Search Results (1,063)

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble of ML models to previously unseen validation data were fitted to a beta distribution. The shape parameters for the fitted distribution were used to calculate the subjective opinion of sample membership into one of two mutually exclusive classes. The subjective opinion consists of belief, disbelief and uncertainty masses. A subjective opinion for each validation sample allows identification of high-uncertainty predictions. The projected probabilities of the validation opinions were used to calculate log-likelihood ratio scores and generate receiver operating characteristic (ROC) curves from which an opinion-supported decision can be made. Three very different ML models, linear discriminant analysis (LDA), random forest (RF), and support vector machines (SVM) were applied to the two-state classification problem in the analysis of forensic fire debris samples. For each ML method, a set of 100 ML models was trained on data sets bootstrapped from 60,000 in silico samples. The impact of training data set size on opinion uncertainty and ROC area under the curve (AUC) were studied. The median uncertainty for the validation data was smallest for LDA ML and largest for the SVM ML. The median uncertainty continually decreased as the size of the training data set increased for all ML.The AUC for ROC curves based on projected probabilities was largest for the RF model and smallest for the LDA method. The ROC AUC was statistically unchanged for LDA at training data sets exceeding 200 samples; however, the AUC increased with increasing sample size for the RF and SVM methods. The SVM method, the slowest to train, was limited to a maximum of 20,000 training samples. All three ML methods showed increasing performance when the validation data was limited to higher ignitable liquid contributions. An ensemble of 100 RF ML models, each trained on 60,000 in silico samples, performed the best with a median uncertainty of 1.39 × ${10}^{-2}$ and ROC AUC of 0.849 for all validation samples. Full article

In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through fuzzy set theory, applies an integrated weighting method to measure development levels, and uses regression models to assess the digital economy’s impact on the real economy. The coupling coordination degree model, kernel density estimation, and Gini coefficient reveal the coordination status and spatial distribution, while the ecological Lotka–Volterra model identifies the symbiotic patterns. The key findings are as follows: (1) The digital economy does not directly determine the state of the real economy. For example, cities such as Zhoukou and Zhumadian have low digital economy levels but high real economy levels. However, the development of the digital economy promotes the real economy without signs of diminishing returns. (2) The two economies are generally coordinated but differ spatially, with greater coordination in the Central Plains urban agglomeration. (3) The digital and real economies exhibit both collaboration and competition, with reciprocal mutualism as the dominant mode of integration. These insights provide guidance for policymakers and offer a new perspective on the integration of both economies. Full article

The purpose of this paper is to analyze how China’s outward foreign direct investment (OFDI), driven by different motivations, affects the bilateral global value chain (GVC) relationship between the home country (China) and host countries, evaluating both bilateral GVC trade value and relative GVC positions. Employing the OECD Trade in Value Added (TiVA) database combined with Chinese listed firm data, we found the following results: (1) Strategic asset-seeking OFDI strengthens the GVC relationship between China and host countries while enhancing China’s GVC position relative to host countries. (2) Efficiency-seeking OFDI increases the domestic value-added exported from host countries to China but does not improve China’s relative GVC position. (3) Natural resource-seeking OFDI enhances bilateral GVC trade volumes but has no significant impact on the relative GVC positions of China and host countries. (4) China’s OFDI, not driven by these motivations, generates a trade substitution effect between home and host countries. We also examined the heterogeneity of these effects. Our findings suggest that China’s OFDI fosters equitable and sustainable international cooperation, supports mutually beneficial GVC trade and host-country economic growth, and therefore, progresses toward Sustainable Development Goal (SDG) 8. Full article

The incorporation of waste polyethylene terephthalate (PET) as a modifier for asphalt presents a promising approach to addressing the environmental pollution associated with waste plastics while simultaneously extending the service life of road surfaces. This study investigates the fundamental physical properties and rheological properties of asphalt modified with waste PET at both high and low temperatures. Utilizing the theory of fractional derivatives, performance evaluation indicators, such as the deformation factor and viscoelasticity factor, have been developed for the assessment of waste PET-modified asphalt. The underlying mechanism of this modification was examined through scanning electron microscopy and Fourier transform infrared spectroscopy. The results indicate that the addition of waste PET enhances the high-temperature stability of the base asphalt but reduces its resistance to cracking at low temperatures. The fractional derivative model effectively describes the dynamic shear rheological properties of waste PET-modified asphalt, achieving a maximum correlation coefficient of 0.99991. Considering the performance of modified asphalt at both high and low temperatures, the optimal concentration of waste PET was determined to be 6%. At this concentration, the minimum creep stiffness of the PET-modified asphalt was approximately 155 MPa at −6 °C. Additionally, the rutting factor of the waste PET-modified asphalt achieved a maximum value of 527.12 KPa at 52 °C. The interaction between waste PET and base asphalt was primarily physical, with mutual adsorption leading to the formation of a spatial network structure that enhanced the deformation resistance of the asphalt. This study provides a theoretical foundation and technical support for the engineering application of waste PET as a modifier in asphalt. Full article

