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

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

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]

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

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

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

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

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

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]

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

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

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

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

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

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

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

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

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

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

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

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

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

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]

Success of economic sanctions hinges on their impact on sanctioned countries' trade. This, in turn, depends on the sanctioned country's opportunity to divert trade to a third party (a country not involved in sanctions). History is witness to third parties facilitating trade diversion, thus busting sanction. Nonetheless, literature does not present conclusive evidence on trade diversion or on motivation for busting sanctions. Therefore, in this paper, we address the following: What bearing do sanctions have on bilateral trade flows and trade diversion? Is diversion dependent on the political and trade alliance third-party shares with the sanctioned and/or the sanctioning countries? We estimate a structural gravity model for globally representative country-dyads, during 1990–2019, using inter-alia the Global Sanctions Database. We find that sanctions depress bilateral trade between sanctioned and sanctioning nations and cause trade diversion via third party. The existence of trade alliance between third party and country involved in sanction has an additional impact on trade diversion. Furthermore, a political alliance between third party and sanctioned country heightens trade between them. However, political alliance between third party and sanctioning country does not explain trade between them. Our findings offer insights into India's trade relations with Russia, since 2022, when Russia was subject to US-led sanctions. [ABSTRACT FROM AUTHOR]

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF)), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC(2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape. [ABSTRACT FROM AUTHOR]

Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic induced hazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India. [ABSTRACT FROM AUTHOR]

Historically, India has been a great investment opportunity for foreigners. However, in the last several decades, the country has experienced a substantial increase in its foreign direct investment (FDI) owing to several reforms made by the government. This study analyzes the determining factors behind Indian FDI inflows from its top source countries using the augmented gravity model. The sample period of this study ranges from 2000 to 2019, thus providing an updated analysis regarding FDI inflows. To attain the objective of this paper, we employ several econometric techniques such as Poisson pseudo maximum likelihood (PPML), feasible generalized least square (FGLS), and Newey-West standard error models. The findings show that the source country's per capita GDP is a negative determinant of FDI inflows in India from selected countries of the world. Moreover, FDI openness, gross fixed capital formation, and exports are found as the positive determinants of FDI inflows in India. The results imply that more export-oriented sectors can be identified for the selected nations, encouraging inflows. The country should look forward to incorporating new elements in old bilateral investment treaties as per the new conditions of the world economy. [ABSTRACT FROM AUTHOR]

India introduced competency-based medical education (CBME) in the year 2019. There is often confusion between terms like ability, skill, and competency. The provided curriculum encourages teaching and assessing skills rather than competencies. Though competency includes skill, it is more than a mere skill, and ignoring the other aspects like communication, ethics, and professionalism can compromise the teaching of competencies as well as their intended benefits to the patient and the society. The focus on skills also undermines the assessment of relevant knowledge. This paper clarifies the differences between ability, skill, and competency, and re-emphasizes the role of relevant knowledge and its assessment throughout clinical training. It is also emphasized that competency assessment is not a one-shot process; rather, it must be a longitudinal process where the assessment should bring out the achievement level of the student. Many of the components of competencies are not assessable by purely objective methods and there is a need to use expert subjective judgments, especially for the formative and classroom assessments. A mentor adds to the success of a competency-based curriculum. [ABSTRACT FROM AUTHOR]

Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future. [ABSTRACT FROM AUTHOR]

The present study majorly focused on the degree of rainfall alteration (DIRA) using an indicator-based novel approach. Most of the previous works majorly focused on only magnitude parameters for measuring monthly rainfall change; however, alteration may also come from other relevant aspects like frequency, timing, rate of change, and duration of rainfall. The present paper considered 33 indicators to assess alterations in rainfall, encompassing various such aspects. Subsequently, it computed the degrees of rainfall alteration categorized as low, moderate, and high range of variability (RVA). This computation was based on the gridded rainfall data endorsed by the Indian Meteorological Department (IMD). Getis-ord was applied to compute the significance of rainfall patterns. The study revealed that middle RVA-based DIRA is low (< 0.33) in the case of magnitude indicators across IMD-defined meteorological sub-divisions, but moderate DIRA (0.33–0.67) was detected in some parts in the case of rate of change, frequency, duration, and timing indicators. This change was detected rather high (moderate to high) over wider parts of India (six and sixteen sub-divisions) in the case of low and high DIRA. In monsoon and post-monsoon seasons, north-eastern hilly hot spots (95–99% level) were weakened, while it was found enhanced in north-western India. A new rainfall hot spot (95%) was identified in Odisha, Telangana, and Vidarbha transition in 1991–2021. Taking into account the notable success achieved through the IRA approach, the study strongly recommends its application across various related domains. [ABSTRACT FROM AUTHOR]

