Search Results (5,224)

Web application firewalls (WAFs) are critical defenses against persistent threats to web applications, yet their security evaluation remains challenging. Traditional manual testing methods are often inefficient and resource-intensive, while existing reinforcement learning (RL)-based automated approaches face two key limitations: (1) attackers cannot perceive opaque WAF rule logic; (2) boolean feedback from WAFs results in sparse/delayed rewards—sparse rewards trap agents in blind exploration, and delayed rewards hinder the association between early actions and final outcomes, adversely affecting learning efficiency. To address those challenges, we propose Ouroboros—a framework integrating genetic algorithm-based symbolic rule reconstruction (translating WAF rules into interpretable RNNs for fine-grained confidence scoring), timing side-channel analysis (evaluating rule-matching depth), and a multi-tiered reward mechanism to enable self-evolving RL testing. Experiments show that the framework reaches 89.2% bypass success rate on signature-based WAFs. This paper presents an efficient solution for automated WAF testing and delivers insights for optimizing rule logic and anomaly detection mechanisms. Full article

Since the initiation of two variants of the bootstrap method by Efron and Rubin in the late 1970s, a variety of advancements has emerged in the literature. The subsampling of blocks enabled the estimation of the actual variance of the sample mean. The equivalence of the data-level and the estimator-level resampling is easily established for the sample mean and estimators alike. For Rubin’s variant of the bootstrap we apply an algorithm by Diniz et al. which allows for the numerically stable computation of the sample-based cumulative distribution function of the estimator under investigation. No actual Monte-Carlo resampling is necessary in this setting and we demonstrate how we get access to the very small probabilities of the tails and moreover to confidence intervals. We do this at the example of a well-known test model that exhibits geometrically decaying spatial correlations. The analysis naturally applies to temporally correlated systems or to the correlations occurring in Markov chains, as well. Full article

There are limited data on sequential computed tomographic (CT) evaluation and objective CT assessment of the metacarpal condyles in Thoroughbred racehorses. This longitudinal study aimed to document changes in attenuation of the metacarpal condyles during the first two years of training and racing. Fan-beam CT examination of the metacarpophalangeal regions was performed on 40 non-lame Thoroughbred yearlings, and repeated four more times, approximately six months apart. Mean Hounsfield Unit (HU) measurements were obtained on sagittal reconstructions of the dorsal and palmar halves of the medial and lateral condyles and parasagittal grooves. One-way ANOVA with a post hoc Tukey’s Test was used to investigate differences between mean HU values over time at the different regions of interest. Multivariable mixed-effects linear regression models assessed the association between dorsal and palmar HU and potential explanatory variables. Mean HU increased significantly with training, especially during the first six months, with a maximal sequential mean increase found in the medial parasagittal groove (119.8 [95% confidence interval 85.3, 154.30], p < 0.001). Dorsal regions had higher HU than palmar regions, with the highest HU recorded in the dorsal aspect of the medial condyle at time 3 (mean HU 1120.1 ± 63.4). Condyles had higher HU than parasagittal grooves (p < 0.001), the palmar half of the right condyles had higher HU than the left (p = 0.045) and the dorsal aspect of the medial condyle had higher HU than the lateral (p < 0.001). An increasing number of race starts and higher body weight:height ratio were associated with higher HU (p < 0.001). The main limitation was the loss of horses to follow-up as the study progressed. In conclusion, density of most regions of the metacarpal condyles increased with time spent in training, reflecting adaption to racehorse training. Full article

