The futurologist Alvin Toffler said “Yesterday violence was power, today wealth is power and tomorrow knowledge will be power”. Dr. R. Chidambaram, the Chairman, Board of Governors of IIT Jodhpur frequently paraphrases Toffler’s words to say “Those who have the ability to transform knowledge into technology have power”.
Invention and Innovation: Changing Dynamics of the S&T Initiatives
Prof. Santanu Chaudhury
Science and technology initiatives over the years have been projected as national drivers for economic growth. In India major investments in the Science and technology sector have come through public funding. The proposed STI policy 2021 also emphatically states, “Science, technology and innovation (STI) are the key drivers for economic growth and human development.”.
If we look at the past, the decade of 2010 to 2020 was declared as the ‘Decade of Innovation’. It was expected that this would lead to creation of innovative institutions and mindsets for national progress. In 2013, government formulated Science, Technology, and Innovation Policy 2013 (not just S&T policy which was the past practice.) The key features of this policy were to build S&T -based innovation ecosystem in the country.
Science and technology initiatives over the years have been projected as national drivers for economic growth. In India major investments in the Science and technology sector have come through public funding. The proposed STI policy 2021 also emphatically states, “Science, technology and innovation (STI) are the key drivers for economic growth and human development.”.
If we look at the past, the decade of 2010 to 2020 was declared as the ‘Decade of Innovation’. It was expected that this would lead to creation of innovative institutions and mindsets for national progress. In 2013, government formulated Science, Technology, and Innovation Policy 2013 (not just S&T policy which was the past practice.) The key features of this policy were to build S&T -based innovation ecosystem in the country. In addition, it was expected that private sectors will increase investments in R&D. In contrast to the past scenario, the proposed STI policy 2021 has emerged through a challenging scenario of COVID pandemic which brought S&T and researchers to the national focus. The pandemic challenge has highlighted the need for long term fundamental research, mission mode outcome-oriented projects and a powerful mechanism for delivery of research outputs for benefit of all. These initiatives cannot operate in isolation. Appropriate political will and financial commitments are essential for such ecosystems to emerge. We, academicians and researchers have critical role. At times we need to make difficult decisive choices, which may not be obvious, but necessary to contribute meaningfully and remain relevant. Science and innovation can only ensure sustainability of the humanity against known and unknown global threats.
2.0 Science and Invention
Science is pursuit and application of knowledge for understanding nature and social world through systematic unbiased observations, experimentations and evidence based reasoning. (This is how Science Council of UK defines science which provides a comprehensive way of looking at both social and natural sciences). Science represents the spirit of inquiry and discovery. Questioning is the basis for evolution of science. Science tells not to believe anything no matter what be its source until and unless it is consistent with evidences and reason. This essence of science gets epitomised in the motto of “The Royal Society” - 'Nullius in verba' meaning 'take nobody's word for it'. It expresses the determination of the scientific community to withstand attempts of unscientific domination and to “verify all statements by an appeal to facts determined by experiments”. Science fundamentally leads humanity to new knowledge which are basic principles of natural and social world until and unless those are proven wrong and replaced by new knowledge.
Engineers make use of scientific knowledge to design processes, structures and equipment meeting a variety of human needs. Each engineering discipline is founded upon a set of theories derived from core science. Basic or fundamental research in engineering is the development of such theories through attempts to establish a basis for empirical observations and develop new methods for engineering analysis. Research aims to advance state of the art by framing newer techniques and causal basis for design of engineering systems. Using these principles and methodologies solutions ranging from tangible artefacts to complex socio-technical systems are delivered by engineers.
Fundamental research leads to discovery of new principles which helps us to understand natural and social world. To discover is to bring something into existence that was not known. Discovery may be accidental and need not be an outcome of a structure process. A discovery is illumination of a pre-existing thing, such as the discovery of a natural law. Thus, discoveries in that sense are limited to what is already here or to the world of the possible. Discovery adds to the body of human knowledge and explains some unresolved problems. Creativity in problem solving leads to discovery. Quest for solving problems to make human life better, to satisfy human aspirations, leads to inventions. Inventions, such as transistors or cellular communication, have uniqueness - they are new to the world. Inventions emerge through a process of exploitation of natural phenomenon/laws discovered by scientists to synthesize something new and unique.
Ever since the prehistoric stone tools were invented, humans have lived in a world shaped by inventions. Paleolithic stone weapons made hunting possible. The printing press, introduced in the 15th century, once and for all democratized the process of the expression of thoughts. The typewriter, which came to market in 1870s, was instrumental in freeing women from housework in the western world and changing their social status for good. Internet and cellular phones have completely changed the way we interact with each other. From ideation to experimental validation to conversion into a product for general/popular use is, however, a long cycle.
There is a continuous spectrum of scientific activity linked with the process of discovery, invention and productization. At one end of the spectrum is basic scientific research; at the other end, engineering development. Moving from the pure-science end of the spectrum to the engineering end, the goals become more closely defined and more closely tied to the demand of the solution of a specific practical problem or the creation of a practical product. Inventions, in this context, can be divided into two broad classes: fundamental inventions and incremental improvements on existing technologies. It is clear that discovery and basic inventions generate fundamental knowledge and know-how to solve problems. Investments in this invention life cycle are not expected to yield products but generate knowledge to make a product. An engineering researcher is more likely to be involved in invention rather than scientific discovery. Discovery and invention life cycles effectively convert investment to intangible knowledge for humanity.
Throughout the course of history a number of disruptive scientific or technological changes have happened only when people have ventured into projects with wildly ambitious goals, may be fraught with possibilities of failure. A moonshot project typically has ambitious, exploratory goals expected to produce ground breaking results. Normally there are no expectations of near term achievements. These projects also have a very high risk of failure.
The idea of moonshot projects has an interesting genesis. In 1962, the then US president, John F. Kennedy in his speech at Rice University, disclosed his dream to put a person on the moon by the end of the decade. Audacity of this challenge not only inspired motivation and passion of the scientific community but also public imagination. Public support and political will to take the project forward despite setbacks were exceptional. A project with a smaller goal possibly could have never triggered this level of commitment.
Today, Japan has well organised Moonshot Research and Development programmes that aims to create disruptive interventions to solve issues facing future society by supporting projects which are much more than just extensions of conventional technologies. We can look at some examples:
|1.||Realization of sustainable medical and nursing care systems to prevent and overcome major diseases by 2040, for everyone to enjoy life without health anxiety until 100 years old. This has a number of moonshot goals|
|(i).||Realization of a society where everyone can prevent diseases spontaneously in daily life|
|(ii).||Realization of a medical network accessible for anyone from anywhere in the world.|
|(iii).||Realization of drastic improvement of QoL without feeling load (realization of an inclusive society without health disparity)|
|2.||Realization of a society in which human beings can be free from limitations of body, brain, space, and time by 2050.Moonshot goals of this project are|
|(i).||The Realization of an Avatar-Symbiotic Society where Everyone can Perform Active Roles without Constraint|
|(ii).||Liberation from Biological Limitations via Physical, Cognitive and Perceptual Augmentation|
|(iii).||Cybernetic Avatar Technology and Social System Design for Harmonious Co-experience and Collective Ability|
Outside Japan, the European Union, the United States, and China aim to introduce disruptive innovation by announcing their ambitious moonshots and setting their goals for resolving difficult issues in a manner that was unthinkable in the past. Research institutions and universities have also initiated moonshot projects. MIT launched Intelligence Quest in January 2018. It has two parts – Core and Bridge. The key output of the “Core” will be machine-learning algorithms which can advance understanding of human intelligence with insights from computer science. The second entity – “the Bridge” is positioned to explore application of MIT discoveries in natural and artificial intelligence to all disciplines. Key questions being pursued in this initiative, in words of MIT president – ““How does human intelligence work, in engineering terms? And how can we use that deep grasp of human intelligence to build wiser and more useful machines, to the benefit of society?”. An active industry player pursuing moonshots is X - formerly Google X, now a separate subsidiary of its parent company Alphabet.
The dream of the moonshot to put human being on moon was a one-time engineering feat. Today’s Moonshots would require a new set of technologies to be invented and then integrated for the benefit of humanity. Present challenges, like medical care for all or transporting billions of people, are also fundamentally different as scales involved here are different. It is not just more challenging but qualitatively different. Engineering devices or systems, that are both effective and affordable at a global scale for billions, will be difficult and will be a problem of different kind.
Moonshot thinking is pursuing things that appear impossible, but if achieved has potential to redefine the future of humanity. Moonshots have multi-dimensional implications - it can be in any field, not necessarily only in science and technology. These are initiatives which would appear today impossible science fiction like but if successful will affect million or billions of people. Getting into moonshot thinking requires an spirit of adventure, ability to imagine with audacity, love failures as opportunity to learn, willingness to work in multi-disciplinary teams. Even there are games to get initiated into moonshot thinking (https://x.company/moonshots-game/setup). However, all moonshots are big budget items – risk investments with potential of huge return or huge loss.
Encyclopedia Britannica defines innovation in the following way: “Innovation, the creation of a new way of doing something, whether the enterprise is concrete (e.g., the development of a new product) or abstract (e.g., the development of a new philosophy or theoretical approach to a problem).” While invention requires the creation of new ideas and processes, innovation requires implementation of the invention. Innovation targets to derive a positive outcome from the invention.
Transformation from invention to innovation is not straight forward. There are questions, challenges, trade-offs and financial implications. Key issues are:
|(i).||Solution Readiness: How can one generate a solution from an invention? what problem is the solution ready to solve? When is a solution really ready for the market?|
|(ii).||Production Readiness: How can one build/manufacture a single instance of a new solution? 10? 10,000? What sort of facility is required for production? And how can one fund this?|
|(iii).||Team Readiness: What type and size of team is needed? How can one build, prepare, and manage that team? And what sort of characteristics is expected of the team?|
|(iv).||Stakeholder Readiness: Which stakeholders are most important (e.g. regulators, investors) and how can you best manage them? How does one engage them in the solution readiness activities to ensure that they too are ready?|
An invention may be feasible and novel in an experimental set-up. However, utility of the invention can only be established if it addresses economic and operational constraints of the target application in the context of a market. Creating a market value for an invention requires design of appropriate techniques and technologies to transform the invention to a marketable solution. This productization process requires a precise understanding of the intended market and the requirements of the customers. In many cases, artistic creativity in design of the solution enhances the value of innovation. Following is interesting excerpt: “We’re all searching for the next iMac or VW Beetle—any worthwhile innovation that captures the public’s imagination and strengthens the company’s brand (Excerpt From: Tom Kelley. “The Art of Innovation”).
5.0 Changing Dynamics
Innovation requires knowledge and strategies which go beyond the realm of traditional academic research. Discovery and invention consumes financial resources to generate knowledge. Innovation transforms knowledge to financial assets. Innovation ecosystem is critical and requires careful nurturing in the academic system. Start-ups provide the pathway for academic knowledge production system to get engaged actively with the innovation ecosystem and financially exploit discovery and inventions. Consequently, we find globally, a strong support system for start-ups and technology parks in the academic institutions. Incubators nurture start-ups for generating tangible financial value for institutional discoveries and inventions. On the other hand technology parks are expected to house matured industries to provide inputs for use inspired research to the academic ecosystem. The application scenarios and problems faced in delivery of solutions for practical problems can lead to generation of knowledge by the academic ecosystem which has value for industry.
Presence of these essential enablers for taking research to the field are expected to offer academia new benchmarks to evaluate their research. Not just citations or high impact publications but patents of commercial value, start-ups promoted, consultancy for use inspired research and finally marketable outcome of research have become indicators of contributions by a faculty. Obviously, in today’s academic ecosystem a faculty is not just knowledge producer but also knowledge consumer for value generation along with imparting education to the students. Even education for the students are not just acquisition of analytical skills to solve problems but also to acquire the ability to identify problems to create knowledge and consume knowledge for creation of value through start-ups’s or similar ventures.
Start-up’s from a practical perspective begin their journey typically somewhere in the invention life cycle and not typically in the discovery phase. An academic research provides in many cases the core inspirational input for innovation. However, its journey to become a solution requires a variety of investigations for putting in place auxiliary components required for creating value out of the solution. In many cases, start-ups engage themselves in those aspects of inventions in collaboration with faculty mentors. However, the most important contribution of start-ups are their effort in transforming knowledge into a marketable prototype through a process of refining the output so that performance parameters are adapted to meet market demands, so that the solution has repeatable, reliable and consistent performance in different operational situations and designing an unencumbered process for manufacturing the solution. Subsequent scaling up and productization including refinement of usability aspects are another stage in the process of innovation. Typically start-up’s by this stage attracts commercial funding which then are clear indicators of commercial value of the innovation. Whether, this will be a successful product or not depends number of other factors including market dynamics.
Dynamics of research ecosystem today expects a close synergy between academic research and innovation process. Policy for funding research, in many cases, is getting oriented towards estimating return of the investment in terms of tangible value creation along with intangible knowledge outcome. Academicians are therefore, expected to pursue research projects, may be in association with start-up’s so that there is a linkage with innovation ecosystem for possible value creation. We need to position basic and fundamental research and use driven research in a new way.
Solutions of critical problems we are facing today in Climate Change, Energy, Food, Water, Health and others require long term fundamental moonshot efforts. For example an Energy moonshot can be: To find a energy source that is cheaper than today’s hydrocarbon energy, that has zero (carbon dioxide) emissions, and that is as reliable as today’s overall energy system. These Moonshots require miraculous discoveries. These discoveries frequently do not come from extensions of known science and technology but from foundational conceptual revolutions. These also do not emerge from vacuum. There is a dynamic interaction between scientific insights and the technologies, financing, engineering, as well as the standards, regulations, and policies that complement, enable, and develop them. They can form the nucleus for a dedicated knowledge and value producing ecosystem with a long term possibility. But they are not goal directed research.
However, the ability to perceive moonshots requires intellectual attributes of a different kind. Thinking about moonshots is an exercise in logical imagination – generating novel problems which can have long term attention of research groups. Some of the sub-problems emerging out of this exercise can be pursued with limited funding but can have substantial impact if they can navigate the discovery-invention-innovation life cycle to reach end-users.
