Search Results (642)

This scholarly article focuses on a specific application of discrete optimization methods in the emergency services. The search for the optimal deployment of service centers is one of the strategic decisions made in the field of urgent pre-hospital healthcare management. Since the consequences of the decisions are important for everyone and can directly affect the availability of the emergency medical service, different opinion groups are often taken into account when formulating a mathematical model. If there are two or more different conflicting objectives, the Pareto front of solutions usually needs to be constructed. It may serve as a good basis for finding the final system design. Since the construction of the exact Pareto set is very time-consuming and requires large computing resources, the efforts of many experts are focused on the development of efficient algorithms enabling the approximation of the original Pareto frontier in a short time. This paper introduces one of such heuristics. Even if the proposed algorithm of gradual refinement follows the idea of sequential processing of the current set of non-dominated solutions item by item inspecting the neighborhood of each element for possible extension of the Pareto front approximation, it can be simply adjusted and generalized making use of several parameters. Such an adjustment naturally raises the question of their optimal settings. Therefore, we gradually tried several procedures, from simple experimental verification of suitable values up to the development of sophisticated tuning of parameters based on machine learning methods. In this way, we created a complex advanced algorithm with elements of artificial intelligence. A series of numerical experiments are carried out utilizing real-world benchmarks that have their Pareto fronts applied in order to quantify and measure the efficacy of the proposed heuristic method. Full article

Explainable artificial intelligence (XAI) is increasingly important in scientific and engineering applications where predictive performance alone is insufficient and model outputs must also be physically credible, transparent, and reliable under unseen conditions. This study proposes a domain-informed XAI framework for predicting total suction in xanthan gum-treated clays using 139 experimental observations covering different mineralogical, moisture, polymer-dosage, and curing conditions. Eleven linear, kernel-based, ensemble, boosting, and physics-guided algorithms were evaluated using leakage-resistant five-fold grouped cross-validation, including a matched constrained–unconstrained HGB comparison with identical model settings. The methodological contribution is an evidence-linked XAI validation protocol in which model explanations are not accepted from feature attribution alone, but are audited through their agreement with leakage-resistant grouped generalization, physically constrained response directions, matched experimental contrasts, residual behavior, predictive uncertainty, and applicability-domain support. Selective monotonic constraints, physics-guided residual learning, SHAP explanations, and nonlinear response visualization are integrated within this protocol as complementary sources of evidence rather than treated as independent indicators of interpretability. The unconstrained histogram–gradient-boosting model achieved the highest out-of-fold predictive performance (R² = 0.958, RMSE = 0.098, and MAE = 0.070 in log10(MPa)). The corresponding monotonic model produced R² = 0.935, RMSE = 0.121, and MAE = 0.094 but eliminated the directional violations detected in the unconstrained response, revealing a measurable trade-off between predictive accuracy and guaranteed physical consistency. Explanations identified moisture content as the dominant negative control and revealed that xanthan-gum effects were non-monotonic and dependent on curing, moisture, and mineralogy. The residual model remained interpretable but underperformed the leading ensembles. Overall, the framework validates explanations against experimental contrasts, physical directions, grouped generalization, residual behavior, uncertainty, and domain support, offering a transferable strategy for trustworthy XAI in structured scientific datasets. Full article

