Search Results (5,921)

Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability of developing NHD in children with CP. The system utilizes an ensemble of three machine learning (ML) algorithms: Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR). The development and evaluation of the CDSS followed the DECIDE-AI guidelines for AI-driven clinical decision support tools. The ensemble was trained on a data series from 182 subjects. Inclusion criteria were age between 12 and 18 years and diagnosis of CP from two specialized units. Clinical and functional data were collected prospectively between 2005 and 2023, and then analyzed in a cross-sectional study. Accuracy and area under the receiver operating characteristic (AUROC) were calculated for each method. Best logistic regression scores highlighted history of previous orthopedic surgery (p = 0.001), poor motor function (p = 0.004), truncal tone disorder (p = 0.008), scoliosis (p = 0.031), number of affected limbs (p = 0.05), and epilepsy (p = 0.05) as predictors of NHD. Both accuracy and AUROC were highest for NN, 83.7% and 0.92, respectively. The novelty of this study lies in the development of an efficient Clinical Decision Support System (CDSS) prototype, specifically designed to predict future outcomes of neuromuscular hip dysplasia (NHD) in patients with cerebral palsy (CP) using clinical data. The proposed system, PredictMed-CDSS, demonstrated strong predictive performance for estimating the probability of NHD development in children with CP, with the highest accuracy achieved using neural networks (NN). PredictMed-CDSS has the potential to assist clinicians in anticipating the need for early interventions and preventive strategies in the management of NHD among CP patients. Full article

This paper addressed the problem of oil diagnostics lubricants applied to the predictive maintenance of industrial gearboxes, proposing the integration of an artificial intelligence (AI) system into the process analysis. The main objective was to overcome the critical issues of the traditional method, characterized by long analysis times and a marked dependence on the subjective interpretation of operators. The method includes a detailed statistical analysis of the common ways to assess the condition of lubricants, such as optical emission spectroscopy, particle counting, measuring viscosity and density, and Fourier-transform infrared spectroscopy (FT-IR). These methods are then combined with an artificial intelligence model. Tested on commercial gearbox data, the proposed approach demonstrates agreement between IA and expert evaluation. The application has shown that it can effectively support diagnoses, reduce processing time by 60%, and minimize human errors. It also improves knowledge sharing through an increase in the stability and repetitiveness of diagnoses and promotes consistency and clarity in reporting. Full article

Background and Objectives: The uric acid to HDL-cholesterol ratio (UHR) has recently emerged as a promising biomarker reflecting systemic inflammation and metabolic disturbances. Elevated liver transaminases are clinical indicators of hepatic injury and underlying metabolic dysfunction. Many Middle Eastern countries face constrained clinical and laboratory resources, where access to comprehensive diagnostic tools may be limited. In such settings, identifying simple and easily accessible markers could offer significant practical value in detecting and monitoring health disorders. This study investigates the potential association between UHR and elevated liver transaminases levels in the Saudi general population. Materials and Methods: This retrospective cross-sectional study included 9618 subjects, and the association between the UHR and elevated liver transaminases, alanine transaminase (ALT), and aspartate transaminase (AST), was comprehensively analysed. In addition, the study assessed risk indicators including the prevalence ratio (PR) and odds ratio (OR) as well as the diagnostic accuracy of UHR and C-reactive protein (CRP) in detecting liver transaminases abnormalities, with analyses stratified by age and gender. Results: UHR was significantly elevated in subjects with increased ALT and AST activities, and this pattern was consistent across all age and gender categories. High UHR was significantly associated with elevated ALT (OR = 2.32, 95% CI: 2.12–2.53, p < 0.001) and AST (OR = 1.38, 95% CI: 1.25–1.52, p < 0.001), with stronger associations observed in males and for ALT activity. In addition, elevated UHR was more prevalent among individuals with increased liver transaminase activities. Receiver operating characteristic (ROC) analysis showed that UHR outperformed CRP in identifying elevated liver transaminases, with better discriminative ability for ALT than AST activity. Conclusions: These findings highlight a significant association between UHR and liver transaminase abnormalities in the general population, underscoring the potential utility of UHR as a simple and accessible indicator for liver function assessment in clinical settings. Full article

