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Background, Motivation, and Objective – Inculcating critical thinking, teamwork, and lifelong learning are the basic tenets of Outcome Based Engineering Education (OBEE). Collaborative learning is one such pedagogy of engagement to achieve the same. In addition, the implementation of OBEE demands the adoption of corporate best practices in deploying new initiatives, which streamlines the process, removes waste in the process, and ensures sustainment of the improved process. Lean Thinking is one such approach adopted by the industry to ensure productivity, performance, and sustainment of new initiatives. The objective of this paper is to study the scope of collaborative learning in OBEE based on the Lean Thinking approach, and to explore its successful deployment in Indian higher education. Statement of Contribution/Methods – The research reported in this paper is based on Action Research methodology carried out using the Lean Thinking PDCA (Plan-Do-Check-Act) approach and its application in improving OBEE through collaborative learning in India. STAD (Student-Teams-Achievement-Division) collaborative learning was deployed through heterogeneous group formation for enhancing the teaching-learning process. The Indo-Universal Collaboration on Engineering Education (IUCEE) consortium knowledge base and assistance has been effectively used to chalk out the strategy for effective deployment. Basic statistics and outlier analysis is used to draw the inferences. Results, Discussion, and Conclusions – The study describes the effective deployment of collaborative learning to enhance the effectiveness of the OBEE ecosystem through students' performance by utilizing the power of Lean Thinking. The study shows that not only Turn-Around-Time (TAT) of the deployment process is enhanced, but also the attainment of the students' performance. Moreover, it improves team spirit and team performance during the process. The paper is based on a single case study executed in an engineering college and hence, there is limitation in generalizing the specific results from the study. But the approach adopted and the learning from this study can be generalized. The study is of use to academicians, researchers, practitioners, and policymakers seeking to implement collaborative learning for an OBEE set- up with optimal utilization of the available resources based on the Lean Thinking platform in their organizations. This approach ensures value-added activity throughout the process and effective utilization of industrial best practices to higher education. Although collaborative learning and Lean Thinking have been extremely successful in the education sector and the corporate sector, respectively, the application of both to the engineering education has been limited. In addition, it describes the end-to-end implementation strategy of OBEE starting from planning, execution, meticulous assessment, and sustainment of the improved process in a step-by-step approach. This study illustrates how its application can improve the performance of the process and thus, productivity in the course. [ABSTRACT FROM AUTHOR]

In India, around 1.5 million engineers graduate every year from various colleges and universities but 80 percent of them do not have the skills required to get employed. If they do, only 3% of them have the skills. Some of the reasons for this are student's attitude towards learning and teacher's attitude towards teaching. This leads to problems such as lack of student's attendance, lack of student's attention in the classroom, high failure rates, marks conflict between teacher and students, students unable to excel in the placement interviews, etc. However, it is the responsibility of the colleges/institutions to provide necessary tools/trainings to the teacher and the students so that the above problems could be solved without compromising the quality of the education. Therefore, the main objective of this paper is to develop a teaching-learning framework which addresses the above problems. In the teaching-learning framework, several teaching pedagogies are introduced. In this paper, 16 different teaching pedagogies are used in various classrooms of undergraduate and graduate programs. The teacher should use different teaching-learning methodology (not same methodology) in a classroom to enable the maximum learning. Based on the past seven years (2011-2018) of handling undergraduate (2135 students) and graduate (340 students) in a total of 60 courses data, five teaching-learning methodologies are proposed. Some of the outcomes of this study are: (a) improved the classroom attendance by 14%; (b) reduced the course failure rate to 2.02%; (c) improved the industry-academia relationship by getting more student projects (90%); (d) twelve journal papers published along with students, etc. [ABSTRACT FROM AUTHOR]

In the past few decades, many urban areas around the world have suffered from severe air pollution and the health hazards that come with it, making gathering real-time air quality and air quality forecasting very important to take preventive and corrective measures. This paper proposes a scalable architecture to monitor and gather real-time air pollutant concentration data from various places and to use this data to forecast future air pollutant concentrations. Two sources are used to collect air quality data. The first being a wireless sensor network that gathers and sends pollutant concentrations to a server, with its sensor nodes deployed in various locations in Bengaluru city in South India. The second source is the real-time air quality data gathered and made available by the Government of India as a part of its Open Data initiative. Both sources provide average concentrations of various air pollutants on an hourly basis. Due to its proven track record of success with time-series data, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model was chosen to perform the task of air quality forecasting. This paper critically analyses the performance of the model in two regions that exhibit a significant difference in temporal variations in air quality. As these variations increase, the model suffers performance degradation necessitating adaptive modelling. [ABSTRACT FROM AUTHOR]

• Weather and climate services (WCS) for farmers in India and Norway reveal similar governance challenges related to format, relevance, and tailoring of knowledge communicated. • A 'most different case study' comparison underscores that WCS remain supply-driven. • More deliberate approaches to institutional design is suggested to foster co-creation and improved engagement of farmers and extension agents in developing the services. • Active use of social media, such as crop-based WhatsApp groups, can facilitate integration between external science-based knowledge/models and local practice and learning. High-quality weather and climate services (WCS) can be critical for communicating knowledge about current and future weather and climate risks for adaptation and disaster risk management in the agricultural sector. This paper investigates the structure and performance of weather and climate services for farmers from a governance perspective. Empirically the paper compares the institutional design and operations of agro-meteorological services in Maharashtra/India and Norway through a 'most different case study' approach. The two cases were selected to represent great diversity in location, scale and institutional design. A governance approach based on semi-direct interviews and policy and institutional analysis was combined with local survey data of farmers' perceptions and use of the services. Despite the fact that the context for the two agromet advisory services was very different from a climate-weather, eco-agriculture and socio-institutional angle, the analysis reveals great similarities in the services structures and critical governance challenges. In both countries the agromet services communicated knowledge that was largely perceived not to be well tailored to farmers' needs for decisions in specific crops- and farm operations, spatially too coarse to address local issues, and, often unreliable or inaccurate in terms of the quality of data. Farmers did, however, respond positively to specific and locally relevant information on e.g., warnings about high rainfall and spread of pests. Observing such similarities across very diverse contexts enhances the generalization potential, precisely because they evolved under very different circumstances. Similar observations find support in the wider WCS literature. Based on the empirical findings, we propose a more deliberate approach to institutional design of WCS in order to enhance governance performance and co-creation of the services at local, district and national scales. It is suggested that greater participation of farmers and agricultural extension agents in the co-creation of these services is a necessary means of improving the services, supported by the WCS literature. However, we insist that greater participation is only likely to materialize if the deficiencies in institutional design and knowledge quality and relevance are addressed to greater extent than done today. The comparison between the two services shows that Norway can learn from India that a more ambitious scope and multiple forms of communication, including the use of social media/WhatsApp groups, can facilitate greater awareness and interest among farmers in multi-purpose agromet services for multi-way communication. India can learn from Norway that a more integrated and decentralized institutional design can strengthen the network attributes of the services, foster co-creation, and improve participation of both poor and large-scale farmers and extension agents. [ABSTRACT FROM AUTHOR]