Artificial intelligence (AI)-powered personalized recommendation systems can enhance relevance and convenience, but their reliance on extensive personal data processing may also heighten users’ perceptions of privacy intrusion. This study examines the relationship between perceived privacy invasion and consumer adoption willingness, with psychological resistance as a statistical mediator and perceived algorithm transparency (PAT) as a moderating condition. Drawing on the privacy-cost perspective of privacy calculus theory and psychological reactance theory, this study focuses on the privacy-related inhibition pathway of AI recommendation adoption. Survey data were collected from 518 Chinese adult Internet users with recent experience of AI-powered personalized recommendation services and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that perceived privacy invasion was negatively associated with consumer adoption willingness (β = −0.357) and positively associated with psychological resistance (β = 0.279), whereas psychological resistance was negatively associated with adoption willingness (β = −0.286). Psychological resistance statistically partially mediated the association between perceived privacy invasion and adoption willingness (indirect effect = −0.080). The interaction between perceived privacy invasion and PAT was negative and statistically significant (β = −0.162), indicating that the positive privacy invasion–resistance association was less pronounced at higher levels of PAT. Supplementary conditional process analysis further showed that the negative indirect association through psychological resistance weakened as PAT increased, with a significant index of moderated mediation (index = 0.0464, 95% bootstrap CI [0.0191, 0.0816]). Additional analyses using common-method-bias diagnostics, PLSpredict and CVPAT, and a targeted Gaussian copula robustness check for PAT provided complementary evidence regarding measurement robustness, predictive relevance, and the robustness of PAT-related estimates. Age-based multigroup analysis revealed no statistically significant age-based heterogeneity in the structural path coefficients across the 18–29, 30–49, and 50+ age groups. This study contributes to the literature by identifying psychological resistance as a motivational pathway associated with privacy-related adoption responses and by conceptualizing PAT as a user-level perceptual boundary condition in AI-powered personalized recommendation contexts. Full article

With the rise of artificial intelligence (AI), an increasing number of AI-based diagnostic tools are being developed. Before clinical implementation, these tools must be validated against existing gold standards. This requires trials that quantify the agreement between AI predictions and reference measurements. However, designing such agreement studies poses methodological challenges that differ substantially from classical superiority trials. This paper aims to provide statistical methods for assessing diagnostic agreement. Methods were categorized according to the measurement scale of the data (nominal, ordinal, continuous)—with a separate group for methods that apply across several scales—and according to the number of raters or measurements involved. A decision tree is provided as a simplified educational framework for method selection rather than as a general method-selection algorithm: design features such as repeated measurements, clustering, spectrum effects, dependence between raters, and an imperfect reference method are not encoded in it and are discussed separately, together with the circularity and confounding issues specific to the validation of AI-based tools. For each method, we summarized assumptions, appropriate use cases, interpretation of results, and available open-source software for sample size calculation and analysis, and we illustrate the sample size calculations in three fully worked examples covering binary, ordinal, and continuous outcomes. We further distinguish conditional inference about one fixed, frozen model version from the broader generalization to a class of algorithms or to future model versions, which require additional sources of algorithmic and dataset variability to be represented in the design and analysis. We conclude by outlining open methodological questions—including Bayesian approaches to agreement estimation, methods for complex AI outputs, agreement models for clustered and repeated-measures designs, and the limited software support for Gwet’s AC1/AC2 sample size planning—that warrant further work as diagnostic technologies and statistical methodology continue to evolve. Full article

Background: Stage II/IIIA colorectal cancer (CRC) patients have a relatively high 5-year overall survival rate after surgical resection alone, but more than 50% of patients receive adjuvant chemotherapy. This study investigates a technology that applies artificial intelligence (AI) to conventional histopathology images to identify patients at risk of recurrence. Methods: HistoNav, a novel AI deep-learning algorithm based on Vision Transformer, Graph Neural Networks and Convolutional Neural Networks was designed to analyse H&E-stained formalin-fixed paraffin-embedded (FFPE) samples (n = 2095) to stratify Stage II/IIIA CRC patients into low-, intermediate-, and high-risk groups. Results: Data analysis revealed a 5-year disease-specific survival (DSS) of 93.2%, 84.3%, and 69.3% for low-, intermediate-, and high-risk groups, respectively. The hazard ratio for the high versus low-risk group (HR = 4.611, 95% CI: 2.776–7.662; p < 0.000001) was statistically significant, demonstrating HistoNav’s potential to stratify patients based on recurrence risk. Conclusions: HistoNav can effectively identify CRC patients with good prognosis using the digital images of H&E-stained resection samples and will support clinical decisions around the use of adjuvant chemotherapy. Full article