The ABT system holds power producers accountable for stabilizing the grid, preventing over-drawl and under-drawl of power. Before the introduction of ABT, frequent over-drawl and under-drawl beyond the limits caused grid disturbances. It results in fewer generators getting tripped due to the wide variation range in the grid frequency and leads to power shortages for large-scale consumers. CERC in India introduced DSM to penalize over-drawing and under-drawing in power buying/selling schedules. Staying within limits earns incentives tied to grid frequency also preventing producers from charging higher rates for exploiting the lower frequency range. This paper delves into the seamless integration of ABT with India's regional and national grids. Furthermore, we meticulously scrutinize the power situation in India by examining the interconnection between MCP, DSM, and frequency. The DSM data were diligently collected from the Indian state of Madhya Pradesh for testing. The result shows that MCP linked with the DSM vector provides information on the deviation limit for DAM, power producers, and consumers. Linking of MCP with DSM and frequency makes power utilities more responsible and accountable for a power system. [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]

Mumps is a global public health problem caused by mumps virus, a member of paramyxoviridae family. MMR (Mumps, Measles, Rubella), an effective vaccine, has been incorporated into routine immunization schedules in over 100 countries. On the contrary, in India, vaccine against mumps has not been included in the routine immunization schedule as mumps is still not viewed as a significant public health problem by the government to warrant such an intervention. An increasing number of mumps outbreaks being reported from many parts of the country in the recent past, is matter of concern. The current paper reviews the situation of mumps in India including the recent surge, and discusses the remedial measures to contain these outbreaks. We conclude that inclusion of Mumps component as MMR vaccine in the Universal Immunization Programme of India along with strengthening surveillance is required to tackle the situation. [ABSTRACT FROM AUTHOR]

The present study explores the status of nuclear energy developments for generation of electric power. Notably, India's pivotal role in advancing nuclear technology is highlighted, particularly with the development of a cutting-edge nuclear Fast Breeder Reactor (FBR) technology-based power plant. India has been developing the latest nuclear Fast Breeder Reactor (FBR) technology-based power plant having power generation capacity 10 times higher than the existing nuclear power plants. The review incorporates the most important component of the FBR system i.e. cold trap. Model Sodium Cold Trap (MSCT) is used by India to demonstrate the regeneration of secondary sodium cold trap.The development, advancements and challenges towards the Model Sodium Cold Trap (MSCT) are discussed in detail. It is possible to increase the maximum in-situ regeneration of MSCT up to 82%. This paper also reviews status of energy production using renewable and non-renewable energy sources. Finally, the scope in the advancements of nuclear reactor technology and related future opportunities has also been included. [ABSTRACT FROM AUTHOR]

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

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

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

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

Clean and sustainable energy holds preeminent importance from a socio-environmental standpoint and can be achieved through the implementation of a hybrid renewable energy system (HRES). As well as ensuring the stable and reliable operation of power systems amidst the increasing integration of renewable energy sources, precise source forecasting becomes indispensable. Thus, this paper presents a pioneering HRES design incorporating photovoltaic (PV), wind turbine (WT), and biogas generator (Bio) technologies, wherein animal manure is utilized as biomass for the sustainable valorization of animal waste. The preliminary objective of this study is to address the prevailing gaps to attain sustainable energy production that is both cost-optimal and technically efficient. Consequently, this paper investigates source forecasting through time series modeling, and assesses the techno-environmental and economic viability of HRES (both off-grid and on-grid) within the northeastern state of India, specifically focusing on the case study of the Cachar district in Assam state. Moreover, real data analysis is conducted to model and optimize the hybrid system. The findings reveal promising outcomes in terms of cost, emissions, and electrical energy potential, in which the optimal configuration PV/Bio with grid results in a net present cost (NPC) of 0.659 M$, cost of energy (COE) of 0.00684 $/kWh, and CO2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_2$$\end{document} emission of 2368 kg/yr. For the off-grid, the optimal configuration PV/Bio/Battery obtained with NPC of 2.49 M$, COE of 0.218 $/kWh, and CO2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$_2$$\end{document} emission of 346 kg/yr. The analyzed results of the off-grid case are compared and validated with existing literature on different energy economics with optimum configurations. The obtained optimum system is more cost-effective than the existing systems in the literature. Furthermore, a sensitivity analysis is performed to assess the system’s robustness. Finally, this study endeavors to project a future power system for one of the prominent regions in Assam, an Indian state. [ABSTRACT FROM AUTHOR]

