Search Results (12,393)

Accurately classifying gait abnormalities is crucial for the effective monitoring and rehabilitation of stroke patients. This study proposed a lightweight machine learning framework for distinguishing healthy from abnormal gait patterns using statistical features extracted from wearable gyroscope data. Statistical z-axis angular velocity values from both limbs were derived and used to evaluate the performance of multiple classifiers, including logistic regression, support vector machines, and ensemble methods. A leave-one-out cross-validation strategy was employed to enhance generalizability across subjects. The results indicated that several classifiers achieve accuracy and area under the curve (AUC) values exceeding 0.95, with random forest and support vector machine-based models demonstrating near-perfect class separability, with an AUC of 0.98. These findings highlighted the effectiveness of using minimal set of biomechanically relevant gyroscope features for gait classification in real-world healthcare applications. The proposed pipeline is computationally efficient, making it well suited for implementing in wearable and remote monitoring systems. Full article

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes. Full article

Accurate identification of key nodes in complex networks is vital for optimizing system robustness and controlling information spread. Existing centrality metrics struggle to balance the continuous extraction of global topological features with the fine-grained perception of local structures, while traditional heuristic algorithms also face severe resolution limitations. To address these issues, this paper proposes a node importance evaluation method based on fractional-order topological propagation and local information entropy (FSEC). This method overcomes the limitations of discrete integer-order propagation inherent in traditional graph walks. It constructs a continuous fractional-order topological propagation operator within the spectral graph theory framework. This enables the smooth projection of node degree features into the global topological space, thereby yielding high-order global impact factors. Furthermore, an information theory mechanism is introduced to quantify the probability distribution of a node’s information contribution within its local neighborhood. The local structural information entropy is then calculated to reflect the node’s asymmetric control over micro-level information flow. Deliberate attack simulations were conducted on nine real-world networks and three types of artificial network models. The results show that the proposed FSEC algorithm significantly outperforms baseline algorithms like Autoencoder and Graph Neural Network (AGNN), Degree Centrality, k-shell, PageRank, and Mixed Degree Decomposition (MDD) in degrading the largest connected component (LCC) and global network efficiency (NE). The proposed method also achieves the minimum Area Under the Curve (AUC) values globally. Its monotonicity is slightly lower than that of AGNN but superior to all other baseline algorithms. In addition, SIR simulations further confirm the effectiveness of the FSEC method. This approach successfully resolves the ranking tie problem among nodes in the same topological layer. Full article

False-positive results are known to occur frequently in newborn screening (NBS) for carnitine palmitoyltransferase II (CPT II) deficiency, highlighting the need to identify appropriate screening markers. The present study aimed to compare the performance of two markers currently used in NBS for CPT II deficiency, (C16 + C18:1)/C2 and C14/C3, with two promising alternative markers, C12/C0 and C12/C2. We analyzed non-patient data from the 2023 fiscal year NBS program together with patient data for CPT II deficiency and very-long-chain acyl-CoA dehydrogenase deficiency derived from previously reported case series. Marker performance was assessed using precision–recall curves, including an analysis in which patients with the myopathic form of CPT II deficiency who passed NBS using (C16 + C18:1)/C2 and C14/C3 were reclassified as true positives. The area under the precision–recall curve values for C12/C2 and C14/C3 were 0.711 (95% confidence interval, 0.492–0.923) and 0.569 (0.341–0.779), respectively, indicating superior performance compared with the other markers. When cases with the myopathic form of CPT II deficiency were included as true positives, the performance of all markers decreased markedly. Although some patients with the myopathic form are still likely to be missed, C12/C2 appears to be an effective marker for reducing false-positive results. Full article

