Search Results (17,898)

Identifying critical infrastructure in transportation networks requires metrics that can capture both the topological structure and how demand is redistributed during disruptions. Conventional graph-theoretic approaches fail to jointly quantify these vulnerabilities. This study presents a computational framework for edge-criticality assessment based on the Fused Unbalanced Gromov–Wasserstein (FUGW) distance, incorporating both structural similarity and demand characteristics of network nodes in an optimal transport tool. The three hyperparameters that influence FUGW accuracy—fusion weight, entropic regularization, and marginal penalties—were tuned using Bayesian optimization. This ensures the rankings remain accurate, stable, and reproducible under temporal variability and demand shifts. We apply the framework to a benchmark transportation network evaluated across four diurnal periods, capturing dynamic congestion and shifting demand patterns. Systematic variation in the fusion parameter shows seven consistently critical edges whose rankings remain stable across analytical configurations. It can be concluded from the results that monotonic scaling with increasing feature emphasis, strong cross-hyperparameter correlation, and low temporal variability confirm the robustness of the inferred criticality hierarchy. These edges represent both structural bridges and demand concentration points, offering α indicators of network vulnerability. These findings demonstrate that FUGW provides a solid and scalable method of assessing transportation vulnerabilities. It helps support clear decisions on maintenance planning, redundancy, and resilience investments. Full article

In the operation mode arrangement of bulk power systems, unreasonable reactive power injection data at nodes tend to result in power flow calculation non-convergence. Owing to the extremely high dimension of the variable space and the heterogeneous impacts of different variables on power flow convergence, it is imperative to accurately identify the key variables inducing non-convergence and provide physical justifications. For this purpose, this paper proposes a data-driven key variable identification and adjustment method: firstly, based on the blocking cut-set theory and the characteristic that the active unbalanced power ΔP of intermediate power flow exhibits opposite signs at the sending and receiving ends of the cut-set, a blocking cut-set identification method leveraging the characteristics of the active unbalanced power of intermediate power flow is developed; secondly, relying on the feature that the reactive unbalanced power ΔQ of intermediate power flow is less than zero, a key variable identification method based on the characteristics of the reactive unbalanced power of intermediate power flow is presented; finally, a key variable adjustment method grounded in the numerical value of ΔQ is proposed. The validity of the proposed approach was validated via simulated computations using both the IEEE 39 bus system and a practical bulk power system. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

This study investigates the vegetation dynamics and phenotypic differentiation of five triticale (×Triticosecale Wittm.) varieties under the region-specific agroecological conditions of Southern Dobruja, Bulgaria, across two growing seasons (2024–2025), with the aim of evaluating how local climatic variability shapes vegetation index patterns. UAV-based multispectral imaging was employed throughout key phenological stages to obtain reflectance indices, including NDVI, SAVI, EVI2, and NIRI, which served as indicators of canopy development and physiological status. NDVI was used as the primary reference index, and a baseline value (NDVIbase), defined as the mean NDVI across all varieties on a given date, was applied to evaluate relative varietal deviations over time. Multiple linear regression analyses were performed to assess the relationship between NDVI and baseline biometric parameters for each variety, revealing that varieties 22/78 and 20/52 exhibited reflectance dynamics most closely aligned with expected developmental trends in 2025. In addition, the relationship between NDVI and meteorological variables was examined for the variety Kolorit, demonstrating that relative humidity exerted a pronounced influence on index variability. The findings highlight the sensitivity of triticale vegetation indices to both varietal characteristics and short-term climatic fluctuations. Overall, the study provides a methodological framework for integrating UAV-based multispectral data with meteorological information, emphasizing the importance of region-specific, time-resolved monitoring for improving precision agriculture practices, optimizing crop management, and supporting informed variety selection. Full article

