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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]

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 study aims to find the quality of research papers published in the domain of entrepreneurship in India. This study covers 100 research papers. A standardized measurement tool developed by the earlier researchers was used to evaluate the research quality. The data compiled using the measurement tool were analyzed with the support of the SPSS. The statistical tools such as descriptive statistics, Friedman's test, factor analysis, two-sample 't' test, and ANOVA are applied to analyze the data. The study findings reported that the quality of research papers published in the field of entrepreneurship is not up to the quality standards. The quality of multiple-author papers is better than single-author papers. Similarly, the quality of papers published by foreign authors is comparatively better than Indian authors. Further, the quality of papers published with the combination of foreign and Indian authors is substantially good. The quality of papers published in foreign journals is higher as compared with Indian journals. Further, the standard of papers published under the qualitative approach was comparatively better than the quantitative approach. The authors developed a Conceptual Model of Process and Product of Research (YOVA model). This model clearly shows that the whole research process yields six levels of research products. The study recommended that the researchers need to go for international collaborations to improve the quality of the publication. The funding agencies, higher learning institutions and research institutions should focus on enhancing research infrastructure. The study examined the validity of research articles searched by novice researchers in India in Google by using keywords related to entrepreneurship and, as such this non-focused approach is a big impediment to quality research. [ABSTRACT FROM AUTHOR]

Bacteriophages are key viruses that can kill thousands of harmful microbes generally present at polluted sites. Such bacteriophages are abundantly present in the river Ganga, where millions of people in India and abroad drink its water and take baths every day for spiritual reasons. Besides bacteriophages, several pathogenic and zoonotic microbes are present in the river Ganga. It is interesting to study the diversity and abundance of bacteria and their respective phages present in polluted or non-polluted sites. Thus, the metagenomics study was carried out at the most polluted sites of river Ganga near Kanpur and non-polluted sites at Farakka, which harbors several harmful bacteria and their phages. The results revealed a significantly higher percentage of Microviridae phage family, ssDNA viruses, and Mimiviridae virus family near Kanpur than Farakka. In addition, compared to Kanpur, Farakka has a more significant percentage of Myoviridae, an unidentified phage family, and Retroviridae viral families. Despite heavy drainage of untreated and contaminated effluents from the leather industry, pesticide industry, paper mills, metropolitan cities, and other sources, the vast number of said phages kills several harmful pathogenic microbes in polluted sites to maintain the Ganga water's healing power or natural sterility. In a polluted aquatic environment, the varieties of bacteriophages were identified in the Ganga and their interaction with the microbial host. The taxonomic diversity of several bacteriophages found in pathogenic host systems was investigated to get exceptional knowledge of these small viruses in the aquatic environment. [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]

The paper aimed to investigate the cyclical dynamics of the business, credit, and investment sectors in India. This was achieved by utilizing annual data from 1980 to 2021 and investigating the impact of domestic and global financial cycles on the business cycle. The cycles were derived using the Hodrick–Prescott filter, and structural vector autoregression (SVAR) and Granger causality tests were employed to establish the dynamic interactions among these cycles. The results of the study revealed a clear divergence between domestic and global financial cycles. Additionally, the SVAR analysis confirmed the presence of a long-run relationship between business, investment, and credit cycles. Notably, the findings suggest that credit cycles can provide valuable insights to manage business cycles in India. Finally, robustness checks were conducted to confirm the reliability of SVAR findings. [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]

In this paper based on original fieldwork, I seek to contribute to critical scholarship in international law by providing an investigation into the engagement with international law by actors in civil society working against son preference primarily in Tirupati, India. I suggest that the turn to the international legal order by civic actors should be theorized as something else than as merely coming 'from above', 'from below' or as a 'translation' of 'global' law to 'local' conditions. Instead, I propose that the mobilization of international law within Tirupati's civil society should be seen as an emancipatory undertaking, an act of resistance with the overarching ambition to reclaim the zenana. In that, I argue, the strategies within Tirupati's civil society are more appropriately understood as critical international law put into practice. [ABSTRACT FROM AUTHOR]

