bluuebunny/crossref_metadata_2025_split huggingface dataset:
Woody species composition and diversity of homegarden agroforestry systems and their Contributions to Rural Livelihood in Dendi District, Central Ethiopia.
Authors: Dereje Yeshareg , Mammo Siraj 
Published: August 2024

Abstract

Compared to other agroforestry systems, the homegarden agroforestry system primarily consists of complicated multi-strata. This study assessed the Contributions of homegardens agroforestry to woody plant species' floristic composition, diversity and Rural Livelihood improvements in Dendi District, Central Ethiopia. To undergo this study 4 rural kebeles were selected using a purposive sampling technique from the study district. Homegardens were chosen from each kebeles using a systematic random sampling technique. Woody Plant species inventory was carried out for 48 homegarden agroforestry, from 143 total households. All woody plant species were counted and recorded in 10 x 10 m plots. A total of 37 woody plant species belonging to 33 genera and 26 botanical families were recorded in the study homegardens. The most diversified woody plant family was Fabaceae with five species followed, by Rosaceae and Euphorbiaceae each represented by three species. The average value of Shannon-Wiener diversity indices for the study area is 2.44. The range of species evenness, which was from 0.67 to 0.72, indicates the distribution of species in the homegardens of Dendi District is more or less similar. The respondents classified homegarden agroforestry in the Dendi district into 10 functional uses. The result revealed that about 34.4% of the woody plant species in homegardens served the household's cash income generation. Others are accounted for medicine, building or fuel wood, live fence, home materials, spices, stimulants, shade, and ornamentals. In addition, it contributes to environmental sustainability. Homegarden agroforestry systems conserve several woody species diversity in their system and woody species were more or less evenly distributed, among the study sites. Generally, the present study showed that the homegarden agroforestry system plays a remarkable role in conserving woody species diversity and the improvements of rural livelihoods. Therefore, the focus should be given to extension services, management and sustainable use of woody species in homegarden agroforestry.
Structural health monitoring of railway truss bridge under moving train load using decision tree models and residual neural networks
Authors: Sunchikala Sharan Kumar , C Mohan S , Gopala Sai Dheeraj , P Swetha 
Published: August 2024

Abstract

This paper introduces an efficient machine learning-based structural health monitoring method for railway truss bridges, addressing the time-consuming and error-prone nature of traditional approaches. By utilizing measured vibration responses under train load, the technique employs wavelets, Fourier transforms, and spectrograms to extract damage-induced changes in signals for training machine learning models. Given the slow and impractical data collection from real-world bridges, the paper proposes generating data from a numerical model, onto which a moving train load is applied. The acceleration time history responses from nodes are recorded for various damage cases, forming the dataset. Decision trees and Residual Neural Networks are trained on this data, demonstrating accurate classification of damaged members. Despite the effectiveness, human interpretation remains necessary for structural health monitoring, emphasizing these models as tools to enhance efficiency and reduce human errors in the monitoring process.
Factors associated with unmet medical needs among middle-aged and older adults in China during COVID-19 Pandemic
Authors: Zheng Yiping , Zhang Baoquan , Wei Jin , Xue Dongyu , Li Changle , Dai Yue 
Published: August 2024

