AI, Volume 7, Issue 6 (June 2026) – 51 articles

The Q-Learning Relational Game (QLRG) framework provides a theoretically rigorous method for identifying minimal winning coalitions in online social networks (OSNs) under the restrictive assumption of global agent symmetry or uniform matroid structure. Real-world OSNs, however, exhibit significant asymmetry. This paper introduces the Cluster-Based Q-Learning Relational Game (C-QLRG), a practical extension that relaxes the global symmetry requirement by leveraging community structure. We partition the agent set into communities with bounded internal variation and represent the state solely by community membership counts of the seed set. Because the closure operator already captures all eventual influence spread, the problem reduces to a sequential seed selection task where the agent decides, at each step, from which community to add the next seed. We prove that the optimal Q-function of a suitably regularized reach-efficiency objective is Lipschitz continuous and derive a performance bound for the learned policy. The full algorithm is presented, and its complexity is analyzed. Empirical evaluations on a synthetic asymmetric network and Zachary’s Karate Club demonstrate that C-QLRG is highly sensitive to reward parameters, where default settings lead to premature stopping, but parameter tuning combined with a corrected minimality verification recovers high-efficiency coalitions by removing non-contributing agents. With tuned parameters, C-QLRG produces a near-winning coalition of size 11 and 99% reach on the synthetic network, surpassing the greedy baseline’s efficiency (size 12) despite a one-node coverage gap, while identifying the optimal winning coalition of size 1 on the Karate Club dataset, matching all baselines. The framework thus offers a principled trade-off between model fidelity and scalability, with the reward design choice being critical for practical deployment. Full article

Agentic artificial intelligence (AI) offers new opportunities for intelligent clinical decision support, but deployment in emergency and crisis settings remains challenging because time-critical recommendations must often be generated under incomplete patient information and system constraints. Conventional clinical decision support systems rely on rule-based workflows that degrade when structured data are absent, while standalone language models lack coordination mechanisms to enforce mandatory safety checks. We present TriAgent, a multi-agent framework that unifies adaptive orchestration, iterative retrieval, embedded safety verification, and end-to-end auditability within a single crisis clinical decision support workflow. An Orchestrator Agent dynamically selects specialist modules for clinical assessment, retrieval, treatment planning, safety verification, and system coordination, with routing determined by model reasoning rather than fixed execution paths. A retrieval sub-agent performs iterative query refinement and relevance grading over 49,000 MIMIC-IV discharge notes, while medication-conflict screening and allergy-risk assessment are invoked in parallel only when clinically indicated. A Critique Agent reviews the full reasoning trace before recommendation finalization. In a retrospective evaluation on 1000 real emergency presentations under synthesized incomplete-information inputs, TriAgent achieved 85.0% critical-case recall and 65.7% overall triage accuracy, versus at most 14.7% and 43.4% for matched single-model and retrieval-only baselines, with safety checks executed on every continuation pathway and adaptive routing invoking only the modules each case required. These results support multi-agent orchestration as a promising design pattern for transparent and auditable AI in healthcare. These gains are internal system properties; clinical-safety benefit remains to be established through prospective, clinician-involved validation. Full article

Industrial robot after-sales question answering often depends on multimodal evidence, such as error screenshots, interface displays, and wiring diagrams, which are difficult for conventional text-based retrieval-augmented generation (RAG) systems to exploit effectively. To address this issue, we design a dual-channel multimodal RAG system that converts image content into retrievable textual knowledge through the collaboration of optical character recognition (OCR) and structured semantic description. In the proposed system, OCR is used to extract explicit textual cues, such as error codes, parameter fields, and interface prompts, while expert-authored semantic descriptions complement implicit visual evidence, including device parts, fault phenomena, and contextual scene information. The transformed knowledge is further integrated into a hybrid retrieval pipeline that combines dense retrieval and BM25, followed by Reciprocal Rank Fusion (RRF) and Maximal Marginal Relevance (MMR) reordering to improve both relevance and contextual diversity. Experiments on a real-world industrial robot after-sales dataset show that the proposed method achieves an overall question-answering accuracy of 87.9%, outperforming the LLM-only baseline by 35.6 percentage points. For image-related questions, accuracy improves from 46.7% to 83.3%. These results indicate that the proposed framework provides a deployment-friendly and interpretable system-level alternative to end-to-end multimodal model fine-tuning for industrial after-sales question answering. Full article

Whole-slide imaging has transformed histopathology into a data-rich domain; however, many computational pathology models encode tissue morphology within latent representations, limiting interpretability, reproducibility, and generalization. This review positions histomics as an intermediate phenotype representation layer linking histological images with downstream clinical inference through structured descriptors of tissue morphology, spatial organization, and tissue architecture. Unlike prior reviews focused primarily on feature extraction or predictive performance, the study adopts a representation-centric perspective of histomics. A taxonomy of histomic features across biological scales is presented, and artificial intelligence frameworks, including machine learning, deep learning, weakly supervised learning, and multimodal approaches, are systematically examined. Key challenges, including segmentation dependence, feature instability, aggregation variability, and domain shift, are critically analyzed alongside emerging developments in foundation models, representation learning, and multimodal pathology. The review provides a unified framework for understanding histomic representations and identifies future directions for developing robust, interpretable, and generalizable computational pathology systems. Full article