Ecosystem services—the benefits humans derive from nature—are foundational to environmental sustainability and economic well-being, with carbon sequestration and storage standing out as critical regulating services in the fight against climate change. This study presents a comprehensive financial valuation of the carbon sequestration, storage and product substitution ecosystem services provided by the Hungarian forest-based sector. Using a multi-scenario framework, four complementary valuation concepts are assessed: total carbon storage (biomass, soil, and harvested wood products), annual net sequestration, emissions avoided through material and energy substitution, and marketable carbon value under voluntary carbon market (VCM) and EU Carbon Removal Certification Framework (CRCF) mechanisms. Data sources include the National Forestry Database, the Hungarian Greenhouse Gas Inventory, and national estimates on substitution effects and soil carbon stocks. The total carbon stock of Hungarian forests is estimated at 1289 million tons of CO₂ eq, corresponding to a theoretical climate liability value of over EUR 64 billion. Annual sequestration is valued at approximately 380 million EUR/year, while avoided emissions contribute an additional 453 million EUR/year in mitigation benefits. A comparative analysis of two mutually exclusive crediting strategies—improved forest management projects (IFMs) avoiding final harvesting versus long-term carbon storage through the use of harvested wood products—reveals that intensified harvesting for durable wood use offers higher revenue potential (up to 90 million EUR/year) than non-harvesting IFM scenarios. These findings highlight the dual role of forests as both carbon sinks and sources of climate-smart materials and call for policy frameworks that integrate substitution benefits and long-term storage opportunities in support of effective climate and bioeconomy strategies. Full article

Most research studies have investigated the impact of pet ownership on the mental and physical well-being of elderly populations, supporting the beneficial effect that pets have on their owners. However, few researchers focused on the well-being of both owner and pet. The present study aimed to explore the well-being of pets owned by elderly individuals using an owner assessment tool and the relationship between elderly characteristics and the pet’s health-related quality of life based on the owner’s assessment of their pet’s well-being. Sixty elderly pet owners who made regular visits to veterinary clinics were selected to complete an electronic questionnaire about their pet’s health-related quality of life. The results identified a high agreement percentage on positive indicators related to the pet’s well-being such as “My pet wants to play and My pet responds to my presence” in the happiness domain, “My pet has more good days than bad days” in mental status, “My pet moves normally” in physical status and “My pet keeps him/herself clean” in hygiene which also resulted in a positive relationship with elderly age. Marital status influenced their responses to “My pet responds to my presence and My pet is as active as he/she has been”. The results also support the use of the applied questionnaire to help identify variables that contribute to a pet’s health-related quality of life. The correlation matrix revealed statistically significant positive associations (p < 0.001) among positively phrased items across all domains, as well as among negatively phrased items. These consistent alignments between direct and between reversed items suggest directional coherence and help mitigate potential response bias. Furthermore, the replication of these patterns across multiple domains reinforces the interpretation that the instrument captures a unified construct of pet well-being, In conclusion, based on subjective evaluation of pet-owner relationships, the ownership of pets by elderly individuals could be mutually beneficial to both elderly owners and their pets. Full article

