Search Results (6,349)

Background/Objectives: Age estimation is of fundamental importance in forensic investigations. When traditional methods based on gross bone morphology or morphometric analysis cannot be applied, forensic experts must rely on multidisciplinary approaches. Histomorphometry has consistently proven to be reliable in cases of highly fragmented or incomplete skeletal remains, particularly in older individuals. Building on the foundational study of Amprino and Bairati, this study evaluated the correlations between bone microstructural features in femoral cross-sections and the age and sex of individuals. Methods: The sample comprised 95 femoral mid-diaphyseal thin sections obtained from autopsy specimens housed at the Institute of Legal Medicine, University of Bari (Italy), representing both male and female individuals aged 18 to 92 years. The numbers and densities of primary, intact secondary, and fragmentary secondary osteons, together with osteon circularity and the mean osteonal area, were measured to investigate age-related variation. Statistical analyses included t-tests, Mann–Whitney tests, Spearman’s rank correlation, and General Linear Models (GLMs). Results: No significant differences in histomorphometric variables were observed between males and females. However, the number of intact secondary osteons and osteon population density increased with age, while the mean osteonal area and osteon circularity decreased with age. Although some variables displayed significant correlations with age, residual analysis indicated a lack of heterogeneity in variance, which limited the development of a robust predictive model. Conclusions: The findings highlight both the potential and the limitations of histomorphometry in forensic age estimation. While certain microstructural variables correlate with age, inter-individual variability reduces predictive accuracy. Further research is needed to refine models that account for biological and biomechanical variability, particularly in older adults. Full article

This study focuses on quantifying wind–temperature load combination coefficients for long-span hybrid cable-stayed suspension bridges (HCSSBs) to overcome limitations of traditional methods in ignoring load correlation and geometry nonlinearity. A probabilistic framework is proposed to use site-specific load data to determine load combination coefficients, focusing on load correlation and geometric nonlinearity while assuming that stress reflects load effects and that 100-year samples are statistically representative. Long-sequence meteorological data, including wind and temperature measurements, were used to construct marginal and bivariate joint distributions, which characterize the randomness and correlation of wind and temperature loads. Load samples covering the design reference period were generated and validated via convergence tests. Four load scenarios (individual temperature, individual wind, linear superposition, and nonlinear coupling) were designed, and key control points are screened using indicators reflecting the comprehensive load effect $\overline{EII}$, combined load proportion $\mathsf{\zeta }$, and nonlinear influence $\mathsf{\eta }$. Based on stress responses of key control points, load combination coefficients were derived with probability modeling. A case study for a bridge with span length of 2300 m shows that the load combination coefficients for the main girder are 0.60 (east wind) and 0.59 (west wind), while they are 0.51 (east wind) and 0.58 (west wind) for the main tower. These results demonstrate that the proposed method enables the provision of rational load combination coefficients. Full article

On construction sites, it is crucial and and in most cases mandatory to wear safety equipment such as helmets, safety shoes, vests, and belts. The most important of these is the helmet, as it protects against head injuries and can also serve as a marker for detecting and tracking workers, since a helmet is typically visible to cameras on construction sites. Checking helmet usage, however, is a labor-intensive and time-consuming process. A lot of work has been conducted on detecting and tracking people. Some studies have involved hardware-based systems that require batteries and are often perceived as intrusive by workers, while others have focused on vision-based methods. The aim of this work is not only to detect workers and helmets, but also to identify workers through labeled helmets using symbol detection methods. Person and helmet detection tasks were handled by training existing datasets and gained accurate results. For symbol detection, 14 different shapes were selected and put on helmets in a triple format side by side. A total of 11,243 images have been annotated. YOLOv5 and YOLOv8 were used to train the dataset and obtain models. The results show that both methods achieved high precision and recall. However, YOLOv5 slightly outperformed YOLOv8 in real-time identification tests, correctly detecting the helmet symbols. A testing dataset containing different distances was generated in order to measure accuracy by distance. According to the results, accurate identification was achieved at distances of up to 10 meters. Also, a location-based symbol-ordering algorithm is proposed. Since symbol detection does not follow any order and works with confidence values in the inference mode, a left to right approach is followed. Full article

