bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Kiwifruit Sweetness with Vis/NIR Spectroscopy Based on Scatter Correction and Feature Selection Techniques
Authors: Wan Chang , Yue Rong , Li Zhenfa , Fan Kai , Chen Xiaokai , Li Fenling
Published: May 2024

The sweetness is an important parameter for the quality of Cuixiang kiwifruit. The quick and accurate assessment of sweetness is necessary for farmers to make timely orchard management and for consumers to make purchasing choices. The objective of the study was to propose an effective physical method for determining the sweetness of fresh kiwifruit based on fruit hyperspectral reflectance in 400–2500 nm. In this study, the visible and near-infrared spectral (Vis/NIR) reflectance and sweetness values of kiwifruit were measured at different time periods after the fruit matured in 2021 and 2022. The multiplicative scatter correction (MSC) and standard normal variable (SNV) transformation were used for spectral denoising. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) methods were employed to select the most effective features for sweetness, and then the features were used as the inputs of partial least squares (PLS), least squares support vector machine (LSSVM), back propagation neural network (BP), and multiple linear regression (MLR) models to explore the best way of sweetness predicting. The study indicated that the most sensitive features were in the blue and red regions and the 970, 1200, and 1400 nm. The sweetness estimation model constructed by using the data of the whole harvest period from August to October performed better than the models constructed by each harvest period. Overall results indicated that hyperspectral reflectance incorporated with MSC-SPA-LSSVM could explain up to 79% of the variability in kiwifruit sweetness, which could be applied as an alternative fast and accurate method for the non-destructive determination of the sweetness of kiwifruit. This research could partially provide a theoretical basis for the development of nondestructive instrumentation for the detection of kiwifruit sweetness.
The Bolt Anchorage Performance of Fractured Rock under a Freeze–Thaw Cycle Load
Authors: Han Fengyan , Chen Yu
Published: May 2024

In circumstances influenced by freeze–thaw cycles, the strength of rock diminishes, necessitating an in-depth investigation into its corresponding anchoring support schemes. This study conducted experiments on rocks with and without fractures at angles of 0°, 45°, and 90° subjected to freeze–thaw cycles of 0, 10, 20, and 30 iterations. It explored the effects of fracture inclination, anchoring conditions, and freeze–thaw cycles on the mechanical properties of rock. The primary findings from the experiments are as follows: (1) fracture inclination significantly impacts rock strength, with the most pronounced deterioration observed in samples with a 45° fracture, exhibiting strengths and elastic moduli at 28.4% and 73.4%, respectively, of those of fracture-free samples; (2) anchoring effectively controls deformation but concurrently induces stress concentrations, resulting in Y-shaped crack formation around the anchoring rod; (3) the degree of strength reduction due to freeze–thaw cycles is angle-dependent, with fracture-free and 90° fracture samples exhibiting diminished strength post freezing, while the 45° fracture samples’ strength remains largely unchanged. Additionally, this study employed a numerical model, coupling a discrete element method (DEM) with a finite difference method (FDM), to simulate experimental conditions, yielding conclusions consistent with experimental outcomes, and notably revealing a prevalence of tensile cracks over shear cracks within samples under uniaxial compression.
Disentangled Self-Attention with Auto-Regressive Contrastive Learning for Neural Group Recommendation
Authors: Gao Linyao , Zhang Haonan , Fu Luoyi
Published: May 2024

Group recommender systems aim to provide recommendations to a group of users as a whole rather than to individual users. Nonetheless, prevailing methodologies predominantly aggregate user preferences without adequately accounting for the unique individual intents influencing item selection. This oversight becomes particularly problematic in the context of ephemeral groups formed by users with limited shared historical interactions, which exacerbates the data sparsity challenge. In this paper, we introduce a novel Disentangled Self-Attention Group Recommendation framework with auto-regressive contrastive learning method, termed DAGA. This framework not only employs disentangled neural architectures to reconstruct the multi-head self-attention network but also incorporates modules for mutual information optimization via auto-regressive contrastive learning to better leverage the context information of user–item and group–item historical interactions, obtaining group representations and further executing recommendations. Specifically, we develop a disentangled model comprising multiple components to individually assess and interpret the diverse intents of users and their impacts on collective group preferences towards items. Building upon this model, we apply the principle of contrastive mutual information maximization to train our framework, aligning the group representations with the corresponding user representations derived from each factor of the disentangled model, thereby enriching the contextual understanding required to effectively address the challenges posed by ephemeral groups. Empirical evaluations conducted on three real-world benchmark datasets substantiate the superior performance of our proposed framework over existing state-of-the-art group recommendation approaches.
An Anomaly Detection Ensemble for Protection Systems in Distribution Networks
Authors: Yuan Chenyin , Sun Chenhao , Yu Boxuan , Su Jianhong , Li Runze
Published: May 2024

