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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

Over the years, survival of children with chronic diseases has significantly improved and a large proportion of them now are entering into adulthood. Transition of Care (ToC) of such patients with having childhood onset of chronic diseases to the adult health care system is well organized in developed countries, although it is an emerging concept in India. In situations where the systems for ToC are not in place, such cases are fraught with unsatisfactory health outcomes. With proper ToC in place, these patients are likely to receive uninterrupted care by the adult care physicians and hence reach their full potential. This document highlights the need, rationale and way forward for ToC of youth with special health care needs (YSHCN) across the country. It also describes the standard operating procedures to develop the ToC at a hospital level for clinicians and administrators. [ABSTRACT FROM AUTHOR]

Early childhood development (ECD) refers to the physical, motor, socio-emotional, cognitive, and linguistic development of a young child. The 'Countdown to 2030' global distribution of 'children at risk of poor development' indicates the need for urgent action and investment in ECD. Nurturing care enhances ECD, even in the presence of adversities. Strategic actions should exist at multiple levels: the family, community, health care providers and government. Previously, child health related policies and programs of the Government of India functioned in isolation, but have recently started demonstrating multi-sectoral collaboration. Nonetheless, the status of ECD in India is far from optimal. There is strong evidence that parenting programs improve outcomes related to ECD. This is dependent on key programmatic areas (timing, duration, frequency, intensity, modality, content, etc.), in addition to political will, funding, partnership, and plans for scaling up. Each country must implement its unique ECD program that is need-based and customized to their stakeholder community. Barriers like inadequate sensitization of the community and low competency of health care providers need to be overcome. IAP firmly believes that responsive parenting interventions revolving around nurturing care should be incorporated in office practice. This paper outlines IAP's position on ECD, and its recommendations for pediatricians and policy makers. It also presents the roadmap in partnership with other stakeholders in maternal, neonatal, and child health; Federation of Obstetric and Gynaecological Societies of India (FOGSI), National Neonatology Forum (NNF), World Health Organization (WHO), and United Nation Children Fund (UNICEF) [ABSTRACT FROM AUTHOR]

Technical progress has a tremendous potential to reduce carbon dioxide emissions by reducing energy consumption, a major concern across production units. However, the existing empirical literature concerning technical efficiency and carbon intensity is scanty. Thus, this paper examines the relationship between technical efficiency and carbon intensity for the organized manufacturing sector of two states, Maharashtra and Odisha, and the all-India level from 2001 to 2018. The paper uses data envelopment analysis to estimate technical efficiency scores. It applies the 2006 Intergovernmental Panel on Climate Change Tier 1 methodology for estimating carbon intensity for each 3-digit manufacturing industry in all three sample cases. The study has used static panel regression and fractional logit regression techniques to examine the deterministic relationship between technical efficiency and carbon intensity. The result shows that technical efficiency is highly sensitive to carbon intensity in the Indian manufacturing industries. The findings also addressed that the size of the industries also reduces the technical performance of manufacturing units. This paper also confirmed that increased profit could boost the Indian manufacturing industries' technical efficiency. Thus, this study addresses that carbon intensity as a proxy for the manufacturing sector's potential to affect climate change plays a crucial role in explaining the technical efficiency variations across industries. Thus, it calls for better policies aimed at reducing the emissions of industries specifically to achieve sustainable growth for the Indian manufacturing sector. [ABSTRACT FROM AUTHOR]

Child malnutrition remains a pervasive problem in India though there are significant improvements in several health indicators with country's consistent economic growth. Along with other factors, maternal autonomy is considered to be critical in influencing health outcome of a child. This paper attempts to examine the determinants of nutritional measures of children under age five with the focus to the role of mother's autonomy in India. There are studies which link characteristics of mother, like education, age, with nutrition of her children. But maternal autonomy is a complex subject as this variable is not tangible. Women's autonomy in daily life is more likely to result in their better healthcare which in turn influences children's birth weight and infant nutrition. We used nationally representative India Human Development Survey 2004–05 (IHDS- 1) and 2011–12 (IHDS 2) data for the analysis. This paper studies the association between maternal autonomy and childhood stunting, the indicator of chronic malnutrition, and the risk of having a stunted child. We have also used the panel data using both waves of IHDS and used mothers fixed effect model to evaluate the impact of maternal autonomy. The autonomy index is created using four dimensions-women's decision making, freedom of movement, financial autonomy and attitude toward domestic violence. The two dimensions of autonomy—physical autonomy, financial autonomy—remain significant determinants for childhood stunting. [ABSTRACT FROM AUTHOR]

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India. [ABSTRACT FROM AUTHOR]

Indian securities markets have undergone significant transformations in the recent past. The paper discusses and documents initiatives taken by the market regulator to expand and deepen the securities markets, including encouraging innovations in financial instruments, improving market efficiency by appropriate and timely regulatory interventions, expanding issuers' base and investors' participation for inclusive growth, ensuring regulatory compliance for fair play of market forces. The study helps investors, intermediaries, and regulators outside India to gain insights into recent developments and opportunities they offer to make the Indian securities market their preferred investment destination. The study concludes with the papers' key findings on "Investor interest and innovative instruments" in the special issue. [ABSTRACT FROM AUTHOR]

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country's economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language. [ABSTRACT FROM AUTHOR]

The scholarship and discussion on urbanization and urbanism are restricted to the exploration of a few metropolitans and global cities. In this way, especially in India, several cities and towns do not fit into the understanding of knowledge produced from metropolitan or large cities. Taking the case of Dharamshala a small town in the Western Himalayas, this paper discusses how debate and conceptualization of urban processes, level of urbanization and scale of economies bypass thousands of small towns not only in India but across the global South. The paper draws attention to the categorization of the urban into small and big, metropolitan and non-metropolitan, unable to provide a comprehensive theory or concept to conceptualize activities and processes that coexist in binaries. Employing an ethnographic approach it discusses the ambiguous statistical criteria, infrastructural facilities, social and cultural dynamics, informality and illegality of Dharamshala. Building on this, the paper asserts that small towns and big cities have a lot of similarities in processes and activities, they may be different from each other at the level of size and scale. Drawing any kind of hierarchy among cities and towns that have the foundation in size (population and geographical) and scale (economies) and positioning them in 'subaltern' positions does not produce a comprehensive framework nor contribute to Southern urban theory but position small towns in between position of metropolitan's cities and urban theories. Noting such debates and discussions, the contribution opens a fresh perspective to study small towns beyond their size and scale and focuses on the processes and activities that contribute to an understanding of Indian urban transition, thereby expanding Southern urban theories. [ABSTRACT FROM AUTHOR]

