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The mental health climate crisis: Unveiling the hidden consequences of global warming
Today, the issue of climate change is a matter of great urgency on a global scale, with vast-reaching implications. While the environmental and physical health effects of climate changes are well-documented, its impact on mental health is an emerging area of concern. The issue of climate change necessitates a rigorous and systematic approach to comprehending, evaluating, and addressing climatic anxiety. This approach must underscore the differentiation between adaptive and maladaptive forms of anxiety, and the importance of considering the societal-level response necessary to effectively combat climate change. Studies have documented the impact of natural calamities like Hurricane Katrina on low-income people residing in those areas. The present study rests on the objectives of examining the psychological impact of climate change on individuals, communities, and vulnerable populations with the help of literature. Further, investigation is continued on the role of climate-related stressors, such as natural disasters, heatwaves, and environmental degradation on mental health outcomes. The study also identified the potential interventions and strategies for mitigating the adverse impact on mental health due to climate change. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Pre Packaged Insolvency - Exploring An Alternative Framework For Bankruptcy Resolution In India
This article is a review of literatures on the need for alternative bankruptcy resolution framework in India. The study explores the context & background to the recent initiation of limited Pre-Packaged Insolvency in India. The article makes a strong case for having a private & pre-negotiated mode of debt resolution along with the existing CIRP framework in India. The article provides a comparative perspective of CIRP and Pre-pack driven resolution model in India. The research paper also addresses some of the potential challenges & concerns related to initiation of pre-pack in India & accordingly discusses the relevant safeguards for the same. Lastly, the study also provide a brief view of pre-pack model currently practised in USA. The Electrochemical Society -
Students Satisfaction with Remote Learning During the COVID-19 Pandemic: Insights for Policymakers
Purpose: This study aimed to learn more about the factors influencing student happiness and involvement in remote learning in Indian higher education institutions (HEIs). The study aims to assist administrators, strategists, and politicians in efficiently dealing with educations new normal. Methodology: The study used a quantitative research approach to fulfill the research aims. A sample of 546 students from various Indian HEIs was chosen, and data were gathered using standardized questionnaires. Structural equation modeling, confirmatory factor analysis, and importance-performance analysis (IPA) were used to calculate the student satisfaction index and examine the impact of various factors. Findings: The findings of this study revealed that institutional and faculty support emerged as the most influential factor impacting students satisfaction through remote learning. It also highlighted the need for HEIs to redesign the assessment process and evaluation techniques to adapt to the remote learning environment. Practical Implications: The findings of this study indicated the practical consequences for administrators, strategists, and policymakers at Indian HEIs. It was advised that improving institutional and teacher support should be a major concern in order to improve student happiness in remote learning situations. Furthermore, redesigning assessment procedures and evaluation processes may improve learning outcomes and student engagement. Originality: This study contributed to the existing body of knowledge by specifically investigating the factors influencing student satisfaction in remote learning within Indian HEIs. The findings shed light on the unique challenges and opportunities the shift to remote education presented. They offered valuable insights for managing and improving the quality of education during and beyond the pandemic. 2023, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis
Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individuals sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individuals tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset. 2023 Tech Science Press. All rights reserved. -
An Anomaly Detection Framework for Twitter Data
An anomaly indicates something unusual, related to detecting a sudden behavior change, and is also helpful in detecting irregular and malicious behavior. Anomaly detection identifies unusual events, suspicious objects, or observations that differ significantly from normal behavior or patterns. Discrepancies in data can be observed in different ways, such as outliers, standard deviation, and noise. Anomaly detection helps us understand the emergence of specific diseases based on health-related tweets. This paper aims to analyze tweets to detect the unusual emergence of healthcare-related tweets, especially pre-COVID-19 and during COVID-19. After pre-processing, this work collected more than 44 thousand tweets and performed topic modeling. Non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA) were deployed for topic modeling, and a query set was designed based on resultant topics. This query set was used for anomaly detection using a sentence transformer. K-means was also employed for clustering outlier tweets from the cleaned tweets based on similarity. Finally, an unusual cluster was selected to identify pandemic-like healthcare emergencies. Experimental results show that the proposed framework can detect a sudden rise of unusual tweets unrelated to regular tweets. The new framework was employed in two case studies for anomaly detection and performed with 78.57% and 70.19% accuracy. 2022 by the authors. -
Importance of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in the Banking Industry: A Study from an Indian Perspective