Basic disruptive research is neither divorced from all technological and practical concerns, nor just concerned with mere practical necessity, characterised by rather unpredictability. There has to be dynamic interaction between domains of science and technology, between foundational research and commercial research. We can imagine a 2D space. One of the two dimensions will represent utility - utility in terms of the degree to which the pursuit is curiosity driven and the other dimension can represent the degree to which it is necessity-driven and viable. In this representation the search for extra-terrestrial life belongs to the extreme corner of purely “I’m curious” and have “no idea” how useful the answer is. Any scientific discovery and invention can be placed around in this space as all effort to create something disruptive today is a combination of discovery and invention and not just discovery followed by invention along a linear path.
All these are clear indicators of changing times and changing expectations. It is always a challenge to get transformed with new demands. However, success visits an institution when it can transform itself with time and evolve with changes and more importantly can define the changes.
Department of Computer Science and Engineering
The futurologist Alvin Toffler said “Yesterday violence was power, today wealth is power and tomorrow knowledge will be power”. Dr. R. Chidambaram, the Chairman, Board of Governors of IIT Jodhpur frequently paraphrases Toffler’s words to say “Those who have the ability to transform knowledge into technology have power”. Today, as the world continues to battle an invisible enemy, it is scientific knowledge, translated into technology that has given us power over this virus. Right from identifying new diagnostic / treatment modalities, the development of vaccines, to the genome sequencing of new variants to mitigate their spread, science and technology have been central to our advance against COVID-19. Our advances against COVID-19 have not been without losses. The world has lost innumerable frontline warriors and medical professionals to this onslaught. Along with these immeasurable losses, we also remember Dr. Vandana Sharma, a young, dynamic faculty member from the IIT Jodhpur family, who championed IIT Jodhpur’s cause of effective online education and Mr. Pawan Meena, an Alumnus, from the Class of 2014 of B.Tech. (CSE). Their loss is irreplaceable to the Institute community. Despite the challenging times, IIT Jodhpur remains committed to grow in leaps and bounds to meet the technology needs of India.
As you will recall, the 6th Convocation of IIT Jodhpur was held on December 6, 2020 in an immersive 360-degree mixed-reality environment. Professor Geoffrey Hinton, Turing Award Winner and Emeritus Distinguished Professor at the Department of Computer Science, University of Toronto, Canada graced the occasion and delivered the Convocation address. The full transcript of his address is included in this issue. I am sure that you will find it illuminating. IIT Jodhpur remains committed to strengthening its relationship with industry stakeholders. To this effect, the annual Industry Day 2021 was organized virtually on March 12-13, 2021. The main themes of Industry Day 2021 were green technology, smart infrastructure, medical technology and drug discovery.
In the In Focus section of this issue, is an article by Dr. Heena Rathore, an Alumnus of IIT Jodhpur (Class of 2016 of Ph.D. in Information & Communication Technologies), currently serving as an Assistant Professor at the University of Texas, San Antonio, on neuroscience-inspired approaches for machine learning. Also in this section, Dr. Alok Ranjan shares his perspectives on how the current situation is a wakeup call for strengthening the health system in India. You will also find several other interesting snippets of ongoing research at IIT Jodhpur in this issue of TechScape. These interdisciplinary innovations can have several applications and create a profound social impact. Just to give a flavour for the diversity of research areas covered in this issue, Dr. Prasenjit Sarkar takes us through the process for development of human tissue in laboratory bioreactors, while Dr. Preeti Tiwari explores more fundamental questions on how social entrepreneurial intentions are formed. Dr. Rima Bhattacharya introduces us to Asian American literature and Dr. Jayant Kumar Mohanta discusses the design of spatial robots for lower-limb rehabilitation. Such diversity is and will always be our strength as TechScape strives to make knowledge available across disciplines. We are confident that you will enjoy reading this issue and we thank the contributing authors for making every issue of TechScape an experience in itself for you, the reader.
Department of Bioscience & Bioengineering
Over the last year, the human race is witnessing the lash of novel coronavirus (Covid-19) pandemic that has already claimed over a million lives all over the world. It poses a great risk to the elderly people, especially in the age group of 60 and above, and to those with comorbidities like diabetes, hypertension, heart, and lung disease. As per the research published by The Lancet Infectious Diseases, the mortality rate is maximum for senior citizens. Quite interestingly, according to `World health report on aging and health-2015’ by the World Health Organization, it is predicted that by 2050 the world's population shall be 2 billion people over the age of 60 years, which is almost double the number today.
According to the tenets set down by the World Health Organization (WHO), the well-being of elderly people requires independence, participation, care, self-fulfillment, and dignity. In this age of globalization, it is common for elderly people to live alone. Surprisingly, mental health is the most neglected and under-treated aspect of human well-being for a long time. The problem is particularly challenging as in most cases, patients are unaware of their medical condition or are often obsessed with societal prejudice in dealing with mental disorders. The requirement of mental health monitoring for the geriatric community has hardly got any attention until recent times. The existing solutions to treat the ailment through psychological intervention, rehabilitation, and cognitive behavior therapy along with medicine-based diagnosis do not seem feasible in the foreseeable future. The habitual world is going to change post-Covid-19 pandemic period with increased self-isolation and quarantine life. Even though the elderly were living almost a similar isolated life for some time, research and development for technological aids to improve their quality of life are massively distanced from recent advancements.
With the proliferation of fast and reliable communication technologies and the realization of the Internet of Things(IoT) services, it is possible to remotely monitor the mental health parameters of self-isolated elderly people and allow early-stage detection to enable preventive responses. Smartphones are witnessing prime adoption rates since their inception. Modern-age smartphones, with a wide range of in-built sensors and high-speed internet connectivity through cellular networks or WiFi, have emerged as promising IoT devices. Smartphones have become an essential part of human life that effectively handles the existential crisis, especially under isolated living conditions during the current pandemic. Unfortunately, the elderly population, in general, is deprived of its highly engaging smart services due to cognitive disability and difficulty in adopting new technology. This further brings new challenges in maintaining a healthy mental state under present circumstances.
Worldwide, efforts such as City4Age, SMART4MD, My-AHA, Frailsafe, and SECURE aim to develop Information Communications Technology (ICT) frameworks towards promoting healthy aging and independent living for the geriatric community. Analyzing the Activities of Daily Living (ADL) through either video surveillance system or wireless sensor networks have been long used for remote health monitoring of the elderly. Recent studies have asserted that smartphone-based mental health interventions are more effective to assess mental health disorders of users through continuous behavior analysis. Analyzing smartphone usage patterns[10,11] to derive the user's mood fluctuation or to assess the stress level of the user through monitoring day-to-day activities can be very effective. Data from wearable, ambient, or smartphone sensors, as well as social network activity traces, are usually analyzed using machine-learning algorithms to find anomalies in the user's regular behavior [12-14]. The success of these schemes are contingent upon factors such as digital literacy, internet penetration, and standard of living. As per recent statistics, around 40% of the urban elderly people are using smartphones and social apps for interaction with their kin. The usage pattern of the smartphone-based social app varies significantly based on the mental and physical wellness of elderly users along with other subjective parameters including the user’s socio-economic background, education level, tech-savvy nature, physical/ mental activeness, social interaction interest, and linguistic proficiency. In this context, it is imperative to understand how the usage of smart devices, IoT environment, familial connections, physical condition, and mental well-being are tied together. The fundamental questions that we ask towards this are as follows:
Towards the validation of the above-stated questions, we conducted an online survey with the motivation of finding out how the community, in general, is resorting to smartphone/IoT-based solutions. The questionnaire was created on Google forms without any fields for personal information and was expected to be filled out through a smartphone browser that indirectly simulates IoT enablement. The study was conducted for two weeks in the last week of June and the first week of July 2020. We received around 180 complete responses total among which there was approximately equal participation from the age groups of 18-49 and 50-86. Table 1 outlines the set of information that was collected. The respondents were a mixed group with around 44% in the age group of 50-60, 42% in 61-70, 13% in 71-80, and 1.3% above 80.
Health and Lifestyle
Age of the subject
If regularly connected with family and friends
Living conditions at home
Daily smartphone usage times
If uses social media apps
If uses apps for finance and business news
If uses any kind of health monitoring app
If uses any movie/tv-show/streaming apps
If uses wearable devices and related apps
If uses any smart gadgets
pandnews stopped recently
If stopped following news on pandemic very recently
pandnews started recently
If started following pandemic news very recently
pandnews following constantly
If following news repeatedly specifically for pandemic news
pandnews following forever
If following the news in general irrespective of pandemic
Social Media Usage
social media app timing
Average time for daily social media app usage before lockdowns
social media lockdown
Average change in social media app usage during lockdown periods
messenger apps usage
Frequency of average messenger apps usage
app usage mental state
If mobile app usage changes with mental state
If uses any health monitoring app
Mental Health State
Subject feels he/she may contract COVID
Subject feels his/her life is severely restricted owing to COVID
Subject feels drastic changes in society due to COVID and may not fall back to older ways
Subject feels worried regarding financial downturns
If subject has any other reasons for worry apart from COVID
Figure 1: Smartphone and app adoption correlations of the old and young population with age
In order to understand and implicitly answer the questions raised earlier, we can observe the survey results shown in Figure 1. This clearly demonstrates the smartphone adoption and usage patterns of the general population. In Figure 1, we can see the correlations of different smartphone apps that the subjects are more prone to use across their ages. While the majority of the received responses show equal ease at adopting the smartphones across all age groups, however still some usage patterns can be seen in the data. We can see some natural trends like, with the increase in age, the tendency to use wearables, and health monitoring apps tend to decrease for both age groups. The social media usage times decrease drastically with increasing age. This demonstrates that with increasing age, the involvement in social media is mitigated. On the other hand, the young population shows an opposite behavior in usage patterns of smart IoT devices and social media specifically in the post lockdown period. It is an understandable trend. The older population on the other hand does not digress from the trend observed during pre-lockdown.
Figure 2: Usage patterns pre- and post- lockdown
In Figure 2, we can see six broad categories across which the apps used by the subjects were recorded. We can intuitively infer some observations. While it is natural that the young population is more interested in apps pertaining to social media, games, and entertainment, the elderly show more interest in apps relating to news, health, and finance. A significant amount of increase in usage rate can be observed across all six categories during the lockdown periods. This is quite natural to understand that home confinement has led people to increase their screen time and also diversify into exploring newer apps. A staggering rise of around 233% is seen in the connected apps of the health category, which indicates a dramatic increase in health awareness among the population. A general section of people who were earlier not dependent on such technology also moved to adopt them in order to monitor their health regularly and stay healthy in general. Finally, to answer our last question on whether smart devices are acting as a catalyst to influence our mental health, we perform an Ordinal Logistic Regression on the various parameters detailed in Table 1. The ordinal model provides the odds ratio of the mental health state (which is the dependent variable) with the other parameters. Figure 3 shows the odds ratio that has been found through the model. As an example, from the principle of the odds ratio, we can deduce that if the ratio for “chronic” is 1.44, then the subject, with some form of chronic disease, is 1.44 times more likely to be mentally stressed in comparison to a subject who is not having any chronic disease. The ratios on the negative axis show the chances on the lower side. Among the survey takers, we also tried to capture the general feeling of anxiety through asking some questions regarding the pandemic, specifically through the parameters A-E as described in Table 1. We observe here that for “A”, where the subjects generally feel that they may contract Covid-19 someday are almost 5.71 times more likely to be stressed in comparison to the others who keep a more positive outlook. So, from this analysis, we can clearly deduce that the smartphones, internet, and wearable devices are actually aiding people in coping with the evolving situation. This becomes clearer when we observe the odds ratio for “social_media” where we can see that being connected over social media is actually lowering anxiety and aiding people to cope in a more healthy manner.
Figure 3: Odds ratios for development of mental stress relative to smartphone interactions.
Overall, we summarize the following findings from the survey as follows:
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About the AuthorsDr. Suchetana Chakraborty,
Damien Barr tweeted last year (30 May 2020):
“We are not all in the same boat. We are all in the same storm.
Some of us are on super-yachts. Some have just the one oar.”
While this quote has connotation at all levels of the society in the midst of this seemingly never-ending pandemic, it holds a lot of weight in the context of online education.
According to Capgemini’s 2020 Report on the Great Digital Divide , “...in a world in which socio-economic inequalities are a pervasive problem, the absence of the internet in people’s lives only serves to compound the problem.” While students situated in economically sound backgrounds and urban/suburban areas have coped well with access to high-speed internet and laptops, students in rural areas or remote locations continue to struggle with limited internet connectivity or bandwidth. Even those in urban areas with excellent coverage may not always be able to attend all classes due to power outages, data-on-a-budget, or some unforeseen local circumstances. For students who are now shouldering economic/household responsibilities in the wake of the pandemic, coping with a difficult home, a financial crunch or a single parent struggling to make ends meet, attending online classes would be the least of priority, and understandably so. Many students also feel disconnected and miss the in-class personalized experience of listening to the instructor and studying from the hand-written boardwork. How can an instructor then make the learning process easy and convenient for students in the midst of unprecedented stress and psychological firestorms?
The past year of remote learning has opened a vista of opportunities in digital learning and has allowed instructors to explore a wide range of teaching pedagogies as opposed to traditional classroom lecture delivery. Self-paced learning  is one such approach and has been proven to be very useful, especially during this pandemic.
Self-paced learning through interactive video lectures
Self-paced learning is not self study; it is a learner-centric teaching methodology where the students get the opportunity to design their own learning experience at their own pace. This self-paced learning and flipped classroom experience can primarily be enabled by interactive video lectures followed by live discussions. In this scenario, an instructor creates video lectures for every topic he/she wants to cover in the course. This lecture can be conveniently created at home or office using some easy-to-use tools and software such as digital pentablet/pendisplay, screen recorders, digital whiteboard, and basic video editing software. Once the video is ready, the instructor interleaves questions in various locations of the video as he/she would do in an in-person classroom session to gauge the audience’s understanding and concentration. This is possible through software such as Camtasia or Adobe Captivate (where the created videos need to be hosted on a local server), or directly via online platforms such as Edpuzzle where the video is assigned to a student as a ‘video assignment’. Edpuzzle allows this integration directly with Learning Management Systems (eg. Google Classroom) and allows the Instructor to monitor whether the student has watched the video, how many times a particular section of the video was watched, time spent on the video, and the score obtained by the student . The student cannot fast-forward the video or skip the questions and therefore at some point has to start listening intently. The video should ideally be divided into short videos of less than 30 minutes, keeping in mind both the attention span and bandwidth budget of the students . (IIT Jodhpur is designated as a Pro School by Edpuzzle till July 2021 for unlimited use and has abundant opportunity to make the most of this methodology.)