Due to the complex topology, multi-line branches, and dense spatial distribution characteristics of a distribution network, potential disturbances and failures cannot be eliminated in real scenes, which means that higher levels of both reliability and stability are required in its corresponding protection system. For this reason, the timely monitoring and pinpoint identification of an underlying abnormal operation status in those protection systems must be ensured. To this end, a data-driven-based real-time anomaly detection ensemble is proposed in this paper. First, the kernel principal components investigation (KPCI) process is deployed to compress the dimensionality of input data, which can reduce the computational complexity within such high-dimensional data environments. Next, the isolated forest (IF) model is applied to excavate potential outliers according to the numeric range of the normal operating states of different features. Thus, a better detection performance in biased or sparse distributions can be achieved by reacting swiftly to those outliers. Finally, the operation data of the power distribution network protection system in a certain area is used as a simulation case. It is evident that compared with the single model IF detection method, combining the IF with the data dimension reduction model can effectively reduce data complexity. Due to the addition of kernel functions, KPCI can adapt to high-dimensional data environments better than standard PCI, and it also has certain advantages in calculation efficiency. This validates the theory that the proposed model has a high level of anomaly detection in practical applications, can assist in the automatic identification of and response to power distribution network security risks, effectively dig out potential system operational disturbances and state abnormalities, and achieve real-time anomaly monitoring and early warning.
Research on Multi-Scale Fusion Method for Ancient Bronze Ware X-ray Images in NSST Domain
Authors: Wu Meng , Yang Lei , Chai Ruochang
Published: May 2024

X-ray imaging is a valuable non-destructive tool for examining bronze wares, but the complexity of the coverings of bronze wares and the limitations of single-energy imaging techniques often obscure critical details, such as lesions and ornamentation. Therefore, multiple imaging is required to fully present the key information of bronze artifacts, which affects the complete presentation of information and increases the difficulty of analysis and interpretation. Using high-performance image fusion technology to fuse X-ray images of different energies into one image can effectively solve this problem. However, there is currently no specialized method for the fusion of images of bronze artifacts. Considering the special requirements for the restoration of bronze artifacts and the existing fusion framework, this paper proposes a new method. It is a novel multi-scale morphological gradient and local topology-coupled neural P systems approach within the Non-Subsampled Shearlet Transform domain. It addresses the absence of a specialized method for image fusion of bronze artifacts. The method proposed in this paper is compared with eight high-performance fusion methods and validated using a total of six evaluation metrics. The results demonstrate the significant theoretical and practical potential of this method for advancing the analysis and preservation of cultural heritage artifacts.
Influence of Spring Precipitation over Maritime Continent and Western North Pacific on the Evolution and Prediction of El Niño–Southern Oscillation
Authors: Ma Yifan , Huang Fei , Xie Ruihuang
Published: May 2024

Previous studies suggested that spring precipitation over the tropical western Pacific Ocean can influence the development of El Niño–Southern Oscillation (ENSO). To identify crucial precipitation patterns for post-spring ENSO evolution, a singular value decomposition (SVD) method was applied to spring precipitation and sea surface temperature (SST) anomalies, and three precipitation and ENSO types were obtained with each highlighting precipitation over the Maritime Continent (MC) or western north Pacific (WNP). High MC spring precipitation corresponds to the slow decay of a multi-year La Niña event. Low MC spring precipitation is associated with a rapid El Niño-to-La Niña transition. High WNP spring precipitation is related to positive north Pacific meridional mode and induces the El Niño initiation. Among the three ENSO types, ocean current and heat content behave differently. Based on these spring precipitation and oceanic factors, a statistical model was established aimed at predicting winter ENSO state. Compared to a full dynamical model, this model exhibits higher prediction skills in the winter ENSO phase and amplitude for the period of 1980–2022. The explained total variance of the winter Niño-3.4 index increases from 43% to 75%, while the root-mean-squared error decreases from 0.82 °C to 0.53 °C. The practical utility and limitations of this model are also discussed.
Calibration for Improving the Medium-Range Soil Temperature Forecast of a Semiarid Region over Tibet: A Case Study
Authors: Guo Yakai , Yuan Baojun , Su Aifang , Shao Changliang , Gao Yong
Published: May 2024

