Search Results (632)

The integration of Artificial Intelligence (AI) systems into critical decision-making processes necessitates robust mechanisms to ensure trustworthiness, ethical compliance, and human oversight. This paper introduces trustSense, a novel assessment framework and tool designed to evaluate the maturity of human oversight practices in AI governance. Building upon principles from trustworthy AI, cybersecurity readiness, and privacy-by-design, trustSense employs a structured questionnaire-based approach to capture an organisation’s oversight capabilities across multiple dimensions. The tool supports diverse user roles and provides tailored feedback to guide risk mitigation strategies. Its calculation module synthesises responses to generate maturity scores, enabling organisations to benchmark their practices and identify improvement pathways. The design and implementation of trustSense are grounded in user-centred methodologies, with defined personas, user flows, and a privacy-preserving architecture. Security considerations and data protection are integrated into all stages of development, ensuring compliance with relevant regulations. Validation results demonstrate the tool’s effectiveness in providing actionable insights for enhancing AI oversight maturity. By combining measurement, guidance, and privacy-aware design, trustSense offers a practical solution for organisations seeking to operationalise trust in AI systems. This work contributes to the discourse on governance of trustworthy AI systems by providing a scalable, transparent, and empirically validated human maturity assessment tool. Full article

Intentionality, understood as the capacity of systems to be “about” something, remains a central issue in the philosophy of mind and cognitive science. Classical approaches face significant limitations, especially when applied to artificial systems. Representationalism struggles with the symbol grounding problem, functionalism reduces intentionality to causal roles, and enactivism restricts it to biological organisms. This paper proposes a cybernetic perspective in which intentionality is conceived as a regulatory function. Feedback mechanisms and homeostasis enable systems to maintain stability and adapt to changing conditions. Even simple systems may, in this sense, exhibit minimal intentionality. Such an approach allows intentionality to be treated as a graded phenomenon and highlights new possibilities for understanding the agency of artificial intelligence. Full article

Background: Direct oral anticoagulants (DOACs) have become the standard of care for preventing venous thromboembolism (VTE) and cardioembolic stroke in patients with atrial fibrillation, due to their predictable pharmacokinetics and reduced need for frequent laboratory monitoring. However, long-term DOAC use still carries a risk of complications such as gastrointestinal or occult bleeding and progressive renal decline, particularly in elderly and frail patients. Objective: This study proposes a remote monitoring model integrated with AI supports designed to enhance the safety and personalization of chronic DOAC therapy in both inpatient and outpatient settings. Methods: Building on existing national frameworks in which DOAC prescriptions are regulated by experienced physicians through regional digital platforms, we developed a structured model that integrates automatic alerts for abnormal laboratory trends, potential drug interactions, and changes in clinical status. The system uses artificial intelligence to identify high-risk patterns, such as declining hemoglobin or glomerular filtration rate, before symptoms appear, enabling early intervention. Results: The proposed model is presented as an integrated workflow supported by structured components. This conceptual framework facilitates real-time surveillance of patient data, supports clinical decision-making, and is expected to reduce preventable complications. Anticipated benefits include improved clinical appropriateness, better resource allocation, and reduced avoidable emergency visits. Conclusions: remote monitoring system integrated with AI supports for predefinite items for long term treatment with DOACs can significantly improve safety and continuity of care. By replacing passive surveillance with predictive, automated alerts, this model exemplifies how digitalization can enhance the efficiency and responsiveness of the National Health System. Full article

The factors influencing urban pluvial flooding in cities with complex topography, such as hill–basin systems, are highly nonlinear and spatially heterogeneous due to the interplay between rugged terrain and intensive human activities. However, previous research has predominantly focused on plain, mountainous, and coastal cities. As a result, the waterlogging mechanisms in hill–basin areas remain notably understudied. In this study, we developed a geographically explainable artificial intelligence (GeoXAI) framework integrating Geographical Machine Learning Regression (GeoMLR) and Geographical Shapley (GeoShapley) values to analyze nonlinear impacts of flooding factors in Changsha, a typical hill–basin city. The XGBoost model was employed to predict flooding risk (validation AUC = 0.8597, R² = 0.8973), while the GeoMLR model verified stable nonlinear driving relationships between factors and flooding susceptibility (test set R² = 0.7546)—both supporting the proposal of targeted zonal regulation strategies. Results indicated that impervious surface density (ISD), normalized difference vegetation index (NDVI), and slope are the dominant drivers of flooding, with each exhibiting distinct nonlinear threshold effects (ISD > 0.35, NDVI < 0.70, Slope < 5°) that differ significantly from those identified in plain, mountainous, or coastal regions. Spatial analysis further revealed that topography regulates flooding by controlling convergence pathways and flow velocity, while vegetation mitigates flooding through enhanced interception and infiltration, showing complementary effects across zones. Based on these findings, we proposed tailored zonal management strategies. This study not only advances the mechanistic understanding of urban waterlogging in hill–basin regions but also provides a transferable GeoXAI framework offering a robust methodological foundation for flood resilience planning in topographically complex cities. Full article

