Search Results (5,170)

Objectives: To synthesize evidence on artificial intelligence (AI)-enabled medical history taking (anamnesis)—beyond large language models (LLMs) alone—and to translate findings into implications and research priorities for head and neck surgery. Methods: We performed a PRISMA-informed narrative review. Searches from database inception to 31 December 2025 (updated 3 January 2026) were conducted in MEDLINE (PubMed), Embase, Scopus, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library, supplemented by medRxiv/arXiv screening and citation chasing. We included studies evaluating or describing AI-supported history capture/summarization, conversational interviewing, symptom checker/digital triage, EHR-integrated intake-to-decision support pipelines, voice interviewing, education/training systems, and governance/ethical considerations related to digital anamnesis. Findings were synthesized by system category and by cross-cutting outcome domains, with a head and neck surgery interpretive lens. Results: Fifty studies (2014–2025) were included. Evidence most consistently suggested feasibility and acceptability of pre-consultation computer-assisted history taking and the potential to reduce documentation burden and improve structured capture. In contrast, symptom checkers and digital triage tools showed highly variable diagnostic/triage performance and prominent safety concerns, highlighting the importance of conservative red-flag escalation strategies, continuous monitoring, and clear accountability. LLM-based diagnostic dialogue demonstrated strong performance in controlled evaluations, but prospective real-world validation, governance, and workflow integration remain limited. Conclusions: AI-enabled anamnesis comprises heterogeneous tools with uneven evidence. For head and neck surgery, potential near-term applications may include structured pre-visit intake, clinician-facing summarization, and training applications, whereas autonomous triage warrants harm-oriented, specialty-calibrated validation and robust governance prior to broader clinical reliance. Full article

As exploratory learning (EL) is increasingly observed with the use of large language models (LLMs), students demonstrate notably varied levels of engagement and effectiveness when they interact with such LLM-supported learning environments. However, the underlying mechanisms driving these disparities, particularly in how students interact with LLMs, remain underexplored. To address this gap, this observational comparative study systematically investigates the EL strategies of 46 students in two different regions of Asia, classifying 25 distinct strategies across cognitive stages using the Depth of Knowledge model. Our analysis compares strategy usage between high and low-performing student subgroups. The findings reveal: (1) A declining trend in the utilization of EL strategies across ascending cognitive stages. (2) High AWP students employed EL strategies more frequently than their peers, with ten EL strategies exhibiting significant between-group differences. (3) Among students with different AI experience, only a few EL strategies usage and cognitive stages showed significant differences. These insights can help educators and LLM interface designers develop targeted exploratory learning assistance for different types of students and help them build high-level metacognitive processes for effective human–computer interaction. Full article

Power electronic converters are being pushed toward higher power density and switching frequency, turning both design and operation into multi-objective, multi-physics optimization problems. While analytical rules and gradient-based methods remain essential, they often struggle with non-convex, mixed-integer trade-offs that include thermal behavior, Electromagnetic Interference/Electromagnetic Compatibility (EMI/EMC), and reliability constraints. This review surveys intelligent optimization approaches for power electronics across design-time, commissioning-time, and run-time horizons. We propose a deployment-oriented taxonomy of intelligent optimization approaches covering metaheuristics, surrogate-assisted and learning-guided design, constrained optimization via model predictive control, reinforcement learning-based supervisory policies, and hybrid physics-informed methods. For each family, we summarize typical tasks, computational and data requirements, robustness, interpretability, and validation maturity, highlighting where intelligent methods provide clear benefits and where classical approaches remain preferable. Reliability- and diagnostics-oriented optimization is discussed with emphasis on residual-based monitoring, stress-aware operation, and lifetime proxies. Practical adoption barriers—model–reality mismatch, data scarcity, real-time determinism, and certification—are synthesized into recurring design patterns that improve deployability. Finally, a conceptual cognitive design framework is proposed that couples virtual engineering, physics-informed surrogates, human-in-the-loop validation, and knowledge reuse in a closed-loop workflow, offering a structured perspective on how intelligent optimization may be integrated more reliably into industrial design practice. Full article