Sarcopenia is increasingly recognized as a systemic complication of inflammatory bowel disease (IBD)—more accurately described as an IBD-associated muscle disorder than as a classical extraintestinal manifestation—but it remains inconsistently defined and rarely integrated into routine care. Consensus frameworks require low muscle strength confirmed by low muscle quantity or quality, so studies reporting only computed tomography (CT)-derived muscle area describe low muscle mass rather than consensus-defined sarcopenia; myosteatosis, the fat infiltration of muscle, is a further and partly independent dimension of muscle quality. This narrative review, supported by a structured literature search that was re-run and extended during peer review, synthesized peer-reviewed human evidence published from 1 January 2010 to 5 July 2026 in adult patients. Overall, 152 records were identified through PubMed/MEDLINE and citation tracking; after 19 duplicate or overlapping records were removed, 133 records were screened, 66 full-text reports were assessed, and 54 sources were included: 39 empirical IBD studies, 5 IBD-specific systematic reviews or meta-analyses, 6 consensus or standardization documents and 4 mechanistic or narrative reviews. Sarcopenia in IBD is driven by chronic inflammation, malnutrition, dysbiosis, corticosteroid exposure, inactivity and impaired anabolic signaling. Prevalence is definition- and setting-dependent, from approximately 10% in stable outpatients assessed with functional criteria to more than 40–50% in CT-based or active-disease cohorts. Sarcopenia is consistently associated with—rather than proven to cause—hospitalization, abscess formation, postoperative complications, treatment escalation or failure and impaired function. Muscle ultrasound and automated, artificial intelligence-assisted analysis of opportunistic CT and magnetic resonance imaging (MRI) are emerging as practical routes to routine assessment. We propose a drivers–detection–prognosis–intervention framework, aligned with the sequential European Working Group on Sarcopenia in Older People 2 (EWGSOP2) and Asian Working Group for Sarcopenia (AWGS) 2019 algorithms, to support opportunistic imaging review, strength testing and integrated nutrition–exercise care; this framework is an expert proposal that requires prospective, multicenter validation. Full article

Artificial intelligence (AI) has become a key driver of transformation in educational contexts, influencing both teaching and learning processes as well as the digital competencies required by teachers and students. In this context, understanding how AI is perceived, used, and valued is essential for guiding educational innovation in a critical and sustainable way. This study examines digital competence in artificial intelligence and its relationship with educational innovation, perceived threats, and job satisfaction, comparing university students and in-service teachers. The research adopts a quantitative approach based on a questionnaire administered to 355 participants. The results indicate that students demonstrate higher levels of technical skills, innovative vision, and more positive attitudes toward the educational use of AI, whereas teachers adopt a more cautious stance, mainly due to a lack of specific training and the absence of clear institutional guidelines. Significant differences are also found regarding perceived threats, with students showing greater awareness of risks such as plagiarism, algorithmic bias, and the potential erosion of critical thinking. In contrast, no significant differences are identified in ethics or job satisfaction. In conclusion, the study reveals a competence gap between both groups and highlights the need to redesign initial teacher education and promote continuous professional development in AI. Finally, it emphasizes the importance of integrating artificial intelligence into education from a critical, ethical, and human-centered perspective. Full article

Artificial intelligence has become increasingly important in research on Disassembly Sequence Planning (DSP). AI-based methods can now solve more complex problems and consider several economic, environmental and operational objectives. However, most studies still focus on optimizing the disassembly process after the recovery strategy and target components have already been selected. This study reviews AI-supported disassembly planning in remanufacturing and identifies the main research gaps and future research directions. The review combines bibliometric mapping of 241 publications with a qualitative analysis of 35 selected studies. Of these studies, 23 directly address DSP, seven concern related disassembly problems, and five focus on remanufacturing planning or scheduling outside DSP. The results show progress in metaheuristic optimization, reinforcement learning, digital twins, knowledge graphs, computer vision, large language models and robotic planning. At the same time, the analysis identified several recurring limitations that can be grouped into three broader dimensions: product-state knowledge and adaptability, validation and transferability, and decision scope and strategic-operational integration. The reviewed studies show that uncertainty, adaptive planning, digital twins and multi-objective optimization have already received considerable attention, but their application remains limited by predefined product representations, case-specific validation and weak integration between product-level recovery assessment and operational disassembly planning. The findings suggest that future research should complement further algorithmic development with more integrated, adaptive and knowledge-supported decision-support approaches that connect recovery assessment with the planning of the required disassembly process. Full article