Since the 2000 s the demand for sand has proliferated at the coastal-land interface to fill up the increasing demand straining Blue Economy (BE) activities and provision of water-energy-food resources. Recent studies have revealed that increased sand mining in both coastal and freshwater zones has continued to impact livelihood-ecological systems threatening the provision of livelihood goods and services. These impacts are exacerbated by a lack of comprehensive frameworks to regulate sand mining and trade; creating the need to develop micro and macro-frameworks and guidelines for sustainable sand mining. This paper uses a non-systematic literature review approach to build on this gap to develop an understanding of the resource nexus perspectives and trade-offs due to sand mining. The paper proposes a novel framework based on the Ecosystem Service Assessment advanced from the review of the literature to guide risk assessments toward more sustainable sand mining. In order to add evidence, the paper analyses in-depth the state of Kerala - one of India's coastal states that has experienced unprecedented rates of sand mining since the 1990 s especially along the Chavara coast albeit with less research on the intersectionality of mining on the resource nexus. Both the framework and our case study highlight how sand mining stresses local ecosystems and livelihoods thus increasing vulnerability to both human and environmental impacts. The paper brings to the fore seven (7) key steps that local institutions can use to guide sustainable sand mining and build integrated governance systems that promote interaction among natural capitals in a given area and livelihood considerations. The article further documents that the use of coherent guidelines and the framework can help in amalgamating the various actors in a given system that can guide local participation in local resource management and the development of cooperative agreements for the sustainable utilization of resources among coastal communities. This could further help understand the resource nexus from the perspective of the synergies and trade-offs in the BE. • The Blue Economy and Resource Nexus offer new pathways for sustainable livelihoods. • But, the proliferation of sand mining is increasing trade-offs thus livelihood and ecosystem vulnerability. • A lack of a comprehensive sand mining governance framework increases such vulnerability. • The developed novel framework based on Ecosystem Service Assessment can help reverse these externalities and trade-offs. • Our framework integrated new insights on sand mining governance that could be leveraged at all levels [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

Air pollution control has emerged as one of the key concerns of recent times and business firms are one of the significant stakeholders in deteriorating the quality of the natural environment. The purpose of this paper is to analyse the factors affecting the adoption of air pollution control measures across firms and contribute to the existing knowledge on pollution control among firms in the context of an emerging market. The analysis shows a significant association between enterprise characteristics and the adoption of air pollution control measures and about 44.3 percent firms have reported the adoption of air pollution control measures. Further, the marginal effect analysis indicates that firms older in age, larger in size and having shareholders' equity are more likely to adopt air pollution control measures. Similarly, firms practising lean operations, investing in research & development, availing bank credit, having quality certification and having better capacity utilisation are more likely to adopt air pollution control measures. Finally, firms perceiving obstacles related to business licensing & permits, tax administration, political instability, and business closure due to COVID-19 outbreak are more likely to adopt air pollution control measures. Our study provides useful implications for managers, policymakers and researchers for encouraging air pollution control technologies across business firms. The study also motivates undertaking future research on pollution control measures across various sectors of the economy. [Display omitted] • Air pollution control is becoming a critical agenda across the globe. • About 44.3 percent Indian firms reported the adoption of air pollution control measures. • Firms' characteristics are significantly associated with adopting air pollution control measures. • Business operations, regulatory mechanisms and obstacles influence the adoption of air pollution control measures. • These insights help develop strategies for reducing air pollution by involving managers, policymakers and researchers. [ABSTRACT FROM AUTHOR]

Due to the large-scale integration of renewable energy and inevitable disturbing events (including natural disasters and deliberate attacks), the invulnerability of power grids is facing a great challenge, thus the security criterion is demanded to upgrade from N − 1 to N − k. However, with the increasing of k and the expanding of network scale, resource consumption of network expansion shows an exponential explosion. Therefore, it is necessary to improve the invulnerability of power grids from a new perspective. In this paper, the definition of nodal N − k security criterion is proposed, which divides buses into different security levels based on realistic demand, and decouples the security analysis from the system level to individual nodes. A two-stage robust optimization model is established, cooperating with the nodal N − k security criterion, the load demand of critical buses could be guaranteed. Moreover, the proposed discrepant upgrade scheme is flexible and has strong generalization performance, where enhanced buses and security levels can be selected independently based on the preferences of decision-makers. The effectiveness and efficiency of the proposal have been demonstrated in 5 cases, i.e., the IEEE 9-bus, 24-bus, 57-bus, 118-bus systems and an actual test system in India. • The definition of nodal N − k security criterion is proposed. • A defender–attacker–defender (DAD) model cooperating with the criterion is established. • A gradual and progressive enhancement framework is provided. [ABSTRACT FROM AUTHOR]

India has been voluntarily following the guidelines on space debris mitigation recommended by UN-COPUOS (United Nations Committee on the Peaceful Uses of Outer Space) and IADC (Inter-Agency Space Debris coordination Committee) to the maximum possible extent towards safe and sustainable operations in outer space. As a part of the ongoing efforts for better compliance with these guidelines, post-mission disposal of Low-Earth-Orbit (LEO) objects has been carried out in the recent past. Cartosat-2, which was operational at 630 km altitude, is the first satellite to be de-orbited at its end-of-life through a series of perigee reduction orbit maneuvers to minimize its presence in the LEO region. Consequently, the post-mission orbital lifetime was reduced from more than 30 years to less than 5 years in compliance with the 25-year rule recommended in IADC space debris mitigation guidelines. The de-orbiting exercise utilizing left-over propellant also achieved the objective of minimising any accidental break-up risk. This paper outlines the relevant analyses and planning aspects of post-mission de-orbiting for Cartosat-2. The lessons learnt and recommendations on post-mission disposal for future LEO missions are also presented. [ABSTRACT FROM AUTHOR]

• Identified 30 agro-ecosystem specific sustainability indicators. • Sustainability indicators help in mapping the climate resilience of agriculture. • We propose a framework, "Climate Risk Management Package for Agriculture (CRiMPA)", to aid in planning interventions. The impending threats of changing climate have been well documented across sectors. The climate risks are best addressed through increasing adaptive capacity and building resilience. Ever since the global call during the Rio Summit in 1992 for establishing sustainability indicators across sectors, there have been several studies across the world on developing indicators for sustainability, vulnerability and climate resilience. Agriculture, the most vulnerable system to changing climate, depends on the resilience of both social and ecological systems. This paper focuses on integrating the variability of climate into the agricultural sustainability measurement with a broad base of indicators and bringing in the localized factors for representing the agroecosystem specificities. The paper also aims at identifying indicators for measuring climate resilient agriculture in Indian sub-continent and developing a conceptual framework for profiling the spatial resilience across various agro-ecosystems for appropriate location-specific policy interventions. In the current study 1209 indicators used in various research studies were screened, grouped for similarity and purpose and classified based on the various dimensions viz., social, economic, ecological, etc. After a critical review based on their appropriateness as a measurable indicator, extent of overlap, relevance in Indian context and possible data availability, 41 indicators were shortlisted for validation through a comprehensive structured online survey among subject matter specialists (n = 225). The responses from the experts (n = 36) were analysed using weighted sum model (WSM) and analytic hierarchy process (AHP). The study identifies a list of 30 sustainability indicators for climate resilient agriculture in India, that are particularly suitable for different agro-ecosystems of the sub-continent. The authors advocate an action-oriented model called Climate Risk Management Package for Agriculture (CRiMPA) to aid in planning spatial/agro-ecosystem specific interventions, which in turn could strengthen the National Action Plan for Climate Change (NAPCC) of Government of India. [ABSTRACT FROM AUTHOR]