With the advent of numerous global precipitation estimates (GPEs) in the recent decades, dependability of hydrologists has lessened on the station data as the GPEs can be readily availed and utilized. Since the skills of GPEs may differ from region-to-region, it is vital to analyse their ability in resolving the regional precipitation climatology using appropriate statistical methods. In this study, a total of five GPEs, viz., APHRODITE, PERSIANN-CDR, CHIRPS, CMORPH, and IMERG were evaluated for their abilities in resolving regional precipitation climatology of WHR with respect to gridded precipitation product of India Meteorological Department (IMD). Different performance indicators i.e., Probability of Detection (POD), False Alarm Ratio (FAR), Normalised Root Mean Square Deviation (NRMSD), Pearson Correlation Coefficient (CC) and Skill Score (SS) were used for evaluating the GPEs. Multicriterion Decision Making (MCDM)approaches i.e., Compromise Programming (CP), Cooperative Game Theory (CGT), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Average Technique (WAT), and Fuzzy TOPSIS were used for ranking the GPEs across different grids in WHR. Entropy based weight assignment to NRMSD, CC, and SS were performed while applying them in MCDM methods. Group Decision Making (GDM) approach utilizing spearman correlation coefficient and additive ranking rule was employed to obtain the final ranking of GPEs from multiple rankings assigned through different MCDM methods. Across 115 grids, APHRODITE exhibits superior performance compared to other GPEs in 89 grids. Conversely, CHIRPS and CMORPH emerge as the least favorable products among the five GPEs across more than 70 grids, being consistently ranked either 4th or 5th. Notably, IMERG was identified as the best-performing product in 14 grids and as the second-best product in 63 grids, positioning it as the second most suitable option after APHRODITE for monthly rainfall time series analysis. Similar results, as detailed in the paper, were also obtained for month-wise rainfall time series analysis. [ABSTRACT FROM AUTHOR]

The optimal reactive power dispatch problem optimizes the shunt capacitor bank installation in distribution systems, reducing power loss and also reducing the financial loss for the electricity market associated with power loss. Moreover, the sharing of both active and reactive power from different renewable energy sources like PV and wind in the form of distributed generation also contributes toward reducing power loss and improving the voltage profile of the system. But the installation and maintenance costs associated with these additional set-ups are rarely taken into consideration any optimization problem. This paper aims to reduce the power loss and improve the voltage profile of a radial distribution network with the integration of capacitor banks, PV, and wind energy sources, while taking into account the overall associated cost of each parameter during optimization. The problem is formulated as a novel cost minimization problem aiming to achieve the optimal settings for a life-long capacitor bank-PV-wind integrated distribution network with the least possible installation, operational, and maintenance costs while reducing its power loss significantly for a span of 20 years. The uncertain nature of PV and wind power output has been modeled using the beta probability distribution function and the Weibull probability distribution function, respectively. This unique proposed problem statement of the capacitor bank-PV-wind power integrated distribution network has been tested on the IEEE 33 and IEEE 141 bus systems and solved using the rock hyraxes swarm optimization (RHSO) algorithm. The results were compared with those from other nine well-established techniques, from which it was concluded that the RHSO algorithm has obtained optimal conditions for both systems to operate efficiently. The problem has also been tested on a practical 13-bus 33 kV distribution network in Maharashtra, India, to validate its performance on a practical system. The RHSO has successfully reduced the power loss to almost 17.48% w.r.t. the base case for the practical network while maintaining a minimum overall cost of $51,073,687.7582 for an entire life-span of 20 years. [ABSTRACT FROM AUTHOR]