Limit state analysis provides building designers with a better understanding of fundamental structural resistance and deformation requirements, resulting in an overall material economy and offering clear safety boundary conditions for intelligent structural design. Cold-formed high-strength steel has extensive application prospects in structural engineering due to its excellent mechanical properties and flexible cross-sectional options. However, most existing research focuses on its ultimate strength-related behavior, lacking sufficient investigation into deformation properties. This study aims to comprehensively reveal the continuous variation laws of structural resistance and ductility of cold-formed high-strength CHSs (circular hollow sections) with different cross-sectional selections under axial load. Through reliable finite element analysis, the effects of yield strength (f_sy) of cold-formed CHSs, diameter-to-thickness ratio (D/t), and cross-sectional slenderness (λ) on compressive performance in the limit state, including failure mode, axial load-end shortening curve, ultimate-to-yield strength ratio (N_u/N_y), and ductility indicators (displacement ductility coefficient (μ) corresponding to the ascending stage and post-buckling ductility degradation coefficient (R_0.85)), were systematically investigated. The results indicate that the dominant failure mode of high-strength CHSs exhibits outward deformation. With an increase of f_sy and D/t, the value of N_u/N_y decreases, and the development of multiple compression performance exhibits significant nonlinearity, which indicates that blindly improving material strength may not necessarily be conducive to developing structural compressive performance or achieving efficient and economical design solutions. To better serve the ductile limit design of high-strength CHSs, combined with available experimental data and simulation results, the upper limit of λ is proposed to be 0.22, and the predictive models of μ and R_0.85 are established, respectively. Full article

Background/Objectives: Obesity represents a growing public health concern worldwide. Chrononutrition, a research field examining the timing and regularity of food intake, has been shown in animal models to influence body weight regulation and obesity-related outcomes. Previous research has also explored the association between chrononutrition information and BMI. Using INRAN-SCAI 2005/2006 adult nutrition data based on 3-day diet diaries (n = 2312), this study aims to assess whether chrononutritional information on the distribution of energy intake during the day is able to improve prediction of overweight status (BMI > 25 kg/m²), compared to information on energy from the whole day alone. Methods: This research investigates it using logistic regression and random forest models. For both types of models, three different specifications were compared: a model trained on the mean and irregularity of calorie intake over 3 days for 6 day-time intervals (MI6); a model trained on repeated measures over 3 days of calorie intake from the same 6 time intervals (RM); and a model trained on mean and irregularity of calorie intake over 3 days for the whole day (MID). The performance of the models was compared using risk prediction metrics and ROC curves. Results: When including additional demographic and behavioural predictors beside the energy variables, the results only showed a statistically significant difference in the performance of the logistic regression models if they were trained and tested on the same data. The models trained using chrononutrition information performed better, but the difference in diagnostic accuracy was very small (AUC = 0.7909 for MI6, p = 0.0086; 0.7923 for RM compared to 0.7850 for MID, p = 0.0072) and possibly attributable to overfitting, as it was no longer significant in the comparison within a testing set (70% training and 30% testing samples). For the random forest models, no significant difference was found. In the same models including only the energy variables, the improved performance of MI6 and RM was significantly better than for MID also in the test set (respectively, p = 0.0001 and p = 0.0002), and the gap in AUCs became substantial (AUC = 0.622 for MI6, 0.618 for RM and 0.507 for MID), indicating that socio-demographic and behavioural variables encapsulate information on energy intake by time of the day. Typical under-reporting bias present in nutritional epidemiology and the cross-sectional nature of the sample based on 3-day diaries may have affected these results, although use of diet diaries should minimize recall bias. Conclusions: In conclusion, the impact on health of timing and regularity of calorie intake in the day may act through other mechanisms than via overweight and may be captured by other demographic and behavioural variables; larger and prospective longitudinal studies are warranted to thoroughly investigate the added value of time-of-day information. Full article

