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Nonalcoholic fatty liver disease (NAFLD) is a major cause of chronic liver disease globally and in India. The already high burden of NAFLD in India is expected to further increase in the future in parallel with the ongoing epidemics of obesity and type 2 diabetes mellitus. Given the high prevalence of NAFLD in the community, it is crucial to identify those at risk of progressive liver disease to streamline referral and guide proper management. Existing guidelines on NAFLD by various international societies fail to capture the entire landscape of NAFLD in India and are often difficult to incorporate in clinical practice due to fundamental differences in sociocultural aspects and health infrastructure available in India. A lot of progress has been made in the field of NAFLD in the 7 years since the initial position paper by the Indian National Association for the Study of Liver on NAFLD in 2015. Further, the ongoing debate on the nomenclature of NAFLD is creating undue confusion among clinical practitioners. The ensuing comprehensive review provides consensus-based, guidance statements on the nomenclature, diagnosis, and treatment of NAFLD that are practically implementable in the Indian setting. [ABSTRACT FROM AUTHOR]

The regional coverage satellite constellations are in demand for fulfilling earth observation needs of specific user regions of interest as opposed to global coverage thus enhancing the revisit period for the area. In this paper, we present a few key mission aspects of the regional coverage constellation designed and operated by the Indian Space Research Organization (ISRO) in low earth orbit. ISRO developed three X-Band Synthetic Aperture Radar (SAR) Earth Observation satellites and placed them in three inclined orbit planes to enhance the Indian region's coverage. This is India's first inclined orbit constellation designed for remote sensing applications in low earth orbit. The satellites were built around a standard bus carrying a Synthetic Aperture Radar payload with a deployable Radial Rib Antenna (RRA). They were designed to achieve a high agility capability. As SAR sensors are capable of all-weather, day, and night imaging; thus, most suitable in inclined orbits where the illumination conditions vary continuously for imaging. Though the inclined orbits provide more revisit opportunities to the region of interest, their characteristics pose challenges in mission design and operations. Though the mission design and planning involved a host of elements, this paper brings out the details of a few key mission analysis elements, planning aspects and operations that were critical in the course of various mission phases. The mission analysis included mission-specific requirements like launch windows selection for the constellation and reference attitude generation specific to inclined orbit geometry. Mission planning had to take care of Radial Rib Antenna (RRA) deployment requirements which were very critical for the mission, apart from taking care of individual subsystem requirements. The SAR payload data product quality and targeting accuracy are very sensitive to the satellite image point range (vis-a-vis SAR operation data window). Extensive post-launch calibration activity was carried out to meet all these mission requirements and data product specifications. The challenge was in fixing the payload beam alignment and fixing the uncertainties in onboard and ground computations. The paper also describes the methods of fixing all possible error sources affecting the high-resolution SAR payload performance. • The regional coverage satellite constellations are in demand for area targets coverage. • Inclined orbit constellations are more suitable for regional coverage using SAR sensors. • Mission operations plans need to consider all the specific requirements of spacecraft systems. • Pre-launch and post -launch calibrations of sensors is mandatory for achieving system performance. [ABSTRACT FROM AUTHOR]

Secondary and High School students are usually adolescents, most of them in the urban areas and metropolitan cities are aware about the existence, various opportunities, career paths and advancements of the space industry. They are constantly updated, given they have access to quality and organized education models and a competitive environment. Although, the students in the rural areas who are equally ambitious and talented yet are not fortunate enough to have access to the state of art facilities like the counterparts. Even if they try and gain access with their self-interest, they do not have the motivation to sustain the passion, given their lack of clarity in subject, lack of awareness about engaging in various ways and fields in space other than technological related and mainly lack of driving force. The purpose of this paper is to develop and present an education model, exclusively for the high school students of the rural areas and inculcate sustainable passion for space. As opposed to the primary school students, the secondary school students have a consciousness of the space industry. This paper will talk about various plans and strategies, that will allow them to gain exposure, guide them about the existence of different career paths which are not entirely technological related, yet are still essential in the space like, administrative framework for example. This opens a new dimension in their brain helping them realize how they can be part of the industry in countless ways, and this shall assist them in being on the right track to space. In addition to, hosting creative space themed exhibitions, fun and engaging model design competitions, workshops, all of these would widen their knowledge boundaries and help them challenge their skills. A real time case study example from a school located in the rural southern of India shall be focused on this paper. Including data from a survey thereby the results, with action plans shall be presented in detail, which will aid in the analysis of the need, feasibility, and long-term sustenance of this approach and the requirement to scale it over different rural areas globally. • Need for improvised education models in rural areas elucidated, with data collection. • Data analysis and correlation of space education awareness, for high school students across two geographical locations. • Readily executable action plans age groupwise, for real-time education models in remote areas globally. [ABSTRACT FROM AUTHOR]