Student dropout remains a persistent challenge in higher education, with substantial personal, institutional, and societal costs. We developed a modular dropout prediction pipeline that couples data preprocessing with multi-model benchmarking and a governance-ready explainability layer. Using 17,883 undergraduate records from a Moroccan higher education institution, we evaluated nine algorithms (logistic regression (LR), decision tree (DT), random forest (RF), k-nearest neighbors (k-NN), support vector machine (SVM), gradient boosting, Extreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and multilayer perceptron (MLP)). On our test set, XGBoost attained an area under the receiver operating characteristic curve (AUC–ROC) of 0.993, F1-score of 0.911, and recall of 0.944. Subgroup reporting supported governance and fairness: across credit–load bins, recall remained high and stable (e.g., <9 credits: precision 0.85, recall 0.932; 9–12: 0.886/0.969; >12: 0.915/0.936), with full TP/FP/FN/TN provided. A Shapley additive explanations (SHAP)-based layer identified risk and protective factors (e.g., administrative deadlines, cumulative GPA, and passed-course counts), surfaced ambiguous and anomalous cases for human review, and offered case-level diagnostics. To assess generalization, we replicated our findings on a public dataset (UCI–Portugal; tables only): XGBoost remained the top-ranked (F1-score 0.792, AUC–ROC 0.922). Overall, boosted ensembles combined with SHAP delivered high accuracy, transparent attribution, and governance-ready outputs, enabling responsible early-warning implementation for student retention. Full article

Background: Symptoms of dizziness, syncope, and palpitations are common presentations in outpatient and emergency care, frequently attributed to stress and anxiety when conventional neurological and cardiac evaluations are normal. Joint hypermobility (JH) syndromes including hypermobile Ehlers–Danlos syndrome (hEDS), and hypermobility spectrum disorders (HSD) are under-recognised as potential causes. Methods: Our retrospective cohort study examined the clinical features, diagnostic findings, and responses to treatment in patients with JH syndromes, who are referred to a specialised syncope clinic within a UK teaching hospital. It involved 218 patients with joint hypermobility, predominantly young females (median Beighton score: 6), reporting chronic orthostatic intolerance, dizziness, and palpitations. Common comorbidities included joint pain, chronic fatigue, gastrointestinal dysmotility, and psychiatric conditions. Prevalence of symptoms, cardiovascular abnormalities on investigation (ECG, echocardiography, and tilt-table testing), and treatment responses were analysed. Results: History and examination were often diagnostic. Standard cardiac tests rarely provided diagnostic value except to exclude alternate conditions. Tilt-table testing was abnormal in 82.0% of cases, revealing orthostatic hypotension, reflex syncope, or postural tachycardia syndrome (POTS). Conservative measures (hydration, salt intake, and exercise) were effective in over half of the cases; pharmacological treatments (ivabradine, fludrocortisone) were considered for refractory cases. Conclusions: This study emphasises that JH syndromes are a common cause of palpitations, dizziness, and syncope in young females. They are multi-system conditions affecting both physical and mental health, which remain under-recognised and are often dismissed as ‘functional’, particularly in women—highlighting gender bias in diagnosis. A structured diagnostic approach with routine joint assessments for JH and increased awareness can facilitate early recognition and management in general medical settings, reducing reliance on emergency services and improving patient outcomes. Full article

This work investigates the use of LLMs to enhance automation in software testing, with a particular focus on generating high-quality, context-aware test scripts from natural language descriptions, while addressing both text-to-code and text+code-to-code generation tasks. The Codestral Mamba model was fine-tuned by proposing a way to integrate LoRA matrices into its architecture, enabling efficient domain-specific adaptation and positioning Mamba as a viable alternative to Transformer-based models. The model was trained and evaluated on two benchmark datasets: CONCODE/CodeXGLUE and the proprietary TestCase2Code dataset. Through structured prompt engineering, the system was optimized to generate syntactically valid and semantically meaningful code for test cases. Experimental results demonstrate that the proposed methodology successfully enables the automatic generation of code-based test cases using large language models. In addition, this work reports secondary benefits, including improvements in test coverage, automation efficiency, and defect detection when compared to traditional manual approaches. The integration of LLMs into the software testing pipeline also showed potential for reducing time and cost while enhancing developer productivity and software quality. The findings suggest that LLM-driven approaches can be effectively aligned with continuous integration and deployment workflows. This work contributes to the growing body of research on AI-assisted software engineering and offers practical insights into the capabilities and limitations of current LLM technologies for testing automation. Full article

