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Rise of Populism in Northeast India: A Case of Assam
A blend of historical and contemporary forces has shaped populism in India. The Congress governments shortcomings (20042014), marked by dynastic politics and corruption, paved the way for the rise of populism, particularly under the Bharatiya Janata Party (BJP), which capitalized on anti?elite sentiment. Narendra Modis leadership, characterized by Hindu nationalism and a development agenda, has significantly altered Indias political landscape. This study focuses on the rise of populism in Northeast India, specifically in Assam, where populist movements and leaders have increasingly influenced the socio?political environment. It explores the socio?economic conditions and identity politics that have driven the growth of populist ideologies, often leading to the marginalization of ethnic minorities. By analyzing key political events, movements, and policies, the research seeks to uncover the root causes of populism in Assam and its impact on democracy, social cohesion, and regional stability. Employing a qualitative methodology that includes political speeches, media analysis, and empirical evidence, the study examines how political leaders in Assam have mobilized regional and ethnic sentiments for electoral gains, further exacerbating ethnic marginalization. The article aims to understand the catalysts and consequences of populist governance in Assam, offering insights into the broader trend of populism in Northeast India and its future trajectory. 2024 by the author(s). -
Rising from Covid-19: Hybrid Teaching Experiences of University Teachers
COVID-19 pandemic has affected higher education all over the globe. The unprecedented situation has brought changes in teaching at universities. Universities across the world decided to teach online. By the time, teachers hone online teaching Skills Universities resorted to hybrid teaching. Teachers are oblivious to hybrid teaching and had situational anxiety. In spite of all teachers started their hybrid classes. The present study assumes transtheoretical model for hybrid learning derived from ecosystem theory. Recent research is yet to capture the hybrid teaching experiences of university teachers amid COVID-19. The present study employed qualitative research method to capture in-depth understanding of hybrid teaching experiences. Study interviewed eight University teachers to understand the hybrid teaching experiences and applied interpretative phenomenological analysis (IPA) to interpret the interview data. The findings of the study emerged with themes and sub-themes describing hybrid-teaching experiences and listed major challenges faced by them. Few of the challenges are handling online and offline students group simultaneously, technology integration to classes, technological barriers, and COVID-19 anxiety. Study recommends more research in the area for broader understanding of nuances of hybrid teaching. Nevertheless, to find solution to the challenges faced by the teachers while conducting hybrid classes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Risk Analysis Using Ensemble Learning Model for Smart Energy Sustainability in Indian Cities
Analyzing risks involved in smart energy sustainability entails identifying, assessing, and mitigating diverse types of risks that include financial, operational, and environmental factors that can affect the dependability, efficiency, and ecological friendliness of energy systems in smart cities. The study uses a high-frequency dataset from smart meters in Mathura and Bareilly districts in India collected over 2 years from May 2019 to October 2021 which contains millions of data points. To forecast energy consumption patterns and reveal possible risks we used machine learning models like linear regression, random forest, gradient boosting, and extra tree classifier. By using several machine learning algorithms such as multiple linear regression (MLR), classification trees (CTs), random forests (RFs), and support vector machines (SVMs) this paper developed an empirical model to establish an interrelationship between district heating systems investments influence on the performance improvement variables for sustainable development goals. Notably, the ensemble learning approach had a remarkable precision rate of 94.69% indicating its importance in forecasting and managing demand for power. Moreover, the findings provide insights that could help policymakers and service providers improve urban energy sustainability and efficiency. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Risk and Resilience in Human Emergencies: Pedagogical Directions from a Psychosocial and Neuropsychological Paradigm
This chapter will furnish an introductory sketch of theoretical perspectives and current empirical findings on risk and resilience in human emergencies. While risk is an inherent part of human emergencies, resilience, the ability of individuals and systems to maintain functioning levels post adversity and adapt is equally important. The goal will be to collate conceptual framework and evidence to provide evidence-informed practices and directions for pedagogy. We will review a wide range of theoretical expositions and focus them on the level to explore how risk and resilience influence and are influenced by the socio-political, environmental, and psychological experiences of learners. Practical examples and best practice recommendations for pedagogy and andragogy to reduce risk and develop resilience at the individual and collective levels will be discussed. We will propose a model to include psychological science in pedagogical experiences to improve conceptualisation, experience, analysis, and application of the teaching and learning process to cope with human emergencies. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Risk and Return Analysis of Socially Responsible Equity Investment for Optimum Portfolio
