bluuebunny/crossref_metadata_2025_split huggingface dataset:
Health literacy, social support, and health education, as factors associating with self-management behaviors among elderly patients with hypertension in China: a structural equation model
Authors: Chen Ziyan , Chen Meijun , Gong Litong , Tian Yu , Chang Chun , Cao Wangnan 
Published: July 2024

Abstract

Background
Hypertension is one of the world’s leading risk factors for death and disability. With a higher prevalence of hypertension, older adults need to strengthen self-management behaviors. The synergistic effects on self-management behaviors and the potential underlying mechanisms between self-management behaviors, health education, health literacy, and social support remain unclear.
Methods
A cross-sectional design and stratified multi-stage random sampling method were used to investigate hypertensive patients aged 60 years and older in six provinces from the eastern, central, and western regions of China. We collected data using questionnaires, and examined interactions among the factors using a structural equation model.
Results
1140 participants were included in this study. The SEM had a good overall fit. Health literacy, social support, and health education had a direct positive relationship with self-management behaviors (β = 0.157, β = 0.187, β = 0.158, P < 0.001). Social support had an indirectly positive relationship with self-management behaviors through health education and health literacy. Health education had an indirectly positive relationship with self-management behaviors through health literacy.
Conclusions
The situation of adopting self-management behaviors among elderly patients with hypertension is uneven in this study. Social support, health education, and health literacy all have direct or indirect impacts on self-management behaviors.
Stereotactic body radiation therapy combined with transcatheter arterial embolization as a downstaging treatment to transplant for hepatocellular carcinoma with portal vein tumor thrombus
Authors: Hu Jing-Qiong , Yang Xiao , He Wei-Ping , Sun Jing , Fan Yu-Ze , Ding Jun-Qiang , Wang Hong-Bo , Duan Xue-Zhang 
Published: July 2024

Abstract

Background: Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC). However, as portal vein tumor thrombus (PVTT) is often viewed as a relative contraindication, patients with this condition frequently miss the opportunity for LT. This study aimed to explore stereotactic body radiation therapy (SBRT) combined with transcatheter arterial embolization therapy (TAE) to control PVTT, downstage it to LT, and increase survival.
Methods: Between September 2016 and June 2022, 17 patients with HCC-PVTT were treated with SBRT combined with TAE and underwent LT after a successful downgrade. All patients were followedup and evaluated regularly. The Kaplan-Meier test was used for the analysis of survival.
Results: Of the study population, post-LT,the 12-, 18-, 24-, 36- and 60-month overall survival (OS) rateswere 100%, 88.24%, 64.71%, 47.06%, and 17.65%, respectively. Post-LT, the 12-, 18-, 24-, 36- and 60-month progression-free survival rates were 82.35%, 64.71%, 47.06%, 29.41%, and 17.65%, respectively. The median OS was 37 months.
Conclusions: SBRT combined with TAE as a downstaging therapy, followed by LT,showed a satisfactory effect in patients with HCC-PVTT. However, it is necessary to increase the sample size and perform a long-term follow-up.
Post-operative outcomes associated with anterior mesh location after laparoscopic sacrocolpopexy.
Authors: Habib Nassir , Giorgi Matteo , Tahtouh Tania , Hamdi Amel , Centini Gabriele , Cannoni Alberto , Bader Georges 
Published: July 2024

