Search Results (200)

Background/Objectives: Overcrowding in the emergency department (ED) is a global health issue. Early prediction of expected hospitalizations, based on parameters available from triage, is essential to enhance patient transfer from the ED to departments, thereby reducing ED congestion. Methods: A historical cohort study included patients who visited two tertiary referral medical centers located in the center of Israel. Data derived from one medical center (MC-A) was used to build the prediction model and to test it, and data from the second medical center (MC-B) was used to validate it. Variables collected included age, sex, triage level, vital signs, initial admitting diagnosis, medical referrals, mode of arrival, time of arrival according to hospital shifts (morning, evening, and night), weekday (workdays/weekend), season, fall risk assessment, and significant comorbidities. Logistic regression was used to build the model, and the area under the ROC curve (AUC) and the discrimination slope (DS) were used to evaluate it. Results: The final cohort included 1436 patients: 1256 patients from MC-A and 180 from MC-B. The patients were divided randomly into a learning group (n = 879), a test group (n = 377), and a validation group (n = 180). We found that higher triage level (urgent+: OR 1.45, p = 0.039), lower O₂ saturation (<95%: OR 3.32, p < 0.001), malignancy (OR 1.81, p = 0.044), cardiovascular disease (OR 2.93, p < 0.001), neurologic illness (OR 2.07, p = 0.014), arrival during the weekend (OR 1.57, p = 0.014), and fall season (OR 1.81, p = 0.003) were associated with higher probability of hospital admission. Our model showed a similar acceptable discrimination ability in all groups (learning: AUC = 0.77, 95%CI 0.73–0.80, and DS = 19%; testing: AUC = 0.76, 95%CI 0.70–0.82, and DS = 17%; validation: AUC = 0.71, 95%CI 0.61–0.80, and DS = 18%). Conclusions: The proposed prediction model can be easily implemented in hospital systems to provide management with an expected number of ED patient hospitalizations in the coming hours. The model can enhance patient flow, thereby reducing crowding in the ED. Full article

Therapeutics based on RNA are commonly produced via biocatalytic approaches using RNA polymerases. The most frequently applied enzyme is the RNA polymerase of Enterobacteria phage T7. However, this enzyme has unfavorable properties, like the formation of double-stranded RNA (dsRNA). This undesired by-product can activate the innate immune system via pattern recognition receptors and cause inflammation. Removal of the contaminant is time-consuming and expensive. In this work, we applied a genome mining approach to identify unidentified single-subunit RNA polymerases with minimal dsRNA generation. A large meta database was screened, and 74 sequences were selected. Two RNA polymerases generating barely detectable amounts of dsRNA were identified from the initial sequence portfolio. Their promoters were detected via a fluorescent RNA aptamer screening, and slightly acidic transcription conditions were established. Further activity characterization showed a significant reduction of dsRNA to 0.001% and 0.02%. Due to these beneficial attributes, these RNA polymerases generate mRNA with enhanced stability, which most likely lowers the immune response towards the desired mRNA. This could be especially useful for producing long RNAs, such as self-amplifying RNA, as these typically require improved stability and low dsRNA content. Full article