The growing integration of generative artificial intelligence (GenAI) tools in higher education has potential to transform learning experiences. However, empirical research comparing GenAI-supported learning with traditional instruction lags behind these developments. This study addresses this gap through a controlled experiment involving 96 undergraduate computer science students in a Database Management course. Participants experienced either GenAI-supported or traditional instructions while learning the same concept. Data were collected through questionnaires, quizzes, and interviews. Analyses were grounded in self-determination theory (SDT), which posits that effective learning environments support autonomy, competence, and relatedness. Quantitative findings revealed significantly more positive learning experiences with GenAI tools, particularly enhancing autonomy through personalized pacing and increased accessibility. Competence was supported, reflected in shorter study times with no significant achievement differences between approaches. Students performed better on moderately difficult questions using GenAI, indicating that GenAI may bolster conceptual understanding. However, interviews with 11 participants revealed limitations in supporting relatedness. While students appreciated GenAI’s efficiency and availability, they preferred instructor-led sessions for emotional engagement and support with complex problems. This study contributes to the theoretical extension of SDT in technology-mediated learning contexts and offers practical guidance for optimal GenAI integration. Full article

Achieving Artificial General Intelligence (AGI) requires a unified framework capable of modeling the full spectrum of intelligent behavior—from logical reasoning and sensory perception to emotional regulation and collective decision-making. This paper proposes Constrained Object Hierarchies (COH), a neuroscience-inspired theoretical model that represents intelligent systems as hierarchical compositions of objects governed by symbolic structure, neural adaptation, and constraint-based control. Each object is formally defined by a 9-tuple structure: $O=(C,A,M,N,E,I,T,G,D)$, encapsulating its Components, Attributes, Methods, Neural components, Embedding, and governing Identity constraints, Trigger constraints, Goal constraints, and Constraint Daemons. To demonstrate the scope and versatility of COH, we formalize nine distinct intelligence types—including computational, perceptual, motor, affective, and embodied intelligence—each with detailed COH parameters and implementation blueprints. To operationalize the framework, we introduce GISMOL, a Python-based toolkit for instantiating COH objects and executing their constraint systems and neural components. GISMOL supports modular development and integration of intelligent agents, enabling a structured methodology for AGI system design. By unifying symbolic and connectionist paradigms within a constraint-governed architecture, COH provides a scalable and explainable foundation for building general purpose intelligent systems. A comprehensive summary of the research contributions is presented right after the introduction. Full article

This study explores the effectiveness of artificial intelligence-generated videos (AIGV) as a scalable enabling technology within the e-commerce sector. It investigates the potential of AIGV to enhance marketing efficacy through the simulation of product experiences, with a particular focus on space tourism. A notable gap exists in the current understanding of how the attributes of AIGV and individual perceptions influence critical consumer responses in the context of space tourism e-commerce. This gap specifically pertains to their effects on trustworthiness, awe, and behavioral intentions, with an emphasis on the underlying mediating mechanisms. Purposive sampling was employed to gather samples, and partial least squares structural equation modeling (PLS-SEM) was utilized for data analysis. The results reveal that both AIGV attributes and personal perceptions exert a significant influence on trustworthiness, awe, and behavioral intentions within the context of space tourism e-commerce. Awe serves as a central mediating construct between AIGV attributes and behavioral intention, while also mediating the relationship between perceived risk and behavioral intention. In contrast, trustworthiness solely mediates the pathway between perceived risk and behavioral intention. The findings present novel theoretical insights into AI-driven consumer behavior within experiential e-commerce contexts. They also offer practical guidance for the effective implementation of the AIGV. Moreover, this study underscores the necessity for ethical frameworks to regulate consumer trust in AI-dominated marketplaces. Full article