Extracting gas from unconventional shale reservoirs with low permeability is challenging. To overcome this, hydraulic fracturing (HF) is employed. Despite enhancing shale gas production, HF has drawbacks like groundwater pollution and induced earthquakes. Such issues highlight the need for ongoing exploration of novel shale gas extraction methods such as in situ heating through combustion or pyrolysis to mitigate operational and environmental concerns. In this study, thermally immature shales of contrasting organic richness from Rajmahal Basin of India were heated to different temperatures (pyrolysis at 350, 500 and 650 °C) to assess the temperature protocols necessary for hydrocarbon liberation and investigate the evolution of pore structural facets with implications for CO2 sequestration in underground thermally treated shale horizons. Our results from low-pressure N2 adsorption reveal reduced adsorption capacity in the shale splits treated at 350 and 500 ºC, which can be attributed to structural reworking of the organic matter within the samples leading to formation of complex pore structures that limits the access of nitrogen at low experimental temperatures. Consequently, for both the studied samples BET SSA decreased by ∼58% and 72% at 350 °C, and ∼67% and 68% at 500 °C, whereas average pore diameter increased by ∼45% and 91% at 350 °C, and ∼100% and 94% at 500 °C compared to their untreated counterparts. CO2 adsorption results, unlike N2, revealed a pronounced rise in micropore properties (surface area and volume) at 500 and 650 ºC (∼30%–35% and ∼41%–63%, respectively for both samples), contradicting the N2 adsorption outcomes. Scanning electron microscope (SEM) images complemented the findings, showing pore structures evolving from microcracks to collapsed pores with increasing thermal treatment. Analysis of the SEM images of both samples revealed a notable increase in average pore width (short axis): by ∼4 and 10 times at 350 °C, ∼5 and 12 times at 500 °C, and ∼10 and 28 times at 650 °C compared to the untreated samples. Rock-Eval analysis demonstrated the liberation of almost all pyrolyzable kerogen components in the shales heated to 650 °C. Additionally, the maximum micropore capacity, identified from CO2 gas adsorption analysis, indicated 650 °C as the ideal temperature for in situ conversion and CO2 sequestration. Nevertheless, project viability hinges on assessing other relevant aspects of shale gas development such as geomechanical stability and supercritical CO2 interactions in addition to thermal treatment. Highlights: Insitu thermal treatment of shales for liberation of hydrocarbons and CO2 sequestration. Significant changes in shale geochemistry and pore properties with increasing temperatures of treatment. Visualizing thermally induced pore evolution in shale: microcracks to collapsed pores. Limitations of N2 in accessing complex pore structures. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of obesity in India, body mass index (BMI) has garnered importance as a disease predictor. The current World Health Organization (WHO) body mass index (BMI) cut-offs may not accurately portray these health risks in older adults aged 60 years and above. This study aims to define age-appropriate cut-offs for older adults (60–74 years and 75 years and above) and compare the performance of these cut-offs with the WHO BMI cut-offs using cardio-metabolic conditions as outcomes. Using baseline data from the Longitudinal Ageing Study in India (LASI), classification and regression tree (CART) cross-sectional analysis was conducted to obtain age-appropriate BMI cut-offs based on cardio-metabolic conditions as outcomes. Logistic regression models were estimated to compare the association of the two sets of cut-offs with cardio-metabolic outcomes. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were estimated. Agreement with waist circumference, an alternate measure of adiposity, was conducted. For older adults aged 60–74 years and 75 years and above, the cut-off for underweight reduced from < 18.5 to < 17.4 and < 13.3 respectively. The thresholds for overweight and obese increased for older adults aged 60–74 years old from > = 25 to > 28.8 and > = 30 to > 33.7 respectively. For older adults aged 75 years and above, the thresholds decreased for both categories. The largest improvement in AUC was observed in older adults aged 75 years and above. The newly derived cut-offs also demonstrated higher sensitivity and specificity among all age-sex stratifications. There is a need to adopt greater rigidity in defining overweight/obesity among older adults aged 75 years and above, as opposed to older adults aged 60–74 years old among whom the thresholds need to be less conservative. Further stratification in the low risk category could also improve BMI classification among older adults. These age-specific thresholds may act as improved alternatives of the current WHO BMI thresholds and improve classification among older adults in India. [ABSTRACT FROM AUTHOR]

