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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]

In today's scenario of India most of the youth is preparing for some competitive exam. If we think behind 15–20 years the number of candidates appearing for competitive exams were in thousands but as the population is increasing exponentially in India day by day the number of candidates is increasing in lacks. We can observe by daily newspapers that most of the competitive exams are facing paper leak problems. Although online examination systems are more effective and secure as compared to offline examination systems because it's not easy to open the question paper before the time starts. On the other hand there are also various consequences where the examination process can be hacked online. But the main issue with online examination process is to lack of resources to conduct parallel examination of millions of candidates, lack of techno enabled exam centers in small cities etc. Our focus is to propose a system in which offline examination can be conducted at most of the govt. and private centers in metro cities as well as small techno backward cities with reduced possibility to leak the paper before commencement of examination. [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]

While promoting manufacturing growth is imperative to boost economic growth, potential adverse consequences on natural resources and ecology have created sustainability-related concerns and challenges. In India, with the reform processes deepening further and the business environment becoming more market-oriented over the years, the adoption of appropriate measures for sustainable manufacturing growth under such conditions would require a deeper investigation of the underlying dynamics, particularly to address the impacts of external shocks. This is especially true when external shocks alter market dynamics and, hence, input is used significantly in the manufacturing sector. Given this backdrop, the present paper examines how the major external shocks to the Indian economy during in the post-reform era have altered the resource use patterns in selected manufacturing industries. The paper uses secondary data collected from the KLEMS database of the Reserve Bank of India for the period from 1980–81 to 2019–20 and applies descriptive statistics and econometric techniques to address the research objective. It is found that energy intensity, vertical integration, capital intensity, and rate of growth of total factor productivity decreased over the decades, whereas labour productivity experienced an increasing trend in most industries. However, the trends are mixed in the case of material intensity. Further, the regression results indicate significant effects of external shocks on resource use, factor productivity, and structural changes. There are also industry-specific differences in the nature and extent of such changes. Hence, a deeper investigation of the underlying factors and impacts at a disaggregate industry level is necessary to draw more robust insights. [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]

This paper presents a comprehensive analysis of India's Citizenship Amendment Act (CAA) of 2019, focusing on its origins, objectives, and far-reaching consequences, particularly concerning its alignment with the constitutional principles of secularism and equality. The CAA facilitates the acquisition of Indian citizenship for persecuted religious minorities from Afghanistan, Bangladesh, and Pakistan, explicitly excluding Muslims. This has sparked national and international debate over its potential to undermine the secular fabric of the Indian Constitution, particularly concerning Article 14, which guarantees equality before the law. Through a legal and constitutional lens, the paper critically examines the broader implications of the CAA on religious minorities, its intersection with the National Register of Citizens (NRC), and concerns about statelessness. Drawing on scholarly critiques, it explores the Act's impact on India's inclusive identity, the potential marginalization of minority communities, and the socio-political challenges posed by its implementation. The paper advocates for re-evaluating citizenship laws to align with India's democratic principles, ensuring social cohesion and protection of human rights. [ABSTRACT FROM AUTHOR]

This paper explores the historical context and current dynamics of the transboundary water issues between India and China. Both countries share multiple transboundary river basins, with the Brahmaputra River being one of the most significant. The importance of the Brahmaputra River to both nations goes beyond just providing resources. It has implications for national security, regional stability, and human livelihoods. However, China's upstream control over the river and its extensive dam-building projects have raised concerns in downstream India and Bangladesh about potential impacts on water availability, agriculture, and livelihoods. The river system is critical for the socioeconomic well-being of millions of people, yet the competing demands for water resources have led to geopolitical tensions. The paper highlights China's strategic use of water resources, including plans for large-scale dams, which India views as a potential threat to its water security. It also underscores the importance of a nuanced understanding of hydropolitics, emphasizing the need for human security, particularly water security, in addressing these challenges. It argues that while "water wars" are unlikely, "water tensions" are significant and must be addressed through comprehensive bilateral agreements and collaborative efforts. [ABSTRACT FROM AUTHOR]

