Search Results (21,858)

The features of patients with multiple urothelial tumors remain to be elucidated. We intend to differentiate primary upper tract urothelial carcinoma with synchronous urothelial bladder cancer (UTUC + sUBC) and UTUC with metachronous UBC (UTUC + mUBC) cases to determine whether these temporal patterns reflect biologically distinct processes. A subgroup analysis of a retrospective cohort of UTUC (n = 114) was performed comparing UTUC + sUBC (n = 14) with UTUC + mUBC (n = 29). IHC expression of cytokeratin 5/6 (CK5/6), CK20, GATA3, and p53 was evaluated to assess relevant subtypes. Genetic characterization comprised TERTp, FGFR3, RAS, and TP53 status. Kaplan–Meier analyses estimated the progression-free survival (PFS) and overall survival (OS) of both UTUC subgroups, and the log-rank test was used to assess differences between subgroups. Our study reveals no significant differences in phenotype or genomic profile between synchronous and metachronous UTUC-UBC cases (p > 0.05). Nevertheless, patients with synchronous UBC revealed significantly worse outcomes in PFS (2y-PFS 23.1% vs. 52.1%, p = 0.029) and OS (2y-OS 40.4% vs. 84.4%, p = 0.016) than those with metachronous disease. These discrepancies could arise from as yet-uncharacterized molecular features or microenvironmental influences. Full article

Risk-based management of water quality in drinking water supply systems requires decision-support tools that extend beyond parameter-level hazard assessment and enable prioritization at the level of physical system objects. In this context, hazard assessment refers specifically to drinking water quality parameters and their possible operational and health-related implications, particularly in facilities serving sensitive user groups. This study proposes a class-based extension of the FPOR (Fuzzy Priority of Objects at Risk) framework to support object-level operational prioritization under conditions of limited data availability. Hazard importance is adopted from prior hazard prioritization using the Fuzzy Priority Index (FPI), while priority premises (PP) are represented as object classes reflecting typical functional and operational characteristics. Class-based profiles of local hazard relevance and object vulnerability are defined using expert-informed fuzzy representations and aggregated into FPOR scores to produce a relative ranking of priority premises classes. The results demonstrate how hazard prioritization can be systematically propagated to object-level decision units without reliance on site-specific monitoring data. The proposed framework provides a transparent and scalable basis for early-stage risk-based planning and supports the operational implementation of object-oriented management strategies in drinking water systems, while maintaining a clear conceptual separation from health risk assessment addressed in subsequent studies. Full article

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

This paper proposes a unified approach based on maximal consistent blocks (MCBs) to address the problem of incomplete single-label and multi-label dimensional reduction. The matrix computation method for maximal consistent blocks is improved by introducing a dynamic multi-row detection mechanism and optimizing the block size determination criteria. The complete set of maximal consistent blocks can be efficiently obtained via matrix intersection operations. For incomplete single-label decision information systems, an attribute reduction algorithm is designed based on maximal consistent blocks. Redundant attributes are eliminated by preserving the upper and lower approximation distributions of decision classes. In the multi-label scenario, a complementary decision reduct method integrating coarse and fine decision functions is proposed, and a unified solution paradigm is adopted to accomplish multi-label dimensional reduction. The effectiveness in classification (F1-score, Ranking Loss, Hamming Loss), reduction performance, and runtime efficiency is validated via statistical tests, scalability studies, structured missingness studies, and comparisons with four representative baselines on Birds, Scene, and Yeast datasets (5%/10%/15% missing rates). Full article

