bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of social media increases the risk of anxiety depression globally: results from 113 countries
Authors: Ranabhat Chhabi Lal , Marion Jason W 
Published: July 2024

Abstract

Background
Generalized anxiety and depression is the initial stage of mental illness and reflects a mood disorder reflected in sadness, hopelessness, nervousness, and worry. There are different factors associated with general anxiety and depression (GAD). Together with basic demographic and economic factors, we observed the use of social media by GAD. The aim of this study is to explore globally the influence of social media on self-reported general anxiety and depression.
Methodology
We chose different factors associated with anxiety and depression affected for more than 2 weeks from the 2020 Wellcome Global Monitor from the available secondary data. The samples from each country are nationally representative of the resident population aged 15 and older with access to a phone in 113 countries. The research design process was completed by leading researchers and subject experts; cognitive testing was conducted in ten countries to ensure questions could be understood across countries and by various demographic groups; and pilot tests were conducted in 10 countries. Independent variables were demographic variables: age, gender, economics, education, employment status, belief factors, and trends in social media use. Univariate variables were presented in frequency and percentage; bivariate analysis was performed with cross-tabulation using the chi square test; and logistic regression was used among significant variables by adjusted odds ratios and 95% CI as multivariate analysis.
Results
The prevalence of self-reported generalized anxiety depression (GAD) was 20% out of 119,234 in 113 countries. More than 38.27% were between the ages of 30 and 49, with 51% being male, more than half having completed high school, and 27% falling into the 4th and 5th quintiles (rich group). Similarly, more than 63.4% were employed, 81.3% believed in science, 42.3% believed in traditional healers, and 81.5% used social media, which was significantly associated with self-reported GAD. Adjusted odds ratio (aOR) showed that young age (15–30) years 1.24 times, females 1.21 times, elementary and primary education 1.34 times, the poorest twenty 1.39 times, and those who use social media several times an hour are more likely to be GAD with reference to early old age, males, higher education, the richest twenty, and those who did not use social media in the past 30 days. At the same time, the older age group (65+), those who trusted science and traditional healers and did not use social media in the past 30 days, were less likely to be GAD.
Conclusions: There is an increasing risk of GAD worldwide, and young adults and females are more vulnerable. Excessive use of social media is a challenging and risky factor.
The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: A Prospective Study from the NHANES
Authors: Guo Xinghong , Wu Jian , Ma Mingze , Tarimo Clifford Silver , Fei Fengyi , Zhao Lipei , Ye Beizhu 
Published: July 2024

Abstract

Background
It is currently unclear whether machine learning based methods using lifestyle factors can effectively predict the probability of all-cause mortality and cardiovascular disease mortality.
Method
A prospective cohort study was conducted using a nationally representative sample of adults aged 40 years or older, drawn from the US National Health and Nutrition Examination Survey from 2007 to 2010. The participants underwent a comprehensive in-person interview and medical laboratory examinations, and subsequently, their records were linked with the National Death Index for further analysis.
Result
Within a cohort comprising 7921 participants, spanning an average follow-up duration of 9.75 years, a total of 1911 deaths, including 585 cardiovascular-related deaths, were recorded. The model predicted mortality with an area under the receiver operating characteristic curve (AUC) of 0.848 and 0.829. Stratifying participants into distinct risk groups based on ML scores proved effective. All lifestyle behaviors exhibited an inverse association with all-cause and cardiovascular mortality. As age increases, the discernible impacts of dietary scores and sedentary time become increasingly apparent, whereas an opposite trend was observed for physical activity.
Conclusion
We develop a ML model based on lifestyle behaviors to predict all-cause and cardiovascular mortality. The developed model offers valuable insights for the assessment of individual lifestyle-related risks. It applies to individuals, healthcare professionals, and policymakers to make informed decisions.
Decision-Tree-Based Distributed Learning for IoT Devices
Authors: Yamashita Yui , Taya Akihito , Wada Takumi , Tobe Yoshito 
Published: July 2024