IRNSS/NavIC is an independent navigation system developed by India. The dependence on external satellite-based navigation systems is gradually decreasing as a result of the deployment of our own navigation system, IRNSS/NavIC, which exhibits enhanced reliability. In this paper, a software-based IRNSS/NavIC receiver is developed using the standard Signal-In-Space Interface Control Document as a reference for signal structure, subframe patterns and parameter calculations. The IRNSS/NavIC receiver is validated with the I/Q data file as input which is generated by software based IRNSS/NavIC simulator. Analysis of the effect of a specific error on receiver output position necessitates repeated trials and modification, or in some cases, the development of new algorithms. The intermediate stage data can be viewed or stored as needed; a few examples are provided. In this paper Squared Correlators Code Discriminator (SCCD), a new multipath mitigation method has been designed and developed for IRNSS/NavIC receiver. The SCCD method is derived from Strobe Correlator (SC) and Combination of squared correlator method (CSC). The pseudorange bias is investigated and eliminated from the SCCD approach. The performance parameters of the IRNSS/NavIC receiver using each of the four approaches are compared, and the outcomes are discussed. [ABSTRACT FROM AUTHOR]

This paper empirically examines the effect of domestic monetary policy shock on external commercial borrowings (ECBs) to India for the period 2000:Q2 to 2019:Q4. The impact of domestic monetary policy shock on ECBs is assessed using two variables: interest rate differential and domestic money supply growth. The paper employs a structural VAR model and utilizes impulse response functions and forecast error variance decomposition analysis to derive empirical results. The empirical results based on impulse response functions indicate that the interest rate differential has a positively significant effect on external commercial borrowings to India, while the effect of domestic money supply growth is found to be negative. The forecast error variance decomposition analysis indicates that the interest rate differential explains about 14.6% and domestic money supply growth explains about 2% of the total variation in external commercial borrowings, implying that domestic monetary policy variables together explain about 16.6% of the total variation in ECBs to India. The results suggest that the central bank, through monetary policy interventions, can influence and manage the flow of external commercial borrowings to India. Furthermore, the study finds that the exchange rate, domestic industrial activity, domestic output growth, domestic country's creditworthiness, and domestic macroeconomic instability are other significant determinants of external commercial borrowings. [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]

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]

Sustainable development is essential for the survival of the planet. The massive increase in GHG emissions due to human activities has put the world in unrest. The Indian construction industry is emerging and evolving at a rapid rate and has understood the grave situation, and for saving the environment has come up with innovative solutions for developing green buildings or sustainable construction. In this paper, the two different (the GRIHA Rating System and the IGBC Green New Building Rating System) of India were studied. The sample size was 40 construction professionals working on green building projects. An interview schedule was used for data collection. The data was subjected to a suitable analysis and the AHP method was applied. Based on the research findings, strategies to go green has been suggested for the IGBC and GRIHA rating system which may serve as a ready reckoner for enhancing the effectiveness and efficiency of the construction management process. Therefore, keeping in mind the above view this research paper "Review of Indian Green Building Rating Systems by Using Analytical Hierarchy Process (AHP) on Various Parameters" becomes indispensable. Hence, this research study can be considered an effort to empower construction professionals to create an extensive analysis of green building concepts and develop sustainably in India. [ABSTRACT FROM AUTHOR]

All over the world, waste and its disposal are becoming a more prominent issue, as landfills swell up and growing evidence points to its contribution to climate change and environmental degradation. In the developing world, waste pickers sort through solid waste and sell it to vendors, who will recycle the product into something consumable. The work they do is vital to climate change mitigation, as well as urban cleanliness. In a way, waste pickers, especially in India, are vehicles of environmental justice, but they are often discriminated against and subjected to inhumane working conditions. In this environment, the pandemic hit, making waste pickers' already precarious life even worse. This paper attempts to fill the gap in the academic literature by compiling a systemized literature review of the impacts of COVID-19 on waste-pickers' lives using an environmental justice perspective. The review also uses a Feminist Political Ecology perspective to illuminate the double jeopardy that female waste pickers face in the era of COVID-19, which academia often ignores. Ultimately, this paper shows that COVID-19 exacerbated the preexisting injustices that the waste picker community face and created an immense burden on women. This paper will show that it is important to notice the differentiation between female and male waste picker challenges, especially at the crux of the COVID-19 pandemic. This paper has important policy implications as well as research considerations for both environmental justice and feminist political ecology scholars. [ABSTRACT FROM AUTHOR]