Direct power control (DPC) is a widely accepted control scheme utilized in renewable energy applications owing to its several advantages over other control mechanisms, including its simplicity, ease of implementation, and faster response. However, DPC suffers from inherent drawbacks and limitations that constrain its applicability. These restrictions include notable ripples in active power and torque, as well as poor power quality brought on by the usage of a hysteresis regulator for capacity management. To address these issues and overcome the limitations of DPC, this study proposes a novel approach that incorporates artificial neural networks (ANNs) into DPC. The proposed technique focuses on doubly fed induction generators (DFIGs) and is validated through experimental testing. ANNs are employed to recompense for the deficiencies of the hysteresis controller and switching table. The intelligent DPC technique is then compared to three other strategies: classic DPC, backstepping control, and integral sliding-mode control. Various tests are conducted to compare the ripple ratio, current quality, durability, response time, and reference tracking. The validity and robustness of the proposed intelligent DPC for DFIGs are verified through both simulation and experimental results obtained from the MATLAB/Simulink environment and the Real-Time Interface (RTI) of the dSPACE DS1104 controller card. The results confirm that the intelligent DPC outperforms conventional control strategies in terms of stator current harmonic distortion, dynamic response, power ripple minimization, reference tracking accuracy, robustness, and overshoot reduction. Overall, the intelligent DPC exhibits superior performance across all evaluated criteria compared to the alternative approaches. Full article

Aim: Nephrotic syndrome (NS) represents a complex glomerular disorder with significant clinical heterogeneity across pediatric and adult populations. Although glucocorticosteroids have constituted the mainstay of therapeutic intervention for more than six decades, primary treatment resistance manifests in approximately 20% of pediatric patients and 50% of adult cohorts. Steroid-resistant nephrotic syndrome (SRNS) is associated with substantially greater morbidity compared to steroid-sensitive nephrotic syndrome (SSNS), characterized by both iatrogenic glucocorticoid toxicity and progressive nephron loss with attendant decline in renal function. Based on this, the current study aims to develop a robust machine learning (ML) model integrated with explainable artificial intelligence (XAI) to distinguish SRNS and identify important biomarker candidate metabolites. Methods: In the study, biomarker candidate compounds obtained from proton nuclear magnetic resonance (1 H NMR) metabolomics analyses on plasma samples taken from 41 patients with NS (27 SSNS and 14 SRNS) were used. We developed ML models to predict steroid resistance in pediatric NS using metabolomic data. After preprocessing with MICE-LightGBM imputation for missing values (<30%) and standardization, the dataset was randomly split into training (80%) and testing (20%) sets, repeated 100 times for robust evaluation. Four supervised algorithms (XGBoost, LightGBM, AdaBoost, and Random Forest) were trained and evaluated using AUC, sensitivity, specificity, F1-score, accuracy, and Brier score. XAI methods including SHAP (for global feature importance and model interpretability) and LIME (for individual patient-level explanations) were applied to identify key metabolomic biomarkers and ensure clinical transparency of predictions. Results: Among four ML algorithms evaluated, Random Forest demonstrated superior performance with the highest accuracy (0.87 ± 0.12), sensitivity (0.90 ± 0.18), AUC (0.92 ± 0.09), and lowest Brier score (0.20 ± 0.03), followed by LightGBM, AdaBoost, and XGBoost. The superiority of the Random Forest model was confirmed by paired t-tests, which revealed significantly higher AUC and lower Brier scores compared to all other algorithms (p < 0.05). SHAP analysis identified key metabolomic biomarkers consistently across all models, including glucose, creatine, 1-methylhistidine, homocysteine, and acetone. Low glucose and creatine levels were positively associated with steroid resistance risk, while higher propylene glycol and carnitine concentrations increased SRNS probability. LIME analysis provided patient-specific interpretability, confirming these metabolomic patterns at individual level. The XAI approach successfully identified clinically relevant metabolomic signatures for predicting steroid resistance with high accuracy and interpretability. Conclusions: The present study successfully identified candidate metabolomic biomarkers capable of predicting SRNS prior to treatment initiation and elucidating critical molecular mechanisms underlying steroid resistance regulation. Full article

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions. Full article

The application of Artificial Intelligence (AI) in river basin pollution control shows great potential to improve governance efficiency through real-time monitoring, pollution prediction, and intelligent decision-making. However, its rapid development also brings regulatory challenges, including data privacy, algorithmic bias, responsibility definition, and cross-regional coordination. Based on the SETO loop framework (Scoping, Existing Regulation Assessment, Tool Selection, and Organizational Design), this paper systematically analyzes the regulatory needs and pathways for AI in watershed water pollution control through typical case studies from countries such as China and the United States. The study first defines the regulatory scope, focusing on protecting the ecological environment, public health, and data security. It then assesses the shortcomings of existing environmental regulations in governing AI, such as their inability to adapt to dynamic pollution sources. Subsequently, it explores suitable regulatory tools, including information disclosure requirements, algorithmic transparency standards, and hybrid regulatory models. Finally, it proposes a multi-tiered organizational scheme that integrates international norms, national legislation, and local practices to achieve flexible and effective regulation. This study demonstrates that the SETO loop provides a viable framework for balancing technological innovation with risk prevention and control. It offers a scientific basis for policymakers and calls for establishing a dynamic, layered regulatory system to address the complex challenges of AI in environmental governance. Full article