Mild traumatic brain injury (mTBI) in adults is extremely common worldwide, but only a small fraction of these patients harbor clinically significant intracranial injuries. Computed tomography (CT) of the head is the standard diagnostic tool to detect traumatic brain hemorrhages or lesions, yet indiscriminate CT scanning of all mTBI patients is inefficient, costly, and exposes patients to ionizing radiation. To optimize patient care, numerous clinical decision rules and guidelines have been developed internationally to identify which adult patients with mTBI should undergo head CT. This review provides a global perspective on the indications for head CT in adult mTBI, comparing key decision rules including the Canadian CT Head Rule, New Orleans Criteria, UK NICE Head Injury Guidelines, and others. Methods: We conducted a comprehensive analysis of major international guidelines and decision rules for head CT in adult mTBI, focusing on their inclusion criteria, risk factors, and diagnostic performance. Results: All the examined rules prioritize near-100% sensitivity for identifying patients who need neurosurgical intervention, but they differ greatly in specificity and recommended CT utilization rates. North American rules such as the New Orleans Criteria tend to favor higher sensitivity, scanning almost all patients with any symptom, whereas the Canadian CT Head Rule and certain European guidelines (NICE, Scandinavian) are more selective, significantly reducing CT usage while maintaining safety. Discussion: We discuss how these variations reflect different healthcare settings and risk tolerances, and we examine the implications for neurosurgical practice. We also highlight challenges in guideline implementation, the impact on global CT utilization, and emerging approaches (such as biomarker-assisted triage) that may further refine decision-making. In conclusion, appropriate use of clinical decision rules for head CT in mTBI can safely minimize unnecessary imaging, but local adaptation and clinician judgment remain crucial to ensure that no significant injuries are missed while avoiding over-scanning. Full article

Unmanned aerial vehicles (UAVs) acting as mobile aerial edge platforms can deliver on-demand communication and computing for the Internet of Vehicles (IoV) via flexible deployment and line-of-sight (LoS) links, improving reliability and reducing latency. However, high vehicle mobility, time-varying channels, and limited onboard energy make task offloading and resource coordination challenging. This paper studies joint task offloading and resource allocation in a UAV-assisted IoV system, where the UAV selects its hovering position from discrete candidate sites each time slot and splits vehicular tasks between the UAV and a roadside unit (RSU) to relieve backhaul congestion and enhance edge resource utilization. Considering vehicle mobility, multi-stage queue dynamics, and UAV energy consumption for communication, computation, and movement, the online optimization of position selection, task splitting, and bandwidth allocation is formulated as a constrained Markov decision process (CMDP). The goal is to maximize the number of tasks completed within the latency deadlines while satisfying the UAV energy budget. To solve this CMDP, we propose a graph-attention-based constrained twin delayed deep deterministic policy gradient (GAT-CTD3) algorithm. A graph attention network captures spatial correlations and resource competition among active vehicles, while a Lagrangian TD3 framework enforces long-term energy constraints and improves learning stability via twin critics, delayed policy updates, and target smoothing. The simulation results demonstrate that it outperforms the comparative scheme in terms of task completion rate, delay, and energy consumption per completed task, and exhibits strong robustness in situations with dense traffic. Full article

Background: Artificial intelligence is emerging as a promising tool in surgical oncology, with growing evidence suggesting potential applications in diagnostic support, intraoperative guidance, and perioperative risk assessment. In gastric cancer surgery, emerging applications range from AI-assisted endoscopic detection to data-driven perioperative risk prediction, while some technological developments, particularly in robotic autonomy, derive from broader surgical or experimental models that may inform future gastric procedures. Methods: A narrative review was conducted following established methodological standards, including the Scale for the Assessment of Narrative Review Articles (SANRA) and the Search–Appraisal–Synthesis–Analysis (SALSA) framework. English-language studies indexed in PubMed, Scopus, Embase, and Web of Science up to October 2025 were included. Evidence was synthesized thematically across five domains: AI-assisted anatomical recognition and lymphadenectomy support, autonomous robotic systems, early cancer detection, perioperative predictive and frailty models, and ethical and regulatory considerations. Results: AI-based computer vision and deep learning algorithms have demonstrated promising capabilities for real-time anatomical recognition, surgical phase classification, and intraoperative guidance, although evidence of direct patient-level benefit remains limited. In diagnostic settings, AI-assisted endoscopy and Raman spectroscopy have been shown to improve early lesion detection and reduce dependence on operator experience. Predictive models, including MySurgeryRisk and AI-driven frailty assessments, may support individualized prehabilitation planning and perioperative risk stratification. Persistent limitations include small and heterogeneous datasets, insufficient external validation, and unresolved concerns related to data privacy, algorithmic interpretability, and medico-legal responsibility. Conclusions: Artificial intelligence is progressively emerging as a promising tool in gastric cancer surgery, integrating automation, advanced analytics, and human clinical reasoning. Its safe and ethical adoption requires robust validation, transparent governance, and continuous surgeon oversight. When developed within human-centered and ethically grounded frameworks, AI can augment, rather than replace, surgical expertise, potentially advancing precision, safety, and equity in oncologic care. Full article

