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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]

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 recent decades, the world has witnessed a sudden rise in population and rapid urbanization. This has led to an increase in the water demand and consequently an upsurge in wastewater. Water scarcity, coupled with untreated wastewater, has become a major concern throughout the world. Municipalities are challenged with the task of treating wastewater and meeting domestic, agricultural, and industrial demands. Mismanagement of wastewater results in economic, social and environmental loss. The high concentration of population, lack of sanitation and water security, reduced awareness, and evident pollution have made India a home full of opportunity and innovation in the wastewater management (WWM) business. The paper gives a comprehensive review to understand the wastewater management and business approach. The research uses Total Interpretive Structural Modelling (TISM) to identify, acknowledge, and interpret the major factors for the stakeholders and for future companies who wish to venture into the wastewater management business. The study provides insight into developing a circular economy solution and gives a vivid framework highlighting the relevant factors for a successful WWM business. The study also provides direction to the practitioners who are involved in the WWM industry and helps evaluate their options, prioritizing resource-efficient solutions. [Display omitted] • Develop a strategy for an efficient wastewater management business framework. • Identifying the factors crucial for the success of a wastewater management business. • An integrated multi-attribute interpretive structural decision approach is proposed. • Provides a pragmatic business framework with dependent and driving factors. • Managerial implication & policy insights for implementing the model are discussed. [ABSTRACT FROM AUTHOR]

Despite several research evaluating the connection between sustainability and the bottom line of firms, the existing research is inconclusive. In addition, most studies focus on multinational firms based in the United States or Europe, while research in the context of emerging economies is scant. This paper endeavours to investigate the strength of the association between Environment, Social and Governance (ESG) disclosure scores and the financial performance of 272 Indian firms listed on National Stock Exchange for the period 2015 to 2021 by employing panel data regression techniques. The results indicate that organisations having better ESG performance outperform their industry peers financially and are valued more by the market. Moreover, the data indicates that financial markets for lenders consider ESG disclosure when determining creditworthiness. In accordance with this, businesses can examine their operations and become more aligned with eco-friendly methods. Even though ESG disclosure appears to be associated with improved financial success, this is not the case across all industries and during COVID-19 pandemic. This study is relevant due to the growing significance of sustainability and the role of emerging nations in international commerce. It helps managers prioritize resource allocations to ESG-related activities that may affect financial performance differently across industry sectors, thus adding to the literature on the financial effects of ESG disclosures, particularly industry-specific aspects. This research represents a pioneering effort by assessing the impact of ESG disclosure on the profitability, market value, and cost of financing of Indian firms using both aggregation and disaggregation methods. [ABSTRACT FROM AUTHOR]

In the emerging landscape of Industry 5.0, the integration of innovative technologies such as Cognitive Digital Twins (CDTs) is pivotal for steering industrial processes towards sustainability and inclusivity, particularly in developing nations striving to align with Sustainable Development Goals (SDGs). The study explores the transformative capabilities of CDTs in Industry 5.0, concentrating on India's quest for inclusive and sustainable industrialization in alignment with SDGs. The inclusion of the TOE-HOT framework enriches the research's analytical foundation, providing a nuanced perspective to investigate the multifaceted dynamics of technology, organization, environment, and human aspects within the sphere of industrial evolution. By employing the Best-Worst Method (BWM), Interpretive Structural Modelling (ISM), and MICMAC analysis, the study delves deep into the synergies between industrial strategies, technology, and sustainable development. The findings reveal the paramountcy of the Technological Dimension, emphasizing CDTs strategies like real-time optimization and data analytics, which are instrumental in dynamically adjusting manufacturing processes and fostering alignment with market demands. The research also stresses the roles of various CDTs strategies like strategic alignment, change management, job security, skill augmentation, and environmental compliance in industrial sustainability and inclusivity. The paper concludes with invaluable insights for policymakers, industry leaders, and researchers, emphasizing the necessity for a holistic, interconnected approach to fully harness the potential of CDTs in realizing the aspirations of Industry 5.0 and contributing to India's sustainable industrial advancement by 2030. [ABSTRACT FROM AUTHOR]

• Wall-Frames are appropriate structural system for hospital buildings. • Yield strain limit state of rebar monitored for evaluating Occupiability performance. • Atleast overall 3% SPD should be provided in Wall – Special Moment Resisting Frame. • Minimum column-to-beam strength ratio of 2 is recommended for hospital buildings. In the aftermath of earthquakes, hospital buildings are expected to remain occupiable to treat the injured. For the purpose, firstly, an appropriate structural system that ensures no or limited structural damage should be provided in such buildings; wall-frames are the recommended structural systems in hospital buildings. Secondly, it is also essential to adequately design wall-frames to help meet the preferred Occupiability seismic performance. The work presented in this paper examines the contributing factors for failure of structural elements in hospital buildings and explores structural configuration and design strategies for mitigating these failures. Nonlinear analyses studies of a typical hospital building in high seismic region in India are carried out to help provide quantitative guidelines for: (a) Structural Plan Density (SPD) of structural walls, and (b) design parameters, for achieving the preferred performance. Displacement-based limit state of structural damage adopted from traditional displacement demand estimation rules is proposed. Lateral stiffness, strength, and ductility are evaluated of study buildings, and efficacy of results obtained from nonlinear static analyses confirmed by performing nonlinear time history analyses in commercial software PERFORM 3D. Results demonstrate inadequacy of moment frame structural system in hospital buildings under strong earthquake shaking, identifies optimum overall SPD of structural walls and relative column-to-beam strength ratio of frame members, required to achieve Occupiability. A minimum 3% SPD of structural walls in Wall – Special Moment Resisting Frame (WSMRF), 4% SPD in Wall – Ordinary moment resisting frame (WOMRF) hospital buildings, and a minimum column-to-beam strength ratio (CBSR) of 2 is required in hospital buildings to attain Occupiability performance. [ABSTRACT FROM AUTHOR]

The rapid growth of soybean cultivation in the world augurs well for achieving SDG2 of promoting sustainable agriculture, ending hunger, achieving food security, and improving nutrition. India started promoting soybean cultivation in the 1970s to combat dietary protein deficiencies and augment smallholders' incomes. However, soybean yield remains low and plateaued at one ton per hectare for the past three decades. This paper leverages a large primary dataset and explores the speed of soybean varietal change by applying survival functions and analyzing determinants of varietal turnover time employing a dynamic framework and harnessing duration analysis. The results show that adopting leading soybean varieties has reached saturation and that policy intervention can have an immediate impact. The weighted average age of soybean varieties in farmers' fields is relatively high at 15.6 years, implying a slower varietal change rate. The lack of improved varieties with desirable traits, poor information flow, and growers' preference for traits beyond yield-hamper faster varietal substitution. Further, analysis indicates regional variations in driving the speed of varietal replacement. The findings have significant implications for spurring soybean yields through investments in varietal development, leveraging modern molecular methods for long juvenility and photo-insensitivity, disseminating information, and institutional changes enabling the private sector participation in crop breeding. [Display omitted] [ABSTRACT FROM AUTHOR]