Abstract

Recently, people have known the concept of the Internet of Things (IoT). Moreover, they have widely used smart devices, such as smartphones and wearable devices, because they are getting smaller and easier to wear. These devices have many sensors so that they can collect many data and process these data using machine learning. Most studies processing sensing data use deep neural network (DNN). However, DNN is not suitable for smart sensing devices because of its high computational complexity. Therefore, this paper adopts ensemble learning of decision trees, especially random forest and gradient boost decision trees (GBDT), to obtain prediction models without many computational resources. In addition, machine learning requires a large amount of data to train highly accurate prediction models. An easy way to gather various data is by uploading data obtained by each device to a server. Although this scheme can collect data easily, it requires the server to be maintained. Further, there is the challenge of the concentration of data and models. To solve these problems, a decentralized machine learning algorithm that works without a server should be developed. This paper proposes a decentralized ensemble learning algorithm for decision trees, with which multiple devices communicate directly with each other to share information without a server. The proposed method can be applied to situations where data are non-independent and identically distributed (non-IID).
Magneto electric switching Parameters in Single-Phase co-doped Multiferroic Material for Spintronic Applications: DFT Calculations
Authors: Tariq Muhammad , Chaudhary Kashif , Shaari Amiruddin , Ahmed Rashid , Yamusaa Shehu Aminu 
Published: July 2024

Abstract

Bismuth ferrite, BiFeO3 (BFO), has garnered significant attention as a thoroughly studied multiferroic material with promising prospects for future spintronic applications, owing to its unique combination of magnetic and electric polarization within a single-phase structure. Frist principle calculations were performed to examine the spin-polarized electronic and magnetic characteristics of BFO in the hexagonal phase, both in its pure state and when doped with lanthanum (La) at A-site and cobalt (Co) at B-site for magnetic switching parameters of spintronic RAM. The inclusion of La and Co atoms leads to alterations in spin polarization in both the spin-up and spin-down channels, resulting in an observed upsurge in the density of energy states (DOS). The observed changes in DOS across both spin channels have a pronounced effect on the magnetic moment. Introducing Co atoms into BFO, both individually and in combination with La, results in an enhancement of the magnetic moment. In the La-Co co-doped substitutional system, a significant volume magnetization of 1.26 (MA/cm) and a linear magneto-electric coupling coefficient of 2.63 ×10− 7 (sec m− 1) have been documented. Particularly, for data transmission within a nano-magnet of a spintronic device, Co-doped BFO demonstrates an exceptionally robust magnetic force of 0.05 T.
Genome-wide analysis of the PYL gene family and identification of PYL genes that respond to cold stress in Triticum boeoticum
Authors: Liu Xin , Zhao Xin , Shen Mang , Feng Ruizhang , Wei Qin , Zhang Minghu , Zhang Lianquan , Yan Yue 
Published: July 2024

Abstract

Abscisic acid (ABA) is a key plant hormone that regulates plant growth and stress responses. Pyrabactin resistance 1-like (PYR/PYL) proteins are ABA receptors involved in the initial steps of ABA signaling. Triticum boeoticum is an important germplasm resource for wheat. In this study, we identified 15 PYL genes from wild emmer wheat and found that they were distributed across five chromosomes. Based on phylogenetic analysis, we classified these genes into three subfamilies. Members of each subfamily have similar gene structures and contain a common motif. Furthermore, we found that their promoters have multiple hormone-related elements. We found 7, 33, and 49 collinear gene pairs in three different ploidy wheat species (T. urartu, T. turgidum and T. aestivum), indicating that PYL genes are relatively conserved during evolution. Additionally, interaction networks and miRNA targets were predicted, revealing interactions between PYL proteins and key components of the abscisic acid signaling network. miR9666b-3p may serve as a central factor in PYL involvement in the abscisic acid network. Through RNA-seq analysis and qPCR validation, four genes were found to potentially play a role in cold stress. These findings lay the groundwork for further research on PYL genes in T.boeoticum.
Adaptive Chaotic Gaussian Lens Snake Optimization Algorithm for Improved Cotton Field Sensor Coverage and Utilization
Authors: Ban Chenglong , Liu Xiang , Tian Min , Lu Zhonghua , Xiang Ruoxue , Zhou Jie 
Published: July 2024

