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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]

Indigenous communities and their way of life are facing significant challenges due to globalisation, industrialisation and urbanisation, which have forced them to gradually adopt the architecture, lifestyle, and culture of mainstream society. This paper proposes a Smart Vernacular Architecture (SVA) framework and applies it to the indigenous dwellings of the Todas, a tribal community living in India's Nilgiri mountains. The architecture and indoor space quality of these dwellings are investigated in this paper based on specific thermal comfort parameters like the interior temperature and airflow. The indoor temperatures of Toda dwellings are simulated for the predicted climate of 2100 to evaluate their climate resilience. Finally, the paper illustrates the usage of virtual reality (VR) to explore and visualise the Toda settlement and the interior environment parameters of a Toda dwelling to effectively convey and preserve the knowledge of vernacular architecture. In a nutshell, this paper elucidates the advantages of vernacular architecture and construction methods to arouse the interest of academics, policymakers, students, and professionals in preserving architecture, culture, and indigenous knowledge. [ABSTRACT FROM AUTHOR]

Oral submucous fibrosis (OSF) affecting populations is considered a public health issue in South/Southeast Asia. The purpose of this study was to analyze the scientometric characteristics and research trends of OSF. All the papers on OSF were comprehensively retrieved from the Scopus database. Regional comparison (India versus outside of India) and chronological comparison (before 2015 versus after 2015) were performed. Among all the 1357 papers on OSF, 930 (68.5%) were from India. In India, biology research on antioxidant, oxidative stress, angiogenesis, and extracellular matrix were distinctive keywords. Risk factors of smokeless tobacco and gutkha and the roles of saliva and blood sampling were also distinctive keywords in India. In outside of India, biology research on myofibroblast, alpha smooth muscle actin, microRNA, long untranslated RNA, and protein p53 were distinctive keywords. The trend of biology research on connective tissue, genotype, genetic predisposition, messenger RNA, and cytology before 2015 has changed to research on myofibroblast, biological marker, microRNA, epithelial mesenchymal transition, extracellular matrix, and oxidative stress after 2015. The trend of clinical aspects of surgery and mouth hygiene before 2015 has changed to the aspects of adverse event/effects, complication, and quality of life after 2015. This scientometric study elucidated the current scenario and research trends of OSF, and would help in improving in reciprocal collaboration and communication for this disease control in South/Southeast Asia. [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]

Challenged by complex problems and the speed of technological, social, and environmental change, the joint approach of Lean Management and the Agile Mindset has been explored in business as a management model. This paper aimed to carry out a bibliometric analysis of Lean Agile Management, pointing out trends in scientific research. To this end, an exploratory and descriptive study was developed, with a quantitative approach and based on secondary data from the Web of Science and Scopus, between 1994 and 2022. Bibliometric indicators pointed to 1808 different papers in 897 sources, with 18 citations per paper, and higher scientific productivity of U.S. (276), India (221), and UK (191), with production peaks in 2018 and 2021, suggesting the diffusion and contemporaneity of the research topic. The laws of bibliometrics were not met for this research subject, and dense networks of collaboration and co-citation among researchers were identified, mainly in four application domains, including supply chain, manufacturing, sustainability, and software development as corroborated by the word network. [ABSTRACT FROM AUTHOR]

Agriculture and its allied sector provide the livelihoods of vast rural areas in India. It also plays a vital role in the growth of developing countries like India. Rice, one of the primary components of Indian agriculture plays a significant role in the food security of the country and the world. However, it is stricken by many diseases that affect the quality and quantity of rice yield. This paper proposes a custom CNN architecture for detecting and classifying common diseases found in rice plants by reducing the number of parameters associated with the network. The proposed CNN architecture has been trained using a dataset of four types of common rice plant diseases. In addition, 1400 on-field images of a healthy rice leaf image dataset have been introduced in the paper for the detection of disease-free plants as well. Independent experiments were carried out with and without the inclusion of the healthy leaf image dataset. The performance of the proposed model is evaluated with the use of Stochastic Gradient Descent with Momentum (SGDM) and Adaptive Moment Estimation (Adam) optimization techniques using several performance matrices. Experimental results from the dataset for the classification of four rice plant diseases show that the model using SGDM optimization gives maximum accuracy of 99.66% and the model using the Adam optimization gives maximum accuracy of 99.83% on the test set in the 7th epoch respectively. However, the model with Adam optimizer performed better than the model with SGDM optimizer when the healthy leaf image dataset is included giving a maximum accuracy of 99.66% and 97.61% in the 7th epoch respectively. [ABSTRACT FROM AUTHOR]