Background/Objectives: This study assessed the diagnostic accuracy of two commonly used diagnostic instruments for autism spectrum disorder (ASD), the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) and the Autism Diagnostic Interview-Revised (ADI-R), in comparison to a best-estimate (BE) diagnosis made by a research psychologist. Methods: Two hundred and thirteen children aged 5 years 0 months to 7 years 11 months completed a comprehensive research assessment that included multiple diagnostic measures. Once each research assessment was complete, a research psychologist gave each participant an overall BE research diagnosis of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) ASD based on all available information from diagnostic testing and behavioral observations during testing. We assessed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both the ADOS-2 and ADI-R separately and in combination and used receiver operating characteristic (ROC) curves to compare the areas under the curve (AUCs) of these instruments. Results: Both the ADOS-2 Spectrum Criterion scoring (sensitivity = 96.2%; specificity = 97.5%) and ADOS-2 Autism Criterion scoring (sensitivity = 82.0%; specificity = 100%) had excellent accuracy in comparison to the BE ASD diagnosis. The ADI-R had good accuracy (sensitivity = 78.6%; specificity = 83.5%) compared to BE ASD diagnosis. In receiver operating curve analyses, both scoring criteria for ADOS-2 were significantly more accurate than the ADI-R. Conclusions: Overall, both instruments provide good, if not excellent, classification accuracies when used individually, as well as in combination. Thus, when deciding which measures to use for ASD research, other factors should also be considered. Full article

With the rapid development of the photovoltaic industry, the issue of high-value conversion and utilization of end-of-life photovoltaic modules emerges. This study proposes using them in silicon-air batteries and designs a one-step pretreatment process to obtain two types of anode materials: AB@Si and TC@Si. Additionally, to enhance the electrochemical performance of retired crystalline silicon from PV modules as anodes for silicon-air batteries and improve their mass conversion efficiency, this study introduces Triton X-100 into the KOH electrolyte to inhibit chemical corrosion of the anodes and investigates the mechanism of action of Triton X-100. The results indicate that the surfaces of AB@Si and TC@Si exhibit a pyramidal structure, demonstrating excellent passivation resistance when used in silicon-air batteries, with maximum mass conversion efficiencies of 3.5% and 1.83%, respectively. Under the influence of Triton X-100, the maximum mass conversion efficiencies reach 6.39% and 3.09%, respectively. Polarization curves and mass loss under non-current conditions indicate that Triton X-100 primarily affects the chemical corrosion process of the silicon anode, while its impact on electrochemical corrosion is negligible. Results from contact angle measurements and adsorption energy calculations indicate that Triton X-100 adsorbs onto the silicon surface via benzene ring groups or OH groups, reducing hydrophilicity and delaying the self-corrosion process of silicon, thereby improving the battery′s discharge lifespan and mass conversion efficiency. Full article

Background/Objectives: Recent studies have increasingly focused on the morphological characteristics of surrounding arteries as rupture predictors, particularly because these vessel configurations remain stable before and after aneurysm rupture, providing a reliable anatomical substrate for risk assessment. This study aimed to identify independent predictors of rupture by evaluating both aneurysmal and internal carotid artery (ICA) morphological characteristics. Methods: We retrospectively analyzed imaging data from 64 patients with posterior communicating artery (PcomA) aneurysms who underwent treatment at a single tertiary center between 2018 and 2022, including 25 ruptured aneurysms (39.1%). Only treated aneurysms were included to ensure the availability of high-quality pre-treatment digital subtraction angiography (DSA) suitable for three-dimensional (3D) reconstruction and centerline-based analysis. Seventeen aneurysm morphological parameters and thirteen ICA-related parameters were measured. Because time-to-event data were not available, logistic regression analysis was performed with rupture status as the outcome variable. Receiver operating characteristic (ROC) curve analyses were conducted to evaluate discriminative performance. Results: Multivariate logistic regression revealed that three ICA-associated factors—the tortuosity of the communicating ICA segment (Tcco), the ICA cross-sectional area at the PcomA origin (Pcs), and the angle between the ICA and PcomA (θ2)—were independently associated with rupture. Among aneurysm-related factors, Maximum 3D Diameter remained significantly related to rupture risk. ROC analyses demonstrated that Maximum 3D Diameter had the highest discriminative value (AUC 0.779; cut-off 7.805 mm), followed by Pcs, Tcco, and θ2. Conclusions: Both aneurysm morphology and the anatomical configuration of surrounding arteries significantly contribute to rupture risk in PcomA aneurysms. Incorporating parent-vessel morphological features into rupture-risk assessment may enhance patient-specific decision-making. Full article