Abstract

Soil Temperature Wireless Sensor Networks (STWSNs) are crucial in monitoring soil temperature in cotton fields. These networks assist farmers in optimizing agricultural activities based on real-time temperature data, thereby increasing cotton yields and reducing production costs. However, achieving maximum network coverage while minimizing the number of nodes remains a significant challenge. This paper proposes an Adaptive Chaotic Gaussian Lens Snake Optimization Algorithm (ACGLSOA) to address this issue. The ACGLSOA adaptively tunes parameters to enhance robustness and optimization accuracy. Additionally, it incorporates two new adaptive factors to improve local search capabilities and introduces enhanced chaos operators to refine the initial solution. The improved Gaussian operator and lens reflection mechanism extend the search space, thus enhancing global search performance. Experimental results show that the network coverage of the STWSN optimized by ACGLSOA reaches 98.91%, and the node utilization efficiency is 73.8%. Compared with the optimization results of the Snake Optimizer (SO), Artificial Bee Colony Algorithm (ABC), RIME Optimization Algorithm (RIME), and Particle Swarm Optimization Algorithm (PSO), the coverage of STWSN is improved by 9.74%, 8.24%, 14.45%, and 29.68%, respectively. Furthermore, the node utilization efficiency is improved by 7.27%, 6.15%, 10.78%, and 22.13%, respectively. Therefore, optimizing the node deployment of STWSNs through ACGLSOA enables the network to provide better coverage to the target area more cost-effectively.
Region Gradient-Guided Diffusion Model for Underwater Image Enhancement
Authors: shao Jinxin , Zhang Haosu , Miao Jianming 
Published: July 2024

Abstract

Underwater image enhancement (UIE) is a critical challenge in marine visual perception and underwater robotics due to complex aquatic environments that severely degrade image quality. This paper introduces the Region Gradient-Guided Diffusion Model (RGGDM), a novel framework that addresses the fundamental trade-off between local detail preservation and global consistency in UIE. RGGDM innovatively integrates a region gradient-guided mechanism with a hybrid Swin-ConvNeXt architecture, introducing a spatially adaptive denoising process governed by gradient discrepancies between input and target images. We propose a learnable parameter $\delta$ that dynamically modulates denoising intensity, focusing computational resources on semantically salient regions. Our approach is underpinned by rigorous mathematical analysis, demonstrating convergence properties under mild assumptions and providing theoretical guarantees for the model's stability and effectiveness. The synergistic combination of Swin Transformer and ConvNeXt enhances feature representation, significantly improving both perceptual quality and pixel-level accuracy. Extensive experiments on benchmark datasets demonstrate RGGDM's superior performance, consistently outperforming state-of-the-art methods across multiple evaluation metrics. Notably, RGGDM achieves a Peak Signal-to-Noise Ratio (PSNR) of 25.48 dB and an Underwater Image Quality Measure (UIQM) of 4.37 on the UIEB dataset. Furthermore, enhanced images show substantial improvements in downstream tasks such as SIFT feature matching, with an average increase of 132.19\% in matching points. These results underscore RGGDM's potential in advancing underwater visual perception and its broader implications for marine robotics and environmental monitoring applications.
Photonic generation of frequency agile LFM signals for ISAC systems
Authors: Zhou Yixiao , Zhao Shanghong , Li Xuan , Wang Guodong 
Published: July 2024

Abstract

A compact photonic method to generate frequency agile linearly frequency modulated (LFM) signals is proposed demonstrated by experiment and simulation. A Mach-Zehnder modulator driven by an LFM waveform train and an electrical controlled optical tunable delay line driven by a coding sequence is employed. According to time-frequency linear relationship of LFM signals, the initial frequency of the generated signals can be shifted by adjusting the introduced time delay. Experiments are carried out to verify the feasibility of the proposed generator. Frequency agile LFM signals under 2FSK and 4FSK modulation with symbol rate of 4 Msps and 8 Msps are successfully obtained. 0.15 m range resolution and 4 Mbps communication data rate are achieved with a 1-1.5 GHz driving LFM signal. This scheme features compact structure and excellent tunability, which are promising to find applications in anti-jamming ISAC systems.
Development of Bioplastics with Essential Oil for Bioactive Applications: Characterization and Evaluation
Authors: Raposo Ana Karoliny da Silva , Soares Ana Carolina Soares e , Santos Casi Santos dos , Silva Pablo Daniel Sena , Paixão Louryval Coelho , Brasil Gabrielle Vieira da Silva , Everton Gustavo Oliveira , Arévalo-Pinedo Aroldo , Firmino Wellysson da Cunha Araújo , Júnior Aluísio Alves Cabral , de Menezes Alan Silva , Ribeiro Geyse Adriana Corrêa , Filho Alla Kardec Duailibe Barros , Santana Audirene Amorin 
Published: July 2024

