Search Results (1,763)

Automated code translation plays an important role in improving software reusability and supporting system migration, particularly in scenarios where Python implementations need to be converted into efficient C++ programs. However, existing approaches often rely heavily on large external models or static inference pipelines, which limits their ability to improve translation quality over time.To address these challenges, this paper proposes ODEL, an On-Demand Experience-enhanced Learning framework for Python-to-C++ code translation. ODEL adopts a hybrid inference architecture in which a lightweight internal model performs routine translation, while a more capable external model is selectively invoked upon verification failure to conduct error analysis and generate structured experience records. These experience records are accumulated and reused across subsequent translation phases, enabling progressive improvement through a closed-loop workflow that integrates generation, verification, consideration, and experience refinement. Experiments on the HumanEval-X benchmark demonstrate that ODEL significantly improves translation accuracy compared with competitive baselines. Specifically, the framework increases Pass@1 from 71.82% to 81.10% and Pass@10 from 74.30% to 89.02%, and exhibits a consistent performance improvement across multiple translation phases. These results indicate that experience reuse within a continuous task stream can effectively enhance automated code translation without modifying model parameters. Full article

The detection of defects in Printed Circuit Boards (PCBs) is a critical yet challenging task in industrial quality control, characterized by the prevalence of small targets and complex backgrounds. While deep learning models like YOLOv5 have shown promise, they often lack the ability to quantify predictive uncertainty, leading to overconfident errors in challenging scenarios—a major source of false alarms and reduced reliability in automated manufacturing inspection lines. From a Bayesian perspective, this overconfidence signifies a failure in probabilistic calibration, which is crucial for trustworthy automated inspection. To address this, we propose MFE-YOLO, a Bayesian-enhanced detection framework built upon YOLOv5 that systematically integrates uncertainty-aware mechanisms to improve both accuracy and operational reliability in real-world settings. First, we construct a multi-background PCB defect dataset with diverse substrate colors and shapes, enhancing the model’s ability to generalize beyond the single-background bias of existing data. Second, we integrate the Convolutional Block Attention Module (CBAM), reinterpreted through a Bayesian lens as a feature-wise uncertainty weighting mechanism, to suppress background interference and amplify salient defect features. Third, we propose a novel FIoU loss function, redesigned within a probabilistic framework to improve bounding box regression accuracy and implicitly capture localization uncertainty, particularly for small defects. Extensive experiments demonstrate that MFE-YOLO achieves state-of-the-art performance, with mAP@0.5 and mAP@0.5:0.95 values of 93.9% and 59.6%, respectively, outperforming existing detectors, including YOLOv8 and EfficientDet. More importantly, the proposed framework yields better-calibrated confidence scores, significantly reducing false alarms and enabling more reliable human-in-the-loop verification. This work provides a deployable, uncertainty-aware solution for high-throughput PCB inspection, advancing toward trustworthy and efficient quality control in modern manufacturing environments. Full article

Whole-body vibration (WBV) remains a critical factor influencing ride comfort, driver performance and occupational health in vehicle applications. Despite the widespread use of standardized indicators, assessing WBV exposure and its perceptual implications remains challenging due to the complex interaction between road excitation, vehicle dynamics, seat transmissibility and human biodynamic response. This review provides a comprehensive synthesis of contemporary methods for WBV assessment, emphasizing their theoretical foundations, practical implementation and inherent limitations. The paper examines classical evaluation metrics, including frequency-weighted root mean square acceleration and vibration dose value, alongside complementary approaches such as overall vibration total value, absorbed power and motion sickness indicators. Biodynamic modeling strategies for the human–seat–vehicle system are critically reviewed, highlighting trade-offs between model simplicity and physiological realism. Particular attention is given to road surface representation and excitation modeling, discussing the implications of ISO 8608-based stochastic profiles versus measured, time-domain inputs on WBV assessment outcomes. Simulation frameworks, experimental platforms and driving simulators are reviewed as complementary tools for evaluating vibration exposure and validating predictive models. Emerging methods, including time–frequency analysis and data-driven approaches, are discussed with a focus on interpretability, validation and integration with established standards such as ISO 2631. The review consolidates recent advances in integrated evaluation approaches, including the role of driving simulators and simulation-, hardware- and driver-in-the-loop (SiL/HiL/DiL) frameworks as enabling tools for repeatable testing, objective–subjective comfort correlation and early-stage vibration-control development. By critically examining both established and emerging methodologies, this review aims to support informed selection and interpretation of WBV assessment tools in vehicle design and evaluation. The findings underscore the need for integrated, transparent and application-oriented approaches to advance vibration comfort assessment and guide future research and standardization efforts. Full article