This personalized experience in asynchronous mode is one that possibly comes the closest to a live classroom experience, where the student listens to and watches his/her own instructor write and teach just like in the classroom, and pause and shoot a quick question to the class—just like in a real classroom. Students can be encouraged to post questions as private or class comments on Google Classroom. The advanced features of Google Meet such as Q&A allow ‘upvoting’ of questions that can steer the live class interactions into a more organized and focussed discussion. While the same student might not raise his/her hand in class to ask a question, he/she feels more comfortable upvoting a question gaining confidence in knowing that there are others with the same doubt.
Self-paced hands-on experiments Another remarkable tool for personalized and interactive online learning is through hands-on virtual experiments. Platforms like the Falstad Circuit Simulator  and Tinkercad Classrooms  allow instructors to directly review circuits made by the students on virtual breadboards and help troubleshoot, give feedback and allow both online and offline tinkering of projects. To know more about the EEL1010 and the Online Trimester , visit the course website .
With the concept of attendance losing its meaning in online classes, the use of interactive video assignments and virtual tinkering platforms serve as a remarkable indicator of virtual presence or engagement of the student with the study material. While the proposed solutions do come at a great cost of time and painstaking efforts on the part of the instructors, many of whom may themselves be coping with the stress and digital fatigue, the opportunity of self-paced learning can prove to be a one-time massive investment of efforts resulting in a long-lasting dividend. This can help reuse the created content and build upon and focus on creation of newer more advanced concepts for upcoming batches. This can also help take the course completely on ‘the cloud’ with organized asynchronous (even revenue-generating) online learning modules. In general, all these tools and practices aim to enhance the learning experience and bridge the digital divide for all stakeholders of the education ecosystem. IIT Jodhpur has completed one year of online classes and it has been an eventful year of learning and coping for both students and faculty members. With institutional framework and support in place, IIT Jodhpur was able to swiftly and deftly move into the digital learning space within just a few days of the lockdown. While the online classes have their own success stories and challenges, this is a great time to foster growth and take this conversation forward. It is important to note that the digital divide affects not just the students but many faculty members too simply due to relatively less exposure to digital tools and education technology. It has been a learning curve for teachers and students alike and the lessons learnt in the process hold a great promise in the days to come.
|1.||Capgemini Research Institute, The Great Digital Divide: Why bringing the digitally excluded online should be a global priority, Report, May 2020. [Online]. Available: https://www.capgemini.com/research/the-great-digital-divide/. Accessed on 10 January 2020.|
|2.||W. Dick and L. Carey, The Systematic Design of Instruction, Allyn & Bacon, 6th Ed., 2004.|
|4.||R. Chouhan, “The Digital Classroom Experience,” Video Resource, Aug. 2020. [Online]. Available: https://youtu.be/PsxbXztsFKE.|
|5.||Falstad Circuit Simulator (https://www.falstad.com/circuit/)|
|6.||Autodesk Tinkercad (https://www.tinkercad.com/).|
|7.||EEL1010 & The Online Trimester (https://youtu.be/j0lkrRptUfk.|
|8.||EEL1010 Course Website (https://sites.google.com/iitj.ac.in/eel1010.|
About the AuthorDr. Rajlaxmi Chouhan,
Jodhpur, popularly known as the Sun City, is the second largest city in Rajasthan and one of the popular tourist destinations in India (Figure 1). Spreading over 1005 sq. km, this city is a hub for commercial, industrial, real-estate, and educational activities. As these activities boost the regional economy of the State and create a bundle of employment opportunities, it is necessary to ensure adequate transportation infrastructure and proper planning and management of traffic in the area, for which it is essential to understand and analyse existing traffic conditions and future development plans of the city.
As per the comprehensive mobility plan (CMP) for Jodhpur (Jodhpur Development Authority, 2021), the average annual growth rate of vehicles in Jodhpur district is 7.5%. The road network in the city is radial without any complementing circumferential network. Inter-city trips are centralized through the Central Business District (CBD), leading to traffic snarls. It is reported that vehicle volume to road capacity ratio during peak hours exceeds one (Figure 2). Roadside parking, increased private vehicle ownership, inadequate public transport and terminals, undisciplined driving, and the lack of parking and non-motorized transport infrastructure aggravates traffic woes in the city. Also, there exists a significant number of traffic accidents (14 per lakh of the population) due to improper traffic engineering measures and this echoes the need for effective measures and policies for road safety.
Solving transportation issues
Provision of additional infrastructure w.r.t. the increasing traffic volume on road alone cannot help to resolve traffic congestion or safety issues. Other strategies including access and travel demand management, for example, measures to encourage modal shift to public transit, staggered work hours, and congestion pricing, could also be considered. National Urban Transportation Policy (NUTP) (Ministry of Urban Development, Govt. of India, 2014) highlights transportation as movement of people rather than vehicles. Objectives of NUTP include integrated land use and transport planning, improved and integrated public transit system, improved facilities for no-motorized transport, parking and freight management, capacity building, road safety, and pollution reduction. Recently, the Ministry of Housing and Urban Affairs has come up with an initiative, namely, Transport4All Challenge (Ministry of Housing and Urban Affairs, 2021), to develop solutions for public transit improvement, bringing together cities, citizens, and startups to meet both intercity and intracity travel needs.
Transportation strategies for Jodhpur
The CMP report proposes various strategies that can be adopted for sustainable transportation in the city and includes land use and transport strategy, development of mobility corridors, public transit improvement, non-motorized transport, parking management, and freight management. In terms of land use management, the Old City and its outer parts need to be planned separately as there is little scope for physical expansion in the former one. The ideal way to manage traffic in this scenario is the decentralization of commercial activities and relocation of regional and industrial activities. This will also reduce the number of freight trips in and around the CBD. During peak hours, freight trips can be further reduced with restricted delivery time and designated routes. Along with the existing radial road network in the city, the possibility for circumferential roads needs to be identified. A few roads could be designated as mobility corridors with increased speed of vehicles, where selected modes of transport could be prioritized. Peak hour fluctuations in travel demand can be handled by rescheduling and increasing frequency and fleet size of public transit system. This will also lead to an increase in PT modal share. However, considering increasing travel demand, an alternative mass transit system may be necessary to cater to the public transit load. These public transit systems should not be modeled as a standalone facility; instead, an integrated transport network should be developed with inter-modal transfer stations, park-and-ride facility, uninterrupted Intermediate Public Transit System (IPT) services, etc. Provision of footpaths and cycle tracks along mobility corridors with uninterrupted accessibility to public transport would cause an increase in NMT share. To increase the effective right-of-way of vehicles, on-street parking should be reduced with proper enforcement methods and off-street parking facility could be developed making it more attractive with integrated PT services. For safe and efficient transportation on existing networks, immediate management measures such as signs and markings and street lighting could be deployed. At intersections, geometric redesign using channelizing islands, traffic signal improvement etc. will save travel time as well as increase traffic safety. For smart transportation, Intelligent Transport System (ITS) measures could be deployed such as Advanced Traffic Management System (ATMS) and Advanced Traveller Information System (ATIS). Thus, a synergy of strategies can help manage traffic demand and bring down traffic-related externalities.
The above strategies could be viewed from two perspectives, namely, traffic management and travel demand management (Figure 3). From a traffic control perspective, fluctuation of traffic demand needs to be analyzed by locations over time. From the analysis results, it is possible to devise alternative traffic management measures but impractical to test those on the field, particularly during peak hours. A traffic flow model can simulate the traffic flow of vehicles by type, replicating traffic and travel patterns in the city. Link performance measures such as speed, travel time, delay, density, etc., from such a representative model of the network, could be a better input to control traffic efficiently. The impacts of alternate measures of traffic management on the field could be simulated, and effective ones could be identified. Such models also enable integration with real-time data feed to control field traffic on-time. From a demand control perspective, policies and strategies need to be set, which will influence people's travel behavior, leading to the redistribution of travel demand over space and time to reduce congestion levels. The travel demand in a study region is typically described in the form of origin-to-destination (OD) matrices of the number of trips occurring between each (and every) OD pair in the region. Estimating an OD matrix of travel demand through a travel survey and a travel demand model is the apt option for deriving a sample of the OD travel patterns in the city. The travel demand model could be integrated with traffic flow model, enabling testing and evaluation of several travel demand management scenarios and dynamic routing of vehicles in the network.
In short, to ensure safe, convenient, and affordable travel of citizens, it is necessary to (a) collect data on traffic, travel demand, land use, and employment, (b) integrate land use with transport network to understand and analyse existing traffic conditions and future development plans of the city, (c) identify appropriate transport strategies, (d) develop mobility plan, and (e) timely schedule implementation programs.
Transportation solutions from the above-mentioned perspectives could be implemented as short-, medium-, and long-term projects. To contribute towards a sustainable and efficient transport system in the city, urban local bodies, non-governmental organizations (NGOs) including educational institutes and research organizations, and startups could have collaborations on these projects. Urban local bodies can interact with NGOs to identify transportation issues from a citizen perspective. They can also help in publicizing and campaigning strategies and policies for transport management. Startups can develop innovative solutions that meet the needs of citizens in consultation with the city government and NGOs. NGOs can guide city governments to contextualize transport solutions, mobilize volunteers to test solutions developed by startups, conduct traffic modeling studies, and analyze and document their impact.References
|1.||Jodhpur Development Authority (2021). Comprehensive mobility plan for Jodhpur (Draft). http://188.8.131.52/jda-news/pdf/CMP%20Draft%20-16.01.2021.pdf|
|2.||Ministry of Urban Development, Govt. Of India (2014). National Urban Transportation Policy. https://www.changing-transport.org/wp-content/uploads/E_K_NUMP_India_2014_EN.pdf|
|3.||Ministry of Housing and Urban affairs (2021), Transport4All Challenge. https://smartnet.niua.org/transport4all/wp-content/uploads/2021/05/210502_T4All_Challenge-brief.pdf|
About the AuthorDr. Ranju Mohan,
“Social entrepreneurs are not content just to give a fish or teach how to fish. They will not rest until they have revolutionized the fishing industry.” ― Bill Drayton.
The objectives of this study are twofold. The main goal is to identify the most critical antecedents that affect the formation of social entrepreneurial intention through research surveys conducted among undergraduate students of technical universities in India. The second objective of validating the derived social entrepreneurial intention formation model was attained by sampling nascent entrepreneurs. Social entrepreneurial intentions are defined as a person's conviction to start a social venture in the near future. Understanding the pre- entrepreneurial stage is crucial for policymakers and educators who want to encourage social entrepreneurship. In academic literature, social enterprises are defined as a form of hybrid venture that integrates social mission to profit. Rather than maximizing profits for external shareholders, these entities make a social impact and improve human and environmental well-being. Social Enterprise/Entrepreneurship represents different things, depending on from which perspective one is looking at an enterprise. However, this research study understands social entrepreneurial behavior as using entrepreneurial behavior for social ends rather than mere profit maximization.
Even after more than two decades, research on the concept of social entrepreneurship is still considered to be in its infancy with minimal progress in theory development. Why do people opt for social entrepreneurship as a career choice? How is the intention to become a social entrepreneur formed? Are specific personality characteristics uniquely associated with social entrepreneurs? These questions remain largely unanswered in the area of social entrepreneurship. The extant literature indeed identifies the utility of intentions in predicting social entrepreneurial behavior. This research argues that a more promising theoretical model focusing on individual and external factors will prove to help understand the process of social entrepreneurship.
Data Collection and Sample
Entrepreneurial academic literature suggests that to measure entrepreneurial intentions accurately, the sample should be selected from a population of those who are currently facing major career decisions . Students on the verge of completing their studies (e,g, Bachelor of Engineering students in the third or fourth year of their program) face career decisions, have a wide array of ideas and attitudes, and although they may not have clear business ideas, most have global perspectives regarding their future profession. Therefore, primary data was collected from the students of three premier technical universities in India.
For the second objective of this study, the data was collected from a sample of nascent social entrepreneurs. The rationale for using this second sample is the following; in entrepreneurial intentions' study, intentions are the dependent variable, and there is a possibility of not differentiating between 'dreamers' and 'doers. Therefore, validating the results of undergraduate student samples on a sample of nascent social entrepreneurs who have taken a behavioral step is always advised. Hence, this study validates the results on nascent social entrepreneurs samples.
This research study has attempted to develop a model explaining the process that depicts antecedents to the formation of social entrepreneurial intentions in an individual. This study tried to offer a theory-driven approach to social entrepreneurship research by taking the theory of planned behavior as our basic research framework. A unique aspect of this research study is that besides taking a sample of students for empirically testing the proposed model, this research study has also taken a sample from the population of nascent social entrepreneurs to validate the results.
The findings of the study have provided significant and valuable implications for the policymakers. As self-efficacy, perceived social support, and empathy are the most critical antecedents, policymakers and educators can offer various skill development programs for the individuals to be trained,challenged to take up entrepreneurial activities and sensitized towards social problems. Universities can provide them with minimum resource support to come up with solutions that address social issues. Furthermore, subjective norms should be taken as the central factor that affects the intention process and controls other factors' interaction in future research studies on social entrepreneurship.
Within this context, collaborative efforts between academic institutions, corporations, and societies are required to provide inputs towards a more comprehensive education system that addresses the relevant modus operandi for sustainable development. Once students know about social entrepreneurship, this will encourage them to be self-employed. To facilitate new social ventures created by the younger generation, the government should provide supporting infrastructure and remove the impediments to the social entrepreneurial career path. If policies don’t change, social entrepreneurs can’t thrive.