Accurate acquisition of low-observable targets with a minimal radar cross-section (RCS) poses a significant challenge for multi-source remote sensing systems, such as integrated radar–electro-optical (REO) platforms, particularly in complex electromagnetic environments characterized by strong noise interference and a high false-alarm rate. Conventional methods, which often treat data association and fusion from heterogeneous sensors as separate, offline processes, struggle with the dynamic uncertainties and real-time decision requirements of such scenarios. To address these limitations, this paper proposes a novel Evidence–Reinforcement Learning-based Decision and Control (ERL-DC) framework. It operates through a closed-loop architecture consisting of three core modules: A static assessment model for initial target prioritization, a Dempster–Shafer (D–S) evidence-based multi-source data decision generator for dynamic information fusion and uncertainty-aware target selection, and a Deep Reinforcement Learning (DRL) controller for noise-robust sensor steering. A high-fidelity simulation environment was developed to model the multi-source data stream, encompassing radar detection with clutter and false targets, as well as the physical constraints of the electro-optical (EO) servo system. Based on the averaged results from multiple Monte Carlo simulations, the proposed ERL-DC framework reduced the Average Decision Time (ADT) from 7.51 s to 4.53 s, corresponding to an absolute reduction of 2.98 s when compared to the conventional method integrating threshold logic with Model Predictive Control (MPC). Furthermore, the Net Discrimination Accuracy (NDA), derived from the statistical outcomes across all the simulation runs, exhibited an absolute increase of 37.8 percentage points, rising from 57.8% to 95.6%. These results indicate that ERL-DC achieves a more favorable trade-off in terms of scheduling efficiency, decision robustness, and resource utilization. The primary contribution is an intelligent, closed-loop architecture that tightly couples high-level evidential reasoning for multi-source data fusion with low-level adaptive control. Within the simulated environment characterized by clutter, false targets, and angular measurement noise, ERL-DC demonstrates improved target discrimination accuracy and decision efficiency compared to conventional methods. Future work will focus on online parameter adaptation and validation on physical platforms. Full article

Background: Percutaneous left atrial appendage closure (LAAC) provides an alternative to oral anticoagulation (OAC) in atrial fibrillation (AF) patients who are at high bleeding risk. Prior studies have suggested sex-related differences in procedural outcomes, with women demonstrating higher peri-procedural complication rates. Data on long-term outcomes, however, remain inconsistent. Methods: We analyzed 407 consecutive patients with AF who underwent LAAC between 2010 and 2023 in four Israeli medical centers participating in the LEADER registry. Baseline characteristics, procedural data, and clinical outcomes were compared between men and women. The primary efficacy endpoint was ischemic stroke or systemic embolism at 1 year. The primary safety endpoint was a composite of all-cause mortality, procedural complications, or major bleeding at 1 year. Results: Of 407 patients, 285 (70%) were men and 122 (30%) were women. The mean age was 77 ± 8.4 years with similar CHA₂DS₂-VASc and HAS-BLED scores across sexes. Device implantation exceeded 99% in both sexes. Major peri-procedural complications occurred in 6.4% overall, without significant sex-based differences (men 7.0%, women 4.9%, p = 0.51). At 1-year follow-up, Kaplan–Meier estimates for the primary efficacy endpoint of ischemic stroke/systemic embolism (2.6%), the primary safety endpoint (19.2%), major bleeding (8.9%), and all-cause mortality (9.3%) were comparable between men and women (all p > 0.1). Conclusions: In contrast to prior large registries reporting higher peri-procedural risk in women, this real-world multicenter experience demonstrated no significant sex differences in either peri-procedural or long-term outcomes following LAAC. These findings support LAAC as an effective and safe stroke-prevention strategy in AF, irrespective of sex. Full article

Background: Preterm birth remains a major cause of perinatal morbidity and long-term developmental complications. Existing prediction methods often lack individualized assessment and have limited capability to integrate multi-source maternal–fetal information. This study aims to develop a personalized preterm birth risk prediction model and to construct a visual, interactive digital twin platform that enhances clinical communication and supports early risk identification. Methods: A total of 1157 structured clinical records collected from 2020 to 2024 were preprocessed through automated feature typing, missing-value handling, and normalization. Two complementary machine-learning models—FT-Transformer and Light Gradient Boosting Machine (LightGBM)—were trained and calibrated to produce probabilities. Their outputs were fused using a Stacking Logistic Regression framework to improve prediction stability and calibration. A 3D visualization module was developed using 3ds Max, PyQt6, and PyVista to generate personalized uterine–fetal models based on fetal position, placental location, and Biparietal Diameter (BPD), enabling synchronized display of prediction results. Results: The fused model achieved an AUC of 0.820, PR-AUC of 0.405, a Brier score of 0.040, and an expected calibration error (ECE) of 3.39 × 10⁻³, demonstrating superior discrimination and probability reliability compared with single models. The interactive platform supports real-time data input, risk prediction, and adaptive 3D rendering, providing clear and intuitive visual feedback for clinical interpretation. Conclusions: The integration of machine learning fusion and digital twin visualization enables individualized assessment of preterm birth risk. The system improves model accuracy, enhances interpretability, and offers a practical tool for clinical follow-up, risk counseling, and maternal health education. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

