Search Results (10,997)

The building sector faces sustainability issues due to its substantial resource demand, prompting the exploration of alternative materials of natural origin. Given the diverse environmental conditions buildings experience, assessing the impact of these conditions on the mechanical characteristics of alternative materials becomes crucial. This study focuses on a composite comprising stone, agglomerate cork core and glass fiber-reinforced epoxy skins, designed for ventilated facades. The composite underwent an aging cycle commonly applied in the evaluation of construction building materials to evaluate its flexural behavior. To that end, bending tests on unaged and aged samples were carried out to investigate both the bending strength and stiffness. The composite panels were tested in two configurations: (i) stone facing up and (ii) stone facing down. The results indicated that higher bending strength was found in samples where the stone was facing up, regardless of the aging condition. In the stone facing up configuration, the predominant failure mode was stone crushing, whereas the samples in the stone facing down configuration evidenced a failure mechanism of fiber breakage. Despite the observed morphological differences between aged and unaged specimens, no significant difference was found regarding the bending strength and failure modes in both tested configurations. However, a flexural stiffness reduction of at least 21% was found for every aged specimen. Full article

Background: Serum albumin is a well-known marker of nutritional and inflammatory status and has been associated with adverse outcomes in heart failure (HF). However, its predictive value for length of hospital-stay and short-term mortality in elderly HF patients remains underexplored. Objectives: To investigate the association between serum albumin levels at hospital admission and length of stay, as well as post-admission mortality, in a cohort of elderly patients hospitalized for HF. Methods: We conducted a retrospective analysis of 56 consecutive patients aged ≥65 years admitted for HF. Comorbidities were assessed using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G), and inflammatory status was measured via C-reactive protein (CRP). Negative binomial regression with robust confidence intervals was employed to evaluate the relationship between serum albumin and length of hospital-stay, adjusting for age, comorbidity burden, and CRP. Cox proportional hazards models were used to assess mortality at 6 months and 1 year, adjusting for age, comorbidity, CRP, and HF subtype, with Kaplan–Meier curves illustrating unadjusted survival differences according to albumin levels and HF subtype. Results: Mean age was 78.6 ± 7.5 years, with 69.6% female patients. Mean serum albumin at admission was 3.58 ± 0.60 g/dL, and mean length of stay was 14.8 ± 10.1 days. Each 1 g/dL increase in albumin was associated with a 32% reduction in length of stay (adjusted IRR = 0.68; 95% CI: 0.54–0.85; p = 0.01), independently by age, inflammatory status and comorbidity. Serum albumin was independently associated with reduced risk of death at 6 months (HR 0.30; 95% CI: 0.11–0.82; p = 0.019) and 1 year (HR = 0.41; 95% CI: 0.17–0.96; p = 0.041). Conclusions: Serum albumin at hospital admission independently predicts length of stay and short-term mortality in elderly patients with HF. Albumin measurement, simple, cheap and universally available biomarker, is helpful for early risk stratification and may guide clinical management in this vulnerable population. Full article

Introduction: Allogeneic penetrating limbo-keratoplasty (limbo-PK) is one of the surgical methods for the treatment of limbal stem cell deficiency (LSCD). We report real-life results on different entities. Methods: Patients treated with limbo-PK at the Department of Ophthalmology of the University Medical Center Mainz were evaluated retrospectively. The primary endpoint was the epithelialization of the graft one year postoperatively. In addition, the postoperative best corrected visual acuity (BCVA), ocular concomitant diseases, drug treatment, and the need for further eye surgery postoperatively were examined. Results: We included 14 eyes of 13 patients (4 female) aged 59.8 ± 14.1 years who underwent limbo-PK between 2020 and 2024. Indications for limbo-PK included chemical burns (n = 4), blast injuries (n = 4), thermal burns (n = 2), trauma (n = 1) graft-versus-host disease (n = 1), and ectrodactyly-ectodermal dysplasia (EEC) (n = 1). The mean preoperative BCVA was 2.2 ± 0.6 logMAR (range: light perception to 0.7 logMAR). Four limbo-PK-grafts were HLA-typed. All limbo-PKs were combined with amniotic membrane transplantation; three with cataract surgery and one with tarsorrhaphy. Postoperatively, all patients received local immunosuppression, and 12 (85.7%) received additional systemic immunosuppression. At one-year follow-up mean BCVA increased to 1.0 ± 0.7 logMAR (range: 2.3 to 0.1, p-value = 0.03) and 11 of 14 eyes showed a functional graft with closed epithelium. In the further postoperative course, four patients needed a further Limbo-PK due to graft failure (n = 2), immune graft rejection after stopping local immunosuppressive therapy (n = 1) and perforation of the graft in a severe case of GvHd (n = 1). Conclusions: Limbo-PK is an effective surgical method for the treatment of LSCD. In our study cohort, we observed a significant improvement in mean BCVA one year postoperatively, with a functional, epithelialized graft achieved in 11 of 14 eyes. Full article