Background/Objectives: The presence of a mural nodule (MN) is one of the findings indicating malignant transformation of an intraductal papillary mucinous neoplasm (IPMN). It is difficult to distinguish true MNs from mucus clots (MCs) by endoscopic ultrasound (EUS) alone. This study aimed to evaluate the efficacy of contrast-enhanced (CE)-EUS for differentiating true MNs from MCs and carcinoma from adenoma. Methods: A total of 104 patients who were diagnosed as having branch duct-type IPMNs with MN-like structures by EUS and underwent CE-EUS between January 2016 and August 2022 were included. MN-like structures without perfusion on CE-EUS were defined as MCs and those with perfusion were defined as true MNs. This was a retrospective study with limited pathological confirmation, and diagnoses in non-surgical cases were based on imaging and follow-up. Results: CE-EUS showed MN-like structures with perfusion in 35 patients and without perfusion in 69 patients. Surgical resection was eventually performed in a total of 28 patients and the diagnostic sensitivity, specificity and accuracy of MNs among them were 100%, 66.7% and 96.4% in CE-EUS; 48%, 66.7% and 50% in CE-CT; and 61.9%, 33.3% and 58.3% in MRCP, respectively. Possible risk factors indicating malignancy were statistically evaluated and presence of an MN was the only significant factor. Among the 35 true MNs, the height of an MN in carcinoma was significantly higher than that of an adenoma. The ROC analysis for detecting carcinoma in true MNs showed an area under the curve of 0.92 with the optimal cut-off value of 7 mm. When this cut-off value was used for diagnosing carcinoma, the sensitivity, specificity, and accuracy were 94.1%, 83.3% and 88.6%, respectively. Conclusions: CE-EUS may be useful for differentiating true MNs from MCs, although diagnostic performance should be interpreted cautiously because most non-surgical cases lacked pathological confirmation. Full article

Background/Objectives: Systemic embolization is a common and serious complication of infective endocarditis (IE). This study evaluated the association between vegetation morphology and embolic events and assessed whether echocardiographic parameters provide incremental discriminatory value beyond clinical variables. Methods: We conducted a retrospective cohort study including 164 consecutive adults hospitalized with definite IE between 2011 and 2025 at a regional referral center. Vegetation presence, size, and mobility were assessed using transthoracic (TTE) and transesophageal echocardiography (TEE), according to clinical indication. The primary endpoint was overall in-hospital embolic burden, including embolic events present at admission, occurring during hospitalization, or incidentally detected during diagnostic work-up. Associations were analyzed using univariate and multivariable logistic regression, and model discrimination was evaluated using receiver operating characteristic (ROC) analysis. Results: Embolic events occurred in 96 patients (58.5%). Vegetations were identified in 68.3% of patients and were more frequent among those with embolization (78.1% vs. 54.4%). Mobile vegetations were more common in patients with embolic events (77.1% vs. 27.9%, p < 0.001), as were vegetations > 10 mm (61.5% vs. 38.2%, p = 0.006). Compared with non-mobile vegetations ≤ 10 mm, mobile vegetations ≤ 10 mm were associated with higher odds of embolization (OR 5.4), and mobile vegetations > 10 mm showed a similar association (OR 7.14). In multivariable analysis, vegetation mobility remained independently associated with embolic events. The clinical model demonstrated moderate discrimination (area under the curve [AUC] 0.71), which improved with the addition of vegetation mobility (AUC 0.81; p = 0.005) and size > 10 mm (AUC 0.79; p = 0.016), with no significant difference between the enhanced models. Conclusions: Both vegetation mobility and size > 10 mm were associated with overall in-hospital embolic burden and may provide complementary information for embolic risk stratification. These findings should be considered exploratory and require confirmation in prospective studies with standardized imaging and validation procedures. Full article

