Search Results (23,037)

Background/Objectives: To quantify end-to-end timeliness of the blood culture (BC) diagnostic workflow over one month using operational key performance indicators (KPIs)—transportation time (TT), time to detection (TTD), time to preliminary report (TTPR), and time to antimicrobial susceptibility testing (AST; TTAST)—and to identify actionable bottlenecks. Methods: This retrospective observational analysis included BC bottles processed between 29 September and 29 October 2023 at a large tertiary-care hospital in Italy. KPIs were computed from laboratory information system (LIS) timestamps and structured observations and were summarized as medians (interquartile range [IQR]). Results: 44.7% (2290/5121) of bottles reached the laboratory within 2 h (median 2.2 h, IQR 1.3–3.7), suggesting pre-analytical delays. Among adult bottles (n = 4995), 68.9% were underfilled (<8 mL), 12.9% met the 8–10 mL target, and 18.2% were overfilled (>10 mL). There were 932 positive bottles (18.2%), with a nocturnal peak in instrument flags despite reduced staffing. Median TTD was 12.6 h (IQR 8.9–18.4), with earlier detection for Gram-negatives than Gram-positives and yeasts (11.9, 14.5, and 30.9 h). In bacterial-positive bottles with complete timestamps (n = 294), median TTPR was 3.8 h (IQR 1.7–8.8); median TTAST was 19.2 h (IQR 14.3–27.8). From collection, median times were 17.9 h (IQR 14.2–23.1) to the preliminary report and 36.0 h (IQR 28.8–48.7) to the AST result. Conclusions: Within-laboratory steps were generally rapid, whereas transport planning and collection volumes emerged as major bottlenecks. Targeted interventions—enforcing ≤2 h TT and training to achieve an 8–10 mL fill—should further improve BC turnaround time. Full article

A polyvinyl chloride (PVC)-based tissue-mimicking polymer phantom was developed and mechanically characterised to replicate stiffness ranges relevant to breast elastography and to provide a controlled platform for evaluating shear wave elastography (SWE) measurements. SWE provides quantitative stiffness information that complements B-mode ultrasound in breast imaging. However, measurement variability related to operator technique and tissue continues to limit confidence in clinical interpretation. This study evaluates the reproducibility of SWE using custom-fabricated PVC-based breast phantoms with mechanically defined stiffness properties. Two PVC-based breast phantoms with identical geometry and different background stiffnesses were scanned using a single ultrasound system under a fixed SWE protocol. Each phantom contained four embedded inclusions representing clinically relevant stiffness categories. Six breast imagers independently acquired repeated SWE measurements in transverse and longitudinal planes, blinded to lesion identity and ground truth. Inter-operator reproducibility was assessed using intraclass correlation coefficients, and was high across both phantom backgrounds, with low intra-operator variability following quality assurance exclusion of one dataset due to sampling error. Measurement variability was lowest for solid inclusions and increased for the cyst-like inclusion in the stiffer background. SWE measurements consistently preserved the relative stiffness ordering of inclusions, although absolute values differed systematically from mechanically derived ground-truth stiffness. These findings demonstrate that PVC-based polymer phantoms provide a stable and reproducible platform for evaluating SWE measurement behaviour under controlled conditions. By isolating operator and acquisition effects from biological variability, this polymer-based framework supports methodological standardisation and structured operator training in breast elastography. Full article

Delayed switching faults in hydraulic directional control valves can significantly degrade system performance and reliability, yet their diagnosis remains challenging due to complex fault mechanisms and coupled sensor responses and limited fault samples in industrial applications. While data-driven approaches, including deep learning-based methods, have shown promising performance in fault diagnosis, their practical deployment in industrial quality inspection and condition monitoring is often constrained by limited fault data availability and insufficient physical interpretability of the diagnostic results. In this study, an interpretable fault diagnosis framework for delayed switching faults in hydraulic directional control valves is proposed based on a simulation-guided feature construction method and multi-pressure signal analysis. Instead of using simulation to generate synthetic training data, a physical simulation model is employed to analyze fault mechanisms and to guide the design of valve-level diagnostic features derived from inter-sensor pressure differences. These features are further evaluated using several classical machine learning classifiers, including RF, SVM, KNN, and LR under conditions of limited fault samples. Experimental results demonstrate that the proposed method effectively captures the structural imbalance caused by internal valve faults and achieves high diagnostic accuracy and robustness compared with conventional single-sensor approaches and purely data-driven black-box models. The proposed framework provides a practical and physically interpretable solution for hydraulic valve fault diagnosis under small-sample conditions and offers potential value for industrial quality inspection and maintenance applications. Full article