Abstract

Objective
To investigate the relationship between the position of the anterior mesh, measured by ultrasound through the Bladder neck - Mesh Distance technique and the surgical outcomes after laparoscopic sacrocolpopexy (SCP) for apical prolapse.
Study design:
Retrospective analysis of prospectively collected data in a tertiary care hospital. Between January 2019 and September 2019, 63 women who underwent laparoscopic SCP due to apical prolapse were included. Bladder neck - Mesh Distance was measured immediately after surgery. The pelvic floor was evaluated using the Pelvic Organ Prolapse Quantification (POP-Q) System before, one month, and 2.7 years (mid-term) after the surgery. Postoperative stress urinary incontinence (SUI) and Patient Global Impression of Improvement (PGI-I) scores were also assessed. The correlation between Bladder neck - Mesh Distance and the postoperative outcomes was investigated using the Spearman rank correlation coefficient.
Results
At mid-term follow-up visit, Bladder neck - Mesh Distance was inversely correlated with the correction of apical prolapse and postoperative SUI. No correlation was detected with the anterior compartment prolapse correction. PGI-I scores were high in all patients at mid-term follow-up, irrespective of Bladder neck - Mesh Distance values.
Conclusion
The shorter the Bladder neck - Mesh Distance, the better the outcome for apical compartment repair. Bladder neck - Mesh Distance had no correlation with the anterior anatomical correction. Shorter Bladder neck - Mesh Distance values were positively correlated to better PGI-I scores and a higher risk of SUI.
Identifying Sensitive Periods for the Impact of Physical Abuse on Psychopathology Symptoms
Authors: Xiao Xingyao , Zhu Li , Liu Yunting , Cheng Yihong 
Published: July 2024

Abstract

Extant research lacks completeness and accuracy in identifying the timing of sensitive periods for physical abuse across childhood. Utilizing data from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) following more than one thousand children from early childhood into adulthood, the present study employed a novel, granular approach to indicate physical abuse timing, develop growth curve models that incorporated linear and quadratic terms of the age variable, and more completely and accurately revealed sensitive periods for physical abuse. Results showed that late childhood (8–10 years) and adolescence (14–17 years) are sensitive periods when physical abuse more severely impacts internalizing issues. Additionally, middle to late childhood (6–10 years) and adolescence (12–16 years) are sensitive periods when physical abuse more severely impacts externalizing problems. Overall, this study provides empirical support for theories of sensitive periods across childhood and offers valuable reference for mental health workers to timely intervene.
MonoSPDC：A Novel Monocular 3D Object Detection Using Global-Channel Atten- tion Optimization
Authors: Su Weixing , Guo Zengxin , Liu Fang , Wang Haoyu 
Published: July 2024

Abstract

With the development of autonomous driving and mobile robotics, monocular 3D object detection has attracted attention in the above fields due to its advantages of low cost, fast speed and low power consumption. However, both mainstream monocular 3D object detection network structures currently face difficulties in practical application in these fields. One issue is the low accuracy of convolutional network structures. Another issue is the complex structure, high computational complexity, large training dataset, and LiDAR assisted training problem of the Transformer network structure. To solve the above problems, a novel monocu-lar 3D object detection algorithm named MonoSPDC is proposed in this paper. In MonoSPDC, we propose the Feature Global-Channels Recombination Module (FGCR). FGCR uses the attention mechanism based on convolutional structure to perform attention operations on the global and channel dimensions of the feature map. This can make the feature map more distinct, while also making the generation of heatmap more reliable and accurate. we also propose the Multi-View Predictive Integration module (MVPI) in MonoSPDC. MVPI can realize the uncertain fusion of 3D corner information based on 2D feature regression and depth information based on depth feature map, so as to improve the accuracy of the final depth estimation. In summary, proposed Mono-SPDC does not require other additional auxiliary training data. It can retain the speed advantage of the convo-lutional structure while achieving the accuracy of the Transformer structure. In the test on the public dataset KITTI, our proposed MonoSPDC has achieved very excellent results.
Generation and Genetic Characterization of Salt-Tolerant Wheat (Triticum aestivum) Mutants under a Conservation Agriculture System
Authors: Saber Ayman Anter , Abdelraouf Ramadan , Khaled Khaled Adly , Moustafa Ehab , Samaha Ghada 
Published: July 2024