Background: The global COVID-19 pandemic has brought forth multifaceted healthcare challenges, including the emergence of COVID-19-associated mucormycosis (CAM). This study investigates demographics, clinical presentation, endoscopic grading, radiological correlations, treatment modalities, and outcomes of CAM in Central India. Methods: A retrospective analysis of 312 CAM patients (April-June 2021). Data included demographics, clinical profiles, endoscopic and radiological findings, laboratory results, and treatments. A novel endoscopic grading system was utilized to assess the severity of mucormycosis lesions. Statistical analysis, including chi-squared tests, was performed. Results: Among 312 patients, 65% were male, 82% had comorbidities, and 84% presented with facial swelling. Endoscopic grading correlated with radiological manifestations (p < 0.05) and intraoperative findings. Surgical debridement and antifungal therapy were common treatments, yielding varied clinical outcomes. Mortality was 13% in advanced grades. Conclusion: This study underscores the role of endoscopic grading in risk stratification and prognosis. Findings contribute to CAM management knowledge, underscoring tailored interventions' significance. [ABSTRACT FROM AUTHOR]

Background: India is a large and populous country where reliable data on headache disorders are relatively scarce. This study in northern India (Delhi and National Capital Territory Region [NCR], including surrounding districts in the States of Haryana, Uttar Pradesh and Rajasthan) continues the series of population-based studies within the Global Campaign against Headache and follows an earlier study, using the same protocol and questionnaire, in the southern State of Karnataka. Methods: This cross-sectional study used the Global Campaign's established methodology. Biologically unrelated Indian nationals aged 18–65 years were included through multistage random sampling in both urban and rural areas of NCR. Interviews at unannounced household visits followed the structured Headache-Attributed Restriction, Disability, Social Handicap and Impaired Participation (HARDSHIP) questionnaire in its original English version or in the validated Hindi version. Demographic enquiry was followed by a neutral headache screening question and diagnostic questions based on the International Classification of Headache Disorders edition 3 (ICHD-3), which focused on each respondent's most bothersome headache. Questions about headache yesterday (HY) enabled estimation of 1-day prevalence. A diagnostic algorithm first identified participants reporting headache on ≥ 15 days/month (H15+), diagnosing probable medication-overuse headache (pMOH) in those also reporting acute medication use on ≥ 15 days/month, and "other H15+" in those not. To all others, the algorithm applied ICHD-3 criteria in the order definite migraine, definite tension-type headache (TTH), probable migraine, probable TTH. Definite and probable diagnoses were combined. Results: Adjusted for age, gender and habitation, 1-year prevalences were 26.3% for migraine, 34.1% for TTH, 3.0% for pMOH and 4.5% for other H15+. Female preponderance was seen in all headache types except TTH: migraine 35.7% vs. 15.1% (aOR = 3.3; p < 0.001); pMOH 4.3% vs. 0.7% (aOR = 5.1; p < 0.001); other H15 + 5.9% vs. 2.3% (aOR = 2.5; p = 0.08). One-day prevalence of (any) headache was 12.0%, based on reported HY. One-day prevalence predicted from 1-year prevalence and mean recalled headache frequency over 3 months was slightly lower (10.5%). Conclusions: The prevalences of migraine and TTH in Delhi and NCR substantially exceed global means. They closely match those in the Karnataka study: migraine 25.2%, TTH 35.1%. We argue that these estimates can reasonably be extrapolated to all India. [ABSTRACT FROM AUTHOR]

