Search Results (5,193)

Background: Up to one-third of brucellosis patients develop focal organ involvement, contributing to increased morbidity and therapeutic failure, yet no clinically validated instrument exists to stratify risk at presentation. Methods: In this three-center retrospective cohort from Türkiye (2015–2025), 355 adults with confirmed brucellosis were enrolled. Thirty-two candidate variables spanning demographics, comorbidities, symptoms, routine laboratory values, and composite inflammation indices underwent LASSO-penalized regression with 10-fold cross-validation for predictor selection, after which a nomogram was constructed and internally validated via 1000-iteration bootstrap resampling. Results: Ninety-two patients (25.9%) developed focal complications. Five predictors were retained by LASSO—prognostic nutritional index (PNI), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), chronic disease stage, and hypertension—and combined with age and sex (retained a priori) into a seven-predictor nomogram. PNI was the strongest contributor (OR = 0.901, 95% CI: 0.857–0.948). Apparent C-statistic reached 0.782 (optimism-corrected 0.762), with a calibration slope of 0.894 and Brier score of 0.154. Decision curve analysis indicated net clinical benefit over the 5–55% threshold probability range. Conclusions: This PNI-anchored LASSO nomogram offers a practical bedside risk stratification instrument for brucellosis-related focal involvement. Prospective external validation across geographically diverse endemic regions is warranted before clinical adoption. Full article

Silicon carbide (SiC) is the leading wide bandgap semiconductor for high-power and high-temperature electronics, but the high defect density still limits device performance. This study investigates how inclusions, Basal Plane Dislocations (BPDs), and Threading Screw Dislocations (TSDs) in 4H-SiC substrates affect epitaxial defect formation. Twenty 200 mm SiC wafers were analyzed after epitaxial growth in two industrial Chemical Vapor Deposition (CVD) reactors, one using Trichlorosilane/Ethylene (Reactor A) and the other Silane/Propane (Reactor B). Defects were characterized using Candela (KLA), Altair (KLA), XRTmicron LAB (Rigaku), SICA (Lasertec), and Crossbeam (ZEISS) dual-beam SEM system. Statistical correlation showed that the conversion rate of embedded particles decreases with particle depth and increases with particle size. Reactor A exhibited lower propagation rates, indicating better suppression of substrate-related defects. SEM/FIB-EDX analyses suggested that carbon inclusions generate pits while metallic inclusions induce triangular defects. Dislocation analysis confirmed a strong correlation between TSDs and BPDs with carrots and triangular defects. BPD conversion rates were estimated at about 98.3% (Reactor A) and 99.8% (Reactor B). These results emphasize the importance of substrate quality and buffer layer optimization to minimize defect propagation. Full article

Objectives: Oral leukoplakia (OL) is the most prevalent oral potentially malignant disorder and requires accurate diagnosis, safe excision, and reliable margin evaluation to minimize recurrence and malignant transformation. Diode laser excision is increasingly adopted due to its precision and favorable clinical outcomes; however, laser-induced thermal effects at surgical margins raise concerns regarding tissue integrity and histopathological reliability. This study aimed to evaluate optical coherence tomography (OCT) as a real-time, high-resolution, non-invasive imaging modality for assessing peri-incisional thermal effects during diode laser excision of non-dysplastic OL. The primary objective was to validate OCT for ultrastructural and morphometric tissue analysis while ensuring preservation of diagnostic readability. Methods: A single-center observational case series was conducted at the University of Turin. Thirty patients with clinically and histopathologically confirmed oral leukoplakia without epithelial dysplasia were enrolled and allocated to two groups: 15 lesions excised using a 980 nm diode laser in continuous-wave contact mode (laser group) and 15 lesions removed by conventional scalpel biopsy (control group). Laser excisions were performed with standardized parameters and a circumferential safety margin of 5 mm. Immediately after excision, specimens underwent ex vivo spectral-domain OCT (SD-OCT) imaging to evaluate the epithelial and connective tissue microarchitecture at surgical margins and central lesion areas. OCT acquisition sites were precisely correlated with histological sections. Quantitative OCT measurements of epithelial thickness, lamina propria thickness, and laser-induced thermal alterations were compared with corresponding histological findings. Results: OCT consistently provided high-resolution visualization of oral mucosal microarchitecture in both groups, allowing clear identification of epithelial stratification, basement membrane continuity, and lamina propria organization. In the laser group, OCT detected superficial optical alterations at the surgical margins consistent with laser-induced thermal effects, while deeper tissue layers remained structurally readable. Histological analysis revealed mean epithelial and connective tissue thermal alterations of 288.9 μm and 430.3 μm, respectively. OCT-derived measurements showed high concordance with histology, with an overall agreement of 88.5% and no statistically significant differences between OCT and histological assessments. Importantly, laser-induced thermal effects did not impair definitive histopathological diagnosis in any specimen. Comparison with the control group confirmed preserved tissue architecture in scalpel-excised samples and highlighted OCT sensitivity in detecting laser-related structural remodeling. Conclusions: OCT proved to be a reliable, non-invasive imaging technique for real-time assessment of diode laser-induced thermal effects during OL excision. The technique accurately delineated tissue microstructure and surgical margins without compromising histopathological interpretation. Integration of OCT into the laser-assisted management of oral potentially malignant disorders may enhance surgical precision, optimize margin control, reduce diagnostic uncertainty, and support individualized follow-up strategies. Full article