• Decoupling of GDP from all energy sources under multiple principles are examined. • World economy achieved weak energy decoupling in most years during 2000–2011. • World economy coupled with coal use and tended to couple with cleaner energy use. • None of studied economies achieved long-term GDP-embodied energy decoupling. • Decoupling delusions occurred when only considering direct and total energy use. Decoupling indicators are widely used to understand links between economic growth and energy use. However, traditional decoupling analyses mostly focus on domestic energy consumption (i.e., the production-based principle) and neglect off-site energy use across global supply chains to satisfy an economy's final consumption (i.e., the consumption-based principle). Moreover, analyses for total primary energy conceal an economy's preference for different energy sources. Therefore, this paper evaluates decoupling states of GDP from all types of primary energy use under consumption-based principle, for world economy and eight typical economies during 2000–2011. Regarding total primary energy, world economy witnessed weak decoupling in most years, and most economies studied (e.g., USA, Japan and China) achieved decoupling initially but performed negative decoupling finally. For EU, USA, Japan, Russia and India, production-based decoupling performances were generally better than consumption-based ones. Decoupling phenomena detected under production-based principle even became coupling or negative decoupling under consumption-based principle in some cases. As for each energy source, world economy decoupled from oil use, but still coupled with coal use, and gradually showed a trend to couple with natural gas and renewables use. Different energy sources showed distinct decoupling degrees from GDP, affected by individual embodied energy requirement structure. This paper uncovers potential energy decoupling delusions to deepen the understanding of relationships between energy use and economic growth. [ABSTRACT FROM AUTHOR]

China and India are two major contributors to the growth of global energy demand. As energy is one central part of the geopolitics, the energy security for both countries get complicated by the political environment. The paper investigates the positions of China and India in the trading relationships with different regions of the world. The paper also examines recent events that affect the energy security of China and India. The conclusion is that during the past decade, China and India have diversified sources of energy, especially from the Middle East, though China demonstrates a higher level of diversification and occupies a less passive position in its trading relationships. Shale revolution, the US energy sanctions on Iran and Venezuela and the US-Chinese trade conflicts, can bring China, India and Russia closer to each other due to the shrinking market influence of Russia in Europe and the needs of India and China to further diversify import sources so that energy supplies will not be disrupted by political chaos. [ABSTRACT FROM AUTHOR]

Increasing demand of automotive products and over-consumption of natural resources compel the manufacturers to implement the Extension of Useful Life (EUL) methodology of automotive products through remanufacturing. Remanufacturing is a process in which the original functionality of End-of-Use (EoU) or End-of-Life (EoL) product is restored and the remanufactured product is treated as good as a new product. In spite of these advantages, remanufacturing is still not so popular and organized in developing countries like India. The developing countries encounter several problems in strategic, tactical and operational level of management while implementing the remanufacturing business option. In this paper attempts have been made to identify both the enablers and the barriers in managing business of EUL products through remanufacturing in India. Subsequently the structural relationships among the enablers and barriers have been modelled in this paper. Initially an extensive literature review is carried out to identify the factors depicting the enablers and barriers. Then, Fuzzy Interpretive Structural Modeling (FISM) is applied to develop and evaluate the structural relationships among the identified factors. The application of FISM helps in understanding the effect of major enablers and barriers in extending useful life of automotive products through remanufacturing. The outcome of FISM based analysis shows that low cost, price sensitive market, cheap labour cost, and fast turnaround are the major enablers for OEM in initiating EUL business practice through remanufacturing, whereas the major barriers are lack of proper design of product, stringent foreign trade policy, cannibalization of new product, lack of proper remarketing strategies, lack of reverse network design, lack of proper maintaining the database of the customers, seller reputation and identity. The analysis is further enriched by MICMAC technique, which leads to getting an insight on the significance of the dependence and driving power of both the enablers and barriers. [ABSTRACT FROM AUTHOR]

Recently, Indian manufacturing industries have undergone a significant transformation emphasising customer-centric objectives to meet the dynamic market's demands. To achieve this, existing industries must upgrade their existing infrastructure to a smart environment driven by automatic data exchange, transparency, and interconnected digital systems. These are the key enabling elements of Industry 4.0. As a result, many Indian industries are developing various sophisticated implementation strategies for adopting it. However, few research works and strategies contribute to its effective and successful implementation. This paper proposes two types of implementation strategies for implementing Industry 4.0 enabling technologies based on the requirements and adaptability of India's commercial vehicle manufacturing industry. The first strategy identifies various aspects of implementing Industry 4.0 enabling framework elements and categorizes it for developing implementation strategies based on India's commercial vehicle manufacturing industry. The significance of each was determined using fuzzy-AHP analysis and ranked accordingly based on the linguistic responses of the industry experts. The most significant framework element is found to be 'Decision Making and Management Skills'. The second strategy emphasizes long-term sustainable implementation objectives. The framework elements are reconstructed and developed into a novel, customized sustainable framework based on six dimensions of sustainability. Using the Fuzzy-DEMATEL method, the causal relationships among these elements are investigated to determine a sustainable implementation strategy. 'Central Database Server' is determined to be the most significant sustainable framework element, while the maximum number of casual relationships is obtained from the group, 'Skill upgradation of the workforce'. This paper proposes a novel research direction for implementing Industry 4.0 technologies into the existing industry. The findings provide several insights and strategies for technological upgradation in other industries in this geographical region. The findings of the work enhance the implementation process of I4.0 technologies, transitioning it from a conventional approach to a futuristic sustainable implementation that is economical and effective. • Industry 4.0 enabling framework elements for Indian manufacturing industry were identified and categorised. • Significance of each framework element are determined using Fuzzy AHP. • A customised sustainable framework of I4.0 enabling elements is presented. • Fuzzy DEMATEL approach used to examine their causal relationships and significance based on long-term sustainability. • Sustainable implementation strategies and implications are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we have considered a mathematical model that deals with the effectiveness of the measures that may be helpful for reducing the spread of the COVID-19 virus in the society. Here we have illustrated the importance of lock down in controlling and maintaining the spread of the COVID-19 virus. The impact of the virus on the susceptible population has been considered in the model. Also, we have taken into account the susceptible population, which by taking preventive measures viz., by having strong immunity, maintaining social distancing, wearing PPE kits and masks etc., is able to reduce the possibility of getting infected from the virus. Local as well as global stability of the equilibrium points of the model have been studied using Lyapunov function and the geometrical approach techniques. Basic reproduction number has also been obtained by using the next generation matrix. To show the effectiveness of the model, different cases obtained by varying the parameters involved in the model have been considered. A comparison between the actual number of infected cases in India and that obtained by the proposed model, showing the effectiveness of the proposed model, has also been carried out. [ABSTRACT FROM AUTHOR]

The contrivance of this paper is two-dimensional. The first is to formulate an extended Dombi-Hamy mean operator in an interval-valued neutrosophic environment for assembling inter-related multiple parameters. The second one is to develop a new ranking procedure named as GLF ranking by introducing the concept of Gaussian likelihood function for solving multi-criteria decision-making problems in an interval-valued neutrosophic circumstance. Also, we have proposed a novel algorithm named Gaussian likelihood function based algorithm or GLF algorithm in solving multi-criteria decision-making problem from the field of medical science to find out the vital cause responsible for the occurrence of cancer in India. Finally, we have verified the flexibility and etiquette of GLF method by comparing it with the existing multi-criteria decision-making methods such as Aiwu's method and Jun Ye's method. • The formulation of the Gaussian likelihood function and its characteristics. • Research into a new GLF ranking system has been done. • The Dombi–Hamy mean operator is an aggregation operator that has been created. • A brand-new GLF strategy has been developed to address practical MCDM issues. [ABSTRACT FROM AUTHOR]