A smart home energy management system plays an important role in improving the efficiency of an energy distribution system and also helps to reduce the carbon footprint of the power utility company. For a developing country like India, one of the main challenges faced while integrating an energy management system and renewable energy technology is the migration cost faced by the user from the existing system. The existing energy policy of the nation or the community should be reformed in such a way that the user who is willing to adapt to an energy management system should be properly rewarded. Smart appliances and IoT-enabled devices reduce wiring complexity in any conventional home and the smart metering facility aids in the bidirectional communication between consumers and utility companies. But how does it take care of user privacy? What are the reasons behind the user's negligence on-demand response schemes in India? Through a case study, it was observed that the power consumption of domestic consumers in India increased over the years. It was also observed through an energy survey of 200 low-tension domestic consumers that a simple reengineering of lighting loads can save up to 4.68 Megawatt-hour of energy in a year. The paper also identified the negative impact of the inclining block rate billing scheme by comparing the bimonthly energy consumption pattern of consumers and also proposed a new billing scheme. The paper also reviews the types of optimization methods available for load scheduling. This paper is an attempt to enlighten readers on the importance of adopting a sustainable home energy management system, as a socio-economic commitment towards a green future. Highlights: The connected loads in Indian homes are increasing day by day and so the electricity bill A smart green HEM system helps to reduce the carbon footprint Novel demand response programs policies should be formulated Harvesting renewable energy will have multiple applications in a home Dimming lighting loads considerably reduces usage cost [ABSTRACT FROM AUTHOR]

The lie-detection tests are traditionally carried out by well-trained experts using polygraph machines. However, it is time-consuming, invasive, and, overall, a cumbersome process, not admissible by the court of law. Trained individuals can easily flaunt these tests. On the other hand, facial thermal imaging could be more effective as it is noninvasive and could be a stealth method of tracking the facial blood flow patterns, which have been proven to detect deceit. This paper presents a method based on facial thermal imaging to detect deception in human subjects. The major issue in such research is the lack of proper real-life databases which emulate the crime scenes. In this paper, first, we have developed a database based on almost real-life theft incidents with due diligence using isolated subjects over a period of time at a government hospital under the plea of free health checkup. The experiment has been conducted at Midnapore Medical College and Hospital, West Bengal, India, with proper ethical committee approval. The participants are selected at the behest of the police department with habitual crime records. Most of them have been repeatedly charged with petty crimes of pick-pocketing and stealing. They are invited individually at different instances of time under the plea of medical checkup where they have been enticed to steal cash. It is followed by a two-stage process, a friendly interaction followed by a slightly tougher interrogation. Their forehead and periorbital region skin surface temperature are recorded by a hidden thermal camera. Upon analysis, conspicuous changes in the temperature profile and blood flow pattern have been observed in the individuals who stole money and those who did not. [ABSTRACT FROM AUTHOR]

Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy's health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates. [ABSTRACT FROM AUTHOR]

This paper seeks to design the least-cost delivery routes relating to the distribution of geotowns through the shortest distance matrix estimated by the multiple traveling salesman problems. We construct the initial distance matrix based on road distance considering all the nodes. Based on the adjacency matrix, we use the R package "sna" to calculate the different measures of centrality in the network. Next, we apply the traveling salesman problem to connect all the nodes at once for planning the trip in the proposed circuit. Finally, we use the Louvain community detection algorithm to cluster geotowns for better tourism planning and ease of travel. Based on the proposed geoheritage sites in Northeast India, the tourist spots may be organized and interconnected. This paper promotes and develops an approach to incorporating national geosites with national geoheritages and moves towards geoparks, keeping in view conservation measures of appropriate international impartial organizations. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

This research paper delves into the intricate domain of policy science, focusing on the policy-making process itself. Existing theoretical frameworks in policy science often overlook essential nuances, particularly the role of political willingness causing non-linearity in the policy-making process. Three fundamental questions drive the research at hand. First, the research delves into the adequacy of existing policy frameworks in effectively elucidating the complexities of policymaking in the context of large democracies, with a particular focus on India. Second, it investigates the significant role of political willingness in shaping the trajectory of policymaking, particularly within democratic systems. Lastly, the research aims to construct a comprehensive framework capable of accommodating and explaining the non-linear dynamics introduced by the influence of political willingness throughout the policy-making process. Through an analysis of India's Data Protection Regulation formulation process, this paper attempts to map the existing gap and way forward. As an outcome of the case study analysis, the proposed framework introduces political willingness as a central element extending the policy formulation cycle and showcases its influence on agenda setting, policy formulation, adoption, implementation, and evaluation. This research contributes to the field of policy science by providing a comprehensive framework that accounts for the complexities of policymaking, especially in the Indian context. By acknowledging the pivotal role of political willingness, this framework seeks to bridge the gap in existing theoretical models, offering a more holistic understanding of policy formulation and analysis. This research lays the foundation for a paradigm shift in the study of policy science, addressing the challenges posed by evolving governance mechanisms and the rising influence of political agendas in the modern world. [ABSTRACT FROM AUTHOR]

