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Still Waters Run Deep: Groundwater Contamination and Education Outcomes in India
We investigate the impact of groundwater contamination on educational outcomes in India. Our study leverages variations in the geographical coverage and timing of construction of safe government piped water schemes to identify the effects of exposure to contaminants. Using self-collected survey data from public schools in Assam, one of the most groundwater-contaminated regions in India, we find that prolonged exposure to unsafe groundwater is associated with increased school absenteeism, grade retention, and decreased test scores and Cumulative Grade Point Average (CGPA). To complement our findings and to study the effect of one such contaminant, arsenic, we use a large nationally representative household survey. Using variations in soil textures across districts as an instrument for arsenic concentration levels we find that exposure to arsenic beyond safe threshold levels is negatively associated with school attendance. 2024 Elsevier Ltd -
Impact of sentimental factors on stock portfolio returns an empirical analysis
This study aims to introduce an integrated model for understanding the influence of various sentimental factors in conjunction with macroeconomic factors on portfolio returns across ten industry sectors within the US market. These sentimental factors are categorized into market-wide, consumer, and individual stock market factors to assess their impact on industry portfolio returns. Employing the Autoregressive Distributed Lag (ARDL) model, the study evaluates the effects of macroeconomic and sentimental factors on stock market portfolio returns. The findings reveal a negative relationship between short-term interest rates and portfolio returns in specific industry sectors like manufacturing, telecom, and wholesale/retail. The study finds a positive relationship between the Hi-tech sector's risk spread and portfolio returns. Market sentimental factors positively influence portfolio returns of durable, non-durable, utility, and other sectors. Individual sentimental factors negatively impact portfolio returns in hi-tech, utility, durable, energy, and other sectors. The stock market-related individual, sentimental factor of the number of IPOs has a positive impact on portfolio returns in the energy sector and a negative impact on portfolio returns in other sectors. Consumer sentimental factors are significant positive determinants for portfolio returns in durable, energy, telecom, health, and other sectors. Discounts on closed-end funds may provide vital fundamental information regarding lower future earnings for stocks in the durable and energy sectors. The study provides valuable insights for investors to optimize their portfolio strategies in response to macroeconomic and sentimental factors within specific industry sectors. 2024 The Authors -
Aspect based sentiment analysis using fine-tuned BERT model with deep context features
Sentiment analysis is the task of analysing, processing, inferencing and concluding the subjective texts along with sentiment. Considering the application of sentiment analysis, it is categorized into document-level, sentence-level and aspect level. In past, several researches have achieved solutions through the bidirectional encoder representations from transformers (BERT) model, however, the existing model does not understand the context of the aspect in deep, which leads to low metrics. This research work leads to the study of the aspect-based sentiment analysis presented by deep context bidirectional encoder representations from transformers (DC-BERT), main aim of the DC-BERT model is to improvise the context understating for aspects to enhance the metrics. DC-BERT model comprises fine-tuned BERT model along with a deep context features layer, which enables the model to understand the context of targeted aspects deeply. A customized feature layer is introduced to extract two distinctive features, later both features are integrated through the interaction layer. DC-BERT mode is evaluated considering the review dataset of laptops and restaurants from SemEval 2014 task 4, evaluation is carried out considering the different metrics. In comparison with the other model, DC-BERT achieves an accuracy of 84.48% and 92.86% for laptop and restaurant datasets respectively. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
On bivariate Teissier model using Copula: dependence properties, and case studies
To precisely represent bivariate continuous variables, this work presents an innovative approach that emphasizes the interdependencies between the variables. The technique is based on the Teissier model and the Farlie-Gumbel-Morgenstern (FGM) copula and seeks to create a complete framework that captures every aspect of associated occurrences. The work addresses data variability by utilizing the oscillatory properties of the FGM copula and the flexibility of the Teissier model. Both theoretical formulation and empirical realization are included in the evolution, which explains the joint cumulative distribution function F(z1,z2), the marginals F(z1) and F(z2), and the probability density function (PDF) f(z1,z2). The novel modeling of bivariate lifetime phenomena that combines the adaptive properties of the Teissier model with the oscillatory characteristics of the FGM copula represents the contribution. The study emphasizes the effectiveness of the strategy in controlling interdependencies while advancing academic knowledge and practical application in bivariate modelling. In parameter estimation, maximum likelihood and Bayesian paradigms are employed through the use of the Markov Chain Monte Carlo (MCMC). Theorized models are examined closely using rigorous model comparison techniques. The relevance of modern model paradigms is demonstrated by empirical findings from the Burr dataset. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. -