Abstract Physical, Regulatory and Brand value risks due to climate change challenges are foreseen by businesses globally. This paper is oriented towards these challenges and prepared a comprehensive Greenhouse gases (GHG) footprint plan and identification of mitigation opportunities for ONGC, a fortune 500 and national oil company of India. Internationally recognized standards and guidelines like Greenhouse Gas Protocol, ISO-14064, and American Petroleum Institute compendium have been referred for quantifying carbon emissions in nearly real 400 + operation control facilities. The GHG inventory is based on Operational Control approach to identify the boundary for which emissions are to be quantified and two significant scopes of emissions viz Direct Emissions and Indirect Emissions are identified and quantified. The quantification concluded that GHG emission footprint for the year 2016–17 is in the range of 9 million tons of CO 2 equivalent. GHG emissions mainly account due to stationary combustion, fugitive emissions and wastewater treatment, which share the around 56% of total emissions. Another 24–26% of emissions are contributed due to flaring, processes by glycol dehydrator, acid gas removal units, and by electricity purchased from the grid. As a result, it has been found that ONGC emits 187 kg CO 2 e emissions per tonne throughput and also compared with its global peer organizations. At the organization level, "Key Industrial Sites" have been identified as contributing higher GHG emissions and accordingly key "hot-spots," a list of 15 GHG mitigation opportunities have been proposed in the paper. Two GHG mitigation initiatives based on solar energy and bioenergy application undertaken at ONGC has also been briefed on case studies. Highlights • Comprehensive carbon footprint plan and identification of mitigation opportunities for Oil and Natural Gas Corporation (ONGC), a fortune 500 and national oil company of India. • Guidelines like Greenhouse Gas Protocol, ISO-4064 and American Petroleum Institute compendium are referred for quantifying carbon emissions from direct and indirect sources. • Green policy, incentives and mandatory compliance. • CO2e emissions per tonne throughput and also compared with its peer global organisations. • Key hot-spots, with a list of 15 GHG mitigation opportunities. [ABSTRACT FROM AUTHOR]

The use of non-imaging collectors has a wide scope in process heat applications based on its performance and economics. An experimental investigation of trapezoidal/concave cavity surface receiver (TSR) for non-imaging solar concentrating collector is carried out in this paper. The implementation of surface/coil receiver instead of tubular receiver for non-imaging collector is presented in this paper. A TSR with helical coil is developed for non – imaging concentrating collector, Elliptical Hyperbolic Collector (EHC). Experiments are carried to estimate the thermal performance of the system under various operating conditions such as two operating modes: series and parallel modes of operation of the collector, two circulation modes: passive and active modes. The stagnation temperature of the trapezoidal/concave cavity surface receiver is measured to be 118 °C on a clear sunny day in October and 102 °C on a cloudy day in February. The daily performance tests are performed under different operating conditions. Based on the experimental study, for the flow rate of 0.03 kg/min and 0.5 kg/min, the fluid outlet temperature is estimated to be 87 °C at 768 W/m 2 and 49 °C at 908 W/m 2 respectively. The corresponding instantaneous efficiency was calculated to be 9% and 40% respectively. The numerical model is developed to predict the temperature of the fluid along the receiver. The pressure drop across a receiver is estimated to be 9.3 kPa for a flow rate of 0.5 kg/min. Exergy analysis of the system is carried out and it ranges between 10 and 20%. The costs involved in fabricating the EHC system are compared to that of a non-imaging concentrating collector (CPC) of same aperture area. An economic analysis of the system is also carried out to study the feasibility of the system based on the life cycle savings method by estimating the annual solar savings from the EHC system. The present system can be a suitable option for low and medium temperature process heat applications. [ABSTRACT FROM AUTHOR]

Information and communication technologies applied to education through e-learning innovative solutions emerge as a relevant driver of change in the publishing industry, involving not only the supply of new educational resources, but also a reduction of paper consumption used in the traditional book edition. The dual goal of this paper is, firstly, to identify sustainable business model archetypes that illustrate the behavior and potential growth of firms in the e-learning industry and, secondly, to provide evidence of the existence of these archetypes through the study of strategic actions and managerial perceptions in a case study of rapid internationalization in this industry. The analysis follows a two-stage method. First, the main behaviors of e-learning firms are identified and grouped in sustainable business archetypes and the research proposals defined. Then these proposals are applied to a case study of the e-learning industry. This analysis furnishes evidence to conclude that the alliance of software firms with publishers becomes a driver that allows e-learning firms to overcome some of the limits of their growth model and maintain sustainable business models. [ABSTRACT FROM AUTHOR]

The relationship between rainfall variability and economic growth is complex, and tends to be significant in economies like India where agriculture plays a major role in economic output and food security. This paper seeks to provide insight into this relationship using Indian state-level economic and rainfall data from 1961 to 2012. We examine all 15 Indian states with populations exceeding 20m as of 2000, totalling 920m people, about 12% of the global population. Physical and human geography vary greatly between, and even within, these states, reflecting the global range of water security challenges and providing an analogue for a range of global economic development and environmental conditions. We identify three patterns of interdependence between rainfall variability and economic growth: i) Continuous Correlation of rainfall and economic growth rates, ii) Decayed Correlation from a significant to an insignificant relationship, and iii) Never Correlated i.e. no significant observable correlation between rainfall and growth. Sensitivity to rainfall variability is somewhat less in wetter states. Investment in irrigation infrastructure has helped states to reduce their economic sensitivity to rainfall variability, with three of the four states that have Decayed Correlation of growth with rainfall having the highest percentage expansion in irrigated areas of the 15 states. Greater use of groundwater supplies (rather than surface water) does not, however, appear to influence the sensitivity of economic growth to rainfall variability. The relationship between rainfall-growth correlation and long term income is complex; states which are correlated generally appear to be growing faster than states which are not correlated, but that growth is occurring from a lower per capita income level. Finally, confirming national trends for India, the paper does not find that economic diversification away from agriculture has reduced economic sensitivity to rainfall variability. The observation that growth in economically-diversified states can still be dependent on rainfall invites further research into the ways in which rainfall either directly, or through other hydro-climatic variables, influences the general economy. [ABSTRACT FROM AUTHOR]

This paper focuses on the behaviour of pedestrians when they cross at signalized intersection under mixed traffic situations. Based on a field study at a typical signalized intersection in the commercial hub in the city of Kolkata, the paper describes various characteristics of pedestrian movements. Several attributes like width of the road, age and gender of the pedestrian and if they are carrying any baggage were considered at the time of performing such analysis. The age and gender of pedestrians were found to have an impact on their speeds; however, children were observed to walk faster since they are accompanied by their parents in most occasions. The speed of panic-stricken pedestrians who prefer to run on the zebra marking while crossing the road at intersection was observed to be quite high. Interestingly, the effect of carrying baggage on walking speeds was found to be insignificant at the study site. Accordingly, the current study made an attempt to further investigate the fact by conducting an informal opinion poll. The poll of about 50 road users revealed that since most of them walk towards their office or business centre, they usually carry lighter baggage and quite often they are compelled to walk fast. Further, at crosswalk, speed does not vary to a considerable extent with the increase of flow; this attributes to the absence of confined flow. The observed flow parameters were plotted and the scattered diagrams indicate widespread data points that largely follow the Greenberg’s logarithmic model. [ABSTRACT FROM AUTHOR]

