bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effect of Anxiety and Autism Symptom Severity on Restricted and Repetitive Behaviors Over Time in Children with Fragile X Syndrome
Authors: Moskowitz Lauren , Will Elizabeth , Black Conner , Roberts Jane 
Published: November 2023

Abstract

Background: Restricted and repetitive behaviors (RRBs) are highly prevalent and reduce function in individuals with fragile X syndrome (FXS). As transdiagnostic features of intellectual disability, elevated rates of RRBs in FXS could represent various underlying known co-occurring conditions in FXS such as anxiety or autism spectrum disorder (ASD), yet this distinction has not been investigated. Further, delineating whether RRBs are more indicative of anxiety or ASD in FXS may clarify phenotypic profiles within FXS and improve differential assessment.
Methods: We longitudinally examined the potentially independent or multiplicative effect of ASD and anxiety symptom severity on RRBs in 60 children with FXS. Anxiety was measured using the Child Behavior Checklist (CBCL), ASD severity was measured using the Childhood Autism Rating Scale (CARS), and RRBs were measured using The Repetitive Behavior Scale – Revised (RBS-R). We estimated a series of moderated regression models with anxiety and ASD symptoms at the initial assessment (Time 1) as predictors of RRBs at the outcome assessment two years later (Time 2), along with an anxiety-by-ASD interaction term to determine the potential multiplicative effect of these co-occurring conditions on RRBs.
Results: Results identified a significant interaction between ASD and anxiety symptom severity at the initial assessment that predicted elevated sensory-motor RRBs two years later. Increased sensory-motor RRBs were predicted by elevated ASD symptoms only when anxiety symptom severity was low. Likewise, increased sensory-motor RRBs were predicted by elevated anxiety symptoms only when ASD symptom severity was low. Interestingly, this relationship was isolated to Sensory-Motor RRBs, with evidence that it could also apply to total RRBs.
Conclusions:Findings suggest that ASD and anxiety exert independent and differential effects on Sensory-Motor RRBs when at high severity levels and a multiplicative effect when at moderate levels.
Estimation of Drilling Rate of Penetration Using Hybrid Machine Learning Methods
Authors: Ranjbar Ali , Mohammadinia Fatemeh 
Published: November 2023

Abstract

Rate of penetration(ROP) is one of the most important well drilling parameters, and its estimation and optimization is very important during well planning and reducing related costs. Meanwhile, the prediction of this parameter is challenging due to the complex interactions between the drill bit and the formation rock. In this study, different Machine Learning(ML) estimation techniques including Artificial Neural Networks(ANN), Random Forest(RF) and Least Squares Support Vector Machine (LSSVM) are hybridized with meta-heuristic algorithms, including Crow Search Algorithm(CSA), Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) has been used to estimate ROP more accurately. The aforementioned meta-heuristic algorithms have been used to intelligently adjust hyper-parameters of estimation ML methods based on data. The results show that it will significantly improve the estimation performance. Among the models, RF-GA, RF-CSA and LSSVM-GA were recognized as the top three models, respectively. The value of the correlation coefficients between the estimated and the actual values of ROP in these models was 0.98, 0.974, and 0.972, respectively. Also, the mean square error (RMSE) values for these models were obtained 2.89, 3.25 and 3.37, respectively. Depth, mud weight and rotation speed are identified as the most influential parameters in the response of estimation models. The findings emphasize the effectiveness of combining ML methods with meta-heuristic algorithms to accurately estimate drilling penetration rates. The results provide valuable insights to optimize drilling operations, reduce costs and increase drilling performance in oil fields. The results of this study in the field of drilling optimization can be useful in engineering-based drilling decisions.
A Scoping Review of Digital Technologies in Antenatal Care: Recent Progress and Applications of Digital Technologies
Authors: Mohamed Halila , Ismail Aniza , Sutan Rosnah , Rahman Rahana Abd , Juval Kawselyah 
Published: November 2023