Mining is an important enterprise that will supply essential minerals for the country's infrastructural development. Mining is a key industry and a development engine in the Indian economy. The epidemic has had both immediate and long-term effects on the labour and industries in India. Short-term effects include the cessation of mineral production, salary loss due to absenteeism and pandemic limitations, unemployment, and the selling of minerals on the market. The reputation and economic health of industries, fresh mineral deposit exploration, and new mining complex development have all been impacted in long-term. Aside from the mining industry-specific impact of COVID-19, the social fabric of the mining workers has been impacted since they are living in an environment of fear and uncertainty. This paper attempts to analyse the effect of COVID-19 on extractive industries in India. It also focuses the steps which taken by the Indian government. The paper concluded with the negative effect of COVID-19 which disrupts the mining sector and the contribution declined during COVID-19. However, for instance from mineral rich-Odisha, the mining sector takes up by its growth in post COVID-19 period with remarkable increment from negative growth to high positive rate. [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]

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]

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]

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]

Mercury pollution, with India ranked as the world's second-largest emitter, poses a critical environmental and public health challenge and underscores the need for rigorous research and effective mitigation strategies. Nanocellulose is derived from cellulose, the most abundant natural polymer on earth, and stands out as an excellent choice for mercury ion remediation due to its remarkable adsorption capacity, which is attributed to its high specific surface area and abundant functional groups, enabling efficient Hg(II) ion removal from contaminated water sources. This review paper investigates the compelling potential of nanocellulose as a scavenging tool for Hg(II) ion contamination. The comprehensive examination encompasses the fundamental attributes of nanocellulose, its diverse fabrication techniques, and the innovative development methods of nanocellulose-based nanocomposites. The paper further delves into the mechanisms that underlie Hg removal using nanocellulose, as well as the integration of nanocellulose in Hg detection methodologies, and also acknowledges the substantial challenges that lie ahead. This review aims to pave the way for sustainable solutions in mitigating Hg contamination using nanocellulose-based nanocomposites to address the global context of this environmental concern. [ABSTRACT FROM AUTHOR]

Sentiment analysis is pivotal in facilitating informed decision-making for businesses, governments, and organizations by comprehending public opinion. However, the task becomes challenging when dealing with linguistic diversity and limited resources for specific languages. This paper presents a novel method, KNetwork, for conducting cross-lingual sentiment analysis of Hindi and English text. The KNetwork leverages the feature vectors generated from translated and transliterated text, aiming to enhance the accuracy of sentiment analysis in cross-lingual settings. Specifically, this paper addresses the challenges associated with sentiment analysis in countries like India, which possess a rich linguistic heritage. The KNetwork model is rigorously evaluated on multiple review datasets, showcasing its performance against state-of-the-art models. Moreover, KNetwork achieves superior results in terms of accuracy of 92.5% and an F1-score of 0.922, outperforming existing models. With an AUC-ROC value of 0.934, it excels in cross-lingual sentiment analysis. This study advances the sentiment analysis for languages with limited resources and underscores the KNetwork's efficacy in enhancing accuracy, with far-reaching implications for informed decision-making. [ABSTRACT FROM AUTHOR]

A comparative study of termite diversity in different ecosystems (agricultural, horticultural and silvicultural ecosystems) was carried out in mid-hills of Meghalaya to understand the species diversity, richness and abundanceof termite. A standard transect method and tissue paper bait method were used for sampling the termites.Alpha diversity was analysed by using Shannon wiener index and Margalef's D index for Species richness; and Simpson's index for Species dominance; whereas Beta diversity was analysed by using Routledge Be index, Cody Bc index, Whittaker Bw index and Wilson and Shmida Bt index. In the study, a total of 10 species of termites were identified which belonged to three subfamilies viz., Macrotermitinae, Amitermitinae and Termitinae. Macrotermitinae was the most presiding subfamily among the collected termite fauna. Individual species dominance was observed to differ in different ecosystems however, Odontotermes spp. was widely distributed across the ecosystems.The number of species recorded in silvicultural, horticultural and agricultural ecosystem were 7, 3 and 1 respectively. The termite diversity was highest in silvicultural ecosystem and lowest in agricultural ecosystem. The lowest numbers of species recorded in ecosystem may be attributed to its abrupt human disturbance. Shannon Wiener, Margalef's D and Simpson index in agricultural ecosystem, depicted less values compared to horticultural and silvicultural ecosystem. Among the ten species collected, one species i.e. Pseudocapritermis tikaderi which was sampled from silvicultural ecosystems was soil/humus feeder and the rest were wood/litter feeders. [ABSTRACT FROM AUTHOR]