As the world struggles to cope with the pressing matters of global warming, rising temperature, and environmental degradation, the need for sustainable practices has become more urgent than ever for individuals and other establishments. Organizations across industries recognize the importance of adopting sustainable practices, not only to meet societal expectations but also to achieve long-term success. This research paper studies the influence of sustainable marketing on customer loyalty, focusing on the consumer electronics sector in India. A mixed-methods approach incorporating descriptive and causal research methods was used; with data collected from 302 participants from India's capital, Delhi and its nearby regions. Structural equation modeling was employed to establish multivariate relationships and Smart PLS 4 was used to analyze the data. The results suggest that sustainable marketing practices positively influence brand image and loyalty. Additionally, brand image acts as an intermediary between sustainable marketing and customer loyalty. These results provide valuable insights for managers aimed at fostering customer loyalty through sustainable practices, particularly in the consumer electronics sector. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) are non-biodegradable substances that can sustain our environment for up to a century. What is more worrying is the incapability of modern technologies to annihilate MPs from om environment. One ramification of MPs is their impact on every kind of life form on this planet, which has been discussed ahead; that is why these substances are surfacing in everyday discussions of scholars and researchers. This paper discusses the overview of the global occurrence, abundance, analysis, and remediation techniques of MPs in the environment. This paper primarily reviews the event and abundance of MPs in coastal sediments and agricultural soil of three major Asian countries, India, China, and Japan. A significant concentration of MPs has been recorded from these countries, which affirms its strong presence and subsequent environmental impacts. Concentrations such as 73,100 MPs/kg in Indian coastal sediments and 42,960 particles/kg in the agricultural soil of China is a solid testimony to prove their massive outbreak in our environment and require urgent attention towards this issue. Conclusions show that human activities, rivers, and plastic mulching on agricultural fields have majorly acted as carriers of MPs towards coastal and terrestrial soil and sediments. Later, based on recorded concentrations and gaps, future research studies are recommended in the concerned domain; a dearth of studies on MPs influencing Indian agricultural soil make a whole sector and its consumer vulnerable to the adverse effects of this emerging contaminant. [Display omitted] • Film & fibre shaped microplastics (MPs) are more abundant in India, China and Japan. • Plastic mulching is the root cause of major MPs pollution in soil and sediment. • Estuarian soil/sediment are more prone to MPs pollution than terrestrial and coastal. • FT-IR is highly used for MPs identification; mass spectrometry & microscopy are least. • MPs work as a vector for metals, PPCPs, surfactants and antibiotic resistance genes (ARGs). [ABSTRACT FROM AUTHOR]

In August 2019, India's ruling Bharatiya Janata Party (BJP), led by Prime Minister Narendra Modi, took the historic decision of abrogating Article 370 of the Indian Constitution, which revoked Kashmir's special status. The contentious move resulted in the emergence of two key competing narratives, with political supporters hailing the abrogation as the liberation of the nation from decades of secessionism and terrorism; and opponents representing it as an assault on democracy and the Constitution. This paper will analyze political contrast in the narratives of political supporters and opponents and how each side discursively represented the abrogation to legitimatize their respective versions of reality. To conduct the analysis, I will draw on Author's (2015) theoretical framework of the Discourse of Illusion, with application to analysis of data occurring from three aspects: historicity (use of the past to justify the present or predict the future); linguistic and semiotic action (subjective conceptualizations of the world made apparent through significant metaphorical rhetoric); and the degree of social impact (the rise of delineating categories as a result of one's rhetoric). • Political contrast in India's Modi and Shashi Tharoor's narratives on Article 370. • Legitimizing ideological narratives by competing political groups. • Discourse of Illusion framework for deconstructing socio-political argumentation. • Multi-perspective framework using structured immediacy, metaphor and categorization. [ABSTRACT FROM AUTHOR]

The increase in the usage of internet and social media has enabled people exchange views, opinions and thoughts as never before. This exchange of data has paved the way for sentiment analysis. The basic task of sentiment analysis is to classify the data into positive, negative and neutral. In this paper an effective MapReduce-Based Hybrid NBC-TFIDF (Naive Bayes Classifier -Term Frequency Inverse Document Frequency) algorithm is proposed to mine people sentiment. A Map Reduce-Based Hybrid NBC is employed to classify the data based on the polarity score of each sentence in social media data. The polarity score is calculated using the emotion corpus and the Diabetic corpus is created using food Glycemic Index and physical activity index. This study analyses the correlation of food habits, physical activity and diabetic risk factors among Indian population using social network data. Around two million data has been identified for the study and the study is restricted to India. The experimental result shows that MapReduce-Based Hybrid NBC–TFIDF performs efficiently in multimode cluster. The results reveal that no individual factor is associated with diabetic risk and also a group of common factors contribute to diabetes mellitus. It is found that 60% of the social media data had positive polarity about the food items that are high in Glycemic Index which is the main root cause for type – 2 Diabetes. This Big-Data analysis reveals that the young generations of India are unaware of risk factors of Diabetes mellitus. [ABSTRACT FROM AUTHOR]

• The vulnerability indicators that affect fisheries and coastal households are identified. • A vulnerability assessment framework is piloted in fishing villages of a coastal district of Tamil Nadu, South India. • The decision matrix provided in this paper aids in identifying the drivers and buffers for coastal vulnerability. • This approach for location-specific intervention planning can be validated and used in other tropical coastal villages. The coastal communities are the most vulnerable to climate vagaries, which affect their habitat and livelihood alike. Vulnerability assessments undertaken using quantitative or qualitative methods at macro- or micro-level need to capture the location and context so as to be able to use them towards improving mitigation and resilience strategies at the community level, where the impact is felt. The characteristics of the marine fishing villages differ from those of other villages in any coastal block / district in a State, making a strong case for a marine village-specific vulnerability assessment as well as intervention planning. This work, capturing this grassroots reality and deriving vulnerability indicators that affect fisheries and coastal households, pilots a novel socio-economic vulnerability (SEVI PH) and cumulative vulnerability (CVI PH) framework in marine fishing villages of Thoothukudi, a coastal district of Tamil Nadu in India, and compares it with an existing framework (SEVI SV) that relies on secondary data. A set of 54 indicators reflecting vulnerability components such as exposure, sensitivity (fishery and social) and adaptive capacity (economic, development drivers and alternate livelihood activity) has been developed. Primary data were collected from 1741 households residing in all the 24 marine fishing villages in Thoothukudi district to estimate various sub-indices and indices of SEVI PH , SEVI SV and CVI PH. Significant differences (p < 0.01) in sensitivity index (SI) and adaptive capacity index (ACI) were observed among the fishing villages, which however, got masked at taluk and district level due to aggregation, underscoring the importance of household based village level assessment of vulnerability. Overall, 42 % of the fishing villages were socio-economically highly vulnerable, while about 71 % of the villages had high cumulative vulnerability index. While the overall social and economic status of fishing households were lower compared to non-fishing population, it could be ascertained that there was relatively less inequity among them. The study also found significant disparities and differences (p < 0.05) across the villages/taluks in terms of exposure, sensitivity as well as socio-economic capabilities to respond, adapt and develop resilience. The two dimensional decision matrix plotted against SI and ACI median threshold values presented a greater contrast and helped in identifying the key drivers (or contributing factors) and buffers (or ameliorating factors) for coastal vulnerability of marine fishing villages to aid in location-specific intervention planning. The study also highlighted the need for imparting need-based location-specific training programmes at the village and taluk level for vulnerability reduction. [ABSTRACT FROM AUTHOR]

• This is done to spread awareness among the inhabitants of the country as they will come to know about these unknown people and what they did in the past, and can also be used for investigation to keep a record of the past criminals. • Image classification falls under supervised machine learning technique and uses various classification algorithms to identify a particular image label. • The aim of the paper is to identify the person and provide information that they did before or achieved something to be so famous to be included in this paper. The paper aims to classify the people who are not so famous and have a controversial background such as the politicians, the criminals/most wanted people all around India, the social reformers who are bringing the changes necessary for the nation and the sports athletes who are making the nation proud around the globe. This is done to spread awareness among the inhabitants of the country as they will come to know about these unknown people and what they did in the past, and can also be used for investigation to keep a record of the past criminals. Image classification falls under supervised machine learning technique and uses various classification algorithms to identify a particular image label. The aim of the paper is to identify the person and provide information that they did before or achieved something to be so famous to be included in this paper. This is achieved by implementing three major Machine Learning algorithms namely: Logistic Regression, Random Forest and SVM (Support Vector Machine), among which SVM classifier along with wavelet transformation, and vertical stacking of cropped raw pixel image and the cropped wavelet transformed image to achieve maximum precision and accuracy of 94.61%. [ABSTRACT FROM AUTHOR]

Given the steady growth in the use of electric vehicles in India, and the need to locate charging stations in various places to fulfill the corresponding demand, this paper focuses on an applicable Electricity Tariff Policy Framework in the context of power supply to Electric Vehicle Charging Stations(EVCSs). The framework is conceptualized by a theoretical foundation, an operational/functional foundation, and a transactional foundation. The theoretical foundation incorporates justice risk across the Energy Lifecycle stages by classifying the relevant international, regional, and national issues within the legal layers of Energy Law while examining the core ideas of the 'Energy Trilemma'. The paper identifies two basic dimensions, (a) the target sets of end-users, and (b) EVCSs settings and the applicable tariff schemes, as the foundation of the framework. The operational/functional foundation consists of techno-economic and social criteria, and the transactional foundation involves the consideration of different target sets of end-users. The proposed framework will benefit policymakers in general and those, especially in India. Adaptation and use of the framework in other countries should consider their basic economic, geographic and political structures. • Identification of basic dimensions of the ETPF in the context of EVCSs. • Justifications for developing a dynamic and adaptive ETPF applicable to EVCSs. • Relevance of justice risk in the electric mobility system. • Tariff Framework for Power supply to Electric Vehicle Charging Stations in India. [ABSTRACT FROM AUTHOR]

