Search Results (1,130)

The design of active pneumatic upper limb exoskeletons is complicated by the challenge of reliably determining a kinematically safe workspace. Existing analytical kinematic methods are not sufficient to predict geometric collisions between elements of closed kinematic chains, which poses risks of mechanical damage and threats to user safety during exoskeleton operation. This paper proposes a hybrid algorithm for verifying the workspace of a pneumatic exoskeleton, combining analytical modelling in MATLAB R2020b based on the Product of Exponentials (PoE) method with high-performance static simulation in the Unity environment. At the initial stage, a discrete set comprising 758 million positions of the upper exoskeleton manipulator was generated. Subsequently, a multithreaded two-stage filtering process was implemented: analytical verification of rod stroke limits and angular constraints, followed by the detection of physical intersections of solid-state meshes using the PhysX engine. The results indicate that while the analytical model filters out 99.6% of invalid configurations. Yet, among the remaining positions—formally correct from a mathematical standpoint—up to 50% lead to critical geometric collisions or breaks in the kinematic chain. The computational efficiency of the proposed architecture enabled full static workspace verification in under 20 min. A reachable zone topology was established, revealing pronounced asymmetry and the presence of a “manoeuvrability core” in the user’s anterior hemisphere. The developed algorithm generates a verified set of kinematically safe exoskeleton states, providing a foundation for the kinematic safety layer of a hierarchical control system. These findings demonstrate the necessity of complementing analytical kinematics with physical collision detection when designing hybrid kinematic mechanisms, and the approach can be applied to verify collision-free movement trajectories in various robotic systems. The approach can be applied to verify collision-free movement trajectories in simulation, with physical validation deferred to future work. Full article

The modernization of global energy infrastructure within the Industry 5.0 framework requires the use of high-strength steels and reliable joining technologies to ensure safe, sustainable pipeline transport. This study focuses on the analysis of heterogeneous welded joints formed between high-strength alloy steel (34KhN2MA/EN 34CrNiMo6) and an austenitic welded seam (ER 307). While austenitic welds mitigate the risk of cold cracking, they introduce significant structural and mechanical heterogeneity. To address this, the research proposes and validates a material homogeneity criterion (MHC) derived from the LM-hardness methodology. By analyzing the statistical dispersion of macrohardness (HRC) through indicators such as the Weibull homogeneity coefficient (m) and the coefficient of variation (ν), the study establishes a quantitative approach to assess material degradation and structural uniformity across key weld zones. Results demonstrate that macrohardness profiling effectively distinguishes between structurally heterogeneous regions near the weld axis characterized by low homogeneity coefficients (m = 4.04 < 10, A_m = 0.742 < 0.878), elevated variability (ν = 29.68% > 11.6%), and high technological damageability (D = 0.92 > 0.81, j_D = 11.87 > 4.38) with pronounced step-like variation in macrohardness (HRC ∈ [12.6; 47]), on the one hand, and stabilized homogeneous zones in the base material, where m = 24.89 > 10, A_m = 0.947 > 0.878, ν = 4.39% < 11.6%, D = 0.52 ⟶ 0, j_D = 1.09 ⟶ 0, and characteristic range of HRC = 47–55, on the other hand. This methodology provides a robust, quasi-non-destructive tool for enhancing predictive maintenance, digital twins, and the overall integrity management of “smart” pipeline systems. Full article

Existing flood risk models have improved predictive accuracy, but they prioritize natural and hydrological factors while giving limited attention to fine-grained urban morphology. This study develops an interpretable deep learning framework to examine how high-resolution, three-dimensional urban form shapes two dimensions of flood risk: inundation risk, measured by grid-level inundated area, and infrastructure risk, measured by flood-related disruptions, including water supply interruption, power outage, road blockage, and collapse-related damage. Using Zhengzhou, China, as a case study, we combine multi-source spatial data, convolutional neural networks, ablation analysis, SHAP interpretation, and Gaussian Mixture Model classification to examine how fine-grained urban morphology affects these two risk dimensions. Incorporating urban morphology improved inundation risk prediction, reducing MSE from 0.0431 to 0.0371. The improvement was greater for infrastructure risk, with accuracy increasing from 0.7327 to 0.8218, and ROC-AUC from 0.83 to 0.95. SHAP results show that inundation risk is associated with vegetation, elevation, hydrological proximity, and localized spatial disorder, whereas infrastructure risk is amplified by vertical intensity, imperviousness, building concentration, porosity, and shape. Spatially, very high infrastructure-risk areas accounted for only 2.30% of the city but 12.88% of the central districts, while 74.62% of very high infrastructure-risk zones were concentrated in dense mid- to high-rise morphology. These findings suggest that flood-resilient planning should move beyond hydrology-sensitive flood management toward morphology-sensitive planning. Full article