The paper investigates the seismic vulnerability of single-story precast industrial buildings constructed in Romania during the 1970s, with particular reference to the damage observed following the 1977 Romanian earthquake. More than 800 structures were analytically assessed using a displacement-based evaluation procedure grounded in their original design specifications. Several displacement capacity models for flexure-controlled concrete columns were applied, and their suitability for the analyzed buildings is critically discussed. The study also includes a detailed case study that illustrates the practical application of the assessment methodology and highlights specific structural behaviors under seismic loading. The results demonstrate that the displacement-based assessment provides realistic predictions of seismic performance, consistent with observations from similar buildings constructed after the 1977 Vrancea earthquake. The conclusions indicate that the analyzed buildings generally exhibit favorable seismic behavior, with flexural hinging preceding shear failure and displacement-based methods offering more realistic and less conservative assessments than traditional force-based approaches. The scientific contribution of this work lies in using a comprehensive framework for evaluating the seismic response of existing precast industrial structures, offering insights into the effectiveness of different column capacity models, and establishing a foundation for future research on retrofitting strategies and the interaction of structural and non-structural components under seismic actions. Full article

Tuberculosis (TB) remains a major global health challenge, and early, accurate diagnosis is essential for effective disease control. Chest radiography (CXR) is widely used for TB screening because of its accessibility, yet its limited specificity necessitates confirmatory molecular testing such as polymerase chain reaction (PCR) assays. This study aimed to evaluate the diagnostic performance of a deep learning model (DLM) for TB detection using CXR and to compare its predictive accuracy with PCR results, specifically in a low-burden region. A retrospective dataset of CXR images and corresponding PCR findings was obtained from two hospitals. The DLM, based on the CheXzero vision transformer, was trained on a large imaging dataset and evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics. Internal and external validation sets assessed sensitivity, specificity, and predictive values, with subgroup analyses according to imaging modality, demographics, and comorbidities. The model achieved an AUC of 0.915 internally and 0.850 externally, maintaining good sensitivity and specificity, though performance declined when limited to PCR-confirmed cases. Accuracy was lower for older adults and those with chronic kidney disease, chronic obstructive pulmonary disease, or heart failure. These findings suggest AI-assisted CXR screening may support TB detection in resource-limited settings, but PCR confirmation remains essential. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

Detecting active failures is important for the Industrial Internet of Things (IIoT). The IIoT aims to connect devices and machinery across industries. The devices connect via the Internet and provide large amounts of data which, when processed, can generate information and even make automated decisions on the administration of industries. However, traditional active fault management techniques face significant challenges, including highly imbalanced datasets, a limited availability of failure data, and poor generalization to real-world conditions. These issues hinder the effectiveness of prompt and accurate fault detection in real IIoT environments. To overcome these challenges, this work proposes a data augmentation mechanism which integrates generative adversarial networks (GANs) and the synthetic minority oversampling technique (SMOTE). The integrated GAN-SMOTE method increases minority class data by generating failure patterns that closely resemble industrial conditions, increasing model robustness and mitigating data imbalances. Consequently, the dataset is well balanced and suitable for the robust training and validation of learning models. Then, the data are used to train and evaluate a variety of models, including deep learning architectures, such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), and conventional machine learning models, such as support vector machines (SVMs), K-nearest neighbors (KNN), and decision trees. The proposed mechanism provides an end-to-end framework that is validated on both generated and real-world industrial datasets. In particular, the evaluation is performed using the AI4I, Secom and APS datasets, which enable comprehensive testing in different fault scenarios. The proposed scheme improves the usability of the model and supports its deployment in a real IIoT environment. The improved detection performance of the integrated GAN-SMOTE framework effectively addresses fault classification challenges. This newly proposed mechanism enhances the classification accuracy up to 0.99. The proposed GAN-SMOTE framework effectively overcomes the major limitations of traditional fault detection approaches and proposes a robust, scalable and practical solution for intelligent maintenance systems in the IIoT environment. Full article

