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The future of agriculture may be fully realized using knowledge of artificial intelligence (AI) accumulated by human expertise. With increasing climate change and population pressure challenges, new technologies, such as AI and RS in vital sectors like agriculture is urgently required to assess agricultural suitability, optimize productivity, and then support sustainable development. This paper presents bibliometric and review analyses on AI and geospatial analysis applied to the agricultural field. Based on the prestigious database, Scopus, thousands of documents were retrieved in the period 1992–2021 and processed using Scopus online tool and VOSviewer software. The results show a growing trend in the number of publications during this period. Earth and Planetary Sciences is the top domain, remote sensing (RS) is the leading source, while the United States and India are the most influential countries. RS, machine learning (ML), GIS, land-use, decision-making, decision trees, and satellite imagery are the most occurring topics in this search. Otherwise, research in this field is turning toward ML and deep learning techniques, whereas agricultural robots and antennas are the top trending tools. The current study provides specific information about the connections between artificial intelligence, geospatial analysis, and agriculture in the period 1992–2021, the fruitful period where the AI coupled to geospatial technologies is in full development. [ABSTRACT FROM AUTHOR]

A country that relies on developing industrialization and GDP requires a lot of energy. Biomass is emerging as one of the possible renewable energy resources that may be used to generate energy. Through the proper channels, such as chemical, biochemical, and thermochemical processes, it can be turned into electricity. In the context of India, the potential sources of biomass can be broken down into agricultural waste, tanning waste, sewage, vegetable waste, food, meat waste, and liquor waste. Each form of biomass energy so extracted has advantages and downsides, so determining which one is best is crucial to reaping the most benefits. The selection of biomass conversion methods is especially significant since it requires a careful study of multiple factors, which can be aided by fuzzy multi-criteria decision-making (MCDM) models. This paper proposes the normal wiggly interval-valued hesitant fuzzy-based decision-making trial and evaluation laboratory model (DEMATEL) and the Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE) for assessing the problem of determining a workable biomass production technique. The proposed framework is used to assess the production processes under consideration based on parameters such as fuel cost, technical cost, environmental safety, and C O 2 emission levels. Bioethanol has been developed as a viable industrial option due to its low carbon footprint and environmental viability. Furthermore, the superiority of the suggested model is demonstrated by comparing the results to other current methodologies. According to comparative study, the suggested framework might be developed to handle complex scenarios with many variables. [ABSTRACT FROM AUTHOR]

The cement industry is undeniably critical for the global economy. However, they are also the largest energy consumers in the world. The clinker manufacturing process causes gaseous emissions like nitrogen oxides, sulfur dioxide, carbon dioxide and particulate matter. There is scope for various technologies to be used in the cement manufacturing process for reducing emissions and energy consumption. This paper presents an analysis of various emission reduction approaches using alternative techniques/raw materials, energy saving (thermal and electrical) methods, process optimization/modifications, carbon capture and storage. However, financial and technical challenges remain significant barriers against sustainability initiatives in the clinker manufacturing process. Practical and feasible methods were applied to reduce nitrogen oxides, carbon dioxide and particulate matter emissions during the clinker manufacturing process. This study also shows the application of pinch technology for more economical energy usage. This study was conducted in the premises of Emami Cement Limited, Balodabazar (Chhattisgarh State), India. [ABSTRACT FROM AUTHOR]

The increasing amount of air pollution on a global scale is a major source of worry. The elevated levels of pollution are affecting the health of urban dwellers and motorists to a great extent. Since the situation has become so dire, it has become an absolute necessity to find effective ways to reduce air pollution and mitigate the negative effects of air pollution on human health. In this paper, the task of determining the least polluted route is addressed and how this can be used to curb pollution is discussed. The proposed work uses Dijkstra's algorithm, suggests a weight selection metric for air pollution parameters and analyzes the efficacy of the proposed algorithm in determining the least polluted route. The algorithm is tested on real-time data extracted from the Central Pollution Control Board, a statutory organization under the Ministry of Environment, Forest and Climate Change, Government of India. From the results obtained, it is found that the least polluted routes between a given source location and other different locations of a city can be determined using Dijkstra's algorithm accurately and the algorithm has a time complexity of O(V2). Further, using a least polluted route map will help motorists take less polluted routes, diverting air pollution off congested streets and enabling national governments to create policies like constructing flyovers or widening roads in increasingly polluted areas. [ABSTRACT FROM AUTHOR]

This research paper aims to examine the spatial heterogeneity of malnutrition across districts in India and identify the contributing factors. Previous studies have focused on individual and household determinants of malnutrition, but limited research has explored the spatial distribution of malnutrition and its association with diverse factors at the district level. This study utilizes data from the National Family Health Survey round five (NFHS-5) and creates a district-level dataset with relevant socio-demographic, economic, and nutritional indicators. The analysis employs descriptive statistics, spatial autocorrelation measures, bivariate analyses, and regression models to assess the spatial patterns and determinants of malnutrition. The results reveal significant spatial variations in the prevalence of stunting, wasting, and underweight across districts in India. Economically disadvantaged states such as Bihar, Chhattisgarh, Madhya Pradesh, Jharkhand, and Uttar Pradesh have higher rates of malnutrition, but certain districts in relatively affluent states also exhibit elevated malnutrition rates. The analysis identifies factors such as asset ownership, BMI, women's education, institutional birth, and access to basic amenities as important determinants of malnutrition. The study highlights the spatial clustering of malnutrition indicators and provides insights for targeted interventions and policy formulation to address malnutrition at the district level in India. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was not only a global health emergency but an economic crisis, adversely impacting global economic activities and posing a long-term impact on human livelihood. The pandemic also disturbed the earning competencies of the population, among which the marginalized communities were tremendously affected. In this respect, informal sector workers are marginalized and badly affected by the pandemic. This paper examines the impact of the COVID-19 pandemic on the livelihoods of informal workers in the Kolkata, the third most populated urban agglomeration in India, which was one of the epicenters of COVID-19 in the country. The study mainly based on primary field survey data collected through a well-designed questionnaire and in-depth interviews. A total of 90 participants were selected using purposive sampling, which consisted of six groups of informal workers, i.e., train hawkers, street food vendors, domestic helps, workers in medicine shops, vegetable sellers, and fish sellers were selected. Out of the six groups of informal workers, the first three are from non-essential services, and rest of three are from essential services. For measuring the impact of the pandemic on the livelihood of informal workers, a Livelihood Vulnerability Index (LVI) was prepared by modifying the DFID's Sustainable Livelihood Framework. The LVI is a cumulative outcome of three sub-indices, i.e., Adaptive capacity Index, Sensitivity Index and Exposure Index, comprised of sixteen variables. The results reveal that workers engaged in non-essential services suffered more due to the pandemic, and their livelihood became more vulnerable in comparison to the workers engaged in essential services. Another striking observation is that female informal workers are more vulnerable than their male counterparts, irrespective of the nature of services. The study emphasizes the need for livelihood security policies to protect informal workers from similar future crises. [ABSTRACT FROM AUTHOR]