A simple framework to estimate the potential of solar industrial process heating (SIPH) and corresponding CO 2 emissions mitigation in paper industry in India is presented. Geographical locations with clusters of paper mills have been identified and the annual process heating requirement for paper production (based on raw material use) at each cluster has been estimated. The total process heating potential for the paper industry in India has been estimated at 25.2 PJ/annum. Using the estimated performance of a commercially available parabolic trough based SIPH system with collector area requirement of 1.11 million m 2 annual solar fraction in the range of 0.25–0.30 have been estimated. Annual energy delivery of SIPH systems estimated at 4.34 PJ per annum. Finally at 8 main locations with clusters of paper mills in India considered in this study, adoption of SIPH without storage is expected to mitigate 0.34 million tonnes of CO 2 emissions annually. [ABSTRACT FROM AUTHOR]

We analyse the results from a longitudinal natural field experiment that provides an environmental nudge by reinforcing positive environmental norms. Posters asking individuals to use less paper are pasted on printers (Times) and in office cubicles (Umendall) of two information technology firms around New Delhi, India. A third firm (India Mart) does not get the nudge and serves as our control. During the nudge period, employees of the firms that get the conservation message on average expend a significantly lower number of sheets per day as compared to the pre-nudge period, measured both in terms of paper used and paper wasted. Furthermore, after the nudge is removed, the per capita paper wastage for one of the firms that received it (Times) does not increase significantly over the next month and a half, whereas for the other firm (Umendall) it does but remains significantly lower than the per capita wastage in the pre-nudge period. In terms of total paper used, the nudged firms show a reduction in the number of sheets used several weeks after the nudge is removed. For the control firm, the wastage and usage of printer paper does not change significantly over the duration of the experiment. A robustness check with no nudge intervention done by computing per capita wastage and usage for the same dates exactly one year later does not show a reduction over the same time period and offers support for our conclusions. [ABSTRACT FROM AUTHOR]

An enzyme cocktail (xylanase-15U, pectinase-2U, α-amylase-2.5U, protease-2U and lipase- 1.8U) from Bacillus halodurans was applied for the biobleaching of unbleached and ODL (oxygen delignification) agro based pulp in the R&D set up of Shreyans Paper Mills Ludhiana, India, upscaling from 5 g (at lab scale) to 100 g odp (oven dried pulp). Enzyme pre-treatment was carried out at temperature and pH optima of 65 °C and 9–9.5 for 90 min followed by reduced chemical treatment. In case of unbleached agro pulp, as compared to the total chemical bleaching in mill, enzyme cocktail pre-treatment reduced the chemical usage by 50% without compromising brightness and rather with increased tensile strength (23.55%), burst factor (20.3%) and tear factor (3.17%) and reduced kappa number (19.5%). In case of ODL pulp, enzyme cocktail pre-treatment reduced the chemical usage by 40%, yet increasing brightness (2.24%), tensile strength (20.96%), burst factor (5.1%) and tear factor (8.2%) with reduction in kappa number (4.54%). The enzyme cocktail thus can make the paper manufacturing process greener, safer, economical and eco-friendly and may replace big budget ODL technologies. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) is closely associated with metabolic syndrome. Prevalence of metabolic risk factors including diabetes mellitus, obesity, etc. is rapidly increasing in India putting this population at risk for NAFLD. Patients with NAFLD are at increased risk for liver-related morbidity and mortality and also cardiovascular disease risk and increased incidence of diabetes mellitus on long-term follow-up. Management of patients with NAFLD may require a multi-disciplinary approach involving not only the hepatologists but also the internists, cardiologists, and endocrinologists. This position paper which is a combined effort of the Indian National Association for Study of the Liver (INASL), Endocrine Society of India (ESI), Indian College of Cardiology (ICC) and the Indian Society of Gastroenterology (ISG) defines the spectrum of NAFLD and the association of NAFLD with insulin resistance and metabolic syndrome besides suggesting preferred approaches for the diagnosis and management of patients with NAFLD in the Indian context. [ABSTRACT FROM AUTHOR]

