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India is home to a large number of tribal or Adivasi communities. Particularly vulnerable tribal groups (PVTGs) are one among them. The Indian state initiated several development programs in its affirmative action to benefit PVTGs. However, these initiatives have shown some effects on the ground, still a long way to go. After globalization, a lot of studies claim and argue that the state is slowly withdrawing from its agenda and encouraging multinational companies or corporations (MNCs) to take its role. The MNCs have started their operations by extracting resources without helping Adivasi communities. It led to large-scale protests by the tribal people and civil society organizations. The paper critically discusses development initiatives of the post-independent state to ameliorate the conditions of the PVTGs as part of their affirmative action policies. Furthermore, the paper draws inferences from secondary data sources collected from published and unpublished sources, documents, reports, and online sources. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to gain insight into managers' perspectives on the concept and practice of workers' participation in management (WPM) in Indian organizations. WPM is a critical ingredient in organizational effectiveness. However, the most fundamental aspects of WPM, such as the attitudes, expectations of managers and the factors facilitating or hindering the participation process, are less explored. This paper attempts to answer those questions concerning WPM in the context of Indian organizations. Design/methodology/approach: The study uses data from 138 managers of Indian public and private sector organizations. Data were collected through an online survey as well as through semi-structured interviews. The study explores potential benefits, roadblocks and supporters from the managers' perspective. Further, to test the hypothesis, the author used multiple regression analysis. Findings: The paper demonstrates managers' perception of workers' participation at the board level as the most beneficial and supportive compared to joint consultation and joint decision-making; however, at the same time, it is the one having the most roadblocks in the present Indian Labour laws. Workers, unions and managers' lack of legislative support and intention fuel the fire. The study also found that implementation of WPM is primarily based on statutory norms, and public sector managers are more willing to take the workers on board than managers of private sector organizations. Practical implications: This research is of practical significance as it extracts new visions for policymakers in India already planning to implement four new labor codes, namely, wages, industrial relations, social security and occupational safety, health and working conditions. The data from the study will facilitate the Indian labor ministry to lay down policy decisions concerning WPM. Further, the government can acclimatize this analysis to plug the ambiguities in the present WPM system. Originality/value: To the best of the author's knowledge, this is the first study to give insights into the manager's perspective regarding the WPM using a scenario-based approach. In addition, this paper offers valuable suggestions for policymakers to restructure and streamline the country's existing and overlapping labor laws. [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]

Elections are a critical aspect of democratic governance, providing citizens with the power and right to express their views. A secure voting system with innovative features can improve this process. Blockchain technology is considered a disruptive innovation, and its potential for enhancing the evoting system is significant. The modern voting system is focusing more on blockchain technology to strengthen and secure the process. Blockchain is a reliable, decentralized database that can offer increased security compared to electronic voting machines (EVMs). This research paper presents a detailed study of the design, smart contracts, evaluation of action, and survey on the acceptance of blockchain-based e-voting systems. It examines the requirements for such a system and provides an understanding of the model. As the acceptance of information technology-based services and products increases, future innovation in the e-voting system may depend on blockchain technology. The survey conducted in this paper explores the differences in opinion based on gender, age, and profession among eligible voters from India regarding the acceptance of blockchain technology-based secure e-voting systems. The analysis of these differences sheds light on the potential for blockchain-based e-voting systems to enhance trust and security in the voting process. [ABSTRACT FROM AUTHOR]

Tropospheric systems are widely used by military forces as they provide long-distance, real-time communication. Slow-fading propagation loss reduces link availability and limits its data-carrying capacity. Beam pointing dynamics provides knowledge of favorable heights at different times of the day in different seasons and a useful steering range. Beam steering, based on the beam pointing dynamics of the link, can overcome slow fading. The main contributions of this paper are the derivation of a realistic and accurate tropospheric channel model obtained by making important modifications to Dinc's ray-based model. This paper also presents a method for determining beam pointing dynamics using the modified model. Beam pointing dynamics for two different links located in India have been determined in this paper using real-world data obtained from the Indian Meteorological Department. Another significant contribution of the paper is presenting the prospect of dual data streaming on tropospheric links using a fixed beam and a dynamically steered beam, based on the examination of beam pointing dynamics obtained for the two links. The main result presented in this paper is the comparison of powers received from the most favorable heights in a steerable beam system with the powers received in conventional fixed-beam systems for different days of the year. It has been shown that a higher received power can be achieved with beam steering. Another important result shown is the comparison of the achievable data rates for a single fixed-beam system and a dual-beam (one fixed beam and one dynamically steered beam) system. It has been shown that almost double the data rate is achievable in a dual-beam system. The method for the determination of beam pointing dynamics and the possibility of dual data streaming presented in this paper can significantly enhance the availability and capacity of tropospheric links. [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]

Till now, studies have determined the existence of process innovation using survey data; however, in this paper, we use secondary data to empirically establish its presence in low-tech industries. Our empirical approach consists of data envelopement analysis in the first step followed by regression analysis in the second step. We use a cross-sectional dataset, i.e., Annual Survey of Industries, 2017–2018 for our study. Our results suggest that among Indian low-tech firms, investment in computer equipment and software is the most popular strategy to trigger process innovation followed by investment in plant and machinery. We find that these factors have a greater impact on firms that export products and import inputs, than on firms that export products but do not import inputs. In other words, the export-import orientation of firms induces a differential impact of these factors on process innovation. One policy implication from the study is that government should encourage and incentivize low-tech firms to improve the capabilities of its labour force. This is the first paper in the Indian context that uses secondary data to empirically determine the existence of process innovation in low-tech industries. [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 objective of paper is to study a deep learning strategy for recognizing Indian Sign Language (ISL) using convolutional neural networks CNN. Sign language recognition has become a crucial tool for enhancing communication and accessibility for the deaf in India. ISL is a visual-gestural language used by the deaf population in India, and it is a visual-gestural language used by the deaf community in India. Gesture based communication Application is a characteristic language that involves various methods for articulation for correspondence in day-to-day existence. This paper presents a Programmed interpretation framework for token of manual letters in order in English communication via gestures. It manages pictures of uncovered hands, which permits the client to cooperate with the framework in a characteristic manner. In proposed system a deep learning model using CNN with ISL (Indian Sign Language) Dataset is used. The model consists of preprocessing the dataset to extract features from the image, then train a CNN to recognize the signs and gestures with audio. Experimental results show that the proposed approach is capable of achieving accuracy in recognizing ISL signs and gestures with accuracy of 0.868, recall 0.856, precision 0.863, and F-score is 0.884 respectively. [ABSTRACT FROM AUTHOR]

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