bluuebunny/crossref_metadata_2025_split huggingface dataset:
School bullying prevention among left-behind children: An intervention study based on social ecological model in China
Authors: Huang Jinyu , chen yichao , Chen Yueliang , Da Qingchen , Li Liping 
Published: November 2024

Abstract

Background
School bullying among left-behind children (LBC) is a significant public health concern in China. To promote their healthy development, school bullying prevention intervention based on the theory of social ecological model (SEM) in four levels (individual, relationship, campus and society) among LBC was carried out in this study.
Methods
190 left-behind children were recruited from two middle schools in Shantou city(mean age 13.52 ± 0.70). Differences within groups were analyzed using t-test or chi square test. Furthermore, differences in scores of school bullying prevention knowledge and attitude were examined with differences-in-differences (DID) approach to test the effectiveness of intervention.
Results
Results showed that after the intervention, the knowledge scores in the two group both increased with statistically significant differences (all P < 0.05). While the attitude scores increased in the SEM based intervention group with statistical significance while not in the control group. What’s more, it presented that knowledge and attitude of preventing school bullying were improved in SEM based intervention group more than the control group. However, we didn’t find significant difference in the incidence of school bullying before and after the intervention between the two groups.
Conclusions
Interventions based on the SEM are effective in improving left-behind children's knowledge and attitudes toward school bullying, but additional strategies are needed to achieve actual behavioral change. A comprehensive, multi-level approach that combines SEM-based interventions with behavioral strategies can serve as a model for reducing school bullying in China and globally.
Multi-Scale Attention and Boundary-Aware Network for Military Camouflaged Object Detection using Unmanned Aerial Vehicles
Authors: Liu Keshun , Li Aihua , Yang Sen , Wang Changlong , Zhang Yuhua 
Published: November 2024

Abstract

Military camouflaged object detection using unmanned aerial vehicles (UAVs) is a crucial yet challenging task that can significantly support intelligence interpretation and accurate strike on the battlefield. However, existing methods primarily focus on the semantic segmentation of camouflaged objects, neglecting the specific needs of military reconnaissance and UAV imagery. To address these limitations, we propose a multi-scale attention and boundary-aware network tailored for military camouflaged object detection from UAV imagery. Leveraging a pyramid vision transformer as the backbone, we design feature fusion module to fuse multi-scale features to efficiently transmit feature information. Further, we introduce three interdependent modules: boundary extraction module, boundary guidance module, and context fusion module, to excavate boundary and context semantic information, thereby enhancing the feature representation of camouflaged objects. We also create MCOD-UAV, a new dataset designed specifically for this task. Extensive experiments on MCOD-UAV dataset demonstrate that our network outperforms existing state-of-the-art methods, achieving a mean average precision (mAP) of 48.92%. Our network paves the way for more accurate and preemptive strikes in real-world military operations.
Treatment and counselling preferences of postpartum women with and without symptoms of (childbirth-related) PTSD: Findings of the cross-sectional study INVITE
Authors: Horstmann Rosa Hannele , Seefeld Lara , Schellong Julia , Garthus-Niegel Susan 
Published: November 2024

Abstract

Background
Post-traumatic stress disorder (PTSD) in the postpartum period is a prevalent yet under-researched mental health condition. To date, many women who suffer from postpartum PTSD remain unrecognized and untreated. To enhance the accessibility of help for these women, it is crucial to offer tailored treatment and counselling services that align with their needs. Hence, this study aimed to understand how support preferences differ between women with and without postpartum PTSD, considering the two subgroups of postpartum PTSD: childbirth-related PTSD (CB-PTSD) and general PTSD (gPTSD).
Methods
This study used data from the cross-sectional INVITE study, comprising telephone interviews with N = 3,874 women conducted between six weeks and six months after childbirth. The City Birth Trauma Scale (City BiTS) was used to assess CB-PTSD, while the Primary Care Posttraumatic Stress Disorder Screen for DSM-5 (PC-PTSD-5) was used to assess gPTSD. Service preferences and preferred modes of service provision were measured with self-developed questionnaires. Analyses of variance were used to identify differences between groups.
Results
The support services (family-)midwives and family, friends, or colleagues and the service provision mode as in person communication were preferred most by women across groups. The analyses revealed that women with CB-PTSD had lower overall preferences for services compared to women without postpartum PTSD. Women with CB-PTSD also showed less preference for psychotherapeutic services (e.g. outpatient treatment, inpatient clinics) compared to unaffected women. Regarding modes of service provision, women with gPTSD had a higher preference for all service modes compared to women with CB-PTSD and those unaffected, with a stronger preference for both direct (e.g. in person, video conference) and delayed communication (e.g. chat, email).
Conclusion
This study was the first to explore the support preferences of women experiencing symptoms of postpartum PTSD. The findings suggest that women differ in their preferences, contingent upon the subgroup of postpartum PTSD. According to women's overall preferences, the expansion and further training of (family-)midwife services is recommended. By tailoring support services accordingly to women’s preferences, it may be possible to bridge the treatment gap for postpartum PTSD and to improve the well-being of affected women and their families.
A Novel Passive Pseudo-Negative Stiffness Damper for Seismic Response Reduction
Authors: Pradono Mulyo Harris , Satyarno Iman , Wardi Syafri , Sudarmadi Sudarmadi , Fauzi Hendro Ahmad , Wibowo Leonardus Setia Budi , Rafi Hanif Muzhaffar , Putra Andhika 
Published: November 2024