The paper defines a new model for Takri touching text segmentation (T3S), which uses simpler statistical operations to locate exact segmentation column and generate faster results. An analysis of existing Indian scripts touching text segmentation techniques is also provided in the paper. The analysis inferred higher accuracies with recognition-based approaches which are computationally and time extensive and, work better with huge datasets. Also, most of these have been developed for headline Indian scripts only. The proposed T3S model provides significant segmentation accuracy using a statistical approach thus, generating faster results. Also, it works well with small datasets, which is feasible with the diverse ancient low-resource scripts of India like Takri. The model has been implemented on Takri text, a class of non-headline Indian regional scripts. Thus, it provides a benchmark algorithm for conducting further research in the field. A dataset of 1465 touching consonant pairs in printed Takri script is prepared using connected component segmentation for segmenting Takri text from the manually collected archival data in printed Takri script. The T3S technique is implemented on the dataset prepared and the results are critically analyzed with the existing Indian scripts touching text segmentation approaches, based on the accuracy achieved. [ABSTRACT FROM AUTHOR]

This paper examines the intersection of regional geopolitics and the governance of transboundary rivers using the case studies of multipurpose reservoirs in Himalayan South Asia. It uncovers the various ways Indian hydrocracy uses its institutional and technical expertise to strengthen India's centrality in Nepal's water and hydropower sectors. The practices of security undertaken by the hydrocrats are classified as structural, institutional, and statutory acts. By focusing on practices of an epistemic community like hydrocrats, this paper addresses longstanding weaknesses of the securitisation theory of being elitist and ignoring the agency of mid-level bureaucrats. It also highlights the constructivist nature of international politics. The findings contribute empirically to securitisation theory's 'Paris School' of thought. [ABSTRACT FROM AUTHOR]

This paper focuses on the spatiality of cricket played over public grounds. The paper uses the concepts of space, place and sportscape (Appadurai in Playing the modernity: The decolonisation of Indian Cricket, University of Minnesota Press, 1995) to derive meanings of the way the game is played and experienced. The study is organised under three scales conceptualised as landscape, territory and anthropocentric levels which are interlinked to each other. Drawing insights from the geography of sports literature, the paper extends the boundaries of knowledge about cricket beyond historical and sociological studies of the sport. A combination of methods, namely, ethnographic observations, interviews and focused group discussions were employed to deduce the spatial connection of cricket to urban spaces in India. For this purpose, Nirankari Grounds in North Delhi has been selected as the study area. The location of Nirankari Grounds at the crossroads of a densely populated residential area and the University of Delhi makes it an important site for the study. The paper contributes to the study of geography by expanding the geographical understanding of the sport. At the same time, it attempts to fill in the gaps in literature in domain of sports geography by analysing spatial aspects of sports when the game is in play. [ABSTRACT FROM AUTHOR]

This paper proposes to observe a day's break as the Planet Day (While the proposed day can also be named as the Gaia Day (after James Lovelock), Planet Day seems to be simpler and easier name for better understanding by the masses and greater connect of the issues with them. Hence, here the proposed day is termed as the Planet Day) every month to allow the nature to heal and ensure sustainability of the planet in the long run. Based on the concept of sustainable degrowth, the paper carries out benefit–cost analysis of the proposed Planet Day and presents a framework based on extensive literature review, secondary data analysis and stakeholders' (Here, participants are referred to as one of the "stakeholders" in the sense that every human being who lives on this planet is accountable for the harm done to it and is impacted by ecological degradation. Hence, they are supposed to contribute to healing of the nature through appropriate initiatives both individually and collectively. In addition to the common residents, there are other stakeholders of the ecology as well such as the government, the business enterprises, and manufacturing firms, etc.) perceptions through a non-random convenience sample survey. The paper finds that the net benefit from the Planet Day amounts to be USD 9002.37 billion across the world and USD 102.48 billion for India per annum. The respondents also perceive the proposed Planet Day as ecologically and economically beneficial and thus support the idea of healing time for the planet. However, a critical challenge is to take different stakeholders on board, ensure their active participation, and design appropriate institutional mechanisms for its successful implementation. [ABSTRACT FROM AUTHOR]

This paper develops a Structural Satellite version of the Financial-Macroeconometric Model of India (SSFMMI) to examine whether the surge in Nonperforming Assets (NPAs) in Indian Public Sector Banks (PSUs) post-2015 is due to macroeconomic shocks or better classification of loans and cleaning of bank balance sheets. Specifically, the paper analyses the impact of a rainfall shock, domestic food price shock, world oil price shock, fiscal shock, and monetary shock using counterfactual policy simulations and an out-of-sample forecasting framework to validate the impact of these macroeconomic shocks on NPA levels. The paper's outcomes suggest that the late surges in NPAs are not due to macroeconomic shocks and, therefore, that Indian banks are resilient to such shocks. However, the study reveals that the rise in domestic fuel prices and world food prices can cause a surge in NPAs levels. [ABSTRACT FROM AUTHOR]

This paper investigates the inter-regional intra-industry disparity within selected Indian manufacturing industries and industrial states. The study uses three measures—the Output-Capital Ratio, the Capital-Labor Ratio, and the Output-Labor Ratio—to critically evaluate the level of disparity in average efficiency of labor and capital, as well as capital intensity. Additionally, the paper compares the rate of disparity of per capita income between six major industrial states. The study finds that underutilization of capacity is driven by an unequal distribution of high-skilled labor supply and upgraded technologies. To address these disparities, the paper suggests that policymakers campaign for labor training and technology promotion schemes throughout all regions of India. By doing so, the study argues, the country can reduce regional inequality and improve economic outcomes for all. [ABSTRACT FROM AUTHOR]