Early rainfall prediction is very important to ensure the economic balance of any agriculture-dominated country, such as India. Deep learning has recently received considerable attention for predicting rainfall using historical data. However, rainfall depends on multiple factors like ElNino, rising sea levels, warming of the Atlantic Ocean, etc. The changing climate has also significantly increased rainfall variability. The current deep-learning approaches are either based on a limited set of predictors or do not utilize the time-series properties for the recurrence of similar climate conditions every 12 months. Besides, they perform poorly in predicting accurate rainfall estimates for the diverse areas of India due to limited data availability across various regions. Therefore, this paper presents a groundbreaking solution to address the challenge of limited data by introducing RfGan (Rainfall GAN), a novel variant of generative adversarial networks. RfGan aims to fulfill the crucial need for abundant data to ensure the effectiveness of model training. Moreover, a novel rainfall prediction method, RfGanNet, has been introduced that leverages the strengths of the proposed RfGan for data augmentation, long-short-term memory, and convolutional neural networks for rainfall prediction. The performance of RfGanNet has been validated for India and its clustered regions in terms of nine parameters: precision, recall, specificity, Matthews correlation coefficient, accuracy, F1-score, mean absolute error, mean square error and computation time. The experimental results of RfGan are compared against original and synthetic datasets generated from state-of-the-art methods, namely tabular gan (TGAN), Wasserstein gan (WGAN), and conditional tabular gan (CTGAN). The experimental results indicate that the proposed RfNet model, when trained using RfGan-generated data, surpassed all the other considered methods by achieving 97.22 % accuracy. Moreover, RfNet has been compared with other related deep Learning approaches to validate its efficacy. Additionally, an ablation study investigating various LSTM–CNN combinations has been conducted. The findings of the study indicate that oceanic and climate-related predictors have a noteworthy influence on Indian rainfall. Moreover, the study highlights the suitability of combining Bidirectional LSTM, CNN, and multiple predictors for accurate rainfall prediction. • Novel variant of generative adversarial network- RfGan is proposed. • Different combinations of LSTM and CNN for rainfall prediction are tested. • Novel classifier — RfNet is tested on different augmented datasets. • K-means clustered regions of India have been identified. [ABSTRACT FROM AUTHOR]

Accurately predicting slope reliability under a rainfall/rainstorm event is an important prerequisite for preventing rainfall-induced landslide hazards. However, the predicted probability of slope failure under the rainfall/rainstorm event is often larger than the observed frequency of slope instability. The spatial variability of multiple soil parameters was rarely accounted for. To address this issue, this paper proposes an efficient sequential probabilistic back analyses approach for learning multiple spatially varying soil parameters using Bayesian Updating with Subset simulation (BUS) method. Two survival records of a real slope in India (i.e., the slope stays stable before the rainfall and the slope keeps stable after a 57-day weak rainfall) are successively used in the sequential probabilistic back analyses of soil parameters. The results indicate that the proposed sequential probabilistic back analyses approach can effectively update the distributions of multiple spatially variable soil parameters by the fusion of slope survival records. More accurate statistics of soil parameters can be obtained when additional slope survival records are used in the probabilistic back analyses. Furthermore, two slope failure records under a 3-day heavy rainfall event and a rainfall event ranging from May 1, 2016 to June 30, 2016 in Chibo, India are, respectively, used to predict the slope reliability and further validate the effectiveness of the proposed approach. The predicted probabilities of slope failure under the target rainfall events are well consistent with the actual observation frequency. The proposed approach can provide a powerful and versatile tool for determining the statistics of soil parameters and early warning of landslide hazards under the future rainfall events. • An efficient back analysis approach is proposed for learning multiple spatially variable soil parameters under rainfall. • The BUS method and surrogate models greatly facilitate the sequential probabilistic back analyses of soil parameters. • A real unsaturated slope is investigated to illustrate the effectiveness of the proposed approach. • The proposed approach can effectively update the distributions of soil parameters and slope reliability prediction. • The using of field observations can reduce the uncertainties of soil parameters and enhance the slope reliability prediction. [ABSTRACT FROM AUTHOR]

Autonomous driving is the future of the automotive industry across the globe. Many challenges must be resolved in designing and developing successful Autonomous Driving Systems (ADS), especially in developing countries with poor road infrastructure. Detecting obstacles in lanes is essential for robust ADS to decide whether to change lanes, slow down, or even stop. Developing countries like India present challenges, including poor or no lane line demarcation, poor traffic management, and diverse driving behaviors. That brings many challenges to developing robust lane-keeping or lane-changing decision systems in an ADS. Publicly available driving datasets have an inherent bias towards the road infrastructure of developed countries and hence cannot cater to the needs of the Indian requirements. That makes the existing obstacle-lane segment detection models unsuitable for road infrastructures in developing countries. Due to these factors, the rate of advancement of ADS and Advanced Driver Assistance Systems (ADAS) in developing countries is meager compared to other developed countries. This makes it even more critical for future research to focus on these developing countries. This paper proposes a deep-learning-based novel decision-making network named LaneScanNET to assist the ADS in lane-changing or lane-keeping decision-making. The proposed system assists ADS in detecting obstacles, localization of the Ego Vehicle (EGV) on roads, and estimating lane status in its Field of View (FOV). The proposed LaneScanNET uses a parallel pipeline with an Obstacle Detection Network (ODN) and a Lane Detection Network (LDN) to simultaneously process the incoming image frames for detecting obstacles and segmenting lane lines, respectively. Further, the Obstacle-Lane Fusion Network (OLFN) fuses these results to predict the status of the obstacle lane in the FOV of autonomous vehicles. Vellore Institute of Technology's (VIT's) real-time driving dataset on indigenous roads has been collected to train the proposed LaneScanNET with 2464 obstacle-lane images obtained by driving over 60 km. The dataset generated for obstacle detection and its corresponding lane detection has also been made publicly available to promote research work for these developing countries. The proposed system outperforms all existing networks on Indian roads with an accuracy of 75.28% in obstacle detection and 91.36% in lane detection. Additionally, the proposed system can analyze the lane status with an accuracy of 92.54%. The proposed network performs exceptionally well in unforeseen circumstances like shadows, fog, dust, and occlusions making LaneScanNET a robust network that can be an add-on for ADS to make lane-keeping or lane-changing decisions. The LaneScanNET can be integrated into real-time vehicles to assist drivers or ADS. [ABSTRACT FROM AUTHOR]

In which settings can bicycles help to improve girls' education in low-income countries? This paper analyzes the complementarity between all-weather roads and a bicycle program in India aimed at increasing girls' secondary school enrollment. Using a triple-difference strategy, I find that the program benefits girls living 3–10 km away from schools with all-weather road connections, increasing their enrollment by 60 percent and reducing the gender enrollment gap by 51 percent. There are no effects for girls in villages without all-weather roads or girls living more than 10 km from school. The findings emphasize the importance and interdependence of road infrastructure, mode of transport, and distance to school for improving girls' education in India. • Bicycle program for girls in Bihar, India, reduces the gender education gap. • Bicycle program is only effective if high-quality road infrastructure is available and schools are nearby. • Interdependence of mode of transport (bicycles), road infrastructure, and nearby schools for girls' education choice in India. • Triple-difference estimation. [ABSTRACT FROM AUTHOR]

This paper studies the impact of India's "model" school program which aimed to provide high quality education to economically disadvantaged students. Model schools combine better infrastructure with more accountability, contract teachers, and lower per-pupil spending than regular public schools. Using a fuzzy Regression Discontinuity Design based on entrance exam cutoffs, I find attending a model school for five years increases test scores in math by 0.38 standard deviations, in science by 0.26 sd, and in social science by 0.26 sd on average. Furthermore, model schools increase the probability of joining pre-university by 11.5 percentage points. The results suggest it is possible to deliver substantial improvement of outcomes in public schools at a slightly lower level of school spending through a package of reforms. [ABSTRACT FROM AUTHOR]