The sustainable development goals of industry, innovation and infrastructure aims at building sustainability by paving way for socially responsible investing. Socially responsible investing identifies investment newlineavenues that considers social and environmental responsibilities along with newlinefinancial return. The question of risk and return relationship and whether socially responsible investment outperforms conventional investment has been keen area of interest to empirically drive investors in order to establish an optimal portfolio for socially responsible equity investments. The aim of the study is identifying Equity investments which are Socially Responsible newlinefrom listed Equity investments in India, to examine whether socially responsible equity investments outperformed conventional equity newlineinvestments, to assess the equity investments performance which are socially responsible and equity investments which are conventional across different sectors based on the risk adjustment metrics for establishing an Optimal Equity portfolio which are Socially responsible with Sharpe Index newlineOptimization Model. The study identified socially responsible companies which adhered to sustainability reporting and disclosures of ESG from the total companies listed newlinein BSE and NSE as on 31.12.2021. Annual average return rate, standard deviation, beta and different risk adjustment metrics for evaluating the performance of equity investments which are socially responsible and the equity investments which are conventional was utilized by the study. The newlinesample period of the research between lies between 2012 to 2022. Correlation analysis as well as t-test have been performed using E-views software. Socially responsible equity companies showed significant strong positive newlinerelationship of risk and return than conventional companies. Commodities, Health care, Industrials, Information Technology and Telecommunication sectors outperformed conventional companies of similar sector. -
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Risk Assessment Model for Quality Management System
The ecological and economic risk assessment system and its cost were also factored into the document. The distribution of workplace challenges and hazards, represented by quantitative or subjective occupational risk metrics, was typical in the areas of building safety and environmentally responsible workers. Environmental risk assessment refers to the identification & evaluation of risks, the formulation & application of managerial decisions to lessen the chance of unfortunate conditions, and also the substantial decrease of materials or other damages. Risk assessment facilitates the transition from an area of uncertainty to one where outcomes are more or less expected. The Deming-Shewhart cycle, which would be fully linked to the policy process and performance measurement system, appears to be the implementation technique of the ecological and economic structure under consideration. It would be a cyclical sequence of the associated effective measures. A high degree of adaptability to any internally or externally stressful conditions would be ensured by the synthesis of the fundamentals of the management system & mechanisms for controlling environmental potential costs. This also guarantees the rapid identification of expert hazards, optimization and efficiency gains. 2022 IEEE. -
Risk Behavior Among Emerging Adults: The Role of Adverse Childhood Experiences (ACE), Perceived Family and Interpersonal Environment
Background: Evidence demonstrates that ambiance provided during childhood and the interactions of children with different social agents during childhood have an impact on their adult behaviour. Objective: The current research tries to explore the role of adverse childhood experiences and perceived family and interpersonal interactions in their resultant adult risk behaviour. Method: Around 613 emerging adults (1824 years; Male 343 and Female 270) from the northern districts of Kerala, India took part in the study. The participants were selected using multistage sampling techniques. A Semi-structured Questionnaire was used to understand the perceived family and interpersonal environment. In addition, a checklist (adopted from the risk behaviour scale and youth risk behaviour survey) was also employed. The checklist assisted to understand the presence of actual risk behaviours. Results: Hierarchical Logistic Regression analysis is used to test the hypotheses. The results revealed that 87.2 % of the participants were engaged in at least one type of risk behaviour. Socio-demographic variables (gender and family type) and items of perceived family and interpersonal relationships and adverse childhood experiences were found to be significant predictors of emerging adult risk behaviour. Conclusion: The results further highlight the significance of childhood experiences and the current family environment of emerging adults in understanding their behaviour, and in designing evidence-based intervention program for emerging adults. 2023 The Author(s). -
Risk Factor Based Stage Advancement Prediction of Cataract Using Deep Learning Techniques