This study provides an extended theoretical model to the scholars for investigating entrepreneurial intentions among students of higher learning institutions. The proposed theoretical framework may be referred to by other researchers in their future studies. Eventually, it would be interesting to use the measures developed here to test the model in longitudinal studies for measuring the impact of entrepreneurial social education in the creation of social ventures.References
|1.||Whitton, D. (2006). Social entrepreneurship: Developing robust hope in the next generation. Journal of Asia Entrepreneurship and Sustainability, 2(4), 2-15.|
|2.||Krueger, N. (1993). The impact of prior entrepreneurial exposure on perceptions of new venture feasibility and desirability. Entrepreneurship theory and practice, 18(1), 5-21.|
|3.||Hockerts, K. (2017). Determinants of social entrepreneurial intentions. Entrepreneurship Theory and Practice, 41(1), 105-130.|
About the AuthorDr. Preeti Tiwari,
Brain, which has been called the final frontier, remains an enigma. Despite a focussed international effort given a fillip by the USA declaring 1990’s as the decade of the brain, and the parallel brain projects that it inspired in many countries, we are nowhere near understanding how the brain works. However, we are at an exciting phase in our understanding of the brain due to the rapid progress in neuroscience research.
Earliest explorations into the brain function were from a philosophical point of view for understanding the sense of self, soul and consciousness. Later psychology emerged as a separate branch of knowledge to understand human behaviour and its variations, although in the centres of higher learning it was not considered a part of the science faculty. Brain research was conducted separately from a biological perspective. Structure, connectivity and computations in the brain have been painstakingly unravelled since the beginning of last century, often using various animal models because of easy experimental accessibility. Many parts of Psychology were later merged with neurobiology as the biological basis of brain function and behaviour became impossible to ignore. Many neurobiology laboratories were established in the Departments of Psychology. Molecular biology, which was making a steady progress since 1950’s, saw an explosion of our understanding of cellular and molecular mechanisms in the last decades of the previous century. Neuroscientists studying the brain at the systems level began to accept molecular processes in neurons as the fundamental basis of brain function and this by extension of human perception and behaviour. Understanding the brain became a combined focus of separate branches of learning.
A separate revolution was taking place in parallel since the middle of the last century whereby computer scientists were trying to develop artificial intelligence – machines that were capable of mimicking human brain capability such as self-learning, intelligence and cognition. Progress in computing technology led to neural architecture being incorporated in the algorithms, although their resemblance to human brain function was more implied than real.
Recent technical advances in biology has provided powerful tools to peer into a functioning human brain using Magnetic Resonance Imaging (MRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The tools to observe networks of functioning neurons in real time in behaving animals, and the genetic and epigenetic analysis have become more sophisticated. Data are being collected in ever larger volumes beyond the human capability to analyse or comprehend. At the same time there were ever accelerating advances in the computing power and data storage, and the development of tools such as machine learning resulting in advances in AI. Machines with capabilities such as recognition and reproduction of human speech, and ability to learn and play complicated games became possible. Artificial intelligence was coming closer to the real intelligence. It became imperative that engineering and neurobiology disciplines come together to further mutual progress.
At IITJodhpur an autonomous Centre for Brain Science and Applications (CBSA) has been created under the School of Artificial Intelligence and Data Science with the aim to bring together practitioners of diverse disciplines whose interests converge on understanding the brain, developing technologies to study the brain, developing brain inspired machines, and to apply this knowledge for betterment of humans. This interdisciplinary centre will bring together biologists, physicists, engineers, mathematicians and all those interested in the brain. The neurobiologists will interrogate the brain at the micro- (determining connectivity and functioning if individual neurons), meso- (studying network of groups of neurons such as a single sensory system) and mega-scale (studying the entire brain and interactions of brain in the social context of inter-individual interactions). Other groups will develop tools for data analysis and visualization, and AI and hardware inspired from the knowledge of the brain function. We envisage that working together and generation of knowledge on sensation, perception, intelligence, cognition and consciousness would lead to development of brain inspired machines, intelligent technology for prediction and diagnosis of diseases, brain-computer interface devices, intelligent prosthetics to name a few. Any patient related work will be done in collaboration with AIIMS Jodhpur and other hospitals around the country. Acutely aware that technology often races ahead our comprehension as a society of its moral, ethical and social implications, CBSA will have resident practitioners of philosophy and ethics. An important component of the Centre’s activities will be interdisciplinary teaching at the undergraduate and graduate levels.
About the AuthorProf. Neeraj Jain,
The present day world witnesses to an ever increasing dependence on technologies and sciences. The crucial role of the profession of technocrats and scientists in shaping the world prompts us to think about responsible uses of the knowledge and capabilities linked to the professions. Accordingly, the new curriculum of IIT Jodhpur incorporates the institute wide course, Ethics and Professional Life, in the postgraduate program and the course Professional Ethics I&II in the undergraduate program.
Relevance of the courses
The supreme goal of sciences and technologies is a sustainable and synergistic future, and this goal necessitates normative commitment. The term technology, in its etymological sense, denotes the study (science) of techne (technique), and a comprehensive study of techne demands a fair understanding of manifold norms that are inseparably associated with the design and implementation of technologies. Stated otherwise, flawless designing and successful implementation of technologies are largely dependent on the respect for underlying principles, and technologies are essentially normative. Furthermore, technology may be defined to be an ‘artifactual functional system with a certain degree of stability and reproducibility’ (Radder 2009), and each term in the above definition vindicates the inherently normative nature of technologies. To be specific, the term artifact points to the indispensability of artisan’s constant attention and faithfulness to the rules of the art, functionality expects conducive material and social conditions, and both the terms stability of artifacts in producing identical outputs and the potential of reproducibility in other spatiotemporal contexts require faithfulness to material and social norms. Therefore, professionals must be well informed of the standards and guidelines introduced by technological, epistemological, social, political, and moral norms.The relationship prevailing among science, technology, and society manifests a complementing nature. Advancements in sciences and technologies have contributed enormously in reshaping social perceptions and values, and technologies may have political and social roles as well. Likewise, technology can redefine power relations, social status, and capabilities. At the flip side, designing and implementing technological artifacts involve sociopolitical undercurrents, and hence technologies are innately political artifacts (ibid). Therefore, it is necessary that professionals are able to locate the techne in a sociocultural context. However, astonishing progress of technologies in recent times, particularly of nano, bio, information, and cognitive (NBIC) technologies (Canton 2004) presents promising possibilities for humankind in shaping the future, and thereby adds to the burden of responsibilities on technologists. Ever increasing potentials of technological interventions have made the nature of human actions totally different, ‘not because of the methods, but because of the unanticipated nature of its objects and indefinitely cumulative propagation of impacts’ (Jonas 1984), and therefore, it is no more possible to adopt a morally blind stance towards technological practices. Here, the responsibilities of technologists signify both positive and negative facets. Positively, it is the competence to be responsible, and negatively, it is the liability and accountability to pay for what happened because of you (Davis 2012). Therefore, the profession of technologists involves grave moral obligations for the reason that immense power implies immense responsibility as well
The commitment to moral obligations further dictates on thinking like an engineer, which is significantly dissimilar from the considerations of a manager or negotiations of a politician. The unique framework of deliberations to be adopted by an engineer entails a holistic structure which incorporates norms of the specific techne, codes of professional associations, policies and laws framed by the political machineries, and moral maps provided by ethical positions. The loyalty to ethical standards acts as an armour to professionals while it safeguards possible interests of diverse stakeholders. However, it is possible to identify four major reasons for supporting ethical standards in professional life (Davis 1991). The first is that the commitment to ethical standards protects professionals and others. Secondly, the respect for moral norms shapes a better working environment which ensures welfare of everyone. Thirdly, the trueness to norms helps professionals to practice their professions free from blame, guilt, and shame. Finally, norms dictate rules of fairness that bind oneself and others.
Taking these reasons into consideration, the course analyses major ethical issues arising in professional contexts and attempts to identify the conduct expected from professionals. The analysis is done with the support of the tools and moral maps provided by ethical theories, specific details of the factual data, and reflective assessment. Fig. 1 illustrates the conceptual framework adopted in the course. The framework justifies the relevance of the course explicating the implications of the root, professio, which denotes “to believe/declare”. The set of beliefs and declarations implied here denote rules that have limited regulatory power, professional codes that have a bigger scope, and normative principles that have universal significance.
Since sciences and technologies play a significant role in shaping our lives, it seems to be the need of the hour that technological institutions must frame a curriculum that is capable to mould better accountable professionals. Taking this cue, the course Ethics and Professional Life was offered to all postgraduate students of the Institute and online platforms were used for conducting the classes. The learning process was executed by a team of four instructors and ten student volunteers, and mostly performed through learner centred group discussions. The entire class was divided into ten groups consisting of thirty six members from multiple disciplines. Though the initial thought was to create groups of similar academic background and give them training in their prospective professional areas, a further deliberation compelled us to frame groups of interdisciplinary nature, for most of the ethical dilemmas in professional contexts are to be evaluated from multiple points of view.
The major component of the course is group discussion sessions that analyses cases chosen from real life situations. These sessions help students to think from diverse points of view, accommodate the concerns of the all affected, construe the issues holistically, take well deliberated decisions for resolving dilemmas, respect the views presented by others, and maintain professional integrity throughout. The group discussions were coordinated by the volunteers and mentored by the instructors. The following structure was followed in conducting group discussions. Chosen cases—mostly original and tailor-made for analysing a major ethical issue—were distributed to each group and the leader presented the issue during the first few minutes. If the case requires expert advice, members from the relevant area will offer their advice. At the second stage, group members were expected to share their responses through chats that are eventually collected by the coordinator. The collected data proved that almost all members were actively involved in the process. At the third stage, the coordinator opened the room for group deliberation. The discussions were quite live and vibrant, and the better convincing arguments were recorded as the views of the group. Additionally, by comparing individual responses and the collective report, it is found that individuals change their initial views once they listen to others during the group discussion. Finally, the discussions were codified and the group report was generated. It is possible that certain cases are so interesting and challenging that the groups might find it reasonable to spend more time and resources beyond the constrained frame. The whole learning process planned through group discussions was organised, conducted, recorded and presented by the participants.
Major themes assigned for group discussions include: 1) safety of the products and the role of international and national rules, codes, and norms, 2) consent, privacy, and modifying privacy policies, 3) whistleblowing, 4) experiments involving human subjects, 5) property rights and theft, 6) accountability and the locus of responsibility, 7) paternalistic and non-paternalistic deception, 8) conflicts of interest in general and corruption in particular, 9) gender equality, and 10) fairness at workplace. Since group discussions are likely to overlook certain central ethical issues, the problems were further discussed in the class incorporating i) collective report, ii) coordinator’s presentation, iii) instructors additions, and iv) class responses and queries. Fig. 2 illustrates the methodological structure adopted.
As Fig. 2 vindicates, group discussions were supplemented with a short overview of the nature and significance of professional ethics and tools and techniques employed in ethical adjudications, and focused reflections on conflict of interest, data privacy, plagiarism, fairness, and corporate social responsibility. Accordingly, the entire learning process consists of three major components such as i) basic concepts introduced through short lectures and thought experiments, ii) case studies that present ethical dilemmas in professional contexts, and iii) group discussions which nurture critical thinking, tolerance to values and views of others, ability to execute collective deliberation and decision making, and identifying accountability to diverse stakeholders. Furthermore, the leaning methodology adopted was not expecting students reading long academic papers, but looking around and thinking about the world in which they live. Stated otherwise, the course was mostly practice oriented and designed to encourage students to reflect on urgent issues that appear in day-to-day life. For the same reason, the course evaluation was performed through two components such as a) responses to take home case studies, and b) submission of a small but comprehensive reflection on any ethical problem assigned at the end of the course.
Feedback and concluding remarks
The feedback from the participants underlines the significance of the course and validates the decision to include it in the group of mandatory courses. The feedback was collected through 10 questions arranged in 4 points scale which consists of very good, good, below average, and do not wish to answer. The data vindicate that 49.55 of the respondents, as recorded their responses to the question on usefulness of the course to your career, believe that the course is highly relevant, whereas 46% of them take it as relevant. Therefore 95.5% of the population is convinced of the relevance of including the course in the present curriculum. Likewise, 69.85% of the participants reports that the contents incorporated are very good, 26.7% of the group counts it to be good, and therefore 96.55% of the group find that the course is well designed. On clarity of concepts discussed, 44.1% are marked very clear and 52.5% are marked good. The overall rating attracts 10 points from 19.8%, 9 points from 35.1%, 8 from 27.2%, 7 from 10.4%, 6 from 2.5%, and 5 from 4%. In addition to the convincing results of the survey, the optional space provided for adding additional comments witness to the relevance of the course and its acceptance among the participants.References
|Canton, James (2006). Designing the future: NBIC technologies and human performance enhancement, Annals of the New York Academy of Sciences, https://doi.org/10.1196/annals.1305.010|
|Davis, Michael (2012). ‘Ain’t No One Here But Us Social Forces: Constructing the Professional Responsibility of Engineers, Science Engineering Ethics 18: 13-34.|
|Davis, Michael (1991). Thinking like an engineer: The place of code of ethics in the practice of a profession, Philosophy & Public Affairs 20(2): 150-67|
|Jonas, Hans. (1984). The Imperative of Responsibility, Chicago: University of Chicago Press.|
|Radder, H. (2009). Why Technologies are Inherently Normative?, in A. Meijers (ed) Handbook of Philosophy of Technology and Engineering Sciences. Amsterdam: Elsevier|
About the AuthorsDr. K. J. George,
Institute Vision and Strategy 2021-25
The institute has completed more than a decade in its journey in nurturing talent and achieving excellence. The institute has experienced a significant growth in recent times and by 2025 the student strength will reach to close to 5000 from the current strength of 2564. It is important for a technology institute to assess the changing landscape of the technology and other relevant factors to shape and tune its strategy to contribute significantly and meaningfully. There were several factors including but not limited to New Education Policy, exponential change in technology, changing nature of work and job, financial constraints, expectations from the society, and, the need for virtual mode of education with the traditional brick and mortar model necessitate the need to expand the current Vision and Mission of the institute. Furthermore, high-quality education acquires unprecedented importance in improving the lives and future of the people/planet. The arena and scope of technological education also have to expand far beyond the 20th-century concepts. Technology institutes have to increasingly become more and more multi-disciplinary, and also contribute more directly to the application of emerging technologies for responding effectively to ever-changing challenges/opportunities. They have to become significant contributors to the national development, including in the areas of sustainability, economic growth, and societal problem-solving. The shift in nature of work/ jobs move towards the use of immersive media for blended teaching and the new virtual educational institutions, and growing societal expectations are all calling for a total rethink.