Background: Trastuzumab is a blockbuster monoclonal antibody that has revolutionized the treatment of HER2-positive breast and gastric cancers. With the increasing availability of biosimilar monoclonal antibodies in clinical practice, independent verification of biosimilarity using products sampled from a real-world supply chain is important to assure clinicians and the patients to use these products confidently. Objective: The aim of this study is to assess the biosimilarity of AryoTrust, a trastuzumab biosimilar, in comparison with the reference product Herceptin. AryoTrust and Herceptin products were randomly withdrawn from Iraqi hospitals to reflect medicines administered in real clinical settings. Methods: AryoTrust and Herceptin were compared using an extensive set of orthogonal analytical techniques which included SDS-PAGE, ion-exchange chromatography, capillary isoelectric focusing, peptide mapping, N-glycan profiling, circular dichroism, differential scanning calorimetry, and surface plasmon resonance. In addition to these teste, functional comparability was also tested using an HER2-dependent cell-based proliferation inhibition bioassay. Results: The results showed that both products have highly comparable profiles in all assessed attributes. The analysis showed similar molecular integrity and purity, identical primary structure, comparable charge heterogeneity, similar isoelectric points (pI) of the main isoform, close glycosylation patterns (mainly, by core-fucosylated complex-type glycans), similar higher-order structural features, and thermal stability. The receptor binding studies exhibited comparable binding affinities with Fcγ receptors and FcRn. Finally, the cell-based bioassay revealed comparable dose–response curves with similar EC₅₀ values and relative potency. Conclusions: The integrated analytical and functional data support the biosimilarity of AryoTrust to the reference product Herceptin, which has been marketed and used in Iraq. This study provides real-world scientific evidence supporting confidence in the quality and comparability of this trastuzumab biosimilar and reduces any doubt in the product and at the same time emphasizes the value of independent post-marketing biosimilarity assessments. Full article

Background: We tested whether a contrast-free protocol can reproduce contrast-enhanced VI-RADS scoring and whether reader expertise influences results. Methods: In this retrospective single-center study (January–December 2024), 65 patients (69 lesions) underwent bladder multiparametric MRI. Two blinded radiologists assigned VI-RADS scores using only T2-weighted and diffusion-weighted imaging (biparametric, non-contrast MRI): an expert (>15 years in urogenital radiology) in genitourinary MRI and a non-expert (5 years of experience in genitorurinary radiology). Two complementary reference standards were used. For reproducibility analysis, the reference standard was the VI-RADS score from the original clinical report based on the full multiparametric examination including contrast-enhanced imaging. For diagnostic accuracy analysis, histopathology was used as the reference standard for muscle-invasive versus non-muscle-invasive disease. Agreement was evaluated with confusion matrices, overall agreement, and weighted Cohen’s kappa. Discrimination for high likelihood of muscle invasion (VI-RADS ≥ 4) was assessed with receiver operating characteristic analysis. Results: Reference scores were VI-RADS 2 (34.8%), 3 (14.5%), 4 (20.3%), and 5 (30.4%). Agreement was higher for the expert than the non-expert (73.9% vs. 56.5%; weighted kappa 0.74 [95% confidence interval 0.56–0.89] vs. 0.58 [0.37–0.75]). The area under the curve for VI-RADS ≥ 4 was 0.87 (0.78–0.95) for the expert and 0.81 (0.69–0.91) for the non-expert. Sensitivity at a biparametric threshold of VI-RADS ≥ 4 was 88.6% for both readers; specificity was 85.3% vs. 73.5%. Post-resection cases showed more discrepancies, mainly overstaging. Conclusions: Contrast-free biparametric MRI may approximate multiparametric VI-RADS scoring only in treatment-naïve pre-TURBT cases with clearly low-risk, non-equivocal imaging features, but performance is reader-dependent and less reliable in equivocal, higher-risk, and post-resection examinations. Contrast-enhanced multiparametric MRI remains preferred for staging. Full article

Background: Atrial fibrillation (AF) frequently develops in patients with atrial flutter (AFL), even after successful cavotricuspid isthmus (CTI) ablation. Identifying simple clinical markers for early detection is crucial. Diagonal earlobe crease (ELC), also known as Frank’s sign, has been proposed as a marker of aging and cardiovascular risk. This study investigates the association between ELC and the risk of new-onset AF following CTI ablation in patients with AFL. Methods: We conducted a retrospective cohort study of 292 patients without a prior history of AF who underwent CTI ablation for typical AFL between 2015 and 2024. The presence of ELC was assessed at baseline CTI ablation. The primary outcome was the occurrence of new-onset AF during follow-up, stratified according to the presence of ELC. The median follow-up duration was 49 months, with a minimum follow-up of 6 months. Results: Among the 292 patients, 72 (24.7%) exhibited ELC. Patients with ELC were older (59 ± 11 years vs. 55 ± 14 years, p = 0.05). During the follow-up period, new-onset AF occurred in 31 patients with ELC (43.1%) and 65 patients without ELC (29.5%) (p = 0.03). Kaplan–Meier analysis demonstrated that the occurrence of AF was significantly higher in the ELC group than in the non-ELC group (log-rank test, p = 0.013). Multivariate analysis revealed that ELC was independently associated with an increased risk of AF (hazard ratio 1.67, 95% confidence interval 1.03–2.72, p = 0.039). Conclusions: The presence of ELC is associated with a higher risk of new-onset AF following CTI ablation in patients with AFL. ELC may serve as a simple, non-invasive clinical marker to identify patients who may benefit from closer rhythm surveillance after AFL ablation. Full article