Commit messages are essential for understanding software evolution and maintaining traceability of projects; however, their quality varies across repositories. Recent Large Language Models provide a promising path to automate this task by generating concise context-sensitive commit messages directly from code diffs. This paper provides a comparative study of three paradigms of large language models: zero-shot prompting, retrieval-augmented generation, and fine-tuning, using the large-scale CommitBench dataset that spans six programming languages. We assess the performance of the models with automatic metrics, namely BLEU, ROUGE-L, METEOR, and Adequacy, and a human assessment of 100 commits. In the latter, experienced developers rated each generated commit message for Adequacy and Fluency on a five-point Likert scale. The results show that fine-tuning and domain adaptation yield models that perform consistently better than general-purpose baselines across all evaluation metrics, thus generating commit messages with higher semantic adequacy and clearer phrasing than zero-shot approaches. The correlation analysis suggests that the Adequacy and BLEU scores are closer to human judgment, while ROUGE-L and METEOR tend to underestimate the quality in cases where the models generate stylistically diverse or paraphrased outputs. Finally, the study outlines a conceptual integration pathway for incorporating such models into software development workflows, emphasizing a human-in-the-loop approach for quality assurance. Full article

This paper presents an artificial intelligence (AI)-assisted multi-objective topology optimization of a 48 V interior permanent magnet synchronous motor (PMSM) intended for mid-drive e-bike applications. The machine features a 48-slot, 8-pole stator–rotor combination with $\Delta$-shaped three buried magnets per pole, and is coupled to a multi-stage gearbox that adapts its high-speed, low-torque output to a human-scale crank speed. The design problem simultaneously maximizes average torque and efficiency while minimizing torque ripple by varying key stator slot dimensions and magnet geometries. A modular MATLAB–ANSYS Maxwell framework is developed in which finite element simulations are driven by a Bayesian optimization (BO) loop augmented by a large language model (LLM) with retrieval-augmented generation (RAG). The LLM acts as a memory-based agent that proposes candidates, shapes Gaussian Process priors, and incorporates natural language rules expressing qualitative design knowledge. Two AI-assisted trials are compared against a multi-objective Artificial Hummingbird Algorithm benchmark, RAG + BO with and without natural language input. All three methods converge to a similar Pareto region with average torque around 5.4–5.7 Nm, torque ripple of approximately 12.8–14.2%, and efficiency near 93.3–93.6%, suitable for geared e-bike drives. The LLM-guided trial achieves this performance with a 20.1% reduction in simulation expenses relative to the BO baseline and by about 48% compared to the Artificial Hummingbird Algorithm. The results demonstrate that integrating LLM guidance into Bayesian optimization improves sample efficiency while providing interpretable design trends for PMSM topologies tailored for light electric vehicles. Full article

With the rapid development of intelligent computing and artificial intelligence, there is an increasing demand for efficient, interpretable, and interactive frameworks for fine-grained text analysis. In the field of argument mining, existing approaches are often constrained by sentence-level processing, limited exploitation of key linguistic markers, and a lack of human–AI collaborative mechanisms, which restrict both recognition accuracy and computational efficiency. To address these challenges, this paper proposes KFF-Transformer, a computing-oriented human–AI collaborative framework for fine-grained argument element identification based on Toulmin’s model. The framework first employs an automatic key marker mining algorithm to expand a seed set of expert-labeled linguistic cues, significantly enhancing coverage and diversity. It then employs a lightweight deep learning architecture that combines BERT for contextual token encoding with a BiLSTM network enhanced by an attention mechanism to perform word-level classification of the six Toulmin elements. This approach leverages enriched key markers as critical features, enhancing both accuracy and interpretability. It should be noted that while our framework leverages BERT—a Transformer-based encoder—for contextual representation, the core sequence labeling module is based on BiLSTM and does not implement a standard Transformer block. Furthermore, a human-in-the-loop interaction mechanism is embedded to support real-time user correction and adaptive system refinement, improving robustness and practical usability. Experiments conducted on a dataset of 180 English argumentative essays demonstrate that KFF-Transformer identifies key markers in 1145 sentences and achieves an accuracy of 72.2% and an F1-score of 66.7%, outperforming a strong baseline by 3.7% and 2.8%, respectively. Moreover, the framework reduces processing time by 18.9% on CPU and achieves near-real-time performance of approximately 3.3 s on GPU. These results validate that KFF-Transformer effectively integrates linguistically grounded reasoning, efficient deep learning, and interactive design, providing a scalable and trustworthy solution for intelligent argument analysis in real-world educational applications. Full article