The high complexity of the parameter–simulation problem in land surface models over semiarid areas makes it difficult to reasonably estimate the surface simulation conditions that are important for both weather and climate in different regions. In this study, using the dense site datasets of a typical semiarid region over Tibet and the Noah land surface model with the constrained land parameters of multiple sites, an enhanced Kling–Gupta efficiency criterion comprising multiple objectives, including variable and layer dimensions, was obtained, which was then applied to calibration schemes based on two global search algorithms (particle swarm optimization and shuffled complex evaluation) to investigate the site-scale spatial complexities in soil temperature simulations. The calibrations were then compared and further validated. The results show that the Noah land surface model obtained reasonable simulations of soil moisture against the observations with fine consistency, but the negative fit and huge spatial errors compared with the observations indicated its weak ability to simulate the soil temperature over regional semiarid land. Both calibration schemes significantly improved the soil moisture and temperature simulations, but particle swarm optimization generally converged to a better objective than shuffled complex evaluation, although with more parameter uncertainties and less heterogeneity. Moreover, simulations initialized with the optimal parameter tables for the calibrations obtained similarly sustainable improvements for soil moisture and temperature, as well as good consistency with the existing soil reanalysis. In particular, the soil temperature simulation errors for particle swarm optimization were unbiased, while those for the other method were found to be biased around −3 K. Overall, particle swarm optimization was preferable when conducting soil temperature simulations, and it may help mitigate the efforts in surface forecast improvement over semiarid regions.
A New Analytical Simulation Code of Acoustic-Gravity Waves of Seismic Origin and Rapid Co-Seismic Thermospheric Disturbance Energetics
Authors: Sanchez Saul A. , Kherani Esfhan A.
Published: May 2024

A recent study the detection of coseismic ionospheric disturbances or ionoquakes less than 400 s from the earthquake’s onset. The study also associates these rapid ionoquakes with the seismo-atmosphere–ionosphere (SAI) coupling mechanism energized by acoustic-gravity waves (AGWs) and the subsequent formation of coseismic thermospheric disturbances (CSTDs). The present study outlines a new analytical simulation code for AGWs that resolves the governing equations in the time–altitude and wavenumber domain and confirms the rapid arrival of AGWs in the thermosphere (earlier than the estimated arrival time from the ray-tracing simulation). The rapid arrivals of AGWs are associated with long wavelengths that connect to thermospheric altitudes and propagate with thermospheric sound speeds, avoiding averaging effects from the lower atmosphere. The fast simulation traces the rapid arrival of AGWs in the thermosphere and produces rapid CSTDs within 250–300 s from the earthquake’s onset. The simulation time is much shorter than the formation time of near-field CSTDs, a scenario favorable for the forecasting of CSTDs before observations of ionoquakes. In essence, the fast simulation offers an alternative tool for tracking the evolution of CSTDs.
Comprehensive Analysis of Temporal–Spatial Fusion from 1991 to 2023 Using Bibliometric Tools
Authors: Cui Jiawei , Li Juan , Gu Xingfa , Zhang Wenhao , Wang Dong , Sun Xiuling , Zhan Yulin , Yang Jian , Liu Yan , Yang Xiufeng
Published: May 2024

Due to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both a high spatial and temporal resolution. To solve the above problem, the spatiotemporal fusion (STF) method was proposed and proved to be an indispensable tool for monitoring land surface dynamics. There are relatively few systematic reviews of the STF method. Bibliometrics is a valuable method for analyzing the scientific literature, but it has not yet been applied to the comprehensive analysis of the STF method. Therefore, in this paper, we use bibliometrics and scientific mapping to analyze the 2967 citation data from the Web of Science from 1991 to 2023 in a metrological manner, covering the themes of STF, data fusion, multi-temporal analysis, and spatial analysis. The results of the literature analysis reveal that the number of articles displays a slow to rapid increase during the study period, but decreases significantly in 2023. Research institutions in China (1059 papers) and the United States (432 papers) are the top two contributors in the field. The keywords “Sentinel”, “deep learning” (DL), and “LSTM” (Long Short-Term Memory) appeared most frequently in the past three years. In the future, remote sensing spatiotemporal fusion research can address more of the limitations of heterogeneous landscapes and climatic conditions to improve fused images’ accuracy.
Empowering Electric Vehicles Batteries: A Comprehensive Look at the Application and Challenges of Second-Life Batteries
Authors: Azizighalehsari Seyedreza , Venugopal Prasanth , Pratap Singh Deepak , Batista Soeiro Thiago , Rietveld Gert
Published: May 2024

The surge in electric vehicle adoption has resulted in a significant rise in end-of-life batteries, which are unsuitable for demanding EV applications. Repurposing these batteries for secondary applications presents a promising avenue to tackle environmental and economic challenges associated with their disposal. The second-life battery (SLB) approach emerges as a mechanism to manage this massive amount of retired EV batteries. However, this approach poses significant challenges in determining and monitoring battery degradation and performance. After evaluating different scenarios for reusing or recycling retired EV batteries, this paper examines the main challenges associated with SLBs, including techno-economic aspects, uncertainty from first life, safety, characterization and screening, battery-management systems, and secondary applications. A comprehensive review of current state-of-the-art SLB research and implementations is provided, particularly emphasizing battery characterization and the requisite evaluation processes for SLB eligibility. This paper explores diverse measurement techniques for assessing SLB performance, evaluating them based on accuracy, complexity, and time consumption, which are essential for achieving cost-effective SLB applications. The overarching objective is to thoroughly understand the principal challenges associated with repurposing EV batteries and delineate the research imperatives necessary for their successful implementation and prolonged lifespan.
Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review
Authors: Borna Sahar , Maniaci Michael J. , Haider Clifton R. , Gomez-Cabello Cesar A. , Pressman Sophia M. , Haider Syed Ali , Demaerschalk Bart M. , Cowart Jennifer B. , Forte Antonio Jorge
Published: May 2024