• Three characteristically different plasma structures viz. EMSTID, plasma depletion and non-electrified MSTID were observed simultaneously. • The individual structures underwent evolution, distortion, and dissipation separately. • The lack of mutual interaction between these three structures, despite existing simultaneously, is highly unusual. An unusual event of three characteristically different plasma structures on a geomagnetically quiet night (Ap = 4) of 08 July 2018 has been investigated in this paper using an all-sky imager installed at Hanle, Ladakh (32.7°N, 78.9°E; Mlat. ∼ 24.1°N), India. These structures include a freshly generated electrified medium scale traveling ionospheric disturbances (EMSTID), ambiguous plasma depletions, and a northward propagating non-electrified MSTID. One of the most fascinating aspects of this event was the lack of mutual interaction between the three structures, even though they existed simultaneously and propagated in different directions. They individually underwent evolution, distortion, and dissipation separately. One of the structures was an EMSTID, which got generated within the imager's field-of-view (FOV) and evolved with time. As time progressed, different strip-like structures travelled southwestward, merged with each other to form the EMSTID. The second structure was plasma depletions which appeared in the southeastern part of the FOV. They eventually merged into a single band, which propagated nearly westward. The merged band evolved with time, extended further northward, and dissipated later. Along with these, a very rare non-electrified MSTID structure with east–west aligned fronts was observed which propagated northward. Its fronts underwent distortion, became curved and dissipated. In this study, we have explored the role of electrodynamics behind the observed unique features of the three plasma structures. [ABSTRACT FROM AUTHOR]

• A new stochastic SIRD model with imperfect immunity based on a continuous time Markov process is proposed. • We explore novel stochastic properties like the extinction and alarm time beside the infection and mortality time of a tagged case. • Theorems and recursive algorithms for the computation of the stochastic properties are presented. • An extensive sensitivity analysis reveals monotonous and unimodal tendencies for the considered stochastic indicators. • Health authorities can employ this information to achieve the best balance between health benefits and economic drawbacks. Efficient assessment of epidemic phenomena has an important role in modern epidemiology, while many novel methods propose reliable estimates for epidemic evolution in a population. In this paper, we focus on the stochastic modeling of a novel epidemiological (susceptible-infected-recovered-deceased) SIRD model with imperfect immunity based on a continuous-time Markov process adapted to the characteristics of the epidemic model. We investigate several novel stochastic properties of the SIRD scheme that are both population- and individual-oriented, such as the extinction and alert time of an epidemic, in parallel with the infection and mortality times of a tagged individual. We provide propositions and detailed recursive algorithms for computing probabilities and moments, giving additional information beyond the mean and variance of the stochastic quantities. Important remarks for representing higher-order matrices and system solving are provided, decreasing significantly the operation time of these algorithms. Extensive sensitivity analysis gives light to the influence of the system's parameters, while enhancing the validity of our methodology. Unlike other analyses that focus mainly on fitting the evolution of various diseases, our goal is to complement these studies by highlighting additional noteworthy features that determine an epidemic's future. The proposed methodology is applied on the monkeypox outbreak of 2022 in India and the first COVID infection wave in Barbados. Finally, public health authorities can use the information provided by these indicators to adjust the duration of lockdowns, accordingly, achieving the best possible balance between health benefits and economic disadvantages. Simultaneously, knowledge of mortality probabilities and timing of infection can support early coordination of hospitals and intensive care units, which could notably reduce the high risk of mortality. [ABSTRACT FROM AUTHOR]