Medical anthropologists have not paid enough attention to the variation at the level of the individual practitioners of biomedicine, and anthropological critiques of biomedical psychiatry as it is practiced in settings outside the Global North have tended to depict psychiatrists in monolithic terms. In this article, we attempt to demonstrate that, at least in the case of India, some psychiatrists perceive limitations in the biomedical model and the cultural assumptions behind biomedical practices and ideologies. This paper focuses on three practitioners who supplement their own practices with local and alternative healing modalities derived from South Asian psychologies, philosophies, systems of medicine and religious and ritual practices. The diverging psychiatric practices in this paper represent a rough continuum. They range from a bold and confident psychiatrist who uses various techniques including ritual healing to another who yearns to incorporate more Indian philosophy and psychology in psychiatric practice and encourages students of ayurvedic medicine to more fully embrace the science they are learning to a less proactive psychiatrist who does not describe a desire to change his practice but who is respectful and accepting of ayurvedic treatments that some patients also undergo. Rather than simply applying a hegemonic biomedical psychiatry, these psychiatrists offer the possibility of a more locally-attuned, context sensitive psychiatric practice. [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]

The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learning algorithms is chosen for air pollution and water pollution cases. The purpose of this review is to reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose. [ABSTRACT FROM AUTHOR]

India's evolving digital health strategy leverages innovative technologies to enhance access to healthcare services. This paper explores the key components of India's digital health transformation, including the Ayushman Bharat Digital Mission (ABDM) and India's integration of biometric identification and digital infrastructure to improve healthcare delivery. The lessons learned from India's large-scale implementation of digital health provide valuable insights for global health markets and digital transformations in healthcare systems. [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]

Pines play a significant role in forest biodiversity globally and generate huge forest litter. Dry pine needles due to low ignition temperature and high frictional force with the ground catch fire quickly. Annual forest fires in the northern states of India greatly impact the Indian economy besides causing huge loss to biodiversity, livelihood, and environment. Pine needles are also considered unfit for fodder consumption due to presence of tannins. Although the presence of softwood lignin in pine needles makes it difficult to degrade easily, the presence of holocellulose (68.5%) containing 45–51% cellulose makes this biomass a potential substrate to be used in pulp-making industries for low-grade paper sheets. The good fiber length of pine needles (1.3–1.4 mm) with a diameter of 30–32 μm, maybe considered important property for paper making. The use of pine needles in the pharmaceutical and food industries are due to the presence of secondary metabolites (α-pinene, β-pinene, caryophyllene etc.). The various other potential applications of pine needles are for producing bio-ethanol (yield, 3.98%; purity, 94%), biogas (yield, 23.1 L kg−1), smokeless briquettes (calorific value, 18.77 MJ kg−1), biochar (calorific value, 25.6 MJ kg−1), bio-composites (tensile strength, 21–60 MPa), and bio-pesticides. This paper comprehensively reviews the current applications of pine needles along with its future prospective applications that can have the dual advantage of providing employment opportunities to the people along with environmental protection. [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]

Self-Help Groups (SHGs), the credit based voluntary groups in India are widely known for their potential to alleviate poverty and empower women. This research article explores a lesser-known theme. It tries to analyze the possibility of utilizing the current SHG model of the Indian Government to build and strengthen resilience of the SHG members in case of an external shock like financial stress, food insecurity, dispute in home for joining SHG, and difficulty to repay loan amidst lockdown due to the COVID-19 pandemic. This article also focuses on their coping mechanisms and attitude towards these crises. By utilizing primary data collected from 320 SHG members in one of the aspirational districts in West Bengal, India, the present paper tries to fulfill its objective. It provides a comprehensive understanding of the problems faced by the SHG members by combining both quantitative and qualitative techniques. The findings reveal that from organizing vocational trainings to relaxing loan repayment, SHGs and its women members overcame various adversities with tenacity, especially during the COVID-19 pandemic. Based on the study findings, it is reasonable to conclude that the current SHG model in India presents a promising avenue for enhancing the resilience of its members in the face of external shocks. Article Highlights: Idiosyncratic stress like financial stress and systemic stress like the COVID-19 pandemic were ranked the top two crises faced by the SHG members. Recognizing financial stress and stress due to the COVID-19 pandemic as the top challenges faced by SHG members implies the need for immediate support (that includes access to financial resources, and assistance in adapting livelihood strategies to withstand external shocks), resilience-building efforts (by providing members with skills to diversify their income sources, creating emergency funds, and fostering community solidarity to collectively address crises), and policy attention (implementing policies that provide targeted assistance, such as social safety nets, healthcare access, and economic stimulus). The SHG members mostly had an active attitude towards the crises faced demonstrating their remarkable resilience and adaptive capacity. This highlights the most dominant role of SHGs in their ability to foster a sense of solidarity and collective problem-solving. Members often share experiences, skills, and resources, which can be invaluable during times of crisis. Additionally, the group dynamic encourages mutual support and encourages members to take proactive steps to address challenges rather than feeling overwhelmed by them. Moreover, participating in SHGs often empowers individuals to develop skills, build confidence, and access resources that enable them to better cope with crises. Thus, it implies the importance of community-based approaches like the SHGs to resilience-building. The primary role of SHGs in strengthening resilience among its members, particularly during challenging times like the COVID-19 pandemic was preventive measures (organizing COVID awareness programs), followed by promotional measures (organizing vocational trainings) and protective measures (distribution of food grains). By engaging in these preventive, promotional, and protective measures, SHGs bolstered the resilience of their members by equipping them with knowledge, skills, and resources to navigate challenges effectively. Overall, the implication is that SHGs have played a multifaceted role in supporting their members through the COVID-19 crisis, by focusing on prevention, promotion, and protection measures to enhance their resilience and mitigate the adverse effects of the pandemic on their well-being. [ABSTRACT FROM AUTHOR]