Aflatoxins, particularly aflatoxin B₁ (AFB₁), are among the most potent naturally occurring carcinogens and remain a major food-borne hazard in parts of Asia and Africa. China has generated a uniquely cohesive body of evidence connecting aflatoxin contamination to hepatocellular carcinoma (HCC), especially in settings where chronic hepatitis B virus (HBV) infection is highly prevalent and acts synergistically with aflatoxin exposure. Over five decades, field investigations and laboratory innovations—exemplified by long-term work in Qidong—have assembled a multi-layered causal chain spanning the following: (i) contamination monitoring in staple foods; (ii) quantification of internal dose and biologically effective dose using validated biomarkers (e.g., urinary AFB₁–N⁷–guanine, AFM₁, and serum AFB₁–lysine albumin adducts); (iii) a characteristic molecular fingerprint in tumors and circulation (TP53 R249S); (iv) reversibility demonstrated through randomized intervention trials and policy-driven natural experiments. Chemoprevention and dietary interception studies (e.g., oltipraz, chlorophyllin, and broccoli sprout beverages) showed that enhancing detoxication pathways can lower biomarker burdens in exposed populations. At the population level, a sustained dietary transition from maize to rice, together with strengthened food governance, was accompanied by marked decreases in biomarker distributions and subsequent declines in HCC mortality in endemic regions. Nevertheless, regional heterogeneity, multi-mycotoxin co-exposure, and climate variability are expected to increase exposure volatility and complicate surveillance. Here, we translate and synthesize the Chinese evidence base, highlight biomarker-enabled monitoring and policy evaluation, and propose an integrated “5+1” prevention framework spanning source control, process detoxification, tiered governance, short-course interception, precision follow-up of high-risk individuals, and climate-sensitive early warning along the climate–agriculture–storage–processing–population (CAT–CSPP) chain. Full article

Background/Objectives: Subsequent musculoskeletal injuries are frequent among physically active young adults, yet the roles of body composition, training weekly load (TWL), and physical activity intensity in subsequent injury occurrence remain unclear. This study examined the associations of body composition indices and training-related variables with subsequent injuries in university students and explored whether combining key markers from body composition and training exposure improves discrimination compared with single markers. Methods: The analysis included 418 students from two cohorts merged after confirming negligible between-cohort differences. Participants completed questionnaires on injury history and physical activity and underwent standardized anthropometric and body composition assessments. Intrinsic factors included fat mass index (FMI) and skeletal muscle mass index (SMI), while extrinsic factors comprised training weekly load (TWL), total physical activity (TPA), and vigorous activity percentage (VPA%). Subsequent injury (yes/no) served as the primary outcome. Injuries were assessed retrospectively over the preceding 12 months; subsequent injury was defined as ≥1 injury occurring after a previous (index) injury within this recall period. Analyses used univariate and multivariable logistic regression and exploratory Receiver Operating Characteristic (ROC) analyses for individual markers and combined models. Results: SMI was associated with subsequent injury (OR = 1.09, 95% CI: 1.03–1.15). TWL showed a weak, non-significant association (OR = 1.03, p = 0.307). Models combining SMI and TWL, including their interaction, did not meaningfully improve discrimination compared with SMI alone. ROC analyses indicated limited discriminatory ability across models (AUCs < 0.65), suggesting poor accuracy for identifying individuals with subsequent injury based on these markers. Conclusions: The examined body composition, training weekly load (TWL), and physical activity measures alone or combined showed limited discriminatory utility for subsequent injury status in this cross-sectional sample. These findings support the multifactorial nature of injury susceptibility and indicate that simple anthropometric or TWL-based measures are not suitable as standalone screening tools for subsequent injury in active university populations. Full article