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40–45%), compared before the lockdown period, followed by NO2 (27–35%), Sulphur Dioxide (SO2) (2–10%), PM2.5 (35-40%), but the Ozone (O3) rises (12–25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality. [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]

Municipal solid waste generation in India is increasing as a result of rapid urbanisation and a growing population. Due to gaps in the existing waste collection, treatment and recovery methods, large-scale unscientific disposal and lesser waste recovery have been observed. Insufficient source segregation, unorganised efforts, less awareness and indifference towards malpractice, and a lack of strict implementation were identified as the causes through literature review, surveys and interviews, Therefore, there is a need for a system of interventions, which organises efforts toward waste management and reduces the tolerance for source segregation errors. This paper aims to provide a solution that increases community participation and prevents the generation of mixed waste to ensure waste recovery. The proposed framework integrates municipal solid waste, human elements, and technology into an intelligent app-based system. This includes a smart bin that segregates waste at the source through image classification and an application for communication and data collection. [ABSTRACT FROM AUTHOR]

At the heart of India's livelihood lies agriculture, a foundational and ever-evolving sector. The realm of agriculture faces modern challenges, encompassing unpredictable climate fluctuations, insufficient irrigation infrastructure, and erratic weather conditions. Amidst these challenges, machine learning has emerged as a valuable tool for evaluating soil fertility and Crop assessment in agriculture. Ensemble machine learning methodologies have garnered attention for their potential to enhance predictive capabilities. These techniques involve constructing meta-classifiers that collectively contribute to improved predictive accuracy. This paper centers on the analysis of soil data acquired from testing laboratories, to predict fertility based on a comprehensive dataset. The study employs prominent ensemble machine learning algorithms, specifically focusing on boosting techniques, to elevate predictive accuracy and ensure heightened consistency. The assessment of soil fertility categories involved the examination of 12 carefully chosen attributes. Diverse soil parameters were measured to facilitate the prediction of soil fertility levels. The outcomes of the experimentation revealed that the application of the boosting technique using the Xgboost algorithm yielded superior accuracy compared to alternative ensemble classifiers, achieving a remarkable 96% accuracy rate. [ABSTRACT FROM AUTHOR]

Recognizing specific characters from a large set of handwritten characters is called off-line character recognition. Devanagari, the reading and writing script, is widely used in a broad region of India. This paper proposes using the modified LeNet-5 architecture for recognizing Devanagari off-line handwritten characters using the Devanagari Handwritten Character Dataset (DHCD). The dataset comprises 46 distinct character classes, each with 2000 images. The proposed modified Lenet-5 Base Architecture (MLCNN8) uses more filters and kernels of small sizes with batch normalization. The proposed modified Lenet-5 Base Architecture (MLCNN8) outperformed the Lenet-5 Base Architecture (LCNN) with an accuracy of 99.21% in 53 epochs, whereas LCNN achieved an accuracy of 94.50% in 48 epochs. Hence, the proposed modified Lenet-5 architecture outperforms the Lenet-5 Base Architecture. The proposed model's results were compared with other state-of-the-art and found that it also has an edge for recognizing the Devanagari handwritten characters. [ABSTRACT FROM AUTHOR]

Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector Machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. [ABSTRACT FROM AUTHOR]

Due to the cultural and traditional diversity of the Indian environment, the successful implementation of self-automated and smart architectures is very challenging. As the IoT expands the network of devices, it also increases the hazards with increasing demands. This paper aims to propose an efficient model with the combination of interdisciplinary aspects of Indian building automation and numerous factors of Indian cultural differences. The proposed fuzzy-colored Petri-nets-based ubiquitous framework utilizes the capabilities of ubiquity for the diversified culture of Indian Building Automation systems. The proposed algorithms establish the Wi-Fi component connectivity and the working of the LED pin. Further, the modules of the Wi-Fi component connectivity, set up function and get values function to illustrate the working principle of the proposed model. Lastly, the integrated system using the FCPN tools. The significance of developing the proposed model is in its usefulness, usage of maximum ideas of different modeling techniques, cultural support, and its conversion into a formal qualitative and quantitative model. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 on the Global scale is tremendously drastic. There are several types of research going on across the world simultaneously to understand and overcome this dire pandemic outbreak. This paper is purely a statistical study on a distinct set of datasets regarding COVID-19 in India. The motivation of this study is to provide an insight into the rapid growth of confirmed COVID-19 cases in India. The rapid growth of COVID-19 cases in India started in March 2020. The main objective of this paper is to provide a solid statistical model for the policymaker to handle this kind of pandemic situation in the near future with nonlinear data. In this paper, the data was got from 1st April to 29th November 2020. To come up with a solid statistical model, various nonlinear data such as confirmed COVID-19 cases, maximum temperature, minimum temperature, the total population (state-wise), the total area in km2 (state-wise), and the total rural and urban population count (state-wise) have been analyzed. In this paper, six different Generalized Additive Models (GAM) was identified after a thorough analysis of other researchers' (Xie and Zhu, 2020; Prata et al., 2020) findings. In all perspectives, the results were identified and analyzed. The GAM model regarding total COVID-19 confirmed cases, total population, and the total rural population provides the best average fit of R2 value of 0.934. As the population value is quite high, the author has concise it using logarithm to provide the best p -value of 0.000542 and 0.001407 for a relation between the total number of COVID-19 cases regarding the total population and total rural population respectively. [ABSTRACT FROM AUTHOR]