The Cauvery Basin is one of the prolific hydrocarbon-producing basins of southern India. The pericratonic basin has five sub-basins separated by five basement and structural highs formed by granitic and gneissic rocks of the Archaean Southern Granulite Terrane. The sub-basins have excellent Cretaceous source rocks and hydrocarbon-bearing reservoirs of different geological ages. Commercial hydrocarbons are produced from the Archean basement highs, specifically from the Kumbakonam–Madanam palaeo-highs of the Cauvery basin. The paper addresses basement characterisation using conventional as well as advanced well logs for accurate characterisation of the basement reservoirs. Basement reservoirs are challenging in terms of sporadic porosity and permeability distribution. Most of the porosities and permeability are attributed to secondary generation by fracturing and weathering. The present work aims to understand and compare the petrophysical attributes of two kinds of basement reservoirs (fractured basement and weathered basement) on the Kumbakonam–Madanam High. Two wells from each type lying on either side of the Madanam High were selected. Well log data, including gamma-ray, resistivity, porosity (neutron and bulk density), acoustic logs and advanced logs, such as resistivity images and dipole acoustic and elemental capture spectroscopy logs, from these four wells were analysed for their petrophysical understanding. Furthermore, image and acoustic log data were integrated to characterise the fracture geometry and fracture permeability of the basement reservoirs. Conventional log suites showing low GR, low resistivity, high density, and high neutron porosity are indicative of intricate lithologies, possibly mafic rocks. A notable negative crossover in density and neutron log along with excess Si concentration indicates weathering. A higher fracture density with a crisscross fracture/mesh fracture pattern is indicated by the analysis of image logs, shear wave anisotropy, and Stoneley fractures. Thus, a comparison of the petrophysical attributes of both fields is attempted to understand the fractured and weathered basement reservoirs and their geological characteristics. In conclusion, the basement reservoirs of the Madanam High Field are of interest because of their hydrocarbon-producing ability, and proper synthesis of petrophysical attributes will help develop activities in the basement reservoirs. [ABSTRACT FROM AUTHOR]

In an area like India, where a large number of people are dependent upon agriculture, soil erosion assumes the role of a silent killer. A comprehensive soil management plan requires systematic development and planning at the scale of the natural boundaries, i.e., the watersheds. These watersheds are now actively encouraged to become the planning unit for a wide range of natural resource management. Sub-basin prioritisation with respect to erosion susceptibility, therefore, has become increasingly popular in the last few decades as this helps in optimal distribution, especially in a country like India, where the availability of resources is scarce. Erosion is a complex problem wherein a large number of factors play an important role in influencing this process. Therefore, multi-criteria decision-making (MCDM) techniques are becoming more popular in this domain. However, there are a myriad of MCDM models in literature, and a proper and systematic comparison between such MCDM models may help in deciding on the best possible model in the given categories. This paper has tried to make such a comparison for morphometry-based sub-basin prioritisation in the rocky terrain of the Chaka River Basin, Eastern India. The MCDM models that have been investigated include technique for order of preference by similarity to ideal solution (TOPSIS), VlseKriterijumsk Optimizacija Kompromismo Resenje (VIKOR), multi-objective optimisation on the basis of ratio analysis (MOORA), compound factor (CF), weighted sum model (WSM), weighted product model (WPM) and weighted aggregated sum product assessment (WASPAS). These results were compared with respect to the RUSLE-based annual soil loss of the Chaka Basin with the help of the ROC curve. Results reveal that the MOORA MCDM method is the most reliable (AUC = 0.711), whereas the TOPSIS is the least authentic (AUC = 0.378). The middle domain of the Chaka Basin appears to be highly prioritised as compared to the lower domain. [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 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]