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being.
Enhancement of Vascularization and Ovarian Follicle Survival Using Stem Cells in Cryopreserved Ovarian Tissue Transplantation—A Systematic Review
Authors: Zaninović Luca , Bašković Marko , Ježek Davor , Habek Dubravko , Pogorelić Zenon , Katušić Bojanac Ana , Elveđi Gašparović Vesna , Škrgatić Lana
Published: May 2024

The increase in cancer survival rates has put a focus on ensuring fertility preservation procedures for cancer patients. Ovarian tissue cryopreservation presents the only option for prepubertal girls and patients who require immediate start of treatment and, therefore, cannot undergo controlled ovarian stimulation. We aimed to provide an assessment of stem cells’ impact on cryopreserved ovarian tissue grafts in regard to the expression of growth factors, angiogenesis promotion, tissue oxygenation, ovarian follicle survival and restoration of endocrine function. For this systematic review, we searched the Scopus and PubMed databases and included reports of trials using murine and/or human cryopreserved ovarian tissue for transplantation or in vitro culture in combination with mesenchymal stem cell administration to the grafting site. Of the 1201 articles identified, 10 met the criteria. The application of stem cells to the grafting site has been proven to support vascular promotion and thereby shorten the period of tissue hypoxia, which is reflected in the increased number of remaining viable follicles and faster recovery of ovarian endocrine function. Further research is needed before implementing the use of stem cells in OT cryopreservation and transplantation procedures in clinical practice. Complex ethical dilemmas make this process more difficult.
Chimeric Cell Therapies as a Novel Approach for Duchenne Muscular Dystrophy (DMD) and Muscle Regeneration
Authors: Budzynska Katarzyna , Siemionow Maria , Stawarz Katarzyna , Chambily Lucile , Siemionow Krzysztof
Published: May 2024

Chimerism-based strategies represent a pioneering concept which has led to groundbreaking advancements in regenerative medicine and transplantation. This new approach offers therapeutic potential for the treatment of various diseases, including inherited disorders. The ongoing studies on chimeric cells prompted the development of Dystrophin-Expressing Chimeric (DEC) cells which were introduced as a potential therapy for Duchenne Muscular Dystrophy (DMD). DMD is a genetic condition that leads to premature death in adolescent boys and remains incurable with current methods. DEC therapy, created via the fusion of human myoblasts derived from normal and DMD-affected donors, has proven to be safe and efficacious when tested in experimental models of DMD after systemic–intraosseous administration. These studies confirmed increased dystrophin expression, which correlated with functional and morphological improvements in DMD-affected muscles, including cardiac, respiratory, and skeletal muscles. Furthermore, the application of DEC therapy in a clinical study confirmed its long-term safety and efficacy in DMD patients. This review summarizes the development of chimeric cell technology tested in preclinical models and clinical studies, highlighting the potential of DEC therapy in muscle regeneration and repair, and introduces chimeric cell-based therapies as a promising, novel approach for muscle regeneration and the treatment of DMD and other neuromuscular disorders.
Unlocking the Future of Periodontal Regeneration: An Interdisciplinary Approach to Tissue Engineering and Advanced Therapeutics
Authors: Huang Tsung-Hsi , Chen Jui-Yi , Suo Wei-Hsin , Shao Wen-Rou , Huang Chih-Ying , Li Ming-Tse , Li Yu-Ying , Li Yuan-Hong , Liang En-Lun , Chen Yu-Hsu , Lee I-Ta
Published: May 2024

Periodontal defects present a significant challenge in dentistry, necessitating innovative solutions for comprehensive regeneration. Traditional restoration methods have inherent limitations in achieving complete and functional periodontal tissue reconstruction. Tissue engineering, a multidisciplinary approach integrating cells, biomaterials, and bioactive factors, holds tremendous promise in addressing this challenge. Central to tissue engineering strategies are scaffolds, pivotal in supporting cell behavior and orchestrating tissue regeneration. Natural and synthetic materials have been extensively explored, each offering unique advantages in terms of biocompatibility and tunable properties. The integration of growth factors and stem cells further amplifies the regenerative potential, contributing to enhanced tissue healing and functional restoration. Despite significant progress, challenges persist. Achieving the seamless integration of regenerated tissues, establishing proper vascularization, and developing biomimetic scaffolds that faithfully replicate the natural periodontal environment are ongoing research endeavors. Collaborative efforts across diverse scientific disciplines are essential to overcoming these hurdles. This comprehensive review underscores the critical need for continued research and development in tissue engineering strategies for periodontal regeneration. By addressing current challenges and fostering interdisciplinary collaborations, we can unlock the full regenerative potential, paving the way for transformative advancements in periodontal care. This research not only enhances our understanding of periodontal tissues but also offers innovative approaches that can revolutionize dental therapies, improving patient outcomes and reshaping the future of periodontal treatments.
Neuronavigated Right Orbitofrontal 20 Hz Theta Burst Transcranial Magnetic Stimulation Augmentation for Obsessive–Compulsive Disorder with Comorbid Depression and Anxiety Disorders: An Open-Label Study
Authors: Stubbeman William F. , Yang Jennifer , Converse Julianne , Gencosmanoglu Melodi , Morales Ortega Daisy , Morris Jordyn , Sobocinski Andrew , Li Vicky , Gunawardane Gabriella , Edelen Yana , Khairkhah Raya , Perez Jillian
Published: May 2024