Breast cancer is prevalent and deadly, affecting women worldwide. Increasing research suggests that lysine lactylation (KLA) and DNA damage repair (DDR) play critical roles in tumor progression and that KLA and DDR are interconnected, as KLA can modulate DDR protein function, thereby influencing genome stability and drug response, while DDR signaling can reciprocally reshape lactate metabolism and KLA activity. In this study, we developed a novel prognostic gene signature (KLA and DDR index, KLDRI) based on KLA- and DDR-related genes. Model genes (PGK1, MORF4L2, RAD54B, RPA3, CCND2) were generated via LASSO-Cox regression. Patients were stratified into high- and low-risk groups according to KLDRI, the robust prognostic value of which was demonstrated via survival and validation analyses in the TCGA cohort and the METABRIC and GSE96058 cohorts, respectively. Tumor microenvironment analysis indicated an immunologically suppressed phenotype in high-risk patients, whereas low-risk patients exhibited an immune-inflamed microenvironment. Drug sensitivity analysis indicated reduced sensitivity to multiple chemotherapy and targeted therapy drugs in the high-risk group. Single-cell transcriptomic analysis revealed differential gene expression patterns between risk groups. A prognostic nomogram based on KLDRI was developed to predict overall survival. Furthermore, functional experiments demonstrated that RPA3 knockdown suppressed cancer cell proliferation and migration, sensitized cells to cisplatin treatment, and reduced global lactylation, which may serve as a novel biomarker and potential therapeutic target. These findings enhance our understanding of the interplay between KLA, DDR, and breast cancer progression, facilitating the development of personalized therapeutic strategies. Full article

Objectives: Myelodysplastic syndromes (MDSs) are a heterogeneous group of clonal hematopoietic disorders characterized by ineffective hematopoiesis, genomic instability, and a high risk of progression to acute myeloid leukemia. Oxidative stress (OS) has emerged as a central factor in MDS pathophysiology, contributing to DNA damage, altered cellular signaling, and disease progression. Recent advances in artificial intelligence (AI) and machine learning (ML) offer a transformative approach for integrating multidimensional datasets including oxidative stress markers, hematologic parameters, and molecular profiles to enhance diagnosis, prognostication, and therapeutic monitoring in MDS. Methods: A comprehensive literature search was conducted in PubMed and Scopus, using the keywords “OS biomarkers,” “AI,” and “MDS’’. Results: Modified redox biomarkers can be correlated with oxidative imbalance and disease progression. ML models such as neural networks, decision trees, and support vector machines effectively capture complex relationships among redox biomarkers, enhancing risk stratification and prediction of treatment response. AI-driven proteomic analyses further revealed OS-related protein signatures linked to MDS pathophysiology. Overall, AI and ML enable the transformation of multidimensional OS data into clinically actionable tools for personalized management in MDS. Conclusions: Integrating biomarker research with AI-based analytics holds promise for advancing personalized diagnostics, prognostication, and therapeutic strategies in MDS, paving the way toward precision medicine. Full article

The widespread adoption of photovoltaic systems in residential electrical installations has increased the importance of Automatic Transfer Switches (ATSs) for ensuring power continuity during grid outages. However, many low-cost ATS solutions available on the market prioritize economic efficiency over operational safety, leading to significant risks under fault conditions. This paper investigates a real overvoltage incident in a residential three-phase installation equipped with a photovoltaic inverter and an ATS, which resulted in the failure of multiple electronic loads. The study reconstructs the event and demonstrates that the loss of the neutral conductor during backup operation caused severe phase voltage imbalance, generating overvoltage conditions across lightly loaded phases. A simplified electrical model is used to explain current paths and voltage redistribution under asymmetric loads, highlighting the critical role of correct neutral switching in ATS design. Two commercially available ATS architectures, one based on a changeover-contact mechanism and one employing four-pole miniature circuit breakers, are experimentally evaluated. The evaluation reveals major design deficiencies, including the absence of protective elements for control circuits, reliance on mechanical end-position limiters, and the use of switching devices not intended for frequent source transfer. These shortcomings introduce risks such as uncontrolled actuator operation, overheating, mechanical damage, and potential fire hazards. To overcome these limitations, a new ATS architecture was developed using a phase-monitoring relay, interlocked ABB contactors, and dedicated fuse protection for all control circuits. Detailed laboratory measurements were conducted to characterize contactor switching times and internal relay command delays. By optimizing the command sequence, the proposed ATS achieves predictable, fault-tolerant operation with competitive transfer times, representing a meaningful safety improvement over the evaluated commercial alternatives. The proposed solution is scoped to three-phase residential installations equipped with a hybrid photovoltaic inverter providing a dedicated backup output, operating within TN-S or TN-C-S earthing systems with a maximum grid connection capacity of 21 kW. Full article