This article investigates the role of space in the fiction of Jeffrey Eugenides, focusing on The Virgin Suicides (1993) and Middlesex (2002) through the lens of spatial theory. Drawing on key thinkers such as Henri Lefebvre, Michel Foucault, Edward Soja, Yi-Fu Tuan, and Doreen Massey, the study explores how Eugenides constructs spatial environments that not only frame but actively shape the identities, desires, and traumas of his characters. In The Virgin Suicides, suburban domestic spaces are shown to function as heterotopias—sites of surveillance, repression, and mythologized femininity—while Middlesex engages with transnational and urban spaces to narrate diasporic and intersex identity as dynamic, embodied, and liminal. The analysis reveals that Eugenides uses space as both a narrative device and a thematic concern to interrogate gender, memory, and power. Rather than passive backdrops, the novelistic spaces become charged arenas of conflict and transformation, reflecting and resisting dominant socio-cultural discourses. This study argues that space in Eugenides’ fiction operates as a critical register for understanding the politics of belonging and the production of subjectivity. By situating Eugenides within the broader field of literary spatiality, this article contributes to contemporary debates in literary geography, gender studies, and American fiction. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

The organization of consciousness is described through increasingly rich theoretical models. We review evidence that working memory capacity—essential to generating consciousness in the cerebral cortex—is supported by dual limbic memory systems. These dorsal (Papez) and ventral (Yakovlev) limbic networks provide the basis for mnemonic processing and prediction in the dorsal and ventral divisions of the human neocortex. Empirical evidence suggests that the dorsal limbic division is (i) regulated preferentially by excitatory feedforward control, (ii) consolidated by REM sleep, and (iii) controlled in waking by phasic arousal through lemnothalamic projections from the pontine brainstem reticular activating system. The ventral limbic division and striatum, (i) organizes the inhibitory neurophysiology of NREM to (ii) consolidate explicit memory in sleep, (iii) operating in waking cognition under the same inhibitory feedback control supported by collothalamic tonic activation from the midbrain. We propose that (i) these dual (excitatory and inhibitory) systems alternate in the stages of sleep, and (ii) in waking they must be balanced—at criticality—to optimize the active inference that generates conscious experiences. Optimal Bayesian belief updating rests on balanced feedforward (excitatory predictive) and feedback (inhibitory corrective) control biases that play the role of prior and likelihood (i.e., sensory) precision. Because the excitatory (E) phasic arousal and inhibitory (I) tonic activation systems that regulate these dual limbic divisions have distinct affective properties, varying levels of elation for phasic arousal (E) and anxiety for tonic activation (I), the dual control systems regulate sleep and consciousness in ways that are adaptively balanced—around the entropic nadir of E–I criticality—for optimal self-regulation of consciousness and psychological health. Because they are emotive as well as motive control systems, these dual systems have unique qualities of feeling that may be registered as subjective experience. Full article

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble of ML models to previously unseen validation data were fitted to a beta distribution. The shape parameters for the fitted distribution were used to calculate the subjective opinion of sample membership into one of two mutually exclusive classes. The subjective opinion consists of belief, disbelief and uncertainty masses. A subjective opinion for each validation sample allows identification of high-uncertainty predictions. The projected probabilities of the validation opinions were used to calculate log-likelihood ratio scores and generate receiver operating characteristic (ROC) curves from which an opinion-supported decision can be made. Three very different ML models, linear discriminant analysis (LDA), random forest (RF), and support vector machines (SVM) were applied to the two-state classification problem in the analysis of forensic fire debris samples. For each ML method, a set of 100 ML models was trained on data sets bootstrapped from 60,000 in silico samples. The impact of training data set size on opinion uncertainty and ROC area under the curve (AUC) were studied. The median uncertainty for the validation data was smallest for LDA ML and largest for the SVM ML. The median uncertainty continually decreased as the size of the training data set increased for all ML.The AUC for ROC curves based on projected probabilities was largest for the RF model and smallest for the LDA method. The ROC AUC was statistically unchanged for LDA at training data sets exceeding 200 samples; however, the AUC increased with increasing sample size for the RF and SVM methods. The SVM method, the slowest to train, was limited to a maximum of 20,000 training samples. All three ML methods showed increasing performance when the validation data was limited to higher ignitable liquid contributions. An ensemble of 100 RF ML models, each trained on 60,000 in silico samples, performed the best with a median uncertainty of 1.39x${10}^{-2}$ and ROC AUC of 0.849 for all validation samples. Full article

Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Although current style-transfer methods have achieved promising results when processing photorealistic images, they often struggle with brushstroke preservation in artworks, especially in styles such as oil painting and pointillism. In such cases, the extracted style and content features tend to include redundant information, leading to issues such as blurred edges and a loss of fine details in the transferred images. To address this problem, this paper proposes a multi-scale general style-transfer network based on diffusion models. The proposed network consists of a coarse style-transfer module and a refined style-transfer module. First, the coarse style-transfer module is designed to perform mainstream style-transfer tasks more efficiently by operating on downsampled images, enabling faster processing with satisfactory results. Next, to further enhance edge fidelity, a refined style-transfer module is introduced. This module utilizes a segmentation component to generate a mask of the main subject in the image and performs edge-aware refinement. This enhances the fusion between the subject’s edges and the target style while preserving more detailed features. To improve overall image quality and better integrate the style along the content boundaries, the output from the coarse module is upsampled by a factor of two and combined with the subject mask. With the assistance of ControlNet and Stable Diffusion, the model performs content-aware edge redrawing to enhance the overall visual quality of the stylized image. Compared with state-of-the-art style-transfer methods, the proposed model preserves more edge details and achieves more natural fusion between style and content. Full article

Objectives: To investigate the reliability and validity of the Singer Reflux Symptom Score (sRSS), a new patient-reported outcome questionnaire documenting the severity of reflux symptoms in singing voice is proposed. Methods: Amateur and professional singers consulting the European Reflux Clinic for laryngopharyngeal reflux disease (LPRD) symptoms and findings were prospectively recruited from January 2022 to February 2023. The diagnosis was based on a Reflux Symptom Score (RSS) > 13 and Reflux Sign Assessment (RSA) > 14. A control group of asymptomatic singer subjects was recruited from the University of Mons. The sRSS was rated within a 7-day period to assess test–retest reliability. Internal consistency was measured using Cronbach’s α in patients and controls. A correlation analysis was performed between sRSS and Singing Voice Handicap Index (sVHI) to evaluate convergent validity. Responsiveness to change was evaluated through pre- to post-treatment sRSS changes. The sRSS threshold for suggesting a significant impact of LPRD on singing voice was determined by receiver operating characteristic (ROC) analysis. Results: Thirty-three singers with suspected LPRD (51.5% female; mean age: 51.8 ± 17.2 years) were consecutively recruited. Difficulty reaching high notes and vocal fatigue were the most prevalent LPRD-related singing complaints. The sRSS demonstrated high internal consistency (Cronbach-α = 0.832), test–retest reliability, and external validity (correlation with sVHI: r = 0.654; p = 0.015). Singers with suspected LPRD reported a significant higher sRSS compared to 68 controls. sRSS item and total scores significantly reduced from pre-treatment to 3 months post-treatment except for the abnormal voice breathiness item. ROC analysis revealed superior diagnostic accuracy for sRSS (AUC = 0.971) compared to sRSS-quality of life (AUC = 0.926), with an optimal cutoff at sRSS > 38.5 (sensitivity: 90.3%; specificity: 85.0%). Conclusions: The sRSS is a reliable and valid singer-reported outcome questionnaire for documenting singing symptoms associated with LPRD leading to personalized management of Singers. Future large-cohort studies are needed to evaluate its specificity for LPRD compared to other vocal fold disorders in singers. Full article

With the electrification of generator sets, electric locomotives, new energy vehicles, and other industries, AC motors subject bearings to an electric field environment, leading to galvanic corrosion due to the use of variable frequency power supply drives. The phenomenon of bearing discharge breakdown in subway traction motors is a critical issue in understanding the relationship between shaft current strength and the extent of bearing damage. This paper analyzes the mechanism of impulse discharge that leads to galvanic corrosion damage in bearings at a microscopic level and conducts electric thermal coupling simulations of the traction motor bearing discharge breakdown process. It examines the temperature rise associated with lubricant film discharge breakdown during the dynamic operation of the bearing and investigates how breakdown channel parameters and operational conditions affect the temperature rise in the micro-region of bearing lubrication. Ultimately, the results of the electric thermal coupling simulation are validated through experimental tests. This study revealed that in an electric field environment, the load-bearing area of the outer ring experiences significantly more severe corrosion damage than the inner ring, whereas non-bearing areas remain unaffected by electrolytic corrosion. When the inner ring reaches a speed of 4500_rpm, the maximum widths of electrolytic corrosion pits for the outer and inner rings are measured at 89 um and 51 um, respectively. Additionally, the highest recorded temperatures for the breakdown channels in the outer and inner rings are 932 °C and 802 °C, respectively. Furthermore, as the inner ring speed increases, both the width of the electrolytic corrosion pits and the temperature of the breakdown channels rise. Specifically, at inner ring speeds of 2500_rpm, 3500_rpm, and 4500_rpm, the widths of the electrolytic pits in the outer ring raceway load zone were measured at 34 um, 56 um, and 89 um, respectively. The highest temperatures of the lubrication film breakdown channels were recorded as 612 °C, 788 °C, and 932 °C, respectively. This study provides a theoretical basis and data support for the protective and maintenance practices of traction motor bearings. Full article

