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Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design
The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems. 2023 Elsevier Ltd -
Flight Arrival Delay Prediction Using Deep Learning
This project is aimed to solve the problem of flight delay prediction. This problem does not only affect airlines but it can cause multiple problems in different sectors i.e., commercial (Cargo aviation), passenger aviation, etc. There are a number of reasons why flights can be delayed, with weather being the main one. Our goal in this study is to forecast flight delays resulting from a variety of reasons, such as inclement weather, delayed aircraft, and other issues. The dataset gives itemized data on flight appearances and postponements for U.S. air terminals, classified via transporters. The information incorporates metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. For the purpose of predicting flight delays, the outcomes of several machine learning algorithms are examined, including Ridge, Lasso, Random Forest, Decision Tree, and Linear regression. With the lowest RMSE score of 0.0024, the Random Forest regressor performed the best across all scenarios. A deep learning model using a dense neural network is built to check how accurate a deep learning model will be while predicting the delay and the result was an RMSE score of 0.1357. 2024 IEEE. -
Relating the role of green self-concepts and identity on green purchasing behaviour: An empirical analysis
At present, consumers in emerging economies are becoming more conscious about environmental well-being. Therefore, organizations compete to make their products and practices more eco-friendly. Several studies have tried to explain the relationship between green consumerism and an individual's buying behaviour using traditional theories. However, there is quite a challenge in understanding the influence of green self-concept (GSC) and green self-identity (GSI) in predicting the green purchase intention (GPI) of consumers. Therefore, the authors developed six hypotheses to assess the relation between self-concept and the GPI. The survey was conducted, and the responses were evaluated through the partial least square (PLS) method. The authors analysed the measurement model results (n = 717) and the direct and indirect mediating effect of the latent variable contributing to GPI. The measurement model results show that a significant relationship exists in the proposed model, namely, GSCs ? green purchasing intentions, product self-concept (PSC) ? green purchasing intentions and GSI ? green purchasing intentions. Further, the GSI acted as a mediator for the measurement model. The implications of the study can be used to understand the green consumer behavior in developing new strategies and policies for the organizational practice in emerging economies. 2020 ERP Environment and John Wiley & Sons Ltd. -
Option or necessity: Role of environmental education as transformative change agent
There is a consensus around the importance of environmental education in mitigating the ill effects of environmental problems and preserving the natural environment and promoting green behaviours. The present paper studies the role of environmental education based on transformative learning theory. It intends to present and test a model proposal using sequential mediation analysis of several constructs as the Environmental Education Support (EES) and Volunteer Attitude (VA). A quantitative study was carried out by using data obtained through online questionnaires from several Indian and Brazilian Higher Education Institutions. A multivariate statistical method was employed to analyse the data by using partial least squares structural equation modelling. The results demonstrated that environmental education positively influences students environmental concern, willingness to be environmentally friendly, and volunteer attitude. As a novelty, it reports that environmental education beliefs, concern for the environment and willingness to be environmentally friendly sequentially mediate the relationship between environmental education support and volunteering attitude. 2023 Elsevier Ltd -
The impact of eco-innovation ongreen buying behaviour: the moderating effect of emotional loyalty and generation
Purpose: This study intends to contribute to the literature of eco-innovation by examining the pro-environmental intentions and behaviour among consumers through their understanding of eco-innovation. Thus, the relationship among eco-innovation, general pro-social attitude, generativity, environmental concern, purchasing intentions and buying environmentally friendly products and the differences of the relationship between high and low emotional loyalty and Generation Y and Z were investigated via structural equation modelling (SEM). Design/methodology/approach: Data were collected through an online questionnaire directed to Indian consumers, and analysis was done through partial least square structural equation modelling (PLS-SEM) in two stages, i.e. measurement model and structural model. Findings: Results confirm the relationships established in the proposed model, and some differences were found between the levels of emotional loyalty and the Generations Y and Z. The research shows that individualistic norms and perceived marketplace influence play a purposeful role in transforming environmental concerns into buying behaviour towards eco-innovation-driven products. Practical implications: From a policy and management perspective, the results not only imply the importance of continuous performance and environmental improvement but also those policies hindering diffusion and adoption need to be addressed. Green buying is an elusive task but can be opportunely attained by marketers by adding elements of eco-innovations and understanding mindsets of consumers to create winwin situations for themselves and consumers. Originality/value: The results reinforced that emotional loyalty and Generations Y and Z vitally impact consumers' green buying decision within the framework of eco-innovation and cognitive factors. 2022, Emerald Publishing Limited. -
Being socially responsible: How green self-identity and locus of control impact green purchasing intentions?