Abstract

A set of mutant’s wheat in M3, M4, and M5 generations (2021–2023) and mother varieties were sown in a high salinity environment on six combinations of organic amendments in Ras Sudr, Egypt to develop salt-tolerant cultivars under CA. Ten SSR markers linked with salinity tolerance were used to assess genetic diversity in wheat genotypes. The selection index that contained studied traits could be used as selection criteria to isolate salt-tolerant wheat, as there is a positive significant correlation with grain yield. The average values of polymorphic information content (PIC), gene diversity (GD), and Shannon diversity Index (H) were 0.36, 0.4574, and 0.64 respectively, indicating moderate genetic diversity among the wheat genotypes evaluated can be exploited to produce salt-tolerant wheat cultivars. These markers were initially identified for their association with salt tolerance in wheat. However, subsequent research has revealed their potential involvement in other important agronomic traits, so we associated the results of SSR markers with its related qtls in wheat. This association suggests its potential in influencing yield-related traits in wheat. These findings collectively demonstrate the versatility of SSR markers in identifying loci that govern various agronomic traits in wheat. These markers can play significant roles in improving multiple traits, including salt tolerance, heat stress resilience, grain quality, and yield components. This multifaceted nature makes them valuable tools for developing wheat varieties that can thrive in diverse and challenging environments. This knowledge can be leveraged in marker-assisted breeding programs to develop wheat cultivars with improved salt tolerance, disease resistance, grain quality, and yield-related traits.
Time course of mice-model Kawasaki Disease An echocardiographic study and clinical implication
Authors: Zhang Xue-Mei , Jiao Fu-Yong , Yong Su-Boon , Lin Chun-Ting , Lou Ping , Cao Ling , Zhao Ying , Zhao Xin , Wang Jun-Xiang , Jin Jing , Zhang Dan , Tai I-Hsin , Han Zhen , Kuo Ho-Chang , Hsieh Kai-Sheng 
Published: July 2024

Abstract

Background: Kawasaki disease is a primary cause of pediatric cardiac issues, with the risk of coronary artery complications, heart attacks, and untreated death. Approximately 10% of patients do not respond to initial high dose intravenous immunoglobulin treatment. Our unique model aimed to explore high resolution small animal ultrasound for coronary imaging and its correlation with histopathology and clinical progression.
Methods: Eighteen BALB/c mice were divided into Kawasaki disease and control groups. Kawasaki disease was induced with a single intraperitoneal injection of Lactobacillus casei cell wall extract. High resolution small animal ultrasound monitored coronary changes on multiple days, and histopathological analysis covered various organs.
Results: The findings reveal significant changes in coronary artery diameter, with both the left and right coronary arteries expanding considerably during the experiment. By day 21, the model group exhibited marked coronary artery widening. Ejection fraction and fractional shortening deteriorated significantly by day 14 compared to the baseline. The histopathology report offers a comprehensive timeline of cardiac involvement.
Conclusion: Our model uniquely illustrates coronary arteritis and cardiac lesions in Kawasaki disease mice, emphasizing progressive coronary dilation, notably by day 21. Correlation with pathology supports our findings, shedding light on disease progression in this mouse model, suggesting a delayed course compared to human Kawasaki disease, with potential severe myocarditis.
Women's empowerment and health facility utilisation during childbirth in Cameroon
Authors: Totouom Armand 
Published: July 2024

Abstract

Low women’s empowerment probably constitutes a key obstacle to healthcare utilisation in Africa, given the importance of gender inequalities in this region. However, evidence on the association between women’s empowerment and facility delivery in Cameroon is rare. We provided one of the first studies that examine the association between women’s empowerment and health facility delivery in Cameroon. Our study demonstrates that women’s intra-household decision-making power is associated with a higher probability of using health facility during childbirth. Women that have the final say (alone or jointly with their husband/partner) on their own health care have a higher probability to give birth in a health facility. In addition, the selection equation estimates show those women’s empowerment increases with control over assets, namely land and house. Therefore, policies enhancing land rights equity have the potential to increase women’s empowerment which in turns will improve maternal health through health facility utilisation.
Optimizing Relay Selection in D2DCommunication for Next-Generation WirelessNetworks Using Multi-Agent ReinforcementLearning: A Novel Approach
Authors: SIRMA Muharrem , KAVAK Adnan , INNER A. Burak 
Published: July 2024