• The APPEL (Applied Plasma Physics Experiments in Linear) device is a mid-scale plasma device established at IPR, India, and now in the operational phase. The main objective of the APPEL device is to simulate the plasma surface interactions that occur in the nuclear fusion reactor. • Recently, APPEL device demonstrated the high density of the order 1017-1018 m−3 helium plasma column of length 3.5 m with a DC power of 0.5 kW using a cold hollow cathode-constricted anode plasma source and an axial magnetic field of a few millitesla, which corresponds to the ion flux of the order 1021-1022 m−2s−1 and is suitable for plasma surface interaction studies. • A maintenance-free magnet system provides the magnetic field of 0.41 T for the operating current of 720 A. The APPEL device (Applied Plasma Physics Experiments in Linear Device), recently commissioned at the Institute for Plasma Research (IPR), is an intermediate setup towards building a Plasma Material Interaction (PMI) facility. A particular application of this device is to develop and test high-density, low-pressure plasma sources that can be operated under magnetic fields; optimize the plasma sources for working under various magnetic field configurations; and perform physics experiments relevant to electron heating and plasma interactions with external electrodes. The setup consists of 16 electromagnets that provide a flexible, steady-state axial magnetic field in excess of 0.4 T over an axial distance of 3.5 m, with 2 % radial uniformity across 32.0 cm diameter. This paper gives a brief overview of the APPEL device, emphasizes on the APPEL magnet system, and presents the characteristics of a 3.5 m long, high-density (1017–1018 m−3) helium plasma column, using a hollow cathode plasma source obtained in the pressure range below i.e., p < 2.0 Pa. The corresponding ion flux is found to be in the range of 1021 -1022 m−2s−1, similar to other linear systems currently in use for plasma surface interaction studies. A qualitative discussion is given to explain the characteristics of the plasma column. [ABSTRACT FROM AUTHOR]

[Display omitted] • A high-density (HD) SNP array for goats (Axiom_Cahi) was designed comprising of 626,975 SNPs. • The average coverage of SNPs in the array is one SNP per four kilobase (kb). • Performance of the array was validated by genotyping 443 samples from 26 indigenous goat breeds/populations. • 95.83% markers were highly informative and polymorphic in Indian goats. • No sampling bias and an improved discriminatory power was demonstrated by the goat HD array designed. Goats are the supporting pillars of rural economy contributing significantly to meat and milk production in India. It is a species targeted for fulfilling the interdependent goals of poverty reduction and creation of employment for supporting the rural income. The increased demand for goat products necessitates their genetic characterization and improvement to augment the production of native breeds. Bi-allelic, genome wide, densely placed single nucleotide polymorphism (SNP) markers are most suitable for this purpose. This paper describes the design and validation of an Affymetrix Axiom-based high-density (HD) SNP chip for goats. The array was designed using a panel of 225 samples from 15 diverse goat breeds of India. In total, more than 38 million high quality SNPs were subjected to stringent filtering and 626,975 SNPs were finally tiled on the array. The average coverage of SNPs in our chip is one SNP per four kilobase (kb), providing a denser coverage of the goat genome than previously available arrays. The HD chip (Axiom_Cahi) was validated by genotyping 443 samples from 26 indigenous goat breeds/populations. The results revealed 95.83% markers to be highly informative and polymorphic in Indian goats. Multivariate analysis indicated population structuring, as 15 breeds could be segregated using the designed array. Phylogenetic analysis suggested stratification of breeds by geographic proximity. This HD SNP chip for goats is a valuable resource for genomic selection, genome wide association as well as population genetic studies in goats. [ABSTRACT FROM AUTHOR]

Humans receive a significant portion (˃50%) of the total dose attributed to all the natural radiation sources from indoor radon (222Rn), thoron (220Rn), and their progeny. While progeny contributes an overwhelming part to the dose, in most surveys, only radon gas is measured because of the simplicity of measurement. Progeny concentration is usually estimated by multiplying gas concentration with an assumed factor, called the equilibrium factor, and taken from literature. Recently, results of the measurements of equilibrium factors for 222Rn and 220Rn were reported from various parts of the globe. In India, many such studies have been conducted in the current decade. The studies show a wide variation of equilibrium factors which suggests that they depend on environmental factors and measurement conditions. Therefore, they should be determined site specifically if accurate site-specific dose estimation is targeted. This paper summarizes concepts, definitions, and methods to determine equilibrium factors and reviews literature about reported equilibrium factors worldwide, focusing on data reported from India. • Concepts, definitions and methods to determine 222Rn and 220Rn equilibrium factors. • Review of literature about 222Rn and 220Rn equilibrium factors reported from India. • A significant variation in the equilibrium factors is reported across the country. • Site-specific values of 222Rn equilibrium factors should be used in the dose estimation. • Dose due to 220Rn should be estimated from the directly measured progeny concentrations. [ABSTRACT FROM AUTHOR]

• Indian freshwater ecosystems are not well studied, are degraded due to expansive agriculture and are contaminated with pesticides. • Mesocosms identified malathion was the dominant stressor among co-occurring stressors. • In mesocosms malathion reduced richness, organism densities and caused community homogenisation. • Toxicology test comparisons between countries identify that Indian taxa may be relatively pesticide tolerant. Globally, river pesticide concentrations are associated with regional and local stream invertebrate diversity declines. Pesticides often co-occur with elevated nutrients (e.g. nitrogen and phosphorus) and sediments related to agriculture, making their individual effects difficult to disentangle. These effects are also less well studied in Asia, than in other geographic regions. Within Asia, India is one of the largest producers and users of pesticides and has approximately 60% of total land mass used for agriculture. Here we examine the responses of Indian river invertebrate communities subjected to malathion, nutrients, and sediment additions in a semi-orthogonal design, in three sequential (through time) short-term (120 h) mesocosm experiments. Additionally, a series of single-species toxicity tests were run that used 24 h exposure and 72 h recovery to examine the sensitivity of 13 local invertebrate taxa to malathion, and 9 taxa to cypermethrin, comparing these results to those from other biogeographic regions. Mesocosm results indicate that malathion exposure had a major effect compared to other stressors on communities, with a lesser effect of nutrients and/or sediments. In mesocosms, taxa richness, total abundance and the abundance of sensitive species all declined associated with malathion concentrations. Comparisons of organism sensitivities from other geographic locations and those in the current paper suggest taxa in India are relatively tolerant to malathion and cypermethrin. Our results further reinforce that the high observed aquatic pesticide concentrations known to occur in Asian freshwater ecosystems are likely to be negatively affecting biodiversity, homogenising biota towards those most stress tolerant. [ABSTRACT FROM AUTHOR]

The application of compressed biogas (CBG) in day-to-day livelihood has garnered significant attention in modern day era. It is considered as a potential solution to curb the increasing energy demand and other environmental concerns. The process involves anaerobic digestion (AD) (hydrolysis, acidogenesis, acetogenesis and methanogenesis) of organic substrate under an optimum operating condition (pH, temperature, etc.) via anaerobic microorganisms. This paper aims to describe some major facts about different technological options related to pre-treatment, AD, cleaning and up-gradation system. It also highlights the practical challenges related to high investment cost, inadequate infrastructure, clearance and approval issues, poor feedstock supply chain management etc. in adaption of CBG plants in India. The review examines the significance of both dry and wet continuous digester systems, which includes its advantages and challenges, considering factors, such as feedstock characteristics, process stability, and operational efficiency. It was also found that the type of feedstock and its availability in existing market plays a vital role in smooth functioning of CBG plants. Therefore, the study presents a comprehensive review discussing potential feedstock readily available for CBG plants in Indian market. It was concluded that the usage of agricultural waste (such as Napier grass) has higher CBG potential compared to other feedstock. At the same time, the study presents a broader aspect to policy makers and governing authorities to reframe the existing structure for financial assistantship, regulatory laws, subsidiary schemes etc. for CBG plant owners. It also gives an opportunity to technocrats, industrialist and investors to come-up with an indigenous technology with in-house manufacturing units to promote CBG production in India. [Display omitted] • India has 46 compressed biogas plants with an overall capacity of 272 tonnes/day. • Elaborated the significance of both dry and wet continuous digester systems. • Setting-up a compressed biogas plants requires high investment and operational cost. • Potential market for bio-fuels and bio-fertilizer needs to be explored in India. • Existing financial assistantship and subsidiary schemes need transformation. [ABSTRACT FROM AUTHOR]