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]

Traditional multiple discrete–continuous (MDC) choice models impose tight linkages between consumers' discrete choice and the continuous consumption decisions due to the use of a single utility parameter driving both the decision to choose and the extent of choice. Recently, Bhat (Trans Res Part B Methodol 110:261–279, 2018) proposed a flexible MDCEV model that employs a utility function with separate parameters to determine the discrete choice and continuous consumption values. However, the flexible MDCEV model assumes an independent and identically distributed (IID) error structure across the discrete and continuous baseline utilities. In this paper, we formulate a flexible non-IID multiple discrete–continuous probit (MDCP) model that employs a multivariate normal stochastic distribution to allow for a more general variance–covariance structure. In doing so, we revisit Bhat (Trans Res Part B: Methodol 109: 238-256, 2018) flexible utility functional form and highlight that the stochastic conditions he used to derive the likelihood function are not always consistent with utility maximization. We offer an alternate interpretation of the model as representing a two-step decision-making process, where the consumers first decide which goods to choose and then decide the extent of allocation to each good. We demonstrate an application of the proposed flexible MDCP model to analyze households' expenditure patterns on their domestic tourism trips in India. Our results indicate that, if the analyst is willing to compromise on the strict utility-maximizing aspect of behavior, while also enriching the behavioral dimension through the relaxation of the tie between the discrete and continuous consumption decisions, the preferred model would be the flexible non-IID MDCP model. On the other hand, if the analyst wants the model to be strictly grounded on utility-maximizing behavior (which may also have benefits by way of welfare measure computations), and is willing to assume a very tight tie between the discrete and continuous consumption decision processes, the preferred model would be the non-IID traditional MDCP model. [ABSTRACT FROM AUTHOR]

India houses the third biggest Muslim population in the world but over the years it has experienced massive segregation of people based on their religious identities. This large-scale forced and systemic 'ghettoization' of Muslims is a recent trend largely ascribed to the frequent communal' violence and state's negligence towards minorities. In this backdrop, the paper discusses the communal ghettoization of Muslims and uses the Juhapura area of Ahmedabad as illustration to explain the same. This study is essentially based on field observations and literature to comprehend communal tone of ghettoization process. Muslims in India are a minority (15 percent of total population) and suffer most in case riots occur. In the last few decades, following the 1985, 1992 and 2002 riots, Gujarat emerged as a hotbed of communal tension, forcing the Muslim population to migrate from their original locations and seek refuge in other parts of the state. Residents of these newly populated ghettos such as Juhapura are forced to endure these conditions due to fear, lack of choice and state apathy. These localities are largely ignored and lack all forms of socio-economic and physical infrastructure for a decent living. [ABSTRACT FROM AUTHOR]

The present paper aimed to assess the sediment distribution pattern, mode of transport, and its interaction with hydrodynamic and topographic conditions at different depths and regions along the east coast of India. About 900 surficial sediment samples were collected and analysed on a monthly basis for the Chennai coastal region at 32 stations from 2013 to 2015. The study region is classified into four types, such as beach, inlet, 5 m, and 10 m depth. Sediment textural and grain size trend analyses were conducted to achieve the objectives. Sediment characteristics for the region were recorded as sandy, equally dominated by unimodal and bimodal at the beach, while unimodal at shallow depths (5 and 15 m). The sediments were medium sand to coarse sand at the beach, mostly fine followed by medium at 5 and 15 m depths. The sediment sorting is dominated by moderately well-sorted sediments; the skewness of beach sediments was negative, while nearshore sediments were found positive; average kurtosis values of sediments were noticed to be mesokurtic. The CM plot depicts that the sediments were mostly derived by tractive current, and the modes of transport are "bottom suspension and rolling" and "graded suspension no rolling" at beach locations and shallow water depths, respectively. The GSTA analysis reveals the annual average sediment transport pattern is northerly. The numerical hydrodynamic study confirms the GSTA and CM plot analysis. The study reveals a stable sedimentary environment south of the Chennai port and instability in the northern part. The study includes large spatiotemporal nearshore sediment data with hydrodynamic conditions, immensely helpful to coastal stakeholders and researchers. [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers. [ABSTRACT FROM AUTHOR]