Metal and Ligand-Free Approach Towards the Efficient One-Pot Synthesis of Dipyridopyrimidinimine Derivatives
We report a facile, expeditious, user-friendly, and convenient metal-free synthesis employing base catalysis in a one-pot procedure to construct 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives. This protocol involves a domino process leading to the formation of double C?N bonds utilising KOtBu as the base and DMAc as the superior solvent at 25 C for 2 h. The versatility of this methodology was demonstrated by its successful application to substrates with both electron-withdrawing and electron-donating functional groups, yielding novel functionalized stable 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives in good to excellent yields. Additionally, we have discussed a plausible reaction pathway for the synthesis. 2024 Wiley-VCH GmbH. -
The development and primary validation of employee green behavior scale
Purpose: The increasing adverse impact of human behavior toward the environment has brought in changes in research focus on environmental behavior toward the workplace. Because the employee spends one-third of his day in his workplace, the initiatives taken by the employee also have an impact on the companys environmental stance. Therefore, the researchers gradually focus on employee green behavior (EGB) and its measurement. The study aims to devise a tool for measuring EGB. Design/methodology/approach: Two studies were carried out using the survey method using the purposive sampling technique. The data were collected (Studies 1 and 2) from managers and supervisors working in manufacturing companies located in Kolkata, India. Findings: The first study was done to extract the principal factors using an initial 30 items (N = 220). The result of the principal component analysis shows the emergence of three factors spread over 20 items with loadings above 0.40. The 20-item scale was again administered on managers and supervisors (N = 243). The second study was carried out to examine the convergent and discriminant validity as well as stability of the tool through confirmatory factor analysis (CFA) (N = 243). The result of CFA showed the presence of 16 items spread through three factors: practice and policy, digital use and recycle and reuse. Multiple fit indices support a three-factor model of the 16-item EGB scale. Research limitations/implications: The scale would be a good measure of EGB and can be used for further research. The EGB scale is a composite scale containing three major dimensions that can be used as a complete measure of EGB. Originality/value: The present research aims to fill the current gap by building a comprehensive tool for measuring EGB. The present scale has also addressed the shortcoming of the previous scale and tried to include varied proenvironmental behaviors exhibited in the workplace. 2024, Emerald Publishing Limited. -
Museum visit intervention in K-12 education: a scoping review
This scoping review aims to provide an overview of empirical studies on worldwide museum visit intervention in K-12 education. The study employed Mendeley citation software to identify the articles in the database. A metaanalysis PRISMA statement is used for reporting the items. Out of 135 possibly rich articles, the present study reviewed 18 studies that met the inclusion criteria and were subjected to descriptive and content analyses published between 2017 and 2021. Most of the studies are experimental and from primary school contexts. It is revealed that science is the subject matter context majority of the studies, but philosophy, disaster management, language, and environmental science are also represented. The content analysis resulted in the following learning and social outcomes. It states that social outcome is explored chiefly, followed by learning outcome. The findings indicate that museum visit intervention positively impacts students learning and social outcome. The review also identifies the need for further research on museum visit intervention in the Asia Pacific region. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
An Empirical Framework Using Weighted Feed Forward Neural Network for Supply Chain Resilience (SCR) Strategy Selection