Engineering properties of sands mainly depend on the integrity of the particles which in turn has a strong bearing on their crushing strength. The crushing strength and hence the breakage characteristics of sands are mainly affected by its particle morphology (shape, size, angularity), particle composition (void ratio and gradation), loading conditions and the mineralogical composition. This paper examines the effect of these parameters on the crushing characteristics of six different river sands. One-dimensional compression loading was applied and grain size analyses were performed before and after each stage of the loading. As obtained from the results, more breakage was observed in sands that consists of less quartz percentage. Moreover, there exist distinct relationships between the particle breakage with initial sample void ratio, grain morphology and the applied stress. Progressive change in the shape, size and gradation of the sand samples with increase in the stress is also presented and discussed in this paper. Finally, with the help of a regression analysis, an equation is proposed to determine the particle breakage value of the sands. The computed values are found to be in good agreement with those obtained from the experimental investigations. In view of the fact that the relative breakage is influenced by many of the sand parameters, the model developed in the present study should be very useful for predicting the crushing characteristics of sands. [ABSTRACT FROM AUTHOR]

Responsible management of electronic waste (E-waste) is a major apprehension in contemporary urban India. As a toxic waste stream, E-waste calls for conscientious management practices in order to avoid possible human health and environmental consequences. Publics' consumption, disposal behaviour and awareness/perception are central to any successful E-waste management initiative. This paper is a journey from a review of existing literature on some significant aspects of E-waste (including some widely used conceptual frameworks for waste management studies) towards a new conceptual framework of ‘public understandings of E-waste and its disposal’ in urban India. Accordingly, the paper is divided into two parts: 1) In-depth literature review on a few important aspects of E-waste and 2) Review of publics' perceptions of E-waste and the determinants of their consumption and disposal intention with the help of specific theoretical underpinnings. The concluding conceptual framework underlying our work uses elements that stem from the ideas of the theory of planned behaviour (TPB) and conspicuous consumption. Further, a case study carried out in the city of Bangalore is considered to validate the relevance of the new conceptual framework. Thus, the paper is an attempt not to restrict ourselves to the review of the existing literature alone, but also to test the new conceptual framework formulated through the literature review against a primary field study. [ABSTRACT FROM AUTHOR]

The growth of manufacturing small and medium size enterprises (SMEs) is vital as they have a significant amount of share in total manufacturing output. In this era of globalization, SMEs are compelled to ensure sustainable profitability through cost savings, while being environmentally conscious at the same time. It has been reported in the prior investigations that lean and green practices can achieve excellent results if they are implemented simultaneously. Hence, SMEs need to adopt integrated lean and green manufacturing as this can address economic and environmental concerns together. The drivers for implementation of integrated lean and green manufacturing in SMEs have not been thoroughly explored in the past studies. This sets the motivation for the present research. Thus, in this paper drivers for integrated lean and green manufacturing are identified from the combined support of existing literature and experts' opinions in the relevant field. The drivers are ranked using multi-criteria decision making methods such as technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighting (SAW) in fuzzy environment using environmental, social and economic perspectives, however, consolidated ranking is obtained by borda method. The results reveal that top management commitment, technology up-gradation, current legislation, green brand image and future legislation are five most important drivers for the implementation of integrated lean and green manufacturing in Indian manufacturing SMEs. The research counters the uncertainties in importance of criteria weights using sensitivity analysis which determines the influence of criteria weights on the prioritization. Finally, the paper concludes with some managerial implications which will help the policy makers in government and industry to focus on few important drivers to facilitate the integrated lean and green manufacturing implementation in SMEs with limited resources. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %. [ABSTRACT FROM AUTHOR]

Accelerator-based neutron sources are being developed worldwide due to their wide applications. Institute for Plasma Research in India has developed an accelerator-based high-yield 14 MeV neutron generator based on deuterium-tritium (D-T) reaction to perform fusion-related benchmark experiments. This neutron generator is designed to produce 1012 neutrons per second in both mode continuous and pulse. The emission of neutrons from an accelerator-based neutron generator is not isotropic due to target geometry and angular dependency of reaction cross-section. A detailed and accurate description of neutron source emission and distribution is required to use such a neutron generator for various experiments. The neutron generator is characterized using experiments and simulations to accomplish the request. Energy and angular distributions of emitted neutrons are measured using a calibrated single crystal diamond (SCD) detector. Experimental data of neutron distributions are used to validate source neutron definition for Monte Carlo N Particle (MCNP) simulation. An accurate model of the Titanium Tritide (TiT) target and its holder geometry is prepared for the simulation. Finally, to validate the simulation model, simulated neutron fluxes are compared with measured ones by the SCD detector. This paper discusses the methodology adopted to characterize emitted neutrons and its results. [ABSTRACT FROM AUTHOR]

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners. • A novel Hidden Markov based Deep Learning (DL) models for accurate agricultural commodity price forecasting. • Addresses DL model generalization challenges by integrating sequential dependencies and hidden states obtained from HMMs. • Proposed models outperform baseline models - RNN, CNN, LSTM, GRU, BiLSTM, and BiGRU for price predictions of agriculture commodities. • Evaluated using RMSE, MAPE, MAE and Diebold Mariano test, for accuracy and reliability of the proposed approach. • Offers precision in commodity price forecasts, benefiting stakeholders such as farmers, planners and policymakers. [ABSTRACT FROM AUTHOR]

The rise in environmental awareness and personal well-being has led to a shift in menstrual product preferences. This research, focusing on women from India, utilizes a mixed-methods approach underpinned by the Push-Pull-Mooring (PPM) framework to investigate the transition from sanitary napkins to menstrual cups. Data from 894 menstrual product users from India were analyzed utilizing PLS-SEM. The study found that health concerns and perceived costs were significant push factors, while minimalism and environmental considerations acted as pull factors. However, social stigma and perceived complexity served as mooring factors. This study extends its practicality to product manufacturers, marketers, and health educators, providing actionable strategies to promote menstrual cups with the aim of surmounting prevailing hindrances. The outcomes underscore the importance of this transition in supporting Sustainable Development Goals (SDGs) — particularly SDG 3 (Good Health and Well-being), SDG 5 (Gender Equality), and SDG 12 (Responsible Consumption and Production) — thereby underlining its contribution to the environmental sustainability narrative and cleaner production ethos. • This pioneering study utilizes the Push-Pull-Mooring (PPM) theory and a mixed-method approach to investigate the switch from traditional sanitary napkins to menstrual cups. • Pull factors, including health concerns and perceived cost-effectiveness, are found to be influential in attracting users to menstrual cups. • Users are driven away from traditional sanitary products by push factors such as environmental considerations and a desire for minimalism. • The switch, however, is impeded by mooring factors like social stigma and perceived complexity in using menstrual cups. [ABSTRACT FROM AUTHOR]