The papers primary focus is intelligence creation by AI and fast implementation by RPA. It starts with the applicability of the Turing test propounded by Alan Turing, the father of Artificial Intelligence (AI). At the backdrop lies various events, namely the recent motivation addition of various bank account holders. These factors fuelled the demand for AI and RPA implementation in the banking industry. It pitched how AI and RPA work in real-time scenarios such as financial fraud and money laundering. It discusses how AI builds the knowledge graph and recommends products and services for each customer. This knowledge is implemented and delivered using RPA. The AI application gained prominence in every banking business segment, such as equity, personal, investment and loan. The application of RPA is present in all business segments, although the percentage is increasing yearly. The AI and RPA can help banks to convert the challenges to opportunities. There have been various challenges, and the application of AI and RPA combinations is the key to solving the inefficiencies. Advanced analytical techniques on open-source data have been used in this paper. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Forecasting the Stock Market Index Using Artificial Intelligence Techniques
If the stock market would have a predictable to maximum accuracy, then every stockbroker and investor would have been billionaire. But it is not the ground truth. In a one-to-one interaction with stock analysts, who mention that the stock market is unpredictable and that is why their role is important, else everything would have been black and white. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A review on ensembles-based approach to overcome class imbalance problem
Predictive analytics incorporate various statistical techniques from predictive modelling, machine learning and data mining to analyse large database for future prediction. Data mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. With the improvement in technology day by day large amount of data are collected in raw form and as a result necessity of using data mining techniques in various domains are increasing. Class imbalance is an open challenge problem in data mining and machine learning. It occurs due to imbalanced data set. A data set is considered as imbalanced when a data set contains number of instance in one class vastly outnumber the number of instances in other class. When traditional data mining algorithms trained with imbalanced data sets, it gives suboptimal classification model. Recently class imbalance problem have gain significance attention from data mining and machine learning researcher community due to its presence in many real world problem such as remote-sensing, pollution detection, risk management, fraud detection and medical diagnosis. Several methods have been proposed to overcome the problem of class imbalance problem. In this paper, our goal is to review various methods which are proposed to overcome the effect of imbalance data on classification learning algorithms. Springer Nature Singapore Pte Ltd 2019. -
The rise of new age social media influencers and their impact on the consumers' reaction and purchase intention
The internet revolution and digitisation have created innovative platforms and spokespersons for brands beyond traditional media. The marketing landscape for brands and customers is evolving towards a more personal and authentic approach; adopting various social media platforms and influencers creates more brand engagement. Influencers are the new third-party endorsers, catering to and recommending products to their loyal community of followers. The influencer and their fans/followers are a brands new storytellers. However, selecting the right influencer for a brands promotional strategy requires careful consideration of several factors. This paper aims to study the impact/effect of these variables, namely, endorsers credibility and corporate credibility, on consumers attitudes towards the brand and its impact on purchase intention, with respect to the millennial era. In the present study, 14 Likert-based questions were designed, asking the respondents to rank their choice of agreement on a scale of 1 to 5. The results were obtained through statistical analysis, including measuring the relationship between variables using confirmatory factor analysis and regression techniques. And the study found that corporate credibility has a significantly higher impact (approximately 90%) than individual endorsements (including those by celebrities) in enhancing customers brand perception. Copyright 2024 Inderscience Enterprises Ltd. -
Determinants of Hand Washing Practices among Adolescents in India Findings from CNNS Data, 2016-18
The study attempts to assess the effect of socioeconomic determinants on access to Good Handwashing Practices (GHP) among the adolescent population in India. The Comprehensive National Nutrition Survey (CNNS), 2016-18 dataset is used to identify the predictor and outcome variable for the study. Binary logistic regression established the adolescents age and sex, mothers schooling, wealth index, and the region as a significant predictor for GHP. The study revealed that gender, age, caste, education, individual household wealth, and the region has a significant association with adolescent hand-washing practices, where economic conditions drive the individual practice of handwashing more than the behavioural aspect. It requires government intervention to improve sanitation and water facilities to accelerate hand-washing among adolescents in India. 2022 Tata Institute of Social Sciences. All rights reserved. -
Role of formal restructuring in post-bankruptcy performance of companies: Case study from India
The resolution of distressed assets/NPAs is among the greatest challenges faced by banks in India. Corporate restructuring is a popular method of revival of distressed companies wherein changes are proposed across financial, operational & portfolio & managerial restructuring. This chapter delves into the intricate landscape of court driven corporate restructuring actions and post-bankruptcy performance, particularly within the context of India's dynamic market. Leveraging empirical evidence, the chapter explores the effects of restructuring actions on post-bankruptcy performance. This is also in the background of emerging use of AI tools and technologies in corporate distress resolution. By examining the actual performance data of companies undergoing insolvency resolution process (CIRP) under IBC in India, the chapter offers valuable insights into the efficacy of different restructuring actions and their implications for post-bankruptcy performance. This chapter provides an important contribution in understanding the complexities and effectiveness of bankruptcy resolution processes. 2024, IGI Global. All rights reserved. -
