Search Results (1,438)

Tool wear directly affects surface quality, dimensional accuracy, and manufacturing cost in milling operations, making reliable wear state classification essential for process control. This paper presents an offline deep learning framework for multiclass tool wear classification using the UC Berkeley milling dataset (NASA-Ames). Statistical features are extracted from vibration, acoustic emission, and spindle motor current signals, and dimensionality is reduced from 78 to 9 informative variables using LASSO regression. A four-layer Long Short-Term Memory (LSTM) network then models the temporal evolution of tool degradation across three wear states: healthy, degraded, and failed. Two model variants are compared: Model A uses sensor-derived features only, while Model B additionally incorporates feed rate and depth of cut as inputs. To prevent data leakage, partitioning is performed at the machining-case level rather than at the individual window level. Model A achieves 92% classification accuracy; Model B reaches 95%, demonstrating that cutting conditions provide contextual information that resolves ambiguity between wear states produced under different machining regimes. These results confirm that combining multisensor feature fusion, LASSO-based selection, and sequential deep learning constitutes an effective framework for tool wear classification in milling. Full article

Tail striking is a typical safety event in the area of civil aviation, which is directly related to the aircraft pitch angle at landing. Based on 2933 A319 flights’ non-exceedance quick access recorder (QAR) data from Dali airport, the relationship between key flight parameters during the final approach and landing pitch angle is explored. Functional data analysis and the Group Lasso method are used to select the most important flight parameters, and cluster analysis and weighted logistic regression are used to identify and predict a “high-risk” flight pattern. Here, “high risk” refers to a flight pattern associated with a higher probability of large landing pitch attitude, which is used as a proxy indicator of potential tail-strike risk rather than as evidence of an actual tail-strike event. Finally, flight operation recommendations are provided. The research results indicate that the airspeed, pitch angle and engine speed are most closely related to the landing pitch angle. An unusually high-risk flight pattern is identified, characterized by “high airspeed, high attitude, low thrust” caused by improper energy management of light-load flights. About 32.4% of flights in this pattern land with “large landing attitude”, which means the landing pitch angle is larger than the 95% sample percentile. A prediction model for the high-risk pattern is established using QAR parameters at the heights of 500 ft, 450 ft, and 400 ft, with an accuracy rate of 99.7% on the test data. The prediction in advance at 400 ft can provide pilots with sufficient time to take necessary operations. Full article

Background: Osteoarthritis (OA) is a heterogeneous joint disease characterized by cartilage degeneration. The interplay between extracellular matrix (ECM) remodeling, endoplasmic reticulum (ER) stress, and inflammatory signaling in OA pathogenesis remains incompletely understood. This study aimed to identify robust diagnostic biomarkers and explore the mechanistic convergence of key genes in OA cartilage through an integrated transcriptomic framework. Methods: Three independent cartilage transcriptomic datasets (GSE285234, GSE287861, GSE289464) were integrated after ComBat batch correction. Differentially expressed genes (DEGs) were identified using limma, followed by ORA and GSEA for functional enrichment. LASSO logistic regression identified hub genes for a diagnostic model and nomogram, validated by leave-one-out cross-validation (LOOCV). Consensus clustering stratified OA samples into molecular subtypes. Single-cell RNA-sequencing (scRNA-seq) data (GSE169454, GSE220243) were used to validate cell-type-specific expression. Virtual gene knockout (scTenifoldKnk) and pathway analysis inferred downstream functional consequences. Results: Fifty-eight DEGs (predominantly downregulated) were enriched in ECM and ER protein processing pathways. Six hub genes (EIF2S1, GANAB, STT3A, XBP1, MGP, PMP22) showed robust selection stability. The diagnostic model achieved a LOOCV AUC of 0.769, a well-calibrated nomogram, and superior net benefit. Unsupervised clustering revealed two OA subtypes with divergent unfolded protein response (UPR) and TGF-β pathway activities. scRNA-seq confirmed hub gene expression in chondrocytes and other joint microenvironment cells. Notably, virtual knockout of five hub genes convergently perturbed IL-17, NF-κB, and chemokine signaling pathways. Conclusions: This study identified and validated a six-gene signature reflecting ECM-ER-inflammatory crosstalk in OA cartilage. The convergent perturbation of inflammatory pathways by functionally distinct hub genes reveals a mechanistic core that may serve as a diagnostic panel and a platform for targeted therapeutic investigation in OA. Full article