Complex mechanical systems, such as agricultural combine harvesters, are subjected to dynamic excitations from multiple coupled sources, compromising structural integrity and operational reliability. Disentangling these vibrations to identify dominant sources and quantify their transmission paths remains a significant engineering challenge. This study proposes a robust diagnostic framework to address this issue. We employed a multi-condition vibration test with sequential source activation and an improved Operational Transfer Path Analysis (OTPA) method. Applied to a harvester chassis, the results revealed that vibration energy is predominantly concentrated in the 0–200 Hz frequency band. Path contribution analysis quantified that the “cutting header → conveyor trough → hydraulic cylinder → chassis frame” path is the most critical contributor to vertical vibration, with a vibration acceleration level of 117.6 dB. Further analysis identified the engine (29.3 Hz) as the primary source for vertical vibration, while lateral vibration was mainly attributed to a coupled resonance between the threshing cylinder (58 Hz) and the engine’s second-order harmonic. This study’s theoretical contribution lies in validating a powerful methodology for vibration source apportionment in complex systems. Practically, the findings provide direct, actionable insights for targeted structural optimization and vibration suppression. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

Ship structures are subjected to cyclic loading from waves and currents during operation, which can lead to fatigue failure, particularly at locations with structural discontinuities such as welds. Although various fatigue assessment methods have been developed, there is a lack of experimental data and comparative studies for actual ship structure details. This study addresses this limitation by evaluating the fatigue strength of longi-web connections in hull structures using local stress approaches, including hot spot stress, effective notch stress, notch stress intensity factor, and structural stress methods. Finite element analyses were conducted, and the predicted fatigue lives and failure locations were compared with experimental results. Although there are some differences between each method, all methods are valid and reasonable for predicting the primary failure locations and evaluating fatigue life. These findings provide a basis for considering suitable fatigue assessment methods for welded ship structures with respect to joint geometry and failure mechanisms. Full article

In engineering design, optimization is crucial for achieving sustainable goals. This involves creating environmentally responsible structures. Optimizing the design is the first step in reducing the environmental impact of construction. Topology optimization (TO) is one way to do this. TO is the process of designing the material layout in the design domain according to selected criteria. The criteria can be explicitly defined to promote sustainability. As a result, a new structure topology is proposed to make the structure both lightweight and durable, with the aim of improving its functionality and reducing its environmental impact. In optimal engineering design, it is particularly important to take into account the structure’s special operating conditions, e.g., loads subject to random changes. Predicting topologies under such conditions is important since random load changes can significantly affect the resulting topologies. In this paper, an easy to implement numerical method for this kind of problem is proposed. The basic idea is to transform a random loads case into the deterministic problem of multiple loads. A heuristic method of Cellular Automata is proposed as a numerical optimization tool. The examples of topology optimization have been performed to illustrate the concept, confirming the efficiency, versatility, and ease of its implementation. Full article

This study systematically investigates 50 μm-diameter 631 stainless steel fine wires subjected to both sequential and simultaneous electrothermomechanical loading to simulate probe spring conditions in microelectronic test environments. Under cyclic current loading (~10⁴ A/cm²), the 50 μm 631SS wire maintained electrical integrity up to 0.30 A for 15,000 cycles. Above 0.35 A, rapid oxide growth and abnormal grain coarsening resulted in surface embrittlement and mechanical degradation. Current-assisted tensile testing revealed a transition from recovery-dominated behavior at ≤0.20 A to significant thermal softening and ductility loss at ≥0.25 A, corresponding to a threshold temperature of approximately 200 °C. These results establish the endurance limit of 631 stainless steel wire under coupled thermal–mechanical–electrical stress and clarify the roles of Joule heating, oxidation, and microstructural evolution in electrical fatigue resistance. A degradation map is proposed to inform design margins and operational constraints for fatigue-tolerant, electrically stable interconnects in high-reliability probe spring applications. Full article