Inference after model selection is a very important problem. Model selection algorithms for ARIMA time series, with criteria such as AIC and BIC, tend to select an inconsistent model with positive probability, making data-splitting inference for testing and confidence intervals unreliable. One technique was fairly reliable for sample sizes greater than 600, and a modification also worked. Model selection is often useful for prediction, since the selected submodel tends to have fitted values and residuals that are highly correlated with those of the full model. A few prediction intervals perform fairly well even after model selection. A useful technique for handling outliers is to replace the outliers with missing values. Full article

The deep integration of machine learning technology with geological prospecting has brought to the forefront a key challenge: how to construct geological-mineralization models by fusing multi-source data, select model features with guidance from metallogenic factors, build multi-source metallogenic prediction models with geological constraints, and ultimately achieve a thorough integration of domain knowledge and machine intelligence. The Eastern-Central Tianshan region is one of China’s most important copper–nickel mineral resource bases, predominantly hosting magmatic copper–nickel sulfide deposits with significant resource potential. In this context, this paper proposes a metallogenic prediction model based on multi-modal feature fusion technology. The model employs a Residual Neural Network (ResNet) incorporating a Squeeze-and-Excitation (SE) attention mechanism and a Multi-Layer Perceptron (MLP) to extract features from different modalities. It integrates multi-source data, including geochemical information, geological metallogenic factors, and aeromagnetic data. A cross-modal feature interaction module, constructed using attention weighting and a gating mechanism, enables deep fusion of the features. After training, the model achieved a prediction accuracy of 97% on the test set. Compared to a unimodal model constructed using Random Forest, the confidence and discriminative capability of the training results were significantly enhanced, validating the effectiveness of multi-modal feature fusion. Applying the trained model to the study area, a total of 11 prospective metallogenic zones were delineated. These include 4 zones in the peripheries of known deposits and 7 zones in previously unexplored (blank) areas. Notably, some known mineral occurrences fall within the predicted blank-area targets, validating the feasibility and significant value of multi-modal feature fusion in mineral prediction. This work provides a novel methodology for the subsequent integrated processing of multi-source data. Full article

Reservoirs are critical infrastructure for water and energy security, and require accurate and timely monitoring of reservoir water extent to make informed decisions. Optical remote sensing provides frequent, large-area observations; however, automated water extraction is often complicated by dam operation and surface heterogeneity, which increase spectral variability. Supervised methods, though widely used, generally require manual labels and often perform poorly when transferred across sensors and regions, limiting operational deployment. In this paper, we develop a geo-spectral feature-guided Self-Supervised Water Detection (SWD) framework, an automated algorithm designed for multi-source optical imagery. SWD consists of two stages: pixel-level classification and object-level refinement. Initially, SWD integrates spatial priors with spectral features to automatically derive high-confidence samples, which are then utilized to parameterize Gaussian mixture model to represent multimodal spectral distribution throughout the image. Furthermore, superpixel-constrained region growing is applied to refine shoreline and ensure object-level consistency. We validated SWD across 36 test cases comprising three sensors, six reservoirs, and two hydrological conditions. Compared with Random Forest and U-Net, SWD achieved the best performance. Specifically, (1) in cross-scale tests, SWD achieved high consistency with IoU ≥ 0.774; (2) in cross-region transfers, SWD maintained stable generalization (SD: 0.010); and (3) in hydrological response assessments, SWD captured water-level fluctuations with minimal bias variation (ΔRE < 1%). In addition, SWD framework is computationally efficient, with processing times of 0.49–1.29 s/Mpx on a standard CPU. This study demonstrates that SWD effectively addresses spectral variability and surface complexity in reservoir water area detection across multi-source optical imagery. It operates without manual labels or model training, enabling automated, large-scale and multi-temporal reservoir water monitoring. Full article