Background: Age at diagnosis and histologic differentiation are clinically relevant in early gastric cancer (GC), as poorly differentiated tumors and those diagnosed in younger patients often demonstrate more aggressive characteristics. Serum microRNAs (miRNAs) may provide insights into the molecular basis of these features. Methods: We compared expression profiles between undifferentiated and differentiated early GC cases to identify differentially expressed miRNAs (DEmiRNAs) and associated enriched pathways. Using Lasso regression, we developed and cross-validated a histologic differentiation classifier based on miRNA profiles from 1399 early GC serum samples. Finally, cancer-specific miRNA differences between adolescent and young adult (AYA) and non-AYA patients were evaluated using samples from cancer cases and normal controls. Results: We identified 75 differentiation-associated DEmiRNAs targeting genes enriched in cancer hallmark pathways such as TP53 and PI3K/AKT/mTOR signaling. In the validation set, the combined Lasso model predicted differentiation status with a sensitivity of 69.2%, specificity of 75.3%, positive predictive value (PPV) of 66.9%, negative predictive value (NPV) of 77.2%, an overall accuracy of 73.1%, and an area under the curve (AUC) of 79.7%. Comparison of AYA and non-AYA groups identified 52 cancer-specific and age-related miRNAs. Notably, three components of a previously reported four-miRNA GC diagnostic signature were significantly associated with age. Conclusions: Age-related variation in miRNA expression suggests that patient age may influence the performance of the existing four-miRNA diagnostic signature in early GC. Overall, our findings demonstrate the utility of miRNA profiling for predicting differentiation status in early GC and reveal age-associated variation in cancer-specific miRNAs. Full article

Background/Objectives: Anthropometric measurements provide essential normative datasets that form the foundation for clinical practice and forensic identification. The human ear is a highly informative structure due to its complex morphology and individual specificity, making it a valuable tool for biometric systems. This study aimed to estimate biological sex based on auricular morphometric measurements, develop a logistic regression model for this purpose, and validate its performance using ROC analysis. Materials and Methods: This cross-sectional study included 120 adult participants (60 males, 60 females). Standardized digital photographs were analyzed in ImageJ to record 22 linear and 6 angular measurements using established anatomical landmarks. LASSO logistic regression was employed for variable selection and model shrinkage. The final model’s discriminative performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer–Lemeshow test, and the Brier score. Results: A comparative analysis revealed that most linear and angular measurements showed significant sexual dimorphism. Almost all linear dimensions (A1–A22) were significantly larger in males (p < 0.001). Auricular width (A2) and width at the level of the tragus (A3) emerged as the most robust indicators, demonstrating “very large” effect sizes. Conversely, the angle between the preauricular line and the vertical plane (A28) was significantly greater in females, providing a unique inverse relationship for sex estimation. A parsimonious 5-predictor model (incorporating A2, A3, A5, A10, and A28) achieved exceptional discriminative performance with an AUC of 0.980. Conclusions: Auricular morphometry is a highly effective tool for sex estimation. The findings confirm significant sexual dimorphism in the external ear, particularly in linear dimensions. The developed model may serve as a preliminary morphometric reference for future automated biometric recognition studies, although no artificial intelligence-based classification model was developed in the present study. Full article

Accurate monitoring of the leaf area index (LAI) throughout the entire growth cycle of walnut trees using UAV multispectral imagery is essential for digital orchard management. In this study, focusing on the ‘Wen 185’ walnut variety in Xinjiang, we simultaneously acquired UAV multispectral images and ground-measured LAI data during four critical growth stages: expansion, hard shell, oil conversion, and maturity. A total of 25 vegetation indices and 48 texture features derived from the gray-level co-occurrence matrix were extracted. Hybrid feature selection combining linear (Pearson correlation), nonlinear (maximum information coefficient and random forest importance), and multiple consensus strategies was employed to reduce redundancy. LAI prediction models were constructed using four algorithms: Random Forest (RF), Support Vector Machine (SVM), LASSO, and Ridge Regression (RR), with model interpretability enhanced by SHAP analysis. Results showed that the multiple consensus screening reduced feature redundancy by an average of 69.6%. SHAP identified five core features: Redge_750_Mean, NDVI, B_Mean, RENDVI, and G_Homogeneity. Importantly, predictor importance shifted significantly with phenology: texture features dominated during the expansion stage, while red-edge indices (RENDVI and Redge_750_Mean) became predominant during the hard shell and oil conversion stages, effectively mitigating the saturation problem commonly observed in traditional indices such as NDVI within the LAI range of 1.5–5.8 in this study. The hybrid feature subset combining “red-edge spectrum + spatial texture” with the Random Forest algorithm achieved superior performance across all stages, with the RPD value exceeding 2.0 during the oil conversion stage, indicating excellent estimation capability. This study demonstrates that a “quality over quantity” feature selection strategy not only reduces model complexity but also enables high-precision, dynamic LAI monitoring throughout the entire walnut growth cycle, providing a scientific basis for intelligent management of large-scale orchards in arid regions. Full article