The prevalence of poverty has bothered Indian economists, policymakers, and politicians alike. The rapid economic growth was unable to trickle down to the poorest which has led to the persistence of poverty both in urban and rural India even after 75 years of independence. Keeping this in view this paper has tried to examine the regional variations in the spatial pattern of incidence of urban poverty in India across 17 major states using data from the 28th and 68th NSS rounds. It also attempts to isolate the key determinants responsible for these variations. The objective of this paper is twofold; one is to bring out changes in the pattern of regional variations in urban poverty across 17 major states of India from 1973 to 74 to 2011–12 and the second is to identify the determinants responsible for these variations. The selection of regional units is based on the availability of comparable data as well as on theoretical considerations. For this study, therefore, we have considered each state as a single regional unit as they are home to largely homogeneous populations with common history and language, topography, institutions, and laws and at the same time each state is different from the other on account of unique factors. Therefore, interstate variations in this paper imply regional variations across 17 major Indian states. The results obtained through an instrumental variable estimation suggest that this effect is causal and is largely attributable to the positive spillovers of urbanization on the rural economy rather than to the movement of the rural poor to urban areas. Concerning the growth process, both urban–rural and sectoral (output) growth poverty relationships are suggestive of stronger intersectoral linkages in the economy, whereby growth in one sector transmits its gains elsewhere. [ABSTRACT FROM AUTHOR]

Modeling surface water quality has become crucial in providing better strategies for managing surface water resources, and adequate findings need accurate and geographically dispersed data. Hydrogeological modeling of these data sets is possible using empirically-based models. The other statistical models are also an alternative approach. In this study, a process with maximum probability is considered with the help of machine learning tools (MLT) to have optimized and valid output. The proposed method combines remote sensing and geographic information systems (RS and GIS) and MLT, which are appropriate for the predicament of neither small, large scale, nor long-term simulations. MLT methods such as VAR and ARIMA are developed in the Python programming with Jupyter notebook and tested according to the data in the spatial prediction for surface water quality parameters such as Tr, pH, Ec, TDS, AL, Ca++, NO−3, So, Cl, F−, Fe, and Mg2+ in the Krishna District, Andhra Pradesh, India—lower delta part. The delta with susceptible zones was identified using RS and GIS as those areas are prone to direct exposure to surface water contaminants from aquaculture, agricultural runoff, small- and medium-scale businesses, and household trash. Achieving effective surface water management for this ecosystem is critical for regional water management. The geographical information about the concentrations acquired via the RS and GIS was compared to the statistical modeling findings and verified using real-time measurements. MLT modeling seems more realistic than the experimental setting; data from the previous 20 years (2000–2020) were used for modeling, and the predicted values presented in the paper are predicted for the year 2021. The computed R2 value of ranges between 0.75 and 0.96% is recorded with ARIMA, and VAR posted range between 0.56 and 0.75% with the trained and tested data. The findings show the potential for MLT of geographically dispersed hydrogeological data to be used for pollution-free surface water management. From the surface water management perspective, combining RS and GIS and MLT offers an alternate data analysis approach for obtaining quick results utilizing a less laborious process that produces acceptable results. [ABSTRACT FROM AUTHOR]

The cross-correlation of two-dimensional digital images is fundamental to solar adaptive optics computations. It can be used in a simple tip-tilt correction system to identify the relative shift between consecutive images and correlating sub-aperture images of a Shack–Hartmann wave-front sensor. The typical frequency of computation is about 1 kHz. While the software-based optimized cross-correlations may be sufficient when a small number of sub-apertures are used in a wave-front sensor, hardware-accelerated (FPGA), correlations may be required when a large number of sub-aperture images are involved, e.g., in the case of the proposed National Large Solar Telescope in India. This paper presents SolarAccel: An FPGA-based acceleration of a basic two-dimensional cross-correlation of two images. We accelerate the FPGA-based design by pipelining the individual components of the cross-correlation process. We implemented our RTL logic on a few sets of 128 × 128 pixel images and 32 × 32 pixel images on a Xilinx Zynq Ultrascale + MPSoC on the ZCU104 FPGA evaluation platform. SolarAccel performs a 2D FFT on a 128 × 128 image faster than existing work. The cross-correlation on a 32 × 32 image is also faster than the existing work. This demonstrates that FPGA acceleration is beneficial in solar adaptive optics applications. [ABSTRACT FROM AUTHOR]

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model's performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model. [ABSTRACT FROM AUTHOR]

This paper attempts to capture the missing 'distance' puzzle in the globalization process driven by trade flows. It is widely recognized that the growing global economic integration process play a major role in the spatial restructuring of many countries at varying geographical scales. The paper illustrates a model of international trade flows that builds upon existing research on the geographies of global trade. Empirical results of the current study indicate that the basic and augmented gravity model provides a useful framework for understanding international trade. The evidence of map pattern in residuals across temporal scale is noted. The results further indicate the importance of distance in the Indian context. An identification of the patterns and determinants of India's international trade with 87 countries suggests that potentially important differences in regional demands and country specific factors may impact the trade interaction over the years. [ABSTRACT FROM AUTHOR]

Background: Murraya koenigii (L.) Spreng. has several well-established nutritional and therapeutic applications. Following our desire to investigate the global and scientific community's knowledge of medicinal plants, this study was intended to examine the evolution of knowledge related to M. Koenigii studies. The primary purpose of this paper is to clarify the status of these studies, investigate their methods, findings, and trends, and define their significance within the current research landscape. Results: To achieve these goals, bibliometric analysis was conducted, retrieving, and analyzing 934 original articles published between 1965 and 2023 based on Scopus Dataset results. Data were exported as CVS (comma-separated values) and BibTex files and analyzed using Bibliometrix and VOSviewer software. Articles from 502 sources have been identified, averaging 21.8 citations per document. The research in this plant has had exponential growth (R2 = 0.77). International co-authorship is 13.08%. India and Malaysia are the top publishing countries. Debajo, A.C. (Nigeria), Phatak,R.S. (India), and Sukari,M.A. (Malaysia) are the most productive authors. The top source is the Journal of Ethnopharmacology. "Green synthesis," "nanoparticles," "oxidative stress," "Asian citrus psyllid," "apoptosis," "antimicrobial," "anticancer," "Chromatographic profile," "bioactive compounds," and "alkaloids" are strongly related to the current trends in M. Koenigii research. Regarding the specialized topics, M. Koenigii's study concentrated on using this plant as an antioxidant agent in manufacturing and biological systems. Dynamic subjects like chromatographic profiles, essential oils, and Asian citrus psyllids were included in the motor theme. Conclusions: The current study used bibliometric techniques to evaluate research on M. Koenigii and identify trends and potential future research hot spots. [ABSTRACT FROM AUTHOR]

