Search Results (3,769)

With the development of generative artificial intelligence (AI) technology, collaboration between humans and AI is expected to improve productivity, efficiency, and safety in various industries. This study presents and empirically analyzes the factors affecting collaboration between humans and AI. This study presents and empirically analyzes a research model based on the antecedents of calculative-based, cognition-based, knowledge-based, and social influence-based trust. A total of 305 valid data points were collected through questionnaires completed by experts, office workers, and graduate students, and were analyzed using structural equation modeling. The analysis showed that all antecedents except familiarity, an antecedent of knowledge-based trust, significantly affected trust. Full article

The green macroalgae Ulva Linnaeus, 1753, also known as sea lettuce, is one of the most ecologically and economically significant algal genera. Its representatives occur in marine, brackish, and freshwater environments worldwide and show high adaptability, rapid growth, and marked biochemical diversity. These traits support their ecological roles in nutrient cycling, primary productivity, and habitat provision, and they also explain their growing relevance to the blue bioeconomy. This review summarizes current knowledge of Ulva biodiversity, taxonomy, and physiology, and evaluates applications in food, feed, bioremediation, biofuel, pharmaceuticals, and biomaterials. Particular attention is given to molecular approaches that resolve taxonomic difficulties and to biochemical profiles that determine nutritional value and industrial potential. This review also considers risks and limitations. Ulva species can act as hyperaccumulators of heavy metals, microplastics, and organic pollutants, which creates safety concerns for food and feed uses and highlights the necessity of strict monitoring and quality control. Technical and economic barriers restrict large-scale use in energy and material production. By presenting both opportunities and constraints, this review stresses the dual role of Ulva as a promising bioresource and a potential ecological risk. Future research must integrate molecular genetics, physiology, and applied studies to support sustainable utilization and ensure safe contributions of Ulva to biodiversity assessment, environmental management, and bioeconomic development. Full article

The Pulsed Electric Field (PEF) technique represents a modern technology for treating and processing food and agricultural raw materials. The application of high-voltage electric pulses has been shown to modify macrostructure, improve extractability, and enhance the microbial safety of the treated matrix. In this study, we investigated metabolomic changes occurring during the individual technological steps of malting following PEF treatment. Methanolic extracts of technological intermediates of malting barley were analyzed using metabolomic fingerprinting performed with UHPLC-HRMS/MS. For data processing and interpretation, the freely available MS-DIAL—MS-CleanR—MS-Finder software platform was used. The metabolomes of the treated and untreated barley samples revealed significant changes. Tentatively identified PEF-related biomarkers included 1,2-diacylglycerol-3-phosphates, triacylglycerols, linoleic acids and their derivatives, octadecanoids, N-acylserotonins, and very long-chain fatty acids, and probably reflect abiotic stress response. Monitoring of the profiles of selected biomarkers in PEF malting batch indirectly revealed a potential enhancement of enzymatic activity after the PEF treatment. These results contribute to fundamental knowledge regarding the influence of PEF on final malt from a metabolomic perspective. Full article

Deep learning models have achieved significant progress in developing self-driving algorithms. Despite their advantages, these algorithms typically require substantial amounts of data for effective training. Critical driving data, in particular, is essential for enhancing training efficiency and ensuring driving safety. However, existing methods for identifying critical data often rely on human prior knowledge or are disconnected from the training of self-driving algorithms. In this paper, we introduce a novel data distillation technique designed to autonomously identify critical data for training self-driving algorithms. We conducted experiments with both numerical simulations and the NGSIM dataset, which consists of real-world car trajectories on highway US-101, to validate our approach. In the numerical experiments, the distillation method achieved a test root mean squared error of 1.933 using only 200 distilled training data samples, demonstrating a significant improvement in data efficiency compared to the 1.872 test error obtained with 20,000 randomly sampled training samples. The distilled critical data represents only 1% of the original dataset, optimizing data usage and significantly enhancing computational efficiency. For real-world NGSIM data, we demonstrate the performance of the proposed method in scenarios with extremely sparse data availability and show that our proposed data distillation method outperforms other sampling baselines, including Herding and K-centering. These experimental results highlight the capability of the proposed method to autonomously identify critical data without relying on human prior knowledge. Full article