This paper investigates the influence of green self-identity (GSI) and two attributes of locus of control, namely external environmental locus of control (ExLOC) and pro-environmental locus of control (PELOC), to predict perceived consumers effectiveness (PCE) on green purchase intentions (GPI) using attribution theory. For this study, data from 391 Indian consumers were analyzed using PLS-SEM via SMARTPLS version 3.2.9. Results show that GSI positively influences both ExLOC and PELOC. Furthermore, both aspects of locus of control are significant positive predictors of PCE and have partial mediation roles. The results not only imply comprehensively expound the process of green buying intentions of consumers through self-identity but also addresses the process of attribution. The study applied the Importance Performance Map Analysis (IPMA) to compare the relative importance and performance of three antecedents (i.e., ELOC, GSI, and PCE). The finding is of utmost importance for practitioners and public authorities to design more focused strategies to increase GPI among the masses to enhance the sales of green products. 2022 The Authors -
Hazard identification of endocrine-disrupting carcinogens (EDCs) in relation to cancers in humans
Endocrine disrupting chemicals or carcinogens have been known for decades for their endocrine signal disruption. Endocrine disrupting chemicals are a serious concern and they have been included in the top priority toxicants and persistent organic pollutants. Therefore, researchers have been working for a long time to understand their mechanisms of interaction in different human organs. Several reports are available about the carcinogen potential of these chemicals. The presented review is an endeavor to understand the hazard identification associated with endocrine disrupting carcinogens in relation to the human body. The paper discusses the major endocrine disrupting carcinogens and their potency for carcinogenesis. It discusses human exposure, route of entry, carcinogenicity and mechanisms. In addition, the paper discusses the research gaps and bottlenecks associated with the research. Moreover, it discusses the limitations associated with the analytical techniques for detection of endocrine disrupting carcinogens. 2024 Elsevier B.V. -
A comprehensive review on the need for integrated strategies and process modifications for per- and polyfluoroalkyl substances (PFAS) removal: Current insights and future prospects
Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review. 2024 The Authors -
Microplastic residues in clinical samples: A retrospection on sources, entry routes, detection methods and human toxicity
Microplastics (MPs) are emerging toxicants which have been detected in varying environments. Despite MPs adverse effects, reports on MPs detection from human clinical samples are only a few. This is due to several reasons such as inefficiency of current MPs detection techniques to detect them from human clinical samples, lack of understanding about the MPs toxicity to human organs and ethical regulations that restricts study with human placental exposure to MPs. This review gives a comprehensive outlook on the major sources MPs sources and routes into human system and their human toxicity mechanisms. Further an in-depth discussion on the significance and limitations of various MPs detection methods is elaborated in the review. Challenges in current research framework for detection of MPs from human clinical samples and the possible future directions in this imperative research domain are also focused in this review. 2024 Elsevier B.V. -
I Can Live Without Banks, but Not Without Banking: Role of Trust on Loyalty and Evangelism
The purpose of this paper is to examine the antecedents of e-banking loyalty and evangelism via threefold construct of WEQUAL (usability, information quality, and service interaction) of public sector banks operating in India. Moreover, it also investigates the mediating role of consumers' trust on the website quality of these banks and their impact on e-banking loyalty and evangelism. The data was collected from 243 respondents through online questionnaire. In order to develop the model and test the hypotheses, partial least square structural equation modeling (PLS-SEM) was done through Smart PLS version 3.2.9. Results assert that website quality of banks positively influences the trust of consumers via usability, information quality, and service interaction. Also, consumer trust plays a mediation role between WEBQUAL constructs and e-banking loyalty and evangelism. 2021 IGI Global. All rights reserved. -
The Role of ChatGPT to improve teaching and learning in higher education
This chapter critically explores the role of ChatGPT and AI in higher education, examining their effects, challenges, and contributions to teaching and learning. It reviews studies highlighting ChatGPT's ability to personalize education, enhance student engagement, and boost research. Yet, it also addresses AI-related challenges like misinformation and dependency issues. The chapter recommends a balanced AI integration, focusing on ethical use, bridging the digital divide, and promoting continuous learning for educators and students. Concluding with future perspectives, it emphasizes AI's role in enriching education while cautioning about its careful application. The chapter offers an insightful analysis of AI's intricate role in higher education and strategies for its responsible integration. 2024, IGI Global. -
INVESTING IN WOMEN, INVESTING IN THE PLANET: QUANTIFYING THE IMPACT OF WOMEN'S EMPOWERMENT ON ENVIRONMENTAL SUSTAINABILITY; [INVESTIR NAS MULHERES, INVESTIR NO PLANETA: QUANTIFICAR O IMPACTO DO EMPODERAMENTO DAS MULHERES NA SUSTENTABILIDADE AMBIENTAL]; [INVERTIR EN LAS MUJERES, INVERTIR EN EL PLANETA: CUANTIFICAR EL IMPACTO DEL EMPODERAMIENTO DE LAS MUJERES EN LA SOSTENIBILIDAD AMBIENTAL]
Objective: This study finds out the correlation between the indicators of womens empowerment, including variables like gender parity index in tertiary education, female labour force participation and seats held by women in national parliament, and a variable of environmental sustainability such as CO2 emissions (metric tons per capita). The aim is to analyse existing datasets to know the impact of independent variables on dependent variable. Method: The study uses multiple linear regression to evaluate the effects of independent variables indicators of women's empowerment on the dependent variable, CO2 emissions, using secondary data from the World Bank covering the years 1990 to 2022. The Breusch-Pagan and Breusch-Godfrey LM tests are used to look at heteroskedasticity and autocorrelation, respectively, and VIF is used to find multicollinearity. Results and Conclusion: The study concludes that there is a statistically significant relationship between lower CO2 emissions and increases in the percentage of female seats in the national parliament (-3.73) and higher female labour force participation (-6.04). The gender parity index (GPI) in tertiary education, which is -0.2997, does not, however, appear to have a statistically significant impact on CO2 emissions. Implications: This research can serve as a cause for redesigning gender-responsive environmental initiatives and promoting a more sustainable and equitable future. Originality/Value: This study contributes empirical knowledge to the body of literature by showing the potential contribution of women's empowerment in addressing environmental issues and emphasising the significance of taking gender into account in environmental policy and decision-making processes. 2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved. -