Artificial intelligence (AI) is increasingly adopted in manufacturing for tasks such as automated inspection, predictive maintenance, and condition monitoring. However, the opaque, black-box nature of many AI models remains a major barrier to industrial trust, acceptance, and regulatory compliance. This study investigates how explainable artificial intelligence (XAI) techniques can be used to systematically open and interpret the internal reasoning of AI systems commonly deployed in manufacturing, rather than to optimise or compare model performance. A unified explainability-centred framework is proposed and applied across three representative manufacturing use cases encompassing heterogeneous data modalities and learning paradigms: vision-based classification of casting defects, vision-based localisation of metal surface defects, and unsupervised acoustic anomaly detection for machine condition monitoring. Diverse models are intentionally employed as representative black-box decision-makers to evaluate whether XAI methods can provide consistent, physically meaningful explanations independent of model architecture, task formulation, or supervision strategy. A range of established XAI techniques, including Grad-CAM, Integrated Gradients, Saliency Maps, Occlusion Sensitivity, and SHAP, are applied to expose model attention, feature relevance, and decision drivers across visual and acoustic domains. The results demonstrate that XAI enables alignment between model behaviour and physically interpretable defect and fault mechanisms, supporting transparent, auditable, and human-interpretable decision-making. By positioning explainability as a core operational requirement rather than a post hoc visual aid, this work contributes a cross-modal framework for trustworthy AI in manufacturing, aligned with Industry 5.0 principles, human-in-the-loop oversight, and emerging expectations for transparent and accountable industrial AI systems. Full article

This paper presents a broad study on the evaluation of explanatory capabilities of machine learning models, with a focus on Decision Trees, Random Forest, and XGBoost using a pancreatic cancer data set. We use Human-in-the-Loop-related techniques and medical guidelines as a source of domain knowledge to establish the importance of the different features that are relevant to select a pancreatic cancer treatment. These features are not only used as a dimensionality reduction approach for the machine learning models but also as a way to evaluate the explainability capabilities of the different models using agnostic and non-agnostic explainability techniques. To facilitate the interpretation of explanatory results, we propose the use of similarity measures such as the Weighted Jaccard Similarity coefficient. The goal is to select not only the best performing model but also the one that can best explain its conclusions and better aligns with human domain knowledge. Full article

High Mobility Group ^{Box 1} (HMGB1) and S100 proteins are major ligands of Receptor for Advanced Glycation End-products (RAGE) and have causal roles in endometriosis lesions. Yet the AGE–RAGE pathway that unifies Advanced Glycation End-products (AGEs) with these ligands has not been assessed in endometriosis. In diabetes, atherosclerosis, and chronic kidney disease, AGE–RAGE links insulin resistance and oxidative stress to inflammation, fibrosis, and organ harm. Endometriosis shares key drivers of AGE accumulation, including insulin resistance, oxidative stress, and chronic inflammation. Endometriosis is also linked to higher vascular risk and arterial stiffness. We asked whether AGE–RAGE could bridge metabolic stress to pelvic lesions and systemic risk. We did a focused review of mechanisms and an evidence map of studies on AGEs, RAGE, or known RAGE ligands in endometriosis. We grouped findings as most consistent with a driver, amplifier, consequence, or parallel role. We included 29 studies across human samples, cell systems, and animal models. Few studies measured AGE adducts directly. Most work tracked RAGE ligands (mainly HMGB1 and S100 proteins) and downstream immune and angiogenic programs. Across models, this pattern fits best with a self-reinforcing loop after lesions form. RAGE expression often aligned with lesion remodeling, especially fibrosis. Blood and skin readouts of AGE burden were mixed and varied by cohort and sample type. A central gap is receptor proof. Many models point to shared Toll-like receptor 4 (TLR4)/ nuclear factor kappa B (NF-κB) signaling, but few test RAGE dependence. Overall, current evidence supports AGE–RAGE as a disease-amplifying loop involved in chronic inflammation and fibrosis rather than an initiating trigger. Its effects likely vary by stage and site. Priorities now include direct lesion AGE measurement, paired systemic–pelvic sampling over time, receptor-level studies, and trials testing diet or drug interventions against clear endpoints. Outcomes could include fibrosis, angiogenesis, immune state, pain, and oocyte and follicle function. Full article