An increasing number of organizations are encouraging and accepting the research information management system in the institute level to assess the research progress of the organization and bring out a report on scholarly communication activities. Open and commercial product on the Current Research Information System (CRIS) is emerging in the last decade. The Directory of Current Research Information system listed 25 CRIS system across European countries. The European Current Research Information System (euroCRIS) support the CRIS thought the European countries through the conference, working group and pioneer in the advocacy since 2002. This paper considers the recent development in the CRIS related activities around the world and recent progress in India. This paper describes the various challenges to setting up the RIM for the higher education institutions including identification of nodal officers, collecting faculty profile information, retrieving scholarly communication activities from internal and external sources, etc. Also, discuss the training programme to the nodal officer level and institute level to promote the academic identities and their importance. The paper also discusses the impact of the IRINS in the higher education system in India and its use case with different stakeholders such as students, scholars, faculty members, scientists, research administrators, funding agencies and policymakers, etc. [ABSTRACT FROM AUTHOR]

The paper revisits the Porter-Wagner dilemma about the association between environmental management (EM) and firm performance (FP). The context of informal Micro, Small and Medium Enterprises (MSMEs) provides an interesting case since they are away from regulatory scrutiny or corporate/social obligations. The study resorts to labor productivity as the measure of FP and EM is represented by the various solid waste management practices (WMPs) adopted by the informal enterprises. Using the latest available cross-sectional data on un-incorporated firms by National Sample Survey Office (NSSO) 73rd round 2015-16, we found the positive impact of WMPs was higher in the case of more productive firms in the quantile, indicating the relevance of the Porter-Wagner dilemma. The direction of causality was further validated using the propensity score matching technique by decomposing the average treatment effect. [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]

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 World Wide Web Consortium (W3C) has provided the most important set of guidelines for web accessibility which is popularly known as Web Content Accessibility Guidelines (WCAG). The accessibility analysis of higher education websites becomes paramount important to make them inclusive considering the growing number of enrollments of persons with disabilities (PwDs) in higher education, in countries such as India. This paper presents the accessibility analysis of higher education websites with the case study of college websites (N = 44) affiliated with the University of Kashmir and Cluster University Srinagar. The study has been carried out with two major accessibility evaluation tools called web accessibility test, denoted as TAW and the accessibility engine powering browser extensions called the accessibility engine, denoted as aXe. This paper lists the major accessibility barriers exposed by these sites in terms of metrics such as a number of problems, warnings and a status of success criteria violations. With respect to TAW tool, a number of problems observed were 2646, a large number of warnings to the scale of 15995 and the not reviewed items were 1356. With aXe tool, the total violations observed were 1951 and items needing review were 1733. Findings of the statistical analysis are also presented in this paper. This paper presents a roadmap of steps for making these websites inclusive and barrier-free for PwDs. [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]

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]

Mortality statistics are fundamental to understand the magnitude of the COVID-19 pandemic. Due to limitation of real-time data availability, researchers had used mathematical models to estimate excess mortality globally during COVID-19 pandemic. As they demonstrated variations in scope, assumptions, estimations, and magnitude of the pandemic, and hence raised a controversy all over the world. This paper aims to review the mathematical models and their estimates of mortality due to COVID-19 in the Indian context. The PRISMA and SWiM guidelines were followed to the best possible extent. A twostep search strategy was used to identify studies that estimated excess deaths from January 2020 to December 2021 on Medline, Google Scholar, MedRxiv and BioRxiv available until 0100 h, 16 May 2022 (IST).We selected 13 studies based on a predefined criteria and extracted data on a standardised, pre-piloted form by two investigators, independently. Any discordance was resolved through consensus with a senior investigator. Estimated excess mortality was analysed using statistical software and depicted using appropriate graphs. Significant variations in scope, population, data sources, time period, and modelling strategies existed across studies along with a high risk of bias. Most of the models were based on Poisson regression. Predicted excess mortality by various models ranged from 1.1 to 9.5 million. The review presents a summary of all the estimates of excess deaths and is important to understand the different strategies used for estimation, and it highlights the importance of data availability, assumptions, and estimates. [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]