As it is known that "reconnecting to nature" is essential for sustainability, there are few studies on why reconnecting humans to nature is essential and how it can be accomplished. This study is aimed at finding innovative, yet sustainable ways to reconnect people with nature. The objective of the paper is to enhance the biodiversity knowledge of local residents through conservation of non-timber forest products (NTFPs). In the study, urban respondents' knowledge about biodiversity with particular reference to non-timber forest products (NTFPs) was analysed for the first time. To achieve the objective of this study, participatory appraisal, one-on-one interviews, dual moderator focus groups, ethnographic field research, and semi-structured questionnaire were used. Plant name boards with local names and their NTFP values for the selected species were also installed. Results suggest that NTFP-producing species played a vital role in making people interested in biodiversity. It can be considered as one of the innovative ways to improve surrounding biodiversity, for further enhancing biodiversity knowledge, based on archival study and local community's knowledge about NTFP producing species, NTFPs plantation also done across the study area like parks, roadside, open space. This paper also explored how this study could contribute to the formulation of policies in terms of creating various urban green covers in Indian cities, following the strategies of joint forest management, social forestry, and agroforestry. [ABSTRACT FROM AUTHOR]

The sole purpose of this review paper is to reveal the land use and land cover (LULC) changes in the study region (Northern India) from 2017 to 2021. The researchers discovered that no systematic review of the LULC dynamics of the study region had been conducted so far. This study used a novel systematic review procedure (i.e., the ICA framework) to conduct the review process. The data for this study were retrieved from the Web of Science of Clarivate Analytics, using keywords related to LULC. A thorough review of 29 full-text articles was done to examine the primary land use classes (LUC), techniques used, factors responsible, and dynamics of the major land classes. Most articles included agriculture, built-up, water bodies, and plantations as the main LULC categories. Most of them used on-screen digitisation and supervised algorithms to classify LULC classes and incorporated accuracy assessment to validate their results. It was also found that almost all the papers cited climate change, increasing population, urbanisation, and economic considerations as the main drivers responsible for the changes that occurred in different LULCs. The findings of the various studies revealed that horticulture and built-up areas have increased while agricultural, forest cover and open spaces have decreased to a greater extent. Environmental negatives of LULC dynamics of the region suggested that LULC change is not favourable everywhere. Thus, to control the haphazard LULC change, the government must come up with a substantial land conversion policy in the region with ground-level implantation that will restrict the people from blind land conversion. [ABSTRACT FROM AUTHOR]

To meet the national development goals in India on poverty alleviation and to trail the Sustainable Development Goals (SDG), policy makers need to have adequate information on poverty in the country. To this end the aim of this paper is to analyse the trends in multidimensional poverty at the all-India level, across geographic locations, castes and religious groups. We have used Alkire–Foster methodology to measure multidimensional poverty index (MPI) and utilized National Family Health Survey unit level data of the latest three rounds 2005–06, 2015–16 and 2019–21. To the best of our understanding this is the first paper that utilizes the latest data sets to analyse the trends of MPI in India. We have also made a comparative assessment of trends of poverty in India measured by Global MPI approach and India's MPI approach. Over the period 2005–2006 to 2019–2021 the absolute decline in MPI was larger in rural areas of India. However, the contribution to MPI in rural areas is relatively high and that increased over time. There was no evidence of reduction of inter-caste and inter-religious based disparities. There are larger doses of decline in MPI in the sub period 2015–2016 to 2019–2021 but the decline across the states of India continues to be non-regressive. The states which were worst performing in 2005–2006 continue to be the same in 2019–2021. The paper concludes that by exploring the MPI over time one can decipher the urgencies in policy development to move towards the SDGs. [ABSTRACT FROM AUTHOR]

Rural–urban migration is the most adopted strategy in rural areas to overcome the risk associated with subsistence economy and to diversify income. The Garhwal Himalaya has the highest rate of rural–urban migration in India, which has serious repercussions. Despite this, there is a dearth of literature on the implications of rural–urban migration in the region. This paper attempts to fill this void. The objectives of the paper are to investigate various types, patterns, determinants, and implications of rural–urban migration in the Garhwal Himalaya. In order to meet the objectives, 15 villages were studied from five districts of the Garhwal Himalaya. A comprehensive questionnaire with diverse set of questions from various dimensions was employed in the study. Several migration related questions from both origin and destination were considered while framing and conducting the survey. Several focused group discussions and interviews were conducted in the study villages to understand the real drivers and implications of rural–urban migration in the region. The study reveals that rural–urban migration is a serious issue in Garhwal Himalayas, posing various socio-economic problems. Without immediate action, it will have serious repercussions. Finally, this study suggests minimizing the current rate of rural–urban migration in the Garhwal Himalaya. [ABSTRACT FROM AUTHOR]

Smart city imaginaries have emerged in southern cities driven by neoliberal logics in the urban space. Scholarly work in India has continued to engage with sweeping accounts of cities as opposed to detailed empirical studies of local projects. This paper attempts to address this gap through an in-depth ethnographic inquiry of a slum redevelopment project in the city of Bhubaneswar, India. The key objective is to understand the ways in which informal residents adapted to and changed smart city policies in India in recent years. Using an evolutionary lens, and drawing on participant observation; document analysis; and semi-structured interviews, the paper puts forth a descriptive cases that advances the notion that smart cities imaginaries have resulted in abrupt changes in the institutional context while getting entangled itself within the legal system. The paper also demonstrates how smart cities discourses counter-intuitively result in emergent spaces of resistance in the form of counter-hegemonic practices, thus allowing spaces for the evolution of new actors and imaginaries from unfamiliar territories. The paper concludes by discussing that city planning and governance pathways in India risk creating complicated path dependencies and rigid governance future pathways that may amplify conflict. [ABSTRACT FROM AUTHOR]