Background/Objectives: College student health has become a global public health concern, with campus environments serving as critical resources for supporting healthy lifestyles. This study aimed to identify heterogeneous associations between health-related lifestyle parameters and health status among university students, as well as the relationships between these parameters and campus environmental factors. Methods: A two-stage analytical approach was applied to 909 student responses from five Chinese universities. Stage One employed hierarchical regression to identify lifestyle parameters significantly associated with health status. Stage Two used EFA-derived factors and LASSO robustness checks to examine campus environmental factors linked to these key parameters. Results: Six lifestyle parameters were significantly associated with student health: physical exercise frequency, physical exercise duration, active commuting frequency, nature contact frequency, healthy diet frequency, and self-rated health literacy. Each parameter exhibited distinct patterns of environmental association. Conclusions: These findings provide empirical evidence for redefining health-promoting campus design through targeted environmental interventions. Full article

Background/Objectives: In this study, we develop and validate an interpretable machine learning (ML) model that integrates a hybrid swarm intelligence (SI)-based feature selection method with multiparametric magnetic resonance imaging (MRI)-derived RFs to estimate overall survival (OS) in glioblastoma multiforme (GBM) patients. Methods: A cohort of 276 GBM patients with open-access pre-treatment MRI data was used to perform comprehensive radiomic analysis. In the training (discovery) dataset, we employed five-fold cross-validation combined with bootstrapping to ensure robust methodological validation. Model evaluation covered the concordance index (C-index) with 95% confidence intervals (CIs). Additionally, survival stratification was performed using Kaplan–Meier curves and the log-rank test to separate patients into low- and high-risk groups for OS. The final survival model integrates patient age and ten independent RFs. Results: The model’s performance in the holdout test dataset was evaluated by a C-index of 0.71 (95% CI: 0.63–0.80), exhibiting statistically significant risk stratification (p = 3 × 10⁻⁴). Upon external validation, the model achieved a C-index of 0.67, maintaining statistical significance (p = 1 × 10⁻²). Conclusions: The research combined a traditional regularized Cox regression (Cox-LASSO) model with a new SI-based LASSO-PSO method, yielding significant stratification. To our knowledge, the present study offers one of the first studies to document the use of an interpretable ML model with an SI-based approach for successful risk stratification based on OS. Full article

Aquaculture development increasingly faces the dual requirement of increasing economic output and reducing environmental pressure under limited aquatic resources. Existing studies have examined aquaculture efficiency, environmental performance, and production optimization separately, but region-specific strategies that jointly address economic improvement and environmental-emission mitigation remain insufficiently developed. This study proposes a data-driven modeling and computational framework to identify regional green modes of fishery production, with dual properties of higher economic output and lower environmental-emission intensity. In this framework, data-analysis techniques, including missing-value imputation, regional aquaculture classification, nonlinear variable reconstruction, and Lasso regression, are integrated with scenario-based optimization models under alternative management priorities. By applying the proposed framework to provincial fishery data from China during 2017–2024, the results reveal clear heterogeneity in green fishery production modes across different aquatic-resource systems. In particular, under the economic-priority scenario with emission-reduction constraints, the optimized outputs increase by 11.19% and 6.54% in Zone 1 (an inland freshwater system) and Zone 2 (a coastal-intensive system), respectively. Under the environmental-priority scenario with required economic-growth condition, moderate emission-reduction potential is identified in Zone 1, whereas substantial emission reduction is observed in Zone 2. Furthermore, in view of the determined green fishery strategy by our framework, the nearest-optimum province is identified for each zone. By elasticity analysis, it is further found that technology-extension funding and fishery medicine expenditure are two synergistic production investments in Zones 1 and 2, whereas seedling and feed-related investments display properties of region-specific coordination. Summarily, the proposed computational framework in this paper provides an efficient tool of analyzing the regional green fishery production strategies and the regional heterogeneity in virtue of data-driven modeling and advanced optimization techniques. Full article