Carbon footprints (CFs) are the measure of extent of green house gases (GHGs) emitted from any organization or activity. Reporting of extent of CFs in terms of CO 2 e from educational campus may be considered as a first step towards sustainable educational practices. The present study employs a bottom up approach where CFs due to consumption of resources from each academic department is evaluated separately and net is obtained by adding contributions from individual departments. This method has the advantage of avoiding the potential overlaps in the calculation. Hence this approach to calculate carbon footprints can lead to their precise CF evaluation. In order to justify above hypothesis, CF evaluation was carried out at Shikshana Prasarak Mandali's Sir Parashurambhau College, Pune for three consecutive years starting from 2015 to 2016. The average carbon footprints over the study period were 3630.57 ± 296.79 tones CO 2 e per year with per capita emission of 0.81 ± 0.06 tones CO 2 e. The emission density determined from the average value was found to be 0.0361 ± 0.0029 tCO 2 e m−2 of the campus. It was observed that emission sources classified as scope-1, scope-2 and scope-3 contribute 28%, 48% and 25% respectively to total emission. Out of all the emission sources identified, apart from human contribution through respiration, electricity (35%), biodegradable and non-biodegradable waste, stationary paper, 7%, use of chemicals in the laboratories (5%), paper for official purpose (12%), LPG (3%) and transport (27%) were main contributors. As this is a multi faculty college, science faculty contributes almost 62%, commerce 9% and arts 11% of the total emission. • Present paper highlights carbon footprints from an Indian HEI using bottom up approach. • A questionnaire based survey was used to arrive at departmental and net footprints. • Carbon footprints of HEI in India are much less as compared to developed countries. • The carbon footprints of Indian HEIs are expected to grow at 5–6%. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are among the most revolutionary breakthroughs by the context of decarbonization of road transport. The adoption of EVs has drawn increasing attention and is currently considered a potential route towards sustainable transportation. Businesses approaching the EV market must establish lucrative business models that overcome the adoption obstacles for EVs while assuring long-term growth. The intent of the current research exploration is to create a cutting-edge framework for EV enterprises that aims to maximize profit and save delivery time leading to high customer satisfaction. The volume discount strategy is used for profit escalation. The model presented in this paper is highly adaptable for a real-world problem as it incorporates uncertainty in the EV industry through generalized triangular neutrosophic numbers, an extension of fuzzy numbers. A modified version of the neutrosophic compromise programming approach is proposed in this paper. The results from this approach are validated using genetic algorithm. The proposed optimization-based framework is encapsulated in the form of study of a case from Bangalore city, in India. An in-depth exploration of two cases along with sensitivity analysis is carried out that helped in prioritizing some discrete customers and gave insights on which manufacturing site should be designated more focus. • Profit maximizing business model for electric vehicle (EV) merchants is proposed. • Uncertainty in EV industry is captured using fuzzy number. • A modified neutrosophic compromise programming technique is proposed. • Case study is considered involving particular case and sensitivity analysis. [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission, which is scheduled to launch in 2022, would offer India with unprecedented hydrologic applications. This study presents the preliminary results of the CNES Large-Scale SWOT Hydrology Simulator to illustrate how SWOT data can be used to monitor reservoir water levels. Except for some factors such as effects due to topography, and error due to idealized water detection, the CNES simulator accounts for practically all of the probable noise expected in future SWOT data. In this paper, we look at how SWOT can be used to monitor reservoirs in difficult situations. SWOT will monitor the reservoir in two swaths in this case, with an unmonitored zone in the middle. This paper provides a strategy for extrapolating data across two swaths to determine reservoir volume. The results demonstrate that the proxy water surface elevation (WSE) created by the CNES simulator has a great potential in scientific research relating to reservoir characteristics and surface water dynamics. It is observed that the reservoir volume estimated using proxy SWOT data has a maximum bias of −7.65 % and a minimum bias of −2.19 %. [ABSTRACT FROM AUTHOR]