Exit is not an easy task for a sex worker. Academic investigations of the reasons and barriers to exit from the sex industry are lacking heavily. Sex work is a stigmatised profession, even though the workers find it difficult to exit the same. The current study attempted to understand the barriers faced by female sex workers in Puducherry, a union territory in the southeast part of India, to exit from sex work. The participants comprise 19 female sex workers (FSW) who work in Puducherry. The data were collected through in-depth interviews. All of the participants had thoughts about quitting. The barriers to exit were identified. The barriers were recognised at the individual, interpersonal (microsystem), and structural (macro system) levels within the framework of Bronfenbrenner's Ecological Systems Theory, which are discussed in detail in the paper. The study also identified a lack of support systems for the targeted population. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has left an indelible mark on societies worldwide, challenging governments to respond swiftly and effectively to mitigate its impact. India, with its vast population and complex healthcare landscape, faced unique challenges in formulating and implementing a pandemic response strategy. The article examines India's policy response to the COVID-19 pandemic and explores the valuable lessons it offers for shaping a more resilient and prepared society in a post-COVID world. It provides a comprehensive analysis of India's multifaceted approach to managing the pandemic, highlighting key elements such as lockdowns, testing and contact tracing, healthcare infrastructure, vaccination drives, and economic relief measures. By delving into both the successes and shortcomings of these policies, it seeks to extract valuable insights for policymakers and public health officials globally. As the world transitions into a post-COVID era, the lessons learned from India's experience offer a roadmap for building stronger healthcare systems, improving disaster preparedness, and enhancing social safety nets. The article underscores the importance of proactive governance, community engagement, data-driven decision-making, and international collaboration in the face of global health crises. The paper demonstrates that India's journey through the pandemic provides a wealth of knowledge that can inform policy development, foster greater resilience, and help societies better navigate the uncertainties of a post-COVID world. By reflecting on the successes and challenges of India's response, this article offers actionable insights for shaping a more equitable, sustainable, and prepared society in the wake of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Unexpected and sudden spread of the novel Coronavirus disease (COVID-19) has opened up many scopes for researchers in the fields of biotechnology, health care, educational sectors, agriculture, manufacturing, service sectors, marketing, finance, etc. Hence, the researchers are concerned to study, analyze and predict the impact of infection of COVID-19. The COVID-19 pandemic has affected many fields, particularly the stock markets in the financial sector. In this paper, we have proposed an econometric approach and stochastic approach to analyze the stochastic nature of stock price before and during a COVID-19-specific pandemic period. For our study, we considered the BSE SENSEX INDEX closing pricing data from the Bombay Stock Exchange for the period before and during COVID-19. We have applied the statistical tools, namely descriptive statistics for testing the normal distribution of data, unit root test for testing the stationarity, and GARCH and stochastic model for measuring the risk, also investigated drift and volatility (or diffusion) coefficients of the stock price SDE by using R Environment software and formulated the 95% confidence level bound with the help of 500 times simulations. Finally, the results have been obtained from these methods and simulations are discussed. [ABSTRACT FROM AUTHOR]

Slope instability problems due to widening of roads are very common in mountainous terrains particularly in Indian Himalayan region. Slope instability may occur due to action of different natural as well as anthropogenic activities or through combined action of both. The present paper deals with the slope stability assessment of natural and cut slopes along the Imphal–Moreh section of NH-39 in Manipur state, India. The factor of safety (FoS) for pre- and post-excavations is evaluated, and the FoS with the mitigation measures is examined using limit equilibrium and finite element method. Construction sequences are modeled, and the stability and deformation patterns are evaluated for each construction stage. It is found that about 78% of the slopes is having an angle more than 45° after cutting. The present study suggests that a FoS greater than 1.25 can be achieved by installing a 5.5 m anchor at 18° with the horizontal at a spacing of 1 m. Confirmatory field tests on the anchor are also conducted to ensure the accuracy of the adopted design parameters. This study provides useful informations to researchers and practitioner about the stability assessment of slopes and potential mitigative measures. Similar schemes of mitigative measures are effective for the same/similar geologic conditions in the Indian Himalayan terrain. [ABSTRACT FROM AUTHOR]

Small and medium enterprises (SMEs) in India are suffering from the long-standing challenges related to asymmetric information, high transaction costs, SMEs' opacity and limited access to credit. Blockchain technology, which is still in its infancy in terms of adoption in India, can facilitate SMEs to counter these challenges. Fuelled by this motivation, the study aims to investigate the significant barriers to blockchain adoption in supply chain finance practices by Indian SMEs. Using fuzzy-analytic hierarchy process, sensitivity analysis, and fuzzy-decision-making trial and evaluation laboratory this paper identifies the blockchain barriers, prioritises them and examine their cause and effect relationships. The results of the study indicate that technology barriers are the most influential barriers that impede blockchain adoption. The findings will help the policymakers and practitioners to take suitable measures to overcome these barriers and fuel the adoption of blockchain in Indian SMEs. [ABSTRACT FROM AUTHOR]

This paper explores the scope of services and challenges in implementing Time Bank in India. This community-based initiative allows people to exchange skills and services without the involvement of money. A qualitative study was conducted using semi-structured telephone interviews with 20 participants aged 18 to 35. Thematic analysis revealed four major themes: Services, Challenges, and Limitations of Time Bank, Factors Affecting Help-Seeking Behavior, and Community and Gender Dynamics. Participants expressed interest in offering a wide range of services, from household tasks to professional services. However, challenges and limitations were also identified, such as a lack of awareness and understanding of the concept, social stigma, and concerns about trust, privacy, and safety issues. The study underscores the need to consider cultural and social factors while implementing community-based initiatives. Despite these challenges, participants believed Time Bank could be a valuable platform for building social connections and community support. The study's implications highlight the need for further research on the impact of Time Bank on social capital and the sustainability of the initiative in different cultural contexts. [ABSTRACT FROM AUTHOR]