Long-term particulate matter (PM) chemical composition measurements were performed in Cyprus at two different sites (an urban/traffic site (“LIMTRA”) and a remote/background site (“AGM”)) in an effort to assess (i) the spatial and temporal variability of fine (PM2.5) particulate matter in the eastern Mediterranean; (ii) the main sources contributing to their levels and their relationship with the characteristics of the sampling location; and (iii) the enhancement effect of local anthropogenic and natural biogenic sources on PM levels. To this end, the simultaneous determination of 118 individual Secondary Organic Aerosol (SOA) components (carboxylic acids, organosulfates, and organonitrates) was performed. The “AGM” station showed average SOA yields more than three times higher than those at the “LIMTRA” station (15 ng∙m⁻³ and 4.4 ng∙m⁻³, respectively), whilst the organonitrate levels were higher at “LIMTRA” than at “AGM” (3.3 ng∙m⁻³ and 1.8 ng∙m⁻³, respectively). The most abundant SOA species were hydroxy-acetone sulfate, glycolic acid sulfate, and lactic acid sulfate (21 ng∙m⁻³ at “LIMTRA” and 84 ng∙m⁻³ at “AGM”). The highest SOA load was observed in spring at “AGM” (18 ng∙m⁻³) and in summer at “LIMTRA” (6.8 ng∙m⁻³). Two statistical factorization tools, Principal Component Analysis and Positive Matrix Factorization, were applied to extract common patterns and point to possible SOA sources and SOA formation pathways; the different categorization approaches produced similar results. Full article

This study examines the relationship between income and municipal waste generation within the Waste Kuznets Curve (WKC) framework, with a focus on selected disaggregated household waste fractions (paper and cardboard, glass, bulky waste, and biowaste). The aim is to assess whether increases in earnings per capita are associated with non-linear waste dynamics once spatial interactions and local socio-demographic characteristics are taken into account. The study employs a spatial panel dataset for 378 Polish counties over the period 2017–2024. Fixed-effects panel models, supplemented with random-effects panel models with Mundlak’s approach, are estimated alongside spatial panel specifications. Control variables include population ageing, urbanisation, and tourism, while spatial effects are decomposed into direct and indirect impacts. The results indicate that, in non-spatial models, an inverted U-shaped relationship between earnings and waste generation is observed for most waste fractions. However, once spatial dependence is explicitly incorporated, income effects weaken. In contrast, demographic structure—the share of retirement-age population—emerges as a robust and spatially persistent determinant of waste generation. Urbanisation and tourism exert only a limited influence across waste fractions. The paper advances WKC research by using spatial econometric methods and disaggregated waste fractions at the county level. The evidence suggests that conclusions about income-driven waste decoupling are sensitive to spatial dependence, emphasising the need for locally tailored waste management strategies. Full article

Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article

Water quality monitoring faces increasing challenges due to accelerating industrialization and urbanization, demanding accurate, real-time, and reliable prediction technologies. This study presents a novel ensemble learning framework integrating Gaussian Process Regression, Support Vector Regression, and Random Forest algorithms for high-precision water quality pH prediction. The research utilized a comprehensive spatiotemporal dataset, comprising 11 water quality parameters from 37 monitoring stations across Georgia, USA, spanning 705 days from January 2016 to January 2018. The ensemble model employed a dynamic weight allocation strategy based on cross-validation error performance, assigning optimal weights of 34.27% to Random Forest, 33.26% to Support Vector Regression, and 32.47% to Gaussian Process Regression. The integrated approach achieved superior predictive performance, with a mean absolute error of 0.0062 and coefficient of determination of 0.8533, outperforming individual base learners across multiple evaluation metrics. Statistical significance testing using Wilcoxon signed-rank tests with a Bonferroni correction confirmed that the ensemble significantly outperforms all individual models (p < 0.001). Comparison with state-of-the-art models (LightGBM, XGBoost, TabNet) demonstrated competitive or superior ensemble performance. Comprehensive ablation experiments revealed that Random Forest removal causes the largest performance degradation (+4.43% MAE increase). Feature importance analysis revealed the dissolved oxygen maximum and conductance mean as the most influential predictors, contributing 22.1% and 17.5%, respectively. Cross-validation results demonstrated robust model stability with a mean absolute error of 0.0053 ± 0.0002, while bootstrap confidence intervals confirmed narrow uncertainty bounds of 0.0060 to 0.0066. Spatiotemporal analysis identified station-specific performance variations ranging from 0.0036 to 0.0150 MAE. High-error stations (12, 29, 33) were analyzed to distinguish characteristics, including higher pH variability and potential upstream pollution influences. An integrated software platform was developed featuring intuitive interface, real-time prediction, and comprehensive visualization tools for environmental monitoring applications. Full article