The topology identification (TI) of low-voltage distribution networks (LVDNs) is the foundation for their intelligent operation and lean management. However, the existing identification methods may produce inconsistent results under measurement noise, missing data, and heterogeneous load behaviors. Without principled multiple method fusion and meter-level confidence quantification, the reliability of the identification results is questionable in the absence of ground-truth topology. To address these challenges, a confidence-aware TI (Ca-TI) method for the LVDN based on weakly supervised learning (WSL) and Dempster–Shafer (D-S) evidence theory is proposed, aiming to infer each meter’s latent topology connectivity label and quantify the meter-level confidence without ground truth by fusing different identification methods. Specifically, within the framework of data programming (DP) in WSL, different TI methods were modeled as labeling functions (LFs), and a weakly supervised label model (WSLM) was adopted to learn each method’s error pattern and each meter’s posterior responsibility; within the framework of D-S evidence theory, an uncertainty-aware basic probability assignment (BPA) was constructed from each meter’s posterior responsibility, with posterior uncertainty allocated to ignorance, and was further discounted according to the missing data rate; subsequently, a consensus-calibrated conflict-gated (CCCG)-enhanced D-S fusion rule was proposed to aggregate the TI results of multiple methods, producing the final TI decisions with meter-level confidence. Finally, the test was carried out in both simulated and actual low-voltage distribution transformer areas (LVDTAs), and the robustness of the proposed method under various measurement noise and missing data was tested. The results indicate that the proposed method can effectively integrate the performances of various TI methods, is not adversely affected by extreme bias from any single method, and provides the meter-level confidence for targeted on-site verification. Further, an engineering deployment scheme with cloud–edge collaboration is further discussed to support scalable implementation in utility environments. Full article

Internet gaming disorder (IGD) and nonmedical prescription drug use (NMPDU) are prevalent, co-occurring concerns among young adults. Although prior research links problematic gaming and substance misuse, few studies have examined this relationship in non-college populations or whether student status modifies this association. This study examined the relationship between Gaming Addiction Scale (GAS) score and NMPDU among 1022 Rhode Island young adults aged 18 to 25. In the total sample, 44.6% identified as cisgender heterosexual female, 42.4% as sexual or gender minority (SGM), and 13.0% as cisgender heterosexual male. Multivariable logistic regression estimated the adjusted association between GAS scores and NMPDU, and an interaction term between GAS and student status was tested. Overall, 12.1% reported lifetime NMPDU. Higher GAS scores were associated with increased odds of NMPDU (adjusted odds ratio [AOR] = 1.05; 95% confidence interval [CI]: 1.01–1.09). Student status alone was not significantly associated with NMPDU; however, a significant interaction was observed between GAS and student status (AOR = 1.09, 95% CI: 1.01–1.18, p = 0.031). Higher GAS scores were positively associated with NMPDU, with student status strengthening this association. Findings support screening for problematic gaming, particularly among students, and integrated prevention strategies addressing both behavioral and substance-related risks. Full article

Background/Objectives: Predicting 30-day mortality after in-hospital cardiac arrest (IHCA) remains challenging. We developed an interpretable CatBoost model that incorporates the m-NUTRIC score, age, and selected micronutrient biomarkers (i.e., magnesium, zinc, vitamin D, and vitamin B12). We compared its performance with that of logistic regression and quantified variable contributions using SHAP. Methods: Variables were extracted from the electronic medical records of 880 patients with IHCA admitted to a medical intensive care unit. The CatBoost and logistic regression models were trained on a stratified 80/20 split. The decision threshold was optimized using the Youden index (0.482). Discrimination (ROC-AUC with bootstrap confidence intervals), classification metrics, precision–recall analysis, calibration, and decision curve analysis were reported. Results: CatBoost achieved a ROC-AUC of 0.850 (95% confidence interval [CI]: 0.822–0.879) in the training set and 0.827 (95% CI: 0.760–0.887) in the internal test set, outperforming logistic regression (0.797; 95% CI: 0.720–0.861). The test set accuracy, precision, recall, F1-score, specificity, and average precision were 0.761, 0.847, 0.790, 0.817, 0.702, and 0.909, respectively. The Brier score was 0.186. Decision curve analysis showed net benefit across threshold probabilities of 0.20–0.70. The SHAP analysis identified m-NUTRIC and age as the dominant predictors, whereas micronutrients served as complementary contextual factors. Conclusions: The CatBoost model consistently outperformed the logistic regression and warrants prospective multicenter validation. Full article