Accurate detection of tea buds is a key technology for enabling automated tea harvesting. However, in natural environments, tea buds present challenges such as scale variation, dense distribution, and high similarity to the background, making it difficult for traditional methods to balance accuracy and efficiency. To address these issues, this paper proposes a lightweight detection framework, PCM-YOLO. The model introduces a cascaded gated feature modulation network into the YOLOv11 architecture, combining feedforward structures and gating mechanisms to selectively emphasize informative features, thereby improving tea bud detection performance. In addition, a feature-enhanced downsampling module is proposed, which employs a stepwise pooling-based feature enhancement mechanism to progressively expand the receptive field while preserving feature resolution, effectively incorporating multi-scale contextual information. Finally, a multi-scale feature enhancement module is designed to reduce the computational complexity of the model while maintaining detection performance as much as possible. Experimental results on public datasets demonstrate notable performance improvements over YOLOv11-N: Precision increases from 86.7% to 90.6% (an absolute increase of 3.9 percentage points), mAP50-95 increases by 1.6%, and the number of parameters is reduced by 20.6%. These results indicate that PCM-YOLO achieves a substantial reduction in model complexity while effectively improving detection accuracy, providing a feasible technical solution for deploying high-precision, real-time tea bud detection systems at the edge in tea plantation environments. Full article

The introduction of noise during image acquisition and transmission is inevitable, leading to a significant reduction in the accuracy of image processing tasks, such as target classification, localization, and recognition. To address this issue, this paper proposes a novel robustness-oriented pooling module called Gaussian adaptive pooling. Drawing on the principles of Gaussian filters, the method introduces a Gaussian weight for feature values in the pooling operation, thus integrating filtering and pooling in a novel manner. This approach is both lightweight and versatile, requiring no additional learnable parameters, and enables seamless integration into neural network architectures with pooling layers. Rigorous mathematical derivations and simulation experiments show that our proposed Gaussian adaptive pooling method surpasses conventional methods (average-pooling and max-pooling) in noise handling. Furthermore, its robustness is comparable to traditional pooling methods in addressing challenges such as rotations, scalings, and translations. Extensive evaluations across multiple computer vision tasks—including image classification (CIFAR-10/100), object detection (MS COCO and RTTS), and semantic segmentation (CamVid)—confirm its effectiveness. Specifically, under varying levels of noise and degraded conditions, Gaussian adaptive pooling achieves significant improvements in standard performance metrics compared to conventional pooling methods. For instance, it delivers notable quantitative gains across different tasks including up to a 12.67% increase in mean intersection over union on the CamVid dataset for semantic segmentation and a 1.1% mAP50 enhancement on the real-world RTTS dataset for object detection. Full article

Relation extraction is an important task for structuring information from unstructured text. However, the Romanian language still lacks dedicated datasets and benchmarks for this task. To address this gap, we introduce RoRED, a Romanian relation extraction dataset built by combining two complementary data construction strategies: translating existing high-quality English resources and applying distant supervision to native Romanian Wikipedia data. We leverage a powerful open-source large language model to automatically translate English examples into Romanian. For the native subset, we align Romanian Wikipedia entities with Wikidata relations to obtain naturally occurring Romanian examples. To better reflect real-world relation extraction scenarios, we also introduce synthetic negative examples generated using existing Romanian named entity recognition models. Finally, we validate the dataset by fine-tuning and evaluating multiple baseline models. Our strongest model, LUKE-RoRED, achieves a macro-F1 score of $0.8744$ on the RoRED test set, demonstrating that the dataset can support relation extraction for Romanian. Overall, RoRED provides a strong first native benchmark for Romanian relation extraction. Full article

Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies and describe the ribs qualitatively, this study aims to automate thoracolumbar stump rib detection and analyze their morphology quantitatively. To this end, we train a high-resolution deep learning model for rib segmentation using nnUNet and achieve significant improvements over existing models (Dice score 0.997 vs. 0.779, p-value < 0.01). In addition, we employ a novel iterative algorithm and piecewise linear interpolation to estimate rib length, achieving a success rate of 98.2%. When analyzing morphological features, we show that stump ribs articulate more posteriorly at the vertebrae ($-19.2\pm 3.8$ vs. $-13.8\pm 2.5$ mm, p-value < 0.01), are thinner ($260.6\pm 103.4$ vs. $563.6\pm 127.1$${\mathrm{mm}}^2$, p-value < 0.01), and are oriented more downwards and sideways within the first centimeters in contrast to full-length ribs. We show that with partially visible ribs, these features can achieve an F1-score of 0.84 and an AUC of 0.98 in differentiating stump ribs from regular ones. We publish the model weights and masks for public use. Full article