In this paper, we contend that urban middle-class older Indians engaged in "serious leisure" as a way to reimagine and reconfigure the structure of everyday life during the pandemic-led epochal downtime. In particular, we heuristically show that leisure activity patterns and constraint negotiation strategies among older Indians followed conceptual semblances with the dominant leisure-based typology of Serious Leisure Perspective. By thematically analysing household surveys (n = 71), time-use diaries and in-depth interviews (n = 15) of middle to upper middle-class individuals (55–80 years), we show how both men and women distinguished between serious leisure that is marked by motivation, agency and perseverance with that of unstructured, routinized free-time (or causal leisure). Time-use diaries suggested that despite the changed realities of heightened domestic time available to both genders due to the pandemic, women recorded higher proportion of their daily hours in household management and caregiving. Although women were governed by moral-cultural self-descriptions in their engagement with leisure, it was often associated with an enhanced sense of self-actualisation, self-management and identity. Overall, we show how the social codes of age and gender were inextricably linked with the practice of leisure during the pandemic. [ABSTRACT FROM AUTHOR]

This paper examines the impact of structural change, economic growth, hydro energy consumption, and coal energy consumption on ecological footprint, in the case of India. We employ a novel dynamic autoregressive distributed lag (DYNARDL) method to improve the existing studies' limitations and weaknesses. Thus, the current research contributes empirically and methodological to the literature. The study used the newly proposed DYNARDL simulation method and frequency domain causality using data from 1970–2017 to inspect the impact of structural change, economic growth, hydro energy consumption, and coal energy consumption on ecological footprint in the case of India. Empirical results reveal that institutional structural change decreases ecological footprint in the short and long term, whereas hydropower usage decreases ecological footprint only in the long term. Conversely, economic growth positively affects ecological footprint in both the short and long term. Likewise, coal energy usage increases ecological footprint in the long term. In addition, the findings confirm the validity of the environment Kuznets curve (EKC) hypothesis in India. Furthermore, the results of the frequency domain causality test revealed that structural change, hydro-energy usage, and coal consumption could predict ecological footprint in the long term. In contrast, economic growth can predict the medium- and long-term ecological footprint. We observed that as India's production level continues to surge, so does environmental deterioration, which is associated with the scale effect. [ABSTRACT FROM AUTHOR]

The study is undertaken to examine international trade and tourism nexus in case of India and SAARC countries. To achieve the objective, panel cointegration is employed over the period of 22 years (1997 to 2018). The results from both first and second generation cointegration tests indicated the existence of long run equilibrium among tourism, export, and import. Albeit there are differences between short-term and long-term associations, the findings from autoregressive distributed lag approach indicates strong long-term linkages between trade and tourism. A possible channel of exports exerting a positive influence on tourist arrival is that the products in foreign markets increase the recognition and image of a country eventually leading to more tourists. Moreover, increased exports are expected to increase business trips further increasing the leisure trips and hence a strong linkage between export and tourism. On the other hand, a possible explanation of positive effect of imports on tourism could be that the destination countries which provide tourists with products according to their home taste encourage tourists to travel to these countries. The findings provided in the paper have strong implications for expanding India's tourism footprint in the region. [ABSTRACT FROM AUTHOR]

Numenes grisa Chao, 1983, previously recorded only from China, Laos and Thailand, is recorded from India for the first time. Comments on the other two species, N. flagrans Tams, and N. siletti Walker, that are known from India are provided with live habitus images. N. flagrans Tams, 1928 has been rediscovered from India after 95 years since its original description. A provisional checklist of Numenes species that are described so far is also provided in this paper. [ABSTRACT FROM AUTHOR]

A diet high in sodium contributes to a significant proportion of Disability Adjusted Life Years (DALYs) due to cardiovascular diseases. This paper describes the awareness, behaviour and determinants related to dietary salt intake in an adult population of 18–69 years that were assessed as part of the National NCD Monitoring Survey (NNMS) in India. A sub-sample of 3000 adults selected through simple random sampling from 150 nationally representative Primary Sampling Units (PSUs) was included. Data regarding awareness and behaviour related to dietary salt intake were collected. Urinary sodium excretion in spot urine samples was estimated and used to calculate dietary salt intake. The dietary salt intake's sociodemographic, behavioural and metabolic determinants were also analysed. Less than one-third of the adults of both genders in all age groups in rural and urban areas were aware that daily high salt intake could affect health. The estimated mean daily salt intake was 8.0 g (8.9 g/day for men and 7.1 g/day for women). The salt intake was significantly higher in men [Adjusted OR = 17.66 (5.24–59.46)], rural areas [Adjusted OR = 6.14 (1.83–20.60)], overweight and obese respondents [Adjusted OR = 17.62 (3.17–98.07)]. The perception of the harmful effects of high salt intake and practices to limit salt intake was low in the study population. The mean daily salt intake was higher than the WHO recommendation of up to 5 g daily. The mean dietary salt intake is high in the Indian population, which calls for planning and implementing control of dietary salt consumption measures. [ABSTRACT FROM AUTHOR]

This paper reviews the ongoing failure of hybrid transgenic Bt (Bacillus thuringiensis) cotton unique to India. The underlying cause for this failure is the high cost of hybrid seed that imposes a suboptimal long-season low plant density system that limits yield potential and has associated elevated levels of late-season pests. Indian hybrid Bt cotton production is further complicated by the development of resistance to Bt toxins in the key pest, the native pink bollworm (Pectinophora gossypiella Saunders, PBW), resulting in increased insecticide use that induces ecological disruption and outbreaks of highly destructive secondary pests. Rainfed cotton production uncertainty is further exacerbated by the variable monsoon rains. While hybrid cotton produces fertile seed, the resulting plant phenotypes are highly variable preventing farmers from replanting saved seed, forcing them to buy seed yearly (i.e., market capture), and effectively protecting industry Intellectual Property Rights (IPRs). The lessons gained from the ongoing market failure of hybrid Bt cotton in India are of utmost importance to its proposed introduction to Africa where, similar to India, cotton is grown mainly in poor rainfed smallholder family farms, and hence similar private–corporate conflicts of interest will occur. Holistic field agroecological studies and weather-driven mechanistic analyses are suggested to help foresee ecological and economic challenges in cotton production in Africa. High-density short-season (HD-SS) non-hybrid non-genetically modified irrigated and rainfed cottons are viable alternatives for India that can potentially produce double the yields of the current low-density hybrid system. [ABSTRACT FROM AUTHOR]