Forest fire is a major disturbance in mountainous ecosystems across the world. It impacts the composition, functioning, and structure of the forest ecosystem. Demographic pressure and the related climatic factors are thought to exacerbate the danger of forest fires, although there is a lack of statistical data to support this, particularly in South Asian countries with limited historical records. The study looks at how forest fires interact with their causative variables, such as population, topography, vegetation, and climate regimes, in the forests of Northern India. In order to do so, the paper presents a 15-year trend analysis of forest burn scars. This investigation confirmed the utility of principal component analysis using multisource spatial data and correlation matrices. The findings reveal that the occurrence and intensity of forest fires in mountainous locations are influenced by anthropogenic activities, forests near roads and settlements are extremely vulnerable. Based on the analysis, we emphasize the need for restructuring the existing policies like National Action Plan for forest fires and forest fire prevention and management scheme into more practical science-based policies. These findings may be utilized in planning effective fire control processes in time, and the technique provided in this study may be used to discover potential fire-risk zones in other areas. [ABSTRACT FROM AUTHOR]

In this paper, we address the effects of two fiscal policy delays on the dynamical analysis of macroeconomics. First, a time gap between the accrual of taxes and their payment is considered. Second, the time spent between the purchasing decisions and the actual expenditure is also taken into consideration. Since both these delays are significant in controlling macroeconomic conditions, this paper incorporates aforementioned delays into the IS–LM model. At first, a mathematical model is developed using delayed differential equations. Then a unique steady state solution is obtained. Around the equilibrium point, linear stability analysis is done. Also, the occurance of Hopf bifurcation is observed when delay crosses a critical point and switches in stability are also detected. Properties of Hopf bifurcation using center manifold theorem are discussed. Lastly, numerical simulations are run to verify our analysis. In this work, we considered a case study to perform simulation wherein GDP of India for last ten years is recorded for estimating some parameters. In different investment scenarios, numerical simulations corroborate the analytical findings of the model. Furthermore, rigorous analysis shows that adding the right mix of delays can help in maintaining/ regaining the stability after periods of instability, or even gaining stability in the long run. [ABSTRACT FROM AUTHOR]

The plan of the paper is to study autism spectrum disorder (ASD) in children as complex conditions that affect cognitive, emotional, social, and physical health. Broadly tracing the medical journey of the condition, it addresses the challenges faced by parents as they make sense of, and navigate their way through the appearance of early symptoms, impediments in correct diagnosis, acceptance, and adapt to live with the condition. The paper attempts to understand autism in medical and neurological terms, as well as the challenges faced by children and parents through the case study of Kyari (meaning a flower bed), a lone centre providing accessible autism care in about 150 km around Moradabad (west UP, India), which has been instrumental in raising an awareness of autism and has a reach that is unprecedented in the city in bringing sensitive autism intervention. It further comprehensively studies the symptoms of ASD, and the role of augmentative and alternative communication, behaviour modification, and the work of organisations like the Centre for Autism, CBSE mandate for inclusive education as applied in the case and Indian government policy for autism. [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]

This paper reports the concentrations of uranium and heavy metals in drinking water samples from Garhwal Himalaya, India determined using LED Fluorimeter and Atomic Absorption Spectroscopy (AAS), respectively. The radiological (carcinogenic) and chemical (non-carcinogenic) risks associated with the consumption of uranium in water were estimated for the public. The concentrations of natural uranium in the analyzed drinking water samples vary from 0 to 21.57 ppb with a mean value of 1.54 ppb. The average concentrations of heavy metals in the analyzed drinking water samples were found to be in the order of iron (3.99 ppm) > zinc (0.32 ppm) > copper (0.04 ppm) > nickel (0.01 ppm) > chromium and lead (Below Detection Level). The estimated risk quantities were found to be lower than World Health Organization (WHO) and United State Environmental Protection Agency (USEPA) guideline values. [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]

