Search Results (7,282)

This study aimed to explore prognostic factors for mortality in dogs and cats following traumatic diaphragmatic herniorrhaphy using both multivariable logistic regression, a traditional statistical method, and the random forest algorithm, a machine learning approach. Associations between demographic and clinical variables and mortality were examined. Overall survival was 78.8% (149/189), 77% (97/126) in cats and 82.5% (52/63) in dogs. Key findings revealed that chronic diaphragmatic hernia (DH) significantly increased the odds of death compared to acute cases (adjusted odds ratio (OR) = 4.01, 95% confidence interval (CI): 1.69–9.53). Elevated blood urea nitrogen (BUN) increased mortality (adjusted OR = 3.24, 95% CI: 1.22–8.57). Cox proportional hazards analysis revealed that chronic DH (adjusted hazard ratio (HR) = 3.31, 95% CI: 1.51–7.30) and elevated BUN (HR = 2.88, 95% CI: 1.23–6.77) were associated with increased one-year mortality risk. The random forest analysis reinforced these findings, identifying hernia duration (Gini importance: 1.90) and BUN (Gini importance: 0.94) as the most crucial predictors. Among chronic DH patients, 55% of those with elevated BUN experienced fatal outcomes based on classification and regression tree (CART) analysis. The consistency of random forest results with logistic regression strengthens the reliability of these prognostic insights for DH patients. Full article

This paper proposes a robust blind algorithm for direction of arrival (DOA) estimation in challenging acoustic environments. The method introduces a novel Time Difference of Arrival (TDOA) consensus framework that effectively identifies and filters outliers using Median and Median Absolute Deviation (MAD) statistics. By combining this consensus approach with whitening transformation and Lawson norm optimization, the algorithm achieves superior performance in noisy and reverberant conditions. Comprehensive simulations demonstrate that the proposed method significantly outperforms traditional approaches and modern alternatives such as SRP-PHAT and robust MUSIC, particularly in environments with high reverberation times and low signal-to-noise ratios. The algorithm’s robustness to impulsive noise and varying microphone array configurations is also evaluated. Results show consistent improvements in DOA estimation accuracy across diverse acoustic scenarios, with root mean square error (RMSE) reductions of up to 30% compared to standard methods. The computational complexity analysis confirms the algorithm’s feasibility for real-time applications with appropriate implementation optimizations, showing significant improvements in estimation accuracy compared to conventional approaches, particularly in highly reverberant conditions and under impulsive noise. The proposed algorithm maintains consistent performance without requiring prior knowledge of the acoustic environment, making it suitable for real-world applications. Full article

Focusing on joint-space time-optimal trajectory planning for industrial robots, this study integrates 3-5-3 piecewise polynomial parameterization with an improved Fire Hawk Optimization algorithm (TFHO). Subject to joint position, velocity, and acceleration limits, segment durations are optimized as decision variables. TFHO employs Tent-chaotic initialization to improve the uniformity of initial solutions and a two-phase adaptive Lévy–Gaussian–Cauchy hybrid mutation to balance early global exploration with late local exploitation, mitigating premature convergence and enhancing stability. On benchmark functions, TFHO attains the lowest mean area under the convergence curve (AUC; lower is better). Wilcoxon signed-rank tests show statistically significant improvements over FHO, PSO, GWO, and WOA $(p\le 0.05)$. Ablation studies indicate a pronounced reduction in run-to-run variability: the standard deviation decreases from 0.3157 (FHO) to 0.0023 with TFHO, a 99.27% drop. In an ABB IRB-2600 simulation case, the execution time is shortened from 12.00 s to 9.88 s (−17.66%) while preserving smooth and continuous kinematic profiles (position, velocity, and acceleration), demonstrating practical engineering applicability. Full article

Background/Objectives: Breast cancer (BC) is the most prevalent cancer worldwide, with approximately 40% of early-stage BC patients developing recurrence despite initial treatments. Current diagnostic methods, such as mammography and solid tissue biopsies, face limitations in sensitivity, accessibility, and the ability to characterize tumor heterogeneity or monitor systemic disease progression. Methods: To address these gaps, this study investigates a fully automated analysis workflow using data derived from fluorescent Whole-Slide Imaging (fWSI) for detecting and classifying rare cells (circulating tumor and tumor microenvironment cells) in peripheral blood samples. Our methodology integrates supervised machine learning algorithms for rare event detection, immunofluorescence-based classification, and statistical quantification of cellular features. Results: Using a fWSI dataset of 534 cancer and non-cancer peripheral blood samples, the automated model demonstrated high concordance with manual annotation, achieving up to 98.9% accuracy and a precision-sensitivity AUC of 83.2%. Morphometric analysis of rare cells identified significant differences between normal donors, early-stage BC, and late-stage BC cohorts, with distinct clusters emerging in late-stage BC. Conclusions: These findings highlight the potential of liquid biopsy and algorithmic approaches for improving BC diagnostics and staging, offering a scalable, minimally invasive solution to enhance clinical decision-making. Future work aims to refine the automated framework to minimize errors and improve the robustness across diverse cohorts. Full article