Lack of a shared vision has been identified as a major obstacle in transdisciplinary research involving both scientists and other stakeholders. Without a shared vision, the implementation of scientific findings is difficult. The diverse partners of collaborative research, however, imply a plurality in the valuation of nature and a need for deliberative mechanisms. If visioning processes are to do justice to local contexts, research must apply deliberative mechanisms to cover the plurality in the valuation of nature. This paper proposes a visioning approach for local communities, based on prior transdisciplinary research. This participatory workshop method invites stakeholders to approach nature conservation and livelihoods via a deliberation of desirable futures, barriers for achieving them and associated responsibilities for taking action. The paper explores this method via a case study of visioning workshops on sacred swamps in the Western Ghats (India), and their role for both freshwater swamp protection and livelihoods. The visioning exercise offered discussion opportunities facilitating conscientization, conciliation and collaboration in local bottom-up nature conservation. For conserving the tropical freshwater swamps, the results show the need for a more participatory forest governance, providing space for shared value creation. They also point to the need for further research on inter-faith nature conservation possibilities, along with innovations on value addition and value chain development for livelihood promotion and protection. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to present a comprehensive overview of the ethical issues and the processes involved in research and publishing in India. The study examines the present ethical norms, guidelines, frameworks and developments in India, providing insights into the nation's current status of research and recommendations for publication. This document will be a useful starting point and reference document for those embarking on research and publication in Orthopaedics in India. Materials: A survey of the literature was done, which included scholarly papers, reports, rules, and policies pertaining to Indian publishing norms and research ethics. the document starts with a general introduction to ethics, followed by the evolution of ethics in research and the current International as well as Indian codes of ethics. Subsequently, the discussion is divided into two broad headings of ethics in research and ethics in publishing. Under each heading, there are many specific areas in orthopaedics that would require the application of a unique set of ethics. These areas are discussed separately as subheadings. Results and Discussion: The review draws attention to the complexity of ethical issues in Indian and international research and publishing in orthopaedics. Where available, specific guidelines about the topic in India or international guidelines are discussed. The importance of informed consent, data integrity, plagiarism, authorship disputes, and conflicts of interest are only a few of the key results. It is obvious that ethical norms and regulations, such as those offered by the University Grants Commission (UGC), the Indian Council of Medical Research (ICMR), and the Council of Publication Ethics (COPE) are crucial in determining how research is conducted and how papers are published. The types of studies discussed include research in humans and animals, research with stem cells, metal implants and devices, orthobiologics, Artificial Intelligence, Robotics, computer modelling, virtual reality, 3D printing and bioprinting, tissue banking and data management. The roles of different personnel in research and publications are discussed. Conclusions: Ethics in research and publishing play a crucial role in establishing the authority and standard of scholarly work in India. This study underlines the key concepts of ethics that guide various types of studies and the publication process. It also highlights the requirement for frameworks and guidelines for certain unique areas of research in orthopaedics. [ABSTRACT FROM AUTHOR]

COVID-19 has laid an impact on every sector of the world. Howsoever severe, vaccines have acted as the sole source of a protective guard to prevent the further spread of the COVID-19 pandemic. In this research, the authors broadly focus on the trends in the vaccination drive of India. The paper revolves around a prediction and evaluation approach, which depending on the past and the current trends of daily vaccinations, obtain comparable results using a self-built recurrent neural network of LSTM layers for this study on time series evaluation. Through the neural network, the study predicts the exact vaccination figures likely to be achieved 1 year after vaccine introduction in the Indian subcontinent. The gathered data from January 16, 2021, until September 30, 2021, follow effective visualization of how the model outputs resemble the vaccination numbers for October 2021 and the predictions until January 16, 2022. Finally, the paper follows an extensive data analysis keeping in mind, the analogy of the number of COVID-19 cases and deaths before and after the vaccination system was centralized, to prove how sustainable the framework has been so far. [ABSTRACT FROM AUTHOR]

The National Sample Survey (NSS) collects reliable data on morbidity, health care, and health spending through its annual multi-subject and multi-round health surveys. Evidences from these surveys have been extensively used for research and policy. While these surveys collect comprehensive information on morbidity, hospitalisation, health expenditure, information on household consumption expenditure (which is used to explain the economic gradient in health outcomes) is collected through a single question. Literature suggests that having a single question on consumption expenditure results in measurement errors. In this paper, we examine the effect of measurement errors of household consumption expenditure on estimates of catastrophic health expenditure (CHE) in India using data from the 68th round of the consumption survey (2011–12) and the 71st round of the health survey (2014), carried out by the National Sample Survey (NSS). The consumption survey canvassed a detailed schedule on consumption and interviewed 101,651 households, whereas the health survey interviewed 65,932 households from across the country. Descriptive statistics, estimates of CHE, and logistic regression models were used in the analysis. We used both the budget share approach and the capacity-to-pay approach for estimating CHE. The NSS health survey was found to have underestimated monthly per capita consumption expenditure (MPCE) in India by 32%, with the level of underestimation being significant across the states. Using the budget share approach, the CHE of India in 2014–15 was estimated at 23.4% without adjusting for the underestimation of consumption and 21.1% after adjusting for it. Similarly, using the capacity-to-pay approach, CHE was estimated to be 13.4% without adjustment and 10.4% with adjustment. The estimates differed considerably across the states. In general, it was observed that the use of a single question on consumption overestimated CHE in India. The pattern was similar regarding the intensity of CHE. The predictors of CHE were similar using both the methods, but the unadjusted estimates of CHE showed significantly higher predicted probabilities of incurring CHE across household characteristics. It is recommended to include disaggregated questions on household consumption in the future rounds of the NSS-based health surveys. Researchers using NSS data need to be aware of the effect of measurement errors of consumption expenditure on estimates of catastrophic expenditure. Adjusting for the underestimation of MPCE may improve the estimation of CHE in India. [ABSTRACT FROM AUTHOR]

This paper analyzes the implications of son preference on birth spacing intervals in India. Sibling sex composition provides a credible source of exogenous variation in the Indian context for births on or before 1990, as sex screening became widespread only after the 1990 economic reforms. I use the sibling sex composition of the first two children to capture its impact on the third birth interval. My analysis shows that, on average, families with two sons face an 8% lower hazard of a third birth relative to families with two daughters. Moreover, I find three channels of heterogeneity: religion, mother's age, and state of residence. Respondents from diverse religious groups respond differently to the sibling composition of the first two births regarding their likelihood of riskier births for the third interval. Moreover, older mothers with at least one son are less likely to have a shorter third birth interval than younger cohorts of mothers. Finally, the respondent's state of residence also drives some heterogeneity, with mothers from non-southern states less likely to have a riskier third birth interval once they have had at least one son among the first two births. [ABSTRACT FROM AUTHOR]

The process of Indian Urbanisation has been taking a different shape due to the emergence of numerous census towns. Existing literature reveals that the phenomenal increase of census town in the last decade does not follow the metropolitan city-based development model rather it is more rooted in the shifting of economic activity from the farm to the non-farm sector. The context of the development of these settlements in India varies widely from state to state. In West Bengal, Murshidabad as a district, known for low economic profile and located far away from the Kolkata Metropolitan area, has experienced high growth rate of census towns, nearly three times in the last decade. This article is trying to identify the processes of urbanisation in the district and whether it follows the contemporary urbanisation theories or not. The study also investigates factors leading to the process of urbanisation. Primary and secondary data have mostly been used for analysis in this article. The paper emphasizes the contribution of contemporary theories that explain the growth of census towns and the role of secondary and primary data in capturing their characteristics. This article finds that agricultural lands are being converted into non-agricultural land due to agricultural distress. Simultaneous out-migration of labourers, especially of younger generation males to other sectors such as construction and itinerant petty trading in other states help to boost non-farm activities, which indirectly ignites the growth of census towns in Murshidabad district. The study also validates theoretical discourse on urbanisation in India. [ABSTRACT FROM AUTHOR]