Background/Objectives: To evaluate and compare the characteristics of clinical trials (CTs) involving patients with primary Sjögren’s syndrome (pSS), using biologics, and focusing on the features of the patients recruited. Methods: This systematic review assessed pSS CTs evaluating biologic drugs published from 2010 to 2024 according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The literature search of the electronic databases was performed individually by the authors. The extracted variables regarding the baseline characteristics of participants and trial-related information were defined a priori, collected, and compared. Results: A total of 16 CTs were included in this review in line with the inclusion criteria. The trials were predominantly multicenter (75%) randomized controlled trials with a placebo arm (93.8%), with only five trials recruiting participants across multiple (≥3) continents. The search included a total of 1607 patients (mean age 51 years, 94% female) with a mean disease duration of 6.47 years. Race and ethnicity were underrepresented variables, found in 37.5% and 12.5% of the trials, respectively, with White patients comprising the majority (77.8%). The EULAR Sjögren’s Syndrome Disease Activity Index (ESSDAI) was reported in 93.8% of the CTs. However, only recent studies have emphasized it as the primary outcome. Conclusions: Recent trials on biologics in pSS patients show better methodological quality, with a more standardized assessment of disease activity using ESSDAI, and an increased focus on patient-reported outcomes. Global participation is increasing, but limited racial and ethnic diversity, endpoint variability, and inconsistent biomarker reporting remain critical issues. Full article

Survey campaigns in non-residential buildings show that occupants are often dissatisfied with the indoor environmental quality (IEQ), including the indoor air quality (IAQ) conditions. Occupant-centric controls (OCCs) have the potential to improve occupants’ satisfaction with IAQ and thermal comfort. Currently, applications of OCC systems with IAQ perceptions are limited due to a lack of a suitable modelling approach to predict occupants’ subjective IAQ assessment. In addition, a comprehensive overview of possible confounding variables for subjective IAQ in non-residential buildings is missing. This paper presents a systematic review of 46 papers on subjective IAQ assessments during field investigations in non-residential buildings. The following characteristics of the studies are examined: (1) the study context, (2) study and survey type, (3) dataset and sample size, (4) subjective IAQ assessment scales, (5) analysis and modelling techniques, and (6) associated variables. The review identified 46 different assessment scales and 20 different analysis techniques, respectively, indicating a lack of uniformity across the studies. The vast majority of studies were conducted in classrooms or offices. Other non-residential buildings, such as hospitals and sports halls, were underrepresented. Moreover, most of the studies failed to elaborate on the choice of a statistical technique and to report on the required sample size, compromising the validity of the statistical results. Furthermore, the review highlighted the limited scope of the subjective IAQ assessment analysis, with half of the reviewed studies investigating no more than four different variables. Lastly, only three of the reviewed papers focused on determining an accurate predictive model for subjective IAQ assessment. Full article

One of the most notable aspects of Aspergillus fumigatus, and related to its dynamic adaptation, is its ability to form biofilm and produce a wide variety of secondary metabolites. The aim of this study is to advance the characterization of biofilms generated by different A. fumigatus strains across their developmental stages and analytically evaluate their structure and composition and their relationship with secondary metabolism activation. An in vitro biofilm model was standardized to investigate structural and analytical differences among strains isolated from distinct clinical settings and associated with different pathologies. We found that all tested strains could form biofilms; however, the characteristics of these structures—including total biomass, cellular viability and overall structure—varied markedly among strains under the evaluated conditions. Strains isolated from cystic fibrosis patients exhibited distinct behaviors in most conducted assays compared to other strains. These findings provide new insights into the variability of biofilm composition and may contribute to a better understanding of the role of biofilms in fungal pathogenesis, persistence and treatment resistance. Full article