Abstract

Background The COVID-19 pandemic may negatively impact the accessibility of medical care in China. This cross-sectional study aimed to identify the factors associated with unmet medical needs among middle-aged and older adults in China during the COVID-19 Pandemic. Methods A cross-sectional analysis using data from the 2020 China Health and Retirement Longitudinal Study. The final sample consisted of 19,123 individuals. Multiple imputation was applied to handle missing values. A binary logistic regression was used to examine factors associated with unmet medical needs. Results During the COVID-19 pandemic, 11.18% of middle-aged and older adults did not receive needed medical care. In both urban and rural areas, middle-aged and older adults who were male, with higher educational attainment, rated poor health, suffering from chronic conditions, residing in a residential area completely shut, and often felt fears were more likely to lead to unmet medical needs. In urban areas, middle-aged and older adults with urban employee medical insurance (OR = 2.30, 95% CI: 1.36, 3.56) and urban and rural resident medical insurance (OR = 1.65, 95% CI: 0.96, 2.44) were more likely to have unmet medical needs. In rural areas, middle-aged and older adults over 75 years of age (OR = 0.46, 95% CI: 0.35, 0.62) were less likely to have unmet medical needs, and middle-aged and older adults who knew the preventive measures (OR = 2.16, 95% CI: 1.42, 3.29) had a higher probability of having unmet medical needs. Conclusions The findings indicate gender, age, educational attainment, occupation, living with a spouse, health insurance, number of health technicians per 10,000, self-rated health, chronic conditions, depression, knowing the preventive measures, and fear of pandemic associated with unmet medical needs. The unmet medical needs of vulnerable groups should receive priority attention in the future and facilitate rationalizing the allocation structure of medical resources.
Evaluating the Potential of Wearable Technology in Early Stress Detection: A Multimodal Approach
Authors: Darwish Basil A. , Salem Nancy M. , Kareem Ghada , Mahmoud Lamees N. , Sadek Ibrahim 
Published: August 2024

Abstract

Stress can adversely impact health, leading to issues like high blood pressure, heart diseases, and a compromised immune system. Monitoring stress with wearable devices is crucial for timely intervention and management. This study examines the efficacy of wearable devices in early stress detection using binary and five-class classification models. Significant correlations between stress levels and physiological signals, including Electrocardiogram (ECG), Electrodermal Activity (EDA), and Respiration (RESP), were found, validating these signals as reliable stress biomarkers. Utilizing the WESAD dataset, we applied ensemble methods, Majority Voting (MV) and Weighted Averaging (WA), achieving maximum accuracies of 99.96% for binary classification and 99.59% for five-class classification. Ten classifiers were evaluated, with hyperparameter optimization and 3 to 10 fold cross-validation applied. Time and frequency domain features were analyzed separately. We reviewed commercially available wearables supporting these modalities and provided recommendations for optimal configurations in practical applications. Our findings demonstrate the potential of multimodal wearable devices for early detection and continuous monitoring of psychological stress, suggesting significant implications for future research and the development of improved stress detection systems.
Development and validation of a machine learning model for prediction of cephalic dystocia
Authors: Huang Yumei , Ran Xuerong , Zhai Jinguo , Wang Xueyan , Wu Defang , Yao Zheng 
Published: August 2024

Abstract

Background Early detection of cephalic dystocia is challenging, and current clinical assessment tools are limited. Machine learning offers unique advantages, enabling the generation of predictive models using various types of clinical data. Our model aims to integrate objective ultrasound data with psychological and sociological characteristics and obstetric treatment data to predict the individual probability of cephalic dystocia in pregnant women.Methods We collected data from 302 pregnant women who underwent examinations and deliveries at Southern Medical University's Nanfang Hospital from January 2022 to December 2023. We utilized basic patient characteristics, foetal ultrasound parameters, maternal anthropometric data, maternal psychological measurements, and obstetric medical records to train and test the machine learning models. Our study analysed the effectiveness of three machine learning models: least absolute shrinkage and selection operator (LASSO) regression, decision tree, and random forest. The precision, accuracy, recall, and area under the receiver operating characteristic (ROC) Curve (AUC) were used to evaluate the performance of the models.Results Among the three machine learning models, the LASSO-based logistic regression model demonstrated the best predictive performance, with an AUC value of 0.833. We found that maternal ischial spine diameter, fetal biparietal diameter, fetal biophysical profile score, artificial rupture of membranes, labor analgesia, childbirth self-efficacy, and other variables were predictive factors for cephalic dystocia.Conclusions This study constructed and validated a prediction model for cephalic dystocia via three machine learning methods, which can help clinicians improve the probability of identifying pregnant women at risk for cephalic dystocia.
Network analysis of key factors influencing subjective well-being among elderly with chronic disorders: A study based on the 2020 CFPS
Authors: Liang Chenyu , Ye Zilan , Yan Haifeng , Tan Jianfeng 
Published: August 2024