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]

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]

• 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]

A declining trend in Indian summer monsoon precipitation (ISMP) in the latter half of the 20th century is a scientifically challenging and societally relevant research issue. Heavy aerosol loading over India is one of the key factors in modulating the ISMP. Using the state-of-the-state-of-the-art chemistry-climate model, ECHAM6-HAMMOZ, the impacts of South Asian anthropogenic sulfate aerosols on the Indian summer monsoon precipitation were investigated against: (1) 2010 La Niña (excess monsoon), (2) 2015 El Niño (deficit monsoon) in comparison to (3) normal monsoon 2016. Sensitivity simulations were designed with 48% enhancement in South Asian SO 2 emissions based on a trend estimated from Ozone Monitoring Instrument (OMI) satellite observations during 2006–2017. The model simulations showed that sulfate aerosols reduce ISMP by 27.5%–43.3 %, while simulations without sulfate loading enhanced ISMP by 23% in 2010 La Niña and reduction by 35% in 2015 El Niño. This paper reports that sulfate aerosols loading over India reduce precipitation by aerosol-induced direct and indirect effects by inducing atmospheric cooling, weakening in the convection, and reduction in moisture transport to Indian landmass. This paper emphasizes the necessity of alternate use of energy to reduce sulfate aerosol emissions to solve water issues in South Asia. [Display omitted] • Sulfate emission reduces Indian summer monsoon precipitation through aerosol-radiation and aerosol-cloud interactions. • Over 27% of the summer monsoon rainfall deficit in a normal year are linked to Asian anthropogenic sulfate emissions. • Sulfate-induced rainfall reduction is the maximum for La Niña year. • SO 2 loading increases atmospheric stability and weakens moisture transport to India. [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]

Sustainability issues at the interface of science, policy and practice are complex and in an urgent need of sound interventions. Producing science without assessing the nature of the conservation challenge can result in science getting undervalued and even ignored. While several models have been developed, they do not provide a blueprint for action. Without adequate guidance, the approaches adopted by stakeholders may be out of touch with the nature of the problem and may result in suboptimal outcomes. What are the types of sustainability challenges in the place-based, knowledge-action interface and how to tackle them? To address this gap, we build on scholarly work, to classify sustainability challenges into four idealized categories: namely, knowledge gap, multifaceted, hegemonic alliance and wicked problem. Then by assimilating insights from sustainability science studies we show that it is possible to suggest a suitable approach to these problems. Using this framework, we analyze case studies from India and show how it can enhance the ability of actors to tackle real-life sustainability issues. The unique contribution of this paper lies in constructing a schematic process for diagnosis and intervention when confronted with complex sustainability challenges. Future studies can further test, expand and refine this framework to make it more comprehensive and practice-based. • Often stakeholders operating on the science, policy and practice interface (SPPI) have to tackle complex issues. • While SPPI models have been deliberated, what is lacking is a consolidation of the SPPI problems and approaches. • By building on existing scholarship we developed a conceptual framework to aid in diagnosis and intervention. • Using this frame, we analyse ten sustainability case studies from India and show the potential of this framework. • We opine that this conceptual framing can guide SPPI stakeholders to make their interventions more impactful. [ABSTRACT FROM AUTHOR]

Environmental policy debates often get polarized along binaries of environment versus economic development or conservation versus rights. Sustainability scientists and practitioners generally suggest finding common grounds as a pathway for building consensus among the diverse perspectives involved in polarized environmental policy discussions. This paper takes a critical stance towards such calls for common grounds by calling attention to the implicit assumptions that underlie these discourses. We study the case of the Forest Rights Act (FRA) in India, which has polarized the forest policy discourse between conservationists and rights activists in India. We examine scientific publications on the FRA using discourse and framing analysis and show that scholarly discourses on FRA lie along a spectrum characterized by underlying epistemic and normative practices. We identify five groups of scholars based on the unique positions they have taken on FRA: a) advocacy-centric, b) welfare-centric, c) community-centric, d) dialogue-centric, and e) techno-centric scholars. We find that negative framings of the forest dweller community lead to scientific endorsement of policy prescriptions that empower socio-political institutions that are external to these communities. We argue that scholarly calls for common grounds often assume uniform or neutral framings for the stakeholders involved. We conclude that recognizing the polarization spectrum in environmental policy discourses can help us move towards pluralistic approaches to environmental governance. • Scientific polarization on Indian forest policy discourses lies along a spectrum. • Epistemic and normative practices shape scientific positions on forest policies. • Negative framings of traditional communities tend to empower external institutions. • Calls for dialogue often do not specify framings for the stakeholders involved. • Recognize the polarization spectrum for enabling pluralistic environmental governance. [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]

Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved. [ABSTRACT FROM AUTHOR]

Crop disease in the plant is a significant issue in the agriculture sector, and it is currently very difficult to detect these illnesses in crop leaves. The foundation of the global economy is agriculture. India ranks second in the production of tomatoes worldwide. The tomato crop is affected by various diseases which lead to a reduction in product quality and quantity. The advancement in computer vision and deep learning opens up the door for predicting diseases that appear in the crops. The aim of this paper is classification among 10 different categories of tomato plant leaves using the proposed novel TomConv model which deploys an improved Convolutional Neural Network (CNN). For this purpose, the publicly available dataset called PlantVillage comprising of more than 16000 images of tomato leaves, both diseased and healthy was used for the experimentation purpose. The proposed model is the simplest model among all the available state-of-the-art models. The tomato leaf images were preprocessed for reducing the size in 150 × 150 dimension. The model constitutes four layered CNN followed by a max pooling layer. The model splits the corpus into training and validation datasets in 80:20 ratio, is trained under 105 epochs for tomato leaf images, and achieved an accuracy of 98.19%. The proposed model is compared with existing models under different parameters such as no. of classes, no. of layers, and accuracy. The results are promising as they outperform all the available state-of-the-art models. [ABSTRACT FROM AUTHOR]

Agricultural sector is one of the main sectors in India. Productive growth and high yield production of fruits is important and required for the agricultural industry. Application of computer vision and image processing has aid agriculture to boost yield rating, disease spotting, irrigation, fruit classification and maturity grading. Computer vision and image processing approach can be used to lower the time utilization and has made it economical. A convolutional neural network (CNN) is a type of artificial neural network used in image identification and processing that is mainly planned to process pixel data. This paper utilized a convolutional neural network (CNN) to classify bananas into raw or ripe without requiring labour. Bananas were classified by their colour appearance in the CNN classification and not only this it can be also used to classify many other types of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Agriculture is a significant contributor to India's economic growth. The rising population of country and constantly changing climatic conditions have an impact on crop production and food security. A variety of factors influence crop selection, including market price, production rate, soil type, rainfall, temperature, government policies, etc. Many changes are required in the agricultural sector in order to enhance the Indian economy. In this research work authors have implemented various machine learning techniques to estimate the crop yield in Rajasthan state of India on five identified crops. The results indicate that among all the applied algorithms; Random Forest, SVM, Gradient Descent, long short-term memory, and Lasso regression techniques; the random forest performed better than others with 0.963 R2, 0.035 RMSE, and 0.0251 MAE. The results were validated using R2, root mean squared error, and the mean absolute error to cross-validation techniques. This paper intends to put the crop selection method into practice to help farmers solve crop yield problems. [ABSTRACT FROM AUTHOR]

Good health and well being is one of the most essential targets of the Sustainable Development Goals (SDGs). This paper primarily focuses on Preventive and Diagnostic care of Women Health because even today, women are disadvantaged by discrimination in many societies especially in rural sectors. Two main health issues, fetal abnormality in pregnant women and cervical cancer in women are analyzed so that the doctors and patients can be given early signals to take proactive measures. As per National Health Portal of India, around 1.7 million birth defects occur in India every year. So Antenatal Care(ANC) should be given utmost importance during Pregnancy in a woman's life. Cervical cancer is another issue prevalent amongst women, especialy over the age of 30. It's critical to catch it early and eliminate any risks that come with it. Here arises the need to develop a system that can analyze and predict the aforementioned anomalies at an early stage. This work proposes approximate query processing using different dynamic machine learning algorithms to analyze and predict the abnormalities. Further, a web application is built to facilitate the stakeholders, especially doctors, to interact with the system and iteratively query the system to understand the relationships amongst the various data variables and get appropriate predictions about fetal anomaly from CTG scans as well as presence of cervical cancer from various demographic information, habits, and historic medical records. Approximate Query Processing is accomplished using Correlation analysis, linear, and logistic regression algorithms in a dynamic, interactive, and iterative manner. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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