Generative artificial intelligence (genAI) enables educators to build custom learning tools, but the feasibility and impact of educator-driven, AI-assisted development (“vibe coding”) in medical education remain unclear. This study describes the rapid development of a custom ECG learning application using Gemini 3.1 Pro, evaluates its association with exam performance using difference-in-differences (DiD) and triple-difference (DDD) analyses, and assesses student perceptions with the user version of the Mobile App Rating Scale (uMARS). The app was implemented at one WWAMI site (intervention) with five sites as controls; aggregate performance from two first-year medical student cohorts (E24 vs. E25) was analyzed, comparing ECG-focused (focal) to non-ECG (baseline) exam items. DDD effects were inconsistent across exams, with no overall pooled effect on focal performance relative to baseline versus controls. In contrast, students rated the app highly (overall uMARS 4.57/5), particularly for quiz customization and waveform annotations. These findings support the feasibility of rapidly building and deploying tailored educational tools via genAI-assisted workflows and suggest strong perceived usability and acceptability among students. However, the study did not demonstrate a definitive short-term learning effectiveness effect on exam performance. Vibe coding is therefore positioned as a practical model for faculty-driven, context-specific educational innovation that requires further evaluation across broader implementations. Full article

Background: Radiology underpins diagnosis and treatment across pediatrics, yet most artificial intelligence (AI) tools are developed for adults and validated on adult datasets only. Of more than 200 AI systems cleared by the United States (U.S.) Food and Drug Administration (FDA), only about 3% include pediatric validation. Because children differ from adults in anatomy, physiology, pathology, epidemiology, and imaging protocols, adult-trained models often perform sub-optimally in pediatric settings. Methods: A narrative review of peer-reviewed literature from 2000 to 2025 was conducted using PubMed, MEDLINE, Google Scholar, and Scopus. Studies involving AI applications in pediatric X-ray, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), echocardiography, and point-of-care ultrasound with quantitative performance metrics were included. Findings were synthesized by imaging modality, clinical task, and differences between high-income countries (HICs) and low- and middle-income countries (LMICs). Results: AI demonstrated strong performance across multiple pediatric imaging tasks. In X-ray interpretation, AI detected fractures with area under the curve (AUC) values up to 0.96 (sensitivity, 90.8%; specificity, 88.7%). Pneumonia classification achieved 76.5% accuracy, and foreign body aspiration detection showed 95.3% specificity in HICs. In ultrasound, AI improved junior sonographers’ detection of intussusception (AUC 0.857 to 0.966) and reduced scan time by more than 50%. AI-assisted bone age estimation achieved a mean error of 0.39 years. In echocardiography, AI-derived ejection fraction showed excellent agreement with experts’ interclass correlation coefficient (ICC 0.983), and AI support improved atrioventricular septal defect detection (84.4% to 86.5%). In MRI, the use of AI enhanced lesion detection and supported quantitative analysis. Deep-learning models trained on routine T1- and T2-weighted sequences predicted liver stiffness across multi-site datasets, while advanced neuroimaging pipelines improved the identification of subtle epileptogenic lesions that are often missed on conventional pediatric MRI. However, adult-trained models showed limited generalizability to children. Still, excluding children under the age of two years improved the reading accuracy of pediatric chest X-rays (CXRs) by adult-trained models from 88% to 97%. AI faces challenges beyond the development of age-specific models. Substantial heterogeneity, limited pediatric-specific datasets, and unresolved medicolegal responsibility further restrict adoption worldwide. Challenges are amplified in LMICs, where unstable electricity, limited radiology resources, weak digital infrastructure, and scarce pediatric providers limit implementation. Additionally, many large language models underperform and lack inclusive algorithms suitable for pediatric radiology in many LMICs. Conclusions: AI can enhance diagnostic accuracy, efficiency, and access to pediatric imaging, particularly in resource-limited settings, through task-shifting and decision support. However, it cannot replace pediatric radiologists as of today. Safe adoption requires pediatric-specific model development, standardized validation metrics, diverse datasets that include LMIC populations, stronger digital infrastructure, robust radiologist training in AI capabilities, and the establishment of clear guidelines and medicolegal policies. Full article

Background: Biodegradable implants manufactured from Mg-based alloys are one of the most commonly used in orthopedics. However, their overall clinical acceptance is influenced by their fast corrosion speed and hydrogen emission. Based on an innovative manufacturing route previously described, this study introduces a preliminary proof-of-concept for a Gemini-assisted Digital Twin (Gemini-DT),which is an AI-augmented in silico framework designed to consider a MgF₂ conversion coating on the implant surface and to model the synchronization of the degradation process with new bone formation. Methods: Based on the integration of experimental data for Mg-Nd and Mg-Zn alloys and by considering the implant geometry and coating formation, we developed, in collaborative work with LLM Gemini 1.5 Flash (Google), a four-module cognitive framework (surface thermodynamic synergy (Module 1), degradation analysis and alloy extract concentration management (Module 2), micro-channel fluidics and mechanical stability (Module 3), and bio-mechanical synchronization and regenerative evaluation (Module 4)) to evaluate simulated implant behaviors). Results: Using a 10,000 iteration Monte Carlo stability simulation, the model demonstrated a potential 12% reduction in false-negative design screening errors compared to rigid rule-based systems, achieving strong internal decision consistency in sustaining the mandated parametric compliance window. Computational verification supports the projected biocompatibility trends of Mg-Zn alloys, as previously demonstrated in our in vivo studies. Conclusions: Our research leads to a consistent computational architecture dedicated to Mg-based implants and offers a robust platform for virtual design and optimization. These observations suggest that the developed model can recover viable designs, whereas traditional linear models may reject them. Full article