Artificial intelligence (AI)-based systems are normally data driven applications, where the model is trained to think on its own based on the external circumstances. The power of AI has reached every facet of business and common life and is even being largely explored to be adopted in life sciences and medical domains. It supports the human in decision-making through the cognitive utilities which arises out of self-learning capabilities of a model. With the exponential growth of data, supply chain management and analytics have attracted a large community of researchers to build intelligent systems which can lead to re-invention of data-driven decision systems powered by AI. Systems and literature of the past shows that AI-based technologies are promising in intelligent supply chain management (SCM) and building resilient SCMs. There is a gap in literature which addresses on the framework for decision support systems in SCM and application of AI methods for building a robust supply chain resilience (SCR) leading to more exploration on the topic. In this paper, a decision framework is proposed by incorporating fuzzy logic and recurrent neural networks (RNN) for disclosing the patterns of various AI-enabled techniques for SCRs. The proposed analysis involved data from leading literatures to determine the most adoptable and significant applications of AI in SCRs. The analysis shows that techniques such as fuzzy programing, network based algorithms, and genetic algorithms have large impact on building SCRs. The results help in decision-making by exhibiting an integrated framework which can help the AI practitioners for developing SCRs. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Biotechnological advancements in microplastics degradation in drinking water: Current insights and Future perspectives
Microplastics (MPs) have emerged as persistent toxicants in the recent decade. MPs are reported to present in different samples such as soil, water, wastewater, and human samples including placenta, urine etc. Recent studies have reported its presence in drinking water. MPs presence in the drinking water is of concern to the research because MPs are associated with several toxicities in animal models including human. The presented review is focused on understanding MPs abundance, sources, detection, analysis, and biotechnological approaches for its degradation. The paper discusses MPs sources, distribution, and transport in drinking water. In addition, it discusses the MPs identification in drinking water, and advances in biotechnological, metagenomics, system, and synthetic biology approaches for MPs degradation. Moreover, it discusses critically the major challenges associated with the MPs degradation in drinking water. Heterogeneity in the MPs size and shape makes it its identification difficult in the drinking water. Most of the methods available for MPs analysis are based on the dried samples analysis. Development of MPs in liquid samples may bring a breakthrough in the research. 2024 The Authors -
Mahe's Memorialisation of French Colonialism
[No abstract available] -
Aspect based sentiment analysis using a novel ensemble deep network
Aspect-based sentiment analysis (ABSA) is a fine-grained task in natural language processing, which aims to predict the sentiment polarity of several parts of a sentence or document. The essential aspect of sentiment polarity and global context have deep relationships that have not received enough attention. This research work design and develops a novel ensemble deep network (EDN) which comprises the various network and integrated to enhance the model performance. In the proposed work the words of the input sentence are converted into word vectors using the optimised bidirectional encoder representations from transformers (BERT) model and an optimised BERT-graph neural networks (GNN) model with convolutions is built that analyses the ABSA of the input sentence. The optimised GNN model with convolutions for context-based word representations is developed for the word-vector embedding. We propose a novel EDN for an ABSA model for optimised BERT over GNN with convolutions. The proposed ensemble deep network proposed system (EDN-PS) is evaluated with various existing techniques and results are plotted in terms of metrics for accuracy and F1-score, concluding that the proposed EDN-PS ensures better performance in comparison with the existing model. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Deep Belief Network-Based User and Entity Behavior Analytics (UEBA) for Web Applications
Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot. 2024 World Scientific Publishing Company. -
A framework for national-level prevention initiatives in Indian schools: A risk reduction approach
India's mental health policies predominantly prioritize treatment and rehabilitation. While acknowledging the significance of youth well-being, the initiatives undertaken are fragmented, lacking comprehensive data on reach and utilization. Mounting evidence supports the preventability of mental illnesses, highlighting the cost-effectiveness of prevention initiatives, particularly within school-based programs. This paper aims to delineate a preventive framework centered on schools, employing the six-step OrigAMI (Origins of Adult Mental Illnesses) model. This model targets modifiable risk factors to stop the development of mental illnesses. Each step of this model is dissected and examined within the context of the school environment, elucidating the unique and influential role that educational institutions can undertake in preventive initiatives in India. In the initial step, the paper identifies modifiable risk factors in children and adolescents that can be addressed within the school environment. The second and third steps involve pinpointing the target demographic and utilizing data from comprehensive reviews of mental health initiatives. The fourth and fifth steps delineate the workforce structure, advocating for task shifting to non-specialists, engaging school stakeholders and parents, and establishing a systematic workforce framework. The final step delves into policy implications, exploring the potential to reduce the prevalence of mental illness by focusing on risk factors with a high Population Attributable Fraction. This section also contrasts the proposed approach in terms of expenditure against the current budget allocations. The paper culminates with a recommendation to integrate these preventive programs into existing healthcare policies, positioning schools as central to these prevention efforts. The integration of prevention programs into healthcare policies aims to reduce prevalence rates and alleviate the burden on the healthcare system. 2024 Elsevier GmbH -