Slope instability can cause severe disasters, making stability prediction essential. Machine learning has become a key tool for this purpose, as it avoids complex mechanical calculations and efficiently handles high-dimensional data. Currently, the data used in machine learning primarily originate from real-world cases. However, such cases are inherently limited in quantity and often fail to comprehensively represent all potential slope conditions. To address these limitations, this study proposes a method for constructing numerical simulation databases. Based on this, we develop a model establishment method for rapid evaluation of slope stability integrating numerical simulation with engineering cases. This study uses six characteristic parameters to assess slope stability, including unit weight γ, cohesion c, internal friction angle φ, slope angle α, slope height H, and pore pressure ratio r_u. Through extensive literature mining, we established a database of 684 engineering cases. Based on statistical analysis of input parameters, a numerical simulation scheme was designed. Batch calculations were performed using MATLAB to determine simulation results. The engineering case database was then partitioned into training and testing sets for model development and validation. Subsequently, the numerical simulation database was incorporated into the training set for retesting. Results demonstrate that when considering all predictive indicators, the prediction accuracy of the GRNN-based model improved from 85% to 88.3%, while the PNN-based model showed an increase from 69% to 88.3%. This study offers new insights for optimizing numerical simulation design and enhancing machine learning performance in slope stability prediction. Full article

Background: Anastomotic leakage (AL) remains one of the most feared complications after colorectal surgery. This study aimed to identify preoperative risk factors for AL using a five-year dataset from two Romanian surgical clinics. Materials and Methods: A retrospective cohort of 155 patients undergoing colorectal resection with primary anastomosis (105 from “Colentina” Hospital and 50 from “Dr. I. Cantacuzino” Hospital) was analyzed. Preoperative demographic, clinical, and laboratory data were extracted and assessed using univariate and multivariable logistic regression. Statistical analyses were performed using IBM SPSS. Results: The overall AL rate was 10.3%. Multivariable analysis identified high ASA class (OR 17.6; p = 0.001), emergency surgery (OR 32.2; p = 0.0007), and heavy alcohol use (OR 15.3; p = 0.004) as independent predictors of leakage. While low preoperative albumin and smoking were associated with leakage in a bivariate analysis, these did not remain significant after adjustment. Notably, all laboratory markers were based on preoperative values, distinguishing our approach from prior studies that commonly evaluated postoperative biomarkers. No statistically significant effect was found for neoadjuvant chemotherapy or radiotherapy after controlling for other covariates. Conclusions: High ASA score, alcohol abuse, and emergency surgery were the strongest independent predictors of AL in our cohort. The lack of predictive power of certain widely reported factors, such as low albumin, may reflect our dataset’s focus on preoperative optimization. These findings support the use of individualized risk assessment and reinforce the role of preoperative preparation in reducing leak incidence in colorectal surgery. Full article