Total electron data (TEC) from GPS nowadays can be used as a tool for understanding the space weather phenomena. The development of prediction model for TEC is quiet crucial and challenging due to the dynamic behavior of the ionosphere, since it depends on different factors such as seasonal, diurnal and spatial variations, solar geomagnetic conditions etc. In this paper, an attempt is made for predicting the GPS derived TEC values for different GNSS stations over India using a hybrid method based on Ensemble empirical mode decomposition (EEMD) and Long Short-Term Memory (LSTM) deep learning method. The daily TEC time series data from the IISc Bangalore (Latitude 13.021, Longitude 77.570), Lucknow (Latitude 26.912, Longitude 80.956) and Hyderabad (Latitude 17.417, Longitude 78.551) stations over India during the period 2008 to 2015 of solar cycle 23 and 24 is used for analysis. The assessment of model performance for testing predicted output compared with LSTM and EMD-LSTM models, and their comparison results show that the hybrid EEMD-LSTM model presents better than the other models. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is an important tropical atmospheric phenomenon that plays a significant role in modulating weather patterns across India. Heat Waves (HW) are extreme weather events that have severe impacts on human health, agriculture, and various socio-economic sectors. This research paper aims to investigate the dynamical relationship between MJO phases, as measured by the Real-time Multivariate MJO (RMM) index, and the occurrence of HW in India, as defined by the Indian Meteorological Department. This paper demonstrated the relationship among three aspects, i.e., intensity, duration, frequency of HW during the different MJO phases. The differential dynamics associated for about two-fold rise in the HW occurrence in the South East, Eastern and North West India in the MJO active phase. Inactive phase witnessed enhancement (suppression) of HW during wet (dry) over the regions. The novel finding of this paper is that, the weaker the MJO, the higher probability of HW occurrence in respective regions depending on the position of the MJO convection center. The maximum duration is observed to be significantly decreased by about 2 days in the active MJO phase over NW and north central parts of the country. It is also inferred from this study that there is a significantly decrease of 0.5 °C in HW intensity during the active wet phase of MJO over the NW India. To explain the linkage of HW with MJO, geopotential height (GPH), wind circulation, velocity potential (VP), sea surface temperature (SST) and outgoing longwave radiation (OLR) composite anomalies have been computed and used in the analysis. It is observed that all these parameters have a direct impact on the evolution, strength and spread of the HW in India. This dynamical study mechanism can be prescribed in the numerical weather prediction models for the improved prediction of such extreme weather events during summer for proactive disaster management. Spatial distribution of change in probability of Heat Wave occurrence during all phases of MJO (phase 1 to 8) over the Indian region for the period 1979–2022. [Display omitted] • Relationship between MJO phases and intensity-duration-frequency of Heat Wave in different regions of India is explored. • Weaker the MJO (inactive phase), higher probability of HW occurrence with increased intensity and duration in wet phase. • The HW over north western Indian region are mainly driven by Rossby wave train in the wet phase of MJO. [ABSTRACT FROM AUTHOR]

• The paper examines the effect of social identity on learning for adults under a hierarchical social setting. • Using primary data from a randomized controlled experiment in India where illiterate adult female learners were randomly assigned to a free technology-based literacy program, we found positive and significant impact of matching an upper caste teacher with a lower caste student on literacy scores. • We find suggestive evidence of an increase in students' confidence measures when matched with an upper caste teacher. The paper examines the effect of social identity on adult learning within a hierarchical social setting— an important yet often understudied issue for effective adult education. We leverage the random matching of students and teachers from a randomized controlled experiment in India, where illiterate adult female learners aged 18–45 were randomly assigned to a literacy program. We find a positive and significant impact of matching an upper caste teacher with a lower caste adult student on literacy scores. We also find suggestive evidence of an increase in students' confidence measures when matched with an upper caste teacher, indicating a plausible impact mechanism. Our findings highlight the need for future research on social identity and its influence on adult learning, particularly in countries with existing deep-rooted hierarchical social constructs. [ABSTRACT FROM AUTHOR]