Port seaside scheduling, involving berth allocation, quay crane, and tugboat scheduling, is central to intelligent port operations. This survey reviews and statistically analyzes 152 academic publications from 2000 to 2025 that focus on optimization techniques for port seaside scheduling. The reviewed methods span mathematical modeling and exact algorithms, heuristic and simulation-based approaches, and agent-based and learning-driven techniques. Findings show deterministic models remain mainstream (77% of studies), with uncertainty-aware models accounting for 23%. Heuristic and simulation approaches are most commonly used (60.5%), followed by exact algorithms (21.7%) and agent-based methods (12.5%). While berth and quay crane scheduling have historically been the primary focus, there is growing research interest in tugboat operations, pilot assignment, and vessel routing under navigational constraints. The review traces a clear evolution from static, single-resource optimization to dynamic, multi-resource coordination enabled by intelligent modeling. Finally, emerging trends such as the integration of large language models, green scheduling strategies, and human–machine collaboration are discussed, providing insights and directions for future research and practical implementations. Full article

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide. Timely and accurate diagnosis and staging are crucial for treatment decisions. Objective: To assess the feasibility, safety, and diagnostic/staging yield of a bronchoscopy-based pathway supported by EBUS-TBNA in a regional Romanian center. Bronchoscopy combined with endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) may reduce the need for surgical confirmation, yet its implementation in regional centers is inconsistent. Materials and Methods: This retrospective study included 67 patients with suspected lung cancer evaluated at a regional oncology center between December 2023 and February 2024. All patients underwent bronchoscopy, and EBUS-TBNA was performed in those with mediastinal lymphadenopathy on imaging, with endoscopic tissue biopsies (endobronchial/EBUS-TBNA). Demographic, clinical, histological, and molecular data were collected and analyzed using descriptive statistics and chi-square/Fisher’s exact tests. Results: Among the 67 patients, 42 (62.7%) underwent EBUS-TBNA. The majority were diagnosed in advanced stages (stage III–IV: 83.6%), with adenocarcinoma being the most frequent histological subtype. PD-L1 expression was positive in 52.2% of cases, and p63 in 67.2%. No significant procedural complications occurred, and adequate tissue sampling for histopathological and molecular analyses was achieved in all cases. Associations were found between PD-L1 and advanced TNM stage (p = 0.026), as well as between p63 status and TNM stage (p = 0.002). Conclusions: This study supports the feasibility and safety of a bronchoscopy-based diagnostic and staging algorithm supported by EBUS-TBNA, achieving reliable sampling and avoiding surgical confirmation in a regional oncology setting. Further prospective studies are warranted to validate these findings. Full article

The application of tuned mass dampers (TMDs) to high-rise buildings or slender bridges can significantly decrease the dynamical vibrations due to external excitation, such as wind or earthquake loads. However, the individual properties of a TMD such as mass, stiffness and damping have to be designed carefully with respect to the dynamical properties of the investigated structure. In real-world structures, the influence of uncertain system properties might be critical for the performance of a TMD and thus the whole structure. Therefore, the design under uncertainty of such systems is an important issue, which is addressed in the current paper. For our investigations, we consider linear single-degree-of-freedom (SDOF) systems, where analytical formulas for the deterministic design already exist, and linear multi-degree-of-freedom (MDOF) systems, where a time integration and numerical optimization algorithms are usually applied to obtain the optimal TMD parameters. If the numerical optimization should be coupled with a sampling-based uncertainty quantification method, such as Monte Carlo sampling, the design procedure would require the evaluation of a coupled double-loop approach, which is very demanding from the computation point of view. Therefore, we focus the following paper on an efficient analytical uncertainty quantification approach, which estimates the mean and scatter from a Taylor series expansion. Additionally, we introduce an efficient mode decomposition approach for MDOF systems with multiple TMDs, which estimates the maximum displacements using a modal analysis instead of a demanding time integration. Different optimal design problems are formulated as single- or multi-objective optimization tasks, where the statistical properties of the maximum displacements are considered as safety margins in the optimization goal functions. The application of numerical optimization algorithms is straightforward and not limited to specific algorithms. As numerical examples, we investigate an SDOF system with single TMD and a multi-story frame with multiple TMDs. The presented procedure might be interesting for the design process of structures, where the dynamical vibrations reach a critical threshold. Full article