This work investigates the geometric design and optimisation of a dynamic inductive power transfer coupler for two-wheeled electric vehicles under misalignment and magnetic-field exposure constraints. A computational three-dimensional finite-element model of a shielded rectangular coupler is developed to characterise coupling coefficients and magnetic flux density levels on control planes along the longitudinal travel range and under lateral and angular misalignments. Two simulation datasets are generated: one varying only geometric parameters at a nominal position for surrogate construction and global sensitivity analysis, and a second jointly sampling geometry, the travel range and misalignments for optimisation. Sparse Polynomial Chaos Expansions and Canonical Low-Rank Approximation surrogates are built to quantify Sobol’ indices, revealing that a small subset of primary-side geometric variables dominates both coupling efficiency and magnetic field levels. Random forest regressors are then trained on the extended dataset and embedded in the Non-dominated Sorting Genetic Algorithm II to solve a multi-objective optimisation problem that maximises worst-case coupling, improves robustness to misalignment, and enforces magnetic-field leakage limits. Optimal designs were obtained, and a subset was selected for re-evaluation using the finite-element method. The results confirm that the proposed surrogate-assisted framework yields coupler geometries with enhanced coupling and reduced magnetic field leakage while respecting the mechanical constraints for the electric motorcycle system. Full article

Streptococcus suis is an emerging zoonotic pathogen that typically causes bacteremia or meningitis in humans, whereas vertebral osteomyelitis with epidural abscess is exceedingly rare and may be missed. We describe a 65-year-old farmer with fever and severe low back pain after long-term bare-handed handling of raw pig lungs. Pre-treatment blood cultures yielded S. suis identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). After transient improvement on empirical therapy, fever recurred with worsening lumbar pain. Contrast-enhanced magnetic resonance imaging (MRI) demonstrated multilevel thoracolumbar pyogenic spondylitis with an epidural abscess and a sub-ligamentous abscess beneath the posterior longitudinal ligament (PLL) extending from L2 to L5. Computed tomography-guided lumbar biopsy followed by tissue metagenomic next-generation sequencing (mNGS) detected S. suis, providing concordant evidence supporting pathogen involvement at the vertebral focus. The bloodstream isolate (SS-JX2025-01) was serotype 2, sequence type 7 (ST7). It remained susceptible to β-lactams and glycopeptides but was resistant to macrolide–lincosamide and tetracycline classes, consistent with erm(B), tet(O), tet(40), and ant(6)-Ia detected by whole-genome sequencing (WGS). Virulence profiling revealed an epf⁺/sly⁺/mrp⁻ pattern with multiple adhesins and immune-evasion factors, whereas canonical 89K pathogenicity island markers were absent. Core-genome phylogeny placed SS-JX2025-01 within the Chinese ST7 lineage associated with previous outbreaks. This biopsy-supported case expands the clinical spectrum of invasive S. suis infection, highlights the value of tissue mNGS as an adjunct for supporting deep-seated foci in zoonotic infections, and underscores the importance of occupational prevention in small-scale farming households. Full article

Background: Epicardial adipose tissue (EAT) is an imaging-derived biomarker increasingly associated with cardiovascular inflammation and metabolic risk. Computed tomography (CT) allows for accurate volumetric quantification of EAT, but the clinical interpretation of longitudinal changes remains challenging. Artificial Intelligence (AI) may provide additional value by identifying patterns and predictors of EAT variation. Purpose: To evaluate longitudinal changes in CT-derived EAT volume and to assess the feasibility and performance of AI-based models in discriminating patients with EAT increase after immunotherapy. Methods: In this retrospective single-center study, EAT was volumetrically segmented on baseline and follow-up CT scans. EAT change (ΔEAT) was calculated, and patients were dichotomized according to EAT increase (ΔEAT > 0). Three supervised AI models—Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost)—were trained using imaging-derived and clinical variables. Given the limited sample size and class imbalance, stratified two-fold cross-validation was adopted. Model performance was assessed using AUC, accuracy, and F1-score, and model interpretability was explored using permutation importance. Results: EAT volume showed a statistically significant increase at follow-up. In the AI analysis, SVM and ANN demonstrated good discriminative performance, with ANN achieving the highest AUC (~0.90). XGBoost failed to show meaningful predictive capability. Baseline EAT volume and follow-up duration emerged as the most relevant features. Conclusions: AI-based models, particularly SVM and ANN, are feasible tools for the analysis of CT-derived EAT changes, even in small cohorts. These results support the integration of AI-assisted EAT assessment into imaging-based cardio-oncology research. Full article