Residual oil zones (ROZ) arise under the oil–water contact of main pay zones due to diverse geological conditions. Historically, these zones were considered economically unviable for development with conventional recovery methods because of the immobile nature of the oil. However, they represent a substantial subsurface volume with strong potential for CO₂ sequestration and storage. Despite this potential, effective techniques for assessing CO₂-EOR performance coupled with CCUS in ROZs remain limited. To address this gap, this study introduces a machine learning framework that employs artificial neural network (ANN) models trained on data generated from a large number of reservoir simulations (300 cases produced using Latin Hypercube Sampling across nine geological and operational parameters). The dataset was divided into training and testing subsets to ensure generalization, with key input variables including reservoir properties (thickness, permeability, porosity, Sorg, salinity) and operational parameters (producer BHP and CO₂ injection rate). The objective was to forecast CO₂ storage capacity and oil recovery potential, thereby reducing reliance on time-consuming and costly reservoir simulations. The developed ANN models achieved high predictive accuracy, with R² values ranging from 0.90 to 0.98 and mean absolute percentage error (MAPRE) consistently below 10%. Validation against real ROZ field data demonstrated strong agreement, confirming model reliability. Beyond prediction, the workflow also provided insights for reservoir management: optimization results indicated that maintaining a producer BHP of approximately 1250 psi and a CO₂ injection rate of 14–16 MMSCF/D offered the best balance between enhanced oil recovery and stable storage efficiency. In summary, the integrated combination of reservoir simulation and machine learning provides a fast, technically robust, and cost-effective tool for evaluating CO₂-EOR and CCUS performance in ROZs. The demonstrated accuracy, scalability, and optimization capability make the proposed ANN workflow well-suited for both rapid screening and field-scale applications. Full article

Background/Objectives: We provide a framework for determining how far into the future the spatiotemporal dynamics of tumor growth can be accurately predicted using routinely available magnetic resonance imaging (MRI) data. Our analysis is applied to a coupled set of reaction-diffusion equations describing the spatiotemporal development of tumor cellularity and vascularity, initialized and constrained with diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI data, respectively. Methods: Motivated by experimentally acquired murine glioma data, the rat brain serves as the computational domain within which we seed an in silico tumor. We generate a set of 13 virtual tumors defined by different combinations of model parameters. The first parameter combination was selected as it generated a tumor with a necrotic core during our simulated ten-day experiment. We then tested 12 additional parameter combinations to study a range of high and low tumor cell proliferation and diffusion values. Each tumor is grown for ten days via our model system to establish “ground truth” spatiotemporal tumor dynamics with an infinite signal-to-noise ratio (SNR). We then systematically reduce the quality of the imaging data by decreasing the SNR, downsampling the spatial resolution (SR), and decreasing the sampling frequency, our proxy for reduced temporal resolution (TR). With each decrement in image quality, we assess the accuracy of the calibration and subsequent prediction by comparing it to the corresponding ground truth data using the concordance correlation coefficient (CCC) for both tumor and vasculature volume fractions, as well as the Dice similarity coefficient for tumor volume fraction. Results: All tumor CCC and Dice scores for each of the 13 virtual tumors are >0.9 regardless of the SNR/SR/TR combination. Vasculature CCC scores with any SR/TR combination are >0.9 provided the SNR ≥ 80 for all virtual tumors; for the special case of high-proliferating tumors (i.e., proliferation > 0.0263 day⁻¹), any SR/TR combination yields CCC and Dice scores > 0.9 provided the SNR ≥ 40. Conclusions: Our systematic evaluation demonstrates that reaction-diffusion models can maintain acceptable longitudinal prediction accuracy—especially for tumor predictions—despite limitations in the quality and quantity of experimental data. Full article

Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management of bearings has become an important issue. Although deep learning has shown remarkable advantages, its performance still relies on the assumption that the training and testing data share the same distribution, which often deteriorates in real applications due to variations in load and rotational speed. This study focused on the scenario of domain generalization (DG) and proposed a Meta-Learning with Gradient Alignment and Data Augmentation (MGADA) method for cross-domain bearing fault diagnosis. Within the meta-learning framework, Mixup-based data augmentation was performed on the support set in the inner loop to alleviate overfitting under small-sample conditions and enhanced task-level data diversity. In the outer loop optimization stage, an arithmetic gradient alignment constraint was introduced to ensure consistent update directions across different source domains, thereby reducing cross-domain optimization conflicts. Meanwhile, a centroid convergence constraint was incorporated to enforce samples of the same class from different domains to converge to a shared centroid in the feature space, thus enhancing intra-class compactness and semantic consistency. Cross-working-condition experiments conducted on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves high classification accuracy across different target domains, with an average accuracy of 98.89%. Furthermore, ablation studies confirm the necessity of each module (Mixup, gradient alignment, and centroid convergence), while t-SNE and confusion matrix visualizations further illustrate that the proposed approach effectively achieves cross-domain feature alignment and intra-class aggregation. The proposed method provides an efficient and robust solution for bearing fault diagnosis under complex working conditions and offers new insights and theoretical references for promoting domain generalization in practical industrial applications. Full article