Air-to-air (A2A) and air-to-ground (A2G) communication links are typical link types for unmanned aerial vehicle (UAV) communication networks, where radio propagation channels are fundamental for the design and optimization of corresponding communication systems. In this paper, a UAV channel measurement system based on two unmanned aerial vehicles (UAVs) is developed, which is capable of simultaneous A2A and A2G measurements. This system adopts an integrated hardware and signal processing architecture that ensures time and frequency synchronization among multiple aerial and ground nodes. Several data postprocessing steps, including the back-to-back calibration, sliding-correlation-based channel impulse response (CIR) extraction, and constant false alarm rate (CFAR)-based multi-path extraction, are performed to achieve accurate channel data. A channel emulator is used to validate the accuracy of the developed system. Finally, the developed channel measurement system is applied to conduct field channel measurements in a campus scenario. Measured channel characteristics, including path loss (PL), shadow fading (SF), Rician K-factor, root mean square delay spread (RMS-DS), and small-scale fading (SSF) are analyzed, which reveal distinct propagation behaviors between the A2A and A2G channels. These results provide valuable experimental insights and channel measurement data for modeling UAV channels. Full article

Psoriasis and psoriatic arthritis (PsA) occasionally coexist with antinuclear antibody (ANA) positivity, cutaneous lupus erythematosus (CLE), or systemic lupus erythematosus (SLE), creating one of the most challenging therapeutic overlap scenarios in immunodermatology. Divergent immune pathways—IL-23/Th17-driven psoriatic inflammation versus type I interferon-mediated autoimmunity—generate unique vulnerabilities when systemic treatments are used. To synthesize treatment outcomes, lupus-related safety signals, and mechanistic insights across systemic therapies in patients with psoriasis or PsA who also exhibit ANA positivity, CLE, or SLE. A systematic review following PRISMA 2020 guidelines was conducted across PubMed/MEDLINE, Embase, the Cochrane Library, Scopus, and ClinicalTrials.gov from database inception through 31 October 2025. Thirty-three eligible reports (29 unique clinical studies; 1429 patients) were included and organized into six prespecified overlap subgroups. Mechanistic and translational studies—including ustekinumab and deucravacitinib SLE trial data and reports of IL-17 inhibitor-associated CLE—were reviewed separately to provide contextual interpretation. IL-23 inhibitors were consistently associated with a favorable cross-disease safety profile, with no clear signal for CLE worsening, SLE flares, or drug-induced autoimmunity. IL-17 inhibitors maintained strong psoriatic efficacy but were associated with an increased frequency of de novo or exacerbated CLE. TNF-α inhibitors showed the strongest association with ANA seroconversion, anti-dsDNA induction, drug-induced lupus, and lupus flares. Ustekinumab demonstrated a stable safety profile across lupus-spectrum disease despite variable efficacy in formal SLE trials. TYK2 inhibition provided dual modulation of IL-23 and type I interferon pathways and showed emerging utility in psoriasis or PsA coexisting with CLE or SLE. Apremilast, methotrexate, and mycophenolate mofetil remained reliable non-biologic systemic options. Phototherapy was associated with potential risk in ANA-positive or lupus-susceptible populations and therefore requires careful consideration. Interpretation is limited by the predominantly observational nature and heterogeneity of the available evidence. IL-23 inhibition and TYK2 inhibition appear to offer a balanced profile of efficacy and lupus-related safety in psoriatic disease complicated by lupus-spectrum autoimmunity. IL-17 inhibitors and TNF-α inhibitors may be associated with higher risk in CLE- or SLE-prone patients and therefore warrant particular caution. Personalized treatment strategies should integrate the relative dominance of psoriatic versus lupus disease, ANA/ENA profile, CLE subtype, and underlying mechanistic considerations. Prospective, biomarker-driven studies are needed to guide therapy in this increasingly recognized overlap population (PROSPERO registration: CRD420251241279). Full article