Motivated by the growing significance of solar energy, the paper presents the findings of the maiden study on the long-term seasonal performance assessment of three PV module technologies—monocrystalline (m-Si), polycrystalline (p-Si), and amorphous silicon (a-Si) housed in a 1 MW solar plant installed at a local steppe climate in Gandhinagar, Gujarat, India using the key performance indicators (KPIs), daily power generation, final yield (Y f), reference yield (Y r), total energy loss (T EL) and performance ratio (PR). Notably, the am-Si PV modules were found to be the best performing technology, with an average PR of 71.26% and the lowest average total energy loss of 631 h during the monitored period. Moreover, PV systems have become more popular as a source of green, clean energy; yet, they have low base-load energy sustainability. Explainable AI (XAI) aids in understanding predictions made by the machine learning models. The importance of XAI lies in the comprehension and trust which is embedded into the results of machine learning algorithms. Therefore, the present work builds a comprehensive XAI model for predicting KPIs and assesses the performance of several learning algorithms using R-value, MAE, the number of iterations, and execution time. The Levenberg-Marquardt algorithm predicts the KPIs for p-Si, am-Si, and m-Si with prediction accuracies of 98.63%, 98.58%, and 90.09%, respectively. In summary, this study not only advances our understanding of PV module performance in a steppe climate context but also contributes to the broader field by integrating an XAI model for transparent and reliable predictions of the KPIs. [Display omitted] • Comparison of m-Si, p-Si, and a-Si PV modules in a 1 MW solar plant, Gandhinagar, India, showed long-term performance. • a-Si PV modules outperformed with a 71.26% PR and the lowest 631 hours energy loss, surpassing m-Si and p-Si technologies. • Study underscores XAI's role in ML predictions, developing a robust model to predict KPIs and evaluate learning algorithms. • Levenberg-Marquardt showed high accuracy: 98.63% for p-Si, 98.58% for a-Si, and 90.09% for m-Si PV modules. [ABSTRACT FROM AUTHOR]

• An optimize ensemble machine learning model has been proposed for breast cancer classification on the basis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Crow Search algorithm. • The proposed model improves the overall performance metrics consists of accuracy, recall, precision, AUC, F1 score, Kappa and MCC. • Even with the limited number of samples, the proposed model performs better. In this paper, we propose a novel framework for breast cancer classification using optical coherence tomography (OCT) images. As the incidence of breast cancer in India continues to rise, early detection is essential for increasing survival rates. For precise classification, an efficient classifier model is required. Due to the variety of datasets and performance metrics, a single classifier may not be sufficient. Thus, we propose an efficient ensemble classifier based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) integrated with the Crow Search Algorithm (CSA). TOPSIS, a technique for Multi-Criteria Decision Making (MCDM), ranks and selects the ensemble's base classifiers. The CSA fine-tunes the weights of the base classifiers. In addition, we employ SHAP (SHapley Additive exPlanations) values to visualize feature attributions at the observation level, thereby facilitating model interpretation and comprehension. The proposed classifier performs admirably on the experimental testing dataset, with values of 0.921, 0.921, 0.923, 0.921, 0.846 and 0.846 for precision, recall, F1-score, Kappa, and MCC, respectively. This model's ultimate goal is to reduce reliance on skilled pathologists, reduce interobserver variability, and speed up the processing of breast tissue assessments. [ABSTRACT FROM AUTHOR]

In India, there are various codes, standards, guidelines and rating systems launched to make energy intensive and large sized buildings energy efficient whereas independent residential buildings are not covered even though they exist most in numbers of total housing stock. This paper presents a case study methodology for energy performance assessment of existing residential stock of Hamirpur that can be used to develop suitable energy efficiency regulations. The paper discusses the trend of residential development in Hamirpur followed by classification based on usage, condition, predominant material use, ownership size and number of rooms, source of lighting, assets available, number of storey and plot sizes using primary and secondary data. It results in identification of predominant materials used and other characteristics in each of urban and rural area. Further cradle to site embodied energy index of various dominant building materials and their market available alternative materials is calculated from secondary literature and by calculating transportation energy. One representative existing building is selected in each of urban and rural area and their energy performance is evaluated for material embodied energy and operational energy using simulation. Further alternatives are developed based on other dominant materials in each area and evaluated for change in embodied and operational energy. This paper identifies the energy performance of representative houses for both areas and in no way advocates the preference of one type over another. The paper demonstrates a methodology by which energy performance assessment of houses shall be done and also highlights further research. [ABSTRACT FROM AUTHOR]

Green logistics is an important aspect of production system in today’s world. It combines various efforts to quantify and control the environmental effect of production activities in an enterprise. This research is intended to emphasize upon the ways by which organizations can accomplish positive ecological effects simply by modifying their logistics practices. This paper presents a case study of an Indian automobile company by analyzing alternatives for green practices along with organizational objectives. It is imperative to focus on the green practices in context of Indian automobile sector as the volume of vehicles in India has increased considerably during last few decades. Accordingly, this paper attempts to improve the performance for sustainable adaptation of green practices of an Indian company by selecting the best alternative obtained from the use of Analytical Hierarchy Process to analyze alternatives for green practices in the assembly and packaging processes. Alternatives have been evaluated on the basis of three criteria i.e. green efficiency, factor of safety and ease of operation. The study revealed that clinch joint is the most appropriate alternative for assembly operation while carbon positive packaging material is the best alternative for packaging. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) are playing a vital role nowadays in every aspect of computer vision applications. In this paper we have used the state of the art CNN in recognizing handwritten Tamil characters in offline mode. CNNs differ from traditional approach of Handwritten Tamil Character Recognition (HTCR) in extracting the features automatically. We have used an isolated handwritten Tamil character dataset developed by HP Labs India. We have developed a CNN model from scratch by training the model with the Tamil characters in offline mode and have achieved good recognition results on both the training and testing datasets. This work is an attempt to set a benchmark for offline HTCR using deep learning techniques. This work have produced a training accuracy of 95.16% which is far better compared to the traditional approaches. [ABSTRACT FROM AUTHOR]

Even after 35 years of the National Leprosy Eradication Program (NLEP) and 15 years post-elimination, leprosy continues to be a public health challenge in India. This paper discusses the current awareness of leprosy among people living in urban slums of western Maharashtra. The study was conducted in an urban slum of western Maharashtra with 400 participants. A closed-ended questionnaire regarding the knowledge, attitude, practices, and stigma existing among the people was administered, followed by a small awareness talk and screening for leprosy. Of the total 400 participants, 205 (51.25) were females and 195 (48.75) were males. Only 154/400 (38.5%) people were aware of leprosy. 130/400 (32.5%) people thought that it is treatable; however, 71/130 (54.6) of them thought that it would recur even after completing the treatment. Only 103/400 (25.75) said that they would marry a person with leprosy, denoting prevalent stigma in the society, and 79/400 (19.75) were aware of government services for leprosy and NLEP. Screening of all the participants surveyed did not reveal any new or doubtful cases of leprosy. The present study shows a lack of awareness and knowledge of leprosy among the target population. With only 20% of them being aware of government services and the NLEP, combined with an extremely low knowledge about the disease; it shows the need to further augment the government programs. There is also an increasing need to educate people to accomplish a positive attitude of the community towards leprosy patients. [ABSTRACT FROM AUTHOR]

