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

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

The 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 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]

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]

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