Abstract

The concept of a pseudo-negative stiffness (PNS) damper using a fluid viscous variable damper has been introduced since early 2000s, which employed a semi-active control to produce PNS hysteretic loops by using a variable damper. The hysteretic loops were proven to be effective in reducing the seismic response of a structure. In this research, the development of a passive damper system capable of producing a PNS hysteretic loop is reported. The damper is based on fluid viscous damping. To produce the hysteretic loops passively, the tube of the damper is modified so that the damping force is controlled by the flow of viscous fluid between the piston and the tube by providing grooves at the tube's inner wall. The groove's dimensions are smaller at one end and larger at the other end. When the piston moves from the position of smaller grooves to larger grooves, the fluid flows from smaller grooves to larger grooves. This will make the damping force shift from large to small. When the piston moves back from large to small grooves, a valve will make the fluid flow freely through the orifice at the piston. This will make the damping force small. Combination with another damper that has opposite characteristics produces complete PNS hysteretic loops. Based on the designed passive damper, the theoretical model of the hysteretic loops was numerically simulated under seismic loading for a base-isolated structure employing the damper. The hysteretic loop was proven effective in reducing the seismic response of the structure.
The spiritual needs of individuals with suicide ideation or suicide attempt: a qualitative content analysis
Authors: Ghahramani Marziyeh , Memaryan Nadereh , Ghahari Shahrbanoo , malakouti Kazem 
Published: November 2024

Abstract

Subject Suicide is a major public health concern and people with suicide ideation (SI) or suicidal attempt(SA) are exposed to many complex difficulties, which needs psychological and spiritual care. Method This exploratory, qualitative study consisted of interviews with a number of respondents in order to collect data for use within Gernheim and Lundman's model depicting through content analysis. We used the coding software MAXQDA 2020. The study was conducted on parents of the participants with suicidal indivituals that were referred to psychology clinic department, School of Behavioral Sciences and Mental Health or they hospitalized in Iranian psychiatric hospital during September 2023 till June 2024. A content analysis approach is adopted by focusing at understanding their spiritual needs. Study sample included 15 individuals who were either SI or SA and available along with their parents. Results The study's findings indicate that there are six needs for them, including The need for divine connection, The need to Meaning-based self-care, The need for lovely interpersonal communication, The need for humane-emotional support, The need for contemplation, and The need for purpose. Conclusion Results indicating that tailored interventions addressing each of these needs could potentially offer an effective strategy to reduce suicide risk in bereaved college students should be explored further. This will hopefully lead to positive steps being taken.
Statistical Analysis and Prediction via Neural Networks of water quality in the Middle Paraíba do Sul (Rio de Janeiro State, Brazil) region in the period (2012 - 2022)
Authors: Abraão Ricardo Pereira , Sampaio Nilo Antônio de Souza , von Mühlen Carin 
Published: November 2024

Abstract

This study presents a 10-year temporal assessment (2012–2022) of water quality in the Middle Paraíba do Sul hydrographic region, using the Water Quality Index (WQI) and statistical tools, with predictions via General Regression Neural Network (GRNN). The analysis, based on INEA data, highlights climatic events such as the 2014/2015 drought and differences between the WQI in rainy and dry seasons. The preservation of water quality in this region is crucial for public health, sustainability, and the economic development of the Rio de Janeiro metropolitan area, which relies on the Paraíba do Sul River. Increasing urbanization, agricultural expansion, and climate change pose challenges to water quality. Statistical tools such as Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) reveal significant variations between monitoring points, and GRNN predicts WQI trends for 2023. This predictive approach is vital for informed decision-making in water resource management, particularly as environmental pressures increase.
Formation of Sense of Agency in Novel Motor Learning
Authors: Tanaka Takumi , Imamizu Hiroshi 
Published: November 2024