Background: Despite the availability of pharmacotherapy and psychotherapy for treating obsessive–compulsive disorder (OCD), alternative approaches need to be explored due to the high likelihood of treatment resistance. Neuronavigated 20 Hz theta burst stimulation (TBS-20 Hz), targeting the bilateral dorsolateral prefrontal cortex (DLPFC) augmented with the right orbitofrontal cortex (ROFC), was tested for treating OCD comorbid with depression and anxiety disorders. Methods: A retrospective chart review was performed on fourteen patients treated for moderate-to-severe OCD in a private outpatient clinic. Twelve patients had comorbid major depressive disorder (MDD), and thirteen patients had either generalized anxiety disorder (GAD) or panic disorder (PD). Patients completed the Y-BOCS-SR, BDI-II, and BAI rating scales weekly, which were used to measure the changes in OCD, depression, and anxiety symptoms, respectively. Results: Neuronavigated TBS-20 Hz was sequentially applied to the right DLPFC (RDLPFC), left DLPFC (LDLPFC), and ROFC. A total of 64% (9/14) of patients achieved remission from OCD (Y-BOCS-SR ≤ 14) in an average of 6.1 weeks of treatment (SD = 4.0). A total of 58% (7/12) of patients remitted from MDD (BDI < 13) in an average of 4.1 weeks (SD = 2.8), and 62% (8/13) of patients remitted from GAD/PD (BAI < 8) in an average of 4.3 weeks (SD = 2.5). Conclusions: The neuronavigated TBS-20 Hz sequential stimulation of RDLPFC and LDLPFC, followed by ROFC, significantly reduced OCD, MDD, and GAD/PD symptoms. Randomized sham controls are warranted to validate these results.
Assessment of Executive Functions in Children with Sensorineural Hearing Loss and in Children with Specific Language Impairment: Preliminary Reports
Authors: Lauriello Maria , Mazzotta Giulia , Mattei Antonella , Mulieri Ilaria , Fioretti Alessandra , Iacomino Enzo , Eibenstein Alberto
Published: May 2024

Executive functions (EFs) are related abilities, associated with the frontal lobes functions, that allow individuals to modify behavioral patterns when they become unsatisfactory. The aim of this study was to assess EFs in children with sensorineural hearing loss (SNHL) and in children with “specific language impairment” (SLI), compared with a control group of children with normal development, to identify specific skill deficits. Three groups of preschool children aged between 2 and 6 years were assessed: 19 children with normal hearing, cognitive, and language development, 10 children with SNHL, and 20 children with SLI. The FE-PS 2-6 Battery was used for the assessment of preschool EFs, supplemented with the Modified Bell Test for the analysis of selective attention. Statistically significant differences were found between the two experimental groups and the control one, regarding the investigated skills. Children with SNHL showed a clear deficit in flexibility, whereas children with SLI had greater problems in self-regulation and management of waiting for gratification. Selective attention was found to be deficient in all three groups, with no statistically significant differences. This study shows that the skills investigated were found to be deficient in both SNHL and SLI patients. It is essential to start targeted exercises based on specific deficient skills as part of the rehabilitation program. It is of great importance to understand the consequences of EF deficit in preschool children to achieve an accurate diagnosis and carry out customized rehabilitation programs.
Social–Emotional Competence among School-Aged Children in the Chinese Context: Validation of the Washoe County School District Social–Emotional Competency Assessment
Authors: Cheung Rebecca Y. M. , Ng Ocean O. S.
Published: May 2024