Gond painting, an integral part of the Gond cultural identity of central India, has emerged as highly appreciated and recognised tribal art form worldwide in the recent times. These traditional paintings are believed to have started with the transformation of Gond oral folklores. The Pradhan Gond, a subgroup of the Gonds, who were traditionally assigned to sing songs of the glorification of the gods, goddesses and nature with a Bana, a traditional single-stringed musical instrument, was assumed to initiate the paintings of Gond folktales. They narrated the collective beliefs, values and legends of the Gond tribe on the origin, relationship and divine powers in the form of paintings. The traditions of performing the assigned responsibilities of Pradhan Gond were believed to have lost during the Mughal era and British rule. This research paper focuses on the study of the reflections of three popular Gond folk stories, i.e. Basin Kanya, Mahua tree and Bada Dev, in the contemporary Gond paintings of Madhya Pradesh. The paintings of Jangarh Singh Shyam and Durga Bai have been chosen for the study. [ABSTRACT FROM AUTHOR]

The captivating tribal communities living across the length and breadth of the state of Central India and mostly in Odisha have enriched the cultural heritage of the region by their cultural diversities. Gadaba's religious life reflected through their rich folk traditions of songs, dance, music, rituals and festivals celebrated round the year adds colourful dimensions to their very existence. The amazing conglomeration of traditions, beliefs, sorrows and philosophies that together constitute and vitalize the religion of tribes has descended from antiquity and has been preserved unimpaired to the present day. Every dimension of their life covering round the year activities is intimately connected with religion and the world view associated with it. It is these aspects of their culture that give meaning and depth to their lives and solidarity to their social structure. However, in recent days many changes have been taken place in traditional religious life of Gadaba tribe. Against this backdrop, this paper has tried to explore some of that core structure of Gadaba's religious life as well as the changing dimensions of their religious manifestation. [ABSTRACT FROM AUTHOR]

Competitiveness is a key requirement for modern companies to survive. Effective corporate governance practices are also fast emerging as a fundamental obligation owed to shareholders and other stakeholders. The connection between competition and good corporate governance is derived from the product market competition hypothesis (PMCH) that projects the idea that operating in competitive markets would increase x-efficiency and decrease agency costs in companies, leading to superior corporate governance. Although a significant amount of work, both theoretical and empirical has been carried out on the efficacy of the PMCH, none of the research has been conclusive. Moreover, there has been no concentrated effort into determining the impact of the PMCH on the specific types of agency problems that are endemic to companies with largely concentrated shareholding structures like India. The paper highlights that a part of the reason behind the waning and unconvincing research on the PMCH (specially in India) has been the focus on competitiveness as an abstract concept or empirical construct, rather than on the regulatory aspect of competition, that is, competition law. This paper examines the connection between competition law and corporate governance, specifically the utility of competition regulation in being used to improve corporate governance in companies. It also considers the effectiveness of competition law in dispelling some of the pervasive Indian corporate governance problems such as tunneling and inefficient related party transactions. Some of the methods that are considered which would have such an impact and thus are recommended by the researcher, are a redefinition of 'dominant position' in the competition law discourse, to include within it pyramidical groups of companies as well as viewing tunneling practices as having an 'appreciable adverse effect' on competition. Doing so would result in numerous efficiencies as competition law could be used to kill two birds with one stone: increasing both competitiveness and good corporate governance. [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]