Despite growing health concerns and increasing water scarcity, adequate wastewater treatment and reuse remains limited in India. Low-cost, decentralised, and nature-based on-site sanitation systems could play a key role in a circular water approach. This paper evaluates the performance of a Wastewater Fertigated Short Rotation Coppice (wfSRC) system based on willow, poplar, and bamboo species as a potential solution, offering low-cost, efficient water treatment, and high biomass production. Through a full-scale wfSRC pilot plant (250 m3·day−1 municipal wastewater on 6864 m2) established at Aligarh Muslim University, Uttar Pradesh, India, treatment capacities, biomass production rates, and contamination tolerance of different willow, poplar, and bamboo species were investigated. The chemical compositions of incoming wastewater, percolation water, soil, and biomass were monitored regularly. Despite the high load, all plant sections of the wfSRC system showed efficient removal of wastewater-originated pollutants. BOD5 and COD removal efficiencies were up to 99% and 95% respectively. Removal efficiencies for PO4-P reached 93% and for TN 91%. In general, total nitrogen and phosphate loads appear to be the limiting design parameters under the conditions at Aligarh to meet all discharge limits in India. Biomass production was projected to be >225 tDM·ha−1·yr−1 for bamboo, 65 tDM·ha−1·yr−1 for willow, and 206 tDM·ha−1·yr−1 for poplar. The pilot, conducted from March 2022 to June 2023, showcased how these densely planted agroforestry systems can effectively treat wastewater through natural processes such as oxidation, microbial degradation, and plant uptake. The findings demonstrate wfSRC's potential to serve as a nature-based, sustainable approach to wastewater treatment, particularly beneficial for suburban and rural areas in India and similar regions worldwide. This paper also provides recommendations for future implementation of wfSRC systems, emphasizing the need for careful planning regarding plant selection, system design, and operational strategies to maximize treatment efficiency and biomass production. [ABSTRACT FROM AUTHOR]

This paper delves into the integration of Industry 4.0 technologies in the manufacturing sector, providing a framework tailored for adoption in India, a rapidly advancing digital economy. The current literature does not go into detail on industry 4.0 technology in the context of agile manufacturing. Addressing this critical gap in the literature, we identify 15 criteria across six categories, leveraging the entropy method to compute their weights. We prioritize Industry 4.0 technologies in the context of agile manufacturing using a comprehensive analysis that employs the TOPSIS and Fuzzy DEMATEL methodologies. Results indicate that cybersecurity, Cloud, and the Internet of Things (IoT) are the most commonly used technologies in agile manufacturing. The fuzzy DEMATEL-based framework identifies cybersecurity, Big data, Cloud, simulation, augmented reality, and IoT as 'cause' technologies, while additive manufacturing, robotics, mobile devices, and systems integration are identified as 'effect' technologies. The paper proposes practical strategies for managers in India to facilitate the adoption of Industry 4.0 technologies, thereby enhancing firms' agility in the digital age. By highlighting the specific technologies critical to agile manufacturing, our study not only addresses a notable gap in the literature but also provides a roadmap for their practical implementation, empowering businesses to thrive in an increasingly digitalized landscape. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer's productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices. • Data Envelopment analysis was estimated to derive the paddy efficiency among the tribal villages of eastern India. • The DEA estimates show that average Technical efficiency in the study region is much below the national average. • Irrigated farmers are more efficient than the rain-fed farmers. Surface irrigation is the dominate factor. • Rain-fed paddy cultivators' farm efficiency would improve by 6.5%, if they had irrigated. • Semi-mountainous regions with little access to surface irrigation must explore micro-irrigation practices such as drips and sprinklers. [ABSTRACT FROM AUTHOR]