This study examines the complex process of Islam’s localization in Sudan, focusing on how hospitality, Sufi dhikr, and Mawlid celebrations integrate with Islamic practices. Drawing on three years of qualitative fieldwork, it demonstrates how Sudan’s geography, ethnic diversity, and historical heritage enable the blending of core religious principles with local customs. Sufi brotherhoods—particularly Qādiriyya, Tījāniyya, Shādhiliyya, and Khatmiyya—play a pivotal role in local culture by incorporating traditional musical, choreographic, and narrative art forms into their rituals, resulting in highly dynamic worship and social interaction. In Sudan, hospitality emerges as a near-sovereign social norm, reflecting the Islamic ethics of charity and mutual assistance while remaining deeply intertwined with local traditions. Islam’s adaptability toward local customs is further illustrated by the vibrant drumming, chanting, and dancing that enhance large-scale Mawlid al-Nabi celebrations, uniting Muslims under a religious identity that goes beyond dogmatic definitions. Beyond their spiritual meanings, these Sufi practices and networks also serve as tools for social cohesion, often functioning as support systems in regions with minimal state presence. They help prevent disputes and foster unity, demonstrating the positive impact of a flexible Islam—one that draws on both scripture and local traditions—on peacebuilding in Sudan. While highlighting the country’s social realities, this study offers insights into how Islam can function as a transformative force within society. Full article

Accurate pig counting is crucial for precision livestock farming, enabling optimized feeding management and health monitoring. Detection-based counting methods face significant challenges due to mutual occlusion, varying illumination conditions, diverse pen configurations, and substantial variations in pig densities. Previous approaches often struggle with complex agricultural environments where lighting conditions, pig postures, and crowding levels create challenging detection scenarios. To address these limitations, we propose EAPC-YOLO (enhanced adaptive pig counting YOLO), a robust architecture integrating density-aware processing with advanced detection optimizations. The method consists of (1) an enhanced YOLOv8 network incorporating multiple architectural improvements for better feature extraction and object localization. These improvements include DCNv4 deformable convolutions for irregular pig postures, BiFPN bidirectional feature fusion for multi-scale information integration, EfficientViT linear attention for computational efficiency, and PIoU v2 loss for improved overlap handling. (2) A density-aware post-processing module with intelligent NMS strategies that adapt to different crowding scenarios. Experimental results on a comprehensive dataset spanning diverse agricultural scenarios (nighttime, controlled indoor, and natural daylight environments with density variations from 4 to 30 pigs) demonstrate our method achieves 94.2% mAP@0.5 for detection performance and 96.8% counting accuracy, representing 12.3% and 15.7% improvements compared to the strongest baseline, YOLOv11n. This work enables robust, accurate pig counting across challenging agricultural environments, supporting precision livestock management. Full article

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

With the rapid proliferation of Android smartphones, mobile malware threats have escalated significantly, underscoring the need for more accurate and adaptive detection solutions. This work proposes an innovative deep learning hybrid model that combines Convolutional Neural Networks (CNNs) with Bidirectional Long Short-Term Memory (BiLSTM) networks for learning both local features and sequential behavior in Android applications. To improve the relevance and clarity of the input data, Mutual Information is applied for feature selection, while Bayesian Optimization is adopted to efficiently optimize the model’s parameters. The designed system is tested on standard Android malware datasets and achieves an impressive detection accuracy of 99.3%, clearly outperforming classical approaches such as Support Vector Machines (SVMs), Random Forest, CNN, and Naive Bayes. Moreover, it delivers strong outcomes across critical evaluation metrics like F1-score and ROC-AUC. These findings confirm the framework’s high efficiency, adaptability, and practical applicability, making it a compelling solution for Android malware detection in today’s evolving threat landscape. Full article

Background/Objective: Frontline healthcare staff who contend diseases and mitigate their transmission were repeatedly exposed to high-risk conditions during the COVID-19 pandemic. They were at risk of mental health issues, in particular, psychological stress, depression, anxiety, financial stress, and/or burnout. This study aimed to investigate and evaluate the occupational stress of medical doctors, nurses, pharmacists, physiotherapists, and other hospital support crew during the COVID-19 pandemic in Saudi Arabia. Methods: We collected both qualitative and quantitative data from a survey given to public and private hospitals using methods like correspondence analysis, cluster analysis, and structural equation models to investigate the work-related stress (WRS) and anxiety of the staff. Since health-related factors are unclear and uncertain, a fuzzy association rule mining (FARM) method was created to address these problems and find out the levels of work-related stress (WRS) and anxiety. The statistical results and K-means clustering method were used to find the best number of fuzzy rules and the level of fuzziness in clusters to create the FARM approach and to predict the work-related stress and anxiety of healthcare staff. This innovative approach allows for a more nuanced appraisal of the factors contributing to work-related stress and anxiety, ultimately enabling healthcare organizations to implement targeted interventions. By leveraging these insights, management can foster a healthier work environment that supports staff well-being and enhances overall productivity. This study also aimed to identify the relevant health factors that are the root causes of work-related stress and anxiety to facilitate better preparation and motivation of the staff for reorganizing resources and equipment. Results: The results and findings show that when the financial burden (FIN) of healthcare staff increased, WRS and anxiety increased. Similarly, a rise in psychological stress caused an increase in WRS and anxiety. The psychological impact (PCG) ratio and financial impact (FIN) were the most influential factors for the staff’s anxiety. The FARM results and findings revealed that improving the financial situation of healthcare staff alone was not sufficient during the COVID-19 pandemic. Conclusions: This study found that while the impact of PCG was significant, its combined effect with FIN was more influential on staff’s work-related stress and anxiety. This difference was due to the mutual effects of PCG and FIN on the staff’s motivation. The findings will help healthcare managers make decisions to reduce or eliminate the WRS and anxiety experienced by healthcare staff in the future. Full article