The 2030 Agenda for sustainable development was launched to achieve Sustainable Development Goals (SDGs) across the globe. This paper is based on the primary database to assess the nutritional status of 5–10 years children and the incidence of deprivation in their households of a backward district (Purulia), India in the context of the first two SDGs, e.g., no poverty and zero hunger. We conclude that around 74% of children are undernourished. The proportion of households multidimensionally deprived is 90%, and the majority of them live on less than $1.25 a day. Results reveal that the BMI of mother and the education of father are the two most statistically significant predictors of child malnutrition. Purulia has long been witnessing the persistent nature of deprivation, which is well reflected in the child's health. The district is quite far from the national targets in achieving the SDGs. Government, private sector, and civil society must come together to accelerate the progress of SDGs. [ABSTRACT FROM AUTHOR]

Wetlands provide numerous ecological and economic services to mankind. The soils of wetlands are one of the largest sinks of carbon (C) among the terrestrial ecosystems and can play an important role in offsetting the emission of C as a greenhouse gas (GHG) to the atmosphere. India is bestowed with enriched wetland ecosystems that support diverse and unique habitats. The potential of the wetlands in terms of C capture and sequestration has not been quantified. Therefore, in the present paper, an assessment of C capture from different sources and its ultimate deposition in soils leading to sequestration has been done in three different types of wetlands, one created sewage-fed and two natural floodplain oxbow lakes, in the West Bengal state of India. Multiple seasonal sampling of water, macrophytes, and soil was done to assess the primary productivity, dissolved C, and deposition of C in soils of the wetlands in comparison to reference upland sites. All these wetlands are productive ecosystems as indicated by the physicochemical parameters of water and soil. The quantity of C accumulated up to 0.3 m depth in the oxbow lakes was to the tune of 144–166 Mg/ha, which was 3.43–4.78 times higher than that in the corresponding reference upland sites. In the sewage-fed wetland, the C accumulation estimated as 50 Mg/ha was 1.27 times higher than its corresponding upland site. So, the wetland ecosystems, particularly the floodplains, are highly efficient in accumulating C in their soils and thus can somewhat negate the GHG emission. [ABSTRACT FROM AUTHOR]

Opencast coal mining produces trash of soil and rock containing various minerals, that are usually dumped nearby the abandoned sites which causes severe environmental concern including the production of acid mine drainage (AMD) through oxidation pyrite minerals. The current study entailed assessing the potential production of AMD from an opencast coal mining region in Northeast part of India. In order to have a comprehensive overview of the AMD problem in Makum coalfield, the physico-chemical, geochemical, and petrological characteristics of the coal and overburden (OB) samples collected from the Makum coalfield (Northeast India) were thoroughly investigated. The maceral compositions reveal that coal features all three groups of macerals (liptinite, vitrinite, and inertinite), with a high concentration of liptinite indicating the coal of perhydrous, thereby rendering it more reactive. Pyrite (FeS2) oxidation kinetics were studied by conducting the aqueous leaching experiments of coal and (OB) samples to interpret the chemical weathering under controlled laboratory conditions of various temperature and time periods, and to replicate the actual mine site leaching. Inductively coupled plasma-optical emission spectroscopy (ICP-OES) was operated to detect the disposal of some precarious elements from coal and OB samples to the leachates during our controlled leaching experiment. The Rare earth element (REE) enrichment in the samples shows the anthropogenic incorporation of the REE in the coal and OB. These experiments reveal the change in conductivity, acid producing tendency, total dissolved solid(TDS), total Iron(Fe) and dissolved Sulfate(SO42−) ions on progress of the leaching experiments. Moreover, the discharge of FeS2 via atmospheric oxidation in laboratory condition undergoes a significant growth with the rise of temperature of the reaction systems in the environment and follows pseudo first order kinetics. A bio-remediative strategies is also reported in this paper to mitigate AMD water by employing size-segregated powdered limestone and water hyacinth plant in an indigenously developed site-specific prototype station. Apart from neutralisation of AMD water, this eco-friendly AMD remediation strategy demonstrates a reduction in PHEs concentrations in the treated AMD water. [ABSTRACT FROM AUTHOR]

Conventionally, volcanic margins have been considered devoid of hydrocarbon, but many discoveries and research in recent past have proved the presence of hydrocarbon prospects within them. However, hydrocarbon exploration within volcanic margin is constrained by seismic imaging. Further, identification of continent to oceanic boundary (COB) is critical to hydrocarbon search as hydrocarbon is found mostly over continental crust. Seaward dipping reflectors, associated with volcano-sedimentary sequences, located along rifted continental margins and represented by highly dipping strong amplitude seismic reflectors, play an important role to study volcanic margins. Keeping in view the problems of hydrocarbon exploration within volcanics, seaward dipping reflectors (SDRs) in Indian context have been studied in detail in this paper. It facilitates mapping of continent-oceanic boundary (COB). It has impact on hydrocarbon prospectivity in volcanic margins as it can provide formidable seal and secondary induced maturity. In India, several discoveries have been made in volcanic margins along west coast, from weathered, fractured basalt and sub-basalt sediments. In the present work, long offset regional seismic data (18 dip lines and 1 cross line), along east coast, have been interpreted. SDRs help in hydrocarbon prospectivity assessment for both discrimination of interbedded sediments and preferential accumulation of hydrocarbon. Mapping of linear features not only helped to demarcate COB but also their presence at 6–10 km depth. For the first time, such comprehensive study on SDRs has been done along Indian peninsular region with indications for hydrocarbon prospectivity at deeper levels along Indian peninsular region. [ABSTRACT FROM AUTHOR]