Abstract

Device-to-device (D2D) communication offering a direct communication channel between devices has been introduced as an alternative communication technique for next generation wireless networks, which aims to alleviate the workload traditionally shouldered by base stations (BS) in cellular systems. However, D2D pairs encountering connectivity issues or requiring extended communication ranges will necessitate the involvement of a relay node (RN) to facilitate communication. For the cases wherein an efficient communication with the target device is only possible over a relay device, finding the candidate relay among source-relay-destination devices with the best link availability and optimized end-to-end throughput are challenges to be considered in relay assisted D2D communication. Despite the plethora of studies on relay selection in D2D communication, there persists a need for a method that systematically integrates multiple disruptive factors inherent in wireless channels. While Reinforcement Learning (RL) has primarily found application in resource management tasks like power control, resource block (RB) availability, and spectrum allocation, its utilization for relay selection in D2D communication within dynamic wireless environments remains largely unexplored. In this paper, we propose to use multi-agent reinforcement learning based relay selection (MARS) in which the source device and/or pairing devices act as learning agents. The resource selection agent (RSA), the link agent (LA), and the transmission agent (TA) are involved cooperatively in the MARS method to find the optimum relay. Source nodes in D2D pairs iteratively update their strategies through interactions with the wireless environment and other devices in order to maximize its cost function to select the most convenient relay in the multi hop D2D communication scenario. The MARS method takes into account the combined effect of SINR, link reliability, and throughput values for the estimation of cost function order to select the optimum relay node. We have performed extensive simulations for different device density scenarios in wireless environment and compared the performance of our MARS method with SINR based relay selection approach. The findings show that our relay selection approach for D2D communication outperforms SINR-based methods in terms of end-to-end link reliability and throughput performance.
Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection
Authors: Ye Zhiwei , Huang Ruoxuan , Zhou Wen , Wang Mingwei , Cai Ting , He Qiyi , Zhang Peng , Zhang Yuquan 
Published: July 2024

Abstract

Feature selection (FS) is a significant dimensionality reduction technique, which can effectively remove redundant features. Metaheuristic algorithms have been widely employed in FS, and have obtained satisfactory performance, among them, grey wolf optimizer (GWO) has received widespread attention. However, the GWO and its variants suffer from limited adaptability, poor diversity, and low accuracy when faced with high-dimensional data. The hybrid rice optimization (HRO) algorithm is an emerging metaheuristic algorithm derived from the hybrid heterosis and breeding mechanism in nature. It possesses a robust capacity to identify and converge towards optimal solutions. Therefore, a novel approach based on multi-strategy collaborative GWO combined with the HRO algorithm (HRO-GWO) for FS is proposed in this paper. The HRO-GWO algorithm is enhanced by four innovative strategies including dynamical regulation strategy and three search strategies. First, to improve the adaptability of GWO, the dynamical regulation strategy is devised for parameter optimization of GWO. Then, a multi-strategy co-evolution model inspired by HRO is designed, which utilizes neighborhood search, dual-crossover, and selfing techniques to bolster population diversity. Finally, the study develops a hybrid filter-wrapper framework to efficiently select pertinent and informative feature subsets, enhancing the classification performance while conserving time. The performance of HRO-GWO has been rigorously assessed across benchmark functions and the effectiveness of the proposed framework has been evaluated on high-dimensional biomedical datasets. Our experimental findings demonstrate that the approach on the basis of HRO-GWO outperforms state-of-the-art methods.
Empirical modeling of residual stress profile for multi-axis milling GH4169G
Authors: Zhou Jinhua , Zhang Zhongda , Liu Cijie , Shu Ailing , Jiang Xiaohui , Ren Junxue 
Published: July 2024

