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Religiosity, Theism, Perceived Social Support, Resilience, and Well-Being of University Undergraduate Students in Singapore during the COVID-19 Pandemic
No full textThe COVID-19 pandemic infection control measures severely impacted mental well-being, allowing insight into possible protective parameters. With religion playing a role during challenging times, this study investigated theism and religiosity on the mental well-being of university students during the COVID19 pandemic and how social support and resilience can mediate this effect. One hundred eighty-five university students between 17 and 42 years old responded to online surveys on their theism, religious affiliations, religiosity, well-being, perceived support, and resilience. Pearson’s correlations and single and sequential mediation analyses showed that theism did not significantly predict well-being (r = 0.049), but religiosity mediated the relationship (r = 0.432, effect size = 0.187). Sequential mediation analysis showed that resilience did not mediate the relationship between religiosity and well-being, but perceived social support significantly positively mediated religiosity and well-being with an effect size of 0.079. The findings reveal that factors, such as religiosity and social support could thus aid in the mental well-being of future challenging times such as the pandemic
Publisher Correction: Significance of nanoparticle radius on EMHD Casson blood-gold nanomaterial flow with non-uniform heat source and Arrhenius kinetics (Journal of Thermal Analysis and Calorimetry, (2023), 148, 17, (8945-8968), 10.1007/s10973-023-12288-w)
No full textIn the original publication of the article, the figures 1–18 were published incorrectly due to typesetter’s mistake. The corrected Figs. 1–18 are given in this Correction article. The first sentence in the abstract “For its biomedical applicability, the dynamics electro-magnetohydrodynamic flow …………. numerically elucidated.” should have read “For its biomedical applicability, the electro-magnetohydrodynamic flow ………………numerically elucidated.” The original article has been corrected. (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) (Figure presented.) Geometrical scheme Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with hVariations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with (Formula presented.) Variations in (Formula presented.) with
Federal work life programs and generational perception: an exploratory study using evidence from OPM’s work life survey
No full textUnderstanding the work-life needs of all public sector employees is key to revitalizing government agencies and the services they deliver. Using the U.S. Office of Personnel Management’s very first Federal Work Life Survey (2018), this study examines intergenerational variations of perceived satisfaction from federal work-life programs, supervisory support to participate in federal work life programs, and the moderating effect of work-life programs on an employee’s intention to leave. Our study confirms the existence of generational differences amongst millennials, generation X, baby boomers, and traditionalists and highlights the benefits of work-life programs for employees across generations, with particular emphasis on their attractiveness to younger workers, and the need to discover and create innovative ways to retain workers across generations in the federal workforce
Water quality prediction based on AR and LSTM model
No full textPrediction for water quality is essential because poor water quality can result in serious health problems. Turbidity is one indicator to measure water quality. This research uses time series models, the Autoregressive (AR) model and the Long short-term memory (LSTM) model, to predict the turbidity of water based on the dataset collected from Chicago Park District with a long-term time series from 2013 to 2021. Data cleaning and data exploration are done before building the model, and the stationary test and seasonality test are executed to prepare qualified time series. Also, ACF and PACF plots are drawn to figure out the order of AR to select the best model for prediction. AR Model is a traditional time series model which can make a prediction based on the previous values of time series, and LSTM Model is an advanced Recurrent Neural Network (RNN) designed to prevent the gradient from either decaying or exploding when learning from long-term sequences. The research applies these two models to the water quality data and makes an evaluation and comparison between them. It is found that the LSTM Model has a better performance in forecasting a long-period time series than the AR Model
Multiple solutions and stability analysis in MHD non-Newtonian nanofluid slip flow with convective and passive boundary condition: Heat transfer optimization using RSM-CCD
No full textThis study explores the effect of Williamson nanofluid in the presence of radiation and chemical reaction caused by stretching or shrinking a surface with convective boundary conditions. After implementing two-component model and Lie group theory, the transformed ODEs are solved using the Runge–Kutta Dormand–Prince (RKDP) shooting approach technique. The dual solutions are predicted for certain range of physical nanofluid parameters, such as Williamson parameter ((Formula presented.)), stretching/shrinking parameter ((Formula presented.)), and suction parameter ((Formula presented.)) with different slip (Formula presented.) and magnetic M parameters. Contour plots are generated for the stable branch of the Nusselt number ((Formula presented.)) for different combinations, providing insights into the heat transfer characteristics. The eigenvalue problem is solved in order to predict flow stability. The optimization of heat transfer in nanoliquid is conducted by RSM-CCD. The resulting quadratic correlation enables the prediction of the optimal Nusselt number for (Formula presented.), (Formula presented.), and (Formula presented.). This investigation is motivated by various applications including manufacturing processes, thermal management systems, energy conversion devices, and other engineering systems where efficient heat transfer is crucial
UNLOCKING THE GATE UNDERGRADUATE TEACHING ASSISTANTS AND GATEKEEPER MATHEMATICS COURSES IN THE HISTORICALLY BLACK COLLEGE AND UNIVERSITY SETTING
No full textMathematics continues to be a gatekeeper in limiting participation in the sciences especially among underserved, underrepresented, and racially minoritized students. The contribution of Historically Black Colleges and Universities (HBCUs) to broaden the participation of racially minoritized students in science, technology, engineering and mathematics (STEM) in the United States is significant. This paper reports the findings of a quasi-experimental study on the use of undergraduate teaching assistants (UTAs) in gatekeeper mathematics courses in the context of an HBCU. UTAs were assigned to gatekeeper mathematics courses to disrupt the individualized and deficit-oriented milieu commonly associated with learning mathematics. A total of 1,188 undergraduate students of African descent completed an end-of-semester survey on the use of UTAs in gatekeeper mathematics courses. Results reveal evidence of significant and positive effects of UTA use in gatekeeper mathematics courses on student outcomes. The significant positive results are attributed to the comparative proxies of UTAs who shared similar race and ethnicity with students enrolled in gatekeeper mathematics courses. The significant results of UTA use in gatekeeper mathematics courses bode well for meaningful and practical application to HBCUs and other similar higher education settings seeking to increase STEM outcomes for students of African descent
COLLABORATIVE LEARNING IN TERTIARY EDUCATION CLASSROOMS: WHAT DOES IT ENTAIL?