A novel map matching algorithm for real-time location using low frequency floating trajectory data
The continuous enhancement of technologies and modern well-equipped infrastructures are necessary for easy life. Road accident and missing vehicle ratio are very challenging in preventing misshapenness because these are continually increasing due to traffic hazards. The single way to protect human life from such type of conditions that is more reliable navigation services such as correct location tracking of vehicles on the road network. The real-time location tracking methods fully depends on the map matching algorithms, which also compute a reliable path on the road network. A smart vehicle can provide more reliable tracking services during or before any misshaping using proposed map matching algorithm. This work contributes to ensure correct location for necessary action during misshaping, alert accident zone and communicate messages without wasting valuable time. The proposed approach is validated on the real tracking data and is compared against poor GPS service. Copyright 2023 Inderscience Enterprises Ltd. -
A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field
In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. This research paper introduces a unique Machine Learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Nae Bayes, Support Vector Machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. This method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. Through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than. 2024 IEEE. -
Preface
[No abstract available] -
Internet of healthcare things: Machine learning for security and privacy
The book addresses privacy and security issues providing solutions through authentication and authorization mechanisms, blockchain, fog computing, machine learning algorithms, so that machine learning-enabled IoT devices can deliver information concealed in data for fast, computerized responses and enhanced decision-making. The main objective of this book is to motivate healthcare providers to use telemedicine facilities for monitoring patients in urban and rural areas and gather clinical data for further research. To this end, it provides an overview of the Internet of Healthcare Things (IoHT) and discusses one of the major threats posed by it, which is the data security and data privacy of health records. Another major threat is the combination of numerous devices and protocols, precision time, data overloading, etc. In the IoHT, multiple devices are connected and communicate through certain protocols. Therefore, the application of emerging technologies to mitigate these threats and provide secure data communication over the network is discussed. This book also discusses the integration of machine learning with the IoHT for analyzing huge amounts of data for predicting diseases more accurately. Case studies are also given to verify the concepts presented in the book. 2022 John Wiley & Sons Ltd. All rights reserved. -
Breeding distrust during artificial intelligence (AI) era: howtechnological advancements, jobinsecurity and job stress fuel organizational cynicism?
Purpose: This study examines how technological advancements and psychological capital contribute to job stress. Furthermore, the paper examines how job insecurity, job stress and job involvement influence the cynicism of recently laid-off employees. Despite various research studies, there is a lack of understanding of employees views on their work future and its probable influence on their job behaviors in this era of technology. Design/methodology/approach: A quantitative method was used to collect a sample of 403 recently laid-off employees. The research tool of this study was a questionnaire, and the sampling technique was stratified random sampling. IBM SPSS and AMOS software were utilized to ensure the trustworthiness and accuracy of constructs via factor analysis. The proposed hypotheses were tested using structural equation modeling. Findings: The analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism. Job involvement is negatively associated with employees cynicism. Practical implications: The current study adds to the comprehension of shifts in the perceived behavior of employees toward their organizations due to factors like the adoption of new technology in the organization, job stress, job insecurity and job involvement. Accordingly, there will be a need to form a favorable working atmosphere so that employees can perform their jobs with positive psychology and without any insecurity or stress. Originality/value: The study is thought to contribute to the literature in terms of measuring organizational cynicism while layoffs continue due to AI advancements. 2024, Emerald Publishing Limited. -
Application and challenges of optimization in Internet of Things (IoT)
[No abstract available] -
AI and Machine Learning Applications in Predicting Energy Market Prices and Trends
The worldwide energy market is intricate and unstable, shaped by several aspects including geopolitical occurrences, supply-demand variations, and regulatory modifications. Precisely forecasting energy prices and trends is essential for stakeholders, such as energy producers, dealers, and policymakers. This study investigates the utilization of artificial intelligence (AI) and machine learning (ML) to improve energy price forecasting models. Conventional forecasting methods frequently fail to account for the dynamic and non-linear characteristics of energy markets; however, AI/ML techniques, including neural networks, decision trees, and reinforcement learning, provide enhanced prediction precision. By including external variables such as meteorological conditions and economic metrics, AI models can produce more accurate and useful insights. Case studies illustrate the effective implementation of AI in energy markets, showcasing its capacity to surpass traditional methods. This article addresses difficulties such as data quality and computing expenses while delineating potential developments in AI-driven energy market forecasts. The Authors, published by EDP Sciences.