To meet the demand of the growing population, there exists pressure on food production. In this context, appropriate prediction of crop yield helps in agricultural production planning. Given the inability of the traditional linear models to provide satisfactory prediction performance, there is a need to develop a crop yield prediction model that is simple in complexity, accurate in prediction, and less time-consuming during training and validation phases. Keeping these objectives in view, the present paper focuses on building an adaptive, low complexity, and accurate nonlinear model for the prediction of crop yield. A time series dataset for the period 1991–2012 of Karnataka, a southwestern state of India, is used for yield prediction. An empirical nonlinear relation between crop yield and the four independent attributes has been obtained from the proposed ANN model. The independent attributes employed are total rainfall, the cumulative distribution of temperature, the proportion of irrigated land, and the average amount of fertilizer used. It is demonstrated that the developed model exhibits better prediction accuracy, less root mean square error in the range of 0.07–0.14, less mean square error in the range of 0.01–0.04, and mean absolute error in the range of 0.07–0.15 compared to its corresponding linear regression model. It is recommended that the proposed ANN model can also be applied to predict other agricultural products of the same or other geographical regions of the globe. [ABSTRACT FROM AUTHOR]

Coal is the major source of power in India and world over. Coal mining is an essential industry which has a major role in the economic development of the country. Most major mining activities contribute directly or indirectly to air pollution. Coal dust is a major air pollutant which affects the personal working in the mines and also people residing in villages near the mines. Air pollution due to coal particulates can affect human health and cause damages to the environment. Hence effective pollution control mechanisms are needed to keep the pollution levels within permissible levels. The easiest and most common method employed for dust suppression worldwide is sprinkling of water. In majority of mines, water sprinklers are operated manually and can lead to wastage of water due to over sprinkling. It can also prove to be ineffective in dust suppression if sprinkling is not done properly. The paper proposes a system which can be deployed to automate the dust suppressions sprinklers. The system will monitor the concentration of PM10 and PM2.5 in the air and initiate sprinkling operation when the particulate matter content exceeds preconfigured limits. [ABSTRACT FROM AUTHOR]

The Indian economy is currently passing through a critical phase of economic development as its structural transformation in employment has stalled, whilst both the youth unemployment rate and the number of youths "Not in Employment, Education, and Training (NEET)" have increased to an unprecedentedly high level. In the context in which the share of the youth population is continuing to rise despite the declining fertility rate to below the replacement rate, increased educated youth unemployment has caused an upsurge in the Discouraged Labour Force (DLF). This paper explores the trends, composition, and determinants of rising DLF in India using national level employment-unemployment surveys and macro-level panel data. Based on Multinomial logit and System GMM regression results, it is argued that policies aiming to enhance human capabilities through an improved base of technical education and the promotion of industry are necessary to enhance the growth of quality jobs in order to combat the problem of rising educated youth unemployment and DLF. Moreover, these measures could help in the process of harnessing the demographic dividend in India through an increased level of labour productivity in the long run. [ABSTRACT FROM AUTHOR]

This paper analyzes the work of two Indian sociologists who defined the contours of sociology in India in the immediate post-independence decades, M. N. Srinivas and A. R. Desai. It argues that their scholarship can be linked to sociology's legacy as anthropology in India and its embeddedness in the episteme of colonial modernity. It contends that Srinivas's methodology, the field view, attempted to make a break with earlier methods, such as book view. However, his three concepts, that of dominant caste, Sanskritization and Westernization were perceived as civilizational attributes and which had organized social change in India. A. R. Desai, a Marxist historical sociologist, made an incisive critique of capitalist exploitation and elaborated the material conditions that led to peasant and working-class revolts. However, his sociology could not unravel the caste-class linkages that organized the Indian 'social' which was embedded in Indian nationalism. This paper suggests that a definitive understanding of modernity emerges in Indian sociology in the late 70s when the feminist, dalit and tribal movements interrogated the material basis of contemporary India's developmentalism and its capitalist and exploitative character. [ABSTRACT FROM AUTHOR]

This paper presents the salient features of a study undertaken to characterize the geotechnical properties of dredged sediments from a typical reservoir in India by preparing GIS maps of geotechnical data based on the Inverse Distance Weighted technique. Potential of using the fine sediments (< 425 µm) of the dredged material after stabilization in earth work applications is subsequently examined through a series of compaction tests, UCC and CBR tests. The stabilizers used are Class C fly ash, hydrated lime and Ca-lignosulfonate. Results of the study indicate that the prepared spatial maps can serve as useful tools in proper planning of cost-effective dredging operation. The study also indicates that Class C fly ash, in general is a superior stabilizer among the various stabilizers considered for improving fine reservoir sediments, improvement in undrained shear strength and unsoaked CBR value being about fourfold and sixfold, respectively. However, hydrated lime is seen to be a more effective stabilizer than fly ash under sustained rainfall (soaked) condition. Addition of lignosulphonate is seen to improve the ductile nature of the stabilized dredged sediment without much loss of shear strength. SEM analysis provided deep insight into the flocculation and agglomeration mechanism and nature of the stabilized material. [ABSTRACT FROM AUTHOR]

In recent decades, exponentially expanding population and urbanization have prompted an unplanned and unsustainable urban sprawl, an emerging major threat in developing countries. Temporal examination of land-use/land-cover (LULC) and the identification of transition zones are vital for the sustainable improvement of the city. Therefore, an appropriate methodology is needed to critically analyse the degree, causes and effects of urbanization. This paper analyses the spatio-temporal dynamics of the urbanization process in Gautam Buddha Nagar, one of the important cities of Ganga alluvial Plain. The study quantitatively evaluates the extent, direction and growth of urbanization with Landsat data of year 2001, 2010 and 2016. Urbanization's magnitude, trajectory and expansion were assessed quantitatively in this study using Landsat data from 2001, 2010, and 2016. To determine the changes and trends, LULC categorization maps were generated from 2001 to 2016. A micro-level urban footprinting analysis was carried out on the basis of urban densities and was classified in seven sub-categories (i) urban built-up, (ii) suburban built-up, (iii) rural built-up, (iv) urbanized open land, (v) captured open land, (vi) rural open land, and (vii) waterbody. The results of the urban landscape quantification and transition points were also compared with land surface temperature (LST) indicating overall rise in LST over the time. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