Legal judgment prediction (LJP) increasingly relies on large language models whose full fine-tuning is memory-intensive and susceptible to catastrophic forgetting. We present LawLLM-DS, a two-stage Low-Rank Adaptation (LoRA) framework that first performs legal knowledge pre-tuning with an aggressive learning rate and subsequently refines judgment relations with conservative updates, using dedicated LoRA adapters, 4-bit quantization, and targeted modification of seven Transformer projection matrices to keep only 0.21% of parameters trainable. From a structural perspective, the twenty annotated legal elements form a symmetric label co-occurrence graph that exhibits both cluster-level regularities and asymmetric sparsity patterns, and LawLLM-DS implicitly captures these graph-informed dependencies while remaining compatible with downstream GNN-based representations. Experiments on 5096 manually annotated divorce cases show that LawLLM-DS lifts macro F1 to 0.8893 and achieves an accuracy of 0.8786, outperforming single-stage LoRA and BERT baselines under the same data regime. Ablation studies further verify the contributions of stage-wise learning rates, adapter placement, and low-rank settings. These findings demonstrate that curriculum-style, parameter-efficient adaptation provides a practical path toward lightweight yet structure-aware LJP systems for judicial decision support. Full article

Pb (II) contamination in wastewater represents a grave threat to the environment and ecosystems. Consequently, there is an urgent need to prepare low-cost and highly efficient Pb (II) adsorbents. To address this need, abundant and low-cost natural silica-based desert sand (DS) was innovatively utilized as a carrier to develop efficient and selective Pb (II) adsorbents. Modified desert sand (MDS) was first prepared via 1 M HCl pretreatment for 2 h and subsequent KH550 silane modification. Pb (II)-imprinted composites (Pb (II)-IIP@MDS) were then fabricated via ion-imprinted polymerization, using Pb (II) as the template ion and N-hydroxymethacrylamide (NHMA)/hydroxyethyl methacrylate (HEMA) as dual functional monomers with a molar ratio of 1:1. The synthesized Pb (II)-IIP@MDS was comprehensively characterized by X-ray photoelectron spectrometer (XPS), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT-IR). The adsorption capacity, selectivity, and reusability of this material for lead ions were evaluated through three experiments conducted within the optimized pH range of 6–7, with error bars indicated. In adsorption isotherm experiments, the initial Pb (II) concentration ranged from 50 to 500 mg·L⁻¹, conforming to the Langmuir model (R² = 0.992), with a theoretical maximum adsorption capacity reaching 107.44 mg·g⁻¹; this indicates that the adsorbate forms a monolayer adsorption on the homogeneous imprinted sites. Kinetics data indicate that the process best fits a quasi-first-order kinetic model (R² ≥ 0.988), while the favorable quasi-second-order kinetic fit (R² ≥ 0.982) reflects the synergistic effect of physical diffusion and ion-imprinting chemistry, reaching equilibrium within 120 min. Thermodynamic parameters (ΔH⁰ = 12.51 kJ·mol⁻¹, ΔS⁰ = 101.19 J·mol⁻¹·K⁻¹, ΔG⁰ < 0) confirmed endothermic, entropy-increasing, spontaneous adsorption. In multicomponent systems, Pb (II)-IIP@MDS showed distinct Pb (II) selectivity. It retained 80.3% adsorption efficiency after eight cycles. This work provides a promising strategy for fabricating low-cost, high-performance Pb (II) adsorbents, and Pb (II)-IIP@MDS stands as a practical candidate for the remediation of Pb (II)-contaminated wastewater. Full article