Monolepta hieroglyphica Motschulsky (Coleoptera: Chrysomelidae) (M. hieroglyphica) is widely distributed in China. Its larvae are soil pests that cause severe damage to the seeds and roots of economically important crops such as corn, cotton, and millet. This study investigated the effects of four temperatures (25, 28, 31, and 34 °C) on the survival rate, food consumption (3rd instar), pupation rate, emergence rate, biometric indices (weight and length), and antioxidant enzyme activity of immature M. hieroglyphica. High temperatures (31 °C and 34 °C) adversely affected developmental duration, survival rates, and feeding efficiency. The highest pupation rate, emergence rate, and biometric indices were observed at 28 °C, after which these metrics steadily declined as the temperature increased. Notably, emergence was completely inhibited at 34 °C, resulting in the absence of biometric data. These changes correspond with the temperature-dependent regulation of antioxidant enzyme activities (SOD, CAT, GST, and POD). This study identified the optimal temperature range and critical high-temperature threshold for immature M. hieroglyphica, providing key biological parameters for predicting population dynamics and outbreak risks under climate warming, and offering a scientific basis for precise monitoring and temperature-based integrated pest management strategies. Full article

Background/Objectives: Long-term outcomes in systemic lupus erythematosus (SLE) are largely driven by irreversible organ damage, yet the relative contribution of comorbid conditions remains insufficiently characterized. We aimed to characterize damage accrual and identify comorbidities associated with damage severity and mortality. Methods: A retrospective study of adult patients with SLE followed at a single-center (2014–2023). The Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI), was used to assess damage at last follow-up. Damage was categorized as none (0), mild–moderate (1–2), or severe (≥3). Demographic, clinical, laboratory, treatment, and comorbidity data were extracted from electronic medical records. Multivariable logistic regression and Cox proportional hazards models were applied to identify factors associated with damage severity and mortality. Results: Among 182 patients (84.1% female; mean follow-up 15.6 ± 11.4 years), 59.5% accrued damage, including 30.8% with severe damage. Damage predominantly involved cardiovascular, ocular, neuropsychiatric, and musculoskeletal domains. It was associated with older age, longer disease duration, hematologic and renal involvement, and corticosteroids and immunosuppressive medications. In multivariable analysis, antiphospholipid syndrome (APS) and hypertension emerged as the dominant independent predictors of damage accrual with an odds ratio of 15.70 (95% CI 4.26–57.89, p < 0.001) and 6.46 (95% CI 2.54–16.40, p < 0.001), respectively. Mortality increased with damage severity (16.1% in SDI ≥ 3, 1.9% in SDI 1–2, none in SDI = 0; p < 0.0001). Damage was also associated with increased hospitalizations. Conclusions: Damage accrual is common and strongly predicts mortality. APS and hypertension emerge as dominant, modifiable drivers, supporting integrated cardiovascular and thrombotic risk management in SLE. Full article

The work environment in automated laboratories and industrial sites exposes workers to the risks associated with chemical gas and vapor leaks caused by unforeseen incidents. Such leaks may result in severe health hazards as well as damage to equipment or infrastructure at the leak site. Therefore, the development of systems capable of early detection and highly accurate localization of chemical leaks is of high importance for occupational safety. In this work, a low-cost, portable sensor node based on the Internet of Things (IoT) is proposed for the detection and localization of gas and chemical leaks in indoor environments. The sensor node features a modular design that enables flexible integration and replacement of gas and environmental sensors depending on the target application. In addition, the system includes an ultra-wideband (UWB)-based positioning and tracking unit, allowing operation across multiple indoor zones. The main contribution of this work lies in the combined integration of (i) multi-sensor-based environmental event detection and prediction and (ii) high-precision location within a dynamic multi-zone tracking architecture. The system automatically selects the most relevant anchors in each zone and applies trilateration and least-squares estimation, enhanced by Kalman filtering techniques. In particular, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are employed, with sensor fusion incorporating inertial measurement unit (IMU) data to mitigate the effects of on-line-of-sight (NLoS) conditions and signal degradation caused by obstacles. Experimental results demonstrate that both the EKF and UKF significantly reduce positioning errors and improve tracking stability compared to baseline methods under challenging indoor conditions. The UKF shows superior performance in highly nonlinear scenarios. A quantitative evaluation using manually surveyed reference points showed that the UKF achieved the best overall performance, with a mean error of 39.72 cm and an RMSE of 43.03 cm. These findings confirm the effectiveness of Kalman filter-based sensor fusion for reliable indoor positioning and highlight the suitability of the proposed system for real-time safety monitoring applications. Full article