This paper attempts to estimate long-run forecasting of Indian GDP for the post-COVID period using the factor error correction model (FECM). The model builds on a dynamic factor model that directly and indirectly captures many dimensions affecting the cycles of GDP. The availability of big data enables the extraction of a few common factors from large dimensions, which essentially produces better precision in forecasting estimates. We first extract leading factors and then add proxy policy variables to establish their long-run relationship with the GDP which produces insignificant in-sample bias. The estimated long-run relationship has been employed to predict GDP for 2022–35. We found three major dynamic factors that capture 80% of variations from 56 quarterly variables of the Indian economy. These three factors with their four lags and four exogenous policy instruments have been included in the FECM model for forecasting estimation. We find that the real GDP is expected to grow at 4–8% annually, depending upon the actual realisation of external shocks and policies. The expected rise in temperature and oil prices seems to be dampening the growth. Whereas, the institutional reforms for making more effective public investment and the currency digitalisation that reduces cash requirements could play an expansionary role. If the oil price and the temperature remain at the current level, the growth rate can go closer to approximately 8%. [ABSTRACT FROM AUTHOR]

The Kanyakumari coast is known to be a high background natural radiation area due to the placer deposits of heavy minerals such as ilmenite, monazite, and rutile. The Kanyakumari river sediments that could be the source of the elevated amounts of natural radionuclides in the coastal sands have been studied in this paper. The activity concentrations of primordial radionuclides 226Ra, 232Th, and 40K were determined using high-purity germanium (HPGe) gamma-ray spectrometry. The mean activity concentrations of 226Ra, 232Th, and 40K were found to be 75 Bq kg−1, 565 Bq kg−1, and 360 Bq kg−1, respectively. The mean absorbed dose rate was 395 nGy h−1. Radiological hazard parameters were studied and compared with the world average values. The contribution of 232Th to the total dose rate was found to be higher than that of the two other radionuclides. The high mean ratio of 232Th/226Ra suggested an enrichment of 232Th and the occurrence of 226Ra leaching due to an oxidizing environment. Principal component analysis (PCA) was carried out for the radionuclides in order to discriminate the source of the sediments. This study provides new insights into the distribution of natural radionuclides in sediments of rivers and streams. [ABSTRACT FROM AUTHOR]

The main aim of the study is to present an assessment report of radon concentration in the underground water of Bokaro district, Jharkhand, India. Water samples were collected from tube-wells at 100 different locations scattered throughout the district. The radon (222Rn) concentration level varies widely between 3.5 and 598.9 Bq/l with an average value of 145.8 Bq/l. In this paper, total annual effective dose of the samples and associated excess lifetime cancer risk (ELCR) were estimated also to assess the health risk due to the consumption of the radon rich water. [ABSTRACT FROM AUTHOR]

The Indian economy and food grain production are intricately tied to the spatial distribution and the amount of rainfall during the summer monsoon period (June to September). Numerous studies have delved into modeling methods for forecasting Indian summer monsoon rainfall. This paper utilizes monthly rainfall data spanning 72 years (1950–2021) to analyze, model, and predict the monsoon over Northwest India using Dynamic Linear Modeling (DLM). In view of this, the well-accepted dynamic linear model with significant components is selected from the parsimonious models of DLM based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The dynamic linear model comprises a significant linear time trend component, a deterministic trigonometric seasonal component, a deterministic cycle component, and a stochastic irregular component has been selected. The validation of the selected model passed through the normal and correlation diagnostics of model residuals. The analysis reveals a subtle decreasing trend in monsoon rainfall, showcasing consistent behaviour in deterministic seasonality, and highlighting the deterministic nature of cyclical component with a period of approximately 2.5 years, alongside irregular variations. Utilizing this model, future monsoon rainfall over Northwest India has been forecasted for the years 2022–2027, providing a 95% confidence interval. [ABSTRACT FROM AUTHOR]

A systematic correlation is established between the axial load-carrying capacity of the socketed piles and empirical methods. The axial load-carrying capabilities were calculated with the help of pile load tests for ten single piles in Navi Mumbai, India. Pile load tests were carried out to evaluate the skin friction and base resistance in overlying soil and rock. The test results of axially loaded bored piles socketed into the rock are mentioned in this paper. The pile socket involved rocks of various strengths such as gypsum, limestone, and basalt. In reality, axial capacity is calculated from skin friction of the pile, which occurs owing to a very small settlement in the rock socket. The unconfined compressive strength of the rock is collected from the laboratory test. The load-bearing capability of the socketed piles is determined using several empirical methods, which are usually evaluated by a back-assessment of the in-situ pile load test. However, the significant load-carrying capacity values derived from empirical correlations are found to be reasonably good compared with those estimated from pile load tests. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper focuses on the segmentation of unconstrained handwritten documents containing bilingual data at the word level. Most of the official documents available in India are bilingual, i.e., in the regional language as well as English language. For the purpose of current research work, the handwritten documents from the academic domain containing Punjabi from Gurumukhi script and English language from Latin script have been considered. A heuristic approach based on projection profiles, statistical and structural properties of script has been designed to segment the textual data of documents. The proposed approach can segment the closed, curved, skewed and touched text lines having large variations in pattern and size. For word segmentation, an end-point detection algorithm has been designed to segment the words with intra word gap. The proposed approaches have been evaluated by designing a domain-based bilingual handwritten dataset after having consultations with academicians, and experts in the field. For text line segmentation, an average accuracy of 95.89% and 92.95% has been achieved for IAM and bilingual dataset respectively. However, for word segmentation, there has been an accuracy of 89.74% and 92.25% respectively for IAM and bilingual dataset. As many as 280 documents with various writing styles and content have been selected for the purpose. [ABSTRACT FROM AUTHOR]

In order to survive, mankind needs air. The quality of life depends on the purity of the air we breathe in. Hazardous pollutants are stirred up in our environment by various activities everyday. In a developing country like India, which has huge population, protecting public health is important. In general, air quality is measured using the air quality index, which records the pollutants level in the air. These recordings are to be mentioned across various places, to know about the air quality level. The advancement of the recent artificial intelligence can be replaced by the human efforts and to automate the system in flowless manner. Hence, an effort has been taken in this paper, by proposing a framework of predicting the air quality seasonally using regularized combinational LSTM (REG-CLSTM). For an efficient air quality level prediction with an improved error rate, time, and accuracy, the study has implemented Reg-CLSTM with a large amount of real-time dataset. To improvise the comprehensiveness and the potentiality of the proposed model, the significant feature is extracted by the rough set-wrapper method. The significant challenge is to provide a seasonal limit range for each considered pollutant in place of a generalized range partition. Aiming at this problem, the proposed model is able to identify the highest concerning pollutants occurrence in individual seasons. Through this study, pyramid learning-based hybridized deep learning framework is developed which can play a crucial role in warning the policy maker to reduce the activities that instigate air pollution. [ABSTRACT FROM AUTHOR]

Indian southwest monsoon rainfall has a huge socioeconomic influence, and any changes in monsoon rainfall patterns will have major consequences for agriculture, water supplies, and other connected sectors of economy. The paper explores the charactering and forecasting of the southwest monsoon rainfall (June to September) patterns over the Central region of India for the period (1901–2021: 121 years) using linear state space modeling. The study period is split into two periods, i.e., earlier period (EP: 1901–1950) and the recent period (RP: 1951–2021). The linear state space model (LSSM) with significant latent components such as trend, seasonal, periodic and random variations or noise is selected for modeling the southwest monsoon rainfall (SWMR) based on Bayesian information criteria (BIC) and statistical fit for the two periods. The result indicates that an increase in linear time trend during the EP and the RP has a decreasing local linear time trend in SWMR, and it is interesting to notice that the EP had no noteworthy periodic variations, whereas the RP exhibits significant periodic variations in SWMR. Further, the selected LSSM forecasts SWMR patterns for the period 2022–2028, and it has been observed that the Central Indian region is expected to receive substantial rainfall in the years 2026, 2027 and 2028. [ABSTRACT FROM AUTHOR]