With increasing awareness towards environmental concern, efforts are made to reduce harmful effects of conventional electricity generation methods and uses of renewable sources of energy to generate electricity. India has enormous potential of renewable energy resources to meet its power generation requirements. India can meet with global footsteps in the economic and social advances through the advancement in field of renewable sources. In remote area applications, where grid approach is either not feasible or very costly, hybrid energy systems are recommended to supply electric power needs of single or multiple user utility for household or irrigation applications. In the areas where grids are not connected, electrical power can be generated by solar cells, wind turbine generators, hydro power plants or diesel engine generators. Multiple sources can be combined for generating the desired electrical energy for remote areas. When majority of the sources are renewable generating sources, then the combination is known as Hybrid Energy System. The purpose of this study is to provide a conceptual framework for hybrid system setup, modeling, renewable energy sources, criteria for hybrid system optimization and control strategies, and software utilized for optimal sizing. In addition, this paper gives a mathematical model of the hybrid system components, highlighting the significance of power reliability and system cost, and discusses a comparison of the most popular topologies utilized for the implementation and sizing of HRES. This article concludes with a comprehensive review of the various algorithms and software programmes utilized for size optimization. Then the paper also discuss the various uncertainty analysis in HRES size optimization. • This paper present a review of HRES size optimization and forecasting methodologies. • The comprehensive review of HRES optimization and software tools is presented. • For knowledge collection of HRES, various reviews and surveys are conducted in past. • Old literature not give social, technical, economic, environmental, reliability aspect. • Review of solar and wind forecasting methods are not discussed for HRES optimization.. • Effect of using forecasted weather data on HRES is not properly analyzed till date.. • Performance of HRES optimization algorithm and forecasting effects are analyzed. [ABSTRACT FROM AUTHOR]

India with 2.4% of total global land area is home for about 8% of global biological diversity. In providing goods and services to 18% of world's human population this megadiverse country is losing its biological wealth at an alarming rate. The degradation of terrestrial (forests, grasslands, agriculture, etc.) and aquatic (small to large fresh and marine water bodies) ecosystems has been the key factor in accelerating this irreversible and unrepairable loss. Although ecosystem restoration targets are well reflected in various programs and policies of India, the scientific insights and preparedness on this topic and critical analysis of this knowledge to guide policy initiatives for ecosystem restoration was lacking. Therefore, this editorial paper reviewed 22 papers, published in special issue on ' Ecological Restoration of Degraded Ecosystems in India: Science and Practices ' in Ecological Engineering journal, to analyse the scientific knowledge to inform policy and practices towards achieving ecosystem restoration goals in India (https://www.sciencedirect.com/journal/ecological-engineering/special-issue/109W0PCNQBM). Most studies in this special issue focus on providing solutions for restoration of different degraded ecosystems. A few of them are solving the dilemma of native and non-native species selection for reforestation and afforestation activities. One study provides insights on management of an invasive species in grassland ecosystems. Three studies suggest adopting conservation agriculture practices to enhance agricultural biodiversity, carbon sequestration and socio-economic benefits for marginal farmers. Two out of five papers dealing with aquatic ecosystems provide a framework to quantify the magnitude of degradation, other two propose restoration technologies for coastal zones on India and one assessing the effectiveness of relocating collar reef from degraded urban coasts to reference coastal sites. • Twenty-two papers published in a special issue are reviewed here. • Papers cover terrestrial and aquatic ecosystem restoration studies from India. • 87% papers provide pragmatic solutions for restoration of degraded ecosystems. • Timely collection on scientific preparedness of India for ecosystem restoration. [ABSTRACT FROM AUTHOR]

The increasing nature of biomedical waste and its improper disposal has become a significant concern, resulting in a negative impact on the environment. These are associated with the direct consumption of input materials along with generated co-products and wastewater. This paper aims to address the environmental burden of existing biomedical waste management in the northern part of India. It also aims to suggest some constructive steps which result in a less impactful environment through the Life cycle Assessment (LCA) methodology. LCA is a widely used sustainable assessment tool to evaluate the environmental performance of various waste management systems. The present study's functional unit is all the waste collected at a common treatment and disposal facility. A gate-to-gate approach has been performed based on the foreground data gathered from field surveys, semi-structured interviews, and background data of Eco invent database v1.03. The data has been further evaluated using LCA software SimaPro 9.1.0.4 utilizing the CML-IA impact assessment method. The impact categories include Abiotic Depletion (AD), Abiotic Depletion (fossil fuels) (ADF), Global Warming (GWP), Ozone Layer Depletion (OLD), Human Toxicity (HT), Freshwater Aquatic Ecotoxicity (FAE), Marine Aquatic Ecotoxicity (MAE), Terrestrial Ecotoxicity (TE), Photochemical Oxidation (PO), Acidification (AF), and Eutrophication (EP). The results indicate that the integrated system, including incineration with energy recovery, autoclave-shredder, chemical disinfection, and Effluent Treatment Plant (ETP), reduces the environmental burden compared to the present state. The reliability of the model is tested using sensitivity and uncertainty analysis. The analysis identifies the influential key processes, parameters, and impact categories. This paper suggests that LCA is a beneficial tool for the decision-makers and managers to take strategic decisions for reducing environmental burden and developing a life cycle inventory database for the developed economies. [Display omitted] [ABSTRACT FROM AUTHOR]

In view of school closures due to the COVID-19 pandemic, this paper examines how a Home-Based Learning program affects learning outcomes of children in under-resourced communities. To overcome limited internet connectivity, the program provides remote instructions via phone calls and simple text messages along with automated voice calls to engage children enrolled in grades one to five in activity-based learning content. This intervention was conducted in three districts in the state of Odisha in India. Using a difference-in-differences framework, we find that the intervention led to a statistically significant improvement in basic number recognition and arithmetic operations, and language learning scores of children by 4.69 percentage points and 5.52 percentage points, respectively. Our results are robust to alternative methods of estimation and application of Lee bounds, thus indicating that well-designed low cost interventions could be a useful supplement for continued learning in the face of sudden shocks in low income countries. With a rise in hybrid format of teaching and learning, such interventions have the capability to cushion the decline in learning levels and provide a safety net in the event of school closures. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated workflow that effectively evaluates environmental and health risks of dioxin-like Persistent Organic Pollutants (dl-POPs) at industrial hotspot regions. The developments of validated, cost effective and user-friendly analytical strategies which can be field deployable are quintessential for routine monitoring of dl-POPs, particularly in developing countries. This study addresses the lacunae by enabling an exclusive gas chromatography triple quadrupole mass spectrometer based analytical workflow substituting conventional magnetic sector high resolution mass spectrometer technique and validated the methodology as per the European Union regulation 644/2017. The viable monitoring utility of the methodology for predicting enviro-food-health nexus was field-tested by analyzing fish and sediment samples from the Eloor-Edayar industrial belt, a solitary POPs hotspot in India. The profiles of congeners indicate that dl-POPs were formed through precursor pathways, suggesting the potential release of chlorinated precursor species from surrounding industrial area as the root cause. Fish samples from hotspots were observed to have 8 times higher levels of polychlorinated dibenzo-p-dioxin/furans (PCDD/Fs) and 30 times higher levels of polychlorinated biphenyls (PCBs) than the control sites. A strong statistically significant (p < 0.05) positive correlation was observed between dl-POPs levels in fish and sediment samples at the study site and the Biota sediment accumulation factors for PCDD/Fs and dl-PCBs ranged from 0.019 to 0.092 and 0.004 to 0.671 respectively. The estimated weekly intake from fish consumption in the study region was observed to be 3 to 24 times higher than the maximum levels set by the European food safety authority (2 pgTEQ kg−1bwweek−1). Hence, the periodic surveillance of dl-POPs employing user friendly/validated confirmatory tools stands highly imperative to safeguard human health and environment. Keywords : Dioxin and PCBs, GC-MS/MS, POPs Hotspot, Biota-sediment accumulation factor, Correlation analysis, Health risk assessment. [Display omitted] • Critical validation of cost effective GC-MS/MS methodology for dl- POPs monitoring. • Solitary POPs hotspot in India was assessed and baseline levels established. • Higher dl- POPs levels in fish samples indicating potential consumption risks. • Strong positive correlations observed between sediment and fish dl-POPs levels. • Developed analytical methodology can aid the dl-POPs routine monitoring processes. [ABSTRACT FROM AUTHOR]

