bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Study of the Effect of the Concentration of Water / PolyAlkylene Glycol Solutions on Heat Transfer in Steels Subjected to Quenching
Authors: Farrera-Buenrostro Brandon , Bocanegra Constantin Alberto Hernández , Banderas José Ángel Ramos , Valdovinos Luis Enrique Molina , Granados Nancy Margarita López , Vapalahti Samy 
Published: September 2023

Abstract
In this work, the heat transfer coefficients were determined utilizing an experimental study for cooling a 15B35H boron steel, using aqueous solutions of polyalkylene glycol (PAG) as a cooling fluid. A cooling tank was designed and built to allow the fluid recirculation, where the effect of agitation and the PAG content in a concentration range of 2-6% vol. were analyzed. The thermal histories of cylindrical probes instrumented with K-type thermocouples were obtained, and the heat transfer coefficients associated with the types of refrigeration were calculated, solving the IHCP.It was found that 4% PAG concentration exhibits better quench uniformity regardless of the degree of agitation, but higher cooling rates in the martensitic transformation zone, increasing the risk of distortion and cracking.
WITHDRAWN: New Perspectives for the Intelligent Rolling Stock Classification in Railways: An Artificial Neural Networks-Based Approach
Authors: Dias Ualison Rodrigo Ferreira , Pinto Arthur C. Vargas e , Monteiro Henrique Luis Moreira , de Aguiar Eduardo P. 
Published: September 2023

Abstract
In railway operations, there are several factors that must be analyzed, such as operation cost, maintenance stops, failures, and others. One of these important topics is the analysis of the hot box and hot wheel due to the failure of these components. It can compromise the entire operation, resulting in serious accidents, such as train derailments. Thus, the use of a method that is able to classify a failure is essential for accident prevention. Therefore, as these failures in hot box and hot wheels are binary classification problems and nonlinear data, this work proposes a new method based on the Multilayer Perceptron combined with Set-Membership. The Multilayer Perceptron is very flexible and can be used generally to learn complex problems with the aforementioned characteristics; meanwhile, the Set-Membership leads to reduced computational complexity, fast convergence, and high accuracy. To validate the performance, we compare twelve models applied in eight datasets, seven of which are benchmarks, and one is composed of hot box and hot wheels problems. The results showed that the methods had a good performance when applied to these problems.
The Prevalence and Associated Factors of Suspected Occupational Respiratory Disease Among Construction Workers in Construction Industry in Selangor, Melaka and Pahang State: Secondary Data Analysis of Registry for Occupational Disease Screening (Rods) Database 2023
Authors: Norman Fatimah Zahra , Ismail Nurhuda Binti , Yasin Associate Professor Dr Munira , Mohamad Associate Professor Dr Mariam , Muzaini Kamarulzaman , Samsudin Ely Zarina binti , Noor Ahmad Faiz Azhari , Kadir Mohd Fizal Izwan Ab , Shahadan Sazreza , Manan Mohd Hafiz Abdul , Ismail Prof. Ahmad Izuanuddin 
Published: September 2023