Biofloc technology (BFT) is an aquaculture technology that uses microbial communities to produce a balanced aquatic environment for fish and crustaceans. This study evaluated the global research trends and measured the performance of researchers, organizations or research centers, countries, and funding agencies participating in BFT research in the last decade. Scientific knowledge mapping was also performed to understand the social and conceptual structure of the research publications. The study was conducted based on journal article publications indexed in the Scopus database between 2013 and 2022. The analysis showed that BFT research was increasing steadily, with a total of 911 documents published in the past 10 years. Brazil, China, and India were the three most productive countries in the field. The most productive journals were Aquaculture, Aquaculture Research, and Aquaculture International. Regarding research topics, the largest share of documents was related to bioflocs and their technologies, followed by Litopenaeus vannamei, water quality, growth performance, shrimp, Nile tilapia, Oreochromis niloticus, etc. Co-authorship network analysis revealed that the largest cluster comprised researchers from Brazil, China, and India. Moreover, the study of societal attention revealed that the BFT research needs more public attention as the research was discussed and mentioned only a few social media platforms. However, the BFT research had significant readers on Mendeley. This study provides a comprehensive overview of the current state of BFT research and the research performance of scientific actors. It can serve as a valuable reference for researchers and policymakers. [ABSTRACT FROM AUTHOR]

Globally, financial well-being has received considerable attention from researchers and policymakers as an indicator of financial health in the aftermath of financial shocks. Additionally, financial inclusion has been a priority for developing countries in their efforts towards poverty alleviation and bringing vulnerable populations into the mainstream financial system. Moreover, the shift in digital technologies and financial services has overcome access and usage barriers through the acquisition of digital financial literacy. This paper develops novel multidimensional indices and investigates the relationship between financial well-being, financial inclusion, and digital financial literacy. The study utilizes data from InterMedia's Financial Inclusion Insight (FII) 2016 Survey conducted in India. The results indicate that both financial inclusion and digital financial literacy have a significant positive impact on financial well-being. However, considering the potential endogeneity bias, instrumental variable 2SLS regression reveals that financial inclusion plays the most crucial role in defining financial well-being. Our findings help policymakers recognize the importance of demand-side financial inclusion and digital financial literacy in promoting financial well-being, which may be achieved through robust financial and digital literacy programs. Additionally, the study contributes to the financial well-being literature by incorporating essential attributes of financial inclusion and digital financial literacy. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed two decadal long term (2003–2022) data of AOD and cloud parameters to understand the spatial and temporal variations in aerosol radiative characteristics over a highly polluted urbanized region of Northern India. The aerosol and cloud data were derived at spectral wavelength of 550 nm from the MODIS based Terra/Aqua satellites. Detailed AOD quantitative analysis considering monthly, seasonal and interannual changes was investigated in relation to cloud characteristics and zonal synoptic climatic disturbances. HYSPLIT back trajectory 5-day analysis was made to know the pathways of air masses ending at Delhi region of India. Moreover, we applied Student's t-test to mean Terra AOD and cloud properties to examine and further validate the relationship of aerosol-cloud interaction over the urban tropical Delhi zone close to Himalaya range of Northern India. Results indicated that AODs were found to be increased significantly (> 68%) during the 20 years period of 2003–2022, which might be accounted to continuously increased aerosol pollution in atmosphere. On an annual basis, mean Terra AOD exhibited an increasing trend of ~ 0.0009 per year, whereas seasonal mean AOD was observed to be maximum for winter (~ 0.96) and minimum for summer (~ 0.55), respectively. The correlation coefficient was highest (+ 0.73) between AOD and precipitable water vapor, whereas lowest (− 0.46) between AOD and cloud top pressure (CTP). The findings of AOD and cloud parameters have been compared with the earlier findings made on different Indian cities. [ABSTRACT FROM AUTHOR]

Fresh water is vital for all living creatures and maintains the hydrological cycle. Surface water bodies conserve freshwater and exhibit dynamic changes yearly due to high/low rainfall and over/underutilization. Therefore, extracting water bodies and determining their extent is imperative for effective water resource management. Water body extraction using Water Indices (WI) and Machine Learning (ML) face threshold selection and feature optimization challenges, respectively. This paper proposes a Lightweight Attention-based Multiscale Neural Network (LWAMNNet) for surface water body extraction from Linear Imaging Self Scanning‒III (LISS-III) remote sensing images. The LWAMNNet is an encoder-decoder architecture designed using a modified residual block in both the encoder and decoder to extract high-level features. Feature Extraction Module (FEM) is sandwiched between encoder and decoder to extract global contextual features. The LWAMNNet replaces convolutions with depthwise separable convolutions to reduce computation complexity. In the decoder, the attention module is incorporated to provide attention to fused features (i.e., combined deep features with spatial encoder features) at different scales. The LWAMNNet effectively extracts different-sized water bodies with non-linear boundaries. The proposed LWAMNNet qualitatively and quantitatively outperforms other DL models in performance metrics (accuracy of 99.5%) and computation complexity (in terms of trainable parameters and time). Additionally, the water extent of five major reservoirs in south India was determined annually from 2016 to 2019. Also, the reason for water dynamics is analyzed with the help of rainfall and water availability data provided by the Indian Metrological Department (IMD). [ABSTRACT FROM AUTHOR]

The present paper investigates the impact of future scenarios for the past two storms (namely, Vardah and Madi). These two storms had a different intensity over Bay of Bengal, India. The recent study in this region shows that the influence of these Tropical Cyclones (TC) may be severe in future climatic conditions. Different future Representative concentration pathways (RCP) scenarios are investigated in this study. The combined sea level rise (SLR) and high wind intensity in future scenarios has been investigated in the present study for their influence on maximum water level (MWL), wave climate and coastal inundations. The coupled surge and wave models are used to analyze the influence of SLR and wind intensity for different RCPs. The variations of MWL and inundation extent with increase and decrease in wind increment for different RCPs of the TC's were reported. Overall, for the Far Future RCP 8.5 scenario, the relative percentage of difference (compared to the current scenario) in MWL has increased by 135% for TC Vardah and 180% for TC Madi. Further, the flood area for TC's Vardah and Madi will increase by 70% and 95% compared to the current scenario. Finally, the study reveals that the extreme wind intensity of the TC's in future scenarios plays a significant contribution of up to 50% in coastal inundations. [ABSTRACT FROM AUTHOR]