Background/Objectives: To develop and validate a predictive model that combines morphological features and dual-energy CT (DECT) parameters to non-invasively distinguish metastatic from benign axillary lymph nodes in patients with breast cancer (BC). Methods: In this retrospective study, 117 patients (median age, 65 years; 111 women and 6 men) who underwent DECT followed by axillary lymphadenectomy between April 2015 and July 2023, were analyzed. A total of 375 lymph nodes (180 metastatic, 195 benign) were evaluated. Two radiologists recorded morphological criteria (adipose hilum status, cortical appearance, extranodal extension, and short-axis diameter) and placed regions of interest to measure dual-energy parameters: attenuation at 40 and 70 keV, iodine concentration, water concentration and spectral slope. Normalized iodine concentration was calculated using the aorta as reference. Univariate analysis identified variables associated with metastasis. Multivariate logistic regression with cross-validation was used to construct two models: one based solely on morphological features and one integrating water concentration. Results: On univariate testing, all DECT parameters and morphological criteria differed significantly between metastatic and benign nodes (p < 0.01). In multivariate analysis, water concentration emerged as the only independent DECT predictor (odds ratio = 0.97; p = 0.002) alongside cortical abnormality, absence of adipose hilum, extranodal extension and short-axis diameter. The morphologic model achieved an area under the receiver operating characteristic curve (AUC) of 0.871. Increasing water concentration increased the AUC to 0.883 (ΔAUC = 0.012; p = 0.63, not significant), with internal cross-validation confirming stable performance. Conclusions: A model combining standard morphologic criteria with water concentration quantification on DECT accurately differentiates metastatic from benign axillary nodes in BC patients. Although iodine-based metrics remain valuable indicators of perfusion, water concentration offers additional tissue composition information. Future multicenter prospective studies with standardized imaging protocols are warranted to refine parameter thresholds and validate this approach for routine clinical use. Full article

Background: Cellulite is a highly prevalent condition with dermal and subcutaneous alterations poorly captured by visual grading systems. Ultrasound has emerged as a non-invasive imaging modality capable of objectively quantifying morphological features relevant to cellulite. This systematic review evaluated the evidence on ultrasound for the diagnosis, structural characterization, and treatment monitoring of cellulite, identifying methodological limitations and research gaps. Methods: This systematic review (PROSPERO:CRD420251185486) followed the PRISMA statement. Searches were conducted in PubMed, Scopus, and CENTRAL up to November 2025. Risk of bias was evaluated using ROBINS-I and the Newcastle–Ottawa Scale. Results: Nine studies involving 785 participants were included. Ultrasound frequencies ranged from 12 to 35 MHz, with some scanners operating across broader bandwidths. Despite variability in devices, acquisition protocols, and clinical comparators, all studies consistently demonstrated that ultrasound quantifies key structural characteristics of cellulite. Diagnostic investigations reported moderate-to-strong correlations (r ≈ 0.31–0.64) between ultrasound-derived measures and clinical severity scores. Interventional studies showed measurable reductions in dermal and subcutaneous thickness, decreased adipose protrusion height, and improved dermal echogenicity across multiple treatment modalities. Ultrasound frequently detected microstructural remodeling not readily visible on clinical examination. Conclusions: Ultrasound is a valuable imaging modality for objectively characterizing cellulite and monitoring treatment-induced tissue remodeling. Standardized acquisition protocols, validated analytic criteria, and larger controlled studies are needed to support integration into routine dermatologic and esthetic practice. The quantitative and reproducible nature of ultrasound-derived parameters also provides a suitable foundation for future integration with data-driven and artificial intelligence–based image analysis frameworks. Full article