Background: Anterior cruciate ligament (ACL) injuries in youth athletes are multifactorial, and the relative contributions of neuromuscular and neurocognitive variables remain inadequately comprehended. Methods: In this prospective cohort study, 220 young handball players (104 girls and 116 boys; mean age 16.3 ± 1.4 years) participated in functional testing with the Back in Action system and baseline neurocognitive evaluation with the ImPACT battery. During the 24-month follow-up period, orthopedic specialists identified ACL damage, which was confirmed by magnetic resonance imaging (MRI). Univariable logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to evaluate predictive capability. Results: During the 24-month follow-up, 26 athletes sustained an ACL injury. Prolonged plyometric ground contact time was significantly associated with ACL injury occurrence in logistic regression analysis (p = 0.019) and demonstrated fair discriminatory ability (AUC = 0.63) (OR = 0.98 per ms; 0.98 95% CI: 0.964–0.997). Female sex emerged as a profound and independent risk factor (OR = 5.74). Conclusions: Neuromuscular performance, specifically plyometric ground contact time and female sex, has predictive ability for ACL damage in youth handball players, while separate cognition assessments failed to independently differentiate injury risk. These findings support the use of objective neuromuscular evaluation in comprehensive injury prevention strategies in youth sport. Full article

Ship collisions pose substantial risks to maritime safety, causing vessel damage, casualties, and environmental impacts. Efficient extraction and analysis of key navigational and causal information from accident reports are important for risk assessment and decision support. This study proposes a framework for synthetic data generation, DistilBERT-based named entity recognition, and structured dataset construction for ship collision accidents. Using a template-based method, 56,000 annotated sentences were generated, covering navigational elements and causal factor trigger phrases. The fine-tuned DistilBERT model showed good performance on both synthetic and real accident reports. Statistical and co-occurrence analyses further indicated that failure to maintain proper lookout, failure to take effective evasive action, and failure to maintain safe speed were the main contributing factors across different environments and accident severity levels. Based on the extraction results, a standardized structured dataset was constructed to support subsequent causal analysis, dynamic risk modeling, and collision risk prediction. The study shows that combining template-based data synthesis with Transformer-based named entity recognition is a feasible approach for extracting information from maritime accident reports and transforming unstructured text into structured datasets. Full article

In pico-hydropower, the use of pumps as turbines is a cost-effective solution, especially for remote areas. The abundant seawater makes it a good fluid for pumped storage. The operation of centrifugal pumps in normal and reverse modes involves thickness loss because of solid particle concentration and vapor cavitation. Some research has been performed to predict cavitation in centrifugal pumps, but this issue still exists in several pico-hydropower plants. Therefore, to analyse the synergy between erosion and cavitation in a seawater centrifugal pump, we performed a CFD analysis to compute the effect of material mass loss due to erosion on cavitation risk. The Euler–Lagrangian method was used to track the released particles combined with the RNG k-ε turbulence model. The first part studied the effect of the surface mean roughness height (R_a) on the performance of the centrifugal pump. Increasing R_a from 0 to 15 μm decreases the pump hydraulic efficiency from 93% to 91%, respectively. The second analysis focused on the distribution of erosion thickness and its temporal evolution for 40 μm and 50 μm particles. For both the pump mode and the turbine mode, the erosion thickness is a polynomial function of power 2 with time. The most eroded regions are the blade leading edge (LE) and the blade trailing edge in pump and turbine mode, respectively. The last section focuses on analysing the effect of erosion thickness on cavitation damage. As the surface roughness increases, the cavitation damage power increases. The cavitation power risk increases from 111 kW to 156 kW in pump mode. In turbine mode, when the erosion thickness is between 0.0011 μm and 0.0022 μm, the cavitation damage is the same, approximately 170 kW, whereas the total gas distribution is uniformly distributed in the blade channel. With respect to seawater, the NPSHr increased compared with that with freshwater, from 3.35 m to 3.67 m. Full article