Modern power systems are becoming increasingly complex due to the rapid integration of renewable energy sources, the widespread use of nonlinear power-electronic devices, and the deployment of microgrids operating in parallel with conventional power grids. These evolving conditions intensify the occurrence of diverse and highly complex power quality disturbances (PQDs), demanding accurate and computationally efficient monitoring strategies. This paper presents a novel multi-stage hierarchical framework for PQD detection and classification, comprising an initial training stage with a dedicated 1D Convolutional Neural Network (1D-CNN), a transfer learning stage, and a subsequent fine-tuning stage. The proposed approach operates directly on raw voltage waveforms, eliminating the need for any signal preprocessing, as the CNN performs internal feature extraction. The framework is evaluated using a comprehensive dataset that includes synthetic signals, Matlab/Simulink (version R2022a) time-domain simulations, and real voltage sag events. Additionally, up to 29 types of disturbances, including complex multi-event combinations defined by the IEEE-1159 Standard, are generated using the PQ-SyDa toolbox. The proposed model achieves an F1-score of 97.8% using a three-cycle analysis window and further improves to 98.86% when five cycles are used. These results highlight the robustness and generalization capability of the proposed approach for the real-time PQD monitoring task in modern electrical networks. Full article

This review examines the evolution and architectural foundations of agentic artificial intelligence (AI), with a focus on collaborative multi-agent systems for complex task execution. The paper analyzes the core components, agent architectures, coordination mechanisms, application domains, and deployment challenges that enable autonomous reasoning and decision-making in real-world environments. To complement the survey, a comparative cryptocurrency market analysis case study is conducted using CrewAI, LangChain, and LangGraph focusing on workflow orchestration characteristics such as tool invocation, task transitions, orchestration depth, and memory integration. The findings are further supported by evidence from real-world financial applications reported in the literature, indicating productivity gains of 50–80% in financial data tasks and up to 20% improvement in stock prediction accuracy, highlighting the growing impact of multi-agent AI systems in market intelligence. The study highlights how architectural design choices influence reasoning continuity, coordination behavior, scalability, and system reliability, providing practical guidance for the design and deployment of agentic AI systems in complex, data-intensive domains. Full article

Objectives: This paper presents a principle and framework for generating social trust in AI outputs as an institutional structure rather than an ethical declaration. Sound technical design alone does not guarantee the institutional trust required to establish social measurement. What is needed is not a declaration of trust but the construction of an infrastructure that supports it. Methods: First, the Extended Kelvin Principle is derived by prepending to Kelvin’s measurement–understanding–control chain the links “no social trust without trust infrastructure; no legitimate social measurement without social trust.” Infrastructure-scale trust requires not declarations but verifiability, recordability, and auditability. Just as GUM and calibration infrastructure underpin trust in measured values, AI output governance requires GLO, a common language for expressing output legitimacy, implemented by a VRAIO-type infrastructure. GLO treats an output candidate as a “claim” and declares the rule-conformity of its purpose and content as a legitimacy confidence L, derived from a fact-based argument accompanied by a legitimacy budget. Results: VRAIO integrates declaration, rule verification, tamper-resistant recording, and independent auditing. A sealed, deterministic verifier makes L reproducible: computational falsity is caught by re-computation, factual falsity by checking authoritative records, and severe sanctions render false declaration irrational. Conclusions: GLO is not a mere AI version of GUM but a common language for an underdeveloped domain, whose effectiveness depends on connection to an enforceable output-governance infrastructure. Full article

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITSs), critical for transportation management and logistics. Despite advances in spatio-temporal prediction methods, existing approaches suffer from two key limitations: (i) multi-scale fusion methods inadequately capture hierarchical constraints between cross-scale features, and (ii) models rely on single spatio-temporal views, neglecting multi-source relationship complementarity. To address these issues, we propose ST-MAFNet, a spatio-temporal multi-scale adaptive fusion network comprising three key components, specifically, a Cross-Scale Hierarchical Anchoring strategy (CSHA) that anchors short-term predictions with multi-scale temporal patterns to mitigate noise; a Dual Spatial Perception Module (DSPM) that learns node heterogeneity and dynamic correlations through node embeddings and adaptive graph attention; and a Spatio-Temporal Adaptive Fusion Module (STAFM) that captures time-varying connectivity by integrating multi-scale temporal features with multi-source spatial relationships. Experiments on four real-world datasets demonstrate that ST-MAFNet is particularly effective for short-term traffic forecasting. Compared with the best previously reported MAE results, ST-MAFNet reduces MAE by 2.95%, 1.43%, 1.25%, and 0.37% on PEMS03, PEMS04, PEMS07, and PEMS08, respectively, and achieves the best or second-best performance on most evaluation metrics. Full article