Abstract

Introduction:
Integrating digital health technology into antenatal care has revolutionised patient monitoring, diagnosis, and management. As expectant mothers increasingly embrace social media, the Internet, and smartphone applications, leveraging digital health interventions presents immense potential for enhancing maternal healthcare.
Objective
This review aims to assess digital health modalities, their applicability, and outcomes in maternal care.
Method
We conducted a scoping review by searching four electronic databases (Scopus, Web of Science, PubMed, EBSCOhost), performing manual searches of Google Scholar, and examining the references of relevant studies. Eligible studies included original research published in English between 2018 and January 2023 involving the usage of digital health interventions for antenatal care, complying with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping review guidelines.
Results
A total of 61 eligible articles were identified, where most (n = 37) were conducted in high-income countries such as the US, UK, and Australia. Over the past five years, digital intervention studies motivated by increased telehealth adoption in affluent nations have increased consistently. These interventions primarily encompass patient–provider consultations, remote monitoring, and health education, often supplementing or even replacing in-person visits. Notably, acceptance and satisfaction levels among patients and healthcare providers engaging with digital health interventions were high. The interventions primarily addressed general maternal care (n = 22, 36.06%), mental health issues (n = 7, 11.47%), high-risk pregnancies (n = 5, 8.19%), hypertensive disorders (n = 4, 6.55%), gestational diabetes mellitus (n = 7, 11.47%), antenatal education (n = 5, 8.19%), and gestational weight gain (n = 3, 4.91%). The review findings consistently demonstrated that digital health interventions yielded positive results in effectively managing clinical conditions, improving knowledge, and promoting birth preparedness during the perinatal period. Additionally, these interventions digitally enhanced antenatal care service access and utilisation.
Conclusion
Digital health is gaining importance in maternal and child care, where patients and healthcare providers demonstrate positive outcomes and high acceptance. Safety and privacy concerns are rarely discussed, but no adverse effects have been reported, and standard care is maintained. The increasing prevalence of digital technologies paves the way for the widespread adoption of digital health in antenatal care, shaping the future of maternal care.
Correlation between Regional Differences in Disability and Health Resources in Middle-Aged and Elderly People
Authors: Liu Ying , Wang Daofeng , Ma Xueqi , Li Jiantao , Zhao Zixin , Liu Wanheng 
Published: November 2023

Abstract

Background: With the aging and disability of the population, it is bound to bring a series of social, economic and health risks. Medical and health resources and services also provide more life extension opportunities for the disabled elderly and delay the process from disability to near death, but increase the total disability years of the group. There are differences in economic development level, distribution of medical resources and health service coverage in different regions. The demand for long-term care and medical services for middle-aged and elderly people has increased, which will lead to a heavier social medical burden and greater challenges for the health care system.
Objective: To explore the impact of health resource allocation equity on regional differences in disability among middle-aged and elderly people.
Methods: The disability data is based on 2018 China Health and Retirement Longitudinal Study (CHARLS), and health resource allocation data are derived from 2018 China Statistical Yearbook, and a total of 19,170 people aged 45 years and older were included in the study.
Results: The results of this study show that there are differences in disability rate and degree among middle-aged and elderly people in east, central and west China and the differences was related to the equity of health personnel and health financial expenditures, and the equity of practicing (assistant) physicians and rural doctors were the main factors.
Conclusion: It is necessary to promote the cross-regional cooperation and sustainable development of health resources and establish a sound health resource information management system to gradually reduce the regional differences in the disability rate and degree of middle-aged and elderly people.
Pathophysiology characterization of Alzheimer’s disease in South China’s Aging Population: for the Greater-Bay-Area Healthy Aging Brain Study (GHABS)
Authors: Liu Zhen , Shi Dai , Cai Yue , Li Anqi , Lan Guoyu , Sun Pan , Liu Lin , Zhu Yalin , Yang Jie , Zhou Yajing , Guo Lizhi , Zhang Laihong , Deng Shuqing , Chen Shuda , Yu Xianfeng , Chen Xuhui , Zhao Ruiyue , Wang Qingyong , Ran Pengcheng , Xu Linsen , Zhou Liemin , Sun Kun , Wang Xinlu , Peng Qiyu , Han Ying , Guo Tengfei 
Published: November 2023