Abstract

Sense of agency (SoA) refers to the sensation of controlling one’s body and the external environment. The traditional comparator model posits that SoA arises from a match between predicted and actual action outcomes. Yet, when learning new motor skills, individuals lack reliable predictions of outcomes. This study explored SoA development in novel motor learning where participants controlled a cursor via finger movements. Across learning phases, we evaluated participants’ SoA for cursor movements that either conformed to the learned hand-to-screen mapping or incorporated spatial or temporal biases. Initially, temporal congruence between finger and cursor movements dictated SoA. As learning progressed, SoA increased for the cursor following the learned mapping over that with spatial discrepancies. Such changes did not occur by just memorizing the mapping in a gesture imitation task. These findings enrich existing SoA theories by elucidating the origin of the comparator process, highlighting the pivotal role of motor exploration.
How Competitiveness, Commitment, and Profitability Affect ESG Performance: An Empirical Investigation of Chinese Firms
Authors: Abdullah Shamsuln Nahar , Nazir Muhammad Waqas , Marwat Athar , Rauf Fawad , Naveed Khwaja 
Published: November 2024

Abstract

The rapidly transforming global economic landscape emphasizes ESG performance integration to enhance corporate profitability and competitiveness. This study investigates concern construct relationship of China’s non-financial companies Shanghai and Shenzhen stock market listed during 2016-2022. Utilizing data from credible sources, including Bloomberg, this research evaluates ESG performance through a comprehensive ESG score and examines its relationship with corporate profitability, and commitment. Hence, study employs robust methods to assess ESG practices impact on corporate outcomes, considering various control variables like return on assets, firm age, financial leverage, ownership concentration etc. The findings suggest that ESG performance positively correlates with enhanced corporate performance, increased market competitiveness, and strengthened corporate commitment, thereby underscoring the importance of sustainable practices in achieving long-term business success. This research also contributing by providing empirical evidence on ESG performance and its impact towards fostering profitability, competiveness and enhance employee commitment.
Geospatial and Climate Dynamics in West Africa: Analysing Impact of Weather Conditions on Food Security in Burkina Faso and Nigeria
Authors: Omenka Loveth C. Ode , Osabuohien Evans S. , Urhie Ese , Ode Godwin O. , Eric Kabore N. , Al-Faryan Abdulaziz Saleh 
Published: November 2024

Abstract

West Africa's food security is seriously threatened by climate change. There are a number of reasons for the current food crisis in West Africa, but climate change is one of the main ones. While the debate about the precise influence of climate change on food security lingers, the question of whether climate change really impacts on food insecurity remains largely unanswered, considering the fact that there are other factors which tends to impact on food security. Thus, the study explores the relationship between climate change and food security in West Africa using the case of Burkina Faso and Nigeria by analysing the extent of impact of weather conditions such as precipitation, temperature and humidity on food security. The study utilises Fully Modified Ordinary Least Square (FMOLS) and geospatial analysis. The findings of the study, among others showed that the included variables (Humidity, Precipitation, and Temperature) collectively account for a significant portion of the variability in food production across the observations, noting specially the impact of humidity on food security which has limited studies. The use of Geospatial analysis showed the impact of climate identifying the various state and the extent of climate change impact. The FMOLS results underscored the importance of considering weather conditions such as humidity, precipitation, and temperature in understanding food production dynamics. Policymakers and agricultural stakeholders can utilise these insights to develop adaptive and resilience strategies for mitigating the adverse effects of weather conditions on food agricultural productivity focusing interventions on communities with adverse impact, thereby promoting food security and sustainability.
Enhanced Prediction of Molecular Properties Using Transfer Learning on Sigma Profiles
Authors: Yin Tianzhixi , Gao Peiyuan , Xu Audrey , Saldanha Emily G. 
Published: November 2024

Abstract

The performance of machine learning techniques for the prediction of a wide range of molecular properties has seen rapid improvements in recent years due to developments in both molecular representations and deep learning modeling techniques. Sigma profiles, which are a computational descriptor representing the surface charge distribution of molecules, have shown promise as a molecular representation to support robust property prediction. Meanwhile, large-scale pretrained deep learning models based directly on molecular structure inputs, such as Uni-Mol, have demonstrated strong performance as general-purpose molecular representation learners. In this study, we seek to enhance the prediction of molecular properties by integrating information from sigma profiles with these advanced deep learning techniques. Our methodology involves fine-tuning the Uni-Mol model to accurately predict sigma profiles, which capture detailed molecular structural information important for determining molecular interactions. We then utilize transfer learning to apply the learned weights to predict specific molecular properties, replacing the final output layer to adapt to each new task. The results demonstrate improvements in predictive accuracy across various datasets, showcasing the effectiveness of combining sigma profiles with state-of-the-art machine learning models and demonstrating a path forward for leveraging theory-driven descriptor development to enhance large-scale data-driven molecular property modeling.
Assessment of acrolein development during frying and its relation in acrylamide formation
Authors: Gutiérrez-Flores Isaac Alonso , Garcia-Galindo Hugo Sergio , Moran Jessica Sampieri , Cavazos-Garduño Adriana , Serrano-Niño Julio César 
Published: November 2024