Xylella fastidiosa (Xf) is among the most devastating phytosanitary threats to Mediterranean agriculture, causing Olive Quick Decline Syndrome (OQDS). Since containment efficacy depends on timely intervention, scalable in-field screening tools are needed. This study evaluates a portable digital electronic nose, based on a carbon-nanotube sensor array combined with artificial intelligence algorithms, for the in-field screening of Xf through volatile organic compound (VOC) profiling. Three replicate acquisitions were performed on 130 olive trees (390 measurements) across four cultivars (Cellina di Nardò, Ogliarola Salentina, Ogliarola Barese, and Leccino) in four Italian regions (Apulia, Calabria, Lazio, and Tuscany). Plant status was assigned from the official status of the sampling area (demarcated OQDS focus versus Xf-free area) and supported by real-time quantitative PCR (qPCR) on every plant; within demarcated sites, plants with undetectable DNA in sampled twigs were retained as Xf+ following phytosanitary criteria, giving 216 infected and 174 healthy samples. The multidimensional sensor signals were processed with an optimized Shallow Neural Network. Under plant-grouped 80/20 validation, keeping each plant’s replicates in the same subset, the model achieved (93.3 ± 4.0)% accuracy, (97.7 ± 3.5)% sensitivity, and (88.2 ± 8.3)% specificity (mean ± SD). A feature-importance analysis revealed a reproducible, though not chemically resolved, VOC-related response pattern. This low-cost, portable Internet of Things (IoT) device offers a proof-of-concept screening approach for Xf surveillance, pending plant-level and external validation. Full article

Intelligent Transportation Systems (ITS) increasingly combine sensing, communication, computation, data platforms, and mobility services. This article presents a structured, literature-based mapping review of ITS applications from a standards-oriented perspective. The study analyzes 42 studies organized into five thematic groups and evaluated through 63 article–standard assessments using selected ITU-T Recommendations as an analytical lens. The rubric used in this work examined whether each study reported, or allowed reviewers to infer, evidence on architecture, data handling, interoperability, security and privacy, deployment assumptions, and digital-twin capabilities. Partial alignment was the most frequent outcome, accounting for 25 of 63 article–standard assessments (39.7%). At group level, satisfactory or optimal alignment occurred in 5 of 8 assessments (62.5%) in the digital-twin group and in 3 of 13 (23.1%) in the Big Data group; in the latter, 6 of 13 assessments (46.2%) showed limited or no alignment. Stronger alignment was usually found when studies described architectures, data flows, sensing mechanisms, service workflows, physical–virtual modeling, or system-management components relevant to the Recommendation, and weaker alignment when they focused mainly on algorithms, datasets, prediction accuracy, authentication, or secure dissemination without sufficient detail on interfaces, data governance, gateway roles, deployment conditions, or platform integration. The review proposes a five-dimension standards-facing reporting checklist addressing interoperability, data lifecycle and governance, security and privacy, operational readiness, and standards-facing evidence. It supports traceable reporting through explicit evidence-status categories and locations, and can be implemented as a Standards and Interoperability Reporting Statement (SIRS) for authors, reviewers, and editors. Overall, the findings show that standards-oriented assessment depends not only on technical performance but also on explicit and traceable integration evidence, while the proposed reporting profile provides a practical mechanism for making such evidence more systematically visible in future ITS studies. Full article