Abstract

Progress in the development of bioplastics has been widely studied, especially as an alternative to non-biodegradable products derived from petrochemicals, which have significant environmental impacts after disposal. One promising approach is the addition of essential oils of plant origin, aiming to impart bioactive properties to bioplastics and accelerate the healing of skin wounds. In this study, we used the casting method and an experimental design to investigate the characteristics of a bioproduct resulting from the combination of copaiba oil, glycerol alginate and a crosslinking agent. We evaluated parameters such as moisture content, water solubility, thickness, and water vapor permeability (PVA). The results showed variations in moisture levels (from 7.87% to 48.87%), water solubility (from 15.38% to 93.13%), thickness (from 0.060 to 0.165 mm) and PVA (from 1.784 to 8.018 g·mm/m²·day·kPa). With the use of optical and scanning electron microscopy (SEM) techniques to characterize the morphology of the films, while Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to identify the presence of sesquiterpenes and caryophyllene, active components of copaiba oil in the selected films. The results showed the formation of crystals observed in the SEM, as well as crystalline peaks detected in the XRD, indicative of the desired structure. The FTIR analysis confirmed the effective incorporation of the bioactive compounds, suggesting that the films maintain the antioxidant and antibacterial properties of copaiba oil.
Zooming Fusion Network: A Multi-scale Approach for Precise Crowd Counting and Individual Localization in Low-Angle Scenarios
Authors: Liu Yiran , Li Yu , Zhang Qi , Liu Chengao , Guan Yu , Jiao Jianbin , Li Yuan 
Published: July 2024

Abstract

Crowd counting is pivotal in critical domains like security surveillance, urban planning, and traffic management. It is typically accomplished byaccurately detecting the number of individuals in a scene, thereby offering essential early warning and preventative mechanisms. Currently, the widely adopted method in the industry for dense crowd counting is based on density maps. However, this method relies on the use of Gaussian kernels to create intermediate representations (density maps), making it susceptible to the accumulation of background noise and loss of density. Moreover, it fails to provide precise individual location information, which hinders downstream tasks such as crowd movement trajectory analysis and abnormal behavior detection. To overcome these limitations, we propose a point-based framework named the Zooming Fusion Network (ZFNet), which integrates multi-scale inputs with an attention mechanism module to enhance the model's adaptability to complex scenes and its focus accuracy on crowd regions.. To validate the effectiveness of the proposed method, we conduct experiments on two public datasets. The results demonstrate that ZFNet achieves leading performance, compared to other existing state-of-the-art crowd counting methods. Furthermore, to address the lack of low-angle scenes in public datasets, we introduce a LIB_CC_500 dataset, specifically tailored for low-angle crowd counting. LIB_CC_500 The model's performance is validated on this new dataset, with results indicating its good generalization capabilities.
Electron transfer mediated contaminant activates periodate to generate 1O2 by charge-confined single-atom catalyst
Authors: Zou Jianping , Tang Qianqian , Wu Bangxiang , Huang Xiaowen , Ren Wei , Liu Lingling , Tian Lei , Chen Ying , Zhang Longshuai , Sun Qing , Kang Zhibing , Ma Tianyi 
Published: July 2024