In this study, supervised machine learning (ML) regression models are employed to predict water uptake during the sorption process in a sorption reactor for thermal energy storage applications. Two main methods are used to study sorption storage systems: experimental studies and numerical simulations. Experimental studies involve physical testing and measurements but are often costly and time-consuming. Numerical simulations are more flexible and cost-effective, though they can require significant computational resources for large or complex systems. To address these challenges, researchers are increasingly employing various machine learning techniques, which offer strong potential for data analysis and predictive modeling. In this study, CFD-based sorption simulations are integrated with machine learning models to predict the spatiotemporal evolution of water uptake. Several ML techniques including support vector regression (SVR), Random Forest, XGBoost, CatBoost (gradient boosting decision trees), and multilayer perceptron neural networks (MLPs) are evaluated and compared. A fixed-bed reactor equipped with fins and tubes is considered within a closed adsorption thermal storage system. Numerical simulations are conducted for three different fin lengths (10 mm, 25 mm, and 35 mm) to generate a comprehensive dataset for training the ML models and capturing the complex temporal evolution of water uptake, thereby enabling predictions for unseen fin geometries. The results indicate that neural network-based models achieve superior predictive performance compared to the other methods. For water uptake training, the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination $\left(R^2\right)$ are approximately 2.83, 4.37, and 0.91, respectively. The predicted water uptake shows close agreement with the numerical simulation results. For the prediction cases, the MAE, MSE, and $R^2$ values are approximately 1.13, 1.2, and 0.8, respectively. Overall, the study demonstrates that machine learning models can accurately predict water uptake beyond the training dataset, indicating strong generalization capability and significant potential for improving thermal management system design. Additionally, the proposed approach reduces simulation time and computational cost while providing an efficient and reliable framework for modeling complex sorption processes in thermal energy storage systems. Full article

Advances in Generative Artificial Intelligence have enabled the development of models capable of generating text, images, and audio that are similar to what humans can create. These models often have valuable general knowledge thanks to their training on large datasets. Through fine-tuning or prompt-based adaptation, this knowledge can be applied to specific tasks. In this work, we propose a modular approach to automated news generation using Large Language Models, composed of an information retrieval module and a text generation module. The proposed system leverages both publicly available (open-weight) and proprietary Large Language Models, enabling a comparative evaluation of their behavior within the proposed news generation pipeline. We describe the experiments carried out with a total of five representative Large Language Models spanning both categories, detailing their configurations and performance. The results demonstrate the feasibility of using Large Language Models to automate this task and identify systematic differences in behavior across model categories, as well as the problems that remain to be solved to enable fully autonomous news generation. Full article

Active non-line-of-sight (NLOS) human detection aims to infer the presence of hidden individuals by analyzing indirectly reflected photons between a relay surface and occluded targets. In this study, a single-photon avalanche diode (SPAD) and time-correlated single-photon counting (TCSPC)-based acquisition system were used to measure time–photon waveforms in controlled NLOS environments designed to represent post-disaster rubble scenarios. Although the effective temporal resolution of the system is limited by the detector timing jitter and laser pulse width, the recorded transient signals retain distinguishable intensity and temporal delay patterns associated with the primary and secondary reflections. To construct a representative dataset, measurements were collected under varying subject poses, orientations, and surrounding object configurations. The recorded signals were processed using a unified preprocessing pipeline that included normalization, histogram shaping, and signal windowing. Three machine learning models, namely, Convolutional Neural Network, Gated Recurrent Unit, and Random Forest, were trained and evaluated for human presence classification. All models achieved full sensitivity in detecting human presence; however, notable differences emerged in the classification of human-absent scenarios. Among the tested approaches, random forest achieved the highest overall accuracy and specificity, demonstrating stronger robustness to statistical variations in time–photon histograms under limited photon conditions. These results suggest that tree-based classifiers capture amplitude distribution patterns and temporal dispersion characteristics more effectively than deep neural architectures under the present acquisition constraints. Overall, the findings indicate that low-cost SPAD-based NLOS sensing systems can provide reliable human detection in indirect-observation scenarios. Full article