Abstract

The superalloy GH4169G is used to manufacture integral blisks for aero engines. Torsional bending deformation caused by residual stresses from multi-axis machining is one of the major factors contributing to excessive dimensional errors in blisk blade manufacturing. However, few studies have focused on predicting the residual stress profile in multi-axis milling of the superalloy GH4169G. Therefore, in this paper, an empirical model was established to predict the residual stress profile in multi-axis milling of the superalloy GH4169G. First, 36 sets of multi-axis milling experiments were conducted. Then, the residual stress profile was fitted using the Exponential Decay Cosine (EDC) function and the Firefly algorithm (FA) based on the experiment. Support Vector Machine (SVM) was used to establish the mapping relationship between the process parameters and the coefficients of the EDC function. In addition, the effects of process parameters including spindle speed, feed per tooth and tilt inclination angle on surface residual stress, maximum compressive residual stress and residual stress depth were investigated. Finally, the NSGA-III algorithm was used to obtain the recommended process parameters for multi-axis milling of the superalloy GH4169G blades.
Study on the law of ore dilution and loss and control strategies under brow line failure in sublevel caving mining
Authors: Ming Jian , Pan Yuehao , Xie Jingpeng , Li Zhihui , Guo Ronghua 
Published: July 2024

Abstract

To improve ore recovery ratio and the quality of medium-length hole blasting in non-pillar sublevel caving method, this study first investigated the effects of brow line failure on rock movement and ore recovery indexes based on rock blasting fragmentation mechanisms, the granular dynamics theory and ore drawing theory. Subsequently, control strategies to enhance blasting quality and reduce brow line failure were explored through energy impact crushing experiments, experiments on the radial rock-breaking effects of blast holes with different detonation velocities, and underground physical simulation experiments using full blast hole length detonating cord blasting. The experimental results indicate that as the degree of brow line failure increases, the ore recovery ratio gradually decreases, while the waste ratio gradually increases. Bottom initiation and the use of the full blast hole length detonating cord were identified as the optimal initiation method and charging structure for medium-length hole blasting in non-pillar sublevel caving method, effectively improving blasting quality and reducing damage to the brow line.
DKDL-Net: A Lightweight Bearing Fault Detection Model via Decoupled Knowledge Distillation and Low-Rank Adaptation Fine-tuning
Authors: Petrosian Ovanes , Li Pengyi , He Yulong , Liu Jiarui , Sun Zhaoruikun , Fu Guofeng , Meng Liping 
Published: July 2024

Abstract

Rolling bearing fault detection has developed rapidly in the field of fault diagnosis technology, and it occupies a very important position in this field. Deep learning-based bearing fault diagnosis models have achieved significant success. At the same time, with the continuous improvement of new signal processing technologies such as Fourier transform, wavelet transform and empirical mode decomposition, the fault diagnosis technology of rolling bearings has also been greatly developed, and it can be said that it has entered a new research stage. However, most of the existing methods are limited to varying degrees in the industrial field. The main ones are fast feature extraction and computational complexity. The key to this paper is to propose a lightweight bearing fault diagnosis model DKDL-Net to solve these challenges. The model is trained on the CWRU data set by decoupling knowledge distillation and low rank adaptive fine tuning. Specifically, we built and trained a teacher model based on a 6-layer neural network with 69,626 trainable parameters, and on this basis, using decoupling knowledge distillation (DKD) and Low-Rank adaptive (LoRA) fine-tuning, we trained the student sag model DKDL-Net, which has only 6838 parameters. Experiments show that DKDL-Net achieves 99.48% accuracy in computational complexity on the test set while maintaining model performance, which is 0.58% higher than the state-of-the-art (SOTA) model, and our model has lower parameters. Our code is available at Github link: https://github.com/SPBU-LiPengyi/DKDL-Net.git.