Abstract

Objectives
This study aims to delve into the mechanisms influencing the subjective well-being of elderly individuals with chronic disorders within the context of aging population. It comprehensively analyzes these factors, including cognitive schemas, social relationships, and demographics to provide a scientific basis for enhancing the subjective well-being of elderly individuals.
Methods
This study utilized data from the 2020 China Family Panel Studies (CFPS) and selected 863 individuals aged 65 and above with chronic disorders as subjects for analysis. Network analysis was conducted using JASP.
Results
This study has identified all the relationships between factors in the research and subjective well-being among the elderly with chronic disorders, and we have visualized them in graphical form. The findings indicate that social relationships (S1) are most closely associated with subjective well-being (W1), followed by level of confidence in the future (C1) and social status (B7). Further analysis found that C1 and S1 could be a mediator affecting the impact of B7 on W1.
Conclusion
Identify and delineate the key factors influencing the subjective well-being of elderly individuals with chronic disorders, as well as their interrelationships. This helps to further investigate the factors influencing the subjective well-being of the elderly.
Optimizing Ventilated Disk Brake Design for Enhanced Thermal Performance: An Analytical and Experimental Approach
Authors: Agrawal Vikash K. , Patil Lalit N. , Panwar Vikas Singh , Chavan Kaustabh Vijay , Nimbalkar U. D. 
Published: August 2024

Abstract

The primary purpose of the present investigation is to contribute to the invention of a methodology for determining the innermost temperatures in disc brakes at any range from its mid plane employing the Fourier transformation and Laplace transforms. These methods were utilized to investigate the interior surface temperature throughout braking under pad stress and disc tension circumstances. The initial segment of the mathematical simulation of the innermost temperature within the brake disk showed that the disk surface were the most relevant factor among the parameters under investigation. In the next segment experimentation approach, the ambient temperature along the interface among the pads and disk fluctuate between 97.8°C to 306.7°C because the rotation speed fluctuates across 300 rpm to 1100 rpm and the load from 20 N to 80 N. Another benefit section studies the flow of air, convection heat exchange parameters, and speeds, with validations using CFD simulation. The interface ranges (36.45°C to 91.668°C) were estimated with CFD. The vented brake rotors component was created as a three-dimensional model in modeling software and then loaded analysis software for estimating the disc's entire heat transfer including total thermal surface flux. It is possible to examine brake disks with complex geometries and concepts, especially tapered rotor disks, three-hole disks, among four-hole disks. The tapered rotor four-hole design has the greatest heat flux (489.32 W/m°K) and the greatest heat transfer factor (171262.8 W/m²) relative to other designs, and reaches the lowest temperatures (101.2°C). Mathematical, CFD, and testing examination of the suggested tapered rotor four-hole disk brake design revealed optimal thermal dissipation.
Network Security Anomaly Node Detection Based on Graph Neural Network and Attention Mechanism
Authors: Zhang Deng , Wang Juan , Gao Hanjun , Ni ZhouWei , Zhang Hao 
Published: August 2024

Abstract

Protecting networks from malicious access is a highly challenging task. Identifying and detecting anomalous paths can effectively filter out potential network threats. Despite extensive research in this field, the advent of new network technologies and the increase in connected devices have led to more diverse network attacks. Traditional anomaly detection methods struggle to capture complex relationships between nodes. Graph Neural Networks (GNNs), with their multi-layer structures, can efficiently extract multi-attribute features of nodes.we propose an efficient method for anomalous path detection. By incorporating a multi-head attention mechanism into GNNs, we dynamically adjust node weights to capture complex dependencies. To accurately identify anomalous nodes, we also designed a new threat evaluation scoring formula, enhancing the robustness and accuracy of anomaly detection. Experimental results on the CIC-IDS-2017 dataset show that our model achieved nearly 100\% accuracy and the fastest response time. Compared with previous work, our model provides faster decision-making and significantly improved evaluation performance.Our research demonstrates the potential of GNNs and multi-head attention mechanisms in network security, offering strong support for addressing increasingly complex network threats.
A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security
Authors: Arreche Osvaldo , Abdallah Mustafa 
Published: August 2024