Introduction: Hospitalization of children creates significant emotional and psychological stress for parents, highlighting the importance of addressing their needs in pediatric care settings. Aims: This study examines the perceptions of both parents and pediatric nurses regarding the needs of hospitalized children. Method: A cross-sectional survey using the validated Needs of Parents of Hospitalized Children (NPQ) was administered to 218 parents and 218 pediatric nurses in four hospitals in Jeddah, Saudi Arabia. Key domains assessed included trust, information, and support. Group differences were evaluated using non-parametric statistical analyses. Results: Trust was prioritized more by parents (83.9%) than nurses (72.4%) (p < 0.05). Both groups deemed information important, but parents (87.2%) rated it as more necessary than nurses (74.1%) (p = 0.02). Parents (79.8%) expressed a greater need for support compared to nurses (67.3%) (p = 0.03). Conclusions: This study identified perceptual differences between parents and nurses regarding trust, communication, and support. Some differences were statistically significant at the p < 0.01 level, while others were suggestive (p-value between 0.01 and 0.05) and require further investigation. These disparities suggest a need to foster mutual understanding and improve communication practices to better align healthcare delivery with family expectations and strengthen family-centered care. Full article

Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE–MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE–MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE–MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended. Full article

To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detection method named SDA-YOLO. First, in the backbone network, the LSKA module is embedded into the SPPF module to construct an SPPF-LSKA fusion module, enhancing multi-scale feature representation for peach targets. Second, an MPDIoU-based bounding box regression loss function replaces CIoU to improve localization accuracy for overlapping and occluded peaches. The DyHead Block is integrated into the detection head to form a DMDetect module, strengthening feature discrimination for small and occluded targets in complex backgrounds. To address insufficient feature fusion flexibility caused by scale variations from occlusion and illumination differences in multi-scale peach detection, a novel Adaptive Multi-Scale Fusion Pyramid (AMFP) module is proposed to enhance the neck network, improving flexibility in processing complex features. Experimental results demonstrate that SDA-YOLO achieves precision (P), recall (R), mAP@0.95, and mAP@0.5:0.95 of 90.8%, 85.4%, 90%, and 62.7%, respectively, surpassing YOLOv11n by 2.7%, 4.8%, 2.7%, and 7.2%. This verifies the method’s robustness in complex orchard environments and provides effective technical support for intelligent fruit harvesting and yield estimation. Full article

The rapid development and adoption of artificial intelligence (AI) technology has sparked debates about its implications for labor markets, yet the micro-level relationship between AI and labor share remains underexplored. Based on the theory of skill-biased technological change, this study aims to examine whether AI technology increases labor share by labor structure upgrading at the enterprise level. Using panel data for China’s listed companies from 2012 to 2022, this study tests this relationship using a two-way fixed effects model. The empirical results reveal that AI technology significantly increases labor share, with labor structure upgrading playing a mediating role in this relationship. Heterogeneity analysis reveals that the influence of AI technology on labor share is stronger for enterprises characterized by low labor market rigidity, high labor market supply, and talent policy support in external environments, as well as among labor-intensive, high-tech, and non-state-owned enterprises. Notably, this study finds that advancements in AI technology have achieved mutually beneficial outcomes of improving labor share and enhancing total factor productivity. Our research findings provide detailed empirical evidence for enterprises to formulate and implement AI strategies. Full article