How far the de-linking and morphological and hydrological degradation of tie channels connecting river to the wetland are caused for the areal and hydrological transformation of a wetland was not received enough attention in previous literature. This paper tried to explain this about the confluence reach of the Dwarka and Brahmni rivers in Moribund deltaic India. In order to explain the linkages 16 tie channels' change depicting variables and seven wetland area and hydrological transformation (consistency, hydro-period, water depth, water richness etc.) related variables were taken. Ordinary least square (OLS) regression was applied for explain the linkage. Machine learning approaches were applied for water richness mapping. Tie channel evolution was digitized from the Survey of India (SOI) toposheet (1974) and historical Google Earth images. The result revealed that, the tie channel witnessed morphological and hydrological degradations like channel constriction, channel clogging, flow lowering etc. and these were further identified as some vectors of areal shrinkage and growing hydrological insecurity in the linked wetlands. The total wetland area declined from 44.89 km2 to 16.86 km2 from 1991 to 2021. The rate of areal loss, shallowing WD, growing inconsistency of water presence, narrowing HP, and weakening WR was found high in recent times due to de-linking and degradations of tie channels. This approach of explanation of hydrological insecurity of wetland in relation to tie channel degradations, de-linking and findings are quite unique. So, re-linking and morphological correction of the tie channels would be a good policy for wetland restoration. • The floodplain wetland area was reduced by 37.55% between 1991 and 2021. • Tie channels were either lost or degraded losing sufficient water-carrying capacity. • Consistency, hydro-period, wetland depth declined in relation to tie channel loss. • De-linking of tie channel deteriorated water richness zone in wetlands. • Re-linking of tie channel could improve the hydrological condition of the wetland. [ABSTRACT FROM AUTHOR]

Municipal solid waste management (MSWM) has always been a typical issue in India. The rapid population growth, impetuous adoption of modern lifestyle, and fast urbanization resulted in India's swift generation of complicated municipal solid waste (MSW). Consequently, municipalities of different cities in India face various difficulties in MSW collection, treatment and disposal methodology. Inefficient or poorly managed MSW causes numerous troubles, including air pollution, soil fouling, contamination in drinking water and health hazard, and it also impacts socio-economic deterioration. MSWM technique selection is based on several criteria, which are generally imprecise, uncertain, and vague. This paper uses the cubic Pythagorean fuzzy number to comprise the fuzzy characteristics of the criteria value concerning the five alternatives (Thermochemical methods in MSW treatment and disposal). The best MSW treatment and disposal method is selected amongst the five alternatives under the novel multi-criteria group decision-making (MCGDM) approach, the cubic Pythagorean fuzzy EDAS (Evaluation based on Distance from Average Solution) in the Indian context. The weight information of the criteria has yet to be wholly known. A non-linear optimization scheme is proposed to determine the criteria weights that are partially known to maximize the generalized total distance measure between alternatives corresponding to all criteria. The present method is explained with a case study. Finally, a comparative analysis is drawn between the present and some existing methods, showing that the present method is consistent and robust enough to apply in practice. • CuPyF extended EDAS approach is developed for solving MCGDM problems. • Optimization model is applied for computing incomplete weights. • Create an algorithm for MCGDM problems using the CuP-EDAS technique. • A case study for municipal solid waste management is considered for recycling. • Sensitivity and comparative analysis are verified to validate the results. [ABSTRACT FROM AUTHOR]

Competitiveness of a country depends on the innovation capacity of its industries while sustainability relies upon the adoption of cleaner production processes. Hence, to promote economic development without negative environmental impact, it is imperative to have appropriate development policies and also a sound instrument to monitor the effectiveness of the supporting ecosystem. India, one of the world's largest economies with a huge base of Micro, Small, and Medium Enterprises (MSMEs), makes it a perfect sample for creating an Index to measure innovation for sustainability and adds to the empirical literature. The study identifies the determinants of innovation and the key drivers using multivariate tools on the empirical data collected from the MSMEs in 4 key sectors that have Social, Economic, and Environmental relevance. The findings suggest that "Market sources" is the most influential determinant of innovation, followed by "Institutional sources", "Market sophistication" and "Business Membership Organisations". Further, findings of the Principal Component Analysis revealed that "Productivity and Profitability" consideration is the most important driver for adoption of innovation for sustainability by MSME units. The other drivers are "Regulatory and Occupational Health and Safety Needs"; "Immediate Survival Needs"; "Raw Material and other Cost Issues"; "Developmental Support", and "Competition from Similar Units". Based on the relative importance of these factors, an index is created to measure the effectiveness of the innovation ecosystem. The paper also suggests the use of this index to assess the strength of the ecosystem at the national, sectoral, regional or state, and district or cluster level. This study offers promising avenues for future research ideas for evidence-based policy formulation. [Display omitted] [ABSTRACT FROM AUTHOR]

While the disparities between women and men in respect of healthcare, education, nutrition, livelihood opportunities, and political participation are global phenomena, development of human and social capitals is crucial in this regard. In particular, emphasis on improvement in female education is imperative for bridging such gaps towards a gender indifferent society. Accordingly, policy initiatives and institutional arrangements have been made to promote female education, especially in the developing and underdeveloped countries. Nonetheless, it is uncertain whether such initiatives can necessarily improve female education. For example, the initiatives in India have mostly focused on improving physical infrastructure and other amenities in schools, raising awareness, improving nutrition, and providing financial and other supports. While such interventions are crucial, lack of basic amenities at home can also potentially hinder female education in various ways, particularly in rural areas. This paper examines how household basic amenities influence female education in rural India. Based on secondary data and applying panel data estimation techniques for Indian states, it is found that sanitation facilities at home, access to electricity, use of cleaner cooking fuels, time taken to collect water, and girl sex ratio have significant impact on school attendance of girl child and female literacy rate. While time take to collect water affects female education adversely, the rests have positive impact on the same. Furthermore, public spending on education also impacts female literacy and female literacy relative to the male literacy rate positively, though they do not have any significant influence on school attendance of girls. Further, poverty does not have any significant impact on female education. The findings, therefore, suggest that success of development policies requires inclusive and comprehensive approach to explore the complementarity across the interventions. In addition to development of school infrastructure and provision of other services, emphasis should also be given on improving household basic amenities for better outcome on female education. [ABSTRACT FROM AUTHOR]

• The study depicts societal issues associated with the development projects. • A road map developed that focuses on avenues related to establishment of new development projects. • The SEETA model, based on Multi-Criteria Decision Making techniques is proposed. • Model is demonstrated using comparison of fourteen hydropower plants. • The hydropower plants are compared around five main and fifteen sub-criteria. Selecting an appropriate location for a large infrastructure project poses difficult situations. It shall satisfy sustainability indicators and establish harmony among multiple goals of multiple stakeholders. In such a situation, Multi-Criteria Decision Making techniques allow assessing qualitative and quantitative attributes, analysing and removing subjective biases and help in arriving at objective decisions. However, a single Multi-Criteria Decision Making technique may not be an effective tool to generate concrete results as there is no validation of the results. By utilising hybrid Multi-Criteria Decision Making methods, one can incorporate, gauge, and assess the range of social, economic, and environmental impacts precisely and achieve an accurate result by validating results by other methods. Therefore, this paper develops a model called as Social, Economic, Environmental and Technical Assessment model. It combines four fuzzy Multi-Criteria Decision Making techniques viz. Fuzzy Stepwise Weighted Assessment Ratio Analysis, Fuzzy Multi-Objective Optimization by Ratio Analysis, Fuzzy Weighted Aggregated Sum Product Assessment and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution. This paper utilises the case of hydropower plants in India, how the policy and decision-makers can arrive at the selection of the best location for an infrastructure project. According to the assessment value, Teesta Low Dam IV (Darjeeling, West Bengal) is the preferred hydropower plant for all selected methods. The State could bring social justice, ecological stability; achieve economic benefits and sustainability by appropriating this proposed model in other development fields. [ABSTRACT FROM AUTHOR]