The present study aims to validate the 40-item and the brief 17-item Washoe County School District Social–Emotional Competency Assessment (WCSD-SECA), a self-report measure of social–emotional competencies, among Chinese school-aged children residing in Hong Kong. A total of 349 children (M = 9.86 years, SD = 1.22; 45.82% girls) and their parents (77.84% mothers) completed a set of questionnaires independently. The factor structure of both versions of the WCSD-SECA was assessed via confirmatory factor analyses. Structural equation models were then conducted to examine the predictive validity of the WCSD-SECA. The findings indicated that both the 40-item and the 17-item versions of the WCSD-SECA fit the data adequately. Both versions were also associated with self-reported positive and negative affect and parent-reported internalizing problems and externalizing problems. Additionally, social–emotional competencies as measured by the 40-item version were significantly associated with parent-reported prosocial behavior, whereas those as measured by the 17-item version were associated with prosocial behavior with a marginal significance. The findings demonstrated an adequate factor structure and predictive validity of the full version and the brief version of the WCSD-SECA in assessing social–emotional competencies. Hence, they serve as a useful tool for researchers, educators, and mental health practitioners to evaluate school-aged children’s social–emotional competencies in the Chinese context.
Empathy and Coping Strategies Predict Quality of Life in Japanese Healthcare Professionals
Authors: Shoji Kotaro , Noguchi Norihito , Waki Fumiko , Saito Taku , Kitano Masato , Edo Naoki , Koga Minori , Toda Hiroyuki , Kobayashi Nobuhisa , Sawamura Takehito , Nagamine Masanori
Published: May 2024

Burnout and secondary traumatic stress (STS), also referred to as compassion fatigue, are undeniable negative consequences experienced by healthcare professionals when working with patients. As frontline healthcare professionals are essential to communities, it is crucial to understand their mental health and how they cope with negative psychological responses. This study investigated the relationships between burnout, STS, compassion satisfaction, dispositional empathy, and stress management among Japanese healthcare professionals and students taking care of patients in clinical practice. The participants were 506 Japanese healthcare professionals and students (doctors, nurses, medical students, and nursing students) affiliated with Japanese Ministry of Defense Hospitals. The data were collected from March 2020 to May 2021. We assessed burnout, STS, and compassion satisfaction using the Professional Quality of Life Scale, dispositional empathy using the Interpersonal Reactivity Index, and coping with stress using the Coping Orientation to Problems Experienced Inventory (Brief-COPE). Exploratory factor analysis of the Brief-COPE yielded three factors: active coping; support-seeking; and indirect coping. Personal distress, a self-oriented emotional empathy index, was related to higher burnout and STS scores and lower compassion satisfaction. Empathic concern, an other-oriented emotional empathy index, was associated with lower burnout and higher compassion satisfaction. Active coping strategies were associated with lower burnout and higher compassion satisfaction, whereas indirect coping strategies were associated with higher burnout and STS scores. In a comparison of empathy in professional categories, nurses presented higher personal distress than nursing students, and medical doctors showed lower fantasy tendencies than medical students. These results imply the complex relationships between empathy, coping strategies, and psychological responses among healthcare professionals. Further longitudinal study is needed to explore these complex relationships and to develop more precise and effective psycho-educational interventions to prevent burnout and STS.
Drinking While Stressed and Drinking to Cope Differentially Relate to Mental Health
Authors: Vornlocher Carley , Shiota Michelle N.
Published: May 2024

Excessive alcohol use, as well as drinking to manage distress, are known to undermine mental health. The current study examined the unique associations of simply consuming alcohol while stressed, versus using alcohol to cope with distress, with mental health during the early stages of COVID-19. Participants (N = 264) reported their alcohol use and use of alcohol/substances to cope with stress daily for 22 days and completed measures of mental health at baseline and every 7 days thereafter. Hierarchical regression models were tested to predict drinking while stressed, drinking to cope, and mental health. At baseline, drinking while stressed was not significantly associated with mental health when coping motives were accounted for. However, drinking to cope was significantly associated with greater stress, anxiety, depression, and loneliness. Drinking while stressed was significantly predicted by baseline stress and loneliness, while drinking to cope was significantly predicted by baseline anxiety. Drinking while stressed was not a significant predictor of change in mental health when coping motives were accounted for. Drinking to cope was a significant predictor of increases in depression but not of change in stress, anxiety, or loneliness. The motivation to manage distress appears to be driving the negative effects of alcohol use on mental health.
Postural Control and Muscle Activity during Dual-Task in Young Adults
Authors: Saraiva Marina , Vilas-Boas João Paulo , Castro Maria António
Published: May 2024