Background: Persistent infection with high-risk human papillomavirus (HPV) is the key driver of cervical carcinogenesis and post-treatment recurrence. Although excisional treatment effectively removes dysplastic tissue, it does not directly target viral persistence. While HPV vaccination is well established in primary prevention, its potential role as an adjuvant strategy in HPV-positive women, particularly with respect to viral clearance, remains incompletely defined. Methods: This retrospective cohort study included HPV-positive women with at least 12 months of follow-up who were managed at a tertiary gynecology clinic. Patients were stratified according to HPV vaccination status with the nonavalent vaccine (Gardasil 9) and excisional treatment status with loop electrosurgical excision procedure (LEEP). HPV clearance at 12 months was defined as the primary outcome, while histological outcomes were evaluated as secondary and independent endpoints. Analyses were performed in the overall cohort and stratified by LEEP status. Multivariable logistic regression was used to identify factors independently associated with HPV persistence, adjusting for baseline disease severity and clinical covariates. Results: A total of 935 HPV-positive women were included in the final analysis. Completion of the three-dose HPV vaccination schedule was associated with significantly higher HPV clearance rates at 12 months compared with no vaccination. This association was consistently observed in women who underwent LEEP as well as in those managed without excisional treatment. In multivariable analysis, HPV vaccination emerged as an independent protective factor against HPV persistence, whereas LEEP status itself was not independently associated with viral clearance after adjustment for baseline histological severity. Histological outcomes differed according to baseline disease severity and did not demonstrate a direct one-to-one relationship with HPV clearance. Conclusions: Adjuvant vaccination with the nonavalent HPV vaccine is independently associated with increased HPV clearance in HPV-positive women at 1-year follow-up, irrespective of excisional treatment status. HPV clearance and histological regression represent related but distinct biological processes and should be evaluated as independent outcomes. These findings support a broader role for HPV vaccination beyond primary prevention and suggest potential clinical benefit of vaccination as an adjunctive strategy in the management of HPV-positive women. Full article

Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 $\mathrm{m}/{\mathrm{s}}^2$, exceeding the vestibular perception threshold of 0.17 $\mathrm{m}/{\mathrm{s}}^2$ defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation. Full article

Foreign direct investment (FDI) has often been cast as a straightforward engine of growth, yet its record across Central and Eastern Europe tells a more tangled story where outcomes hinge on the interplay of education, governance, and the timing of external shocks. This study embeds fixed effects panel econometrics within a systems framework, treating FDI as a subsystem of socio-economic dynamics. Using a long-run panel of eleven economies from 2000 to 2023, the analysis models path dependence and regime shifts through interaction terms and period-specific dummies set against a systems-thinking backdrop. The analysis shows that for the average CEE economy, FDI’s contribution has waxed and waned: it dragged on growth during the early transition years (2000–2007), settled into a neutral role after the global financial crisis, and proved unpredictable in the pandemic era. Romania stands out, however, with a marked “FDI premium” quantified as approximately 0.7 pp of growth per pp of FDI that seems to stem from reinforcing loops between rising tertiary enrolment and productivity spillovers. Mapping these feedbacks brings to light virtuous circles where human capital and resilience make or break the benefits of foreign capital. The policy message is plain: nurture the positive loops through investment in skills and firm linkages, keep institutions nimble enough to adapt, and watch for early warning signs of systemic strain. Full article