This review deals with the development and progress of micro-electromechanical systems (MEMS) actuators, which are needed in microfluidic applications, such as lab-on-a-chip and diagnostics. In the last 10 years, there have been tremendous advances in materials, microfabrication and computational modeling that have increased the functionality and scope of MEMS-based microfluidic actuation. This study classifies MEMS actuators on the basis of the physical method of actuation, including electrostatic, piezoelectric, and pneumatic actuation designs, in comparison with their application in pumping, valving, and droplet control. It examines the suitability of emerging structural and functional materials, such as piezoelectric thin-films and electroactive polymers, paying special attention to their reliability and biocompatibility. It also highlights the progress in multiphysics modeling that incorporates electrical, thermal, mechanical, and fluidic models, which facilitates the efficient design and performance optimization procedures. Other trends are multifunctional actuators with built-in sensing capability and the use of artificial intelligence (AI)-assisted design in production. With these developments, however, there exist issues of power efficiency, thermal control, fabrication uniformity and operational durability, and also the absence of standardized benchmarking. Finally, future research directions are outlined, including hybrid MEMS actuation, intelligent microfluidic operations, to improve the performance of the system and enable the transfer of the lab demonstrations to the large scale application of the system. Full article

Brain–computer interfaces (BCIs) have great potential for consumer electronics, as they enable the decoding of brain activity to control external devices and assist human–computer interaction. However, current decoding methods for BCIs face several challenges, such as low accuracy, poor stability under electrode shift, and slow processing for real-time use. In this paper, we propose a hybrid decoding framework designed to address the challenges of current EEG decoding methods. Our method combines manifold learning with contrastive learning. The core of our method lies in a dual-manifold model that uses non-negative matrix factorization (NMF) and a contrastive manifold learning framework to extract clear and useful features from brain signals. To improve decoding stability, we introduce a joint training strategy that enhances feature learning. Furthermore, the system is optimized for real-time interaction, reducing the system latency to 100 ms. We collect EEG signals from 15 subjects performing motor execution tasks and 10 subjects performing motor imagery tasks to construct a motor EEG dataset. On this dataset, the proposed method achieves superior decoding performance, reaching F1-scores of 0.7382 for the motor imagery tasks and 0.8361 for the motor execution tasks. Furthermore, the method maintains robustness even with reduced electrode counts and altered spatial distributions, highlighting its potential as a decoding solution for reliable and portable BCI systems. Full article

Human locomotion relies on a proximal–distal organization of joint mechanical work that adapts to task constraints, such as those imposed by inclined walking. In individuals with transtibial amputation, loss of the biological ankle disrupts this organization, leading to proximal alterations and inter-limb asymmetries. Active mechatronic prosthetic feet have been developed within a biomechanical biomimicry framework to restore distal positive mechanical work. This exploratory study quantified the effects of an active mechatronic prosthetic foot on joint mechanical work during inclined walking. Four individuals with transtibial amputation performed instrumented treadmill walking at −3°, 0°, and +3° using their habitual passive foot and a powered foot. Positive and negative mechanical work at the ankle, knee, and hip were computed using inverse dynamics and compared with a normative reference database (n = 20). The powered foot induced modest, task-dependent modifications, mainly at the ankle and knee. In downhill walking, it promoted a more symmetrical redistribution of negative mechanical work, particularly at the knee, suggesting a partial reduction in contralateral overload. In uphill walking, distal assistance increased prosthetic-side positive work, reflecting slope-dependent reallocation rather than normalization. Although a multivariate deviation score indicated reduced deviation under the powered condition, full convergence toward the asymptomatic organization was not achieved. Full article