Background: Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide, highlighting the need for improved prognostic tools. The integration of artificial intelligence (AI) and machine learning (ML) with multi-omics data offers new opportunities for biomarker discovery and risk stratification. Methods: We integrated bulk transcriptomic data from GSE116918 (training, n = 248) and three cross-cohort consistency evaluation cohorts (TCGA-PRAD, GSE70769, GSE46602), focusing on 1087 epithelial–mesenchymal transition (EMT)-associated genes. Using consensus clustering, weighted gene co-expression network analysis (WGCNA), and 91 machine learning algorithm combinations (including Random Forest, Lasso, and CoxBoost), we constructed a prognostic signature. SHAP analysis was used for model interpretability. Single-cell RNA sequencing (scRNA-seq, GSE268307, 10,672 cells) and spatial transcriptomics (10× Genomics Visium FFPE) provided hypothesis-generating evidence; spatial analysis was based on one tissue section. Results: A three-gene signature (INHBA, FAP, ITGBL1) effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly worse metastasis-free survival (HR = 1.61, 95% CI: 1.39–1.87; 4-year AUC = 0.93 in the training cohort; external AUCs ranged from 0.62 to 0.77). CytoTRACE inferred high differentiation potential of COMP+ fibroblasts, and Monocle3 inferred a transcriptional transition from COMP+ toward NELL2+ fibroblasts. BayesPrism deconvolution suggested that high inferred COMP+ fibroblast abundance was associated with poor prognosis and advanced T stage. NicheNet analysis prioritized BMP7 as a key upstream ligand, with downstream targets enriched in TGF-β signaling and stem cell pluripotency pathways. Conclusions: This study presents a machine learning-based multi-omics framework for prostate cancer risk stratification. The three-gene signature provides a new exploratory prognostic model while inferring a COMP+ to NELL2+ transcriptional transition. These findings may inform future hypothesis-driven studies of treatment sensitivity, pending experimental validation, and demonstrate the value of AI-driven multi-omics integration for precision oncology. Full article

Most existing driver behavior classification technologies have focused on highways and other primary road infrastructures, despite secondary roads accounting for a disproportionately large number of traffic fatalities worldwide. Compared with highways, secondary roads present greater variability in road geometry, infrastructure quality, and traffic interactions, making driver behavior recognition considerably more challenging. This paper investigates the classification of driver behavior on secondary roads using machine learning techniques. Naturalistic driving data obtained from the publicly available UAH-DriveSet dataset were analyzed using two complementary feature groups describing lane detection and traffic status. Four supervised machine learning algorithms, namely, Logistic Regression (LR), gradient boosting (GB), Random Forest (RF), and Artificial Neural Networks (ANNs), were evaluated to classify driving behavior into three categories: Normal, Aggressive, and Drowsy. The extracted features were first analyzed through statistical profiling and exploratory feature analysis before training and evaluating the classification models. The experimental results show that gradient boosting consistently achieved the highest performance for both feature groups, attaining an overall classification accuracy of approximately 67% while providing balanced precision, recall, and F1-scores across all behavioral classes. Logistic regression and random forest produced competitive but lower performance, whereas the Artificial Neural Network yielded the lowest classification accuracy. The obtained results demonstrate the effectiveness of ensemble learning methods for driver behavior recognition under secondary-road conditions and highlight their potential for integration into intelligent driver monitoring and Advanced Driver Assistance Systems (ADASs). By enabling earlier identification of aggressive and drowsy driving behaviors on secondary roads, the proposed approach could support timely driver warnings and safety interventions, potentially reducing accident risk. Furthermore, the findings provide a benchmark for future machine learning models designed for real-world secondary-road environments, where driving conditions are more variable and challenging than on highways. Full article