Accurate prediction of blasting-induced peak particle velocity (PPV) is critical for assessing structural damage risk and ensuring safe tunnel construction. This study proposes an AI agent-based Evaluator-Optimizer workflow that automates the model-development pipeline from prepared dataset input through model training, performance evaluation, hyperparameter optimization, and ensemble construction, with limited manual intervention after dataset definition. The framework employs a multi-agent architecture comprising three collaborative agents—an Orchestrator, an Evaluator, and an Optimizer—supported by a large language model (LLM) reasoning layer. The Evaluator agent analyzes model performance across multiple metrics and generates diagnostic insights; the Optimizer agent translates these insights into structured optimization plans; and the Orchestrator coordinates the evaluate-optimize loop and stopping logic. The workflow was applied to a dataset of 102 tunnel blasting events. Nine candidate regression models spanning tree-based, kernel-based, neural network, and regularized linear families were trained and evaluated. The results show that the workflow enables three substantive observations: (i) across five tree-based models the powder factor is the dominant predictor (28.7–50.5% relative importance); (ii) under 50 Monte-Carlo repeated 80/20 splits, KNN and the Voting ensemble are statistically indistinguishable and form the most stable performance cluster, while Gradient Boosting lies within the same cluster with larger variance; and (iii) under nested 5 × 5 cross-validation, the un-leaked R² for the top models is about 0.84–0.86, which quantifies the small-sample over-optimism that any future PPV study on single 80/20 splits should expect. The study therefore contributes both a portable agent architecture for tabular geotechnical regression and a concrete cautionary result about single-split benchmarking. Full article

Although ombitasvir/paritaprevir/ritonavir plus dasabuvir (OPrD) therapy is highly effective for chronic hepatitis C (CHC), clinicians frequently encounter transient hyperbilirubinemia, which can be misidentified as hepatotoxicity. This study investigated the role of SLCO1B1 (OATP1B1) and SLCO1B3 (OATP1B3) genetic polymorphisms in predicting bilirubin spikes and distinguishing transporter-mediated interference from hepatocellular injury. In this prospective study of 65 patients with HCV genotype 1, genotyping for OATP1B1 (c.388A>G, c.521T>C) and OATP1B3 (c.334T>G, c.699G>A) was performed using PCR-RFLP and capillary electrophoresis (QIAxcel Advanced System). Clinical and biochemical parameters were monitored over a 12-week treatment period. Hyperbilirubinemia (total bilirubin >1.1 mg/dL) developed in 18.5% of the cohort, typically within the first month. A distinct ‘AST-dominant’ biochemical signature, elevated bilirubin and AST paired with stable ALT, was identified, suggesting transporter-specific interference rather than hepatocyte damage. Statistical analysis pinpointed the OATP1B3 c.699G>A (rs7311358) variant as the sole genetic driver (p = 0.007). Carriers of the c.699G>A allele faced a 6.3-fold higher risk of developing hyperbilirubinemia (OR: 6.30, 95% CI: 1.48–26.80, p = 0.032), while no significant associations were found for OATP1B1 variants. We conclude that OATP1B3 c.699G>A is a potent predictor of OPrD-induced hyperbilirubinemia. Identifying this genotype pre-treatment allows clinicians to anticipate transient, benign bilirubin elevations and prevent unnecessary drug discontinuation, thereby mitigating therapeutic inertia and ensuring treatment continuity for CHC patients. Full article

Background and Clinical Significance: Over recent decades, percutaneous nephrolithotomy (PCNL) has emerged as a primary treatment, firmly establishing itself as the cornerstone approach for managing large kidney stones. Postoperative bleeding commonly stems from an arteriovenous fistula (AVF), a connection between a damaged artery with high flow and a damaged vein with low flow, or from a pseudoaneurysm (PA), which involves arterial blood leaking into the tissue, causing a localized hematoma. The preferred technique for addressing such vascular complications is selective trans-arterial angioembolization, widely regarded as the gold standard. Case Presentation: In this article, we present the case of a 42-year-old woman who experienced delayed bleeding eight years after PCNL and a previous angioembolization. The patient presented with macroscopic hematuria, and further investigations, including cystoscopy, contrast-enhanced abdominal-pelvic CT, and angiography, were performed. To stop the bleeding, we identified and performed selective angioembolization (SAE) of a small arterial branch arising from an inferior branch of the right renal artery. Conclusions: To the best of our knowledge, this is the initial documented instance of delayed bleeding manifesting eight years post-PCNL and angioembolization. This occurrence is exceptionally rare, given that the patient exhibited no urological signs or symptoms over the intervening years, and no predictive or risk factors were identified. Full article