In modern world, Cataract is the predominant causative of blindness. Treatment and detection at the early stage can reduce the number of cataract sufferers and prevent surgery. Two types of images are generally used for cataract related studies- Retinal Images an Slit lamp Images. The quality of Retinal images is selected by utilizing the hybrid naturalness image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is and Deep newlinelearning convolutional neural network (DCNN) categorizes the images based on quality newlinescore. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid GMHE-HF) is utilized for enhanced noise filtering. The Slit lamp image quality selection is done using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) model. Further a new algorithm Normalization based Contrast limited adaptive histogram equalization (NCLAHE) is used for image enhancement. Images are pre-processed utilizing the wiener filtering (WF) with Convolutional neural network (CNN) with adaptive atom search optimization (CNN-AASO) for removing the noise. Further, the denoised image is enhanced by Gaussian mixture based contrast enhancement (GMCE) for contrast enhancement. The cataract detection and classification is performed using two phases. In phase I, the cataract is detected using Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model. Phase II uses slit lamp images and detects the type and grade of cataracts through proposed Batch Equivalence ResNet-101 (BE_ResNet101) model.This work also proposes the risk factors for cataracts and classify the cataracts risk using deep learning models. The dataset is pre-processed by missing values and the string values are converted into numeric values. -
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Risk management of future of Defi using artificial intelligence as a tool
This chapter explores AI's pivotal roles in managing risks within DeFi, emphasizing strategic implementation to enhance risk assessment, management, and decisionmaking processes for a better user experience. The convergence of AI and DeFi presents unprecedented opportunities, fostering transparency and decentralization. Drawing from diverse sources, the study evaluates AI's effectiveness, particularly in machine learning, in addressing emerging risks. It focuses on how AI can guide DeFi's future while managing market and credit risks through tasks like data preparation, modeling, stress testing, and validation. Additionally, AI aids in data quality assurance, text mining, and fraud detection. Emphasis is placed on identifying and managing risks that could hinder DeFi's future, highlighting key AI techniques. Given the financial industry's ongoing transformation, these insights are increasingly vital. 2024, IGI Global. All rights reserved. -
Risk management of technological accidents triggered by natural-hazards (Natech): A review of relevant indian legislation
The ill-effects of technological sites on the environment have been researched substantially across the world with particular reference to pollution. However, the threats posed by the environment to technological sites have rarely been studied. Such events, where natural forces trigger technological accidents, are called Natech accidents. It has been observed that developed countries are aware of this emerging hazard and they have responded to it by creating various legislative frameworks for managing Natech risk. In contrast, in developing countries, it has not yet received due attention. The present study has been done to understand the Indian perspective of the legal framework for Natech risk reduction. The study revealed that India has an elaborate legislative framework for disaster risk identification and management. Though there is attention to routine disaster risk, the risk from natural forces to the technological sites is rarely considered. Apart from recognizing natural forces as a threat, no specific legislation is available for Natech risk reduction. A developing country like India must manage the risk posed by natural forces to its technological infrastructure. There is a need for specific legislation to manage Natech risk, which will be an initiating force for the state of the art Natech risk management. 2021, World Research Association. All rights reserved. -
Risk-Based Authentication System Using Hierarchical Sub-Feature-Based Model-(HSFBM)
Password-based authentication system recently has been more secure as risk-based authentication system (RBA) is indentured. The RBA system monitors the parameters extracted during the user login process, and based on the proposed model, the system raises a multi-factor authentication to the user. As the vulnerability has increased concerning passwords, fingerprints easy access to any web application may result in a security flow. Several best practices have addressed these issues, but the security threats have been challenging during the initial login sessions. Hence, this paper proposes a novel method for an effective risk identification method during the initial login phase using a hierarchical sub-feature-based model for different categories of users in an RBA system. The FAR is comparatively better in our proposed model, with minimal re-authentication requests for the user. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Risks and ethics of nanotechnology: an overview
Environmental nanotechnology is thought to be important to current environmental engineering and scientific techniques. The biomedical, textile, aerospace, manufacturing, cosmetics, oil, defense, agricultural, and electronics industries can all benefit from the use of nanotechnology to enhance a wide range of material properties, including physical, chemical, and biological properties. However, nanotechnology-based products or nanomaterials (e.g., nanofibers, nanowires, nanocomposites, and nanofilms) may be harmful to human health. Since nanomaterials are usually manufactured using novel manufacturing techniques and have a variety of sizes, shapes, and surface energies, there can also be uncertainties in their manufacture and handling. This chapter provides a detailed account of ethical issues related to nanotechnology, particularly environmental toxicity, risk management, health risk evolution, and environmental significance of nanomaterials. In addition, environmental challenges, toxic effect of nanoparticles on the environment, ethics of nanotechnology, and social, ecological, biological, and other legal issues are highlighted. The potential of nanomaterials in environmental remediation and their use in environmental protection is also emphasized. 2023 Elsevier Inc. All rights reserved. -