Hybrid architecture of Multiwalled carbon nanotubes/nickel sulphide/polypyrrole electrodes for supercapacitor
A hybrid electrode structure consisting of amino functionalised multi-walled carbon nanotube, nickel sulphide, and polypyrrole is successfully synthesized using a two-step synthesis such as hydrothermal and in-situ polymerization method. The resulting MWCNT/NiS/PPy composite exhibits a distinct tube-in-tube morphology with excellent stratification. The combination of different components and the unique structure of the composite contribute to its impressive specific capacitance of 1755 F g?1 at 3 A g?1. The prepared ternary composite enables ample exposure of numerous active sites while improving structural stability, ultimately leading to enhanced energy storage capabilities. They do this by combining the advantages of constituent components, a hierarchical assembly approach, and an integrated composite structure. Furthermore, even after undergoing 10,000 charge-discharge cycles, the supercapacitor retains more than 97% of columbic efficiency. An asymmetric coin cell was fabricated using MWCNT/NiS/PPy//AC device which delivered an energy density and power density of 33.12 Wh Kg?1 and 6750 W kg?1 respectively. These findings highlight the exceptional potential of the fabricated device for future applications in hybrid energy storage systems. 2024 Elsevier Ltd -
A hybrid technique linked FOPID for a nonlinear system based on closed-loop settling time of plant
Wind and hydroelectric systems are more cost-effective and environmentally beneficial. A hybrid technique is proposed for the fractional-order proportional-integral-derivative (FOPID) controller to regulate the wind and hydro system. The proposed hybrid technique combines the feedback-artificial-tree (FAT), and atomic-orbital-search (AOS); together known as FAT-AOS approach. The proposed technique is utilized to decide the optimum controller parameters, and it guarantees system constancy in large disturbances using less computation and overshoot by restraining the parameter variation. The FAT is used to predict the optimum gain parameter of FOPID, and minimizing the system error is accomplished with the AOS approach. The performance metrics are peak time, rise time, settling time, and peak overshoot, are analyzed. The performance of the proposed method is done in the MATLAB platform. The simulation result of proposed approach for the rise time as 0.001 sec, settling time is 0.012 sec, and the overshoot percentage is 0.02 %. By comparing the existing methods, like Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Particle swarm optimization (PSO), the proposed approach rise time and settling time overshoot, is less. The comparison proves that the proposed system delivers improved outcome than existing systems. 2024 -
Comparative Analysis of Digital Business Models
This paper discusses the comparative analysis of different attributes of Google and Facebook business model and their novel features for handling innovative business framework. We have compared Google and Facebook business model on different key attributes and also discussed the statistical analysis of business models using Google business analytics platform. We have argued performance analysis of these models. One important point which we discuss and analyze in this paper is that a business model is not about just building revenue generating machine, but it is indeed more than that. It explores the strategy and business approaches of both the models of revenue generating line of attacks. Our research contributes a considerate understanding of Google and Facebook architectural model and its influence on business framework. Statistical enactment and results are analyzed, precisely when big data and media are applied. This paper also provides better understanding of the digital marketplace for both of the platforms and its earning methodology. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Partner betrayal trauma and trust: Understanding the impact on attachment style and self-esteem
Dismissal of an individual's emotional experience by their significant others can have a massive impact on the psychological well-being of the individual. Betrayal trauma discusses the prevalent social phenomenon and its short- as well as long-term impacts on an individual. This study focused on betrayal trauma in romantic relationships. It aimed to find its relation with an individual's self-esteem and attachment styles, with trust as a mediating variable. The tools used in the study- were the partner betrayal trauma trust scale, adult attachment scale and self-esteem scale, each of which was a self-report measurement scale circulated among young adults in the Indian population. The study consisted of 140 participants (n = 140) with a mean age of 21.7 and a standard deviation (SD) of 2.05. The participants included 85% female, 16% male, 3% of the participants identified as genderfluid, and 2% of the participants preferred not to mention their gender. The results from the study show that betrayal trauma in romantic relationships is related to an individual's attachment style and self-esteem. A positive significant correlation was found between betrayal trauma, self-esteem and attachment style, which reveals the impact of betrayal trauma on the psychological well-being of an individual. These findings may aid mental health practitioners in helping young adults resolve their relationship crises and enhance their lifestyles in India. 2024 Elsevier Masson SAS -