This study evaluates zero-shot domain transfer for multilingual sentiment analysis in sustainable urban mobility using XLM-RoBERTa, a transformer pre-trained on social media data and applied to transport reviews without task- or domain-specific fine-tuning. Starting from a manually annotated English corpus of 375 transport-related user reviews, we created sentence-aligned translations in Spanish, French, German, and Italian, yielding a multilingual evaluation dataset of 1875 instances. Results show that the model assigns consistently high confidence to polarized content (mean: 0.76–0.85) and lower confidence to neutral or ambiguous expressions (0.58–0.65), with visible but preliminary cross-lingual variations that require further linguistic validation. Confidence scores are treated as diagnostic indicators of model certainty, not as evidence of correctness or calibration. A qualitative analysis of 113 categorized low-confidence predictions identifies six recurring linguistic patterns associated with model uncertainty (led by translation drift, mixed sentiment, and idiomatic expressions) with substantial inter-annotator agreement (κ = 0.664). By releasing the annotated multilingual dataset and code publicly, this work provides a reproducible exploratory evaluation framework for annotation-scarce, domain-specific multilingual NLP. Full article

Conventional recommender systems often rely on shallow collaborative signals, which limits their performance under sparse and popularity-skewed conditions. To address this, we propose a knowledge-aware framework that combines an item hypergraph induced by user interaction histories, a top-k user similarity graph, and one-hop, relation-aware knowledge-graph aggregation. The hypergraph branch learns high-order item co-occurrence representations, which are aggregated into initial user vectors and then refined through user similarity propagation. On the item side, user-conditioned relation attention aggregates one-hop KG neighbors to produce semantic item representations. User and item representations are fused by an MLP scorer, and a lightweight popularity-aware post-scoring adjustment can optionally be applied to moderate head-item dominance. Experiments on MovieLens-1M, Last.FM and Book-Crossing show strong performance among the compared baselines in AUC, ACC, and Recall@K. Full article

Heterogeneous anomaly detection ensembles improve robustness by combining complementary detectors, yet existing approaches often rely on heuristic detector selection, fixed contamination assumptions, and equal weighting. We investigate whether compact ensembles of complementary detectors can outperform substantially larger heterogeneous configurations through diversity-aware weighting and adaptive contamination estimation. Experiments on 22 benchmark datasets show that a compact ensemble of four complementary classical detectors outperforms an eleven-detector ensemble containing deep learning components, while requiring only 13.8% of the computational cost. Across the benchmark, the proposed ensemble variants achieve strong rankings while remaining competitive with the strongest individual detectors (Friedman ${\chi }^2=71.58$, $p<0.001$). These findings suggest that detector diversity, rather than ensemble size or architectural complexity, is the primary driver of robust unsupervised anomaly detection performance in resource-constrained environments. Full article

Non-invasive blood glucose estimation from exhaled breath has been proposed as a painless alternative to repeated capillary measurements; however, performance evaluation remains challenging in small-sample settings. This study investigates the estimation of blood glucose from human breath using volatile organic compound (VOC) signals acquired with an electronic nose. Responses from three metal-oxide sensor channels sensitive to CO, alcohol, and acetone were collected from 58 individuals, with one measurement per subject, and analyzed using strictly patient-level five-fold cross-validation, in which test folds comprised only real subjects. Two experimental factors were examined. First, model performance was evaluated with and without an additional interpretable alcohol–acetone log-ratio capturing relative variation between compounds. Second, model training was performed using either real data only or fold-wise tabular synthetic augmentation generated via a Gaussian copula fitted exclusively on training subjects, while evaluation remained strictly real-only. Under real-only training, classical machine learning models achieved the lowest prediction errors (approximately 6–7 mg/dL), whereas under synthetic augmentation FTTransformer was the best-performing deep learning model. This findings should be understood as a constrained proof-of-concept analysis rather than as evidence of diagnostic capability or clinical readiness. Full article

Background: The rapid advancement of generative artificial intelligence is transforming music composition from an exclusively human-centric activity into a hybrid human–algorithmic domain. Despite technological progress and growing commercial integration, consumer acceptance of AI-generated music remains empirically underexplored. Methods: This study formulates and empirically evaluates a multidimensional theoretical model integrating nine frameworks—including UTAUT2, parasocial interaction theory, anthropomorphism theory, authenticity theory, and innovation resistance theory—through a quantitative cross-sectional survey of 466 young adults aged 17–28. Confirmatory factor analysis and multiple regression analysis (with robust standard errors) were employed. Results: The model explained 63.6% of the variance in behavioral intention (R² = 0.636). Five constructs emerged as significant predictors: hedonic motivation (β = 0.136, p = 0.017), parasocial relationships (β = 0.121, p = 0.002), social influence (β = 0.126, p = 0.002), performance expectancy (β = 0.102, p = 0.019), and innovation resistance (β = −0.089, p = 0.029). Authenticity concerns, ethical AI concerns, anthropomorphic perceptions, and technological substitution fears were non-significant in the multivariate model. Conclusions: Young consumers’ acceptance of AI-generated music is primarily driven by experiential, social, and relational factors rather than ethico-cultural concerns. These findings have substantive implications for creative industries navigating algorithmic cultural production. Full article