To achieve carbon neutrality goals, large amounts of renewable energy sources (RESs) are being integrated into power systems. In particular, high penetration of distributed photovoltaic (PV) makes distribution networks highly stochastic, calling for dynamic distribution network reconfiguration (DNR). Existing DNR approaches can be broadly categorized into model-driven optimization-based methods and learning-based methods, with deep reinforcement learning (DRL) being a representative paradigm for fast online decision-making. Existing DNR models typically belong to mixed-integer linear programming, which requires solution methods such as deep reinforcement learning (DRL). However, existing methods commonly struggle to account for human factors, i.e., the time-varying preferences of distribution network operators in DRL decisions. To this end, this paper proposes a natural language-driven, human-in-the-loop DNR framework, which combines a DRL base policy for hour-level dynamic reconfiguration with a large language model (LLM)-based instruction supervision layer. Based on this human-in-the-loop framework, commands from operators in natural language are translated into online adjustments of safety-screened DRL switching actions. Therefore, the framework demonstrates the fast, model-free decision capability of DRL while providing an explicit and interpretable interface for incorporating temporary and context-dependent operator requirements without retraining. Case studies on IEEE 16-bus and 33-bus distribution networks show that the proposed framework reduces network losses, improves voltage profiles, and limits switching operations. It also achieves markedly higher compliance with operator instructions than a conventional model-based method and a pure DRL baseline. These results highlight a viable path to embedding natural language guidance into the data-driven operation of active distribution networks. Full article

We study control synthesis under Signal Temporal Logic (STL) specifications for driving scenarios where strict rule satisfaction is not always feasible and human experts exhibit context-dependent flexibility. We represent such behavior using robustness slackness—learned rule-wise lower bounds on STL robustness—and introduce sub-goals that encode intermediate intent in the state/output space (e.g., lane-level waypoints). Prior learning-based MPC–STL methods typically infer slackness with VAE priors and plug it into MPC, but these priors can underrepresent multimodal and rare yet valid expert behaviors and do not explicitly model intermediate intent. We propose a diffusion-guided MPC–STL framework that jointly learns slackness and sub-goals from demonstrations and integrates both into STL-constrained MPC. A conditional diffusion model generates pairs of (rule-wise slackness, sub-goal) conditioned on features from the ego vehicle, surrounding traffic, and road context. At run time, a few denoising steps produce samples for the current situation; slackness values define soft STL margins, while sub-goals shape the MPC objective via a terminal (optionally stage) cost, enabling context-dependent trade-offs between rule relaxation and task completion. In closed-loop simulations on held-out highD track-driving scenarios, our method improves task success and yields more realistic lane-changing behavior compared to imitation-learning baselines and MPC–STL variants using CVAE slackness or strict rule enforcement, while remaining computationally tractable for receding-horizon MPC in our experimental setting. Full article

Insulin resistance (IR) is a central driver of cardiometabolic disease and an increasingly recognized modifier of inflammatory and vascular pathology. Beyond impaired glucose homeostasis, IR emerges from chronic, metabolically induced inflammation (“meta-inflammation”) and convergent cellular stress programs that propagate across tissues and organ systems, ultimately shaping endothelial dysfunction, atherogenesis, and cardiometabolic complications. Here, we synthesize multilevel links between insulin receptor signaling, intracellular stress modules (oxidative, endoplasmic reticulum, inflammatory, and fibrotic pathways), tissue-level dysfunction, and systemic inflammatory amplification. This work is a conceptual narrative review informed by targeted database searches and citation tracking, with explicit separation of mechanistic/experimental evidence from human observational and interventional data; causal inferences are framed primarily on mechanistic and interventional findings, whereas associative statements are reserved for observational evidence. We propose an integrative framework in which stress-response pathways are context-dependent and become maladaptive when chronically activated under nutrient excess and persistent inflammatory cues, generating self-reinforcing loops between IR and inflammation that accelerate vascular injury. This framework highlights points of convergence that can guide mechanistic prioritization and translational hypothesis testing. Full article