Mental calculation is key to the development of number sense and flexibility in thinking, but in practice, it is often neglected in favour of written algorithms. The aim of this study was to examine the relationship between the success in mental calculation and the number of strategies used, as well as to explore differences between age groups and genders. The study included 233 participants from various age groups, and data were collected through a mental calculation test and individual interviews regarding the strategies employed. The study follows quantitative, cross-sectional, correlational-comparative design, and the data was analyzed using key statistical techniques including the Kolmogorov–Smirnov test, Pearson’s correlation analysis, linear regression, one-way ANOVA with Bonferroni post hoc correction, and two-way ANOVA to examine main effects and interactions. The results showed a statistically significant positive correlation between the number of strategies and success in mental calculation. Differences between age groups were marginally significant; it was found that upper primary and secondary school students used a greater number of strategies. Additionally, boys, on average, applied more strategies than girls. In conclusion, the variety of mental calculation strategies positively correlates with accuracy in mental calculation, and teaching a greater number of strategies may contribute to the development of flexibility and confidence in mathematical thinking. It is recommended that greater emphasis is placed on the development of mental strategies within formal education. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

Microarray gene expression data are characterized by high dimensionality and small sample sizes, which complicates cancer classification tasks. To address these challenges, this study proposes a hybrid gene selection approach that integrates a filter-based dimensionality reduction method with a metaheuristic optimizer. Specifically, the method applies the F-score statistical filter to rank and reduce gene features, followed by Nuclear Reaction Optimization (NRO) to refine the selection. This combination is referred to as the F-score-based Nuclear Reaction Optimization method or F-NRO. The performance of F-NRO was evaluated on six publicly available microarray cancer datasets (Colon, Leukemia1, Leukemia2, Lung, Lymphoma, and SRBCT) using Support Vector Machines (SVMs) and Leave-One-Out Cross-Validation (LOOCV). Comparative analysis against several existing hybrid gene selection algorithms demonstrates that F-NRO achieves high classification accuracy, including perfect accuracy on five datasets, using compact gene subsets. These results suggest that F-NRO is an effective and interpretable solution for gene selection in cancer classification tasks. Full article

Background/Objectives: Thyroid cancer encompasses distinct histological subtypes with varying biological behavior and treatment implications. Accurate preoperative subtype differentiation remains challenging. Although ultrasound (US) is widely used for thyroid nodule evaluation, qualitative assessment alone is often insufficient to distinguish between papillary (PTC), follicular (FTC), and medullary thyroid carcinoma (MTC). Methods: A retrospective analysis was performed on patients with histologically confirmed PTC, FTC, or MTC. A total of 224 standardized B-mode ultrasound images were analyzed. A set of fully quantitative features was extracted, including morphological characteristics (aspect ratio and perimeter-to-area ratio), internal echotexture (echogenicity and local entropy), boundary sharpness (gradient measures and KL divergence), and structural components (calcifications and cystic areas). Feature extraction was conducted using semi-automatic algorithms implemented in MATLAB. Statistical differences were assessed using the Kruskal–Wallis and Dunn–Šidák tests. A Random Forest classifier was trained and evaluated to determine the discriminatory performance of individual and combined features. Results: Significant differences (p < 0.05) were found among subtypes for key features such as perimeter-to-area ratio, normalized echogenicity, and calcification pattern. The full-feature Random Forest model achieved an overall classification accuracy of 89.3%, with F1-scores of 93.4% for PTC, 85.7% for MTC, and 69.1% for FTC. A reduced model using the top 10 features yielded an even higher accuracy of 91.8%, confirming the robustness and clinical relevance of the selected parameters. Conclusions: Subtype classification of thyroid cancer was effectively performed using quantitative ultrasound features and machine learning. The results suggest that biologically interpretable image-derived metrics may assist in preoperative decision-making and potentially reduce the reliance on invasive diagnostic procedures. Full article