Background and Objectives: Tumor-infiltrating lymphocytes (TIL) and the neutrophil-to-lymphocyte ratio (NLR) are each associated with prognosis in melanoma, yet their combined prognostic value remains insufficiently defined. We aimed to assess whether integrating NLR and TILs into a combined immune phenotype improves prediction of recurrence-free survival (RFS) in patients with resected cutaneous melanoma. Materials and Methods: A total of 203 patients were included. Receiver operating characteristic analysis identified an NLR cut-off of 2.75 for RFS, defining low (<2.75) and high (≥2.75) groups. TIL status was dichotomized as present or absent. According to the combined NLR–TIL profile, patients were initially categorized into three immune phenotypes: favorable (low NLR and TIL-positive), intermediate (low NLR and TIL-negative or high NLR and TIL-positive), and unfavorable (high NLR and TIL-negative). For the dichotomized analysis, the intermediate and unfavorable phenotypes were combined and compared with the favorable phenotype. Associations of clinicopathological factors with RFS were evaluated using Kaplan–Meier curves and Cox regression models. Results: The median follow-up was 56 months. In the univariate analysis, stage III disease, greater Breslow thickness, increased mitotic rate, and absence of adjuvant therapy were associated with worse RFS. In addition, patients with an unfavorable immune phenotype had a markedly increased risk of recurrence compared with those in the favorable group (HR 2.86, 95% CI 1.43–5.71; p = 0.004). In multivariate Cox regression analysis, both the unfavorable immune phenotype and stage III disease independently predicted RFS (HR 2.25, 95% CI 1.11–4.54; p = 0.024 and HR 2.13, 95% CI 1.03–4.43; p = 0.041, respectively). Conclusions: Combined assessment of systemic inflammation and tumor-local immune response using NLR and TILs may provide meaningful prognostic stratification in resected cutaneous melanoma. Full article

Background/Objectives: Prior spontaneous preterm birth (sPTB) and short cervical length predict the occurrence of sPTB with low sensitivity, highlighting the need for better detectors of at-risk pregnancies. PreTRM^® is a validated, biomarker-based sPTB predictor that we aimed to improve in this study by developing models that incorporate parity and key risk factors. Methods: A Model was developed and validated through retrospective analysis of a cohort of singleton pregnancies that resulted in a live term or preterm birth (PTB) (n = 976). The Model’s ability to predict sPTB and PTB was assessed and its clinical utility compared to PreTRM. Results: The Model predicted sPTB with 77.1% sensitivity, 74.4% specificity, 21.4% positive predictive value (PPV) and 97.3% negative predictive value (NPV), an improvement over PreTRM’s sensitivity (75.0%) and PPV (14.6%), and a higher PPV than short cervix (16.2%). PTB was predicted by the Model with 76.8% sensitivity, 74.6% specificity, 31.6% PPV and 95.5% NPV. Relative to PreTRM, the Model achieved significantly higher area under the receiver operating characteristic curve (AUC) results when predicting whether a PTB or sPTB would be associated with a neonatal hospital stay ≥5 days (p = 0.001 for PTB; p = 0.044 for sPTB). The Model also achieved significantly higher sensitivity than PreTRM in predicting a ≥5 day hospital stay associated with PTB (p = 0.009) and higher sensitivity for a ≥5 day hospital stay associated with sPTB, showing improved clinical utility over PreTRM. Conclusions: The Model achieved substantially higher performance than standard of care risk predictors, and an improvement in clinical utility over PreTRM, demonstrating the robustness of the Model as a sPTB and PTB predictor. Full article