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 aims to apply a multi-criteria decision-making (MCDM) framework to evaluate the environmental performance of the medical service supply chain using a case study in India. An application of MCDM method, the fuzzy TOPSIS (Technique for order of preference by similarity to ideal solution) model, is used for Medical Support Service Provide firms (MSSPF) environmental performance evaluation. We require multiple and conflicting criteria to fix real-life decision-making challenges, which in turn led to the inception of (MCDM). However, MCDM is contracted with various contradictory requirements in which the knowledge of decision-makers is generally vague and thereby more challenging. In this situation, inclusions of fuzzy set theory in MCDM Methods would be good to solve the MCDM problem. In this study, the authors used a fuzzy TOPSIS MCDM model to solve the evaluation framework. Data were collected from the experts from the MSSPF about the more perfect criteria to assess environmental performance using a questionnaire. Case assesses the environmental performance, based on defined criteria, of three MSSPF by the experts. Fuzzy TOPSIS Method ranks three MSSPF according to environmental performance in the following order: Firm B, Firm A and Firm C. The result suggested that 'Firm B' performed superior environmental performance followed by 'Firm A' and 'Firm C'. This evaluation framework will help MSSPF to identify potential areas of further improvement in Environmental performance, and thereby creates strategies to overcome and formulate new innovations. Notably, this is one of the few studies to have used this method to gauge the environmental performance of the medical service supply chain in India. [ABSTRACT FROM AUTHOR]

In this paper, detailed petrology, mineral chemistry and geochemistry of lamprophyres from the Rongjeng–Nongchram area, Meghalaya, northeast India, is being reported. These dykes are intruded into the Proterozoic Assam Meghalaya Gneissic Complex and Neoproterozoic porphyritic granites with limited field exposures in two separate clusters controlled by lineaments. These lamprophyres are alkaline, potassic to ultra-potassic, and metaluminous and have been classified into camptonite and sannite from their petrography and geochemical affinity. Resorption texture of biotite in camptonite and compositional zoning in clinopyroxene of sannite indicate their crystallization under in-equilibrium conditions and subsequent interaction with changing composition of the magma(s). Detail geochemistry indicates fractionation as the imperative mechanism during the evolution of the magma(s) and shows heterogeneity at the mantle source and evidence for minimal crustal contamination. Various tectonic discrimination diagrams indicate the emplacement of the lamprophyres in a post-collisional and within plate setting, signifying a possible plume mechanism involved in their emplacement. The affinity of the lamprophyres towards an OIB setting was recorded from the chondrite-normalized REE and primitive mantle-normalized multielement patterns. The geochemistry of the lamprophyres points towards the presence of possible enriched metasomatic pods in the mantle, which might have melted due to the heat from the mantle plume and triggered the emplacement of the lamprophyres in the Rongjeng–Nongchram area. Research highlights: New lamprophyre bodies from Garo Hills, Meghalaya Plateau, NE India, are being reported with detailed petrology, geochemistry, and mineral chemistry. The lamprophyres are of alkaline affinity. The Kerguelen plume played a vital role in the generation of these lamprophyres. There are geochemical signals of the presence of enriched metasomatic pods in the mantle, which were melted due to plume interaction. The study also indicates a distinctive mantle source heterogeneity across the Meghalaya Plateau. [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]

Macro-level studies on intergenerational mobility among women in India suggests that, there is a need to further probe certain excluded communities that have a higher likelihood of persistence of educational status among women through micro-level studies. The present study investigates the extent and drivers of high/low intergenerational educational mobility among young women (vis-à-vis their mothers) belonging to the weavers' community of Varanasi, India. Using transition/mobility matrices and mobility measures, along with qualitative methods, the paper builds on a mixed-methods study conducted among 364 households of weavers' community from Varanasi. The qualitative data includes a total of 8 respondents with 5 KIIs and 3 IDIs. The study shows that the overall intergenerational educational mobility among women (vis-à-vis their mothers) from the weavers' community of Varanasi is 0.30 and there is no downward mobility. The qualitative findings further highlight various reasons enabling or preventing high educational mobility in the given context. In conclusion, the paper illustrates several micro-level issues and disparities in intergenerational educational mobility that are not clearly evident through the macro-level studies and further helps in making informed policy decisions. [ABSTRACT FROM AUTHOR]