Abstract

Vegetable oils have the ability to transfer heat to the food by carrying out a frying or frying process at 150–200 °C, providing different desirable sensory properties to the food, however, heating the oil to high temperatures leads to degradation processes that lead to the formation of acrolein, which is toxic to human health; likewise, vegetable oil, when interacting with food, carries out complex reactions that could lead to the formation of acrylamide, which, like acrolein, is considered toxic to health. In the present work, the relationship between the formation of acrolein in oils heated to 220 °C over 7 hours of heating is analyzed, as well as the concentration of acrylamide that is generated when frying French fries under the same conditions. The results suggest that the formation of acrolein depended on the heating time and the type of oil used and, in addition, the formation of acrolein had an influence on the formation of acrylamide, with soybean oil being where there was the most formation of both compounds.
Comparison of Systemic Health and Oral Health in Korean Elderly in Aged-care Facilities
Authors: Lee Na-Young , Bak Hee Kyeong , Kim Han-Na 
Published: November 2024

Abstract

Background
This study compared the systemic health and oral health of elderly residents in nursing homes and daycare centers in South Korea. The study also identified the factors by which elderly health status (systemic health vs. oral health) differed between those living in the nursing home and daycare center attendees.
Methods
This study surveyed one nursing home and one daycare center in Korea (a total of 64 elderly residents) from March to September 2024. Five trained dental hygienists conducted physical examinations and surveys to assess the participants' systemic health and oral status. A chi-square analysis was performed to analyze the general, systemic, and oral health status by facility type. A multivariate binary logistic regression analysis was also conducted to identify the factors that distinguish between nursing homes and daycare center for the elderly.
Results
An analysis of the elderly residents in nursing home and daycare center revealed significant differences in their long-term care grade, activities of daily living (ADL), independent activities of daily living (IADL), medications, blood pressure, walking ability, InBody, oral moisture (tongue and buccal), diet, tongue pressure, oral hygiene, tongue coating, and halitosis. Key factors influencing facility type were long-term care grade (OR = 4.8), tongue moisture (OR = 4.1), halitosis (OR = 1.1), and Skeletal Muscle Mass Index (SMI) (OR = 2.7), with higher values indicating a greater likelihood of daycare center placement over nursing homes.
Conclusions
The comparison of systemic health and oral health between elderly residents in nursing home and daycare center identified significant differences in their long-term care grade, tongue moisture, halitosis, and SMI, which are distinguishing variables between the facility types. Given the differences in systemic and oral health status based on care facility type, it is essential to develop tailored oral health programs specific to each facility.
Test Bench for Economic Science: Stock Market Forecasting in “n” Dimensions
Authors: Briet Timoteo 
Published: November 2024

Abstract

Econophysics, supported by mathematics, has proven to be a promising field for studying complex economic systems. However, the absence of a general theory limits its applicability. This work proposes a novel framework based on fluid dynamics and artificial intelligence to jointly model and predict dependent economic events. After the model “WAVES”, the model termed “ABE” (Acceleration Balance Equation), defines a space with a potential field, where geodesics represent the trajectories of economic events (the “GT” Geodesic Trajectories model) as the metric is defined by the Hamiltonian action. Artificial intelligence is utilized to optimize the model and enhance its predictive accuracy based on existing projections, working with the model described as a neural network. Comprehensive tests are conducted to validate the proposed approach, both for individual events and sets of events. The results demonstrate the model's capability to capture underlying dynamics and predict future behavior with minimal uncertainty. This method holds significant potential for real-time applications, as well as short, medium, and long-term analyses. By minimizing or removing human intervention and leveraging the laws of physics, particularly through transport and diffusion phenomena, the proposed approach offers a robust, targeted solution for the joint modeling and forecasting of economic events, built on the foundation of the so-called natural dynamics of the economy. Consequently, a range of useful tools are defined, providing economists with means to regulate the economy and intervene appropriately at critical moments, even creating new structures. These tools enable decision-makers to design and implement effective public policies that promote economic and social welfare, as well as optimal equilibrium. The 2024 Nobel laureate in Economics59 proposes the proper identification of institutions and the economic structure necessary to achieve sustainable growth. This growth also requires effective cooperation among individuals, as well as the preservation of both individual and conditional freedom. This article offers a key tool to accomplish that goal.