Search Results (322)

Background/Objectives: The study aimed to evaluate the most significant factors that impact arterial blood gas parameters: pH, pO₂, pCO_2, and concentration of lactates. Methods: The study was a retrospective analysis of clinical data obtained from the patients’ records hospitalized at the Department of Pediatric Anesthesiology and Intensive Care. A total of 71 patients were enrolled in the study. A total of 479 measurements were performed for arterial blood, 41 were excluded. The analysis was performed for 438 results. The artificial neural network (ANN) regression models were applied, and the Least Absolute Shrinkage and Selection Operator (LASSO) regression was used. ANNs were built considering the following activation functions: hyperbolic tangent, linear, exponential, and logistic. The following three sets were separated: training, testing, and validation. In the case of LASSO regression, the regularization was applied, excluding insignificant variables from the model. Besides the machine learning techniques, the correlation between the variables was calculated. Results: The correlation coefficients for regression ANN models exceeded the value for testing set of 0.92. According to the sensitivity analysis, the most significant variable for pH was cCl⁻, for pO₂ it was pO₂/FiO₂, for pCO₂ it was Fshunt, and for concentration of lactates it was pH. In the case of LASSO regression for pH, the most significant factor was pCO₂, for pO₂ it was pO₂/FiO₂, for pCO₂ it was cCl⁻, and for concentration of lactates it was pCO₂. Conclusions: The results show the usefulness of machine learning methods in analyzing complex physiological relationships. Such techniques can help improve diagnostic accuracy and optimize therapeutic management in pediatric patients. Full article

Online reviews on platforms such as Google Maps strongly influence consumer decisions. However, aggregated ratings mask nuanced opinions about specific aspects such as food, drinks, service, lounge, and price. This study presents a multi-aspect sentiment analysis framework for Arabic café reviews. Specifically, we combine machine learning (Linear SVC, Naïve Bayes, Logistic Regression, Decision Tree, Random Forest) and a Convolutional Neural Network (CNN) to perform aspect identification and sentiment classification. A rigorous preprocessing and feature-engineering with TF-IDF and n-gram was implemented and statistically validated through bootstrap confidence intervals and Friedman–Nemenyi significance tests. Experimental results demonstrate that Linear SVC with optimized TF-IDF tri-grams achieved a macro-F1 of 0.89 for aspect identification and 0.71 for sentiment classification. Meanwhile, the CNN model yielded a comparable F1 of 0.89 for aspect identification and a higher 0.76 for sentiment classification. The findings highlight that effective feature representation and model selection can substantially improve Arabic opinion mining. The proposed framework provides a reliable foundation for analyzing Arabic user feedback on location-based platforms and supports more interpretable and data-driven business insights. These insights are essential to enhance personalized recommendations and business intelligence in the hospitality sector. Full article

Background: Left atrial strain (LAS) derived from speckle-tracking echocardiography (STE) provides a sensitive, load-dependent measure of atrial function and ventricular filling pressures. Data on LAS in paediatric cardiomyopathies are still scarce; therefore, this study aimed to assess LA phasic function in dilated (DCM) and hypertrophic (HCM) cardiomyopathy and to determine its relationship with clinical and echocardiographic indices of disease severity. Methods: We conducted a cross-sectional case–control study that included 84 children (DCM n = 29, HCM n = 29, control n = 26) who underwent comprehensive clinical and echocardiography evaluation, including LAS parameters (reservoir—LASr; conduit—LAScd; and contractile—LASct). Group comparisons were performed using ANOVA or Kruskal–Wallis tests with post hoc adjustments, and correlations were analysed using Pearson’s or Spearman’s coefficients. Multivariable linear and logistic regression models were adjusted for age, body surface area (BSA), heart rate (HR), and blood pressure (BP) percentiles. Results: LASr and LAScd were significantly reduced in both cardiomyopathy groups compared with controls (p < 0.001), following a graded pattern (DCM < HCM < control). In DCM, lower LASr was independently associated with higher left atrial volume index (LAVi) and elevated E/E′ ratio, whereas in HCM, septal hypertrophy (IVSd Z-score) and log NT-proBNP were dominant determinants of impaired LASr. In logistic regression, LASr (OR = 0.93, p = 0.016) and LAScd (OR = 1.21, p = 0.001) independently predicted severe NYHA/Ross functional class after covariate adjustment, while LASct showed no significant association. Conclusions: These findings demonstrate that LA reservoir and conduit strain are markedly impaired in paediatric cardiomyopathy and are strongly linked to structural remodelling and functional limitation, underscoring their value as sensitive non-invasive markers of disease severity. Full article