Pastoral practices throughout the world are in a state of flux, and Gaddi pastoralism in India's Western Himalayas is no exception. Often in literature and common parlance, these practices are predicted to end with the current generation of practising pastoralists. For Gaddi, an agro-pastoral community located in the hill state of Himachal Pradesh in India, these gloomy predictions have remained persistent over decades. Irrespective of these claims, pastoralism continues to remain a viable livelihood option for many within the community even today. However, the institutional dynamics in which these practices are embedded have undergone several changes. In this paper, we discuss the changes in Gaddi pastoralism and its resilience by stressing a critical aspect of labour, often referred to as puhals in their vernacular dialect. Out of the various contextual meanings, we adopt the applied translation of this term as hired herders to understand their role in the larger socio-ecological system amidst the on-going livelihood shifts and declining interest among the Gaddi youth to pursue pastoralism. The data presented in this paper was collected through ethnographic fieldwork conducted during 2018–2019 at Bharmour region of Chamba district in Himachal Pradesh, India. Qualitative tools including in-depth interviews, focus group discussions and participant observations were administered. We discuss various aspects of puhal practices among the Gaddis including their recruitment process, need for hiring, involved negotiations over remunerations, skill sets and access to the resources, to highlight the institutional dynamism. Our findings suggest that hiring herders remains a crucial adaptation for the continuation and sustenance of pastoral practices that are facing contingent external pressures. It also facilitates transmission of local knowledge, livelihood and income diversification, accumulation of wealth, social mobility and cultural continuity. We conclude that hired herding, in the face of increasing labour shortages among the Gaddis, offers an innovative proposition to halt the decline in pastoralism and shape its future. [ABSTRACT FROM AUTHOR]

This paper aims to illustrate the crucial role of preserving, protecting and managing the environment by Indigenous Peoples in Japan and India. Traditional Knowledge has provided the ability to detect, understand and respond to environmental changes. However, modern science and applied technology often fall short of sustainable practices, acquired through 'centuries of local observation'. Our consumer-centric world has unravelled a sad situation that has contributed to an irretrievable climate crisis. Multilateral instruments, however, have come to the rescue. In this journey, Japan and India have shown the world some constructive initiatives with positive outcomes which could go a long way in achieving the UN-SDGs. [ABSTRACT FROM AUTHOR]

Nucleic acids are prominent biomarkers for diagnosing infectious pathogens using nucleic acid amplification techniques (NAATs). PCR, a gold standard technique for amplifying nucleic acids, is widely used in scientific research and diagnosis. Efficient pathogen detection is a key to adequate food safety and hygiene. However, using bulky thermal cyclers and costly laboratory setup limits its uses in developing countries, including India. The isothermal amplification methods are exploited to develop miniaturized sensors against viruses, bacteria, fungi and other pathogenic organisms and have been applied for in situ diagnosis. Isothermal amplification techniques have been found suitable for POC techniques and follow WHO's ASSURED criteria. LAMP, NASBA, SDA, RCA and RPA are some of the isothermal amplification techniques which are preferable for POC diagnostics. Furthermore, methods such as WGA, CPA, HDA, EXPAR, SMART, SPIA and DAMP were introduced for even more accuracy and robustness. Using recombinant polymerases and other nucleic acid-modifying enzymes has dramatically broadened the detection range of target pathogens under the scanner. The coupling of isothermal amplification methods with advanced technologies such as CRISPR/Cas systems, fluorescence-based chemistries, microfluidics and paper-based sensors has significantly influenced the biosensing and diagnosis field. This review comprehensively analyzed isothermal nucleic acid amplification methods, emphasizing their advantages, disadvantages and limitations. [ABSTRACT FROM AUTHOR]

The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ML target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ML algorithm proposed to improve the revenue of agriculture field. [ABSTRACT FROM AUTHOR]

Mining and quarrying provide the basic raw materials for sustaining human well-being and are critical for achieving economic developments. At the same time, environmental degradation and its associated social impacts and inequalities have become a grave reality of mining sector that affects all nations, individually and/or collectively. Assessment of the environmental impacts arising from mining and quarrying is critical to limit the environmental problems within the barest minimum levels. Although many impact assessment studies are available on mining/quarrying of different major and minor minerals, not many studies exist on quarrying for laterite blocks which is being widespread in many of the fast developing tropical and sub-tropical regions of the world like India. Therefore, this paper evaluates the impact of laterite quarrying for construction blocks, in one of the twin river basins in SW India, the Netravati–Gurpur river basin, where the activity is widespread. The Rapid Impact Assessment Matrix (RIAM) method was used to evaluate the impacts of laterite quarrying as it allows a comprehensive analysis of the results based on the individual environmental score obtained for each component. RIAM is a valuable assessment tool, owing to its capability in quick, collective and reliable evaluation of the impacts that can aid decision making and minimization of environmental impacts, especially at early planning stages. Data pertaining to resource extraction, identification of impacting actions, mapping of mining hotspots, etc., were collected from primary and secondary sources through systematic field work and sample collection, questionnaire surveys within the local community and other stakeholders such as mine operators, labourers, officials of Government departments, etc. A total of 21 laterite quarries are located in the basin with a total production of 5.7 million laterite bricks/year (0.115 × 106 ty−1). The impact assessment study revealed that the activity not only disturbs the natural environment especially, hydrology, air quality and noise levels, ecology, land use and soil stability but has profound influence on the socio-economic factors of human health and immunity, displacement, etc., of the quarrying-hit areas. The activity also recorded both long-term and short-term positive impacts as a source of employment and income generation. Additionally, the activity favours groundwater replenishment and agriculture productivity of the area where appropriate mine closure measures were taken up. However, the positive impacts of the activity are far outweighed by the fact that most impacts of laterite quarrying are of class − C (moderate negative impact) and − D (significant negative impact) owing to the long-term socio-environmental and bio-ecological implications of the activity. Thus, it is imperative that there is significant improvement in policy and regulatory framework and its implementation for mining and quarrying of building materials which is vital for meeting future development requirements. [ABSTRACT FROM AUTHOR]

This paper presents a report on the activities and the proposed action plan of the Education and Public Outreach Working Group (EPO WG) of Square Kilometre Array–India Consortium (SKAIC). Details of a set of flagship programs as well as supporting activities are presented, in consonance with the scale of India's involvement in Square Kilometre Array Observatory (SKAO), as well as the educational and science literacy contexts in the country. Ongoing independent EPO activities by some of the member institutions are also included. [ABSTRACT FROM AUTHOR]

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]

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]