Due to the high population of hearing impaired and vocal disabled people in India, a sign language interpretation system is becoming highly important for minimizing their isolation in society. This paper proposes a signer independent novel vision-based gesture recognition system which is capable of recognizing single handed static and dynamic gestures, double-handed static gestures and finger spelling words of Indian Sign Language (ISL) from live video. The use of Zernike moments for key frame extraction reduces the computation speed to a large extent. It also proposes an improved method for co-articulation elimination in fingerspelling alphabets. The gesture recognition module comprises mainly three steps – Preprocessing, Feature Extraction, and Classification. In the preprocessing phase, the signs are extracted from a real-time video using skin color segmentation. An appropriate feature vector is extracted from the gesture sequence after co-articulation elimination phase. The obtained features are then used for classification using Support Vector Machine(SVM). The system successfully recognized finger spelling alphabets with 91% accuracy and single-handed dynamic words with 89% accuracy. The experimental results show that the system has a better recognition rate compared to some of the existing methods. [ABSTRACT FROM AUTHOR]

For smart cities, the successful large-scale implementation of solar PV technology, Quality Certification and Standards are mandatory. The International Electrotechnical Commission (IEC) is a global organization for standardization consisting of all IEC national committees. The IEC PV standards comprise IEC technical committee 82 solar PV Energy System (IEC TC82) which develops and adopts all Photovoltaic related standards. There are nearly 80 standards applicable to photovoltaic and five working groups in IEC TC82. For necessary safety requirements 'Quality and Standards' technologically need to be revised and up to date. This paper presents PV standards developed by various technical committees worldwide, mainly focusing on various IEC PV standards, gaps identified by them and the recommendations provided by the committee in recent times. The breakup costs of the various sub-systems of a PV installation with an illustrative example for India is also discussed. It is intended to provide a guideline for consumers and investors, opting for solar PV installations. [ABSTRACT FROM AUTHOR]

Dynamics of COVID-19 outbreak in India are different from other countries for her huge population and different administrative approaches. This paper presents a six compartments (SHIQRD) model with different time delays between them, to analyse and forecast the COVID-19 pandemic in India. The model introduces separate compartments for 'home-quarantined', 'quarantined infectious', and 'undetected infectious' pertaining to the Indian scenario. It also incorporates incubation time, sample testing time, and recovery window as time delays between blocks. With the proposed model, reproduction number of different phases during lock-down are evaluated. Besides, dynamics of pandemic for the next one year are also explored. [ABSTRACT FROM AUTHOR]

This paper presents a detailed approach for an efficient blood bank database management system. The database is the single most useful setting for caching data, and it is also an ideal tool for contriving, managing, updating, and modifying data from different angles. The benefits of a well-structured blood bank database are limitless and yield the benefits of improving efficiency and saving time. Here, our motive is centred on this area. India faces a shortage when it comes to the amount of blood donated. The gap in demand and supply in widened due to mismanagement and inefficient databases. We have modelled a well-organized database to try and reduce this gap. Alongside, we have developed an application that reminds donors when they become eligible again, gives locations of nearby blood donation camps, makes requesting blood easier for blood recipients etc. as well as promoting a healthy community. IOT is used for interlinking the application to the server as well as for inter-application communication. With the help of IOT this collection and exchange of data becomes more efficient. [ABSTRACT FROM AUTHOR]

In this article, some misuses of Kappa statistic in the original paper [Chemosphere, 307, 135831] are discussed. By using DRASTIC and Analytic Hierarchy Process (AHP) models, the authors have assessed the groundwater vulnerability of Totko, India. High nitrate concentrations in groundwater have been found in highly vulnerable areas, and the accuracy of the models has been assessed through Pearson's correlation coefficient and Kappa coefficient. However, using Cohen's Kappa to estimate the intra-rater reliabilities (IRRs) of the two models is not appropriate on the condition of ordinal categorical variables in five categories in the original paper. We briefly introduce the Kappa statistic and propose to use weighted Kappa to compute IRRs under such conditions. To conclude, we recognize that this does not significantly alter the conclusions of the original paper, but it is necessary to ensure that the appropriate statistical tools are used. [Display omitted] • Cohen's Kappa assesses IRR for 2 or ≥3 unordered categorical variables categories. • Weighted Kappa measures IRR for ordered categorical variables with ≥3 categories. • Fleiss' Kappa evaluates IRR for three or more raters. [ABSTRACT FROM AUTHOR]

This paper emphasizes the usage of Retired Electric Vehicle Batteries (REVB) to support a system with a Centralized Charging Station (CCS) for charging electric vehicle batteries. This paper also focuses on power flow from the Grid to the Centralized Charging Station (G2CCS), power flow from solar PV to the Centralized Charging Station (PV2CCS), PV to the Grid (PV2G), and power flow from Retired Electric Vehicle Batteries to the Centralized Charging Station (REVB2CCS), Retired EV batteries to the Grid (REVB2G), various schemes for EV's in India, and barriers to the adoption of EV respectively. The review of the modelling and optimization is mainly based on retired EV batteries for EVs charging with recent trends, and future possibilities of retired EV batteries are also discussed. This review paper provides valuable references for researchers and assists them in continuous improvement in this field. [ABSTRACT FROM AUTHOR]

In this paper, we suggest how India can become atmanirbhar (i.e. self-reliant) in development of green energy technologies- solar, battery storage and green hydrogen, given its stated commitments in clean energy. First, we introduce a generalized policy design – a set of ten policy measures: seven necessary and three sufficient- with the former being on the supply side and the latter on the demand side. The paper argues that while the ideal policy mix would be technology dependent, based on technology maturity and the strategic positioning of the country, it should draw both from supply side (i.e., for domestic development) and demand side (i.e., for scale deployment). Then, we suggest the required individual strategies for each of the three technologies- such as a manufacturing focused policy for solar solely to develop competence in manufacturing; a bottom-up, manufacturing-led policy for battery storage with focus on deployment in parallel with technology development; and traditional top-down, research-led strategy for green hydrogen by entering via development through supply side and complementing via deployment through demand side. Finally, we compare these with the existing domestic energy industrial policies as well as those prevalent in other jurisdictions such as the US and China. • Create self-reliance for India in green energy technologies. • Develop generalized policy design with supply and demand side measures. • For solar, manufacturing focused policy for competence solely in manufacturing. • For battery storage, bottom-up manufacturing-led policy with deployment alongside development. • For green hydrogen, top-down research-led strategy where deployment follows development. [ABSTRACT FROM AUTHOR]

This paper explores from the view of the data recipient and user the complexities of creating a common licensing scheme for the access and use of satellite earth observation (EO) data in international disaster management (DM) activities. EO data contributions in major disaster events often involve numerous data providers with separate licensing mechanisms for controlling the access, uses, and distribution of data by the end users. A lack of standardization among the terminology, wording, and conditions within these licenses creates a complex legal environment for users, and often prevents them from using, sharing and combining datasets in an effective and timely manner. It also creates uncertainty among data providers as to the types of licensing controls that should be applied in disaster scenarios. This paper builds from an ongoing comparative analysis of the common and conflicting conditions among data licenses that must be addressed in order to facilitate easier access and use of EO data within the DM sector and offers recommendations towards the alignment of the structural and technical aspects of licenses among data providers. [ABSTRACT FROM AUTHOR]