Predicting carbon dioxide (CO₂) solubility in brine is critical for carbon capture and storage. This study employs the Ant Colony Optimization (ACO) algorithm to enhance the predictive accuracy of four machine learning models: Neural Network (NN), Decision Tree (DT), Support Vector Regression (SVR), and Gradient Boosting Machine (GBM). The models were trained and validated on a mineral compound dataset. Performance was evaluated using the coefficient of determination (R²) and error metrics including RMSE and MAE. The GBM model achieved the highest test accuracy (R² = 0.986) with low errors (RMSE = 0.0478, MAE = 0.0362), demonstrating superior ability to model complex, non-linear relationships with minimal overfitting. The optimized NN, featuring three layers and fifteen neurons, delivered strong performance (R² = 0.930) with balanced errors across datasets. The DT model offered excellent interpretability and a strong test score (R² = 0.912), while the SVR model provided robust generalization (R² = 0.889). The results indicate that ACO is an effective tool for hyperparameter tuning across diverse model architectures. For maximum accuracy, GBM is recommended, whereas DT is ideal when interpretability is required. The NN presents a strong middle-ground option with competitive accuracy. This comparative framework assists in selecting the optimal model based on specific project priorities of accuracy, transparency, or computational efficiency for geochemical forecasting. Full article

Unsupervised visible–infrared person re-identification (USL-VI-ReID) plays a pivotal role in cross-modal computer vision applications for intelligent surveillance and public safety. However, the task remains hampered by large modality gaps and limited granularity in feature representations. In particular, channel augmentation (CA) is typically used only for data augmentation, and its potential as an independent input modality remains unexplored. To address these shortcomings, we present a multimodule collaborative USL-VI-ReID framework that explicitly treats CA as a separate input modality. The framework combines four complementary modules. The Person-ReID Adaptive Convolutional Block Attention Module (PA-CBAM) module extracts discriminative features using a two-level attention mechanism that refines salient spatial and channel cues. The Varied Regional Alignment (VRA) module performs cross-modal regional alignment and leverages the Multimodal Assisted Adversarial Learning (MAAL) to reinforce region-level correspondence. The Varied Regional Neighbor Learning (VRNL) implements reliable neighborhood learning via multi-region association to stabilize pseudo-labels and capture local structure. Finally, the Uniform Merging (UM) module merges split clusters through alternating contrastive learning to improve cluster consistency. We evaluate the proposed method on SYSU-MM01 and RegDB. On RegDB’s visible-to-infrared setting, the approach achieves Rank-1 = 93.34%, mean Average Precision (mAP) = 87.55%, and mean Inverse Negative Penalty (mINP) = 76.08%. These results indicate that our method effectively reduces modal discrepancies and increases feature discriminability. It outperforms most existing unsupervised baselines and several supervised approaches, thereby advancing the practical applicability of USL-VI-ReID. Full article

Background: Aorto-esophageal fistula is a rare but life-threatening condition most often linked to thoracic aortic aneurysms and significant upper gastrointestinal bleeding. Thoracic endovascular aortic repair (TEVAR) is a crucial, life-saving procedure, but delayed complications, such as secondary esophageal fistulas caused by endograft erosion, can develop. Prompt recognition and multidisciplinary management are vital for survival. Case Presentation: We describe a 57-year-old patient with cardiovascular comorbidities and a saccular thoracic aortic aneurysm, who initially presented with massive hematemesis, melena, and hemodynamic instability. Imaging showed an aorto-esophageal fistula. Emergency treatment included placing a fully covered esophageal stent followed by TEVAR. Three weeks later, he experienced fever, chest pain, and worsening dysphagia. Laboratory tests indicated elevated inflammatory markers and hypoalbuminemia. Computed tomography revealed a new retrocardial esophageal fistula at T9, caused by mechanical erosion from the thoracic endograft. Endoluminal vacuum-assisted closure (EndoVAC) therapy was performed, leading to clinical improvement and the return of esophageal function. Conclusions: This case highlights a rare instance of a delayed secondary esophageal fistula after TEVAR beneath a preexisting stent, likely due to chronic contact between the endograft and esophagus. Despite advancements in endoscopic therapy, secondary fistulas after TEVAR are associated with high morbidity. Early diagnosis, aggressive infection management, structured nutritional support, and a multidisciplinary approach are essential. Extraluminal or intraluminal vacuum-assisted closure offers a promising minimally invasive option for managing complex esophageal defects. Full article