Abstract

Background: Construction workers who are exposed to hazardous at the construction site expose to the risk of developing occupational respiratory disorder (ORD). Numerous studies have examined the effects of Occupational Respiratory Disease (ORD), which have become a global issue because they are thought to be a significant cause of morbidity and mortality. However, important data on the prevalence and associated factors of occupational respiratory disease among Malaysian construction workers due to occupational hazard exposure are insufficient. By examining the prevalence and risk factors for suspected ORD among employees in the construction workers in three states of Peninsular Malaysia, this study seeks to close this gap.
Methods: An analytical cross-sectional study was conducted using secondary data from the Registry for Occupational Disease Screening (RODS) Database 2023, involving construction workers. Suspected ORD was defined as the presence of at least one symptom from the British Medical Research Council (BMRC)questionnaire, plus at least one of the work-relatedness domains. The association between individual factors, occupational factors and organizational factors was investigated using descriptive analysis and logistic regression.
Results: The study findings revealed that the prevalence of suspected ORD was 12.1 (95% CI: 9.5-15.8)N=412. Regarding educational level, workers with non-tertiary education had a higher likelihood of suspected ORD compared to those with tertiary education (Adjusted Odds Ratio (AOR): 0.29, 95% Confidence interval [CI]: 0.94-0.88, p < 0.05). Individuals with any comorbidity exhibited a significantly greater likelihood of suspected ORD compared to those without comorbidities (AOR: 3.68, 95% CI: 1.14-11.9, p <0.05). Not using PPE N95 were associated with higher odds of suspected ORD (AOR: 9.35, 95% CI: 1.04-84.3, p < 0.05). Similarly, working in dust environment was significantly associated with a higher likelihood of suspected ORD (AOR: 4.93, 95% CI: 1.21-20.1, p < 0.05).
Conclusion: The findings indicate that occupational health management and interventions should consider educational level, comorbidities, usage of PPE (N95), and dust exposed in the workplace. Further study utilising complete diagnostic examination is required, however, to corroborate these findings and establish causal links. These findings will also help to create effective preventive measures and intervention techniques to reduce the negative impact of ORD on worker health.
The Nexus between Green Intellectual Capital, Blockchain Technology, Green Manufacturing, and Sustainable Performance
Authors: Umar Muhammad , Ahmad Arslan , Sro Robert , Muhammad Zikri 
Published: September 2023

Abstract
Enterprises across the globe are facing increasing pressure to effectively utilize resources and reduce costs through green supply chain practices. Emerging technology, such as blockchain technology which enables green practices, has become a contemporary industrial paradigm. However, enterprises need to build green intellectual capital to implement blockchain technology, which can be key to realizing green supply chain practices. This research examines the impact of green intellectual capital (GIC) on blockchain technology and its role in implementing green manufacturing to achieve sustainability. Partial Least Squares Structural Equation Modeling was utilized for assessing the proposed hypotheses, and cross-sectional data was accumulated from manufacturing firms. As per the results, GIC, which includes green human capital, green structural capital, and green relational capital, has a crucial role in the implementation of blockchain technology. The outcomes also indicated that the adoption of blockchain technology significantly influences green manufacturing. Moreover, green manufacturing (GM) has a substantial role in improving business sustainability. This empirical research provides a deeper understanding of how GIC and blockchain technology contribute to the implementation of GM. This research also provides guidelines that managers, policymakers, and producers can use to facilitate the incorporation of GM practice into business activities.
The impact of the pandemic and the COVID-19 vaccine hesitancy on people living with HIV: a single-center cross-sectional study in Caracas, Venezuela
Authors: Forero-Peña David A. , Carrión-Nessi Fhabián S. , Forero-Peña José L. , Camejo-Ávila Natasha A. , Mendoza-Millán Daniela L. , Omaña-Ávila Óscar D. , Maricuto Andrea L. , Velásquez Viledy L. , Mejía-Bernard Mario D. , Rodriguez-Saavedra Carlis M. , Marcano-Rojas María V. , Contreras Yoesmir , Guerra Luis J. , Alvarado María F. , Carballo Martín , Caldera Jocays , Guevara Rafael N. , Redondo María C. , Landaeta María E. 
Published: September 2023