Construction accident reports contain rich causal evidence; however, their unstructured narratives make systematic analysis difficult. Recent advances in large language models (LLMs) have created new opportunities to leverage such information at scale. This study develops an integrated LLM–BERT–network framework for analyzing construction accident causation. Based on 347 official construction accident investigation reports, a DeepSeek-based pipeline with human-in-the-loop quality control was used to extract causal keywords describing direct and indirect causes, yielding 2572 keywords. A BERT-based semantic normalization procedure then consolidated synonymous expressions, reducing 811 deduplicated keywords to 104 normalized terms (an 87.2% reduction in vocabulary size). A manual sample-based evaluation further supported the reliability of the LLM-based extraction and BERT-based normalization procedures. The normalized keywords were further organized into a hierarchical taxonomy and used to construct a directed keyword-association network linking indirect and direct causes for structured relational analysis. To strengthen methodological rigor, additional validation and analytical experiments were conducted, including manual sample-based evaluation of keyword extraction, sensitivity analysis of normalization settings, and examination of representative failure cases. The results support the reliability and robustness of the proposed framework. The analysis indicates that behavior-related factors and management deficiencies occupy structurally important positions in the directed network. Overall, the findings suggest that construction accidents arise from the interaction of human, managerial, environmental, material, and technical factors rather than isolated single causes. Effective prevention therefore requires system-oriented interventions that strengthen worker competence, supervision, training, accountability, and hazard identification. Full article

Background: Exercise modulates the immune system and may enhance anti-cancer activity, offering potential synergy with cancer immunotherapy. Tumours with low immune cell infiltration (“cold” tumours) often respond poorly to immunotherapy and are associated with poor prognosis. Here, we demonstrate that exercise can reshape the immune landscape of tumours across the cold spectrum. Methods: C57BL/6 mice underwent orthotopic implantation of PANC02 (murine pancreatic adenocarcinoma) cells and BALB/c mice underwent intraperitoneal injections of AB-1 (murine mesothelioma) cells. Mice were then divided into groups; exercise with anti-Programmed Cell Death Protein 1 (PD-1), exercise with isotype, no exercise with anti-PD-1 and no exercise with isotype. Treadmill-running was performed for 20 min/day, 4 days/week at a speed of 12 metres/minute. Resistance training consisted of hanging upside down on a wire-mesh screen for 1 min 2 days/week. Flow cytometry was used to measure TME immune populations. Tumour and liver samples were harvested, paraffin wax-embedded/sectioned and analysed using SlideViewer 2.9.0™. A total of 22 healthy volunteers underwent a single bout of high-intensity interval cycling. Blood was collected pre- and post-exercise. Flow cytometry was used to measure leucocyte subpopulations. MSTO-211H (mesothelioma) and PANC-1 (pancreatic cancer) cells were cultured with pre- and post-exercise serum, with/without HSV1716, and viability determined using alamarBlue^®. PANC-1 apoptosis and migration were assessed using caspase-3/7 and scratch assays, respectively. Results: In an orthotopic pancreatic cancer mouse model, combining exercise with immunotherapy significantly increased tumour necrosis and reduced metastatic potential. In both pancreatic cancer and mesothelioma models, this combination remodelled the tumour microenvironment, enhancing cytotoxic CD8⁺ T cell infiltration, upregulating Programmed Cell Death Protein 1 (PD-1), and reducing Myeloid-Derived Suppressor Cells and regulatory T cells (Tregs). Complementary human studies revealed an acute systemic release of Natural Killer cells and a reduction in Tregs following high-intensity interval exercise in healthy volunteers. Moreover, exercise-conditioned serum from these participants exerted anti-cancer effects on pancreatic cancer and mesothelioma cell lines. Conclusions: Altogether, these findings highlight exercise as a promising adjunct to immunotherapy for poorly immunogenic cancers such as pancreatic cancer and mesothelioma. Full article

Under severe ice and snow weather, ice-covered pantograph–catenary arcs affect the safe operation of high-speed trains. This study investigates the impact of ice-covered arc electrical characteristics, plasma parameters, and material ablation mechanisms. By constructing a comprehensive pantograph–catenary icing experimental platform, arc voltage, current signals, high-speed dynamic images, and emission spectra were synchronously collected under different icing thicknesses ranging from 0 to 15 mm. Research indicates that ice coverture causes frequent “extinction–reignition” phenomena during the arc initiation stage due to the latent heat absorbed by melting ice, significantly reducing the initial stability of arc combustion. Spectral analysis confirms that the arc excitation temperature and energy density are positively correlated with the concentration of hydrogen ions produced by water vapor ionization, reaching a peak under the 5 mm icing condition. Experimental results show that the average energy density of ice-covered arcs is approximately double that of the non-iced condition, causing the ablation pits on the carbon strip to exhibit characteristics of greater depth and wider copper deposition zones. This study reveals the unique mechanisms and damage characteristics of icing pantograph–catenary arcs, providing an important basis for the safe design and maintenance of pantograph–catenary systems in high-cold railway environments. Full article