Abstract

The electron transfer process (ETP) is able to avoid the redox cycling of catalysts by capturing electrons from contaminants directly. However, the ETP usually leads to the formation of oligomers and the reduction of oxidants to anions. Herein, the charge-confined Fe single-atom catalyst (Fe/SCN) with Fe-N3S1 configuration was designed to achieve ETP-mediated contaminant activation of the oxidant by limiting the number of electrons gained by the oxidant to generate 1O2. The Fe/SCN-activate periodate (PI) system shows excellent contaminant degradation performance due to the combination of ETP and 1O2. Experiments and DFT calculations show that the Fe/SCN-PI* complex with strong oxidizing ability triggers the ETP, while the charge-confined effect allows the single-electronic activation of PI to generate 1O2. In the Fe/SCN + PI system, the 100% selectivity dechlorination of ETP and the ring-opening of 1O2 avoid the generation of oligomers and realize the transformation of large-molecule contaminants into small-molecule biodegradable products. Furthermore, the Fe/SCN + PI system shows excellent anti-interference ability and application potential. This work pioneers the generation of active species using ETP’s electron to activate oxidants, which provides a new perspective on the design of single-atom catalysts via the charge-confined effect.
Evaluation of Stroke-Related Dysphagia using surface electromyographic biofeedback and their management by Related Exercise
Authors: Yang Yuan , Zhu Zhang , Star Sparkle 
Published: July 2024

Abstract

Introduction: Poststroke oropharyngeal dysphagia increases pneumonia risk and lowers life quality. Speech therapy uses unfamiliar tasks to change habits. The repeatability of tests limits the use of precise feedback for motor learning. Neuromuscular biofeedback from surface electromyography (sEMG) benefits muscle function. In dysphagia therapy, biofeedback improves control, engagement, and brain activation. Biofeedback's efficacy in dysphagia is unclear, however, swallow (ES) exercises are advised.
Aims and Objectives: This study examines the feasibility of using surface electromyography (sEMG) biofeedback to assess stroke-related dysphagia and guide exercise-based treatment.
Method: From September 2022 to August 2023, a Chinese hospital observed healthy volunteers using the Functional Oral Intake Scale (FOIS) to determine dysphagia severity. Participants were randomly assigned to biofeedback or non-biofeedback activities during the Effortful Swallow exercise in two sessions to verify its repeatability and applicability. Regular and effortful swallowing was examined using neck electrode sEMG data and questionnaire feedback.
Result: Table 1 shows demographics for 30 stroke patients and 40 healthy controls. Age variability is shown by stroke patients' median age of 74.5 years and controls' 75.00. Both categories have more men. Healthy and stroke participants have different sEMG biofeedback questionnaire preferences, as shown in Table 2. Figure 1 shows age-related sEMG amplitude changes during effortful swallowing. Figure 2 shows how feedback helps both groups swallow. Figure 3 illustrates that electromyographic biofeedback increases swallowing activity in healthy people, suggesting therapeutic potential that needs additional study.
Conclusion: In conclusion, sEMG biofeedback may improve muscle engagement and patient motivation in stroke dysphagia treatment, warranting future investigations.
Burst-like swarms and periodical VT events in the accelerating unrest phase of Campi Flegrei caldera (2021-2024)
Authors: Macedonio Giovanni , Giudicepietro Flora , Avino Rosario , Sessa Eliana Bellucci , Bevilacqua Andrea , Bonano Manuela , Caliro Stefano , Casu Francesco , De Cesare Walter , De Luca Claudio , De Martino Prospero , Traglia Federico Di , Vito Mauro Di , Dolce Mario , Esposito Antonietta , Lanari Riccardo , Manunta Michele , Minopoli Carmine , Tobar Fernando Monterroso , Neri Augusto , Ricciolino Patrizia , Rufino Francesco , Santi Alessandro , Scarpato Giovanni , Striano Pasquale , Tramelli Anna , Chiodini Giovanni 
Published: July 2024

Abstract

Since 2021, peculiar seismic sequences became evident and frequent in Campi Flegrei caldera (Italy), while deformation, seismicity and gas emission showed an acceleration. We distinguished burst-like swarms and periodical VT sequences. The earthquakes of both types of sequences resulted located in an area that includes the main hydrothermal field, and a zone affected by a geodetic anomaly, which clearly appeared in 2021. Burst-like swarms (max Md = 4.4) are accompanied by a pseudo-tremor, suggesting a mechanism involving near-continuous brittle failure. The periodical VT sequences are shallow and appear linked to the dynamics of the Mt Olibano lava dome, which deforms non-uniformly compared to the rest of the caldera and coincides with the geodetic anomaly. This peculiar seismicity, described in the Campi Flegrei for the first time in this study, has been associated with phreatic explosions and critical phases of unrest in other volcanoes, and currently characterizes the rapidly evolving state of activity of this high-risk volcano.