Slope stability prediction is a critical task in geotechnical engineering, but machine learning (ML) models require large datasets, which are often costly and time-consuming to obtain. This study proposes a domain-driven teacher–student framework to overcome data limitations for predicting the dry factor of safety (FS dry). The teacher model, XGBoost, was trained on the original dataset to capture nonlinear relationships among key site-specific features (unit weight, cohesion, friction angle) and assign pseudo-labels to synthetic samples generated via domain-driven simulations. Six student models, random forest (RF), decision tree (DT), shallow artificial neural network (SNN), linear regression (LR), support vector regression (SVR), and K-nearest neighbors (KNN), were trained on the augmented dataset to approximate the teacher’s predictions. Models were evaluated using a train–test split and five-fold cross-validation. RF achieved the highest predictive accuracy, with an R² of up to 0.9663 and low error metrics (MAE = 0.0233, RMSE = 0.0531), outperforming other student models. Integrating domain knowledge and synthetic data improved prediction reliability despite limited experimental datasets. The framework provides a robust and interpretable tool for slope stability assessment, supporting infrastructure safety in regions with sparse geotechnical data. Future work will expand the dataset with additional field and laboratory tests to further improve model performance. Full article

Coffee silverskin (CS), the principal solid by-product from coffee roasting, is a promising raw material for sustainable food applications aligned with circular economy principles. Due to its high flammability at roasting temperatures, effective management of CS is not only an environmental but also a safety concern in coffee processing facilities. To the best of our knowledge, this is the first study evaluating the chemical composition, bioactivity, safety, and environmental impact of torrefacto (CT) and natural (CN) coffee silverskin. CT (from Arabica–Robusta blends subjected to sugar-glazing) and CN (from 100% Arabica) were characterized in terms of composition and function. Oven-dried CT showed higher levels of caffeine (13.2 ± 0.6 mg/g vs. 8.7 ± 0.7 mg/g for CN), chlorogenic acid (1.34 ± 0.08 mg/g vs. 0.92 ± 0.06 mg/g), protein (18.1 ± 0.2% vs. 16.7 ± 0.2%), and melanoidins (14.9 ± 0.3 mg/g vs. 9.6 ± 0.2 mg/g), but CN yielded more total phenolics (13.8 ± 0.6 mg GAE/g). Both types exhibited strong antioxidant capacity (ABTS: 48.9–59.2 µmol TE/g), and all oven-dried samples met food safety criteria (microbial loads below 10² CFU/g, moisture 7.9%). Oven drying was identified as the most industrially viable, ensuring preservation of bioactives and resulting in a 19% lower greenhouse gas emissions impact compared to freeze-drying. Sun drying was less reliable microbiologically. The valorization of oven-dried CT as a clean-label, antioxidant-rich colorant offers clear potential for food reformulation and waste reduction. Renewable energy use during drying is recommended to further enhance sustainability. This study provides scientific evidence to support the safe use of coffee silverskin as a novel food, contributing to regulatory assessment and sustainable food innovation aligned with SDGs 9, 12, and 13. Full article

Obesity is a complex, multifactorial disease and a growing global health challenge associated with type 2 diabetes, cardiovascular disorders, cancer, and reduced quality of life. The existing pharmacological therapies are characterized by their limited number and efficacy, and safety concerns further restrict their utilization. This review synthesizes extensive knowledge regarding the role of the endocannabinoid system (ECS) in the pathogenesis of obesity, as well as its potential as a therapeutic target. A thorough evaluation of preclinical and clinical data concerning endocannabinoid ligands, cannabinoid receptors (CB1, CB2), their genetic variants, and pharmacological interventions targeting the ECS was conducted. Literature data suggests that the overactivation of the ECS may play a role in the pathophysiology of excessive food intake, dysregulated energy balance, adiposity, and metabolic disturbances. The pharmacological modulation of ECS components, by means of CB1 receptor antagonists/inverse agonists, CB2 receptor agonists, enzyme inhibitors, and hybrid or allosteric ligands, has demonstrated promising anti-obesity effects in animal models. However, the translation of these findings into clinical practice remains challenging due to safety concerns, particularly neuropsychiatric adverse events. The development of novel strategies, including peripherally restricted compounds, hybrid dual-target agents, dietary modulation of endocannabinoid tone, and non-pharmacological interventions, promises to advance the field of obesity management. Full article

Background: Anatomical variations of the extensor hallucis longus (EHL) tendon hold significant implications for foot and ankle surgery, yet they remain underrepresented in orthopedic literature. Accurate recognition of these variants is crucial for minimizing iatrogenic injuries and improving surgical outcomes. Aim: This narrative review aims to summarize current anatomical knowledge on EHL tendon morphology, with a particular focus on the classification system proposed by Olewnik et al. Emphasis is placed on its diagnostic, radiological, and surgical relevance. Methods: A comprehensive literature review was conducted, integrating findings from cadaveric dissections, imaging studies, and clinical observations. The Olewnik classification—based on the number and insertion of EHL tendon slips—serves as the organizing framework for the anatomical and surgical discussion. Findings: The Olewnik classification delineates three primary types: Type I (single slip), Type II (two slips, subdivided into IIa–IIc), and Type III (three slips). Each type is discussed in terms of anatomical features, diagnostic challenges on MRI and ultrasound, and implications for surgical exposure, tendon transfer, and graft harvesting. Comparative analysis with prior typologies underscores the enhanced clinical utility of the Olewnik system. Conclusions: The reviewed classification offers a reproducible, imaging-compatible, and surgically applicable framework for understanding EHL tendon variability. Incorporating this system into preoperative planning may enhance procedural safety and precision. Further clinical validation and broader integration into surgical education are warranted. Full article