Background/Objective: Long-term tenofovir disoproxil fumarate (TDF) therapy is associated with progressive bone mineral density loss in patients with chronic hepatitis B (CHB), yet existing fracture risk algorithms, such as FRAX, were not designed for this population. We aimed to develop and internally validate a clinical nomogram for identifying TDF-associated osteoporosis using penalized regression on demographic, virological, and biochemical predictors. Methods: In this single-center retrospective cohort study, 237 adult CHB patients receiving TDF for at least 12 months underwent dual-energy X-ray absorptiometry (DXA). Osteoporosis was defined as a T-score of −2.5 or lower at the lumbar spine or femoral neck. Thirteen candidate predictors were evaluated using LASSO regression with 10-fold cross-validation; selected variables were entered into an unpenalized multivariable logistic regression model; internal validation employed bootstrap resampling with 200 replications to derive optimism-corrected estimates of discrimination and calibration. The clinical utility was assessed using decision curve analysis (DCA). Results: Osteoporosis prevalence was 15.2% (n = 36). LASSO selected three predictors: prior fragility fracture (OR 11.45, 95% CI 4.82–27.15), the Charlson Comorbidity Index (OR 1.45 per unit, 95% CI 1.15–1.85), and alkaline phosphatase. The model demonstrated strong discrimination (apparent C-index 0.860; optimism-corrected 0.845) with excellent calibration (slope 0.94, intercept 0.02; Brier score 0.095). At a 0.15 probability threshold, sensitivity was 86.0%, specificity 78.0%, and negative predictive value 97.0%. DCA confirmed superior net clinical benefit over default strategies across the 0.10–0.30 threshold range; a pre-specified sensitivity analysis excluding fracture history retained meaningful discrimination (corrected C-index 0.791). Conclusions: This nomogram offers a clinically actionable, disease-specific tool for stratifying osteoporosis risk in TDF-treated CHB patients, particularly well suited for safely deferring DXA imaging in low-risk individuals. External validation in multicenter and ethnically diverse cohorts is required before widespread implementation. Full article

Background/Objectives: Computed tomography (CT)-based radiomics shows promise for non-small cell lung cancer (NSCLC) prognosis prediction, but model performance varies widely by feature selection and machine learning strategies. Optimal combinations remain unclear. This study aims to systematically compare feature selection methods and machine learning algorithms for 12-month overall survival prediction using CT radiomics in NSCLC patients. Methods: We analyzed 385 patients from The Cancer Imaging Archive (TCIA) NSCLC-Radiomics dataset. Radiomic features from primary tumor volumes were combined with clinical variables. Three feature selection methods—sequential forward selection (SFS), maximum relevance minimum redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO)—were compared across five classifiers: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), and gradient boosting classifier (GBC). Performance was assessed using area under the receiver operating characteristic curve (AUC) and accuracy on independent test sets. Cox regression and Kaplan–Meier analyses evaluated survival risk stratification. Results: Logistic regression showed the most stable classification performance across feature selection strategies (test AUC 0.60–0.65, accuracy 0.72–0.73). The mRMR-LR model achieved highest AUC (0.65); LASSO-LR showed highest accuracy (0.73). For survival analysis, LASSO-based Cox modeling demonstrated superior risk stratification with significant separation between high- and low-risk groups in both training and testing sets (p = 0.0095). Conclusions: Simpler models like logistic regression provide robust performance in CT radiomics, while LASSO excels for survival risk stratification. As we employed single-dataset validation, clinical applicability remains limited because validation was performed within a single public dataset. Nevertheless, the findings provide methodological insights into the selection of feature selection and machine learning strategies for CT radiomics-based prognostic modeling in NSCLC. Full article

Background: Spinal deformities in children represent a relevant public health issue, with possible long-term consequences. Timely identification of their determinants is essential for adequate prevention. Methods: This study was a secondary analysis of data from the 2019 Serbian National Health Survey, including 1309 children aged 5–14 years. Logistic regression with LASSO regularization and multiple ML algorithms were tested, with XGBoost selected as the optimal model. Class imbalance was addressed using class weighting and SMOTE. Model interpretability was achieved using SHAP analysis. Results: The prevalence of spinal deformities was 8.6%. Univariable analyses showed that age, poorer self-rated health, chronic illness, recent injuries, and pes planus were significantly associated with spinal deformities. Family-related variables showed no significant associations. Among the evaluated models, XGBoost demonstrated the most stable performance across the applied evaluation metrics and the best balance between predictive performance and interpretability. SHapley Additive exPlanations (SHAP) analysis showed that pes planus was the strongest determinant, followed by age and chronic illness, while socio-demographic and family factors had minimal influence. Conclusion: Explainable machine learning models, particularly XGBoost combined with SHAP, can allow for the identification and interpretation of key determinants of spinal deformities in children. Pes planus was shown to be modifiable and relevant associated determinant, supporting its importance in early screening and preventive strategies. Full article