Understanding the complex drivers of water yield is essential for ensuring basin water resource security, yet existing linear approaches often overlook the critical nonlinear effects arising from factor interactions. Previous studies combining the InVEST model with attribution methods have typically treated climate and land use as independent factors, failing to quantify their interactive effects beyond additive assumptions. This study addresses this gap by introducing a coupled framework that explicitly isolates and quantifies nonlinear climate–land interactions through scenario-based residual decomposition and spatial interaction detection. Focusing on the Minjiang River Basin, this study first applies a locally calibrated InVEST model to analyze the spatiotemporal patterns of water yield from 2000 to 2023. Through scenario analysis and the Geographical Detector method, we decoupled the contributions of climatic factors, land use, and their interactions. The results show significant spatiotemporal heterogeneity in water yield, averaging 1053.59 mm, with a spatial pattern aligned closely with precipitation. Climatic factors dominated the changes (average contribution 93.43%), while the direct contribution of land use was minimal (−1.56%). Importantly, a significant nonlinear interaction effect was identified (average 8.13%), with the interplay between precipitation and forest land proportion showing the strongest explanatory power for spatial differentiation (q-statistic up to 96.4%). These findings highlight the necessity of an integrated climate-land regulatory strategy that enhances climate resilience and optimizes key land uses to promote sustainable water management, providing a methodological framework for analyzing complex hydrological drivers. Full article

Alkali-activated materials can significantly reduce carbon dioxide emissions compared with cement. However, their durability remains insufficiently understood. This study investigated the effects of calcium hydroxide (Ca(OH)₂, CH), an expansion agent (calcium sulfoaluminate, CSA), and a shrinkage-reducing agent (SRA) on the compressive strength and length change and determined the optimal content levels for each agent. Experiments were conducted to evaluate the compressive strength and length change of 17 mortar mixtures containing CH, CSA, and SRA. The substitution ratios of CH, CSA, and SRA were fixed at three predefined levels for each factor. The microstructural changes induced by the use of each agent were analyzed using pH measurements, porosity analysis, and X-ray diffraction. In addition, the water desorption behaviors associated with CSA and SRA were assessed. Experimental and statistical analyses demonstrated that the optimal contents of CH, CSA, and SRA for simultaneously improving the compressive strength and length change were 8.54, 10.0, and 0.76 wt.%, respectively. The use of CSA significantly enhanced the compressive strength development and dimensional stability of the mortar. This improvement was associated with a reduction in the porosity, which was attributed to ettringite formation. Furthermore, while the SRA slightly reduced the compressive strength, it significantly improved the dimensional stability. Full article

Background/Objectives: Serotonin transporter (SERT) inhibition represents a central pharmacological strategy in the treatment of major depressive disorder. In this study, an integrated computational framework combining quantitative structure–activity relationship (QSAR) modeling, molecular docking analysis, and in silico ADMET profiling was applied to identify and prioritize novel candidate structures. Methods: Conformation-independent QSAR models were developed using local molecular graph invariants and SMILES-based descriptors optimized through a Monte Carlo learning procedure, while a genetic algorithm–multiple linear regression (GA–MLR) was employed to derive statistically robust predictive models from a large descriptor pool. Model quality, robustness, and external predictivity were rigorously evaluated using multiple statistical validation criteria. In parallel, a field-based contribution analysis was applied to construct a three-dimensional QSAR model, enabling spatial interpretation of structure–activity relationships. Fragment-level contributions associated with activity enhancement or attenuation were subsequently identified and used to design new candidate inhibitor structures. Results: The designed compounds were further evaluated by molecular docking, InducedFit Docking and Binding Pose MetaDynamics (BPMD) into the SERT binding site, providing a structure-based assessment consistent with the trends observed in QSAR modeling. In addition, in silico ADMET analysis was performed to assess key pharmacokinetic and safety-related properties relevant to central nervous system drug development. Conclusions: The proposed workflow demonstrates the utility of combining data-driven QSAR modeling with structure-based and pharmacokinetic considerations to rationalize and prioritize novel serotonin transporter-focused scaffold optimization, offering a transferable strategy for early-stage antidepressant drug discovery. Full article