Background/Objectives: As part of the human adaptation to dairy consumption, the presence of the rs4988235-T variant in the MCM6 gene primarily determines lactase persistence in adult European populations, increasing the expression of the lactase-encoding LCT gene. Carriers of the C/C variant are lactose intolerant, while carriers of the T/T or T/C variant have persistent lactase enzyme activity and are able to digest lactose in adulthood. While the association between lactose intolerance and increased cardiovascular risk (CVR) is well-known, the underlying causes have only been partly explored. The present study aimed to investigate the association of rs4988235 polymorphism with significant lipids affecting cardiovascular health and estimated CVR. Methods: The rs4988235 polymorphism was genotyped in 397 subjects from the general Hungarian population and 368 individuals from the Roma population. To characterize the overall lipid profile, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), apolipoprotein AI (ApoAI), and apolipoprotein B100 (ApoB100) levels were measured, and their ratios (TG/HDL-C, LDL-C/HDL-C, and ApoB100/ApoAI) were calculated. Cardiovascular risk was estimated using the Framingham Risk Score (FRS), Pooled Cohort Equations (PCE), Revised Pooled Cohort Equations (RPCE), and the Systematic Coronary Risk Evaluations (SCORE and SCORE2) algorithms. Adjusted linear and logistic regression analyses were performed, with p < 0.05 considered significant. Results: The Roma population had a significantly higher prevalence of the C/C genotype than the general population (65.5% vs. 40.3%, respectively). The results of the adjusted linear regression analysis showed a significant association between the C/C genotype and higher LDL-C level (B = 0.126, p = 0.047) and ApoB100 level (B = 0.046, p = 0.013), as well as a higher LDL-C/HDL-C ratio (B = 0.174, p = 0.021) and a higher ApoB100/ApoAI ratio (B = 0.045, p = 0.002), as well as a lower HDL-C level (B = −0.041, p = 0.049). The C/C genotype was also significantly associated with an increased cardiovascular risk (CVR) as estimated by the SCORE (B = 0.235, p = 0.034), SCORE2 (B = 0.414, p = 0.009), PCE (B = 0.536, p = 0.008), and RPCE (B = 0.289, p = 0.045) but not the FRS. After adjusting the statistical model further for ApoAI and ApoB100 levels, the significant correlation with the risk estimation algorithms disappeared (SCORE: p = 0.099; SCORE2: p = 0.283; PCE: p = 0.255; and RPCE: p = 0.370). Conclusions: Our results suggest that the C/C genotype of rs4988235 is associated with significantly higher ApoB100 and lower ApoAI levels and consequently higher ApoB100/ApoAI ratios, potentially contributing to an increased risk of cardiovascular disease. The results of the statistical analyses suggest that the association between lactose intolerant genotype and cardiovascular risk may be mediated indirectly via modification of the apolipoprotein profile. Full article

This paper introduces a new optimization problem-solving method based on how the stingless bee Tetragonula carbonaria builds and regulates temperature in the hive. The Tetragonula carbonaria Optimization Algorithm (TGCOA) models three different behaviors: strengthening the structure’s hive when it is cold, building combs in a spiral pattern at medium temperatures, and stabilizing the hive when it is hot. These temperature-dependent strategies dynamically balance global exploitation and local exploration within the solution space, enabling a more efficient search. To validate the efficiency and effectiveness of the proposed method, the TGCOA algorithm was tested using ten unimodal and ten multimodal benchmark functions, twenty-eight constrained problems with dimensions set to 10, 30, 50, and 100 taken from the IEEE CEC 2017, and seven real-world engineering design challenges. Furthermore, it was compared with ten algorithms from the literature. Wilcoxon signed-rank and Friedman statistical tests were performed to assess the outcomes. The results on the benchmark problems showed that the approach outperformed 80% of the algorithms at a 5% significance level in the Wilcoxon signed-rank test and ranked first overall according to the Friedman test. Additionally, in multidimensional problems, the TGCOA was ranked first in dimensions 30, 50, and 100. Moreover, in engineering problems, the approach demonstrated a high capacity to solve constraint problems, obtaining better results than the algorithms that were compared. Full article

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

Background: Spontaneous pneumothorax (SP) in adolescents has a high recurrence risk, particularly without surgical treatment. This study aimed to predict recurrence using machine learning (ML) algorithms applied to chest computed tomography (CT) and to visualize CT features associated with recurrence. Methods: We retrospectively reviewed 299 adolescents with conservatively managed SP from January 2018 to December 2022. Clinical risk factors were statistically analyzed. Chest CT images were evaluated using ML models, with performance assessed by AUC, accuracy, precision, recall, and F1 score. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visual interpretation. Results: Among 164 right-sided and 135 left-sided SP cases, recurrence occurred in 54 and 43 cases, respectively. Mean recurrence intervals were 10.5 ± 9.9 months (right) and 12.7 ± 9.1 months (left). Presence of blebs or bullae was significantly associated with recurrence (p < 0.001). Neural networks achieved the best performance (AUC: 0.970 right, 0.958 left). Grad-CAM confirmed the role of blebs/bullae and highlighted apical lung regions in recurrence, even in their absence. Conclusions: ML algorithms applied to chest CT demonstrate high accuracy in predicting SP recurrence in adolescents. Visual analyses support the clinical relevance of blebs/bullae and suggest a key role of apical lung regions in recurrence, even when blebs/bullae are absent. Full article