With this backdrop, an institute level committee deliberated on various aspects of the institute and proposed a draft version of the vision document. The committee reimagined the core constituents of the institute i.e., all academic units, administrative offices, and other activities following four steps namely “Reimagine, Redefine Disrupt, Innovate,”. Furthermore, drafting vision document was also inspired by principles of Foresight− a field which predicts most probable futures. This document was debated in series of meetings with different stakeholders and subsequently feedback received through various discussion sessions was incorporated.
Vision statement reflects the proposed nature of the institute; it is envisaged as a future driven knowledge institute, with emphasis on the use of Transformational Technologies/ Interventions with a multidisciplinary approach. The Vision has been translated into a Mission with a five-point Mandate, and a Strategic Architecture to create a holistic institute for knowledge creation and dissemination of all traditional and emerging technologies and their fusion, and its application for national/societal purposes.
The Mission will be achieved through ten Goals. These Goals relate to Curriculum, Pedagogy, Research, Outreach, Institutional Collaboration, Industry Connect, Infrastructure, Student Life Cycle, Financial Plan, and Agile Organisation. The main objectives relate to offering a flexible curriculum, enhancing translational research ecosystem, inculcating professional internal culture, efficient collaboration with industries and institutions, fostering humanitarian values and passion for learning, and to develop socially responsible faculty, students, and future leaders, committed to creating a sustainable society. Every goal is also divided into several sub-goals and the institute Vision and Strategy Document documents strategy for each of the sub-goals and respective Key Performance Indicators (KPIs). In what follows, the institute vision statement, Mission and Goals are presented.
"A future-driven institute for nurturing excellence of thought; creating, preserving, and imparting knowledge; and using transformational technologies/interventions with a multidisciplinary approach for responding to societal challenges and aspirations. "
To assimilate balanced, broad-based as well as specialized education in all curricula with opportunities for different kinds of students and their interests.
To establish systems for dynamic development, implementation, and evaluation of futuristic pedagogy including blended-hybrid teaching and experiential learning.
Have a globally engaged research ecosystem with state-of-the-art facilities in place, for attaining leadership in research on academic, social, national, and industrial fronts while capitalizing on emerging and in-demand opportunities.
To be the Institute of Choice for a lifelong learning journey of working professionals, alumni, and the community.
Have an efficient platform in place for forging impactful partnerships with academia, research institutes, business organizations, civil society, governments, and other agencies across the world for contributing to larger goals for humanity.
About the AuthorDr. Deepak Fulwani,
IIT Jodhpur is situated at eastern edges of great Thar Desert in India. IIT Jodhpur was born in 2008 and moved to present main campus in 2017. Campus plan was conceived supposing the campus as a living laboratory. The institute is in its teens with its second phase of physical infrastructure development in its last mile. The ecosystem of the campus pertaining to land-use and land cover has transitioned to completely different one during last decade. Campus needs to be sustainable in all fronts and as per the vision of our Director. The Campus Sustainability Project (CSP) is being developed as part of IIT Jodhpur’s commitment to embed sustainable practices across education, administration, finances, student well-being, landscape, ecosystems, natural resources, global social responsibility of the institute and extremely feeble environment prevailing in the region. IIT Jodhpur’ s ability to achieve desired outcomes in the said areas and maintain the ability to continue programs, processes and activities over next decade will provide definition to sustainability
Energy: In order to enable the campus in such a manner, practices to reduce our dependency on fossil fuels needs to be introduced in a phased manner. New renewable energy sources, rechargeable batteries, energy storages can be opted for use in area of transport and energy production. Our new vendors and collaborators should be following UN (sustainability development goals) SDG norms and should have awareness to upkeep their processes and working within environmental conservation edicts. Student projects related to carbon capture data, carbon sequestration and footprint are proposed for the next two years with the long term plan to make IITJ campus carbon neutral.
Waste: Practice of circular causation is to be enabled through strategies to stop waste by concepts of refusing to create waste, reduce its evolution, reuse it, refurbishing products, redesign to fit, rethink about a process, recycle it, recover valuables from it as well produce energy through processes to rot waste. The construction, biomedical, kitchen, paper, sewage and gardening waste need to segregate at source and properly processed to ensure cleanliness around the campus through small projects envisaging a waste free campus in the long run. Setting SDG targets for individual units or buildings which are achievable with effective use of science and technology products and processes are the main aim
Out-Reach: Water and energy are bound to be audited through competitions between occupants of a different physical infrastructure. Students, staff and faculty will test the sustainability indices of processes, devices, and frameworks which they design, create and implement within the campus during the next two years of this project. These projects will be showcased live to the outside world to disseminate concepts which are aligned to UN SDGs and also to churn the public opinion how to practice sustainability in their surroundings. This culture will build a competitive thought to conserve campus amenities and its limited natural resources.
Education: Education for sustainability at IITJ will motivate pupil to produce technology and services that uses renewable resources and does not damage their ecological habitats. This focuses on process, design and product appropriateness, natural resource conservation and creating ecologically and socially aware engineers and professionals who understand interdependence of environmental, social, cultural, data and economic systems. In this regard, talks on sustainability has already started with thought process on initiating management development programs (MDPs), certificate programs and doctoral programs at IIT Jodhpur. Dissemination of knowledge on SDGs and for attainment of SDGs will be also demonstrated periodically to the nearby districts, communities, schools and neighbors through physical as well as online mode.
External Linkage: During the CSP, global linkages with sustainability accreditation organizations, memberships with higher education organization related to UN, regional institutional collaborations and on-campus initiatives is also necessitated. Projects will be also aiming to position IITJ towards promulgating emission mitigation pathways, initiating newer technologies, studies, standards and policies for achieving net zero (emissions need to fall to zero) in alignment to guidelines proposed in the Paris Agreement on 12 December 2015 (by the 196 Parties to the UN Framework Convention on Climate Change (UNFCCC)).
Support: The institute, through its Office of infrastructure and CETSD will support and enable the project initiation in terms of small financial as well as administrative supports. All stakeholders irrespective of student, faculty, staff, family members and other stakeholder are requested to join hands to take forward this movement towards a vibrant future for the region as well as the campus by proposing projects towards SDGs.
Scope: Projects can be from diverse areas, but not limited to, conservation and carbon capture and fixation, data sensing and collection, water management, digitization, AI based interventions, education -based projects, SDG awareness-survey, framework, management, behavioral, awareness, social outreach, neighborhood village partnerships, environmental aspects, student projects, student SDG awareness projects, waste management, transport, renewable energy use, and flora-fauna sustenance.
About the AuthorDr. Anand Plappally,
I would like to start by thanking the Director, Santanu Chaudhury, for inviting me to give this Convocation Address. I've never given a Convocation Address before, so I'm not quite sure what's expected of me, but what I'm going to do is talk about the interaction between reason and intuition. I'm going to start by talking about something Darwin said in his book, The Voyage of the Beagle.
I would like to start by thanking the Director, Santanu Chaudhury, for inviting me to give this Convocation Address. I've never given a Convocation Address before, so I'm not quite sure what's expected of me, but what I'm going to do is talk about the interaction between reason and intuition. I'm going to start by talking about something Darwin said in his book, The Voyage of the Beagle.
On that voyage, Darwin visited numerous coral islands, and he noted that there were three types of coral island. There were coral islands with the reef close to the island; there were coral islands with a barrier reef that was a long way from the island, like a mile offshore; and there were coral islands that are just atolls with a ring of barrier reef and no island in the middle. Because he was a biologist, Darwin noticed that there's a problem with this: the barrier reefs were in deep water more than 100 feet deep, and the live coral reef, that reach the surface of the ocean, was built on top of a big pile of dead coral. And the question was, how did that dead coral get there, because coral can't grow more than about 100 feet below the surface; there isn't enough light.
After some thought, Darwin realized what must have happened: it started off as a coral island with just a fringe of reef around it, and then both the island and the ocean floor sank, and as they sank, the reef got further from the island, and new coral was built on top of the old coral. So just from visiting these coral islands, and doing some reasoning, Darwin was able to overcome the fundamental intuition that the earth is rigid. Darwin even postulated the Great Barrier Reef, off the coast of Australia, was caused by the whole Australian continental shelf sinking. About 80 years later, someone called Alfred Wegener postulated the theory of continental drift which was even more radical than for example, South America had once been attached to Africa, and they drifted apart opening up the Atlantic Ocean. Geologists mocked this theory as wishful thinking. Even though Wegener, who wasn't a geologist (he was a climatologist), had lots of evidence in favor of it; lots of little paradoxes that could only be explained by continental drift.
For example, the soil types along the coast of Europe and Africa matched up with the soil types along the coast of the Americas. Also, rare fossils that only occurred in certain parts of the coast of Africa matched up with the same rare fossils on the corresponding parts of the coast of South America. There were glacial scrapes, in rocks in the tropics in Africa, i.e. the kind of scrapes that are made by glacier, pulling a big rock over the top of a rock formation. So areas that were now in the tropics used to be covered ice. There are also coal deposits in the Arctic, and the coal deposits are formed from plants that only grow in hot climates. So presumably, areas that are now in the Arctic used to be much hotter. Despite this evidence, the geologists weren't convinced. In fact, they were very strongly against Wegener's theory. They treated it as heresy. They said things like "if Wegener is right, the last 70 years of geology were a complete waste of time." They also said this theory should not be allowed in the textbooks; it will just confuse students. When David Attenborough was a student in the 1940s, he asked his lecturer about the theory of continental drift. And here's what the lecturer said. "I once asked my lecturers why he was not talking to us about continental drift. And I was told sneeringly, that if I could prove there was a force that could move continents, then he might think about it. The idea was moonshine, I was informed."
Now, what's interesting is by the 1940s, people already had the correct mechanism for continental drift. It was proposed by Holmes in the 1930s, and it was that the continents floated on a sea of hot magma. When I was a boy in the 1950s, I listened to a radio program before the theory of continental drift was accepted, and in that radio program, there was some very strong biological evidence in favor of continental drift. There's a genus of water beetles, called the Almaty, that live in fast-running streams and can't fly. If you look on the north coast of Australia, this genus is differentiated into a number of species, which are in different streams on the north coast of Australia. And in about 100 million years, these species are so bad at migrating that they haven't managed to cross the mountain reach between two streams, and yet, if you look at the north coast of New Guinea, you find the same species of Almaty in the same order, but actually in reverse order. Now, this is a weird paradox that needs some explaining, and the only actual explanation that makes any sense is that the north coast of New Guinea, was once attached to the north coast of Australia, and it came off and turned around.
The geologists' response to this argument was: how could beetles move continents?
There's something fundamental here by doing science, which is, if you come across a paradox, you oughtn't dismiss it. You don't want to jump to simplistic explanations of it, but you must bear it in mind. For a good scientist, a paradox is an irritant and it never ceases to be irritating, until the scientists can explain it. A paradox for a scientist is a bit like a piece of grit in an oyster. It irritates the oyster, until the oyster has constructed a pearl to stop it being irritating.
The reason I spent so long talking about the paradigm clash in geology, between people who viewed the earth as rigid, and people who viewed the earth as fluid, but on a much longer time scale, is that a very similar clash of paradigms happened in Artificial Intelligence. In the early days of artificial intelligence in the 1950s, there were two quite different paradigms for how to build an intelligent system. One paradigm was inspired by logic. In the logic-inspired approach, the essence of intelligence is using symbolic rules to manipulate symbolic expressions; what they focus on is reasoning. The alternative is the biologically-inspired approach, where the essence of intelligence is adapting the connection strengths of a large network of neuron-like processing elements, and what they focus on, is learning and perception.
These two different paradigms have completely different views about what internal representation should be like. In the logic-inspired approach, internal representations are symbolic expressions; they're like language, but a kind of cleaned up language that's unambiguous. In the biologically-inspired approach, internal representations are nothing at all like language. They're not symbolic expressions; they are big vectors of neural activity. So in the biologically-inspired approach, the only place you get symbols is in the input and output. Inside you have much richer representations that are big vectors. The two different paradigms also have completely different ways of making a computer do what you want.
In the logic-inspired paradigm, you tell the computer what to do; it's intelligent design. You figure out consciously exactly how you would solve a problem, and then you tell the computer in excruciating detail how to do it. That's called programming. In the biologically-inspired approach, it's quite different. You get a computer to do what you want, by showing it examples of input and output, and having the computer learn how to map the inputs to the outputs. For this to work, you need a general-purpose learning procedure that can cope with all sorts of different tasks.
Of course, you have to program something; you have to program into the computer, the general purpose learning procedure. A good example of an artificial intelligence task is to take an image, and to produce a string of words that describes what's in the image. If you think about what an image really is, it's a very large number of pixels, each of which has red, green, and blue values. And so you have to get from those numbers that represent the intensities of the pixels to a string of words that represents what's in the image. Figuring out a computer program to do that is very difficult. People in the day tried it for 50 years and failed. One problem is we don't know how we do it ourselves. And it seems very unlikely we do the vision by using logical reasoning.
In the earliest days of AI, both the symbolic approach, and the neural network approach had their adherence. In the 1950s, Alan Turing and John von Neumann were both proponents of the neural network approach, and they could hardly be accused of not understanding the symbolic approach. Unfortunately, both of them died young; Turing with some help from British intelligence.
By the 1960s, mainline AI, which was a symbolic approach, was already dismissing the neural network approach as hopeless. There had been working around 1960 by Rosenblatt showing how you could get a simple learning algorithm to solve some fairly straightforward problems. But work by Minsky and Papert, published in 1969, showed that there were all sorts of problems that the simple learning algorithm couldn't deal with, and that led people in mainline AI to completely dismiss the neural network approach; they decided it was hopeless.
The main issue was this. A neural network with many layers and nonlinear processing elements is an extremely powerful processing device, but the question is, could you get it to learn to solve a problem just by showing it examples of input and output. People who believed in neural nets, thought that eventually they will be able to do that. People who believed in symbolic AI thought that that was a completely ridiculous hope. They dismissed it as pathetic wishful thinking, in much the same way as the geologists dismissed Wegener's theory of continental drift.
If you told a symbolic AI researcher in around 1980, that it would one day be possible to do reasonable quality translation from one language to another by using a large neural network that had no prior knowledge about linguistics, and was simply trained on a big set of data of how to translate one sentence to another, they would have dismissed this idea as utterly ridiculous. The prevailing theory in linguistics in the 1980s was that language was not learned at all. Linguists thought that language was innate; that is, it evolved, but you didn't learn it during your lifetime; during your lifetime, your brain matured, and the linguistic knowledge that was innate, revealed itself. From a practical standpoint, this is an absurd theory. This view of language came from Chomsky, and it was based on a bad application of a theorem. Gold's theorem showed that you could not learn a language perfectly every time from positive examples alone, and people took this to mean that you could not learn a language. As soon as you allow occasional mistakes and imperfections, the theorem no longer applies.