A Comprehensive Survey on Deep Learning Techniques for Digital Video Forensics
With the help of advancements in connected technologies, social media and networking have made a wide open platform to share information via audio, video, text, etc. Due to the invention of smartphones, video contents are being manipulated day-by-day. Videos contain sensitive or personal information which are forged for one's own self pleasures or threatening for money. Video falsification identification plays a most prominent role in case of digital forensics. This paper aims to provide a comprehensive survey on various problems in video falsification, deep learning models utilised for detecting the forgery. This survey provides a deep understanding of various algorithms implemented by various authors and their advantages, limitations thereby providing an insight for future researchers. 2024 World Scientific Publishing Co. -
Unleashing economic potential: decoding the FDI-economic growth nexus in G-15 economies amidst unique host country traits
This study examined the impacts of ForeignDirectInvestment (FDI) on economic growth across top the five G-15 countries over a period of 33years, while considering the influence of key host country traits, namely macroeconomic stability, financial development, human capital, and trade openness. The selection of these variables was firmly supported by both theoretical foundations and empirical studies that highlight their significant role in shaping the FDIgrowth interconnection. Panel data derived from World Bank Indicators, spanning the period from 1989 to 2021, were analyzed using a feasible generalized least squares method (FGLS), a rigorous approach, including descriptive statistics, correlation analysis, cross-sectional dependence tests, unit root tests, and multiple regression models. By exploring the interconnection between FDI and the characteristics of the host country, this study sheds light on how these factors collectively contributed to economic growth in the G-15 economies. Descriptive statistics indicated a favorable trend in economic growth, with an average of 3.470 and a standard deviation of 4.289. Correlation analysis revealed significant positive relationships between Economic Growth and Gross Capital Formation, Human Capital, and Liquid Liabilities. Conversely, FDI, Inflation, and Trade Openness displayed insignificant positive correlations with Economic Growth. The findings also demonstrated that favorable host country traits magnified the impact of FDI on economic growth. Specifically, increased Financial Development, Human Capital, and Trade Openness enhanced the positive effects of FDI on economic growth. However, Inflation had a dampening effect on the growth factor. Policymakers in G-15 countries should give precedence to developing strong financial markets, promoting trade liberalization, and investing in human capital to optimize the advantages of FDI. This research addresses a critical gap in the literature as limited empirical work has been conducted on the FDIgrowth relationships specific to the G-15 economies, which hold substantial influence in the global investment landscape and showcase remarkable economic growth. By employing rigorous panel data methodology and a long-term dataset, we provides original insights into the interaction between FDI and host country characteristics, contributing to the existing body of knowledge. The Japan Section of the Regional Science Association International 2024. -
Beyond the first bite: understanding how online experience shapes user loyalty in the mobile food app market
In the competitive landscape of mobile food ordering applications (MFOA) in India, the primary focus is enhancing the customer experience to mirror or even exceed their offline meal acquisition experiences. Existing research underscores the pivotal role of a superior online experience in driving business success. Against the backdrop of a dearth of studies addressing online customer experience (OCE), our current research seeks to gain insight into its state and its implications for attitudes and intentions. Specifically, we investigate the impact of OCE on the continued usage intentions (CUI) of new MFOA users. This study not only sheds light on the relationship between OCE and CUI but also presents a fresh configuration of OCE, addressing its varied conceptualization. Furthermore, drawing on data collected from over 400 first-time users of MFOA, our findings reveal that e-satisfaction and e-trust act as full mediators in influencing CUI. Finally, the study also suggests that e-trust mediates the effect of e-satisfaction on the CUI of MFOA users. Our research contributes to our understanding of OCE by specifically highlighting the experiences and outcomes of first-time users of MFOAs. Practitioners should employ strategies including personalized orientation and data gathering, location-based services, in-app messaging, push notifications and gamification techniques to increase OCE and drive CUI. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.