Background/Objectives: In Saudi Arabia, noncommunicable diseases (NCDs) are an increasing public health concern, with almost 70% of deaths related to chronic diseases. The study aimed to analyze 33-year trends in NCD risk factors and apply machine learning (ML) models to identify ecological associates of premature NCD-related mortality, sex-specific analyses and project trajectories to 2030. Methods: A longitudinal ecological time-series design which used WHO Global Health Observatory (GHO) NCD Indicators (1990–2022; select lipid indicators from 1980). Five supervised regression ML models—OLS, LASSO, Ridge, Random Forest, and Gradient Boosting—were trained with TimeSeriesSplit cross-validation (five folds) to preserve temporal order and prevent data leakage. A formal PELT changepoint algorithm confirmed trend breakpoints. Linear projections to 2030 were estimated with 95% prediction intervals. Results: Adult obesity increased by +20.6 percentage points (pp) over 33 years. Under a no-policy-change scenario, female obesity is projected at 50.3% by 2030 (95% PI: 50.0–50.5%). Premature NCD mortality declined by −5.9 pp. Under TimeSeriesSplit CV, all models yielded negative R², confirming LOOCV R² = 0.98 reflected shared time-trend artefacts; the ML component is reframed as descriptive feature-importance analysis. The obesity sex gap (female minus male) was the strongest ecological associate of premature NCD mortality. Diabetes treatment coverage showed a strong inverse ecological association (r = −0.913). Conclusions: NCD risk factors in Saudi Arabia are evolving in complex ways. Targeted interventions addressing sex-specific disparities and healthcare system performance are urgently needed to meet national and global NCD targets. Full article

Background/Objectives: Insomnia is a prevalent sleep disorder with substantial public health implications, yet epidemiological data from Serbia remain limited. This study aimed to assess the prevalence of clinically significant insomnia symptoms in the adult population of Serbia and to examine associated sociodemographic, comorbidity, psychosocial, and lifestyle factors. Materials and methods: A cross-sectional study was conducted from September 2023 to September 2025, including 2577 adults aged 18–89 years across Serbia. Insomnia symptom severity was measured using the Insomnia Severity Index (ISI), with scores ≥ 15 indicating clinically significant insomnia symptoms. Sociodemographic, comorbidity, psychosocial, and lifestyle factors were assessed via self-reported questionnaires. Multivariable logistic regression with LASSO variable selection was used to identify factors independently associated with clinically significant insomnia symptoms. Results: The prevalence of clinically significant insomnia symptoms (ISI ≥ 15) was 10.9%. Independent factors associated with clinically significant insomnia symptoms included being single (OR = 1.54) or divorced (OR = 1.75), lower educational attainment (OR = 0.71 per level increase), being retired (OR = 1.83) or a student (OR = 1.66), dermatological comorbidities (OR = 2.99), use of anxiolytic medications (OR = 2.44), exposure to stressful life events (OR = 1.88), engagement in late-night activities (OR = 1.37), consumption of coffee/tea (OR = 2.22), energy drink consumption (OR = 1.52), and late-night eating habits (OR = 1.27). Conclusions: Clinically significant insomnia symptoms among adults in Serbia are influenced by a complex interplay of sociodemographic, comorbidity, psychosocial, and lifestyle factors. These findings underscore the need for integrated approaches that address both medical and modifiable behavioral determinants in the prevention and management of insomnia symptoms. Full article

This study investigates the role of symmetric probabilistic models in predicting financial distress in the automotive industry, with a focus on companies operating in the Slovak Republic. Financial distress prediction represents a binary classification problem characterized by an inherent symmetry between healthy and distressed firms. To capture this structure, two widely used symmetric models—logit and probit—are applied and systematically compared. The modeling framework incorporates LASSO regression for variable selection, enabling dimensionality reduction while preserving the most informative financial indicators. The empirical analysis is conducted on a dataset of 351 manufacturing enterprises. The results indicate that both models achieve comparable predictive performance, with the logit model reaching an accuracy of 78.9% and the probit model 77.8%. The area under the ROC curve further confirms the strong discriminatory power of both approaches. The findings highlight that the symmetric nature of the applied link functions contributes to model stability, interpretability, and balanced classification behavior. This study extends existing research by explicitly linking symmetry concepts with financial distress prediction in a sector-specific context. The proposed approach provides a transparent and practically applicable framework for early risk identification in industrial enterprises. Full article