Background/Objectives: Early identification of breast cancer patients who are likely or unlikely to benefit from neoadjuvant chemotherapy (NAC) remains clinically important because ineffective treatment may delay definitive surgery and expose patients to unnecessary toxicity. Quantitative ultrasound (QUS) radiomics offers a contrast-free and repeatable method for extracting tissue-sensitive imaging biomarkers from raw ultrasound data. This study aimed to evaluate whether baseline QUS radiomic features and early treatment-induced changes could predict a pathologic response to NAC in a real-world single-center cohort. Methods: We designed a prospective observational study including 96 consecutive women with biopsy-proven stage II–III breast cancer treated with NAC at Victor Babes University of Medicine and Pharmacy Timisoara. All patients underwent standardized QUS examinations before treatment and again at week 2. The response was defined pathologically at surgery as residual cancer burden class 0/I versus II/III. Clinical, histopathologic, and QUS variables were compared between responders and non-responders. Group comparisons used Student’s t test, Mann–Whitney U test, chi-square testing, and Fisher’s exact test where appropriate. Multivariable logistic regression was used to identify independent predictors of response. Model discrimination was summarized using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Forty-three patients (44.8%) were classified as responders and 53 (55.2%) as non-responders. Responders had higher baseline Ki-67 values (47.8 ± 13.1% vs. 41.9 ± 13.0%, p = 0.033), lower baseline homogeneity (0.3 ± 0.1 vs. 0.4 ± 0.1, p = 0.010), and higher peritumoral heterogeneity (0.9 ± 0.1 vs. 0.8 ± 0.2, p = 0.027). At week 2, responders showed larger increases in mid-band fit (3.0 ± 0.8 vs. 1.2 ± 0.8 dB, p < 0.001), greater entropy change (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), more pronounced spectral intercept reduction (−3.5 ± 1.4 vs. −1.2 ± 1.3, p < 0.001), and greater tumor shrinkage (−24.3 ± 7.0% vs. −11.1 ± 5.7%, p < 0.001). In multivariable analysis, Δ MBF and Δ entropy remained independent predictors of pathologic response. The combined clinical-plus-QUS model achieved an AUC of 0.89. Conclusions: Baseline microstructural heterogeneity and very early QUS-derived treatment changes were strongly associated with the pathologic response to NAC. These findings support the potential role of QUS radiomics as a low-cost, repeatable early-response biomarker in breast cancer. Full article

Remote sensing technologies provide valuable geospatial data for analyzing environmental conditions and for supporting spatial ecological modeling across large, heterogeneous landscapes. In this study, multi-source remote sensing–derived environmental variables were integrated with ensemble machine learning techniques to model the habitat suitability of the common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ&K), Pakistan. Environmental predictors derived from satellite observations included land cover, vegetation condition, terrain attributes, and climate-related indicators. To ensure model reliability, multicollinearity among predictors was evaluated, and spatial clustering patterns of leopard occurrence records were examined using global spatial autocorrelation analysis. Two complementary machine learning algorithms, Maximum Entropy (MaxEnt) and Random Forest (RF), were implemented and integrated through a weighted ensemble approach to improve predictive accuracy and robustness. The ensemble model achieved high predictive performance with an area under the curve (AUC) value of 0.942, outperforming individual algorithms. The resulting habitat suitability map indicates that approximately 30% of the study region is highly suitable habitat, primarily in the northern and central districts, including Muzaffarabad, Neelum, Hattian, Poonch, and Sudhnutti. Variable importance analysis identified remotely sensed land cover, elevation, vegetation cover, slope, and temperature seasonality as the dominant predictors of habitat suitability, whereas anthropogenic indicators such as proximity to roads and population density had secondary effects in fragmented areas. The results demonstrate the potential of integrating remote sensing data and ensemble machine learning for spatial habitat modeling and wildlife conservation planning in mountainous ecosystems. Full article