No full textPurpose - Collaborative learning has been increasingly recognized as an effective approach to promote students’ success in higher education. To better understand the factors that contribute to successful collaborative learning, this study applied the Biggs’ presage-processproduct (3P) general model of learning to investigate the role of teaching quality, student-faculty interaction, and relatedness as presage factors, collaborative learning as process factor, and reflective and integrative learning and higher-order thinking as product factors. Methodology - A cross-sectional approach was applied in this study, which included 1,892 Malaysian undergraduates. The study used the Quality of University Learning Experience (QULEX) survey to measure various constructs. First, confirmatory factor analysis (CFA) was conducted to establish the psychometric properties of the instruments. Thereafter, structural equation modeling (SEM) was employed to evaluate the latent variables relationships. Findings – Based on the findings, collaborative learning fully mediated the prediction of student-faculty interaction, teaching quality, and relatedness on reflective and integrative learning and higher-order thinking. Significance - These findings suggest that collaborative learning with social components and effective teaching maximize students’ learning activities, and they should be fostered in academic institutions to improve students’ academic success. Implications for improving teaching and learning are also discussed in this paper
Early Detection of Breast Cancer Using Thermal Images: A Study with Light Weight Deep Learning Models
No full textThe occurrence rate of cancer is gradually expanding worldwide, and early detection is preferred. Breast Cancer (BC) is a medical emergency, and proper detection is needed to reduce its harshness. The clinical-level screening of BC with Thermal Imaging (TI) is widely adopted due to its accurateness. This work presents the examination of the BC using the TIP and the Pre-trained Light Weight Deep Learning (PLWDL) scheme. The implemented procedure involves (i) Image assortment and modification, (ii) Feature removal and Firefly Algorithm (FA)-based feature optimization, (iii) Binary classification, and (iv) Verification of the clinical significance based on achieved results. Due to its simplicity, the gray-scale version of the thermal images is considered for evaluation using the PLWDL schemes, such as SqueezeNet, MobileNetV1, and MobileNetV2. The detection process is executed using binary classification using SoftMax (SM), Naïve Bayes (NB), and Random Forest (RF), and the experimental outcome achieved is that the SqueezeNet with RF classifier delivers a detection accuracy \u3e90%
Performance Attributes of Environmental, Social, and Governance Exchange-Traded Funds
No full textRecently, interest in socially responsible investing has grown, including new investment vehicles such as environmental, social, and governance exchange-traded funds (ESG ETFs). Despite their rising popularity, few studies have attempted to examine the performance characteristics of these stylized funds. This study aimed to fill this knowledge gap by elaborating on the performance attributes of ESG ETFs and examining fund managers’ security selection and market timing skills. Our results suggest that these funds generally underperform relative to conventional ETFs in many aspects. Additionally, the market timing skills of fund managers require improvement but are comparable to those of conventional ETFs. These results are robust to selecting the individual funds and alternative indices used in the sample. Furthermore, both the security selection and market timing skills of ESG ETF managers deteriorated significantly during the COVID-19 pandemic. Finally, the results indicate a slightly weaker cointegrated relationship between ESG ETFs and their benchmark indices when compared to conventional ETFs, suggesting that potential investors in ESG ETFs should carefully inspect the funds to make informed decisions
Tools and software for computer-aided drug design and discovery
No full textTraditional drug discovery is a time intense, expensive, and complicated procedure. The amalgamation of computer-aided drug design (CADD) and experimental processes can make the whole drug discovery process rational, time efficient, and economical. CADD has two major workflows. One is ligand-based drug design (LBDD) where researchers work with query molecules (ligands), which can be a potential drug for a specific disease. Researchers can check ligands for diverse physicochemical properties and predict their activity against specific diseases. Alternatively, structure-based drug design (SBDD) utilizes protein and enzyme/receptor information and their 3D structures to find the key binding interaction of a prospective drug with specific amino acids. Overall, the CADD approach is highly dependent on applications of commercial and open-source software. With the availability of high-performance computers, supercomputers, cloud-based computing systems, and open-access codes from Github, the CADD process has become extremely viable and effective in terms of reliable drug design and discovery. This chapter discusses the major available tools and software required in different steps of CADD. The provided information can be a valuable resource for the beginners and experienced researchers in the arenas of drug design, computational modeling, chemoinformatics, and medicinal chemistry