Textile-reinforced concrete (TRC) is an alternative class of mechanical reinforcement for cement composites. The biaxial braided reinforcement structure in composite materials with diverse cross-sectional shapes offers high adaptability, torsional stability, and resistance to damage. In general, 3D textile reinforcements improve the mechanical properties of composites compared to 2D reinforcements. This study aimed to verify reinforcement behavior by comparing multidimensional braided textiles, 2D (one- and two-layer) reinforcements, and 3D reinforcement in composite cementitious boards. Experimental tests were performed to evaluate the effect of textile structures on cementitious composites using four-point bending tests, porosity measurements, and crack patterns. All textiles showed sufficient space between yarns, allowing the matrix (a commercial formulation) to infiltrate and influence the composite mechanical properties. All composites presented ductility behavior. The two layers of 2D textile composites displayed thicker cracks, influenced by shear forces. Three-dimensional textiles exhibited superior values in four-point bending tests for modulus of rupture (7.4 ± 0.5 MPa) and specific energy (5.7 ± 0.3 kJ/m²). No delamination or debonding failure was observed in the boards after the bending tests. The 3D textile structure offers a larger contact area with the cementitious matrix and creates a continuous network, enabling more uniform force distribution in all directions. Full article

Additive manufacturing has huge development potential in the aerospace field. The hot-end components of aeroengines work in harsh environments, facing high temperatures and a demand for long service life. In this paper, high-cycle fatigue (HCF) tests of Ti6Al4V alloy at 400 °C by selective laser melting (SLM) under different stress ratios (−1, 0.1, 0.3, 0.5, and 0.8) were carried out, and the fracture surfaces were observed. The results show that the defects existing on the surface or subsurface are prone to become the origin of fatigue cracks. There is a large dispersion of the high-cycle fatigue life of the samples, especially at a low stress ratio. With the increase in the stress ratio, the fatigue failure area on the fracture surface gradually decreases, and the fracture surface gradually presents a mixed pattern of tensile endurance fracture and fatigue failure. Considering the influence of creep damage due to mean stress, models were established, respectively, for the fatigue behavior and time-related rupture behavior to predict fatigue life and conduct an assessment. Then, the two models were combined and the composite models were proposed using the linear damage law. Finally, the single fatigue model and rupture models, as well as the composite models, were evaluated, respectively, and compared with the actual fatigue life, and the best model was obtained for the high-cycle fatigue prediction of SLM Ti6Al4V at 400 °C. Full article

Background: Mantle Cell Lymphoma (MCL) is an aggressive malignancy with variable clinical behavior, largely reflecting the underlying molecular heterogeneity. The genomic landscape of MCL encompasses gene mutations with strong prognostic implications and secondary genetic events, which are also implicated in the pathogenesis and prognosis of the disease. Methods: We evaluated the diagnostic samples of 73 patients with relapsed/refractory MCL that were enrolled in the Fondazione Italiana Linfomi Mantle First-BIO study. All patients had available data for correlating CNVs with the presence of TP53 mutation. Time to first relapse or progression of disease (POD) was used as the primary outcome measure. Results: We identified CNVs associated with MCL, with Del 9p21.3 (CDKN2A) being the strongest predictor of shorter time to POD (p = 0.01), independently of TP53 mutation in multivariable analysis. Unsupervised clustering identified molecularly defined clusters that were associated with significantly different times to POD (p = 0.01). Pairwise log-rank tests confirmed TP53 mutated vs. wild-type (WT) as the strongest prognostic factor, with cluster assessment improving the prognostic predictivity among patients: clusters TP53-mut vs. TP53-WT, p = 0.001, HR = 3.92; and p = 0.014, HR = 2.23, respectively. In conclusion, CNV-based molecular clusters might represent a novel approach to identify patients at higher risk of treatment failure, further contributing to the prognostic predictivity of TP53 mutation. Full article

Background: Drug-induced cardiotoxicity poses a major challenge in drug development and clinical safety. Although machine learning (ML) methods have shown potential in predicting cardiotoxic risks, prior research has largely focused on specific mechanisms such as human Ether-à-go-go-Related Gene (hERG) inhibition. This scoping review systematically examined studies applying ML models to predict a broad range of drug-induced cardiotoxicity outcomes. Methods: A systematic search of PubMed, EMBASE, SCOPUS, and Web of Science identified studies developing ML models for cardiotoxicity prediction. Extracted data included sources, feature types, algorithms, and performance metrics, categorized by evaluation method (training, testing, cross-validation, or external validation). Results: Twenty-five studies met inclusion criteria, addressing outcomes such as arrhythmia, cardiac failure, heart block, hypertension, and myocardial infarction. Structured resources such as SIDER (Side Effect Resource) were the most common data sources, with features including molecular descriptors, fingerprints, and occasionally, target-based or transcriptomic data. Support vector machines (SVM) and random forest (RF) were the most common algorithms, showing robust predictive performance, with externally validated area under the receiver operating characteristic curve (AUC-ROC) values above 0.70 and accuracy exceeding 0.75 in several studies. Despite variability and limited external validation, ML approaches demonstrate substantial promise for predicting diverse cardiotoxic outcomes. Conclusions: This review underscores the importance of integrating heterogeneous data and rigorous validation for improving cardiotoxicity prediction. Full article