Background/Objectives: Abnormal muscle tone and impaired motor control commonly limit gait recovery after stroke. Robot-assisted gait training has been introduced to augment conventional rehabilitation; however, its effects on stage-based motor recovery, functional ambulation, and muscle tone during the subacute phase remain unclear. Methods: This prospective, single-center, randomized controlled trial enrolled 30 patients with subacute stroke who received robot-assisted gait training plus conventional rehabilitation (R-BoT Plus group, n = 15) or conventional rehabilitation alone (control group, n = 15) over 4 weeks. The primary outcome was the change in Brunnstrom recovery stage of the lower extremities (BRS-LE). Secondary outcomes included Functional Ambulation Category (FAC), Fugl–Meyer Assessment for the Lower Extremity (FMA-LE), clinical spasticity measures (Modified Ashworth Scale and Modified Tardieu Scale), and muscle mechanical properties (MyotonPRO). Exploratory analyses were conducted to examine the associations between changes in stage-based motor recovery (ΔBRS-LE), functional ambulation (ΔFAC), and MyotonPRO parameters. Within-group changes were assessed using the Wilcoxon signed-rank test. Between-group effects were primarily evaluated using baseline-adjusted ANCOVA with HC3 robust standard errors, with Wilcoxon rank-sum tests on change scores as sensitivity analyses. Associations between changes in clinical outcomes and MyotonPRO parameters were evaluated using Spearman’s rank correlation coefficient (ρ). Results: BRS-LE (p = 0.014) and functional ambulation (p = 0.041) were significantly improved in the R-BoT Plus group. Changes in FMA-LE and clinical spasticity measures did not differ significantly between groups. Quantitative myotonometry revealed selective muscle- and parameter-specific changes. No robust correlations were observed between MyotonPRO parameters and changes in BRS-LE. Conclusions: The addition of robot-assisted gait training to conventional rehabilitation was associated with greater improvements in stage-based lower-limb motor recovery and functional ambulation in patients with subacute stroke. In contrast, cumulative impairment scores and conventional clinical spasticity measures demonstrated limited changes between groups. Quantitative muscle mechanical assessment revealed selective muscle-specific adaptations, supporting its role as a complementary tool for mechanistic characterization rather than as a surrogate marker of motor recovery. Future studies incorporating dose-matched designs and longer follow-up periods are warranted to clarify the independent and long-term effects of robot-assisted gait training. Full article

Timely identification of canopy decline in commercial strawberry production is challenging because visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling. The framework was applied across three commercial strawberry fields in Oxnard, California using nine UAV surveys collected from December 2022 to June 2023, yielding 159,220 plant-level monitoring units. NDRE- and Redness Index-based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported survival-based modeling of canopy decline progression. Across withheld test plants from all survey dates, overall concordance indices ranged from 0.88 to 0.95 across fields, indicating strong ability to rank plants by time-to-decline risk under heterogeneous field conditions. Spatial risk maps revealed localized high-risk clusters that expanded over time in fields with greater canopy deterioration, while fields with minimal visible decline exhibited diffuse but stable risk distributions. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy decline risk. These results demonstrate that framing repeated UAV observations as a time-to-event process enables fine-scale spatiotemporal modeling of canopy decline dynamics and supports risk stratification for targeted field monitoring in commercial strawberry systems. Full article

Background: Postoperative complications following colorectal surgery remain a persistent clinical challenge. Traditional risk stratification has focused on patient characteristics, while conventional modeling approaches treat procedural factors such as operative duration and surgical approach as independent predictors, potentially obscuring interaction effects. Methods: This study developed a machine learning model stratifying 7908 colorectal surgery patients into four distinct procedural risk categories based on combined surgical approach and operative duration (laparoscopic-short, laparoscopic-long, open-short, open-long), rather than treating these factors as separate variables. A gradient boosting ensemble classifier with RUSBoost resampling was trained on predictor variables including patient demographics, comorbidities, and intraoperative factors. Results: Feature importance analysis revealed that the open-long category emerged as the single most important predictor, substantially exceeding all other variables. Weight loss, body mass index, patient age, and electrolyte abnormalities ranked as the next most important predictors. Stratified complication rates demonstrated a critical interaction: prolonged duration more than doubled complication risk in open procedures (short-duration: 9.99%, long-duration: 20.46%), whereas laparoscopic procedures showed only a modest increase from short-duration (10.45%) to long-duration (14.08%) cases. Logistic regression benchmark analysis confirmed the duration-approach interaction (OR = 1.53, 95% CI: 0.97–2.39), achieving comparable discrimination (c-statistic 0.678 vs. 0.665 for the ensemble model). Decision curve analysis demonstrated logistic regression provided superior clinical utility across most threshold probabilities. Conclusions: The dual analytical framework (i.e., statistical inference for quantifying associations and machine learning for predictive feature ranking) offers complementary insights for clinical application. These findings demonstrate that stratified feature engineering can elucidate complex risk phenotypes that may be obscured when procedural factors are analyzed independently. Full article