The sustainability transition of electricity sectors is a matter of competition between multiple emerging renewable systems and dominant, established conventional systems. These transitions are multi-dimensional and are featured with non-linear and causal interactions between social, technical, economic and political components. Understanding the dynamic of transitions, i.e. how transitions unfold, can inform effective policy interventions. This paper aims to present a narrative-modelling approach to improve the understanding and description of transition dynamics in electricity sectors. The central ideas of the paper are: (1) the use of qualitative transition narratives helps to capture the co-evolving nature of society and technology which are simplified in modelling approaches; (2) narratives, with concepts from the sustainability transitions field, also guide the development of a model structure; and (3) computational models, in return, reproduce the complexity of transition dynamics, i.e. feedback loops, non-linearity, and time delays—the features which are impracticable to analyse with transition narratives alone. We use the historical transition of India's electricity sector to demonstrate the implementation of our proposed approach. First, an overview of the transition narratives is presented and the model structure, developed based on the narratives, is explained. Then, it is argued how the coupling of the narratives and model improves our understating of the positive impacts as well as the side-effects of stable feed-in tariffs and accelerated depreciation on the penetration levels of wind and solar electricity. [ABSTRACT FROM AUTHOR]

It has been observed that there is asymmetry in the approach of Indian banks when it comes to reacting to interest rate signals given by the Central Bank. While they are quick to react to interest rate increases, they drag their feet when it comes to reacting to interest rate cuts. More often banks pass only a small part of the interest rate cuts. This paper attempts to understand the reasons behind such an asymmetric behaviour. The paper identifies two reasons in the case of Indian commercial banks which are leading to such behaviour. The first reason identified is the oligarchic structure of the Indian banking system where there is a dominance of the public sector banks. The second reason identified in this paper is the pro cyclical behaviour of the Indian public sector banks which has given them an asset-liability profile where their flexibility to react to interest rate signals got severely compromised. [ABSTRACT FROM AUTHOR]

In rapidly growing urban slums in India, with little structured urban planning and limited infrastructural facilities, the number of people living without access to basic water and sanitation services is increasing. Tragically in India, a growing slum population and lack of adequate sanitation makes over 50 million men and women to defecate in the open every day. This research paper sought to measure attitudes and behaviour towards toilet construction and its utilization by the poor people in urban areas in India. The survey collected primary data through questionnaire and analysed by cross tabulation, decision tree using chi-Square automatic interaction detector, multiple classification analysis, and logistic regression. Findings guided the design of a mass media communication campaign to impact toilet construction, its increased uptake, universal and consistent use of safe sanitation among poor urban communities, to reduce open defecation in urban areas. Results of the study demonstrate, principally, that while both women, and men, use toilets more during the rainy season, the drop-outs among men in the summer is greater than for women. The paper also documents new evidence on the pathways to decision-making regarding toilet use providing key insights for Behaviour Change Communication (BCC) campaign architects. It also presents evidence on how non-users behave with regard to their usage of community toilets and construction of toilets. [ABSTRACT FROM AUTHOR]

Indian banking is witnessing a dynamic phase due to the continuous change in polices of RBI and the government of India. In 2015, RBI issued licenses for the first time to 11 entities to start payment banksin order to improve banking penetration. Of these 11, three entities have started their payment banks and are trying to woo customers through technology driven services. Riding on technology to achieve competitive advantage, these banks will aid to make banking more convenient for the customers. The paper focuses on assessing customers’ preference in using services of payment banks over universal banks by using bivariate correlation analysis. The paper talks about the services that the customers prefer from the payment banks. It also captures how convenience/ease is helping payment banks to expand customer base. The role of educational background, occupation and age group as deciding factors for customers in choosing payment banks is studied in this paper. The paper further studies the effect of reference groups on the customer’s decision to choose a bank. [ABSTRACT FROM AUTHOR]

According to the United Nations Human Settlements Program (UNHSP), the number of slum households in developing countries continues to grow by a higher proportion as compared to its encompassing city. Traditionally, policy makers have concentrated on population control strategies by focussing on birth rates and rural–urban migration to stem the growth and emergence of slums. However, these strategies have often failed to achieve the desired results. In the present paper we find the key underlying processes that explains the observed differences in household life cycle between slum and non-slum households. We find that the slum households when compared to nonslum urban households, exhibit a large variation in the household size over the course of their life cycle, which in turn leads to inefficiency while building slum resettlement colonies. We have developed an agent based model, namely DynaSlum , to identify the key social determinants that impact the behaviour of a slum household. We use a novel and unique dataset based on the field work from 37 slums in Bangalore combined with the NFHS data to calibrate DynaSlum and validate our findings. This paper presents two major insights to address the challenges. First, we find that high rate of home leaving among young adults is the key determinants for the large variation in the life cycle of slum households. Second, we show that reducing home leaving among young adults will reduce the formation number of new slum households and contribute to a higher but stable household size. This will lead to efficiency and higher per capita resource consumption when building capacity for slum development (resettlement colonies) as policy makers would be able to plan for a stable household size. [ABSTRACT FROM AUTHOR]

This paper focuses on effects of mixed traffic on capacity of two-lane roads. On the basis of field data collected on Indian highways, the present paper makes it clear that capacity reduces if the proportion of slower vehicles increases in the traffic stream. Since such vehicles are responsible for the formation of platoons, their increasing proportion in traffic would accordingly increase the equivalency factor of vehicles, thereby, resulting in variation in capacity. The present study therefore explicates the need of introducing the concept of dynamic passenger car unit and anticipates that this would alleviate the current implication on capacity standards of such roads under mixed traffic. [ABSTRACT FROM AUTHOR]

Social media plays a major role in the propagation of information during disasters. This paper mainly contains a study regarding how people of Chennai used social media especially twitter, in response to the country’s worst flood that had occurred recently. The tweets collected were analysed using machine learning algorithms such as Random Forests, Naive Bayes and Decision Tree. By comparing the performances of all the three it was found that Random Forests is the best algorithm that can be relied on, during a disaster. This paper also targeted the sources of the Twitter messages to explore the most influential users of Chennai flood. [ABSTRACT FROM AUTHOR]

The annual consumption of petroleum products in India was about 221 million metric tons in 2015. Of this, 84% was imported. The Indian industrial sector accounts for about 16%-20% of the total fuel consumption for thermal energy for different heating applications in the temperature range of 50°C--250 °C. Solar collectors can meet these temperature requirements and offer the possibility to mitigate the consumption of oil. This study highlights the fact that conversion efficiency from solar energy is much higher for process heating than for electricity generation and that process heating applications constitute a significant share of industrial energy consumption. In this paper, a methodology has been developed to estimate the potential for integration of solar collectors for process heating. The methodology employs process operating temperatures to select the type of solar collectors. The size of the solar field is estimated taking into account the thermal heat loads, working fluid and temperatures of these processes, the efficiency of the chosen solar collectors, location-based solar irradiance and capacity utilization of the solar collectors. The proposed methodology has been validated with a software tool called System Advisor Model (SAM). The techno-economic analysis will indicate the viability of solar systems for integration in industries. Therefore, the consociated parameters on economic (capital cost, fuel oil savings, monetary benefits), financial (Payback periods, Rates of Returns) and environmental (Carbon savings) are estimated. Further, the methodology has been applied to select Indian industries to verify its potential quantitatively. The industries selected include Textile, Pulp & Paper, Dairy, Leather and Automobile. Process-wise energy demands are considered while estimating the potential as the fuel requirement offset by solar energy in terms of absolute fuel oil savings, monetary benefits and carbon savings. The other economic and financial parameters mentioned above were estimated to verify the capability and present the market position of solar systems. Further, sensitivity analyses have been performed with respect to solar energy penetration and fuel oil prices to address the viability of integration of solar energy for process heating. [ABSTRACT FROM AUTHOR]