Rice milling industries are seen in large numbers in the eastern and southern parts of India. Like the other industries, this is also going through changes because of the introduction of automation using machine vision technologies. In the consumer market, buyers consider that a good quality rice bag must not contain broken kernels. Considering this commercial aspect, during rice production, the estimation of broken rice kernels mixed with whole rice kernels is an essential grading task in the husking mills. In this paper, we propose a method for monitoring the presence of broken rice kernels in the rice outputs during milling. In our proposed method, during milling, rice kernel samples are captured, and image understanding-based processing is applied to realize broken and whole rice kernels. A whole-grain kernel is distinguishable from a broken-grain kernel in terms of its size, perimeter length, etc. But many times, several kernels get attached to one another during image capturing. These attached kernel groups need to be separated into single kernels so that the shape and size information of kernels may be used to estimate the amount of presence of broken rice kernels. Hence, the proposed method works in two stages. In the first stage, attached rice kernels are accurately separated using geometric analysis of the boundary edges of the attached kernel groups, and then, in the second stage, all individual kernels are clustered into either broken or whole kernel categories based on their shape and size information. The proposed method identifies the broken kernels with accuracy of 98.25 % , 96.54 % , and 98.52 % for Shamashri, Lalswarno, and Basmati rice kernel respectively. Finally, we present statistics of the estimation depicting the percentage of broken and whole rice grains in the sample images. Our test results indicate the effectiveness and applicability of the proposed method. [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]

Numerous studies emphasize accuracy in machine learning regression models, yet scalability and execution efficiency are often overlooked, critical for large datasets or extensive computations. This paper introduces a scalable, distributed Spark MLlib regression model through the best subset selection approach to predict Covid-19 statistics in India, demonstrating high accuracy, scalability, and execution efficiency. Notably, limited research focuses on tree-based regression, particularly gradient boost regression, in the context of the Covid-19 dataset. The proposed work optimizes regression models for accuracy and execution time on Spark clusters of varying sizes using the best subset selection approach. Evaluation encompasses Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R2 Error for accuracy, and execution time analysis. Results indicate superior prediction accuracy in tree-based regression, with Gradient Boosted Tree Regression (GBTR) leading, and Random Forest Regression (RFR) surpassing Decision Tree Regression (DTR). Accuracy remains consistent across Python library, Spark MLlib on a single machine, and clusters of varying sizes, with Spark MLlib displaying lower execution times than Python's machine learning library on a single machine. Furthermore, execution times decrease substantially within Spark clusters, particularly for the iterative GBTR. This research uncovers scalability and execution efficiency aspects, highlighting tree-based regression's accuracy and advocating for Spark MLlib's efficacy in enhancing execution efficiency, especially across multi-node clusters. [ABSTRACT FROM AUTHOR]

As per the increasing demand for new sustainable materials and methodologies, green synthesis of metal oxide nanoparticles has drawn the attention of many researchers. However, compared to certain other metal oxides, there has been comparatively less attention towards green synthesis of Ruthenium oxide Nanoparticles (RuNPs). In this paper, RuNPs were synthesized using plant extract of Iris Kashmiriana (found in the high-altitude region of Kashmir in India) and characterized by FT-IR, SEM, EDX, and XRD. Particles were synthesized in the dimensions of 25–30 nm. FT-IR peaks in this 3348 cm-1 infrared spectrum range confirmed Ru-O symmetric stretching while crystalline structure was confirmed by X-ray diffraction. By displaying a zone of inhibition for erythromycin, the synthesized ruthenium oxide nanoparticles exhibit potent antibacterial action against four different pathogens, including Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Streptococcus pneumoniae. [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]

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]

This paper proposed modeling, designing and battery management system for a three-wheeled electric vehicle for the Indian road under different conditions. The system consists of a lithium nickel manganese cobalt oxide cell-based lithium-ion battery with a managing system known as battery management system (BMS) described here. The process for modeling the vehicle, choosing the motor, developing the controller with a programmable circuit and developing the DC–DC buck converter, and battery pack of the lithium-ion cell with an effective BMS is discussed. According to the study, a prototype programmable controller circuit board, including the PWM-controlled driver circuit, is built for the proper flow of electricity on urban roads in India and has been tested successfully on road. This developed electric prototype travels the range of 100 km with an average speed of 32 km/h, while the maximum current drawn from the battery is 30_amp. The efficiency increased by 2% compared to the market-available PWM square wave controller. MATLAB simulation is done to compare the effectiveness of the modeled system with the suggested prototype control system. To verify the system, a small-scale prototype is developed. [ABSTRACT FROM AUTHOR]