Objective: To evaluate the utility of synovial iron quantification using Magnetic resonance imaging (MRI) in assessing structural joint damage in the knee of patients with rheumatoid arthritis (RA). Methods: This cross-sectional study employed a two-stage design. In the initial comparative stage, 6 patients with RA and 5 patients with osteoarthritis (OA) were recruited to compare synovial R2* values, a metric derived from iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) MRI sequences representing synovial iron content. Following this, the RA cohort was expanded to a total of 51 patients to investigate the association between R2* values and clinical parameters, including disease activity and bone erosion. Synovial fluid iron levels were measured with an Iron Assay Kit and synovial iron deposits were semi-quantified via Prussian blue staining. Associations between R2* and clinical and laboratory parameters, including inflammatory factors and joint damage indices, were analyzed using Spearman’s rank correlation. Univariate and multivariate ordered logistic regression models were employed to identify factors associated with bone erosion severity. An R2*-based nomogram was developed and validated using receiver operating characteristic (ROC) analysis and calibration curves. Results: Synovial R2* values were significantly higher in RA patients than those with osteoarthritis (53.66 S⁻¹ vs. 31.38 S⁻¹, p < 0.05), consistent with Prussian blue staining results. While synovial R2* values showed no significant correlation with systemic iron metabolic markers, inflammatory indicators, or the Disease Activity Score 28, they were positively correlated with bone erosion severity (ρ = 0.500, p < 0.001) and negatively associated with the joint space width (ρ = −0.307, p < 0.05). Multivariate analysis identified R2* as an independent indicator linked to bone erosion extent (OR = 2358.336, p < 0.001). The R2*-based nomogram demonstrated good discriminative performance. (AUC = 0.83). Conclusions: The R2* value derived from IDEAL-IQ MRI is a reliable tool for quantifying synovial iron and may represent a promising non-invasive imaging biomarker reflecting bone erosion in RA patients. Full article

Background and Objectives: Dentofacial asymmetries are common in patients with malocclusions, while mild body postural asymmetries are frequently reported in otherwise healthy individuals. However, their interrelationship remains insufficiently investigated in adults without diagnosed spinal disorders. This study aimed to evaluate the association between dentofacial and body postural asymmetries in adults with malocclusions. Materials and Methods: A clinical cross-sectional observational study was conducted on 102 adults (18–45 years) with malocclusions and no spinal pathology. Standardized clinical morphometric examinations assessed dentofacial asymmetries (horizontal and vertical planes), dental parameters (dental midlines deviation and occlusal plane inclination), and body postural asymmetries (head, shoulder, trunk, pelvic, and lower limb alignment). Asymmetries were recorded using predefined clinical thresholds. Statistical analyses included the Wilcoxon signed-rank test, Pearson chi-square test, and Spearman’s rank correlation coefficient. Results: Dentofacial asymmetries were identified in both planes and occurred more frequently on the left side. Horizontal facial asymmetries were most common at the cheek (74.5%), nostril (66.7%), and mandibular angle levels (57.9%), and were influenced by sex, age, facial growth pattern, and facial profile (p ≤ 0.05). Mandibular dental midline asymmetry was present in 55.8% of patients. Body postural asymmetries were also frequent, particularly unilateral (60.8%) or anterior (55.9%) head inclination and shoulder asymmetries (54.9%), with a predominance on the left side and associations with age, body mass index, and postural attitude (p ≤ 0.05). Correlations were identified among facial asymmetries and among body postural asymmetries (p ≤ 0.01), indicating a bilateral distribution pattern. Additionally, right-sided facial asymmetries showed significant positive associations with right-sided body postural asymmetries (ρ = 0.197–0.229; p ≤ 0.05). Conclusions: Dentofacial and body postural asymmetries have been identified in adults with malocclusions and presented side-specific associations regarding the patterns of asymmetry. Full article