Abstract
Background
The COVID-19 pandemic has disrupted multiple health services, including HIV testing, care, and treatment services, jeopardizing the achievement of the UNAIDS 90-90-90 global target. While there are limited studies assessing the impact of the COVID-19 pandemic on people living with HIV (PLHIV) in Latin America, there are none, to our knowledge, in Venezuela. This study aims to assess the impact of the pandemic and COVID-19 vaccine hesitancy among PLHIV seen at the outpatient clinic of a reference hospital in Venezuela.
Methods
We conducted a cross-sectional study among PLHIV aged 18 years and over seen at the Infectious Diseases Department of the University Hospital of Caracas, Venezuela between March 2021 and February 2022.
Results
A total of 238 PLHIV were included in the study. The median age was 43 years (IQR 24), and the majority were male (68.9%). Most patients (88.2%; n = 210) came for routine check-ups, while 28 (11.3%) were newly diagnosed. The majority of patients (96.1%) were on antiretroviral therapy (ART), but only 67.8% had a viral load test, with almost all (95.6%) being undetectable. Among those who attended regular appointments, 11.9% reported missing at least one medical consultation, and 3.3% reported an interruption in their ART refill. More than half of the patients (55.5%) had received at least one dose of the COVID-19 vaccine, while the rest expressed hesitancy to get vaccinated. Most patients with COVID-19 vaccine hesitancy were male (65.1%), younger than 44 years (57.5%), employed (47.2%), and had been diagnosed with HIV for less than one year (33%). However, no statistically significant differences were found between vaccinated patients and those with COVID-19 vaccine hesitancy. Age was a risk factor for missing consultations, while not having an alcoholic habit was identified as a protective factor against missing consultations.
Conclusion
This study found that the pandemic had a limited impact on adherence to medical consultations and interruptions in ART. However, it documented a high rate of COVID-19 vaccine hesitancy among PLHIV seen at the University Hospital of Caracas.
Sucralose (C12H19Cl3O8) impact on microbial activity in estuarine and freshwater marsh soils
Authors: Westmoreland Amelia G. , Schafer Tracey B. , Breland Kendall E. , Beard Anna R. , Osborne Todd Z. 
Published: September 2023

Abstract

As the general population’s diet has shifted to reflect current weight-loss trends, there has been an increase in zero-calorie artificial sweetener usage. Sucralose (C12H19Cl308), more commonly known as Splenda, is a primary example of these sweeteners. In recent years, it has been identified as an environmental contaminant, and cannot be broken down via bacterial decomposition. This study focuses on the impact of sucralose presence on microbial communities in brackish and freshwater systems. Using a BenthoTorch and LI-8000A CO2/H20 Gas Analyzer, microbial respiration and chlorophyll concentration were measured as indicators of microbial activity in dosed samples taken from both fresh and estuarine marsh environments. Results showed a significant difference between microbial concentration and respiration when dosed with varying levels of sucralose. The diatom community showed a negative correlation with the sucralose concentration. The freshwater cyanobacteria community exhibited a positive correlation with sucralose, in direct contrast to its saltwater counterpart. However, further studies are necessary to confirm any potential use for these communities in the breakdown of sucralose in wastewater treatment plants.
Subtyping of COVID-19 samples based on Cell-Cell interaction in single cell transcriptomes
Authors: Jeong Kyeonghun , Kim Yooeun , Jeon Jaemin , Kim Kwangsoo 
Published: September 2023

Abstract
In single-cell transcriptome analysis, numerous biomarkers related to COVID-19 severity, including cell subtypes, genes, and pathways, have been identified. Nevertheless, most studies have focused on severity groups based on clinical features, neglecting immunological heterogeneity within the same severity level. In this study, we employed sample-level clustering using cell-cell interaction scores to investigate patient heterogeneity and uncover novel subtypes. The clustering results were validated using external datasets, demonstrating superior reproducibility and purity compared to gene expression-or gene set enrichment-based clustering. Furthermore, the cell-cell interaction score-based clusters exhibited a strong correlation with the WHO ordinal severity score based on clinical characteristics. By characterizing the identified subtypes through known COVID-19 severity-associated biomarkers, we discovered a "Severe-like moderate" subtype. This subtype displayed clinical features akin to moderate cases; however, molecular features, such as gene expression and cell-cell interactions, resembled those of severe cases. Notably, all patients who progressed from moderate to severe belonged to this subtype, underscoring the significance of cell-cell interactions in COVID-19 patient heterogeneity and severity.
Accelerated Aging Effects on SRAM PUF reliability at various Temperature and Voltage conditions
Authors: Singh Harshdeep , Bhatta Niraj Prasad , Rahman Md Tauhidur , Amsaad Fathi 
Published: September 2023