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RL-Based Online Mutation Strategy Selection Techniques inDifferential Evolution: A Study
In recent years, reinforcement learning (RL)-based online mutation strategy selection techniques have emerged as a principled learning framework for balancing exploration and exploitation in Differential Evolution. Several state-of-the-art (SOTA) DE variants have been proposed that utilize online mutation strategy selection to improve the performance of the canonical DE algorithm. This paper presents a comprehensive review of such DE variants and studies multi-armed bandit formulations for online mutation strategy selection in DE. It systematically categorizes existing DE variants based on their utilization of RL algorithms. The Author(s) 2026. -
RNA-seq DE genes on Glioblastoma using non linear SVM and pathway analysis of NOG and ASCL5
Differentially Expressed genes related to Glioblastoma Multiforme as an output of RNASeq studies were further studied to conclude new research insights. Glioma is a type of intracranial tumor (within the skull), which can grow rapidly in its malignant stages. Gene expression in Grade II, III and IV Gliomas is analysed using non linear SVM models. The enriched GO terms were identified GOrilla. Pathways related to NOG and ASCL5 gene were studied using Reactome. 2020, Springer Nature Switzerland AG. -
Road Accident Prediction using Machine Learning Approaches
Road accidents create a significant number of serious injuries reported per year and are a chief concern of the world, mostly in underdeveloped countries. Many people have lost their near and dear ones due to these road accidents. Hence a system that can potentially save lives is required. The system detects essential contributing elements for an accident or creates a link among accidents and various factors for the occurrence of accidents. This research proposes an Accident Prediction system that can help to analyze the potential safety issues and predict whether an accident will occur or not. A comparative study of various Machine Learning Algorithms was conducted to check which model can help predict accidents more accurately. The dataset used for this paper is the government record accidents that occurred in a district in India. Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbor, XGBoost, and Support Vector Machine are among the Machine Learning models used in this paper to predict accidents. The Random Forest algorithm gave the highest accuracy of 80.78% when the accuracies of the Machine Learning models were compared. 2022 IEEE. -
Road-Traffic Congestion in Bengaluru : Psychological and Social Consequences
The study investigated the commuting experiences of frequent travelers during congestion using a three-phase sequential exploratory design. Using semi-structured interviews, phase-1 explored the experiences of a sample of ten (4 women and 6 men) regular commuters on Bengaluru's congested roads. Thematic analysis revealed that psychological experiences due to travel adversities during congestion generated negative affect that narrowed thought-action repertoire of the commuters into fight or flight responses. Fight responses caused negative road occurrences that intensified travel adversities further, creating a vicious cycle showing a non-linear loop. Social consequences included challenges for personal time and activities, family time, health and health care activities, work, social, community, and recreational activities, increase of virtual socialization, and social Darwinism. In phase 2, a check-list of psychological consequences was developed based on the thematic analysis. Phase 3 statistically validated the vicious cycle in a sample of 190 (87 women and 103 men) commuters using structural equation modelling. The model substantiated the probability of the vicious cycle. Based on the model, a mathematical model was developed that could be used to test the non-linear relationship between the components of the vicious cycle. -
Roadmap of effects of biowaste-synthesized carbon nanomaterials on carbon nano-reinforced composites
Sustainable growth can be achieved by recycling waste material into useful resources without affecting the natural ecosystem. Among all nanomaterials, carbon nanomaterials from biowaste are used for various applications. The pyrolysis process is one of the eco-friendly ways for synthesizing such carbon nanomaterials. Recently, polymer nanocomposites (PNCs) filled with bio-waste-based carbon nanomaterials attracted a lot of attention due to their enhanced mechanical properties. A variety of polymers, such as thermoplastics, thermosetting polymers, elastomers, and their blends, can be used in the formation of composite materials. This review summarizes the synthesis of carbon nanomaterials, polymer nanocomposites, and mechanical properties of PNCs. The review also focuses on various biowaste-based precursors, their nanoproperties, and turning them into proper composites. PNCs show improved mechanical properties by varying the loading per-centages of carbon nanomaterials, which are vital for many defence-and aerospace-related indus-tries. Different synthesis processes are used to achieve enhanced ultimate tensile strength and mod-ulus. The present review summarizes the last 5 years work in detail on these PNCs and their appli-cations. 2021 by the authors. Licensee MDPI, Basel, Switzerland.