As cyber threats such as denial-of-service (DoS) attacks continue to rise, network intrusion detection systems (NIDS) have become essential components of cybersecurity defense. Although machine learning is widely applied to network intrusion detection, its performance often deteriorates due to the extreme class imbalance present in real-world data. This imbalance causes models to become biased and unable to detect critical attack instances. To address this issue, we propose MCH-Ensemble (Minority Class Highlighting Ensemble), an ensemble framework designed to improve the detection of minority attack classes. The method constructs multiple balanced subsets through random under-sampling and trains base learners, including decision tree, XGBoost, and LightGBM models. Features of correctly predicted attack samples are then amplified by adding a constant value, producing a boosting-like effect that enhances minority class representation. The highlighted subsets are subsequently combined to train a random forest meta-model, which leverages bagging to capture diverse and fine-grained decision boundaries. Experimental evaluations on the UNSW-NB15, CIC-IDS2017, and WSN-DS datasets demonstrate that MCH-Ensemble effectively mitigates class imbalance and achieves superior recognition of DoS attacks. The proposed method achieves enhanced performance compared with those reported previously. On the UNSW-NB15 and CIC-IDS2017 datasets, it achieves improvements in accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) by ~1.2% and ~0.61%, ~9.8% and 0.77%, ~0.7% and ~0.56%, ~5.3% and 0.66%, and ~0.1% and ~0.06%, respectively. In addition, it achieves these improvements by ~0.17%, ~1.66%, ~0.11%, ~0.88%, and ~0.06%, respectively, on the WSN-DS dataset. These findings indicate that the proposed framework offers a robust and accurate approach to intrusion detection, contributing to the development of reliable cybersecurity systems in highly imbalanced network environments. Full article

Wheat (Triticum aestivum L.) is a key crop in Spain, especially in Castilla and León Region. However, there are few studies evaluating predictive models based on spectral indices and multivariate analysis to estimate yield in direct seeding (DS) and conventional seeding (CS) systems. This study addresses this need by implementing a split-plot experimental design in the city of Palencia, Spain, analyzing crop physiological data and nine spectral indices derived from multispectral aerial images captured by drones. The analysis included multivariate techniques such as Principal Component Analysis (PCA) and Random Forest (RF), supplemented with statistical tests, ROC curves, and prediction analysis. The results showed that the RF model successfully classified treatments with 93.75% accuracy and a Kappa index of 0.875, highlighting performance, nitrogen, and protein as key variables. Among the vegetation indices, the Soil-Adjusted Vegetation Index (SAVI) and the Advanced Vegetation Index (AVI) were the most relevant in the flowering stage, with ROC curve values of 0.7778 and 0.8025, respectively. Spearman’s correlations confirmed a significant relationship between these indices and key physiological variables, allowing to distinguish between DS and CS systems. The RF-based prediction model for performance showed R² values above 91% in the indices with the highest correlation. However, predictive capacity was higher in DS, suggesting that conditions inherent in non-mechanized handling significantly influence model performance. This highlights the importance of using non-destructive procedures to estimate production, enabling the development of adaptive and sustainable strategies that contribute to efficient agricultural production, since it is possible to anticipate crop yields before harvest, optimizing resources such as fertilizers and water. Full article