The economic and societal advantages of Science, Technology, Engineering, Mathematics, and Medicine (STEMM) occupations are considerable; however, access to STEMM education and training opportunities is unequal, especially for youth from low-socioeconomic-status (SES) areas. Young people from low-SES areas may experience sustained structural, financial, and social barriers that limit their ability to develop identities as STEMM practitioners and to persist in pursuing these fields. This case study describes the design, implementation, and evaluation of a community-based mini-medical summer camp held in a low-SES area to support the development of STEMM identities and to increase 6th–11th-grade students’ biomedical and medical knowledge and career interests. The program utilized partnerships with local entities to provide access to biomedical and medical content. Nineteen students completed the program; fifteen consented to and assented to assessment using pre- and post-tests of STEMM-related knowledge and self-efficacy, and completed all measurements. Students’ STEMM knowledge levels increased significantly; however, their STEMM self-efficacy did not change, possibly due to high initial confidence and the short duration of participation. Students reported high engagement and increased interest in the sciences and medicine. Overall, this study suggests that community-centered outreach programs can increase STEMM engagement and learning in low-SES environments. Full article

Background/Objectives: Noninvasive differentiation of parotid gland tumors remains challenging despite ultrasound being the primary imaging modality for salivary gland lesions. Given its examiner dependence, improving diagnostic consistency and transparency is crucial. We quantified interobserver variability in parotid ultrasound, modeled examiner-specific decision patterns using machine learning surrogates, and tested whether surrogate complexity relates to examiner performance. Methods: In this retrospective, single-center study, six examiners independently rated ultrasound images of 149 parotid tumors using predefined descriptors. Performance was summarized using accuracy and the area under the receiver operating characteristic curve (AUC), with 95% confidence intervals (CIs). AUCs were compared using DeLong tests (Holm-adjusted). Interobserver agreement was assessed using pairwise Cohen’s and global Fleiss’ κ. For each examiner, a decision-tree surrogate was trained from structured descriptors and clinical metadata to reproduce examiner labels and visualize decision pathways; performance was estimated by 5-fold cross-validation. Results: Examiner accuracy ranged from 63.5% to 90.5% and AUC from 0.66 to 0.89 (best 0.89, 95% CI 0.83–0.95); the best performer exceeded the two lowest performers (p < 0.001). Agreement was higher for objective descriptors (size: κ = 0.57–0.97) than for subjective descriptors (echogenicity: κ = 0.11–0.79). Surrogate decision-tree accuracy versus histopathology ranged from 57.2% to 80.0% for unpruned and from 65.1% to 76.5% for pruned models, with high coverage (95.3–98.7%). Tree complexity showed no consistent association with examiner performance. Conclusions: Parotid ultrasound shows substantial interobserver variability. Interpretable surrogates can approximate individual labeling behavior from structured descriptors and clinical metadata, making examiner-dependent decision patterns explicit. Full article

Background/Objectives: Driving cessation is associated with adverse health outcomes. Proactive support that extends safe driving while preparing for life after driving cessation has been emphasized, but empirical evidence remains limited. This study examined the effects of a proactive class for older drivers on awareness and behavior related to driving and mobility (Study 1) and on longitudinal changes in on-road driving behavior (Study 2). Methods: The proactive class was implemented as a municipal program, including information provision, training activities, group discussions, and optional on-road driving evaluations. Study 1 included 71 older drivers who attended the class at least five times annually and completed an anonymous questionnaire assessing perceived changes in awareness and behavior. Study 2 included 29 participants who completed standardized on-road driving evaluations at baseline and at a 1-year follow-up. Paired t-tests or Wilcoxon signed-rank tests with effect sizes were applied. Results: In Study 1, participants reported increased awareness of safe driving, greater confidence in continuing to drive, heightened risk perception, initiation of health-related behaviors, trial use of public transportation, and increased healthcare utilization, particularly ophthalmology visits. In Study 2, total scores on the on-road driving skill test improved significantly at follow-up (Cohen’s dz = 0.805). No significant changes were observed in individual on-road driving skill subitems, physical function, cognitive function, or daily functioning after correction for multiple comparisons, except for a reduction in driving simulator accidents. Conclusions: Participation in a proactive, continuous driving transition support class was associated with multidimensional behavioral changes and improved on-road driving performance among older drivers, potentially contributing to safer mobility and healthier aging. Full article