This paper proposes a transfer learning-based approach to enhance video-driven safety risk detection in industrial environments, addressing the critical challenge of limited generalization across diverse operational scenarios. Conventional deep learning models trained on specific operational contexts often fail when applied to new environments with different lighting, camera angles, or machinery configurations, exhibiting a significant drop in performance (e.g., F1-score declining below 0.85). To overcome this issue, an incremental feature transfer learning strategy is introduced, enabling efficient adaptation of risk detection models using only small amounts of data from new scenarios. This approach leverages prior knowledge from pre-trained models to reduce the reliance on large-labeled datasets, particularly valuable in industrial settings where rare but critical safety risk events are difficult to capture. Additionally, training efficiency is improved compared with a classic approach, supporting deployment on resource-constrained edge devices. The strategy involves incremental retraining using video segments with average durations ranging from 2.5 to 25 min (corresponding to 5–50% of new scenario data), approximately, enabling scalable generalization across multiple forklift-related risk activities. Interpretability is enhanced through SHAP-based analysis, which reveals a redistribution of feature relevance toward critical components, thereby improving model transparency and reducing annotation demands. Experimental results confirm that the transfer learning strategy significantly improves detection accuracy, robustness, and adaptability, making it a practical and scalable solution for safety monitoring in dynamic industrial environments. Full article

Integrating human–robot collaboration (HRC) into construction sites has significantly enhanced efficiency and quality. However, it also introduces new or intensifies existing risks as it brings in new entities, relationships, and construction activities. Safety remains the top priority and a persistent concern in HRC systems. However, the current literature on human–robot collaboration safety (HRCS) is vast yet fragmented, and a systematic exploration of its status and research trends in the construction context is still lacking. This paper explores advances in HRCS over the past two decades through a mixed quantitative and qualitative analysis method. Initially, 287 related articles were identified by keyword-searching in Scopus, followed by bibliometric analysis using CiteSpace to uncover the knowledge structure and track emerging research trends. Subsequently, a qualitative discussion highlights achievements in HRCS across five dimensions: (1) optimization of remote intelligent machinery; (2) hazard analysis and risk assessment in HRCS; (3) digital twin for safety monitoring; (4) cognitive and psychological impacts; (5) organizational management perspective. This study quantitatively maps the scientific landscape of HRCS at a macro level and qualitatively identifies key research areas. It provides a comprehensive foundation for understanding the evolution of HRCS and exploring future research directions and applications. Full article

Introduction: Drug–drug interactions (DDI) are a major, preventable cause of patient harm, a challenge amplified in Jordan by rising polypharmacy and documented high rates of medication errors. To date, no study in Jordan has systematically compared hospital and community pharmacists. This study aimed to conduct the first national, comparative assessment of DDI management among these two cadres. Materials and Methods: A national, cross-sectional study was conducted with 380 licensed pharmacists (175 hospitals, 205 community) recruited via proportionate stratified random sampling. A validated online questionnaire assessed demographics, objective DDI knowledge, professional attitudes, practices, and barriers. Multivariable logistic regression was used to identify independent predictors of high knowledge and optimal practice. All collected data were coded, cleaned, and analyzed using the Statistical Package for the Social Sciences (SPSS V28.0). Results: Hospital pharmacists achieved significantly higher mean objective knowledge scores than community pharmacists (10.3 vs. 8.1 out of 15, p < 0.001), a gap particularly wide for interactions involving high-risk OTC medications. The primary barrier for community pharmacists was a lack of access to patient data (85.4%), contrasting with high workload and physician resistance in hospitals. Optimal practice was independently predicted by higher knowledge (AOR = 1.25), a hospital practice setting (AOR = 3.65), and was inhibited by perceived physician resistance (AOR = 0.45). Conclusions: Jordanian hospital and community pharmacists operate in distinct worlds of knowledge and practice. A tailored, dual-pronged national strategy is essential. For hospitals, interventions should target interprofessional dynamics. For community pharmacies, health policy reform to provide access to integrated patient data is the most urgent priority. These findings highlight a globally relevant challenge of practice-setting disparities, offering a model for other nations to develop tailored, context-specific interventions to improve medication safety. Full article