The paper studies the impact of ethnocentrism and cultural intelligence on employee outcome during corporate restructuring or expansion. The authors propose a theoretical model to test this relationship. In-depth interviews of 122 professionals in Northern India who have either been a part of an international assignment or have expanded their businesses were conducted and analysed for testing the proposed model. Key findings suggest that an ethnocentric person finds it difficult to perform well at his workplace as he is constantly faced with dilemmas of cultural fitness. A culturally intelligent person easily adapts oneself to the new environment without any adverse effect on performance. To provide a smooth transition during restructuring, mangers should take measures to encourage cultural intelligence and promote ethno-relativism among the employees. [ABSTRACT FROM AUTHOR]

In this paper, I examine how an organization narratively constructs its prototypical disabled employee. Data comprise public narratives of the Government of India, the country's largest employer of disabled persons. Narratives during 2008–2016 were considered as this timespan witnessed the design of inclusive legislation that emphasized defining disabled persons and their entitlements. Findings indicate that the label of "disadvantage" was consistently used to portray the target employee. Alongside other narrative material suggesting, for example that the target employee was someone who required employment assistance, this label was supplied to external audiences to convert them into potential partners. This supply of narrative material further reinforced the portrayal of the target employee. Consistent use of this expansive label subsumed changing definitions of who is a disabled person, allowed for aggregations with diverse disadvantaged collectives, and accommodated changes in employment entitlements and ecosystem partners, thereby allowing the reading of change in stable narratives. The contributions of this paper lie in highlighting how the consistent use of an expansive label can cast narrative stability and change as complementary, and in suggesting that narrating a prototypical employee entails shaping the setting outside the employing organization toward employee categorization. [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]

In this reply to the discussion of our paper (Kumar et al. in Nat Hazards 116:2437–2455, 2023), we attempt to rebut the claims made regarding the use of the maximum sustained wind speed of a cyclone event irrespective of its location, inappropriate implementation of fitting methods, and inconsistent use of future projected sea surface temperature to historical records. While the discussion highlights certain observations about our results, the application of such techniques for evaluating PMTC parameters (adhering to AERB criteria) requires further research. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction. [ABSTRACT FROM AUTHOR]

Mathematics education in India is offered in one of the 22 officially recognized state languages or in English even though there are at least 270 languages with more than 10,000 speakers each. Caste, a deep-rooted structure that stratifies Indian society, is integrally linked to shaping state languages. There is minimal research from India that looks at language and mathematics education and practically none that factors in caste. Focusing on Tamil Nadu, a state with a history of anti-caste movement on the one hand and pure Tamil movement (a movement that sought to create a Tamil language with no words from other languages) on the other, this conceptual paper seeks to explore this dimension. More specifically, by using caste as an analytical framework, and by drawing on examples from the mathematics textbooks published by the Tamil Nadu State Board of Education and the experience of a few teachers and learners, the paper seeks to make a theoretical argument that the use of pure Tamil in mathematics textbooks has negative implication for socio-culturally and economically marginalized students who are solely dependent on textbooks as the only source for learning mathematics. There is a strong need for carrying out empirical work that would highlight the nuances and complexities involved in realizing 'mother tongue' education in mathematics, particularly for those who belong to marginalized caste-class backgrounds, and we hope that such work would emerge in the future. [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]

India's Indo-Gangetic Plain (IGP) region has become the global hotspot of air pollution, and stubble burning in northern India has increased the concern due to its impact on air quality and health. The current study focuses on studying the diurnal variation of air pollutants for the first time covering Haryana, India, and identifying the sources responsible for air pollutants emissions during the different seasons of the year. Based on ecology and cropping pattern, the study area was divided in three zones. In all seasons, the diurnal peak concentrations of particulate matter (PM2.5 and PM10) were found highest in Zone 2 as compared to Zone 1 and Zone 3, which can be due to the high polluting districts located in Zone 2 including Hisar where thermal power plants, stainless steel producing industry and pulp and paper industry are located along with district Faridabad which is known as the industrial capital of Haryana. The lowest average hourly concentration of PM10 and PM2.5 was found in late afternoon hours in winter, pre-monsoon and post-monsoon season in all three zones due to a decrease in traffic volumes which lead to low emission rates along with increase in the mixing height which cause favorable dispersion conditions. The lowest hourly mean concentration of CO, NOx and NH3 in all zones was found during the afternoon hours in all seasons. The study also focuses on understanding the day and time patterns and the maxima and minima of pollutants concentrations in different zones of Haryana. [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]