There was something interesting that happened here. A mathematical theorem was used to make people believe something that's manifestly absurd, which is that we don't learn language. I remember watching a TV program - I think it was a Nova program - quite a long time ago, when many leading linguists looked straight at the camera, and said, "there's a lot we don't know about language, but one thing we know for sure is that it's not learned."
That's the power of ideology.
The whole idea that we evolved language, because it's too difficult to learn in your lifetime, is just silly. Learning algorithms that use gradient descent, are a much more efficient way of adapting a large number of parameters than evolutionary algorithms. That's probably why evolution produced a brain so the brain can learn things that are too difficult for evolution to evolve.
I now want to talk a bit more about the nature of intuition. The kind of intuition that makes it just completely obvious that the earth is rigid, or completely obvious that a neural network with a tree and parameters that start off with random values can't possibly learn everything from data.
Where do these intuitions come from?
Well, I think what deep learning has shown is that when you learn about your domain, you develop vector representations for things in the domain, and these vector representations have a very rich internal structure in terms of their features. As you become an expert, the internal structure, the vector representations, make some things just obvious; you don't need to do reasoning; they're just completely obvious given these vectors.
So I'll give you an example. It's an example is hard to explain using traditional logic. Suppose I tell you that you should ignore biology for the time being, and you have to assume either that all dogs are male and all cats are female, or that all dogs are female, and all cats are male. Now, I know it can't be like that, but if it had to be one way or the other, which would you choose?
At least in the culture I come from, it's obvious that dogs are male and cats are female. Now, the question is, why is that obvious? It's based on an analogy, right? It's based on the idea that dogs are large and noisy and chase cats, whereas cats are smaller and more subtle. So the internal structure of the vector representations that we use to stand for things gives us immediate access to a bunch of obvious facts.
What's happening is that if you look at the vector use for men and the vector used for women, there's a similarity between the vector for men and the vector for dogs, and a similarity between the vector for women and the vector for cats, at least in my culture. This is a phenomenon that only occurs if we're using vector representations; you don't have to do any reasoning at all. It's just an automatic fact, that because the vector for women is like the vector for cats, anything that the vector for women causes, is likely to be caused by the vector for cats, or at least more likely to be caused by that vector than the vector for dogs.
One of the nicest examples of deep learning developing intuitive understanding is the AlphaGo system. In that system, there are two neural nets that develop intuition. One can look at a board position and suggest sensible possible moves. The other can look at a board position and say how good it is for one of the players. Initially, they trained the neural net that predicted good moves by using training data taken from games by human experts. By having a net like this, which could intuitively suggest good moves, they were able to greatly cut down the search, i.e. the process by which you say if I go here, and he goes down, I go here and he goes there, where do we end up. But later on, what they did was, they trained the network for predicting what a good move might be, from games that AlphaGo played against itself, and this is a really nice example of the interaction between intuition and reasoning.
The neural network that suggests good moves, I'll call intuitive; it's just a pass through a neural network without any sequential reasoning. The process that says if I go here, and he goes there, and I go here, and he goes there, that's reasoning; that's considering various alternatives and seeing where you end up. Now, the nice thing was that after you've considered some alternatives, you can use your evaluation function -- the neural net that says how good is this position for me -- to decide what really was a good move. So you get better information, after some reasoning, that can be used to refine your intuitions.
I want to finish by showing you another paradox that's motivated a lot of my thinking about neural networks for the last few years. It's a very simple puzzle that I discovered once inside a Christmas cracker, and it's amazing how difficult people find it, and I think the extreme difficulty of the puzzle tells us a lot about how we represent shapes inside our brains.
So it's a two-piece jigsaw puzzle: you take your tetrahedron; you cut it into two pieces; you give someone the two pieces, and all they have to do is assemble it back into a tetrahedron. It sounds completely trivial.
Here's one of the pieces, it looks like this. Here's the other piece; it's exactly the same.
And when you give people these two pieces, they try putting similar faces together. So we try that, that's not a tetrahedron.
They try this, that's not a tetrahedron.
They'll even consider considered trying this, that's not a tetrahedron.
Then they try that again. No, that's still not a tetrahedron.
Then they try that, and that's not a tetrahedron.
And then often they give up and say it's impossible.
So the question is, why is that such a difficult puzzle? Now, in case you're wondering what the answer is, the answer is that - your tetrahedron.
I've cut a tetrahedron in half in such a way you get a square cross-section, which you probably didn't think you could get cutting a tetrahedron in half. Now there's completely different way of thinking about a tetrahedron, and if you think about the tetrahedron in this different way, the puzzle is easy. You can think about a tetrahedron as two lines of right angles, and now join all the points on this edge to all the points on this edge, and that's a solid tetrahedron. When you think about it that way, it's fairly obvious that if you cut it halfway out with a horizontal plane, you'll get a square cross section.
But people don't think about tetrahedral like that they think about them like this: When you think about a tetrahedron like this, you use a frame of reference in which the main axis is the vertical, so there's an apex at the top, and there is a triangular base. Now, if you see this, you think about this shape, with an axis along the shape, along here, another axis across here, and another axis this way; so the natural rectangular coordinate system for this piece doesn't line up at all with the natural coordinate system you use for tetrahedron, and that's why the puzzle is so hard. You can't see how this piece fits into a tetrahedron because you're using a different coordinate system for this piece. Actually got some experimental data on how hard this puzzle is. I tried this on a number of professors from an Institute of Technology, and what I discovered was, the number of minutes it takes you to get the solution is roughly proportional to how many years you've been a professor; that the longer you've been there, the harder you find the puzzle. In fact, I had one professor who looked at the pieces and decided it couldn't be done, and after 10 minutes had a proof that it was impossible. Now, you'll probably be relieved to hear that the Institute of Technology where I did this experiment was in Massachusetts, not in India. I think the difficulty of the tetrahedron puzzle can only be explained by assuming that people represent shapes relative to intrinsic coordinate frames, and then if they use a different intrinsic coordinate frame for the same shape, they have a completely different internal representation. What's interesting about that is that's not the representation that's used by convolution neural networks, so a standard convolution on your network cannot explain why this puzzle is so difficult. It's much more like the representations that are used in computer graphics, and I think the future of computer vision is going to be making vision more like inverse graphics. That's all I have to say. Thank you for your attention.
|Dr. Vandana Sharma was a passionate mathematician, a beloved teacher, and a cherished colleague at IIT Jodhpur. A highly-qualified and dynamic young professional, Vandana earned her Masters degree from IIT Madras, and PhD from University of Houston, and served as a faculty member in Arizona State University from 2014-19. She joined IIT Jodhpur in 2019 and was especially instrumental in the Institute’s successful transition into online teaching during the pandemic. Her research focussed on reaction-diffusion systems, and was published in reputed international journals and avenues. She was especially passionate about innovation in teaching and online learning that could help students visualize the concepts in a simple yet exciting way|
|Vandana’s illustrious career was decorated with honor and recognition, such as Indian Academy of Science Summer Student Fellowship (2006) and Early Career Mathematician by Mathematical Association of America (2015). Looking back, I remember Vandana as a wonderful friend and teammate, with an ever-smiling personality, always giving others hope regardless of her own personal circumstances.|
|Vandana bravely and courageously fought her battle with Covid and breathed her last on May 16, 2021 at AIIMS Jodhpur. Losing her in this way is an irreplaceable loss to her family and friends, the IIT Jodhpur community, and the world of mathematics. She was an exceptionally dutiful daughter to her parents and had dedicated her entire life to service and the pursuit of science. While the anguish of her leaving too soon can never be abated, I hope we can honor her memory by being compassionate to each other in action, more than words, and cultivate her virtues in our conduct with utmost integrity, professionalism and devotion to service.|
|Dr. Rajlaxmi Chouhan, Department of Electrical Engineering, IIT Jodhpur|
|Pawan Meena was a beloved husband, father, son and friend who always provided a shoulder when you needed one. Anyone who had the pleasure of knowing Pawan will describe him to be the most calm, giving man with a heart of pure gold. Adored by all those who knew him, he was the gel that held so many together. He was one of the most organised persons I have ever come across. A man with vision, Pawan always had his life planned, from education to marriage to entrepreneur to being a full time working dad.Tragically, Covid-19 ruined all of the well thought out future plans. A born winner through grit, determination and bravery, Pawan, sadly lost his battle to Covid-19.|
|Pawan was an ultimate champ and a great jugaadu, who strived to be the best in anything he did. This includes his education at IIT Jodhpur in Computer Science and Engineering, turning an entrepreneur at the age of 24, becoming a great Engineer at Toppr and being a Senior Engineer at Kuku FM. Beyond all that, he was a very compassionate team member. Although this article provides a basic view of his life, he was so much more than that! The love Pawan had for his family and friends was incredible and equally matched back by them. He leaves behind a wife and a loving 3 year old son.|
Class of 2014 of B.Tech. (CSE), IIT Jodhpur
Coronavirus diseases -2019 (COVID-19) has taken the world by storm and India is one of the severely affected countries with a total of 20.8 million infected cases and 0.23 million deaths, as of May 4, 2021, . This is one of the biggest crises India is facing post-independence. The second wave of COVID-19, which is much more virulent than the first wave, brings higher mortality along with a devastating impact on the socio-economic front , .
COVID-19 might be a once in a lifetime event, has tested the healthcare system of the entire world and unfortunately, very few countries have passed it successfully. It also exposes the weakness and lacunas in India’s healthcare system which has been the result of constant neglect towards investment in health from the last 74 years, since independence, under various government regimes . The current crisis shows the importance of a resilient healthcare system. Health system resilience can be defined as ‘the capacity of health actors, institutions, and populations to prepare for and effectively respond to crises; maintain core functions when a crisis hits; and, informed by lessons learned during the crisis, reorganize if conditions require it’ . The impact of COVID-19 is devastating for India’s healthcare system which is not able to meet the demand of COVID infected patients, but at the same time it is also not able to provide regular care for other health conditions which will have long term ramification for society and progress towards universal health coverage (UHC)-Goal 3 of Sustainable Development Goals (SDGs)- in coming years, . The current crisis is also a wake-up call to realize the vulnerability of India’s healthcare system which has been neglected for decades. This commentary aims to understand various building blocks of the healthcare system, based on the WHO’s Health System Framework , which needs to be strengthened so that the country is well prepared for managing ongoing and future healthcare emergencies. These six building blocks of the health system are as follows:
1. Service delivery. The organization of service delivery in India’s healthcare system is fragmented, where primary healthcare facilities provide a narrow range of services which include HIV, TB, malaria, leprosy, and mother and child health . As India goes through a socio-demographic and epidemiological transition, the disease burden of non-communicable diseases (NCDs) has increased significantly . However, India’s government healthcare system is not responsive to meet societal healthcare needs. Also, poor continuity of care and weak referral support aggravate the provisioning of care further .
COVID-19 pandemic shows the higher vulnerability of individuals living with comorbid conditions (mainly NCDs) . Regular care for most of the health conditions, for example, TB, has been hampered , . The present crisis shows the importance of comprehensive primary healthcare, if provided, can minimize the impact of the pandemic. For example, Indian states like Kerala and Tamil Nadu, which had better primary healthcare, have lesser mortality per million compared to other states in spite of having a greater proportion of elderly .
Service provisioning under the private sector has been reduced significantly and in many situations, if it is available, they have charged the patient exorbitantly high, which is out of reach for the lower socio-economic population . However, the private sector serves India’s 70% out-patient care and 55% hospitalization, and they need to be partnered in handling this pandemic. There is also a need to revisit the Clinical Establishment Act (2010), and enforce it to ensure a better quality of clinical care delivered through public and private clinical facilities across the country.
COVID-19 reminds us to build surge capacity or planned redundancy in healthcare facilities, mainly in tertiary care with increased bed strength, to meet the healthcare needs during the crisis .
Issues of fragmented care have been pointed out by previous studies and various policy documents including National Health Policy-2017 . To address these issues and provide comprehensive primary healthcare, the Government of India proposed to upgrade the health sub-centre to health and wellness centres (HWCs) under the ‘Ayushman Bharat’ programme in 2018 . The service coverage has been increased from 5 to 13 services which include care in pregnancy, neonatal and infant healthcare, childhood and adolescent healthcare, family planning, management of communicable diseases, outpatient care for acute ailment, screening and prevention of NCDs, dental health, eye care, elderly and palliative care services, emergency medical services, and mental health . It is a welcome step, however, its implementation on the ground needs to be observed. Provisioning of comprehensive primary healthcare promised under HWCs, will provide diversity in service provisioning and make the healthcare care system more resilient to meet future healthcare emergencies.
2. Health Workforce. COVID-19 pandemic has shown an acute shortage in India’s health workforce, . India has 5.76 million health workers which means 16.7 doctors, nurse and midwives for the 10,000 population , and it is much below the WHO’s threshold of 44.5 per 10000 population . Also, India’s health workforce is highly skewed. For example, the southern part of India has only 21% of India’s population but has 44.3% of India’s medical college seats. India’s health workforce policy has been shaped by various high-level expert committees, post-independence, but the country still faces a severe shortage of healthcare providers. The recent policy document, titled “New India @75” by NITI Aayog, aims to generate and add 1.5 million health workers in the public sector by 2022-23 . Various studies have also shown the investment in the health workforce is a driver for progress towards many Sustainable Development Goals (SDGs). Investment in the health workforce is a vital aspect of building a resilient health system that requires long term vision and investment.