In developing nations, solid residential fuels are the major sources of primary energy for various domestic activities. To date, the emission inventory of inorganic trace gases over National Capital Territory (NCT) was prepared using either default or country-specific emission factors. In this paper, we report (for the first time) the spatial variation of emission factors (EFs) of inorganic trace gases (SO2, NO, NO2, CO, CO2, and CH4) from the residential fuels used in slums and rural areas of NCT determined using dilution chamber in the laboratory. 147 residential fuel samples, including fuelwood, dung cake, crop residues, coal, etc., were collected at 149 NCT locations out of 675 slum clusters and 146 rural villages. The range of EF(s) of SO2 (0.02 ± 0.01 to 0.04 ± 0.01 g kg−1), CH4 (0.10 to 0.34 g kg−1), NO2 (0.01 to 0.02 g kg−1) is lower than the CO (3.55 ± 1.72 to 6.07 ± 1.53 g kg−1) and CO2 (0 to 129.45 ± 46.94 g kg−1). The north and north west districts of NCT are emission hotspots for CH4, NO, and NO2 emissions, whereas, the southern and northern areas of NCT are for CO2. These citywide emission inventories (0.05° × 0.05°) of inorganic trace gases are prepared using laboratory-determined EFs and available consumption data determined by recent survey information. Among solid residential fuels, fuel wood, and dung cake are two major contributors to inorganic trace gases in NCT. [ABSTRACT FROM AUTHOR]

Soil erosion is the inherent and destructive threat affecting agricultural production and livelihood of million mouths. The increased frequency of floods and land use/land cover changes has made Upper Jhelum Sub-catchment susceptible to soil erosion risk. Morphometric based watershed prioritization for soil erosion risk may help in sustainable management of natural resources. Thus, this paper endeavors to prioritize watersheds of Upper Jhelum Sub-catchment in India based on morphometric parameters for soil erosion risk using geospatial techniques. Weights to the morphometric parameters were assigned through a multi-criteria decision method. The watersheds in the Sub-catchment have been categorized into low, medium, high and very high priority classes based on prioritization ranks that were determined by computing the compound value for the soil erosion risk, based on prioritization ranks obtained through compound value for the soil erosion risk. The results revealed 1E1D3 and 1E1D8 watersheds accorded very high priority. The watersheds namely IE1D2 and IEID4 were found under high priority. Medium priority for soil erosion risk was determined in IEID5 and IED7 watersheds while 1E1D1 and IE1D6 watersheds were identified for low priority. The study calls for implementing soil conservation practices in the Sub-catchment. The Sub-catchment can be made less hazardous for the soil erosion risk by implementing contour farming, building check dams, terrace farming, afforestation and limiting large scale overgrazing. The findings of this study may offer valuable insights for stakeholders for conservation of soil resource. The approach utilized in the study may be linked with soil loss estimation for effective conservation of natural resources in further future studies. [ABSTRACT FROM AUTHOR]

It is well known that the urban systems, in particular cities, display scaling behaviour regarding socio-economic, infrastructural and individual basic services indicators. However, understanding urbanisation and the links between rural and urban areas is fundamental to making the most of the global transformations happening around the world. In this context, it is important to study the scaling laws based on both the urban and rural regions, going beyond cities. This paper explores the extension of the idea of allometric urban scaling law to study the scaling behaviour of Indian districts, with both the urban and rural population. To proceed, we have chosen districts (both rural and urban) of India, a relatively larger local administrative units, which are more or less independently functional within a country. This interdisciplinary work focus on the scaling analysis of various socio-economic indicators (SEIs) corresponding to the size (population) of four distinct urbanisation classes, namely rural, semi-rural, semi-urban and urban districts. The scaling exponents (β ) were estimated for each classes for the years 2001 and 2011 along with their goodness-of-fit measured by the R 2 values. Our rigorous statistical analysis indicates that the scaling laws indeed exist even at the district level for most of the SEIs considered, related to education, employment, housing, health, etc.; the R 2 values obtained for these SEIs are very high (often greater than 0.8 or 0.9) in both the years. Moreover, linearity of the scaling factors have been statistically tested and it has been found, at 95% level of confidence, that not all the SEIs behave linearly ( β = 1 ); some of them are characterised by super-linear ( β > 1 ) behaviour and some behave sub-linearly ( β < 1 ). Statistical hypothesis tests have also been performed to test the equality of two scaling factors corresponding to two distinct classes and two different years to understand the differences in scaling relationships amongst increasing urbanisation classes and their changes over time. [ABSTRACT FROM AUTHOR]

Managed Aquifer Recharge (MAR) has emerged as a multi-facet, sustainable and effective technique to replenish dwindling groundwater resources. Suitable site selection is an important step in the design phase of MAR process. Conventional methods of site suitability studies using GIS and Multi Criteria Decision Analysis (MCDA) does not address the aquifer's response to MAR, while limited modelling-based studies have considered the effect of surface hydraulic factors. In this paper, a fresh approach is presented, that utilises decision model, K-mean clustering technique and numerical model to identify optimal sites for MAR interventions. The methodology was applied in a semi-arid region of Lower Betwa River Basin (LBRB), India. Four different parameter combinations were employed that incorporated the impact of surface and subsurface parameters. Multiple model runs were executed using MODFLOW–NWT to assess the groundwater head response to the infiltrated volume of water. The results indicated that under the least head change category (< 0.7 m), the north and south–east regions of the LBRB were the most appropriate sites. The coalition of geomorphology and drainage density along with aquifer properties such as hydraulic conductivity, specific yield and aquifer thickness were found to be the best suited combination for site selection in LBRB, with maximum spatial coverage (16%) under it. It was observed that the choice of parameter combinations affected the range of groundwater head variations and depended upon the site-specific criteria of accepted head change ranges for determining the best suitable parameter combinations for MAR site selection. [ABSTRACT FROM AUTHOR]

An effort is made to understand the role of El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events on the malaria transmission intensity over India during the period 1951–2020 (70 years) with the help of a realistically simulated dynamical malaria model. The results suggest that the La Nina years pose a greater threat of malaria disease, especially in the densely populated Indian states. During El Nino years, the malaria transmission intensity and distribution over India greatly reduce, except in the regions such as Orissa, Chhattisgarh, Jharkhand, Western Ghats, parts of Madhya Pradesh, and Andhra Pradesh. It is found that in the positive IOD years, the malaria transmission intensity increases (decreases) over the entire central Indian region and along coastal regions of Tamil Nadu and Kerala (southern peninsular states of India and northeast India). An almost opposite behavior is seen during the negative IOD years. The malaria transmission variability over India is becoming increasingly heterogeneous in recent decades during the El Nino and La Nina years as a result of global warming. The period of 1986–2020 witnessed a substantial decrease (increase) in the malaria transmission intensity during the positive (negative) IOD years, except for a few regions of India. The implications of the results presented in the paper linking the ENSO and IOD signals with the intensity and distribution of malaria over India in a warming world are enormous, especially for the densely populated Indian states. [ABSTRACT FROM AUTHOR]