Managing the transition to a low carbon economy is a complex challenge that needs both early wins and a longer-term alternate technologies leapfrog, to better supplement existing public policies. Relative to the significant investment and policy focus on renewable energy generation and Electric Vehicles (EV) - both globally and in India - Stationary Energy Storage systems (ESS) have received far lower investment and policy attention. This is an important issue to redress for two key reasons. Firstly, ESS is a key rate limiting constraint to achieve the desired benefits of further increasing the share of renewables in the energy generation mix, in India's case from the current 20–25% to a target 40%+ range by 2030. Secondly, several ESS applications are already/very nearly economically viable. Although the dominant discourse focuses on EVs, our analysis in this paper shows that there is a bigger near term opportunity in India for Stationary Battery Energy Storage Systems (BESS) to replace diesel gensets for power backup. Interestingly India offers a meaningful level of scale for power-backup applications, for adoption directly by end-users. BESS as an alternative to Diesel Generator (DG) for power backup is economically viable in the Telecom sector and for roof-top solar installations; further non-subsidy levers e.g. differential tariffs and an annual cess on DG use, can drive economic viability in large campuses e.g. residential, schools and commercial buildings. These applications could offer a BESS demand ranging from 40 to 145 GWh over the next three years – which is more than the BESS demand estimates for EV segment of 40 GWh. Our study is verified and supported by experiential insights derived through primary research, personal interviews and hosting round-table discussions with relevant private and public policy experts. The conclusions from this paper raise the interesting public policy and business strategy implications, 1 of given the economic viability and significant demand why has the adoption up to this potential not yet taken off, and what will it take to achieve this potential? 1 1 These issues are addressed in the author's subsequent working paper, interested readers can contact the corresponding author • Techno-economic feasibility study of battery energy storage systems (BESS). • Identify early adopter consumer scale use cases that are/near economic viability in India. • Demand estimates for BESS as Diesel genset replacement in Telecom, Consumer & Industry. • Our Results confirm greater near term for this application than EVs. • Raises important implications for greater Public policy and Business Strategic focus on this opportunity. [ABSTRACT FROM AUTHOR]

• A novel Equivalent Truss Model (ETM) for nonlinear static analysis of confined masonry structures. • Validation of ETM through results of experimental studies. • Application of ETM for seismic evaluation of a 3-storey confined masonry building in India. • A comparison of force-based and performance-based seismic design approaches. • Seismic evaluation based on the Performance Based Design approach confirmed a signifcant safety margin for a design developed based on the force-based approach. Modern Performance-Based Design (PBD) approach for seismic analysis of building structures is based on the evaluation of nonlinear response to earthquake-induced demand and assessment of performance based on the lateral displacements/drifts. One of the key challenges associated with the application of PBD for seismic evaluation of confined masonry (CM) structures is the development of an adequate numerical model for simulating nonlinear response at different seismic demands. In this paper, an Equivalent Truss Model (ETM) has been proposed for nonlinear analysis of CM structures. The model is based on a truss analogy concept, where a CM wall is idealized as a truss consisting of pin-ended elements. A masonry panel is modelled as a compression element (strut), while horizontal and vertical reinforced concrete (RC) confining elements are modelled either as struts or tension elements (ties). ETM is essentially a macro-model that simulates nonlinear behaviour of a CM wall through lumped nonlinearity of masonry struts, while RC confining elements show linear elastic behaviour. The proposed model has been validated using the results of experimental studies on CM walls subjected to in-plane reversed cyclic loading. The paper also presents a case study, in which ETM was applied for seismic evaluation of an existing three-storey CM apartment building located in an area of moderate seismic hazard in India using nonlinear static analysis. The damage states for PBD were established based on past research studies on CM structures. A comparison of the results of conventional force-based design and PBD analyses showed that ETM can be used in conjunction with the PBD approach to evaluate existing CM buildings and also optimize design solutions for new buildings, which were developed using a conventional, force-based seismic design approach. [ABSTRACT FROM AUTHOR]

• This paper uses instrumental variable approach to study the world's largest free school meal program (i.e., Mid Day Meal Scheme or "MDMS") on primary school enrollment in India. • This paper finds that the MDMS program had positive and significant impact on gross and net primary school enrollment in India. • MDMS increased gross and net enrollment in primary school by 16 to 19 percentage points. • An analysis of on-time enrollment finds that the program had a large effect on net enrollment in the first grade, suggesting a large extensive margin response. • MDMS had a larger impact on (a) girls compared to boys and (b) disadvantaged population groups compared to other population groups in India. • The analysis found the effect of the mid-day meal program on girls to be almost twice its effect on boys for primary school enrollment, suggesting that the program had a positive impact on reducing the gender gap in school participation. • Findings of this paper suggests that school feeding program is an effective policy tool for increasing primary school enrollment in developing countries. This paper studies the impact of the world's largest free school lunch program (Mid Day Meal Scheme) on school enrollment in India. While the program was launched nationally, its implementation has been scattered within and across states. Using an instrumental variable approach, this paper estimates local average treatment effect of receiving free meals in school on children's enrollment in primary school. The findings of this paper suggest that the mid-day meal scheme increased the probability of enrollment in primary school and on-time enrollment in first grade. An analysis of heterogeneity in results shows that the program had larger effect on socially disadvantaged groups and on girls. Specifically, the analysis found the effect of the mid-day meal program on girls to be almost twice its effect on boys for primary school enrollment, suggesting that the program had a positive impact on reducing the gender gap in school participation. [ABSTRACT FROM AUTHOR]

Objective: Proper diagnosis of Low Back Pain (LBP) is quite challenging in especially the developing countries like India. Though some developed countries prepared guidelines for evaluation of LBP with tests to detect psychological overlay, implementation of the recommendations becomes quite difficult in regular clinical practice, and different specialties of medicine offer different modes of management. Aiming at offering an expert-level diagnosis for the patients having LBP, this paper uses Artificial Intelligence (AI) to derive a clinically justified and highly sensitive LBP resolution technique.Materials and Methods: The paper considers exhaustive knowledge for different LBP disorders (classified based on different pain generators), which have been represented using lattice structures to ensure completeness, non-redundancy, and optimality in the design of knowledge base. Further the representational enhancement of the knowledge has been done through construction of a hierarchical network, called RuleNet, using the concept of partially-ordered set (poset) with respect to the subset equality (⊆) relation. With implicit incorporation of probability within the knowledge, the RuleNet is used to derive reliable resolution logic along with effective resolution of uncertainties during clinical decision making.Results: The proposed methodology has been validated with clinical records of seventy seven LBP patients accessed from the database of ESI Hospital Sealdah, India over a period of one year from 2018 to 2019. Achieving 83% sensitivity of the proposed technique, the pain experts at the hospital find the design clinically satisfactory. The inferred outcomes have also been found to be homogeneous with the actual or original diagnosis.Discussions: The proposed approach achieves the clinical and computational efficiency by limiting the shortcomings of the existing methodologies for AI-based LBP diagnosis. While computational efficiency (with respect to both time and space complexity) is ensured by inferring clinical decisions through optimal processing of the knowledge items using poset, the clinical acceptability has been ascertained reaching to the most-likely diagnostic outcomes through probabilistic resolution of clinical uncertainties.Conclusion: The derived resolution technique, when embedded in LBP medical expert systems, would provide a fast, reliable, and affordable healthcare solution for this ailment to a wider range of general population suffering from LBP. The proposed scheme would significantly reduce the controversies and confusion in LBP treatment, and cut down the cost of unnecessary or inappropriate treatment and referral. [ABSTRACT FROM AUTHOR]

This paper attempts to analyze the variations in technical efficiency of the individual farmers of distinct (small, medium and large) size of land holdings and different types (family and hired) of labor with given set of input variables for the agricultural production across different Agro-Climatic Zones. A field survey was conducted to collect the information from a randomly stratified sample of 300 farmers operating in six districts falls under Trans-Gangetic Plains and Semi Sandy Desert zones of the states of Punjab and Haryana in India. A model of stochastic frontier analysis for cross sectional data with half normal truncated features has been used to measure the technical efficiency under the assumptions of Cobb-Douglas production function. The model comprises eight dummy variables for the farmers owning distinct size of land and using distinct type of labor in their production. The results for the estimates of technical inefficiency with family and hired labor depict; small farmers are 50.30 and 68.11 per cent, medium farmers are 61.09 and 67.75 per cent, large farmers are 71.03 and 56.67 per cent and average farmers are 71.94 and 70.07 per cent inefficient respectively. This paper helps the farmers and the policy makers in identifying the second best-alternative crop for obtaining the sustainable agricultural production. [Display omitted] • Ratio of land to labor applied by farmers lead to loss of income and productivity. • The demand for food is increasing, motivating the importance of farmers for sustainable agricultural production and development. • Sustainable agricultural production can deal with help is required to meet the growing demand for food. • Kernel density function measures variations in technical inefficiency of individual farmers. • Second-best alternative crops must be suggested to farmers for cultivation. [ABSTRACT FROM AUTHOR]