Abstract
INTRODUCTION: The Guangdong-Hong Kong-Macao Greater-Bay-Area of South China has an 86 million population and faces a significant challenge of Alzheimer’s disease (AD). However, the characteristics and prevalence of AD in this area are still unclear due to the rarely available community-based neuroimaging AD cohort.
METHODS: Following the standard protocols of the Alzheimer’s Disease Neuroimaging Initiative, the Greater-Bay-Area Healthy Aging Brain Study (GHABS) was initiated in 2021. GHABS participants completed clinical assessments, plasma biomarkers, genotyping, magnetic resonance imaging (MRI), β-amyloid (Aβ) positron emission tomography (PET) imaging, and tau PET imaging. The GHABS cohort focuses on pathophysiology characterization and early AD detection in the Guangdong-Hong Kong-Macao Greater Bay Area. In this study, we analyzed plasma Aβ42/Aβ40 (A), p-Tau181 (T), neurofilament light, and GFAP by Simoa in 470 Chinese older adults, and 301, 195, and 70 had MRI, Aβ PET, and tau PET, respectively. Plasma biomarkers, Aβ PET, tau PET, hippocampal volume, and temporal-metaROI cortical thickness were compared between normal control (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia groups, controlling for age, sex, and APOE-ε4. The prevalence of plasma A/T profiles and Aβ PET positivity were also determined in different diagnostic groups.
RESULTS: The aims, study design, data collection, and potential applications of GHABS are summarized. SCD individuals had significantly higher plasma p-Tau181 and plasma GFAP than the NC individuals. MCI and dementia patients showed more abnormal changes in all the plasma and neuroimaging biomarkers than NC and SCD individuals. The frequencies of plasma A+/T+ (NC; 5.9%, SCD: 8.2%, MCI: 25.3%, dementia: 64.9%) and Aβ PET positivity (NC: 25.6%, SCD: 22.5%, MCI: 47.7%, dementia: 89.3%) were reported.
DISCUSSION: The GHABS cohort may provide helpful guidance toward designing standard AD community cohorts in South China. This study, for the first time, reported the pathophysiology characterization of plasma biomarkers, Aβ PET, tau PET, hippocampal atrophy, and AD-signature cortical thinning, as well as the prevalence of Aβ PET positivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China. These findings provide novel insights into understanding the characteristics of abnormal AD pathological changes in South China’s older population.
WITHDRAWN: Regional Traffic Signal Control Optimization Model based on Quantum Genetic Algorithm
Authors: Wu Jiayi , Zhou Wei , Tan Zhenjiang , Zhao Nan 
Published: November 2023

Abstract

The paper proposes, for the first time in the field of optimizing regional traffic signals, a model based on a quantum genetic algorithm to solve the problem of traffic congestion at intersections under the existing infrastructure conditions. The model introduces four evaluation criteria: vehicle waiting time, standard deviation, collision percentage, and algorithm execution time. It conducts simulation experiments on three typical intersection types: cross intersections, roundabouts, and diamond intersections. A more optimal regional traffic signal control scheme is proposed. In order to verify the effectiveness of the scheme, a large number of subsequent simulation experiments are conducted. The results demonstrate that, compared to other traditional intelligent algorithms, the algorithm presented in this paper performs better at alleviating traffic congestion at intersections.
Influence of liver function markers and apolipoprotein E ε4 on pathogenesis and cognitive decline in Alzheimer’s disease
Authors: Han Sang-Won , Lee Sang-Hwa , Kim Jong Ho , Lee Jae-Jun , Park Young Ho , Kim SangYun , Nho Kwangsik , Sohn Jong-Hee 
Published: November 2023