Background/Objectives: This study aimed to assess the associations between the Lifestyle Inflammation Score (LIS) and gestational diabetes mellitus (GDM), and glucose metabolism alterations (GMA) postpartum. Methods: A secondary analysis was conducted on 378 pregnant women at the end of pregnancy. Anthropometric, clinical, biochemical, and dietary parameters were evaluated. Postpartum reclassification was based on fasting serum glucose (>100 mg/dL), HbA1c (>5.7%), and/or a 2-h oral glucose tolerance test (>140 mg/dL). The LIS was calculated using a proxy index including smoking status, physical activity, and pregestational BMI, applying the beta coefficient from the original LIS model. Tertiles were created, with T3 indicating the highest level of proinflammatory exposure. Statistical analyses included Kruskal–Wallis, one-way ANOVA, linear-by-linear association, and multivariate logistic regression, adjusted for family history, gestational weight gain, carbonylated proteins, and adiponectin to evaluate associations between LIS and GDM, and adjusted for pharmacological treatment, gestational weight gain, and breastfeeding for LIS and GMA. Results: Higher LIS values were more common among women with GDM (T1 = 45.9%, T2 = 62.2%, T3 = 74.8%, p < 0.001) and among those with GMA (T1 = 34.4%, T2 = 45.6%, T3 = 53.7%, p = 0.019). Compared with the lowest tertile, the highest tertile of LIS was associated with greater odds of GDM (OR 3.72; 95% CI: 1.19–11.64, p = 0.024) and GMA (OR 2.69; 95% CI: 1.25–5.76, p = 0.011). Conclusions: A more proinflammatory lifestyle, as reflected by a higher LIS, increases the risk of progression to GDM and later to GMA. Full article

Educational equity and access to quality learning opportunities represent fundamental pillars of sustainable societal development, directly aligned with the United Nations Sustainable Development Goal 4 (Quality Education). Student retention remains a critical challenge in higher education, with early disengagement strongly predicting eventual failure and limiting opportunities for social mobility. While machine learning models have demonstrated impressive predictive accuracy for identifying at-risk students, most systems prioritize performance metrics over practical deployment constraints, creating a gap between research demonstrations and real-world impact for social good. We present an accountable and interpretable decision support system that balances three competing objectives essential for responsible AI deployment: ultra-early prediction timing (day 14 of semester), manageable instructor workload (flagging 15% of students), and model transparency (multiple explanation mechanisms). Using the Open University Learning Analytics Dataset (OULAD) containing 22,437 students across seven modules, we develop predictive models from activity patterns, assessment performance, and demographics observable within two weeks. We compare threshold-based rules, logistic regression (interpretable linear modeling), and gradient boosting (ensemble modeling) using temporal validation where early course presentations train models tested on later cohorts. Results show gradient boosting achieves AUC (Area Under the ROC Curve, measuring discrimination ability) of 0.789 and average precision of 0.722, with logistic regression performing nearly identically (AUC 0.783, AP 0.713), revealing that linear modeling captures most predictive signal and makes interpretability essentially free. At our recommended threshold of 0.607, the predictive model flags 15% of students with 84% precision and 35% recall, creating actionable alert lists instructors can manage within normal teaching duties while maintaining accountability for false positives. Calibration analysis confirms that predicted probabilities match observed failure rates, ensuring trustworthy risk estimates. Feature importance modeling reveals that assessment completion and activity patterns dominate demographic factors, providing transparent evidence that behavioral engagement matters more than student background. We implement a complete decision support system generating instructor reports, explainable natural language justifications for each alert, and personalized intervention templates. Our contribution advances responsible AI for social good by demonstrating that interpretable predictive modeling can support equitable educational outcomes when designed with explicit attention to timing, workload, and transparency—core principles of accountable artificial intelligence. Full article

Stress, workload, and uncertainty characterize occupational tasks across sports, healthcare, military, and transportation domains. Emerging theory and empirical research suggest that coordinated whole-body movements may reflect these transient mental states. Wearable sensors and optical motion capture offer opportunities to quantify such movement dynamics and classify mental states that influence occupational performance and human–machine interaction. We tested this possibility in a small pilot study (N = 10) designed to test feasibility and identify preliminary movement features linked to mental states. Participants performed a perceptual decision-making task involving facial emotion recognition (i.e., deciding whether depicted faces were happy versus angry) with variable levels of stress (via a risk of electric shock), workload (via time pressure), and uncertainty (via visual degradation of task stimuli). The time series of movement trajectories was analyzed both holistically (full trajectory) and by phase: lowered (early), raising (middle), aiming (late), and face-to-face (sequential). For each epoch, up to 3844 linear and non-linear features were extracted across temporal, spectral, probability, divergence, and fractal domains. Features were entered into a repeated 10-fold cross-validation procedure using 80/20 train/test splits. Feature selection was conducted with the T-Rex Selector, and selected features were used to train a scikit-learn pipeline with a Robust Scaler and a Logistic Regression classifier. Models achieved mean ROC AUC scores as high as 0.76 for stress classification, with the highest sensitivity during the full movement trajectory and middle (raise) phases. Classification of workload and uncertainty states was less successful. These findings demonstrate the potential of movement-based sensing to infer stress states in applied settings and inform future human–machine interface development. Full article