Nuclear accidents, despite having an extremely low probability of occurrence, could cause uncontrolled release of radioactive elements (fission and activation products) into the environment, and may ultimately lead to contamination of food products. Such a scenario requires extraordinary measures for control of food, which might be contaminated to a level not suitable for human consumption. Agricultural products (which include grain crops, vegetable, fruits, dairy, meat, eggs and poultry) pass through a series of local, district and state level markets to finally reach consumers. An effective intervention at different stages of distribution by targeted sampling and analysis of suspected (contaminated) foodstuffs will substantially reduce the chances of contaminated food to reach the public. At the same time, it will also ensure food security of the people without imposing unreasonable restrictions in market flow. This can also help in getting the farmers adequately compensated. This paper presents a protocol for sampling and analysis suitable for India, considering the diversity with respect to climate, soil type, land use, crop pattern, population density, etc. The paper also provides an estimate of infrastructure requirement to carry out environmental monitoring following the emergency with respect to human resources and instruments. The paper proposes to use the national web portal for collection of data pertaining to crop pattern, land use and market flow. A web-based decision support system (Web-DSS) on a GIS platform, for sampling, analysis and display of data online would enhance the transparency of decision being taken and enable the administrators to effectively monitor the work flow, details of sample collection, analysis and effective use of human and other resources. [Display omitted] • A GIS and web-based protocol is suggested for agricultural monitoring driven by a predominantly agrarian economy. • This protocol is intended for use by decision makers in India in case of nuclear emergency. • The protocol postulates a centralized real-time control and updates for sampling of food & agricultural produce. [ABSTRACT FROM AUTHOR]

According to WHO (World Health Organization) data, people spend at least 80% of their lives indoors, with the percentage rising to 95% for older people with mobility impairments. Creating a thermally comfortable indoor environment is essential for improving users' productivity and well-being, particularly for those who spend maximum time indoors, such as elderly people. India's ageing population is increasing significantly; however, there is lack of studies in the field of thermal comfort for the elderly. The main aim of this paper is to determine the thermal comfort range of elderly people in residences of warm and humid climates during summers in India and to find out the adaptive actions opted by the elderly population to achieve thermal comfort. 740 responses were collected from elderly people through questionnaire surveys along with simultaneous field measurements during the summer. The linear regression analysis found the occupant's comfort temperature of 30 °C and the comfort range was found to be 28.5 °C- 31.5 °C, which was narrower than the younger people's thermal comfort studies at residences. The neutral temperature was higher for the male elderlies than the female elderlies, which was highly correlated with their clothing adjustments. The probit analysis revealed that 90% of the elderly felt comfortable when the operative temperature varied between 26.9 °C and 30.9 °C. Clothing adjustments and taking cold showers are the most preferred behavioural actions by the elderlies in summer, whereas changing the cooking time and tying up hair are the most preferred behavioural adaptations to overcome thermal discomfort for female elderlies. [ABSTRACT FROM AUTHOR]

In resolving multicriteria decision-making (MCDM) problems, it is observed that the solution process is frequently affected by the available vague information together with intra-personal uncertainties associated with expert's opinion. In most of the time fuzzy set or its existing variants fail to capture such uncertainties for making reasonable balance in decision making processes. To reduce the effects of uncertainties, this paper introduces a new variant of fuzzy set, viz., type-2 Pythagorean fuzzy set (T2PFS). The main advantage of using T2PFS in MCDM is that the membership and non-membership values of each argument are presented by Pythagorean fuzzy numbers rather than using fuzzy number or intuitionistic fuzzy number. In model formulation process, at first, T2PFS is explicitly formulated and some basic operations are defined. Subsequently, four aggregation operators are constructed to aggregate the arguments, and their properties are verified. Afterwards, two information measures and a score function are defined. Moreover, two MCDM algorithms based on the developed aggregation operators and TOPSIS are framed. To show the application potentiality of the proposed method, a case study relating to the selection of sustainable transport system in India is considered and solved. In the case study five different fuel-based vehicles, mostly used in India in recent times, are evaluated by the experts as transport alternatives with the consideration of thirteen sustainability related criteria. From the achieved result it is found that in future electric cars would become the most sustainable vehicle in India. Sensitivity analysis of the developed models and comparative study with existing fuzzy and non-fuzzy MCDM methods establish effectiveness of the developed models. This study may facilitate transport authority to select appropriate transport system in connection with sustainable development of a region. • As a new variant of fuzzy set, type-2 PFS is coined for resolving uncertainties associated with MCDM problems. • Terminologies, ranking of type-2 PFSs with aggregation operators are developed by defining arithmetic operations. • Defining distance and entropy measures, type-2 PF entropy weight model is introduced to determine criteria weights. • Two type-2 PF MCDM algorithms based on aggregation operators and TOPSIS are presented. • A case study is performed for the selection of sustainable transport system in India. [ABSTRACT FROM AUTHOR]

India has a significant importance in achieving the global Paris agreement's long-term goal as it is the world's third-largest consumer of energy. This paper aims to improve the understanding of the opportunities and constraints of decarbonizing India's electricity sector and adds to the growing body of literature by modelling the electricity sector in India with a dynamic optimization approach that assesses options for effective greenhouse reductions through carbon pricing. Attaining significant variable renewable energy penetration in India by 2050 is consistent with the lowest cost power generation options identified through our simulations, where solar and wind can provide 66 % of the electricity needs by 2050, even without a carbon price. With a carbon price of US$50/tCO2e, the simulations project that wind and power generation share in the electricity mix will rise to 81 %, leading to significant emission reductions. In this scenario, power sector emissions will be 164 million tCO2e by 2050, which is only about 14 % of the emissions levels in 2019. The analysis also indicates that fossil fuels remain part of India's electricity mix in 2050, but their role and operation would dramatically change. When a carbon price of US$50/tCO2e is introduced, coal capacity remains valuable to fill demand when solar and wind are not available. This would require modifications in operation schedules and repurposing coal power plants for frequent ramping up and down. [ABSTRACT FROM AUTHOR]

• CRISP-DM methodology is used for crime data mining. • Crime against women dataset is gathered from the national crime record bureau website year-wise from 2001 to 2020. • Crime trends, regression analysis, correlation gradient, correlation heat map and choropleth map are analysed. • Forecasts crime under indian penal code in categories with the accuracy of 72.29, 92.15, 83.30 and 84.33% respectively. Crime against women (CAW) in India is the violence against women that is at par in previous years. India is densely populated has added to the figures of crime against women. This paper aims at study of crime against women dataset given by NCRB (National Crime Record Bureau) from 2001 to 2020 for all the 27 states and 9 union territories. EDA (exploratory data analysis) with linear regression is a powerful combination for understanding the relationship between various factors and the incidence of crime against women. EDA is a process of analysing and summarizing the main characteristics of a data set through visualizations, descriptive statistics, and other techniques. At the same time, linear regression is a statistical method that models the relationship between a dependent variable and one or more independent variables. India's crime against women dataset on various crime categories under Indian Penal Code (IPC) such as rape, cruelty by husband and his relatives, kidnapping and abduction, dowry deaths, assault on women with intent to outrage her modesty, insult to modesty of women and human trafficking are considered to accomplish this. CRISP-DM methodology allows for a consistent and structured approach to data mining, which reduces the risk of errors and improves the chances of success in predicting crime rate. The proposed model has various data analytics steps to pre-process the datasets and visualize the crime rate. The visualization of data helps to uncover trends present in the crime dataset. The proposed predictive model analyses data and predict crime against women under four IPC categories to give accuracy of 72.29, 92.15, 83.30 and 84.33% respectively. [Display omitted] [ABSTRACT FROM AUTHOR]