Evaluation of Flow Resistance using Multi-Gene Genetic Programming for Bed-load Transport in Gravel-bed Channels
Evaluation of flow resistance is necessary for the computation of conveyance capacity in open channels. The significance of the friction factor in channels with bedload conditions is paramount. The response of flow resistance in gravel-bed channels in bedload transport conditions is distinct from that of a fixed bed. The paper studies the different empirical approaches in the literature to determine the friction factor under bedload transport conditions and proposes an expression by genetic programming for the same. Various hydraulic and geometric parameters affect flow resistance in the bedload transport condition. The present study includes bed slope, relative submergence depth, aspect ratio, Reynolds number, and Froude number as influencing factors for such flow conditions. A wide range of experimental datasets is employed to determine the effect of these influencing parameters and develop a customised single expression for the friction factor. The experimental data set has also been moderated for sidewall corrections. The predictability of the proposed model is compared to various empirical equations from the literature. Unlike the existing models, the proposed model provides a more extensive expression for effectively predicting the friction factor for a wide range of datasets. The conveyance capacity of a river is validated from the estimated value of friction factor, as compared to other standard models. The developed Multi-Gene Genetic Programming (MGGP) model reasonably predicts discharge in the rivers, signifying that the model can competently be applied to field study within the specified range of parameters. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Factors influencing purchase intention of online shopping customers: A review of the existing literature
The rapid digital transformation, especially on the internet, has provided businesses with unprecedented opportunities for global expansion. This shift has revolutionized marketing, replacing costly and labour-intensive efforts with cost-effective digital strategies. E-commerce platforms facilitate this process by offering an interactive interface for users to post reviews, comments, and questions, thus enhancing the decision-making experience. Understanding customer purchase intention is vital in this digital age, as it's shaped by various antecedent factors, with digital word-ofmouth (eWOM) being a significant influencer. However, the relationship between eWOM and purchase intention remains underexplored. This chapter reviews 60 previous studies, shedding light on the factors affecting online customers' purchase intentions. The study identifies research gaps, setting a clear direction for future investigations. 2024, IGI Global. All rights reserved. -
Digital image correlation (DIC) for measuring strain in brick masonry specimen using Ncorr open source 2D MATLAB program
This micro-article is focused on the application of DIC in measuring strain for a 2D structural element like brick masonry specimen. Conventional techniques of strain measurements involve devices like strain gauges, LVDTs, dial gauges and extensometers. Even though Commercial DIC packages are available and have been widely used to perform DIC, these setups are quite expensive. In this study a simple DSLR camera along with Ncorr, an open source 2D MATLAB program is used to perform the DIC. Results have also been obtained experimentally by using conventional measuring devices and compared with that of DIC. The results obtained through DIC is well in agreement with that of experiments, and the difference in strain at failure was observed to be 4.7%. Furthermore, the results of this study should encourage researchers to adopt this technique in studies of behaviour and structural health monitoring of brick masonry specimens. 2019 The Authors -
Three-component p-TSA catalyzed synthesis of hydrazinyl thiazole derivatives
A direct single-pot three-component procedure for synthesizing bio-active hydrazinyl thiazole derivatives has been demonstrated. The reaction involves substituted 2-Bromoacetophenones, carboxaldehydes, and thiosemicarbazide to form the hydrazinyl thiazole scaffolds via a simple condensation reaction followed by intramolecular cyclization with p-TSA as a catalyst at room temperature. The ease of product separation, lack of column chromatographic purification, and use of readily available starting materials result in an efficient approach for organic synthesis. 2023 Elsevier Ltd. All rights reserved. -
Environmentally conscious synthesis of novel pyrano[2,3-d]pyrimidines via ternary deep eutectic solvents
Pyrano[2,3-d]pyrimidine and its analogues have gained considerable courtesy because of their diverse biological functions and wide-ranging applications, from pharmaceutical agents to essential natural pigments. However, synthesising pyrano[2,3-d]pyrimidine with multiple reactants is challenging and requires advanced green chemistry solutions. This study investigates the generation of thirteen new pyrano[2,3-d]pyrimidine analogues through a single-step, open-flask, multicomponent reaction (MCR) strategy involving aldehydes, phenylhydrazine, ethyl acetoacetate, and barbituric acid via deep eutectic solvents (DES). These DESs serve as environmentally friendly alternatives to traditional solvents. A ternary deep eutectic solvent (TDES) was evaluated for its catalytic solvent activity among ten different formulations. TDES-7 (5 mL) demonstrated the best performance, achieving 95 % product formation within 30 min at room temperature. Its remarkable catalytic activity and ability to produce high yields across multiple reaction cycles make it a standout choice for this application. The collaboration between MCR and TDES underscores an important blend of two significant green aspects, demonstrating their potential to achieve a green and productive sustainable synthesis method with an noble E-factor of 0.1236. 2024 Elsevier B.V.