The study aims to unravel the impact of Environmental Social Governance (ESG) scores on the firm's market performance of polluting companies. Moreover, the study also finds out moderating effect of green initiatives. The study's population consisted of 67 companies that were chosen from the list of polluting companies given by the Central Pollution Control Board of India for the post-COVID-19 timeframe of 2020–2023. Regression analysis was conducted to analyze the relationship between the variables. The results indicate that the performance of ESG will improve the financial performance of the company. In most cases, the results suggest that firms with heightened ESG performances have better market performance. Furthermore, the results show that there is no moderating effect of green initiatives. This study's findings have important implications for stakeholders. The examination's findings should help managers understand the state of ESG and financial market performance after the coronavirus crisis, as well as the relationship between FP and ESG responsibility fulfilment. The study has significant practical implications that may help managers create plans and guidelines for implementing and improving ESG scores to maximize performance. Population was the major limitation of the study. [ABSTRACT FROM AUTHOR]

The extraction of valuables from waste has gained momentum. Thermal influence alters both the organic and inorganic components of coal. Insufficient knowledge on the association of rare earth elements (REEs) with the parent matrix of thermally altered high-ash coals (63% ash) limits the potential for such coals being utilized for isolation of valuables. In this study, we analyzed the distribution and occurrence modes of REEs within a magmatically altered high-ash coal via nine-step sequential extraction, combining Tessier and BCR methods. The total concentration of REEs in the coal sample, on whole coal basis, was found to be 820 ppm, which is significantly higher than the world average. Major mineral oxides were deduced to be those of Si, Fe, Al, Ca, Mg, and Ti. Sequential extraction confirmed that about 66% of HREE and 25% of LREE were included in the residual fraction. LREEs were concluded to be primarily in ionic form, whereas HREEs were speculated to be associated with the TiO2 phase. XRD analyses showed that thermal alteration affected the dolomite phase specifically, which selectively got removed where carbonate-bound elements were assessed. Petrographic analysis supported the magmatic influence and demonstrated the presence of mosaic structures and pores containing unfused vitrinite, with a reflectance value of 3.6. To summarize, the present study pertaining to delineation of association of valuables in high-ash heat-altered coals from an Eastern coalfield in India can potentially open up new avenues for utilizing such coals, which are otherwise considered waste. Article Highlights: A nine-step sequential chemical leaching process employing eco-friendly lixiviants was implemented to delineate the mineralogical associations in high-ash heat altered coal of Indian origin. Dolomite was completely eradicated at the complexation stage implying alteration of this mineral phase due to magmatic influence. LREEs were concluded to be present primarily in the ionic form while HREEs were strongly associated with anatase phase. Petrographic analysis revealed presence of mosaic structures with porous morphology containing un-fused vitrinite. [ABSTRACT FROM AUTHOR]

Background: Phellinus fastuosus is a wood-eating medicinal fungus from Western Ghats of India. Therefore, we investigated hypoglycemic and hepatoprotective effects of P. fastuosus aqueous extract on streptozotocin-induced diabetic and carbon tetrachloride (CCl4) induced hepatotoxicity in rats, respectively. Result: As compared to the diabetic control group, a 400 mg/kg dose had significant hypoglycemic effects, including a reduction in blood glucose (24.44%) and gain in body weight. Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activity reduced by 31.81% and 32.84%, respectively, were also noted, along with decreases in triglycerides (24.32%) and cholesterol (25.89%) levels. The albumin, bilirubin and creatinine levels were also significantly reduced after administration of P. fastuosus extract in diabetic rats. Administration of P. fastuosus extract showed a substantial decrease in the activity of ALT, AST, alkaline phosphatase (ALP), catalase (CAT) and superoxide dismutase (SOD) in addition a decrease in the level of lipid peroxidation (LPO) as compared to CCl4-intoxicated rats. The cumulative effect of CCl4 increased the erythrocyte membrane peroxidation, whereas P. fastuosus extract reduced the cholesterol and increased phospholipid, thus preventing the alteration of membrane fluidity as compared to CCl4-intoxicated rats. FTIR and HR-LC-MS-based metabolic profiling revealed the presence of various functional groups and bioactive metabolites. Conclusion: The extract showed the hypoglycemic and hepatoprotective effects due to the presence of various bioactive metabolites. Exploration of therapeutic potential of P. fastuosus using bioassay-guided fractionation is needed. [ABSTRACT FROM AUTHOR]