Abstract

New research focuses on creating artificial intelligence (AI) solutions for network intrusion detection systems (NIDS), drawing its inspiration from the ever-growing number of intrusions on networked systems, increasing its complexity and intelligibility. Hence, the use of explainable AI (XAI) techniques in real-world intrusion detection systems comes from the requirement to comprehend and elucidate black-box AI models to security analysts. In an effort to meet such requirements, this paper focuses on applying and evaluating White-Box XAI techniques (particularly LRP, IG, and DeepLift) for NIDS via an end-to-end framework for neural network models, using three widely used network intrusion datasets (NSL-KDD, CICIDS-2017, and RoEduNet-SIMARGL2021), assessing its global and local scopes, and examining six distinct assessment measures (descriptive accuracy, sparsity, stability, robustness, efficiency, and completeness). We also compare the performance of white-box XAI methods with black-box XAI methods. The results show that using White-box XAI techniques scores high in robustness and completeness, which are crucial metrics for IDS. Moreover, the source codes for the programs developed for our XAI evaluation framework are available to be improved and used by the research community.
The Transcriptome dynamics of the pituitary of turkey (Meleagris gallopavo) during broodiness
Authors: Bankizadeh Fatemeh , Sadr Ayeh Sadat , Safari Reza , Banabazi Mohammad Hossein 
Published: August 2024

Abstract

Broodiness is a maternal behavior and an instinct for natural breeding in poultry that inhibits egg production. The transition from the egg-laying to the brooding phase is a complex process that ultimately leads to gonadal regression, changes in neuroendocrine patterns, and termination of ovulation. The turkey is one of the species having strong brooding behavior and affecting the productivity of industry. genetic and environmental factors influencing broodiness in poultry have been extensively studied, but the molecular regulation mechanism of broodiness is somewhat unclear. Recently, the RNA sequencing technique has been applied to investigate gene expression profiles during the egg-laying period and brooding period. In this research, the pituitary transcriptome of commercial turkey birds were studied during the egg-laying and brooding period. A total of 334 differentially expressed genes were identified (fold change ≥ 1, P < 0.05). There were 229 upregulated expressed genes and 104 downregulated at egg-laying period compared with brooding period. In the present study, the results of GO and KEGG analysis revealed that the genes in the protein processing in the endoplasmic reticulum pathway, Spliceosome complex pathway and Regulation of actin cytoskeleton pathway (P < 0.05) are critical for controlling the hormonal cycle and autophagy. In addition, regulation of neurotransmitter secretion and regulation of neuronal synaptic plasticity were highly enriched in calcium ion related biological processes.
Dynamic changes in prokaryotic and eukaryotic communities and networks in a minimally managed cabbage-cultivated field soils
Authors: Ito Sentaro , Murakami Junya , Suzuki Mio , Hirose Yuu , Yamauchi Takahiro , Eki Toshihiko 
Published: August 2024

Abstract

In this study, we used DNA metabarcoding to investigate both terrestrial prokaryotes and eukaryotes in minimally managed cabbage-cultivated and uncultivated agricultural fields from February to August 2021. Analyses of the relative abundances of prokaryotic and eukaryotic sequence variants (SVs) and their β-diversities have clarified the dynamic changes in eukaryotic communities during cultivation. We investigated taxonomic changes in fungi-, protist-, and animal-derived SVs, as well as abundant SVs in each eukaryotic phylum. The results revealed that the fractions of predatory or parasitic protists and animals increased, whereas those of fungi and earthworm Enchytraeus spp. decreased. The fractions of abundant SVs derived from diatoms, Ciliophora, the class Vampyrellidae (Cercozoa), and mites increased and subsequently decreased during this period. These findings suggest that predatory protists and animals fed on bacteria and autotrophic eukaryotes, such as diatoms, propagated in spring, followed by their propagation and parasitism to host eukaryotes. The co-occurrence networks of the top 150 SVs also changed, especially prokaryotic networks that markedly changed from April to May, and those of eukaryotes from May to June– August, supporting the observations mentioned above. These findings indicate dynamic changes in eukaryotes in fields with minimal agricultural practices and are useful for sustainable natural farming.
Suitability evaluation of ecotourism activities in national parks --- a case study of the Qilian Mountain National Park
Authors: Huang Shan , Sheng Danhua , Wang Pengtao , Xiao Jianhua , Li Qiushuang , Liu Hanjun 
Published: August 2024