Remote sensing object detection (RSOD) models play an increasingly important role in modern remote sensing systems. However, during model delivery, sharing, and deployment, RSOD models face increasing risks of unauthorized redistribution, illegal replication, and intellectual property infringement. To mitigate these threats, this paper proposes a white-box watermarking framework for RSOD models that enables reliable copyright verification while preserving the performance of the primary detection task. Specifically, a gradient-based sensitivity analysis of the detection loss is first performed to adaptively identify model parameters that minimally affect detection performance, which are then selected as watermark carriers. Subsequently, a parameter-ranking-based watermark encoding scheme is developed, where watermark bits are embedded by enforcing relative ordering constraints between parameter pairs. To further improve robustness under practical deployment conditions, an attack-simulation-driven training strategy is introduced, in which common perturbations and watermark removal attacks are simulated during the embedding process. In addition, a stealthiness enhancement strategy based on statistical distribution constraints is designed to maintain consistency between the distribution of watermarked parameters and those of the original model, thereby reducing the risk of watermark exposure and localization. Extensive experiments across multiple RSOD datasets and detection architectures demonstrate that the proposed method achieves a high copyright verification success rate with negligible impact on detection accuracy and exhibits strong robustness and stealthiness against a variety of watermark removal attacks. Full article

Urban air pollution is increasingly recognised as a determinant of mental wellbeing, yet most existing studies rely on static exposure estimates and lack spatial granularity. This limits understanding of how pollutant-specific patterns influence psychological states in real-world settings. To address this gap, we integrate real-time environmental and physiological data from 40 participants using the DigitalExposome dataset, applying multivariate and spatial analysis techniques. Our findings confirm that Particulate Matter (PM_2.5) exerts the strongest negative association with mental wellbeing while extending prior work by establishing a preliminary ranking of other pollutants Particulate Matter (PM₁₀), Particulate Matter (PM₁), Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Ammonia (NH₃). We applied statistical and spatial analysis methods, including heatmaps and Voronoi diagrams, to explore links between pollutants and wellbeing and compare the relative influence of air pollution and noise. This enabled identification of pollutant-specific hotspots and multi-level wellbeing patterns across individual, accumulated, and collective scales. These results demonstrate the value of spatial analysis for environmental health research and support targeted urban interventions, such as green space placement and traffic re-routing, to mitigate mental wellbeing risks. Full article

Flooding is a recurrent climate-related hazard in southern Thailand that frequently disrupts schooling and undermines educational continuity. Despite the critical importance of school-based disaster preparedness, there is limited empirical evidence explaining the drivers of flood preparedness among primary school teachers in climate-vulnerable regions. This study aimed to identify the cognitive, experiential, and topographic factors correlated with flood knowledge, flood risk perception (FRP), and flood preparedness (FP) among primary school teachers in Nakhon Si Thammarat province. A cross-sectional survey was conducted with 745 teachers using a structured questionnaire that covered sociodemographic characteristics, flood experience, training, information sources, and regional topography (elevation, slope, and distance to river). Spearman’s rank correlation and Generalized Linear Models (GLMs) were applied to examine the relationships and predictive factors. The findings revealed that topographic factors, specifically distance to the nearest river, were significantly associated with teachers’ flood knowledge, while school elevation was significantly related to FRP. Community-based information was a strong predictor of flood knowledge. Furthermore, prior flood experience, first-aid training, access to school-based information networks, and FRP were identified as key drivers of FP. Moreover, the negative relationships were found between flood knowledge and FP suggest that preparedness is influenced by complex cognitive and behavioral mechanisms rather than knowledge alone. These findings highlight the importance of integrating topographic risk information, experiential learning, and community-based information networks into school-based disaster preparedness programs rather than relying solely on knowledge. These findings offer practical implications for designing targeted teacher training and school-based disaster risk reduction (DRR) strategies in climate-vulnerable settings. Full article