In everyday life, we recurrently perform two tasks simultaneously, which is called dual-tasking. A common dual task is smartphone use while standing or walking. According to previous studies, this task can compromise postural stability. However, few studies have analyzed lower limb muscle activity during dual-tasking using smartphones. This study aimed to assess the postural sway and muscle activity during dual-tasking in young adults. Thirty-six healthy young adults (23.08 ± 3.92 years) participated in this study. They performed a single task (ST: keeping a quiet standing posture) and a dual task (DT: keeping the ST while simultaneously performing a cognitive task on their smartphone). Postural sway was assessed through the center of pressure (CoP) analysis using a force platform: total CoP displacement, CoP displacement in the anterior–posterior and medial–lateral directions, mean total velocity of the CoP, mean velocity of the CoP in the anterior–posterior and medial–lateral directions, and 95% confidence ellipse sway area. A surface electromyography system recorded the muscle activity of the lumbar spinal erector and five muscles of the lower limb (bilaterally). The results showed an increase in postural sway from the ST to the DT in all CoP variables (p < 0.05), and muscle activity in most muscles analyzed decreased from the ST to the DT (p < 0.05). In conclusion, our results reflect a decentralization of attention from motor performance once postural sway increased and muscle activity decreased in dual-task conditions.
Consumer Psychology of Mysterious Consumption: Embracing Uncertainty through a Perception of Control
Authors: Ding Wei , Han Seunghee
Published: May 2024

Mysterious consumption, characterized by product purchases without knowledge of their exact nature, is gaining popularity in the modern marketplace. In two online experiments, we examined how consumers’ psychological states, particularly their sense of personal control, influence their perception and intent to purchase mysterious products in the context of purchasing a random flight ticket. Study 1 demonstrated that when consumers experience low personal control, as opposed to high personal control, they are less likely to value the uncertainty inherent in random flight tickets, resulting in decreased purchase intentions. Study 2 revealed that introducing even limited choice options to random flight tickets can enhance consumers’ appreciation of uncertainty, thereby increasing their purchase intention. This effect was especially notable among those initially hesitant to embrace uncertainty, ultimately boosting their intent to purchase. These findings deepen our understanding of consumer psychology surrounding mystery consumption and provide practical insights for marketers seeking to tailor their products and marketing appeals to consumers’ psychological states.
The Use of Sargasso Seaweed as Lignocellulosic Material for Particleboards: Technical Viability and Life Cycle Assessment
Authors: Duran Afonso José Felício Peres , Lyra Gabriela Pitolli , Campos Filho Luiz Eduardo , Bueno Cristiane , Rossignolo João Adriano , Alves-Lima Cicero , Fiorelli Juliano
Published: May 2024

There have been beaching events of the marine alga pelagic sargassum in coastal regions of the Caribbean Sea, West African countries, and the north-northeast region of Brazil since 2011. Its presence has caused environmental and socioeconomic impacts while several studies were conducted in order to understand the causes of this phenomenon, as well as alternatives to mitigate its impacts. The objective of this research was to evaluate pelagic sargassum biomass from beaching as a raw material for the manufacture of medium-density multilayer particleboards, aiming for an application that can reduce the impacts generated by the disposal of this seaweed on beaches and landfills. These are composed of 30% sargassum particles in their inner layer and 70% sugarcane bagasse particles on their outer layers, which are bonded with castor-oil-based polyurethane resin. A physical and chemical characterization was carried out in order to evaluate sargassum particles while physical and mechanical tests were carried out in order to evaluate the panels. Results were subsequently compared with indications from different particleboard standards. A life cycle assessment was carried out to complement the feasibility study of these panels and to compare their different manufacturing processes. The multilayer panels met the minimum requirements for physical and mechanical properties established by regulations, indicating that the Sargassum spp. biomass can be used as filling. The life cycle assessment study indicates that sargassum panels produced in the Belém, PA, Brazil, region present lower environmental impacts in four of seven evaluated categories when compared to conventional panels. Given the results obtained, the use of sargassum from beaching events as raw material for panels can be presented as an alternative for reducing social, economic, and environmental impacts in the regions affected by these events.
Developing a Manufacturing Industrial Brain in a Smart City: Analysis of fsQCA Based on Yiwu Knitting Industry Platform
Authors: Ma Jinchi , Wang Weidong , Zhou Cheng
Published: May 2024

This article explores the mechanism for constructing and the path for implementing an industrial brain in the development of smart cities, with a focus on the case of the Yiwu knitting industry platform in China. Accordingly, our study involved a literature review, questionnaire survey, data analysis, qualitative comparative analysis (QCA), and discussion. Our key finding was that the manufacturing brain evolves in three distinct stages: platform creation, growth, and expansion. The mechanisms of implementing these are functional development, trust creation, and value co-creation, respectively. Specifically, functional development marks the commencement of the industrial brain’s construction, which involves enterprise demand analysis, capability bottleneck identification, data value formation, and platform architecture simplification. Trust building serves as the central mechanism of evolving the manufacturing brain, comprising institutional, relational, and computational trust. Lastly, value co-creation proceeds, which is pivotal for a business paradigm revolution, encompassing connection, linkage, and integration. The main theoretical contribution of this article is to propose a normative analytical framework for revealing the mechanism of construction and the path of implementation of industrial platforms in smart city development. Meanwhile, in its practical contribution, this article provides policy guidance, as developed through our analysis of how an industrial platform can promote the transformation and upgrading of the urban manufacturing industry, to realize smart city construction and the economy and society’s coordinated development.
Research on Influencing Factors of Knowledge Transfer among Prefabricated Construction Workers
Authors: Cao Xinying , Qin Luping , Li Bei , Qin Peicheng
Published: May 2024