Improvement of water and sanitation conditions may reduce infant mortality, particularly in countries like India where open defecation is highly prevalent. We conducted a quasi-experimental study to investigate the association between the Swachh Bharat Mission (SBM)—a national sanitation program initiated in 2014—and infant (IMR) and under five mortality rates (U5MR) in India. We analyzed data from thirty-five Indian states and 640 districts spanning 10 years (2011–2020), with IMR and U5MR per thousand live births as the outcomes. Our main exposure was the district-level annual percentage of households that received a constructed toilet under SBM. We mapped changes in IMR and U5MR and toilet access at the district level over time. We fit two-way fixed effects regression models controlling for sociodemographic, wealth, and healthcare-related confounders at the district-level to estimate the association between toilets constructed and child mortality. Toilet access and child mortality have a historically robust inverse association in India. Toilets constructed increased dramatically across India following the implementation of SBM in 2014. Results from panel data regression models show that districts with > 30% toilets constructed under SBM corresponds with 5.3 lower IMR (p < 0.05), and 6.8 lower U5MR (p < 0.05). Placebo, falsification tests and robustness checks support our main findings. The post-SBM period in India exhibited accelerated reductions in infant and child mortality compared to the pre-SBM years. Based on our regression estimates, the provision of toilets at-scale may have contributed to averting approximately 60,000–70,000 infant deaths annually. Our findings show that the implementation of transformative sanitation programs can deliver population health benefits in low- and middle-income countries. [ABSTRACT FROM AUTHOR]

While previous studies have examined the influence of store atmospherics and social factors independently, limited research has investigated their synergistic impacts across cultures. In particular, the interactions between ambient scents, companionship influences, and cross-cultural shopping customs remain underexplored. Therefore, this study investigates how ambient scents influence retail shopping behaviors with a companion across cultures. Data were collected via surveys administered to customers in Polish and Indian shopping malls (N = 579), examining the impact of detected fragrances on time spent, money spent, purchase decisions, cognitive responses, and attitudes. Shopping with a companion in a scented environment has a more positive influence on time spent, money spent and impulse purchases in India than in Poland. There is no difference between these two cultures regarding cognitive responses to a scented retail environment, but there are significant differences for attitudinal responses and repurchase intentions. We found a positive association between the consumer rating of the shopping environment in terms of the ambient scent and the amount of money spent in India, but not in Poland. Theoretical and practical implications are discussed for optimizing atmospheric design according to cultural customs around shopping companionships. [ABSTRACT FROM AUTHOR]

Background & Aims: Diabetes and related metabolic syndromes represent a significant global health challenge, with the global burden of diabetes increasing considerably since 1990. In this article, we examined the trend of publications in Endocrinology, Diabetes and Metabolism between 1996 and 2021, focusing on Asian countries. Methods: We obtained and used the data from the Scopus database from the SCImago website (https://www.scimagojr.com/), on 1 April 2023, related to the subspecialty of Endocrinology, Diabetes and Metabolism for country rankings between 1996 and 2021. We did not include any data related to other medical specialties or other fields. Results: There has been a steady rise in global publications on these subspecialties over the past decade, with the number of publications from Asian countries increasing significantly. Western Europe recorded the highest number of publications, followed by North America and Asia. The COVID-19 pandemic also contributed to a surge in publications in this field. In Asian countries, China and India have notably increased their global contribution to publications, with China emerging as the top Asian nation in 2021. Conclusion: Our findings provide valuable insights into the research output from various countries and the productivity trends in Diabetes, Endocrinology and Metabolism-related research. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, allocation plan of ventilators is reinforced for fulfilling its critical needs to the hospitals. The death rate caused by COVID-19 could have been diminished globally if the proposed medical logistics network is possible to be timely deployed between suppliers and demand regions. Owing to operate and organize medical logistics network and to minimize the total delivery time (TDT), we have formulated a mathematical model based on mixed-integer non-linear programming (MINLP). The application of the proposed model is considered employing the real-life pandemic data, which is alarmed to the severely affected regions in India. The results denote that the anticipated model of medical logistics network efficiently performs in a pandemic demand scenario. The responsiveness of the developed model is also examined under three demand scenarios based on Covid-19 pandemic wave-1, wave-2, and wave-3. Lastly, the sensitivity analyses are done to examining the impact of the critical parameters of the model, and findings are discussed. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations of biomass burning (BB) over the Indian region using satellite-based data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003–2021 are analyzed and studied. We have used fire products with a high confidence level (≥ 80%), which is free from false alarm fires. The total fire counts (TFC), fire radiative power (FRP), and burned area (BA) for different land use and land cover (LULC) types over six different regions, namely Central India (CI), Indian Gangetic Plain (IGP), North-East India (NEI), North India (NI), South India (SI) and West India (WI) are studied. The biomass burning shows spatial, seasonal and inter-annual variations. Within the regions, different hotspots are identified for cropland burning, forest burning, etc. It is observed that in the IGP and WI regions, burning activity shows bi-modal seasonal behaviour, which coincides with crop burning after harvesting seasons, while other regions show a single mode. Non-parametric long-term analysis in TFC and TFRP (derived by adding FRP of all the fire hotspots in a respective year) shows a positive trend over all the regions except in the NEI region. The decreasing TFC with increasing precipitation is also observed in all the considered regions, which is attributed to enhanced moisture and decreased temperature. The present study provides the scientific basis for addressing the origin and type of biomass burning in different regions of India, and it is quite useful for developing procedures, awareness, and planning for reducing BB, which is quite harmful to human health as well as the environment. [ABSTRACT FROM AUTHOR]

With the high demand for fossil fuels, exploring the frontier areas for hydrocarbon reserves has become imperative. The recent discoveries in Gojalia, Sonamura, Baramura, and Sundalbari fields emphasize the need to explore additional anticlinal structures in Tripura for hydrocarbon exploration. Tulamura anticline (the study area) produced gas from Upper Bhuban, establishing hydrocarbon prospectivity in the northern part, but the southern part remains largely unexplored. An electro-log interpretation revealed the presence of sand facies deposited in a fining upward sequence, suggesting channel deposition. An integrated geophysical approach using seismic inversion and machine learning techniques was performed to delineate and characterize the litho-facies dispersal patterns in the Tulamura field. Spectral decomposition (12, 20 and 28 Hz) of stacked seismic data were RGB (red-green-blue) blended, revealing the southward striking channel geometry of the Bhuban Formation at a depth of 2220 m. The 3D P-impedance and Vp/Vs ratio volumes were estimated using the model-based pre-stack seismic inversion. Inversion results help discriminate among sand, shale and siltstone litho-facies. Petrophysical property (effective porosity) was predicted by combining the post-stack seismic attributes and well-log data using neural network modelling. The identified sand facies within the channel geometry exhibit relatively moderate to low P-impedance (9800–10600 m/s * gm/cm3), low Vp/Vs ratio (1.68–1.76), and moderately high effective porosity (8–13%) from surroundings, indicating favourable conditions for hydrocarbon accumulations. Shale between channels and major faults can create favourable stratigraphic entrapment, while an upward fining sequence suggests an intact top seal. This study advocates an integrated approach involving geophysical inversion and machine learning to identify optimal conditions for hydrocarbon accumulation within sand facies, supported by structural and stratigraphic entrapment. [ABSTRACT FROM AUTHOR]