This paper mainly aims to identify the key factors that should be included in building of smart cities in a proper efficient way. Due to rapid increase in urban population, it should be responsible to provide a better way in building of a smart city. Smart city is an idea of providing urban development that includes social, financial, ecological parts of a society. A smart and sustainable city has goals to be achieved in an adaptable, reliable, scalable, accessible, and in resilient way which will be discussed in the methodology section. This paper mainly focuses on some important factors in improving sustainability of smart cities such as climate and environmental issues and governance authorities for improving quality of life of citizens. Also, this paper explains 3E's which are environment, economy and equity are plays a crucial role in rapid development of smart cities. This research is focused on discussing various smart technologies recently used in India and the challenges involved in them. Recent literature on smart city development is reviewed, and the challenges that prevailed in recent technologies are analysed. This paper includes how Internet of Things (IoT), Connectivity, Cloud computing and AI etc., should be effectively used for developing a smart city. This paper deals with the requirements to build smart cities in India – regarding various aspects like available resources, lifestyle, transport management system, smart healthcare system, smart waste management, smart Sanitation, smart railways, smart agriculture, home automation, smart energy consumption etc. This paper also discusses 'smart living,"smart infrastructure,"smart governance,"smart economy,' and other various systems which are need for Smart India in particular. When adopted with proper planning, all the above catalysing with smart services and solutions, build a sustainable city. [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]

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]

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]

The outcome of economic growth is visualised as the well-being of citizens or human well-being (HWB). However, it has been a great challenge to measure HWB. Though there are known reasons for considering GDP and its growth as a measure of overall development and progress of nations, yet mostly it is being used as a gospel indicator to compare nations and design appropriate policies. This paper is an effort to develop a comprehensive adjusted GDP to measure HWB through secondary data for thirty years (1990–91 to 2019–20) in India. We make thirty-five adjustments to net national income (NNI) to compute the adjusted national income (ANI) index based on the system analysis approach. The empirical findings show that the gap between NNI and ANI has been growing over time, and the ANI index shows an increasing trend. Through the analysis it is suggested that economic growth should be focused only if it improves HWB (full or partial). The paper attempts to make intervention into policy shift for improving HWB vis-à-vis happiness of people. [ABSTRACT FROM AUTHOR]

Socioeconomic situations in young people's families and communities make them more vulnerable to suicidal ideations. The Youth Helpline has made a significant difference in youth's lives and livelihoods by addressing their social and economic needs with positive mental health support. The quantitative and qualitative data validate the micro- and macro-level impact of the Youth Helpline in Puducherry. However, the multi-stakeholder model faces challenges in ensuring accountability, particularly from government partners. This paper highlights how direct intervention on the socio-economic determinants of youth suicidal ideation has positively affected youth mental health and reduced suicidal ideation in Puducherry. Social identity-based inequalities and access and affordability to mental health services are the major contributors to youth mental health problems. This paper critically discusses the Youth Helpline's multi-stakeholder process and youth-led approach to addressing socio-economic determinants, as well as the Helpline's impact on youth mental health. [ABSTRACT FROM AUTHOR]

In response to the beginnings of the COVID-19 pandemic, India's government instituted a severe nationwide lockdown in late March 2020. In this paper, we analyze household survey data on income from an Indian state, Punjab. We analyze the impacts of the pandemic and lockdown on total household income, as well as its components, including wages, business income and transfers. This paper innovates in this area by using Poisson Pseudo Maximum Likelihood estimation to deal with the skewed distributions of various income components, as well as quantile regressions to examine differential impacts across the income distribution. We examine the differences between rural and urban households, differential impacts on female-headed and laborer households, and the time pattern of impacts. We find that rural households experienced more persistent income declines than urban households, and their wages suffered relatively more. Households with daily laborers were most badly affected, and this was especially true at the bottom of the income distribution. [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]