The vibration signals of Electro-Hydrostatic Actuators (EHAs) exhibit strong non-linearity and non-stationarity, particularly under complex coupling mechanisms, making the extraction of intrinsic fault features computationally challenging. Conventional deep learning approaches often lack mathematical interpretability and struggle to decouple superimposed fault signatures from incomplete datasets. To address these issues, this paper proposes the Enhanced Continuous Wavelet Transform Capsule Network (ECWTCN), an intelligent decoupled diagnosis framework designed for multiscale signal analysis. The architecture integrates a wavelet-kernel convolution layer to extract physically interpretable time–frequency features across multiple scales, effectively capturing transient impulses associated with incipient faults. Furthermore, a novel maximized aggregation routing algorithm is introduced to optimize the dynamic routing process, enhancing global feature aggregation. A distinct advantage of the ECWTCN is its capability to generalize distinct fault patterns, enabling the identification of unseen compound faults by training exclusively on normal and single-fault samples. Comparative experiments show that the proposed method delivers strong multi-label classification performance under operating condition A, achieving a Subset Accuracy of 93.7% and a Label Ranking Average Precision of 0.998. Complexity analysis further confirms the method’s efficiency in terms of FLOPs and parameter size. This work presents a robust, lightweight, and mathematically interpretable solution for the analysis of complex signals in high-reliability equipment. Full article

The recycling of Floating Production Storage and Offloading (FPSO) units has become an important economic and environmental challenge as a growing number of offshore assets reach end-of-life. This study evaluates the comparative economic, environmental, and social performance of alternative FPSO recycling scenarios evaluated using a stochastic Monte Carlo simulation, focusing on five FPSOs that operated in Brazil and were scheduled for recycling either domestically or in Denmark. Twelve performance indicators were aggregated into sustainability indices using a Monte Carlo simulation with 100,000 iterations, enabling analysis of robustness and variability across ten recycling scenarios. The results indicate that Brazilian recycling scenarios (P-32 and P-33) outperform the Danish scenarios in terms of global performance, with Global Sustainability Index values predominantly ranging from 0.59 to 0.75, compared to 0.37 to 0.61 for the Danish cases. Differences in performance are mainly associated with towing distance, cost structure, and emissions. Social indicators show limited variability and act as a stabilizing component across scenarios. Plasma cutting presents slightly better environmental and economic results than LPG cutting, although it does not alter the overall ranking of scenarios. These findings support decision-making on FPSO recycling scenarios by highlighting the role of uncertainty and contextual factors, particularly in emerging recycling markets. Full article

Candidozyma auris (formerly Candida auris) is frequently multidrug-resistant, resulting in limited treatment options and high mortality. Comparable mortality between C. auris candidemia and non-C. auris candidemia in recent studies requires confirmation in the Middle East after adjustment for confounders. This study aimed to compare mortality rates between patients with candidemia by C. auris and non-C. auris Candida species. We retrospectively analyzed 94 cases with candidemia between January 2019 and October 2025, including C. auris candidemia (n = 30) and non-C. auris candidemia (n = 64). Inverse probability weighting was used to balance baseline confounders between groups. The primary analysis used a weighted Cox proportional hazards model. Patients in the C. auris group had more comorbidities, greater healthcare exposure, and longer hospital stays. Crude 30-day all-cause cumulative mortality was comparable between the C. auris and non-C. auris groups (log-rank test, p = 0.8). The 30-day mortality of C. auris candidemia was similar to that of non-C. auris candidemia (adjusted HR 0.40; 95% CI 0.16–1.04; p = 0.060). Large multicenter studies involving diverse populations across different regions are warranted to validate these findings. Full article