To address the problems of enhanced non-stationarity, significant feature distribution shift, and insufficient cross-condition generalization capability of traditional fault diagnosis methods under variable operating conditions such as varying speed and load, a rolling bearing fault diagnosis method integrating group sparse reconstruction and a multi-strategy improved quantum particle swarm optimization-based relevance vector machine (RVM) is proposed. First, group sparse representation learning is employed to reconstruct the original vibration signals, thereby suppressing background noise and enhancing fault-related impulsive components to improve signal separability and stability. Subsequently, a modal component selection criterion combining kurtosis and correlation coefficients is introduced to optimize and reconstruct the decomposed modal components, enabling the reconstructed signals to retain more fault-sensitive information. On this basis, multiple information entropy features are extracted from the reconstructed signals to construct high-dimensional state feature vectors for comprehensively characterizing the dynamic operating states of rolling bearings. To further enhance the parameter optimization capability, Chebyshev chaotic mapping is incorporated into the quantum particle swarm optimization (QPSO) algorithm to improve the uniformity of population initialization. Meanwhile, a Cauchy mutation strategy is introduced to strengthen the global search capability and avoid premature convergence, thereby forming a multi-strategy improved QPSO algorithm. Finally, the improved optimization algorithm is utilized to adaptively optimize the key hyperparameters of the RVM, resulting in a fault diagnosis model with high accuracy, strong generalization capability, and sparse characteristics. Experimental validation on the HUST and XJTU-SY bearing datasets demonstrates that the proposed MIQPSO-RVM framework achieves diagnostic accuracies of 96.70% and 94.83%, respectively. Compared with several representative intelligent diagnosis methods and deep learning models, the proposed method exhibits superior diagnostic performance, robustness, and generalization capability under complex operating conditions. Full article

Stochastic metaheuristic feature selection (FS) may generate unstable feature subsets across repeated runs, undermining reproducibility in Internet of Things (IoT) intrusion detection systems (IDSs). This paper presents Head–Tail Population Initialization (HTPI), a feature-importance-guided initialization approach for enhancing the stability of Adaptive Hybrid Genetic Algorithm–Simulated Annealing (AHGA-SA)-based FS. HTPI uses feature-importance scores to split candidate features into Head and Tail groups and initializes candidate subsets by prioritizing Head features and sampling Tail features with importance-based weights. HTPI is integrated into AHGA-SA as an incremental extension, termed HTPI-AHGA-SA, and modifies only the initialization and reinitialization steps. Experiments on eight IoT-oriented IDS datasets using 50 runs per configuration, with seeds paired across methods, showed significantly higher Nogueira stability under HTPI-AHGA-SA on all datasets after Holm correction, with non-overlapping 95% leave-one-run-out jackknife confidence intervals in every case. These results characterize algorithmic cross-run stability under a fixed data partition. All absolute differences in dataset-level mean F1 Macro remained below $0.003$; formal equivalence at this margin was supported for six datasets, while dataset-specific security-metric trade-offs remained. On three representative datasets, post hoc explainable artificial intelligence (XAI) analyses indicated generally higher permutation importance (PI)-based cross-run consistency and measurable predictive utility in the selected Head and Tail portions under retraining. Full article

To address the scarcity of bridge defect data in the harsh environments of cold regions, as well as the parameter redundancy and edge platform deployment challenges of existing algorithms, this paper proposes an intelligent inspection system for bridge surface defects in cold regions based on parameter sharing and feature enhancement. The system first constructs a large-scale dataset called CRBD (Cold-Region Bridge Defect), which contains 10,129 high-resolution images and finely classifies defects into four standardized categories: Crack, Spalling, Patch, and Seepage. Subsequently, a lightweight detection network called BridgeNet is designed. Its core parameter sharing and feature enhancement detection head stabilizes training via group normalization, significantly reduces the parameter count through cross-scale global sharing and structural reparameterization, and improves bounding-box regression accuracy by incorporating a distribution focal loss mechanism. On this basis, an airborne real-time image processing and intelligent perception pipeline is constructed, which establishes the complete workflow for autonomous unmanned aerial vehicle inspections. The experimental results demonstrate that with a lightweight architecture of only 2.26 M parameters and a model size of 4.98 M, BridgeNet achieves a mean Average Precision of 61.4% and an F1 Score of 60.9%. Furthermore, it exhibits excellent real-time inference speed on heterogeneous edge mobile platforms and maintains robust overall perception stability under various extreme physical disturbances. Full article