3. Health information. Health management information system (HMIS) is crucial building blocks of the health system . A well functioned HMIS system helps in the early detection of the outbreak of diseases, forecast its spread and helps in planning the mitigation strategies. India’s Integrated Diseases Surveillance Programme (IDSP) is a fairly good system for monitoring disease outbreaks in the community. However, at the time of the COVID-19 outbreak, its reporting stopped in February 2020 and a new vertical was created for the reporting of COVID-19 under the Ministry of Health and Family Welfare (MoHFW), Government of India . This provides an overall increase in new COVID infected cases and mortality in different states of India but lacks in providing information related to disaggregate equity analysis. HMIS also faces an interoperability issue where IDSP and HMIS cannot be integrated into one system for synergistic policymaking. Many of the developed, for example, NHS in UK, countries could manage the pandemic better since they had a robust HMIS which could identify various predictors of spread and mortality at the beginning of the pandemic. Also, while handling healthcare emergencies, information required for the mitigation policy might be different from the data required for routine purposes . To meet this there is a greater need for investment in human resource training, innovative and user friendly technology, open accessibility, and good governance.
4. Medical products, vaccines and technologies. Medical technology plays an important role in improving providers’ service delivery capacity and maximizing access to care for individuals . Early and accurate diagnosis of health problems helps in facilitating timely intervention and better outcome. During the COVID-19 pandemic access to healthcare has been increased via m-health and e-health strategies . In this regard, the launch of the National Digital Health Mission in 2020 by the Government of India could be a path changer in improving the access and continuity of care . Learnings from other countries show that electronic health records can improve public health by the ease of workflow and lowering the healthcare cost. Telemedicine consultation during the time of pandemic has shown its potential and importance in reaching a wider section of the population, more so in rural and remote populace.
COVID-19 pandemic and shortage of drugs and diagnostics made us realize that India cannot solely dependent on the global supply chain, where industrialized countries have taken a protectionist stance . There is a need to increase funding and support for frugal technologies. Also to build a resilient health system, technology should not be considered and developed in isolation rather it should be integrated with the needs of the health system and social context of the country. India has been called the ‘pharmacy of the developing world’ but in the present decade its dependency for raw materials on other countries has increased . There is a greater need for investment in medical technology, which should be based on a multidisciplinary approach, frugal and equitable.
5. Health Financing. India spends 1.3% of total gross domestic product (GDP) on healthcare against the recommended 5% by WHO. India’s National Health Policy-2017 proposes to increase this to 2.5% by 2025 but so far it has not been implemented on the ground. Underfunding of healthcare has led to higher out-of-pocket expenditure (OOPE) for the household at the point of care. Expenditure on health is one of the major causes of impoverishment for the household, where 55 million people slip below the poverty line every year in India . It has its ripple effect on various aspects of human development and the overall economy. In India, by mandate, public healthcare facilities aim to provide free access to care but due to lack of consumables and medicine patients have to purchase from outside which leads to high OOPE. On the other hand, the private sector charges considerably high which is unaffordable to the lower and middle-class population. COVID-19 pandemic has exacerbated this problem where a large section of the population had to forgo their healthcare needs. To build a resilient healthcare system there is a greater need for investment in the area of infrastructure, human resource, and drugs and diagnostics. In a time of crisis, only a robust health system can absorb the extra funding and build a surge capacity. Even in India, states like Tamil Nadu and Kerala responded better to the pandemic in terms of lesser mortality per million population compared to other Indian states since they had better infrastructure and health workforce.
6. Leadership and governance. Across all building blocks of the health system, governance plays a vital role, and more so during the time of healthcare emergencies. In most crises‘ top –down’ approach of governance, which is based on command and control, is adopted. It has strengths in terms of streamlining the logistic issues. However, in the long run, the ‘bottom-up’ approach which is more decentralised and based on system learning has to be adopted. For an effective response to crisis policy formulation and action plan should go beyond the health system. In many situations governance of one sector (example: finance) in response to shock ignores the impact on another sector (example: health) which has clear interrelation in the attainment of Sustainable Development Goals (SDGs). COVID-19 pandemic also asks similar trade-off questions to policymakers which do not have an easy answer since livelihood and health are interrelated to each other. However, at the time of crisis governance and leadership requires synchronized team effort keeping societal benefit as the overall goal.
In Summary, India needs a resilient healthcare system not just to respond to healthcare emergencies but also in providing regular health care. It requires systemic thinking and greater investment by the government in providing comprehensive primary healthcare along with good tertiary care referral linkages. Considering the multi-dimensional impact of health in human development, investment in healthcare will always be a more cost-effective strategy that will bring prosperity to the nation.References
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About the AuthorDr. Alok Ranjan,
Humans have a history of building machines that increase productivity. As with any innovative technology, humans are at the center of Artificial Intelligence (AI) development. Just think about how many decisions have been made about you today, or this week, or this year, by AI. In the future, we will, no doubt, spend more time interacting with AI-enabled technology. I believe that the current pandemic situation, where we live in a socially distant world of uncertainty and isolation, has further accelerated this trend. So, does thisese mean humans are turning more into machines and machines are becoming more humans? There is a general perception in our population that robots are going to take over. Rather than focusing on this problem, we should reflect on the crucial difference between human and artificial intelligence and use it to build explainable and responsible AI. Such a system should be accountable to answering hard questions across the machine learning (ML) cycle which includes steps such as problem definition, collection and preparation of data, training model, evaluation, deploying, and monitoring the system.
Recent advances in machine learning research have resulted in state-of-the-art techniques where the Reinforcement Learning (RL) agents are focused on either using value-based methods or policy-based methods with the goal of reducing variance in the reward signal, thereby trying to reach an optimal state in the shortest period. Metrics such as the number of iterations taken to reach optimal reward structure, or the number of interactions needed with the environment to achieve this are generally used as key performance indicators. There is a large body of research work that shows how the agents can achieve this using either large amounts of training data or using complex algorithms that require power and resource-intensive computational elements. Such a strategy, however, may not be applicable for resource and power-sensitive network of Internet of Things (IoT) devices and more importantly differs fundamentally from how humans learn. To overcome this challenge, I have been looking at the field of neuroscience to derive inspiration from how the human brain works, specifically towards the release of dopamine in response to variable reward structure.
Human brain comprises of a number of neurons which are cells specialized for processing and transmitting information. The neurons typically comprise of the cell body, dendrites, and axon. A synapse is a structure present at the termination of axon which transmits the communication by releasing neuromodulation neurotransmitter. Dopamine is produced as a neurotransmitter by neurons whose cell bodies lie mainly in two clusters of neurons in the midbrain: the substantia nigra pars compacta (SNpc) and the ventral tegmental area (VTA). Dopamine plays essential roles in learning, action-selection, most forms of addictions, and disorders such as schizophrenia and Parkinson’s disease. Neurotransmitter imaging technique or the Single-scan Dynamic Molecular Imaging technique allows detection, mapping, and measurement of dopamine released acutely in the human brain during cognitive and behavioral processing . This technique exploits the competition between a neurotransmitter and its receptor ligand for occupancy of receptors on the synapse. In this technique the ligand binding (binding potential) is measured dynamically during performance of a ‘Control’ and ‘Test’ task. The ligand binding reduces significantly in the brain areas where dopamine is released endogenously during task performance (Test condition). This study shows that dopamine activity increases in the brain areas whenever learning and decision making takes place. Furthermore, dopamine is the neuromodulator involved in reward processing. The Reward Prediction Error (RPE) hypothesis of dopamine neuron activity postulates that one of the functions of dopamine-producing neurons is to deliver an error between an old and a new estimate of expected future reward to target areas throughout the brain as shown in Eq. 1.
RPE = Received reward -Predicted reward (1)
Dopamine release is less when the reward error difference is positive implying the received reward signal is greater than predicted reward, and negative value showcases the dopamine release is high.
Typical RL systems focus on singular agents receiving observations from the environment, calculating reward, and then deciding on the next set of actions at fixed intervals or based on fixed responses. Examples of such techniques include Temporal Difference (TD) reinforcement learning  that make changes in the agent’s policy and value estimates with no prior knowledge of the environment. This learning is represented in the form of TD errors which are a special kind of RPE that signal discrepancies between current and previous expectations of reward over the long term as shown in Eq. 2.
𝛿t-1=Rt + γ V(St)- V(St-1) (2)
Where 𝛿t-1is the expected TD error, Rt is the reward, γ is the discount rate chosen by the agent to focus on distant future reward or the immediate future reward, V(St) is the state-value estimate.
Human cognition is organized along the five principles of language, perception, memory, intelligence, and attention . My research takes inspiration from these principles of cognition to categorize the diverse set of information that is acquired using communication devices (language), various sensors (perception), and using pre-stored data on the onboard computer (memory). Early research results using Temporal Difference (TD) RL for the use-case of a self-driving car in an environment modeled using an extension of the 4x4 grid-world problem is shown in Fig 1 . Fig. 1(a) shows the environment, where the RL agent is in the object O1. In this use-case, observation of the environment is enabled using information fused from multiple sensors, such as cameras, radars, lidars and ultrasonic sensors. Fig. 1(b) shows results of TD RL simulation using only the cognitive principle of perception. Fig. 1(c) shows an improvement in performance using both language and memory. Key contribution of this research work is the reduction in number of iterations needed for learning and reduction in variation of error signals using TD with all three cognitive principles of language, memory, and perception.
While these early results are promising, my focus is to significantly expand the state of the art by taking inspiration from scientific research on human brain activity which has shown higher activity in dopamine release in response to rewards received at variable times and in response to learning and decision making happening in different parts of the brain. A popular RL model, called the actor-critic model , is used to for the main modelling of dopamine activity. A generic representation of this model is shown in Fig 2(a) where x1, x2, …, xn are inputs representing the observations from the environment. The ‘actor’ is the component that learns policies, and the ‘critic’ is the component that computes the value function to learn about whatever policy is currently being followed by the actor to ‘criticize’ the actor’s action choices. My ongoing research is to build a system that distributes this processing across two agents (like different parts of the brain) and based on information received from various inputs at different instances of time. On each agent, we leverage the temporal difference learning model that aids the critic in computing the value function that evaluates the total reward for the whole task, not just the immediate, next reward.
Fig. 2(b) demonstrates the application of neuro-inspired actor-critic model to the use-case of self-driving cars. The information can be gathered from multiple cars in a cluster, thereby leading to the scenario of cooperative learning. Self-driving cars communicate with each other using the concept of basic safety messages  which are broadcast at the rate of 10 messages per second. Such messages contain information like position, velocity and can result in new learning experiences at different time instances in a specified time epoch. By using the variable reward concept, the system can generate reward signals at variable duration, which can lead to increased learning. Likewise, distribution of decision making among the vehicles (actors) and central servers (clients) also results in distributed processing, thereby leading to stimulatinge more multiple brain regions to generate more participation for increased learning.
In summary, my research focus lies at the intersection of neuroscience and cognitive psychology. It investigates new techniques and results in areas related to dopamine-based reward-stimulated learning which supports the concept of cooperative learning. It explores novel techniques on how RL techniques can learn from this behavior, especially in a IoT network that may contain several resource constrained nodes. Rather than expecting agents running on all the nodes at fixed time intervals, my research investigates the efficiency gain by invoking agents at different time instances, thereby providing them with an opportunity to receive variable reward signals. Just like variable reward structure results in increased learning stimulated by higher dopamine activity in human brains, it can be postulated that such an approach can help achieve efficient learning in IoT network.References
|1.||R.D., Badgaiyan, A.J. Fischman, and N.M. Alpert, “Striatal dopamine release during unrewarded motor task in human volunteers”. Neuroreport, vol. 14, no. 11, pp.1421-1424, 2003.|
|2.||I. Kotseruba, and J.K Tsotsos, “40 years of cognitive architectures: core cognitive abilities and practical applications”. Artificial Intelligence Review, vol. 53, no. 1, pp.17-94, 2020.|
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|3.||H. Rathore, A. Samant, and M. Jadliwala. “TangleCV: A Distributed Ledger Technique for Secure Message Sharing in Connected Vehicles”. ACM Trans. Cyber-Phys. Syst. 5, 1, Article 6, 2021.|
About the AuthorDr. Heena Rathore is an alumnus of IIT Jodhpur (Class of 2016, PhD, Information & Communication Technologies) and is currently serving as Assistant Professor in Department of Computer Science at University of Texas, SA, USA.
There has been a rapid increase in the demand for high speed access to voice, video, and data over the past few years. Due to this exponential growth in the wireless data traffic, it is becoming challenging to satisfy the data-rate demands of mobile users because the available radio frequency (RF) communication spectrum is very limited. Since the RF spectrum is so congested and the data transmission rate of RF communications cannot satisfy the huge demand for large data transmission, free-space optical (FSO) communication systems have emerged as a possible new technology for the next generation of communication systems. FSO communication systems offer higher bandwidth and capacity in comparison to traditional RF communication systems. In addition, FSO links are license-free and cost-effective compared to the expensive and scarce RF spectrum . High data rate requirement in 5G and beyond communications requires backhaul links with much higher capacity and reliability relative to previous systems, especially in the context of network densification that makes wired backhaul an expensive solution. FSO systems can be explored for backhauling in 5G and beyond communications owing to their numerous benefits. Owing to the directional nature of the laser beam used in FSO transmitters, FSO systems are inherently secure. However, due to the divergence of the transmitted optical beam as a result of the turbulent nature of the FSO channel, if the eavesdropper is able to locate itself close to the legitimate receiver, it will be able to intercept the information -. Hence, it is important to analyse the security of the FSO systems, particularly at the physical layer (called physical layer security (PLS)). PLS techniques are specifically advantageous for 5G scenarios compared to cryptographic techniques due to their independence of computational complexity and decentralized nature of 5G networks. Some key contributions by Dr. Aashish Mathur in the area of secrecy of FSO systems are as follows:
|1.||F. Yang, J. Cheng, and T. A. Tsiftsis, "Free space optical communication with nonzero boresight pointing errors", IEEE Transactions on Communications, vol. 62, no. 2, pp. 713-725, Feb. 2014.|
|2.||G. D. Verma, A. Mathur, Y. Ai, M. Cheffena, "Secrecy performance of FSO communication systems with nonzero boresight pointing errors", IET Communications, vol. 15, no. 1, Jan. 2021, pp. 155-162.|
|3.||A. Sikri, A. Mathur, M. R. Bhatnagar, G. Kaddoum, P. Saxena, and J. Nebhen "Artificial noise injection--based secrecy improvement for FSO systems", IEEE Photonics Journal, vol. 13, no. 2, Apr. 2021, pp. 1-12, Art no. 7900412.|
|4.||Y. Ai, A. Mathur, L. Kong, and M. Cheffena, "Secure outage analysis of FSO communications over arbitrarily correlated Malaga turbulence channels," IEEE Transactions on Vehicular Technology, vol. 70, no. 4, pp. 3961-3965, Apr. 2021.|
About the AuthorDr. Aashish Mathur,
The static testing of a rocket engine is one of the critical and mandatory test procedures for any space mission involving rocket propulsion. Among the static testing of various rocket stages, the most challenging is the testing of the upper stage rocket motors. This is because the upper stage rocket motor should operate at a vacuum pressure condition and maintaining such low vacuum pressure in a test chamber is a challenging task. The conventional method of using vacuum pumps may not provide the required vacuum level due to the enormous mass flux ejected by the rocket motor into the vacuum chamber. An alternative method for creating low back pressure is by pumping out the fluid from the vacuum chamber using the nozzle jet itself. It is well known that a high momentum nozzle jet (primary flow) inducts and entrains the surrounding fluid (secondary flow) due to the momentum exchange between the two streams. This principle can be used to create a vacuum condition at the nozzle exit by exhausting the nozzle jet into a confined chamber with upstream closed and such devices are known as the vacuum ejectors or zero secondary flow ejectors [1-3]. In general, the high-altitude test facilities (HAT) used for testing upper stage rocket motors employ the vacuum ejector principle for maintaining the required vacuum level at the nozzle exit. A schematic of a typical high altitude testing facility is shown in Fig.1 (a). As shown in Fig.1 (a), the nozzle will be kept in a vacuum chamber (secondary chamber) and is connected to a diffuser system for pressure recovery. Two configurations of ejector-diffuser systems are commonly used for HAT systems, 1) straight ejector diffuser (SED) and 2) the second throat ejector diffuser (STED) system . The schematic of the two configurations can be found in Fig. 1(a) and 1(b), respectively. In a straight ejector diffuser (SED), a straight constant area duct is used as the diffuser section and in the second throat ejector diffuser (STED) system a convergent area section followed by a constant area duct is used as the diffuser section. Both these configurations utilize the shock cells developed in the duct for the pressure recovery.