Abstract

BackgroundAlzheimer’s disease (AD) is a complex neurodegenerative disorder influenced by various factors, including liver function, which may impact the clearance of amyloid-β (Aβ) in the brain. This study aimed to explore how the apolipoprotein E (APOE) ε4 allele, a well-known genetic risk factor for AD, affects the relationship of liver function markers with AD pathology and cognition.MethodsUsing two independent cohorts, the Hallym University Medical Center and the Alzheimer’s Disease Neuroimaging Initiative, logistic and linear regression models were used to investigate the associations of liver enzymes (aspartate aminotransferase [AST], alanine aminotransferase [ALT], alkaline phosphatase, total bilirubin, and albumin) with AD diagnosis, amyloid positron emission tomography (PET) burden, cerebrospinal fluid biomarkers for AD (Aβ42, total tau, and phosphorylated tau181]), and cognitive performance. Mediation analyses were conducted to assess whether age and amyloid PET burden mediated these associations.ResultsOnly in theAPOEε4 carrier group, a high AST to ALT ratio and low ALT levels were significantly associated with AD diagnosis, increased amyloid PET burden, poor cognitive function at the last visit, and faster longitudinal decline in cognitive function in both cohorts. However, no such relationship was observed in theAPOEε4 non-carrier group. In particular, the AST to ALT ratio was associated with cerebrospinal fluid Aβ42 levels exclusively in theAPOEε4 carrier group in the Alzheimer's Disease Neuroimaging Initiative cohort but not with phosphorylated tau181or total tau levels. Moreover, mediation analyses in both cohorts revealed that age played a mediating role in the associations between these liver enzymes and amyloid PET burden or AD diagnosis, exclusively in theAPOEε4 non-carrier group, but not in theAPOEε4 carrier group. The association between these liver enzymes and AD diagnosis was mediated by the amyloid PET burden, but this mediation effect was only evident in theAPOEε4 carrier group.ConclusionsThis study provides valuable insights into the significant influence of theAPOEε4 allele on the intricate relationships of liver enzymes with Aβ-related pathogenesis and cognition in AD. Further research is required to elucidate the underlying mechanisms and potential therapeutic implications of these findings.
Prevalence of low back pain and associated factors among bank workers at Hawassa district, northern zone, Sidama Region, Southern Ethiopia
Authors: Jonga Thomas , Samuel Beniyam , Aynalem Amdehiwot , Israel Eskinder , Balta Bargude , Ameno Abdela 
Published: November 2023

Abstract

Background
Low back pain (LBP) is a public health treat that affects people who are frequently enrolled in sedentary working conditions. Thereby, interventions are required to combat the effects resulting from lost productivity and cause a negative economic impact due to their absence from the workplace, seeking medical intervention at a health facility, or being on bed rest many times related to illness. The aim of this study was to determine the magnitude and associated factors among bank workers in Hawassa district, Sidama region, Ethiopia.
Methods
An institutional-based cross-sectional study was conducted from April 20, 2023, to June 30, 2023. A simple random sampling method was employed to select 627 bank workers. Data was collected using a structured questionnaire adopted from the Standardized Nordic Musculoskeletal Questionnaire. Then the data was entered into Epidata version 4.6 and transferred to SPSS version 25 for further analysis. Bivariate and multivariate logistic regression were done to identify independent predictors of lower back pain. Finally, in all analyses, a P-value < 0.05 was considered as significant.
Results
Six hundred-seven bank workers participated in the study, giving a response rate of 96.8%. The overall magnitude of lower back pain among the study population was 55.2% with a 95% confidence level (CI 51.1–59). The magnitude of LBP was independently associated with four baseline factors. Manager position AOR = 4.3; 95% CI= (1.2,15); Level 2 banker AOR = 3.78% CI= (1.9,8.9); Age 30–39; AOR = 4.6; 95% CI= (2,10.6); Age ≥ 40; AOR = 2.4; 95% CI= (1.04,5.5); work space sufficient AOR = 0.5; 95% CI= (0.3,0.8); and physical activity AOR = 0.2; 95% CI= (0.1,0.9) were independent predictors of lower back pain.
Conclusion
The prevalence of low back pain among bank workers is high in the study area. It is recommended and essential to establish a health screening team, promote physical activity, promote healthy working conditions, and give special attention to elderly bankers and bank managers to reduce the risk of developing low back pain
Comparison between methods to predict climate change impacts on tropical shallow lakes
Authors: Amorim Lais Ferrer , Duarte Bárbara Pozzan dos Santos , Martins José Rodolfo Scarati 
Published: November 2023