Point-of-sale (POS) consumer credit represents the most rapidly expanding retail-lending channel within the emerging Eurasian markets, necessitating a stringent operational framework for the underwriting model: the decision must be rendered within a mere few hundred milliseconds during the in-store checkout process, while the inputs are constrained to what the application XML is capable of conveying. This research endeavors to develop, internally validate, and operationally delineate a hybrid, explainable artificial intelligence framework aimed at POS credit scoring within the production portfolio of Kazakhstan’s largest second-tier bank. The architectural framework is delineated along two orthogonal dimensions—client tenure and decision-making channel—resulting in the formulation of three distinct production models: two transparent Weight of Evidence–Logistic Regression scorecards tailored for the real-time channel, and one isotonically-calibrated stacked ensemble (comprising LightGBM, CatBoost, and a three-layer neural network) designated for the batch channel. The selection of hyperparameters was conducted utilising Bayesian optimization within the context of stratified five-fold cross-validation. The digital scorecards achieve an area under the receiver operating characteristic curve (AUROC) of 0.847 and 0.835, whereas the offline ensemble enhances performance to an AUROC of 0.918, accompanied by a Kolmogorov–Smirnov statistic of 0.682 and a Gini coefficient of 0.836. The population stability indices persist below the threshold of 0.07, while isotonic recalibration effectively reduces the Brier score by 18%. Furthermore, an extensive examination of fairness demonstrates variations in approval rates within a margin of ±1.2 percentage points—and equalised-odds gaps below 1.5 percentage points in the true-positive rate and 0.7 percentage points in the false-positive rate—across multiple demographic factors such as gender, age, and distinctions between urban and rural classifications, thus establishing an artificial intelligence framework that is both regulatorily compliant and interpretable, aligning with the directives set forth by the Agency of the Republic of Kazakhstan for Regulation and Development of the Financial Market. Full article

Person Re-identification (Person Re-ID) is essential in surveillance and security. Traditional image processing methods often struggle to identify individuals accurately due to the sensitivity to occlusions and limited discriminative capability of the global feature representation. To address these challenges, this study proposes a deep-learning architecture for Person Re-ID, termed Dynamic Part-Based Fusion (DPBF), which integrates the Salient Part Discrimination (SPD) and the Adaptive Feature Integration and Contextual Fusion (AFICF) frameworks within a unified pipeline. The SPD module enhances representation learning by emphasizing discriminative body regions through an attention-guided part-based mechanism guided by human parsing information. The AFICF component performs the correlation-aware integration of localized part-specific features and global contextual features, reducing redundancy and improving discriminative feature representation. The proposed framework coordinates part-level feature extraction and correlation-aware integration within a unified pipeline to improve robustness under occlusion and appearance variations. Additional analyses demonstrate a stable performance across independent training runs, competitive computational complexity, and robustness under severe occlusion conditions through adaptive local–global feature integration. The method was evaluated on several Person Re-ID datasets, including Occluded-ReID, Market-1501, DukeMTMC-ReID, Occluded-Duke, P-DukeMTMC-ReID, and CUHK03-Labeled. The experimental results demonstrate a competitive performance compared with existing methods, while additional reproducibility, computational-complexity, and occlusion-stability analyses further validate the robustness and practical applicability of the proposed framework. Specifically, DPBF achieves a 10.6% increase in Rank-1 accuracy and a 16% improvement in mAP over the closest competitor on the Occluded-ReID dataset. Full article

Aortic aneurysms (AAs), both abdominal and thoracic, remain one of the most lethal cardiovascular diseases, with increasing prevalence and incidence, especially in sporadic forms, in our populations, primarily represented by elderly individuals. The high mortality risk is primarily due to delayed management, although their management has shown progress, particularly regarding imaging techniques that facilitate diagnosis and otherwise complex surgical procedures. This is due to the clinical decision-making approach, which, unfortunately, is still based, according to guidelines, on the maximum aortic diameter. The maximum aortic diameter, as repeatedly emphasized, fails to capture the biological and biomechanical complexity of these pathological conditions, which are influenced, among other things, by highly individual factors (genetics, gender, lifestyle, etc.). Thanks to the advent of network medicine and omics sciences, diverse and complex clinical, imaging, and biomarker datasets are available. Artificial intelligence (AI) could process this data to facilitate the complex management of aneurysms and accurately predict risk. AI could prove an excellent tool for aneurysm management, improving risk prediction and radically transforming the way we understand, monitor, and manage aneurysm patients, despite some limitations, as well as improving its therapeutic applications towards personalized strategies. This narrative review provides an overview of these aspects based on current evidence. Full article