Abstract

This paper thoroughly investigates the trustworthiness of Static Random-Access Memory (SRAM) Physical Unclonable Functions (PUFs) within hardware security. It presents an overview of SRAM PUFs, their significance in hardware security, and their essential features. The paper explores the realms of evaluation metrics and practical examination of SRAM cell start-up values concerning an 8-bit SRAM dataset. Concurrently, it navigates the encountered challenges. A comprehensive pattern analysis of SRAM cell stability is discussed with accelerated aging. Compared the outputs at multiple trials to authenticate the result. The complex interplay of factors that affect the dependability of SRAM PUFs, encompassing environmental conditions, the accumulation of aging effects, and the complex landscape of process variations, is carefully explained and clarified. The paper concludes by summarizing key findings, emphasizing the importance of reliable hardware security, and suggesting future research directions for enhancing SRAM PUF reliability.
Unraveling New Insights: Exploring Risk Factors of Under-Five Mortality in Somaliland using the First-Ever SLDHS 2020 Data and Count Data Regression Models
Authors: Ismail Mohamed A. , Chesneau Christophe , Muse Abdisalam Hassan 
Published: September 2023

Abstract
The high prevalence of zero counts in child mortality distribution in Somaliland prompts an exploration into whether distinct generating processes exist for child mortality and their frequency per mother. This study aims to identify an appropriate count regression model for the number of children who died per mother in Somaliland, addressing concerns of under-dispersion and zero-inflation. Addition[1]ally, it seeks to determine the risk factors associated with under-five mortality and their frequency. The analysis utilizes data from the nationally representative 2020 Somaliland Demographic and Health Survey (SLDHS), where 28.1% of the 4,615 surveyed women reported no under-five mortality. These zero counts may arise from structural or sampling factors, leading to the investigation of two-part zero-inflated and hurdle-count regression models alongside standard one-part count regression models. Diagnostic tools, such as the likelihood ratio test, The Akaike information criterion (AIC) and the Bayesian information criterion (BIC), are employed to determine the most suitable model. The hurdle Poisson regression model emerges as the best fit. The findings highlight several significant factors associated with under-five mortality per mother, including child sex, number of children ever born, access to clean water sources and toilet facilities, geographic region, wealth index, breastfeeding, mothers’ age at first birth, and mothers’ education. Understanding and addressing these factors can guide targeted interventions and policies aimed at reducing under-five mortality and improving child health outcomes. Furthermore, promoting deliveries at healthcare institutions is recommended for mothers.
Design, Synthesis and Evaluation of Aurone and Indanone Derivatives as Novel Antibacterial Agents
Authors: Xie Baoxing , Zhao Haiqing , Hu Ailin , Aisa Haji Akber , Niu Chao 
Published: September 2023

Abstract
Aurones and indanones were proved to be potential antibacterial agents in our recent work. Based on strategy of bioisosterism and diene insertion, a series of aurone and indanone derivatives were designed and synthesized, then submitted to the antibacterial activity evaluation on C. albicans, E. coli and S. aureus. According to the results, nineteen of thirty synthesized compounds had inhibitory activity against at least one bacterium. The minimum value of MIC activity was 62.5 µM (A2, B1, B3, B4, B8, C6, D1, D2) and the minimum value of MBC was 62.5 µM (B4, C6). In addition, molecular docking and ADMET prediction were also applied to the possible binding mode and the druggability of active compounds. Our research once again demonstrated that aurone and indanone are good scaffolds for the development of antibacterial agents, especially against S. aureus bacterial.
Reliability Based Design Optimization of a novel double-layer staggered octocopter with improved surrogate model
Authors: Zhu He , Nie Hong , Wei Yuanzun , Wei Xiaohui 
Published: September 2023