This study presents an all-optical artificial synapse based on 2D perovskite materials for neuromorphic visual simulation. While conventional optoelectronic synapses, which integrate memory and processing, are prevalent in this field, their inherent optical-to-electrical conversion during signal processing incurs significant energy costs. In contrast, our proposed device operates purely in the optical domain. Under ultraviolet–visible light control, the change in light transmittance of this device can simulate various key biological synaptic plasticity behaviors, including paired-pulse facilitation and learning ability. By integrating these devices into a 28 × 28 synaptic array, we constructed an artificial neural network that mimics the experience-driven enhancement characteristic of human visual perceptual learning. Under light-responsive regulation, the system optimized image recognition learning behavior, and after multiple training sessions, the recognition accuracy stabilized above 97%. This study is based on two-dimensional perovskite materials and provides a new material platform for realizing intelligent visual systems with adaptive learning capabilities. Full article

Remote sensing object detection (RSOD) models play an increasingly important role in modern remote sensing systems. However, during model delivery, sharing, and deployment, RSOD models face increasing risks of unauthorized redistribution, illegal replication, and intellectual property infringement. To mitigate these threats, this paper proposes a white-box watermarking framework for RSOD models that enables reliable copyright verification while preserving the performance of the primary detection task. Specifically, a gradient-based sensitivity analysis of the detection loss is first performed to adaptively identify model parameters that minimally affect detection performance, which are then selected as watermark carriers. Subsequently, a parameter-ranking-based watermark encoding scheme is developed, where watermark bits are embedded by enforcing relative ordering constraints between parameter pairs. To further improve robustness under practical deployment conditions, an attack-simulation-driven training strategy is introduced, in which common perturbations and watermark removal attacks are simulated during the embedding process. In addition, a stealthiness enhancement strategy based on statistical distribution constraints is designed to maintain consistency between the distribution of watermarked parameters and those of the original model, thereby reducing the risk of watermark exposure and localization. Extensive experiments across multiple RSOD datasets and detection architectures demonstrate that the proposed method achieves a high copyright verification success rate with negligible impact on detection accuracy and exhibits strong robustness and stealthiness against a variety of watermark removal attacks. Full article

Background: Predicting genomic alterations from routine hematoxylin and eosin (H&E) whole-slide images (WSIs) may help triage molecular testing. Methods: We retrospectively enrolled 437 patients at Osaka Metropolitan University Hospital across 26 cancers, matched with clinical gene-panel data. We curated 1023 binary endpoints across SNV, CNV, and SV categories. We extracted slide embeddings from five pathology foundation models (Prism, GigaPath, Feather, Chief, and Titan) using a unified feature extraction pipeline and benchmarked them using a lightweight downstream Multi-Layer Perceptron (MLP) classifier. Using the best-performing patch feature system, we trained a multi-instance learning model to assess incremental benefit. Results: Titan achieved the highest and most stable transfer performance, with a median endpoint-wise Area Under the Receiver Operating Characteristic curve (AUROC) of 0.77 in the slide benchmarking; at the patch-level, prediction of APC_SNV reached an AUROC of 0.916, and prediction of KRAS_SNV reached an AUROC of 0.811 on the held-out test set. Conclusions: In a heterogeneous clinical gene-panel setting, pathology foundation models can provide strong baseline genomic-prediction signals without additional fine-tuning. We propose a practical, deployment-oriented two-stage workflow: rapid slide-embedding screening to prioritize robust representations and candidate endpoints, followed by patch-level training for high-value tasks where additional performance gains and interpretable regions are clinically worthwhile. Full article