With the increasing penetration of renewable energy systems, multilevel inverters have been widely adopted to meet the growing demand for high-power and high-quality energy conversion. Among various multilevel topologies, cascaded H-bridge multilevel inverters (CHMIs) are particularly attractive due to their modular structure and improved output voltage quality. However, the increased number of power semiconductor devices and switching states significantly complicates fault diagnosis under practical operating conditions. Currently, most existing neural networks for fault diagnosis are manually designed based on domain expertise. This may limit their adaptability to task-specific fault patterns as well as edge-side inference performance. To reduce the dependence on manually designed diagnostic networks, an edge-oriented fault diagnosis framework based on differentiable architecture search (DARTS) is proposed to automatically design task-specific diagnostic networks. A simplified special cell search strategy is adopted to improve search efficiency and facilitate practical deployment. The searched architectures are lightweight and suitable for deployment on edge platforms. The experiments show that the proposed method achieves an average diagnostic accuracy of 99.44% on the test set under the RL load of $(7\mathrm{\Omega },6\mathrm{mH})$. Furthermore, the searched model contains only 0.2417 M trainable parameters, and edge deployment experiments on the Jetson Orin Nano platform show low-latency inference capability. Full article

Accurate radio map (RM) construction is essential for 6G wireless network optimization, yet faces significant challenges owing to sparse real-world measurements and dynamic environmental obstacles. This paper presents RMF, a novel single-step generative model based on mean flow matching that enables direct mapping from a noise prior to the target radio map distribution in a single forward pass, eliminating the iterative inference required by diffusion-based approaches. The proposed model integrates a multi-feature U-Net backbone with four specialized branches that extract and fuse building-layout features—via dual-path frequency and spatial-domain processing—base station distance fields, graph neural network-encoded sparse measurements, and dynamic obstacle representations, all injected through multi-scale cross-attention. Evaluations on the RadioMapSeer benchmark show that RMF attains the best RMSE and PSNR among the compared methods, with RMSE between 0.0136 and 0.0162 and PSNR between 36.52 and 37.24 dB, SSIM within 0.012 of the leading diffusion baseline, and an order-of-magnitude reduction in per-sample inference time. In the challenging zero-measurement scenario, RMF achieves PSNR gains of 1.45–1.55 dB over competing methods in both static and dynamic environments. The single forward-pass design yields inference times of 0.05 s, making RMF a promising candidate for real-time 6G applications such as coverage optimization and dynamic spectrum management, subject to validation on field-measured data in future work. Full article

Artificial intelligence systems increasingly mediate high-stakes human activities, yet urban navigation remains highly challenging for blind and visually impaired individuals. Although digital navigation technologies have significantly improved route planning and accessibility, many existing systems still rely on generic interaction paradigms that insufficiently account for cognitive load, contextual uncertainty, and the adaptive needs of vulnerable users. This challenge highlights the importance of Human-Centred AI approaches capable of supporting not only functional accessibility, but also cognitively sustainable and trustworthy interaction. This paper introduces LAZAR, a human-centred adaptive AI framework for accessible urban mobility grounded in a user-centred design methodology and formalised through a structured Software Requirements Specification. Rather than focusing exclusively on route optimisation, LAZAR approaches assistive navigation as an adaptive human–AI interaction problem in which instructional granularity, interaction frequency, and feedback mechanisms are designed to support user autonomy and situational awareness whilst limiting unnecessary cognitive burden. The proposed framework integrates high-fidelity prototyping, accessibility-oriented interaction modelling, and a modular multi-agent architecture intended to support adaptive and personalised guidance. Central to the approach is a cognitive load-aware interaction layer designed to regulate the presentation and timing of navigational assistance according to user needs and contextual conditions. The proposed multi-agent architecture is presented as a modular design framework whose interaction principles and interface logic were partially operationalised in the evaluated prototype. The complete integration of all adaptive coordination mechanisms, together with large-scale real-world validation, remains part of ongoing and future development work. This work contributes a structured methodology for the design of adaptive assistive AI systems that integrates accessibility requirements, human-centred interaction principles, and cognitively informed guidance strategies. A formative usability evaluation involving eleven visually impaired participants provides preliminary empirical evidence regarding usability, accessibility, and perceived usefulness of the proposed interaction model. The framework establishes a foundation for future research on inclusive and adaptive AI-based navigation systems in urban environments. Full article

3D scene understanding in industrial environments is crucial for effective operation and maintenance (O&M) and asset monitoring. However, accurate asset detection and localisation face significant challenges due to asset diversity and scene complexity in these environments. Existing learning-based methods rely heavily on labelled training datasets, which are limited for industrial settings due to asset variability and intricate geometries. To address these challenges, this paper presents a novel framework for industrial asset detection and localisation without requiring labelled training datasets, using only point cloud data. Experimental results demonstrate the competitive performance of the proposed framework, achieving an average precision at 25% intersection over union (AP25) of 48.13% and an AP50 of 34.98%, significantly outperforming state-of-the-art (SOTA) methods. This framework can be employed to generate 3D digital models of brownfield industrial plants that lack up-to-date spatial information, serving as a foundational spatial layer for the development of digital twins within industrial environments. Full article