It is seen from the literature that the ejector system in high altitude testing (HAT) facility operates in two modes during the initial transient starting phase, where the stagnation pressure in the combustion chamber builds up to a steady state. During this transient stage, the stagnation pressure (P0) at the inlet of the rocket nozzle increases and the jet expands continuously. In the first operation mode of the ejector, the jet boundary of the nozzle plume is not attached to the outer wall and this mode is called the un-started mode . However, at a specific inlet stagnation pressure P0, the underexpanded jet coming from the nozzle impinges on the outer wall and produces shock cells. These shock cells help in pressure recovery and the outer duct downstream to the impingement point acts as a diffuser. The condition at which the supersonic jet attaches with the outer duct is therefore referred to as the starting of the diffuser and the corresponding nozzle inlet stagnation pressure (P0st ) is called the starting pressure of the diffuser . As the diffuser attains the started mode, the jet boundary seals the vacuum chamber from the diffuser downstream and blocks further induction of fluid from the vacuum chamber. As a result of this, the minimum vacuum chamber pressure can be expected at the onset of started mode operation. The minimum vacuum level and the started mode pressure in vacuum ejectors depends on many geometric parameters, like, diffuser length to diameter ratio (L/D), nozzle position, nozzle area ratio, annular gap between the nozzle and the diffuser etc. There have been plenty of studies in the past to optimize these geometric parameters and yield better performance characteristics in vacuum ejectors used for HAT facilities. However, these optimization studies are limited by the inadequate understanding of the fluid dynamics of vacuum ejector diffusers, particularly during the starting process. Hence, this study investigates the process of vacuum generation in a second throat vacuum ejector system employed for the high-altitude testing of rocket motors using the computational fluid dynamics method.Results and Discussion
Fig.2 shows the nature of vacuum generation for a typical axisymmetric second throat ejector diffuser system used in high altitude testing. It is seen that the vacuum generation progresses with four distinct stages. The initial stage consists of a gradual and oscillatory vacuum generation which is followed by a transition region (stage-2) where the vacuum pressure drops faster. The transition stage progresses to a rapid evacuation stage (stage-3), where a rapid reduction in vacuum chamber pressure is observed and this has been reported by many past studies. However, the present numerical study shows that the rapid evacuation is again followed by a gradual vacuum generation process, before reaching the minimum vacuum level or the started mode (labeled as stage-4 in Fig.2). A recent experimental study by Arun et al. [4-5] also reported the presence of a gradual evacuation stage after the rapid evacuation process. However, the reason behind the reappearance of gradual evacuation after the rapid evacuation was not properly explained in their study.
In order to further investigate the physics behind the staged evacuation, the static pressure histories at the nozzle exit and in the vacuum chamber have been compared (Fig.3 (a)), since the fluid induction from the vacuum chamber strongly depends on the pressure gradient between these two sections. The transient start-up process can be essentially divided into several quasi-steady processes and the vacuum chamber attains a quasi-steady pressure condition during each of these processes through a dynamic pressure equilibrium existing between the vacuum chamber and the primary jet at the nozzle exit plane. This can be clearly noticed from Fig. 3(a), which shows that the vacuum generation in the vacuum chamber closely resembles the pressure history at the nozzle exit. During the stage-1 evacuation, the nozzle exit pressure is relatively higher and it reduces gradually, as shown in Fig. 3(a). The static pressure contours during the stage-1 evacuation (Fig. 3(b)) show that the nozzle is at un-started mode with multiple shock cells appearing inside the nozzle. Due to these multiple shock reflections, the pressure at the nozzle exit will be larger during the stage-1 evacuation, which in turn results in a relatively larger vacuum chamber pressure (Fig. 3(b)). From Fig. 3(a), it is observed that at a time instant close to 0.414 seconds the slope of the static pressure curve suddenly changes and this is identified as the starting of a ‘transition region’ where the gradual evacuation starts changing to a rapid evacuation (point ‘c’ in Fig. 3(a)). It is observed that this ‘transition region’ starts when the second shock cell leaves the nozzle exit (Fig. 3 (c)). It is also seen from Fig. 3(a) that at a time instant close to 0.464 seconds, the ‘transition region’ changes to a rapid evacuation stage, where a rapid rate of vacuum chamber pressure reduction can be seen. (Point ‘e’ in Fig. 3(a)). The methodology by which the onset of rapid evacuation has been computed is mentioned in the appendix. This rapid reduction in static pressure at nozzle exit can be related to the dynamic movement of shock wave in the nozzle during the start-up process, as shown in the pressure contours in Fig. 3(e) to 3(h). As the primary jet total pressure increases the shock structure in the nozzle moves downstream with the shock cells exiting the nozzle. After a certain pressure ratio, only the first shock cell remains inside the nozzle, as shown in Fig. 3(d). A slight increase in stagnation pressure from this stage (point-e in Fig. 3(a)), results in a rapid movement of the first shock cell to the nozzle exit plane, as clearly observable from Fig. 3(e) to 3(h). The first shock cell exhibits a Mach reflection structure with an incident shock, a reflected shock, and a Mach stem meeting at a single point. As the first shock cell is pushed towards the nozzle exit plane, the reflected shock wave in the Mach reflection structure exits the nozzle, as shown in Fig.3 (e) to 3(h). It should be noted that in a Mach reflection shock system, the pressure rise will be maximum behind the reflected shock wave and as it rapidly moves out of the nozzle exit plane, the static pressure at the nozzle exit reduces suddenly which in turn reduces the vacuum chamber pressure. The comparison of the pressure history at the vacuum chamber (points ‘e’ to ‘h’ in Fig. 3 (a)) and the corresponding pressure contours in the nozzle (Fig.3 (e) to 3(h)) clearly shows that the rapid reduction in vacuum chamber pressure happens during the rapid movement of the reflected shock wave out of the nozzle exit plane.
|1.||R. C. German, R. C. Bauer, Effects of diffuser length on the performance of ejectors without induced flow, Technical Report AEDC-AEDC-TN61-89, 1961.|
|2.||W. L. Jones, H. G. Price Jr., C. F. Lorenzo, Experimental study of zero-flow ejectors using gaseous nitrogen, NASA Technical Note D-230, 1960.|
|3.||R. C. German, J. H. Panesci, H. K. Clark, Zero secondary flow ejector-diffuser performance using annular nozzles, Technical Report AEDC-AEDC-TDR-62-196.|
|4.||R. Arun Kumar, Gopalapillai Rajesh, Physics of Vacuum Generation in Zero-Secondary Flow Ejectors, Phys. Fluids 30 (6) (2018) 066102.|
|5.||G. Bharate and R. Arun Kumar, Starting Transients in Second Throat Vacuum Ejectors for High Altitude Testing Facilities, Journal of Aerospace Science and Technology, 113, 2021.|
About the AuthorsGhanshyam Bharate,
It is well known that the study of many processes of the natural sciences, engineering and modern world can be reduced to an integral equation and integro-differential initial and boundary value problems to set up, solve, and interpret complicated mathematical models. The problem concerns differential equations, integral equations, and a system of equations derived from conditions on initial or other points, so in most of the cases these equations become too complicated to be solved explicitly. Thus we need to approximate the solution numerically. There has been a notable interest on solving the integral and integro-differential equations of second kind by various numerical techniques. But it will be more profitable to adopt some efficient, convenient method based on piecewise and global polynomial based projection methods such as Galerkin, collocation, multi-Galerkin, multi-collocation methods and their iterated versions to get asymptotic convergence rates for the approximate solutions.
In , we considered a class of derivative dependent Fredholm-Hammerstein integral equations i.e., the integral equation, where the nonlinear function inside the integral sign is dependent on derivative and the kernel function is of Green's type. We proposed the piecewise polynomial based Galerkin and iterated Galerkin methods to solve these type of derivative dependent Fredholm-Hammerstein integral equations. We discussed the convergence and error analysis of the proposed methods and also obtained the superconvergence results for iterated Galerkin approximations. Some numerical results are given to illustrate this improvement.
In (, ), we developed a Jacobi spectral Galerkin method for weakly singular linear and nonlinear Volterra integral equations of the second kind. We applied some variable and function transformations to convert the equation into the new equation, so that the solution of the transformed equation possesses the better regularity results and theory of Jacobi polynomials can be applied in the approximation. The motivation to consider the Jacobi spectral method is that the singularity of the kernel of the Volterra integral equation is incorporated in the weight function to obtain the superconvergence results. Here, we provided the convergence rates of the approximate solution in two cases, when the exact solution is sufficiently smooth and when the exact solution is non-smooth. Theoretical results are verified by numerical illustrations.
In , we discussed the discrete Legendre spectral and iterated discrete Legendre spectral methods for the convergence analysis for Hammerstein type weakly singular nonlinear Fredholm integral equations, with algebraic and logarithmic type kernels. In fact, by choosing the minimal quadrature rule, i.e. the number of quadrature nodes equal to the dimension of the approximating subspace, we obtained the optimal convergence rates in discrete Legendre spectral method in L2 norm and obtained the optimal convergence rates in iterated discrete Legendre spectral method in L2 and uniform norm. In , we also discussed the discrete Legendre projection methods for a weakly singular compact integral operator to find the error bounds for the approximate eigenfunctions. We showed that eigenfunctions in the iterated discrete Legendre Galerkin method have optimal convergence rates in L2 and uniform norm. Numerical examples are presented to illustrate the theoretical results.
Recently in , we focus on finding the approximate solution for a large class of integro-differential equations of second kind, which is necessary but difficult due to the fact that the differential operator is not bounded in general. Let X= C[-1,1]. Consider the following non-linear Fredholm integro-differential equation of the second kind
where the kernel k, right hand side function f and ϕ are known functions, x is the unknown function to be determined. It is also known that the quality of the approximate solution of the corresponding operator equations in function space depends essentially on the smoothness of the functions, which define the equation. Here the restriction concern continuity or discontinuity of the Green's kernel, which arises due to the semihomogeneous differential operator. In spite of this difficulty, there has been a notable interest on solving the integro-differential equations of second kind by various numerical techniques. However, there is no literature available for the convergence analysis for higher dimensional nonlinear integro-differential equations. Here, our research work concerns the mathematical analysis of existence, uniqueness, convergence rates of the approximate solution by spectral projection methods for n^th order linear and nonlinear integro-differential initial and boundary value problems with smooth kernel given by (1). We also discuss the significant features of these methods as well as shed some light on advantages of one method over the other (For example, for n=2, with the kernel function k (s ,t)=s(s^2-t), and the function f(s)= 5/4-1/3 s^2 and ϕ(t,x(t))=((x(t)))^2 in equation (1), the numerical error comparison of the proposed methods are given in Fig 1 & Fig 2). We also give the numerical implementations to show that the size of the system of equations that must be solved in multi projection methods remain same as in projection methods. Numerical aspects are also considered to illustrate the theoretical results.
|1.||Moumita Mandal, Kapil Kant and Gnaneshwar Nelakanti, Convergence analysis for derivative dependent Fredholm-Hammerstein integral equations with Green's kernel, Journal of Computational and Applied Mathematics 370 (2020), 112599|
|2.||2. Kapil Kant, Moumita Mandal and Gnaneshwar Nelakanti, Error Analysis of Jacobi spectral Galerkin and multi Galerkin methods for Weakly singular Volterra Integral equations, Mediterranean Journal of Mathematics, 17(1) (2020), 20|
|3.||3. Kapil Kant, Moumita Mandal and Gnaneshwar Nelakanti, Jacobi Spectral Galerkin Methods for a Class of Nonlinear Weakly Singular Volterra Integral Equations, Advances in Applied Mathematics and Mechanics (2020) (Accepted).|
|4.||4. Moumita Mandal, Kapil Kant and Gnaneshwar Nelakanti, Discrete Legendre Spectral Methods for Hammerstein type Weakly Singular Nonlinear Fredholm Integral Equations, International Journal of Computer Mathematics (2021) 1-19|
|5.||5. Moumita Mandal, Kapil Kant and Gnaneshwar Nelakanti, Eigenvalue Problem of a Weakly Singular Compact Integral Operator by discrete Legendre Projection Methods, Journal of Applied Analysis and Computation (2021) (Accepted).|
|6.||6. Moumita Mandal and Gnaneshwar Nelakant, Approximation solution for a class of non-linear Fredhlom integro-differential equations by projection method (In process).|
About the AuthorDr. Moumita Mondal,