The fault diagnosis of complex systems is essential for ensuring operational safety. The belief rule base (BRB), a rule-driven framework based on expert knowledge, is widely applied in fault diagnosis because of its ability to manage uncertainty. However, existing BRB models rely heavily on large amounts of high-quality labeled data, and their performance decreases when labels are scarce or noisy. To address this limitation, a perturbed self-training-based BRB method (PS-BRB) is proposed. In this approach, pseudo-labels for unlabeled samples are first inferred by an initial BRB, and Gaussian noise is introduced into the inputs to simulate perturbations. Samples that produce consistent predictions before and after perturbation are retained through class consistency checking. The Jensen–Shannon (JS) divergence then measures the difference between belief distributions, and high-quality pseudo-labels are selected according to the 90th percentile criterion. These pseudo-labels are incorporated into the training set to optimize BRB rules and parameters. The method is validated on two bearing datasets, and the results show improved diagnostic accuracy and applicability, which indicates potential for use in practical engineering scenarios. Full article

The development and function of the heart are governed by a highly coordinated network of regulatory mechanisms, among which miRNAs play a central role. These small, non-coding molecules modulate gene expression predominantly through mRNA degradation. This narrative review aims to summarize current knowledge about biogenesis, its impact on heart development and function, and its clinical implications in pediatric cardiology. We discuss how specific miRNAs contribute to shaping the normal heart and influencing the pathogenesis of congenital malformations. Furthermore, we review disease-specific miRNA signatures identified in the most common congenital heart defects and some acquired diseases, including hypoplastic left heart syndrome (HLHS), tetralogy of Fallot (TOF), bicuspid aortic valve (BAV), septation defects, cardiomyopathies, arrhythmias, and myocarditis. Many studies indicate that circulating and tissue miRNAs can become non-invasive biomarkers for early diagnosis and disease monitoring. Experimental data suggest their potential use in treatment despite many delivery and safety challenges. However, further research is necessary to fully exploit the potential of miRNAs and effectively translate these findings into clinical practice in pediatric cardiology. Full article

Predicting future events is crucial for temporal reasoning, providing valuable insights for decision-making across diverse domains. However, the intricate global interactions and temporal–causal relationships at the document level event present significant challenges. This study introduces a novel document-level future event prediction method that integrates an event knowledge graph and a large language model (LLM) reasoning framework based on metacognitive theory. Initially, an event knowledge graph is constructed by extracting event chains from the original document-level event texts. An LLM-based approach is then used to generate diverse and rational positive and negative training samples. Subsequently, a future event reasoning framework based on metacognitive theory is introduced. This framework enhances the model’s reasoning capabilities through a cyclic process of task understanding, reasoning strategy planning, strategy execution, and strategy reflection. Experimental results demonstrate that the proposed approach outperforms baseline models. Notably, the incorporation of the event knowledge graph significantly enhances the performance of different reasoning methods, while the proposed reasoning framework achieves superior performance in document-level future event prediction tasks. Furthermore, the interpretability analysis of the prediction results validates the effectiveness of the proposed method. This study advances research on document-level future event prediction, highlighting the critical role of event knowledge graphs and large language models in temporal reasoning. It offers a more sophisticated future event prediction framework for government management departments, facilitating the enhancement of government safety management strategies. Full article

Background: Currently, population aging and the growing incorporation of digital technologies into everyday life highlight the need to ensure the digital inclusion of older adults. This is due to the existence of a significant digital divide that affects this population group, limiting not only their access to services and opportunities but also their emotional well-being and quality of life. The lack of digital skills can generate feelings of exclusion, frustration, and dependence, negatively impacting their mental health and autonomy. Methods: The objective of this study is to assess the level of basic digital competence in 404 older adults using the Scale of Basic Digital Competence in Older Adults (DigCompB_PM) in order to identify existing digital divides and provide empirical evidence for the design of educational interventions that promote the digital inclusion of this population group. To this end, we start with the following research question: Are older adults prepared to face the digital and knowledge society, taking into account personal variables such as age, gender, geographical location, place of residence, and type of cohabitation? Results: The findings reveal that participants scored highest in the dimension related to safety and digital device usage while scoring lowest in online collaboration, indicating a disparity between basic digital skills and collaborative competencies. Cluster analysis further demonstrates that age and previous occupational experience significantly influence digital literacy levels. These results highlight the heterogeneity of digital competence among older adults. Conclusions: The study concludes by emphasising the importance of implementing tailored policies that enhance digital literacy in this population. Key factors such as accessibility, training, and motivation should guide such efforts. Additionally, intergenerational learning emerges as a promising strategy, facilitating the development of digital skills through knowledge exchange and sustained support from younger cohorts. Full article