The exponential growth of social media has revolutionized global communication, enabling instant idea exchange and transforming information sharing into a worldwide phenomenon while simultaneously accelerating the spread of abusive and hateful content that threatens online harmony and poses a serious risk to online community integrity and public trust. Although supervised deep learning approaches achieve impressive accuracy for hate speech detection, they remain fundamentally reliant on extensive annotated corpora, and their lack of interpretability makes them insufficient for transparent and scalable real-world hate speech detection. This study presents a category-oriented unsupervised architecture for English hate-speech detection and classification that substantially reduces reliance on large labeled datasets by requiring only minimal supervision (10% of labels for post hoc cluster interpretation), ensuring transparency and a high degree of semantic interpretability. We introduce an unsupervised Subspace Weighting Co-Clustering framework that uses HateBERT-driven contextual embeddings, enabling simultaneous interpretable feature weighting and semantic understanding for robust hate-speech detection. The obtained embeddings are further structured using the Subspace Weighting Co-Clustering approach, which enables the unsupervised discovery of latent subspaces and the organization of tweets into semantically coherent hate categories. The comprehensive evaluation shows that the framework achieves superior accuracy over existing methods, providing a more robust and effective mechanism for digital platforms to identify and mitigate hate speech and promote safer online interactions. Full article

This systematic literature review, guided by Kitchenham and Charters and following PRISMA 2020, analyzes explainable artificial intelligence (XAI) approaches for multiclass classification models, with an emphasis on explaining class differentiation and the relationship between feature contributions and changes in prediction probabilities. The protocol was defined in advance, but it was not preregistered. Searches were conducted in Scopus, Web of Science, SpringerLink, and ScienceDirect (2020–2025) using PICOC-based strings and explicit eligibility criteria. Following the PRISMA flow, 108 studies were included out of 8697 identified records. The most frequently reported approaches are based on feature contribution/attribution (e.g., SHAP, LIME, CAM, and Grad-CAM) and counterfactual explanations, with prominent applications in medicine, finance, and cybersecurity. Although several works analyze local contributions and, separately, probability variations, the synthesis reveals a methodological gap: there is a lack of a formal and explicit instance-level framework that quantitatively connects the differential contribution of a feature (e.g., SHAP values) with the probability variation between classes to explain class differentiation. In practical terms, such a linkage enables instance-level justification of why a model favors class A over a competing class B, improving traceability and decision support in high-stakes settings (e.g., differential diagnosis and risk assessment). These findings point to future directions toward more rigorous comparative local explanations in multiclass settings. Full article

Undertriage—the misclassification of acutely ill patients into low-acuity triage categories—is a persistent patient safety concern, and prior machine learning approaches restricted to vital signs have yielded modest predictive performance. We hypothesized that this ceiling reflects feature restriction rather than an inherent predictive barrier. In this retrospective cohort study of 10,792 adult patients (age ≥ 18) initially triaged as Korean Triage and Acuity Scale (KTAS) level 4 or 5 across two tertiary academic centers during 2025, the primary outcome was triage reclassification—change from initial KTAS 4/5 to final KTAS 1–3 (n = 941; 8.7%). Five nested feature sets of increasing breadth were compared using logistic regression (LR) and gradient-boosting classifiers (GBC). Calibration (slope, intercept, Brier score), sensitivity/specificity/positive and negative predictive values at operating thresholds of 3%, 5%, and 10%, and decision-curve net benefit were evaluated on a held-out test partition. NEWS alone yielded an AUROC of 0.58, whereas the full triage-time panel (Set E; 43 features) achieved a GBC AUROC of 0.72 (95% CI 0.68–0.76; 5-fold CV 0.73 ± 0.02) and an AUPRC of 0.23, approximately doubling the NEWS baseline (0.12). The model was well calibrated, with a Brier score of 0.075, a calibration slope of 0.85 (95% CI 0.70–1.01), and an intercept of −0.30 (95% CI −0.65 to 0.07); both intervals included the ideal values of 1 and 0, indicating that predicted probabilities can be interpreted as approximate absolute event likelihoods. At a 5% operating threshold, sensitivity was 0.79, capturing 79% of reclassifications while flagging 53% of the cohort. Decision curve analysis demonstrated positive net clinical benefit across thresholds of 3–20%, exceeding both a vital-signs-only model and the treat-all/treat-none baselines. Feature importance analysis identified pain score, onset-to-arrival time, heart rate, systolic blood pressure, and age as the dominant predictors. Contextual variables routinely documented at triage—particularly pain score and onset-to-arrival time—together with heart rate and systolic blood pressure form a discriminative composite that exceeds the performance of vital-signs-only models in the KTAS 4/5 subpopulation. The resulting model is well calibrated and provides positive net clinical benefit across the 3–20% threshold range, supporting its potential role as a secondary screening flag for low-acuity patients warranting clinician re-review. External validation in independent cohorts is needed before clinical deployment. Full article