To identify the influencing factors and internal mechanism of knowledge transfer among prefabricated construction workers (PCWs), this study constructs a knowledge transfer behavior model for PCWs from various perspectives, including behavioral attitude, subjective norms, physiological perceived control, behavioral habits, and behavioral intention based on a modified Theory of Planned Behavior (TPB). It also employs a Structural Equation Model (SEM) for hypothesis validation and utilizes System Dynamics (SD) to simulate the knowledge transfer process of PCWs. Through empirical and simulation research, three conclusions are drawn: (1) Knowledge transfer willingness has a promoting effect on knowledge transfer behaviors. (2) Trusted relationships, organizational culture, physiological perceived control, and behavioral habits all have a promoting effect on workers’ knowledge transfer willingness and subsequently promote their knowledge transfer behaviors. (3) Among these factors, the strength of knowledge transfer willingness has the most significant impact on workers’ behavioral habits, followed by trusted relationships, physiological perceived control, and organizational culture. Additionally, when comparing the results of this study with knowledge transfer influencing factors of traditional construction workers (TCWs), it is found that trusted relationships and self-efficacy have a promoting effect on the knowledge transfer willingness of PCWs as well as TCWs. However, the impact of organizational culture, environmental perception, and behavioral habits on the knowledge transfer willingness of PCWs is more pronounced compared to TCWs. Based on TPB, this study constructs a suitable theoretical model to analyze the knowledge transfer process of PCWs by mining their group characteristics, and the research results establish a systematic analysis framework for the study of knowledge transfer behavior of PCWs. It also provides scientific suggestions for the formulation of targeted measures to enhance the willingness and efficiency of knowledge transfer of PCWs.
Experimental Investigation and Design of Novel Hollow Flange Beams under Bending
Authors: Xue Jingya , Ma Shiliang , Chen Xiaomiao , Wu Qing , Wang Yifan , Wang Yunqing , Akbar Muhammad , Yang Ning
Published: May 2024

In this paper, a new type of assembling rivet-fastened rectangular hollow flange beams (ARHFBs) is proposed. The cross-section of the ARHFB consists of two U-shaped and C-shaped components connected by self-locking rivets to form two rectangular hollow flanges. To study the performance and strength of the ARHFB as a flexural member, eight four-point bending tests and more than 40 simulation studies were carried out. The details, results, and comparison of the four-point bending tests, especially the characteristics of the test bench and the lateral support, are presented in this paper. ARHFB sections with varied rivet spacing, web depth, and flange width were experimentally studied. Additionally, a parametric study of ARHFB was conducted using finite element models verified by test results. The influence of span on the loading capacity of ARHFB was discussed. ARHFB can be used in large-span buildings. A more economical ARHFB component selection method was given. The depth of the flange, the strength of the web, and the thickness of the web are important parameters of ARHFB. The loading capacity obtained from the test was compared with the predicted values of the design formulas in the American Iron and Steel Institute (AISI) and the Chinese design standard for cold-rolled steel (GB50018). The calculation and verification of ARHFB flange buckling and lateral torsional buckling were also considered. It is recommended that GB50018 be used to predict the flexural capacity of ARHFBs.
Integrated Decision-Making of Urban Agriculture within the Greyfield Regeneration Environments (UAGR)
Authors: Dong Wenli , Lin Gangjian
Published: May 2024

Various urban environmental and social challenges have emerged during the rapid urban development. Urban agriculture has emerged as one of the practical solutions to address these urban issues and climate change. This study aims to establish a decision model for urban agriculture regeneration that can be applied to improve the implementation of related projects. The study first reviews existing research on Urban Agriculture within the Greyfield Regeneration Environments (UAGR) and outlines the processes involved, including project initiation, construction, and operation management. It identifies 25 factors influencing UAGR and employs the Fuzzy Delphi method (FDM) to prioritize them based on expert judgments. Subsequently, the interpretative structural model (ISM) analysis method is applied to analyze the interrelationships among the 11 most important factors. Matrix operations and MATLAB programming are utilized to establish the influence relationship model based on expert questionnaires to determine the influence between each pair of factors. This results in a hierarchically structured decision model for UAGR. Finally, the decision-making model is applied to analyze the case study in Shanghai and Hangzhou. As urban agricultural activities are proliferating in rapid urbanization, the establishment of a decision-making model for UAGR can offer practical guidance to practitioners, facilitating the development of urban agriculture and mitigating climate change.
Clinical Applications of Artificial Intelligence in Medical Imaging and Image Processing—A Review
Authors: Obuchowicz Rafał , Strzelecki Michał , Piórkowski Adam
Published: May 2024

Artificial intelligence (AI) is currently becoming a leading field in data processing [...]