Time-series Interferometric Synthetic Aperture Radar (InSAR) techniques encounter substantial reliability challenges, primarily due to the presence of gross errors arising from phase unwrapping failures. These errors propagate through the processing chain and adversely affect displacement estimation accuracy, particularly in the case of a small number of SAR datasets. This study presents a unified data fusion framework designed to enhance the detection of gross errors in multi-source InSAR observations, incorporating a robust Least Squares Adjustment (LSA) methodology. The proposed framework develops a comprehensive mathematical model that integrates the fusion of multi-source InSAR data with robust LSA analysis, thereby establishing a theoretical foundation for the integration of heterogeneous datasets. Then, a systematic, reliability-driven data fusion workflow with robust LSA is developed, which synergistically combines Multi-Temporal InSAR (MT-InSAR) processing, homonymous Persistent Scatterer (PS) set generation, and iterative Baarda’s data snooping based on statistical hypothesis testing. This workflow facilitates the concurrent localization of gross errors and optimization of displacement parameters within the fusion process. Finally, the framework is rigorously evaluated using datasets from Radarsat-2 and two Sentinel-1 acquisition campaigns over the Tianjin Binhai New Area, China. Experimental results indicate that gross errors were successfully identified and removed from 11.1% of the homonymous PS sets. Following the robust LSA application, vertical displacement estimates exhibited a Root Mean Square Error (RMSE) of 5.7 mm/yr when compared to high-precision leveling data. Furthermore, a localized analysis incorporating both leveling validation and time series comparison was conducted in the Airport Economic Zone, revealing a substantial 42.5% improvement in accuracy compared to traditional Ordinary Least Squares (OLS) methodologies. Reliability assessments further demonstrate that the integration of multiple InSAR datasets significantly enhances both internal and external reliability metrics compared to single-source analyses. This study underscores the efficacy of the proposed framework in mitigating errors induced by phase unwrapping inaccuracies, thereby enhancing the robustness and credibility of InSAR-derived displacement measurements. Full article

The sine cosine algorithm (SCA) is a population-based stochastic optimization method that updates the position of each search agent using the oscillating properties of the sine and cosine functions to balance exploration and exploitation. While flexible and widely applied, the SCA often suffers from premature convergence and getting trapped in local optima due to weak exploration–exploitation balance. To overcome these issues, this study proposes a multi-faceted SCA (MFSCA) incorporating several improvements. The initial population is generated using dynamic opposition (DO) to increase diversity and global search capability. Chaotic logistic maps generate random coefficients to enhance exploration, while an elite-learning strategy allows agents to learn from multiple top-performing solutions. Adaptive parameters, including inertia weight, jumping rate, and local search strength, are applied to guide the search more effectively. In addition, Lévy flights and adaptive Gaussian local search with elitist selection strengthen exploration and exploitation, while reinitialization of stagnating agents maintains diversity. The developed MFSCA was tested against 23 benchmark optimization functions and assessed using the Wilcoxon rank-sum and Friedman rank tests. Results showed that MFSCA outperformed the original SCA and other variants. To further validate its applicability, this study developed a fuzzy c-means MFSCA-based adaptive neuro-fuzzy inference system to forecast energy consumption in student residences, using student apartments at a university in South Africa as a case study. The MFSCA-ANFIS achieved superior performance with respect to RMSE (1.9374), MAD (1.5483), MAE (1.5457), CVRMSE (42.8463), and SD (1.9373). These results highlight MFSCA’s effectiveness as a robust optimizer for both general optimization tasks and energy management applications. Full article

Listeria monocytogenes is a ubiquitous foodborne pathogen whose occurrence in food and food-processing environments raises public-health concerns, particularly when isolates carry antimicrobial-resistance determinants. Next-generation sequencing (NGS) is increasingly used to detect resistance genes and to predict phenotypic resistance. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, PubMed, Web of Science, and Scopus were searched for original articles (2015–2024) that used second- and/or third-generation sequencing to characterize antibiotic resistance in L. monocytogenes from food and food-processing environments. After deduplication and screening, 58 studies were included from an initial 418 records. NGS reliably detected a set of recurrent resistance determinants across diverse sample types and geographies. The fosX locus (intrinsic fosfomycin-related marker) was effectively ubiquitous across studies, while acquired determinants were variably distributed: lin (35/58 studies, 60.34%), norB (33/58, 56.90%), and tetracycline genes overall in 20/58 (34.48%) with tetM as the most common (11/58, 18.97%). Reported concordance between the genotypes and phenotypes for acquired resistance was very high (>99% for most agents), with notable exceptions (e.g., ciprofloxacin and some fosfomycin cases). Common analysis pipelines and databases included ResFinder, CARD, BIGSdb-Lm, ABRicate, and ARIBA; most sequencing used Illumina short reads, with an increasing use of long-read or hybrid approaches. NGS is a powerful surveillance tool for detecting resistance determinants and for source-tracking, but its predictive value depends on integration with phenotypic testing, standardized reporting, and comprehensive, curated databases. Key gaps include inconsistent phenotype reporting, variable database coverage, and limited assessment of gene expression/regulatory effects. Full article

Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their spatial heterogeneity and the multivariate nature of pollution dynamics. This study presents an integrated framework for detecting spatiotemporal pollution patterns using satellite remote sensing, trend segmentation, hierarchical clustering and dimensionality reduction. Taking Horseshoe Lake (Illinois), a shallow eutrophic–turbid system, as a case study, we analyzed Sentinel-2 imagery from 2020–2024 to derive chlorophyll-a (NDCI), turbidity (NDTI), and total phosphorus (TP) across five hydrologically distinct zones. Breakpoint detection and modified Mann–Kendall tests revealed both abrupt and seasonal trend shifts, while correlation and hierarchical clustering uncovered inter-zone relationships. To identify lake-wide pollution windows, we applied Kernel PCA to generate a composite pollution index, aligned with the count of increasing trend segments. Two peak pollution periods, late 2022 and late 2023, were identified, with Regions 1 and 5 consistently showing high values across all indicators. Spatial maps linked these hotspots to urban runoff and legacy impacts. The framework captures both acute and chronic stress zones and enables targeted seasonal diagnostics. The approach demonstrates a scalable and transferable method for pollution monitoring in morphologically complex lakes and supports more targeted, region-specific water management strategies. Full article

Background: SATB2-associated syndrome (SAS), also known as Glass syndrome, is a neurodevelopmental disorder (NDD) characterized by intellectual disability, developmental delay, absent or limited speech, and distinctive craniofacial and dental anomalies. It is caused by autosomal dominant pathogenic variants in the SATB2 gene, which plays a crucial role in brain, dental, and jaw development. Due to its variable phenotype, clinical diagnosis can be challenging, necessitating genetic confirmation. Methods: We present six new cases of SAS with SATB2 germline variants identified through next generation sequencing (NGS) technologies, expanding the known genetic and clinical spectrum of the syndrome. Detailed clinical phenotyping was performed for all patients. Results: Our cohort exhibits a broad range of clinical manifestations consistent with SAS, encompassing severe intellectual disability, profound speech delay, various palatal and dental abnormalities. We report the oldest adult patient (56 years old) carrying an in-frame duplication, and a pediatric patient with a missense variant who presented a significant reduction in visual acuity, likely of neurological or cortical origin, in the absence of ophthalmological abnormalities. SATB2 variants include three missenses, two in-frame deletion/duplication and one frameshift variant, several of which are novel and classified as likely pathogenic or pathogenic according to ACMG guidelines. Conclusions: This report provides new clinical and genetic insights into the landscape of SAS. Our findings confirm the phenotypic heterogeneity of SAS and highlight the critical role of comprehensive genetic testing for accurate diagnosis in NDD patients. Full article

The safety of offshore structures is a key topic in developing offshore oil and gas and offshore wind energy. Due to the harsh offshore environment and costly offshore field tests, offshore field trials to validate the theoretical models for offshore structures are limited, and testing results can rarely be found in the public domain. The Bayesian updating technique combines existing engineering knowledge with the observed performance data about in-service offshore structures to update model uncertainties. Hence, the Bayesian technique overcomes the shortcomings of limited offshore field trials. This paper compiles performance data on offshore jackets in hurricanes in the Gulf of Mexico (GoM) in the past two decades and calibrates the model uncertainties of the API method using the Bayesian technique. With the updated model uncertainty, this paper evaluates the reliability of generic offshore jackets in the GoM and the case study’s offshore wind substation in China. With a typical reserve strength ratio (RSR) of about 2.0 to 2.2, the reliability analysis reveals that the updated annual failure probability of a generic offshore jacket in the GoM is largely less than $1.0\times {10}^{-3}$, indicating that the extreme weather overload is not a major concern. However, the RSR of the case study platform in China is greater than 4.5, and the annual failure probability for the case study offshore wind substation is about 2–3 orders of magnitude lower than typical oil and gas jackets. Hence, from the extreme metocean condition perspective, the substation under investigation has sufficient structural capacity, and the design practice for offshore wind substations in northern China may be improved. Full article