Introduction: Artificial intelligence (AI) and robot-assisted platforms are increasingly influencing cardiothoracic surgery. AI enhances risk prediction, imaging interpretation, and early complication detection, while robotics improves visualization, dexterity, and minimally invasive access. This systematic review evaluates the current evidence supporting these technologies and their implications for clinical practice. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Web of Science, and Google Scholar (January 2000–May 2025) following PRISMA 2020 guidelines. After screening and eligibility assessment, 67 studies met predefined inclusion criteria and were incorporated into the qualitative synthesis. Additional high-impact reviews and consensus documents were consulted for contextual interpretation. Results: Machine learning models demonstrated modest but consistent improvements in predictive performance compared with EuroSCORE II and STS scores, particularly in high-risk cohorts. Robot-assisted mitral and coronary procedures showed reduced postoperative pain, blood loss, ICU stay, and recovery time in experienced centers, though early learning phases were associated with longer operative, cross-clamp, and bypass times. AI-enabled intraoperative tools, such as video analysis, workflow recognition, and real-time anatomical segmentation, emerged as promising adjuncts for surgical precision. Structured robotic training programs, especially simulation-based and dual-console pathways, accelerated proficiency acquisition. Conclusions: AI and robotic systems act as augmentative technologies that enhance rather than replace the surgeon’s role. Their safe and effective adoption requires standardized training, transparent AI decision pathways, and clear ethical and medico-legal governance. Full article

Background/Objectives: The aim of the study was to evaluate the diagnostic accuracy of a commercial artificial intelligence (AI) algorithm originally developed for screening mammography when applied to symptomatic women presenting to a tertiary outpatient clinic. Methods: This single-center, retrospective diagnostic accuracy study included women who presented with breast symptoms to a tertiary outpatient clinic between January and June 2013 and underwent digital mammography. An AI algorithm cleared by the U.S. Food and Drug Administration (FDA)-cleared AI algorithm was applied to all mammograms and generated continuous malignancy scores ranging from 1 to 100. Mammographic breast density was classified according to the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) by two experienced radiologists. Histopathology, when available, or otherwise a minimum of 2 years of clinical and imaging follow-up served as the reference standard. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis with calculation of the area under the curve (AUC) and 95% confidence intervals (CI) derived by patient level bootstrap resampling (n = 2000). Analyses were performed for the overall cohort and stratified by breast density (non-dense [BI-RADS A–B] vs. dense [BI-RADS C–D]). Results: A total of 78 women (mean age, 55 ± 11 years) were included, of whom 16 had histopathological verification of suspicious lesions with proven breast cancer in 14 patients and 62 were classified based on follow-up alone. In the overall cohort (156 breasts, including 15 breasts with malignancies), the AI algorithm achieved an AUC of 0.96 (95% CI: 0.86–1.00). Performance remained high in non-dense breasts (AUC = 0.96; 95% CI: 0.88–1.00) and dense breasts (AUC = 0.99; 95% CI: 0.93–1.00), with no statistically significant difference observed between density subgroups (DeLong test, p = 0.36), although subgroup comparisons were underpowered. Decision curve analysis suggested a consistent positive net benefit across a wide range of threshold probabilities in both density groups. Conclusions: In this preliminary, single-center retrospective cohort, a screening-trained AI algorithm showed promising diagnostic accuracy when applied to symptomatic mammograms. These findings require validation in larger, contemporary, multicenter cohorts before clinical implementation. Full article

This review examines what constitutes heart rate variability (HRV), the relationship between HRV and the autonomic nervous system, and the physiology driving HRV. HRV is correlated with vagal nerve activity and parasympathetic nervous activation. Higher HRV is correlated with youth, active lifestyle, adaptive capacity, and good health. Next, the review examines the medical significance of HRV, especially the correlation between lower HRV and the presence of medical and psychological disorders. In general, HRV serves as a biomarker for health and disease, an index of autonomic nervous system dysregulation, an index of prefrontal cortical functionality, and a marker for psychopathology across diagnoses. Higher HRV is associated with several characteristics associated with successful psychotherapy and hypnotherapy: social engagement, compassion, emotional regulation, and cognitive flexibility. Given this association, somatic regulation should be regarded as integral to treatment alongside psychotherapy and hypnosis. Understanding HRV can enable the psychotherapist and hypnotherapist to optimize treatment. In effect, the therapist can harness the power of the brain and nervous system to better prepare the patient for therapy and to enhance the process of therapy. This review encourages therapists to utilize several strategies and interventions to increase patients’ HRV levels prior to and during therapy. The review will be most applicable for those hypnotherapists who integrate hypnosis into counseling and psychotherapy. The review describes the process by which HRV biofeedback training guides the individual to voluntarily increase HRV. It also identifies a number of lifestyle parameters and self-care practices (including self-hypnosis) that increase HRV. Encouraging lifestyle and self-care practices to increase HRV can support a greater response to hypnotherapy and psychotherapy. With additional training, hypnotherapists can integrate HRV biofeedback into a hypnosis practice. Further, several simple interventions already within the scope of most hypnosis practitioners can be utilized to enhance HRV at the beginning of a hypnotherapy process, and again during the process of therapy. Full article