Abstract
Surrogate models are commonly used in the aircraft design process to save costs, but the predictions of simplified surrogate models often exist errors. Therefore, improving the accuracy of the surrogate model and conducting uncertainty analysis specifically for the surrogate model are necessary. In this study, a multi-objective optimization was performed on a novel configuration octocopter drone that demonstrated significantly higher aerodynamic efficiency compared to conventional configurations. The aim was to reduce the potential design failures resulting from the use of surrogate models by conducting uncertainty analysis. Under the guidance of experimental design, sample points were selected for computational fluid dynamics(CFD) simulation and a surrogate model was constructed. The errors of the surrogate model were fitted using a normal distribution, and uncertainty analysis and constraints were applied to the surrogate model using the Reliability Index Approach (RIA). The NSGA-III algorithm was employed for optimization. Based on the approximate Pareto front obtained from optimization, additional sample points were selected using orthogonal experimental design to supplement the dataset. The surrogate model was then improved, and the changes in accuracy were analyzed by comparing the probability distribution functions of the errors before and after adding the sample points. The final Pareto front was obtained. Through CFD simulation verification, the surrogate model achieved a prediction accuracy of 99.6%. The optimized unmanned aerial vehicle(UAV) exhibited a 113.341% improvement in thrust-to-weight ratio(y) and a 26.5% increase in aero ratio(a). The research demonstrates that the improved surrogate model has a high prediction accuracy and the methodology based on the improved surrogate model and RIA uncertainty analysis plays an important role in the optimization of novel rotorcraft.
Prevalence and Consequences of Workplace Violence among Nurses: A Study at Accra Psychiatric Hospital
Authors: Battanis Yvonne Yawa , Adjorlolo Samuel 
Published: September 2023

Abstract

Background
Workplace violence has been identified as an endemic problem affecting all healthcare workers, especially mental health nurses. In the health sector, less emphasis has been paid to workplace violence, with most studies on violence taking place in high-income nations and only a handful in low- and middle-income countries. This study investigates the prevalence and consequences of workplace violence among mental health nurses working in Accra Psychiatric Hospital, Ghana.
Methods
Data were collected from 233 mental health nurses using a cross-sectional approach and adapted the Workplace Violence in the Health Sector Country Case Studies Research Instrument to collect data on the prevalence and self-reported consequences of workplace violence among nurses. Data were analysed with SPSS Version 23.0 and Microsoft Excel 2008, using descriptive statistics and results presented in tables.
Results
Workplace violence was identified to be prevalent among nurses working in the hospital with 21.9%, 52.8%, and 6.0% reporting physical violence, verbal abuse and sexual harassment cases, respectively. Consequences of workplace violence include being super alert or watchful and on guard; difficulty avoiding thinking about or talking about the event or having feelings related to it; disturbing images/memories/thoughts of the event and feeling sick.
Conclusion
Workplace violence is prevalent among nurses and impacts their health and safety. Ensuring effective strategies and procedures for reducing and reporting violence will help find a lasting solution to workplace violence among mental health nurses.
Self-Adaption Rapid Screening Model Research on Psychological Symptoms of Prisoners in Community Correction Based on Machine Learning
Authors: Xu Zhifei , Pan Zhigeng , Wang Yan , Zhang Yichao , Leng Pengfei 
Published: September 2023

Abstract

The SCL-90 Symptom Checklist is a widely used screening scale in psychiatry and psychology in clinical practice,has also been adopted by community correction institutions as the main tool for psychological assessment of community correction prisoners.However, in practical work, there are many problems with the evaluation of this scale, such as a complex number of questions, low cultural level of chief examiner, and insufficient specialization level of the community corrections officer (CCO) .For this reason, grassroots community correction institutions generally use the SF-12 Health Survey Brief as a preliminary screening tool. This scale only has 12 questions, improving work efficiency, but it is prone to missed screening. As a widely used evaluation scale, it also lacks group specificity.
Therefore, this study discusses how to compare the use of multi-label classification algorithm and oversampling method to build machine learning model and find the association between the mental health risk of the special group and the group trait data. By using the idea of computerized adaptive testing (CAT) for reference, and according to the trait level of community correction prisoners, the most suitable one or more subscales from the nine subscales of symptom checklist SCL-90 are personally recommended for the subjects.Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale (between question groups) targeting a specific group of community correctional prisoners have been achieved. This method not only simplifies the number of questions, reduces the test time, improves the work efficiency, but also enhances the pertinence of groups and scenes. It is beneficial for community correction institutions to predict, prevent and warn regulatory safety events, and timely play the role of correction and shaping the heart to effectively prevent and reduce the recidivism of released prisoners.