The finite element method (FEM) and discrete element method (DEM) have been widely applied to analyze the deformation and failure processes of embankment slopes. Although both methods can produce promising results, the choice between them has long remained unresolved. In this study, a failure case of a granite residual soil (GRS) embankment was analyzed. FEM and DEM models were established to simulate the instability process of this embankment slope, and the applicability of both methods to GRS embankments was then evaluated. The main conclusions are as follows: (1) Geotechnical parameters of GRS were determined through laboratory testing, and FEM and DEM models were developed to reproduce the deformation and failure behavior of the embankment slope subjected to rainfall and vehicle loading. (2) Similar rainfall infiltration patterns were obtained from both FEM and DEM simulations; however, significant differences in deformation were observed. The FEM-predicted deformation was 0.075 m after rainfall, indicating that the embankment remained stable. In contrast, the DEM-predicted deformation reached 1.4 m, indicating that the embankment slope had already become unstable. (3) The DEM simulation closely reproduced the failure of the GRS embankment slope observed in the field. It realistically captures the process of particle disintegration in GRS caused by rainfall infiltration, as well as the subsequent slope collapse. Therefore, DEM can be regarded as the most appropriate approach for modeling the instability of GRS embankment slopes. Full article

This work aims to evaluate the feasibility of using a COTS-embedded computer as an on-board computer (OBC) for nanosatellites in academic projects. The prototype is based on the BeagleBone Black board, which runs the cFS flight-mission software on the RTEMS operating system. For evaluation purposes, 15.9 MeV proton-accelerated radiation tests were performed at the CNA facility to obtain the soft-error rate of the DDR3 SDRAM. Results show the presence of bit-flips in memory cells, leading to error propagation, and a burst of errors produced by SEEs, affecting the control logic of the SDRAM memory. Despite the errors and accumulated dose, the board continued to function normally, with a worst-case FIT indicating that one failure every two years is expected in the SDRAM memory. This study suggests the possibility of using BeagleBone Black as an OBC for LEO. In addition, the article provides clues on how redundancy-based fault tolerance can be implemented. Full article

Background: Monoclonal antibodies targeting the calcitonin gene-related peptide (CGRP) pathway, such as erenumab (ERE), are effective migraine-preventive therapies for many patients. Identifying clinical and genetic factors associated with treatment failure is crucial for optimizing patient management. Methods: This multicenter, prospective observational study included patients with episodic or chronic migraine treated with ERE for 12 months. Demographics, migraine history, comorbidities, treatment outcomes, and genetic variants in CGRP receptor-related genes (CALCRL and RAMP1) were evaluated for associations with non-response to ERE, defined as a <50% reduction in monthly migraine days. Results: Of the 140 patients starting ERE, 11 were lost to follow up, 12 stopped ERE due to side effects; 18 patients were non-responders and were compared to 99 responders. Arterial hypertension [adjusted OR (aOR): 7.77, p = 0.007], smoking (aOR: 4.98, p = 0.014), and insomnia requiring medication (aOR: 4.51, p = 0.027) were associated with non-responder status. Genetic analysis revealed a nominal association between the RAMP1 rs6431564 polymorphism and non-responder status (nominal p = 0.025), which did not survive Bonferroni correction. The G allele was linked to a reduced risk (aOR per G allele: 0.28, p = 0.025) and caused the increased expression of RAMP1 in an allele-dose manner. Conclusions: Hypertension, smoking, insomnia requiring medication, and, nominally, the RAMP1 rs6431564 polymorphism were associated with non-responder status to ERE in migraine patients. Further validation of the present results in larger cohorts is needed. Full article

Ultra-dense heterogeneous cellular networks in 6G and beyond face an escalating vulnerability to cell outages stemming from complex issues like parameter misconfigurations, hidden conflicts among Autonomous Network Functions (ANFs), multivendor incompatibility, and software/hardware failures. While ANF-based automated fault detection is a core capability for next-generation networks, existing solutions are predominantly reactive, identifying faults only after reliability is compromised. To overcome this critical limitation and maintain high service quality, a proactive fault prediction capability is essential. We introduce a novel Discrete-Time Markov Chain (DTMC)-based stochastic framework designed to model network reliability dynamics. This framework forecasts the transition of a cell from normal operation to suboptimal or degraded states, offering a crucial shift from reactive to proactive fault management. Our model rigorously quantifies the effects of fault arrivals, estimates the fraction of time the network remains degraded, and, uniquely, identifies sensitive parameters whose misconfigurations pose the most significant threat to performance. Numerical evaluations demonstrate the model’s high applicability in accurately predicting both the timing and probable causes of faults. By enabling true anticipation and mitigation, this framework is a key enabler for significantly reducing the cell outage time and enhancing the reliability and resilience of next-generation wireless networks. Full article