Background: High-throughput metabolomics studies have promoted the discovery of candidate biomarkers linked to atherosclerosis (AS). This narrative systematic review summarises metabolomics studies conducted in (1) individuals with subclinical AS (assessed by imaging techniques such as carotid intimal media thickness, IMT, and coronary artery calcium, CAC), (2) patients with established atherosclerotic plaques, and (3) individuals with AS risk factors. Methods: The systematic search was conducted in the PubMed database according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The inclusion criteria were as follows: (i) publication date between 2009 and 2024; (ii) identification of potential biomarkers for AS in subjects with a diagnosis of AS or with one or more traits characteristic of the disease (i.e., CAC or IMT); (iii) identification of potential AS biomarkers in subjects with atherogenic clinical conditions (i.e., Down’s syndrome, DS, polycystic ovarian syndrome, PCOS, and systemic lupus erythematosus, SLE); (iv) metabolomic studies; and (iv) studies in human samples. Exclusion criteria comprised the following: (i) studies on lipid metabolic diseases unrelated to AS, (ii) “omics” results not derived from metabolomics, (iii) reviews and studies in animal models or cell cultures, and (iv) systematic reviews and meta-analyses. Of 90 eligible studies screened, 24 met the inclusion criteria. Results: Across subclinical and overt AS, consistent disturbances were observed in amino acid, lipid, and carbohydrate metabolism. Altered profiles included branched-chain amino acids (BCAAs), aromatic amino acids (AACs) and derivatives (e.g., kynurenine–tryptophan pathway), bile acids (BAs), androgenic steroids, short-chain fatty acids (FAs)/ketone intermediates (e.g., acetate, 3-hydroxybutyrate, 3-HB), and Krebs cycle intermediates (e.g., citrate). Several metabolites (e.g., glutamine, lactate, 3-HB, phosphatidylcholines, PCs/lysophosphatidylcholines, lyso-PCs) showed reproducible associations with vascular phenotypes (IMT/CAC) and/or clinical AS. Conclusions: The identification of low-weight metabolites altered in both subclinical and overt AS suggests their potential as candidate biomarkers for early AS diagnosis. Given the steady increase in deaths from cardiovascular disease, a manifestation of advanced AS, this finding could have significant clinical relevance. Full article

With the increasing demand for safety monitoring of offshore engineering structures, traditional single-modality sensing and centralized data processing models face challenges such as insufficient real-time performance and weak anti-interference abilities in complex marine environments. This research proposes an intelligent monitoring system based on multimodal sensor fusion and edge computing, aiming to achieve high-precision real-time diagnosis of offshore structure damage. The research plans to construct multimodal sensors through sensors such as stress change sensors, vibration sensors, ultrasonic sensors, and fiber Bragg grating sensors. A distributed wireless sensor network will be adopted to realize the transmission of sensor data, reduce the complexity of wiring, and meet the requirements of high humidity and strong corrosion in the marine environment. At the edge computing layer, lightweight deep learning models (such as multi-branch Transformer) and D-S evidence theory fusion algorithms will be deployed to achieve real-time feature extraction of multi-source data and damage feature fusion, supporting the intelligent identification of typical damages such as cracks, corrosion, and deformation. Experiments will simulate the coupled working conditions of wave impact, seismic load, and corrosion to verify the real-time performance and accuracy of the system. The expected results can provide a low-latency and highly robust edge-intelligent solution for the health monitoring of offshore engineering structures and promote the deep integration of sensor networks and artificial intelligence in Industry 4.0 scenarios. Full article

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the voltage, current, and temperature of each cell in a multi-cell pack. These key parameters are essential for ensuring safe operation, prolonging battery life, and optimizing energy usage in off-grid or mobile power systems. The system architecture is based on an ESP32 microcontroller that interfaces with INA219 and DS18B20 sensors to continuously measure individual cell voltage, current, and temperature. Data are transmitted wirelessly via Wi-Fi to a remote time-series database for centralized storage, analysis, and visualization. Experimental validation, conducted over a 15-day period, demonstrated stable system performance and reliable data transmission. Analytically, the findings indicate that utilizing an advanced smart charger for precise cell balancing and improving the physical layout for cooling led to superior thermal performance. Even when load current nearly tripled to 110 mA, the system maintained a stable cell operating temperature range of 29.8 °C to 30.3 °C. This result confirms significantly reduced cell stress compared to previous iterations, which is critical for enhancing battery health and lifespan. The application of this project aimed to demonstrate how a combination of open hardware components and lightweight network protocols can be used to create a robust, cost-effective battery monitoring solution suitable for integration into smart energy systems or remote IoT infrastructures. Full article