Lightning is a sudden release of electrostatic energy that plays a significant role in the ionospheric-magnetospheric coupling process. A short-term increase in the particle count rates (CR) in the Van Allen Radiation Belt (VAB) is known as the particle bursts. Due to lightning events, the increase in the number of high-energy particles in the inner region of the VAB (L < 2) is a fascinating research to implement. This paper tries to determine the number of lightning-induced particles, which can successfully establish a connection between the two regions of the atmosphere (the ionosphere and the magnetosphere). Lightning-generated electromagnetic radiation propagates through the Earth Ionosphere Waveguide (EIWG), where some energy leaks through and scatters particles in the radiation belt. By lowering the pitch angle, some particles precipitate into the ionosphere and cause secondary ionization. This excess particle count caused by lightning strokes is detected using data from the NOAA-15 satellite and data from the World Wide Lightning Location Network (WWLLN) is used to verify the relationship between lightning stroke and particle count. In this paper, we process our work by using lightning locations over a specific region of India and the geographic/geomagnetic conjugate locations. The monthly variation shows a high correlation between the lightning stroke number and the particle counts within the conjugate geomagnetic location, while the correlation with the particle counts within the conjugate geographic location is significantly lower. In the second half of the paper, we use an automated energy filtration method, for the computation of the particle counts induced by maximum energetic lightning (having lightning energy value > 104 J and the particle counts induced by minimum energetic lightning (having lightning energy value < 104 J). The results of this analysis demonstrate that the variation of PCs in the conjugate geomagnetic location is more closely related to the maximum energetic lightning variation than the lowest energetic lighting variation. This paper will demonstrate the coupling process between the ionosphere and the magnetosphere based on counting the particles produced by lightning. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

• A detail study of rain fade at Ka band over tropical region has been done. • Performance comparison of experimental link at 20/30 GHz with ITU-R model done. • "s" parameter relating fade slope to filter characteristics is calculated. Dynamic characteristics of Ka band rain fade (or rain attenuation) over tropical region are relatively less studied, though they are essential for implementation of suitable fade mitigation techniques. In this paper, dynamic characteristics of rain fade at 20 GHz and 30 GHz from GSAT-14 satellite are reported with experimental measurements at Ahmedabad, India. ITU-R model for fade slope probability distribution has been analyzed in this paper and Ka band rain fade has been investigated based on this fade-slope analysis. The ITU-R model is found to closely match 20 GHz measurements, though differences increase at 30 GHz. Fade slopes are found to be within ±0.5 dB/s at both 20 and 30 GHz. Both positive and negative slopes of the fade slope distributions are quasi-similar. ITU-R model's s parameter, relating fade slope to filter characteristics, is found to be very close to the measured value for this region. Distributions of fade slopes are also found to be dependent on the standard deviation values similar to ITU-R recommendation at 20 GHz, but significant deviation is observed at 30 GHz. This observed frequency dependency might be due to increase in cloud attenuation at high frequencies or may be due to limited data set. Results indicate needs of further experimental data at high frequencies from other tropical regions to gain further insight into the fade slope characteristics. This will ultimately help in designing a better prediction model in future. [ABSTRACT FROM AUTHOR]

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

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

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

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

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]

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

In 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 range error caused by ionospheric delay in Global Positioning System (GPS) signals is currently the main factor that affected the positioning accuracy and navigation determination. Ionospheric modeling and optimization of its range error is a practical approach to improve the GPS positioning accuracy. The global ionospheric models are mostly unable to predict ionospheric delay corrections over the India region due to the Equatorial Ionization Anomaly (EIA)/fountain effect and lack of equatorial/low latitude GPS stations data. This paper proposes a method to facilitates regional ionospheric delay corrections based on the Adjusted Spherical Harmonic Function (ASHF) model by considering fewer coefficients into the algorithm. The ionospheric correction is driven with low-resolution harmonics coefficients with order ≤2 (≤9 coefficients), and coefficients are estimated with the Modified Gram-Schmidt (MGS) approach. The performance of the proposed model (ASHF) is compared with the Spherical Harmonic Function and Klobuchar models. Preliminary results reveal that the low-resolution ASHF model improved the ionospheric time delay corrections by 12.98% relative to the Klobuchar model. The results could be useful in the context of ionospheric time delay modeling for regional navigation satellite systems. • Estimation of ionospheric delays is proposed based on Orthogonal Dimensionality Reduction. • Model outcomes are assessed by comparing with the Weighted Least Square estimations. • The SVD-SHF method improves the accuracy of the model results in geomagnetic storm conditions. • The SVD-SHF method resulted in dimensional reduction and low computational load. • The outcomes suggest that the model implementation is used for GNSS and surveying applications. [ABSTRACT FROM AUTHOR]

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

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

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

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

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]

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]