Hearing loss (HL) affects more than 1.5 billion people worldwide and remains a leading cause of disability across the lifespan. While genetic predispositions, otitis media (OM), and cholesteatoma are well-recognized contributors, Eustachian tube dysfunction (ETD) is an underappreciated but pivotal determinant of auditory morbidity. By impairing middle ear pressure (MEP) regulation, ETD drives conductive hearing loss (CHL) through stiffness and mass-loading effects, contributes to sensorineural hearing loss (SNHL) via altered window mechanics and vascular stress, and produces mixed hearing loss (MHL) when these pathways converge. A characteristic clinical trajectory emerges in which conductive deficits often resolve quickly with restored ventilation, whereas sensorineural impairment requires prolonged, physiology-restoring intervention, resulting in transient or persistent MHL. This review integrates mechanistic insights with clinical manifestations, diagnostic approaches, and therapeutic options. Diagnostic frameworks that combine patient-reported outcomes with objective biomarkers such as wideband absorbance, tympanometry, and advanced imaging enable reproducible identification of ETD-related morbidity. Conventional treatments, including tympanostomy tubes and balloon dilation, offer short-term benefit but rarely normalize tubal physiology. In contrast, Eustachian tube catheterization (ETC) has emerged as a promising, mechanism-based intervention capable of reestablishing dynamic tubal opening and MEP regulation. Looking forward, integration of physiology-based frameworks with personalized diagnostics and advanced tools such as artificial intelligence (AI) may help prevent progression from reversible conductive deficits to irreversible SNHL or MHL. Full article

The application of the same global legal regulations to areas with different climates, landscapes, and cultural and urban conditions may ultimately lead to decisions that are unsuitable for the region, which could result in poor investment and development decisions for the municipality. This article examines how sustainability regulations established locally, in response to local conditions, differ from global regulations created without considering the differences between the areas to which they apply. Selected criteria were assessed in relation to global and local regulations, and then, based on these criteria and their weights, rankings of the strengths and weaknesses of municipalities were proposed in relation to the selected criteria, the weights of which were evaluated depending on the adopted global or local regulations. The AHP method was used to conduct this multi-criteria assessment, based both on expert group opinions and artificial intelligence tools. The aim of this analysis was to demonstrate differences in the hierarchies of sustainable development aspects implemented globally and locally, as well as local conditions. The assessment results indicate discrepancies between expert knowledge, which takes into account local conditions, and the priorities resulting from general legal regulations. Some areas important from a local perspective, such as building density or mixed-use development, are insufficiently addressed in legal regulations, both under Polish and EU law and local law. This also contradicts current trends in urban planning theory, which advocates a shift away from zoning. Others, such as energy efficiency in buildings and renewable energy sources, are strongly present in both national and EU law but are not implemented in local regulations. Full article

Predicting phenotypes from genomic data can significantly advance agriculture. Genomic selection, which uses genome-wide DNA markers to identify individuals with high genetic value, enhances the accuracy of breeding programs. While linear models are routinely used for genomic selection (GS), machine learning (ML) models offer complementary potential. In this study, robust ML-based models were developed to predict five phenotypic traits—three related to flowering time and two to leaf number—in Arabidopsis thaliana, a model plant with a fully sequenced genome. Using explainable artificial intelligence (XAI), specifically SHapley Additive exPlanations (SHAP) values, we identified SNPs that contributed most to trait prediction. Many of these SNPs were located in or near genes known to regulate flowering and stem elongation, such as DOG1 and VIN3, supporting the biological plausibility of the model. SHAP also enabled local interpretability at the single-plant level, revealing the genotypic basis of individual predictions. Our results indicate that integrating ML with XAI improves model interpretability and provides predictive performance comparable to traditional methods. This approach confirms known genotype–phenotype relationships and highlights new candidate loci, paving the way for functional validation. The proposed methodology offers promising applications in precision breeding and translation of insights from Arabidopsis to crop species. Full article

Digital transformation has driven the use of artificial intelligence (AI) in local government financial reporting to improve efficiency, transparency, and accountability. This study employs a systematic literature review (SLR) approach to analyze 20 relevant articles, identifying common characteristics of publications, research focus, methods, AI technologies used, key findings, research gaps, and future research directions. The analysis results show the dominance of machine learning and expert systems in detecting fraud, predicting financial performance, and improving reporting accuracy. However, limitations in infrastructure, regulations, and system integration across government agencies remain significant challenges to implementing AI in the public sector. This study proposes the need for the development of practical implementation models, collaboration between academics, government, and technology developers, as well as the formulation of policies that support ethical and responsible AI governance. These findings make a significant contribution to shaping the strategic direction of AI utilization to strengthen local government financial reporting systems sustainably. Full article