While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the statistical reliability and robustness of observed gains largely unexplored. Consequently, it remains unclear which signals provide consistent and robust improvements. This paper presents a taxonomy-driven empirical analysis of graph-derived signals for tabular machine learning. We propose a unified and reproducible evaluation method to systematically assess which categories of graph-derived signals yield statistically significant and robust performance improvements. The method provides an extensible setup for the controlled integration of diverse graph-derived signals into tabular learning pipelines. To ensure a fair and rigorous comparison, it incorporates automated hyperparameter optimization, multi-seed statistical evaluation, formal significance testing, and robustness analysis under graph perturbations. We demonstrate the applicability of the method through an extensive case study on a large-scale, imbalanced cryptocurrency fraud detection dataset. The analysis identifies signal categories providing consistently reliable performance gains and offers interpretable insights into which graph-derived signals indicate fraud-discriminative structural patterns. Furthermore, robustness analyses reveal pronounced differences in how various signals handle missing or corrupted relational data. These findings demonstrate the proposed taxonomy-driven evaluation method’s practical utility for fraud detection and illustrate how it can be applied in other application domains. Full article

Modern agriculture is undergoing a paradigm shift toward eco-friendly methodologies that enhance seed material quality while minimizing chemical inputs. This study evaluates the impact of Effective Microorganism (EM) exposure (variants E1 and E2) on the morpho-physiological parameters and phytosanitary health of potato tubers. The primary objective was to determine the efficacy of microbial priming in suppressing the infection rates of Streptomyces scabies (common scab) and Rhizoctonia solani (black scurf) across 14 genetically diverse cultivars. A three-year field experiment (2019–2021) was conducted using a split-plot design with three replications. The study analyzed the interaction between EM exposure times and the genetic resistance potential of the selected cultivars. Statistical analysis confirmed that pre-planting microbial treatments significantly inhibited pathogen development. EM applications (E1 and E2) reduced the infection rates of both S. scabies and R. solani through an “escape mechanism,” whereby treated tubers exhibited accelerated biomass accumulation and reached physiological maturity before peak pathogen pressure. Furthermore, treatments optimized the physiological state and vigor of the tubers, establishing a robust physiological barrier against soil-borne infections. The application of EMs proves to be a highly effective, non-invasive biostimulation method. A significant difference was observed in the responding varieties between EM treatments and the cultivars innate genetic resistance, particularly in cultivars with higher baseline resistance. The use of EM biostimulants significantly modifies the health of tubers, and the direction of these changes is strictly determined by the variety factors. The results suggest that microbial priming not only enhances plant growth kinetics but also induces systemic resistance, offering a viable ecological alternative to traditional chemical seed dressings in sustainable potato production. Full article

Artificial intelligence (AI)-supported agricultural disease detection has become increasingly important for addressing global food security challenges. In this study, a hybrid meta-heuristic optimization-based feature selection approach is proposed for the detection of peacock eye disease (Venturia oleaginea) on olive leaves. The proposed method combines Artificial Rabbit Optimization (ARO) and Genetic Algorithm (GA) strategies to balance global exploration and local exploitation during feature selection. Comprehensive experiments conducted on a dataset of 954 olive leaf images demonstrate that the proposed approach achieves an F1-score of 99.7% while reducing the feature dimensionality by 95%, selecting only 100 features from ResNet101. Statistical analysis confirms that the method significantly outperforms standalone GA and ARO approaches ($p<0.05$, paired t-tests), demonstrating superior long-term convergence behavior and a 47–56% reduction in performance variance across repeated runs. Compared to existing approaches in the literature, the proposed method attains competitive or superior accuracy with substantially fewer features, indicating a marked reduction in computational complexity. These results suggest that the proposed hybrid feature selection framework has strong potential for deployment in resource-constrained agricultural monitoring scenarios, where efficient inference and reduced model complexity are critical. Full article

Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems. Full article

The Flexible Job-shop Scheduling Problem (FJSP), a pivotal NP-hard challenge in intelligent manufacturing, has been increasingly addressed by Deep Reinforcement Learning (DRL) methods. However, existing approaches face a dilemma: Proximal Policy Optimization (PPO) ensures stability but suffers from conservative exploration, while Soft Actor–Critic (SAC) enhances exploration but lacks stability in discrete scheduling spaces. To resolve this trade-off, this study proposes PPO-Graph Explorer, a novel framework that integrates a Graph Isomorphism Attention Network (GIAN) with an Entropy-Adjusted PPO (EAE-PPO). Unlike generic Graph Transformers, our GIAN employs a structure-aware hybrid design specifically tailored for FJSP’s disjunctive graph topology. EAE-PPO introduces a structured exploration curriculum that enables the agent to mimic aggressive search behaviors early in training without sacrificing on-policy stability. Extensive experiments on standard benchmarks (Brandimarte, Hurink, Dauzère–Pérès) demonstrate our method’s superiority. Compared to state-of-the-art DRL baselines, it achieves an average makespan gap reduction of 5.1 percentage points with zero statistical outliers. Qualitative analysis further reveals an 8.95% reduction in makespan on representative instances, accompanied by a significant increase in average machine utilization from 89.0% to 98.1%. Full article

Optimizing the control parameters of an islanded microgrid with active load integration presents a challenging operational research problem since current methodologies frequently fail to reach the ideal balance or symmetry between transient response, stability, and efficiency. The conventional methods, such as the canonical Particle Swarm Optimization (PSO), have settling time and voltage ripple minimization constraints, indicating possible improvement scopes. This research addresses this gap by employing advanced metaheuristic algorithms such as Accelerated Particle Swarm Optimization (APSO), Accelerated Particle Swarm Optimization with variable α (APSO α), Accelerated Particle Swarm Optimization with Normal Distribution (APSO_G), Rayleigh Distribution Accelerated Particle Swarm Optimization (RDAPSO), Rayleigh Distribution Accelerated Particle Swarm Optimization with variable α (RDAPSO α), and the Dragonfly Algorithm (DA). The algorithms were tested for their performance by using CEC Standard Benchmark functions from 2017, 2019, and 2022, providing a basis for rigorous and symmetrical testing and validation. The optimized RDAPSO α algorithm showed a significant reduction in voltage ripple, which was reduced from 4 V to 0.47 V, with an 88.25% reduction. It also showed a 46.32% improvement in settling time, which was reduced from 184.2 ms to 98.9 ms compared to PSO. A detailed statistical analysis was conducted to enhance the reliability and symmetry of the outcomes using Multivariate Analysis of Variance (MANOVA), the Mann–Whitney U test, the Friedman test, and the Bonferroni test. The results show that RDAPSO α offers a significant edge over the rest of the algorithms, with improvements that can be declared statistically superior in optimizing microgrids with improved symmetry in performance. Full article

This study explores the effects of applying agri-environmental measures and digitalization on cereal farms in Dunărea de Jos Basin, Romania. Through structured interviews with 40 wheat producers, complemented by field observations and data analysis, effects are identified in highlighting planning, data evaluation and approximation to European environmental requirements. Wheat productivity and input efficiency are investigated in partially technologically advanced farms compared to poorly technologically advanced farms. Regression is used in the relationship between average wheat production and the main agricultural inputs. The results show statistically significant correlations, supported by coefficients (R² > 0.45). In partially mechanized farms, wheat production is influenced by the use of plant protection products (R² = 0.943), demonstrating high technological consistency and improved efficiency of phytosanitary applications. In farms not using Geofolia, the application of NPK fertilizers appears as the dominant factor of productivity (R² = 0.968), indicating that chemical fertilization compensates for limited mechanization and restricted access to technological operations. Digitalization of farms contributes to the optimization of resources, reducing diesel consumption by 45% and developing overall efficiency by 34%, reducing the pressure on the environment by 30%. Technology can be responsible for increasing farm productivity and yield performance, while reducing resource intensity and environmental impact. Full article