Abstract

Effective development of ecotourism is a strategy to improve the main functional areas of national parks, including scientific protection and utilization. For best results, suitability of ecotourism activities must be determined but methods and guidelines for suitability studies are lacking. Here, we used the Gansu area of the Qilian Mountain National Park in China as a case study and proposed a model AHP-PROMETHEE-GIS based on landscape units for the suitability evaluation of ecotourism activities. The PROMETHEE method was used in the Python software, and the suitability ranking was obtained for eco-tourism activities in 1641 landscape units. The first four levels of suitability ranking of landscape units were visualized with ArcGIS. The analysis showed that the Gansu area of the Qilian Mountain National Park is suitable for cross-country adventure, professional investigation, forest recreation, and other eco-tourism activities. A spatial layout of ecotourism was established in the non-core protected areas of the national park, with a goal to balance development of ecotourism and ecological protection, and to realise the unity of the ecological, economic, and social benefits of the Qilian Mountains National Park.
A label-free electrochemical aptamer sensor for sensitive detection of cardiac troponin I based on AuNPs/PB/PS/GCE
Authors: Jiang Liying , Li Dongyang , Su Mingxing , Chen Fenghua , Qin Xiaomei , Wang Lan , Gui Yanghai , Zhao Jianbo , Guo Huishi , Qin Xiaoyun , Zhang Zhen 
Published: August 2024

Abstract

Monitoring cardiac troponin I (cTnI) is of great value in the clinical diagnosis of acute myocardial infarction (AMI). In this paper, a highly sensitive electrochemical aptamer sensor was demonstrated by using polystyrene (PS) microspheres as the electrode substrate material, combined with Prussian blue (PB) and gold nanoparticles (AuNPs) for the sensitive and label-free determination of cTnI. PS microspheres were synthesized by emulsion polymerization and then dropped onto the glassy carbon electrode, PB and AuNPs were electrodeposited on the electrode in corresponding electrolyte solution step by step. The PS microsphere substrate provided a large surface area for loading mass of the biological affinity aptamers, while the PB layer improved the electrical conductivity of the modified electrode and the electroactive AuNPs exhibited excellent catalytic performance for subsequent electrochemical measurements. In view of the above-mentioned sensing platform, the fabricated label-free electrochemical aptamer sensor showed a wide detection range of 10 fg/mL ~ 1.0 µg/mL and a low limit of detection of 2.03 fg/mL under the optimal conditions. Furthermore, this biosensor provided an effective detection platform for the analysis of cTnI in serum samples. The introduction of this sensitive electrochemical aptamer sensor provides a reference for clinical sensitive detection of cTnI.
Design and Development on Multiple Physical Fields Simulation of High-Voltage Switchgear
Authors: Hou Kang , Yu Jiuhong , Huang Beiju , Hou Shen 
Published: August 2024

Abstract

This paper presents a simulation system software for the design and assembly of high-voltage switchgear components. For different kinds of high-voltage switchgear, the system uses STL forms to show three-dimensional (3D) solid models in model base, including the parts and assembly. The system software realizes seamless integration with analysis software COMSOL. In the system, the three-dimensional solid model is imported into the finite element analysis software, and the multi physical field (temperature field, electromagnetic field, air flow field) simulation of different high-voltage switchgears is realized through attribute setting, subdivision, parameter definition and other links.