Abstract

Inland waters play a key role in climate change studies, but choosing the correct tool to represent them is challenging. This paper discusses tool's applicability for predicting the impact of climate change on a lake's hydrodynamics. It aims to help determine the most suitable method to utilize. Three different tools, capable of representing the lake's hydrodynamics, were built and evaluated through the required input data quantity, the lake's hydrodynamic representation, and time consumption. Two climate change scenarios were simulated using the thermal stability curve, a unidimensional model (GLM), and a 3D mathematical model (Delft3D). The results were consistent, indicating an increase in the lake's temperature and the required energy to break the stratification, altering thermal patterns. The stability curve requires minimum input data and, with little computing time, can cover a larger simulation window. The unidimensional model requires more input data and knowledge, but with little simulation time, it shows the temperature profile, while the three-dimensional model provides gains in spatial variability representation; however, it needs more input data and advanced knowledge and is time-consuming. In lake management, it will be appropriate to combine the methods, using the curve to analyse the trend and delimitate the period for detailed study.
Resolution for Pandemics Approved by the International Telecommunication Union Plenipotentiary Conference [Past Related Activities and Future Challenges]
Authors: Nakajima Isao , Kawasumi Yasuhiko , Yoo Done-Sik , Androuchko Leonid 
Published: November 2023

The ITU adopted a new resolution at its PP Conference held in 2022 in Bucharest, Romania to utilize ICT to combat pandemics and new infectious diseases. In this paper, we have investigated infectious disease-related contributions and declarations at the ITU-D up to the most recent resolution for pandemics, pointed out issues, and offered a concrete outlook. We are pleased that the new resolution on pandemics has been issued, but the specific details regarding infection detection, efforts to prevent infection (PP-22 Resolution 215), and technology content remain insufficient. The risk of highly pathogenic avian influenza infection and the global spread of Ebola virus disease cannot be eliminated. ICT plays a major role in social infrastructure, and its use in health care is rapidly expanding into fields of application. At the next PP Conference, we would like to request that ITU begin to focus on saving more lives by making full use of AI and ICT technology.
The application of an all-media perspective-oriented teaching in the environmental art and design program
Authors: Xu Xuanyin 
Published: November 2023

Abstract
This paper is oriented to the all-media vision to run the database mechanism in a multidimensional interactive teaching system for environmental art and design majors to maximize the possibility of predicting the probability distribution of the next course. Installing the database on the server enables data interaction within the system. Use a wavelet neural network for training to achieve the balance of communication channels. The results show that the mean square error of the system designed in this paper is under 0.1, the minimum average response time is 120.6ms, and the score of the important factor of student course evaluation is 75. The all-media vision can provide teaching design ideas for the teaching process of environmental art design majors and nurture applied design talents, as demonstrated.
Model Construction and Analysis of Deep Learning-based Cybersecurity Awareness Enhancement for College Students
Authors: Song Chengli 
Published: November 2023

Abstract
This paper constructs a network security intelligence analysis model based on deep learning methods. Firstly, the weights and thresholds of network packets are modeled using the convolutional neural network algorithm to extract the main information features. Then, the backward propagation algorithm is used for layer-by-layer propagation, combined with an unsupervised autoencoder to achieve the network parameter update. The results show that the model can recognize a variety of network viruses, with an average detection rate of 97%, and the error rate is kept around 0.5%. The network security intelligence analysis model is based on the deep learning method to analyze and warn about network intrusion data, effectively improving college students’ awareness about network security.
Modern Human Resource Management System Based on Cloud Computing Platform
Authors: Yu He 
Published: November 2023

Abstract
This paper creates a human resource management system based on a cloud computing platform to achieve effective data analysis and calculation. The effectiveness of resource output is controlled through similarity characterization, fuzzy division of resource data, defining the division area, and then obtaining resource association rules. The system classifies the information of working employees according to the probability data, determines the characteristic attributes of the talent level vector of new employees, and can determine the initial level of new employees through the matching value. It is found that the gender R in the human resource management system increases significantly by 0.132, the R*R value is 0.624, and the behavioral performance is relatively high at 0.09, which indicates that the cloud computing platform can provide a scientific basis for the important decisions of the enterprise.