The present study aimed to investigate individual differences in practice effects during progressive matrices based on Carpenter et al.’s taxonomy of abstract rules. To this end, data from a non-verbal reasoning test, the Abstract Reasoning Test (ART), were used. Because the ART was developed from Carpenter et al.’s theory, the impact of extraneous factors unrelated to the theoretical model is minimized, thereby allowing for a more precise identification of practice effects. The sample consisted of 765 military recruits who responded to 34 items on the ART. Analyses were conducted using a random weights operation-specific learning model (RWOSLM), in which practice parameters were treated as random effects allowed to vary across individuals. The model measures within-test practice effects specific to each examinee, allowing the hypothesis of rule learning during the ART to be assessed at the individual level. Correlations between practice effects and external measures associated with intelligence were examined to investigate the nature of the practice effects. The results suggest individual differences in rule learning within the ART. Decreases in difficulty were observed for both pairwise progression and figure addition or subtraction, although between-person variability was evident only for the latter. Additionally, the results revealed between-person variability in decreases in difficulty associated with one of the items’ figural properties, which suggests the existence of individual differences in the process of increasing familiarity with this feature throughout the test. Individual differences in practice effects during the ART significantly correlated with external measures of abilities and intellect, suggesting that practice effects during progressive matrices are conceptually tied to intelligence. Full article

Background: China’s rapidly aging population has led to a growing burden of dementia, marked by cognitive decline and heavy social and economic costs. Dietary patterns have been identified as a critical means for prevention. Methods: This study drew on data from the China Longitudinal Health and Longevity Survey (CLHLS). Three logistic regression models were applied to examine the association between the Chinese version of the Mediterranean-Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay (cMIND) diet and dementia. To test the stability of the results, we conducted two sensitivity analyses. Restricted cubic spline (RCS) models were used to assess the potential for a nonlinear relationship. Subgroup and interaction analyses were conducted to explore heterogeneity across covariates and main effects. Propensity score matching (PSM) was performed as a secondary analysis to minimize the influence of confounding factors. Results: The study included 9142 participants, with a dementia prevalence of 10.7% among Chinese older adults. After adjusting for all covariates, each one-unit increase in the cMIND diet score was associated with an 11% lower prevalence of dementia (OR = 0.89; 95% CI: 0.84–0.93). After full adjustment, the RCS model confirmed a significant and linear dose–response association between adherence to the cMIND diet and dementia. Comparable associations were observed across most subgroups. Conclusions: Adherence to the cMIND diet was significantly associated with a lower prevalence of dementia in Chinese older adults, with evidence of a clear dose–response effect. These findings highlight the potential of the cMIND diet as a preventive strategy against dementia in this population. Full article

Background and Objectives: Evidence on epilepsy surgery from Central Asia is limited, reflecting the real-world challenges of developing this service in low- and middle-income settings. We evaluated one-year seizure outcomes after resective surgery for drug-resistant focal epilepsy at a single center in Kazakhstan, and we assessed whether the use of advanced presurgical imaging was associated with seizure freedom. Materials and Methods: A retrospective cohort study was conducted, including consecutive adults who underwent curative-intent resective epilepsy surgery from 2017 to 2023. Outcomes at 12 months or more post-surgery were classified using the Engel criteria. Logistic regression was used to examine associations between the advanced presurgical diagnostic tool and achieving an Engel class I outcome. Crude and adjusted risk ratios (RRs) for not achieving Engel I were estimated using modified Poisson regression with robust SEs. Results: Among 112 patients (median age 31 years; median epilepsy duration 19 years), 76% underwent temporal lobe procedures and 71% had lobectomies. At one year, 74 patients were seizure-free (Engel II: 15.2%, III: 11.6%, IV: 7.1%). Year-to-year Engel I rates varied without a significant linear trend from 2018 to 2023. In bivariable analyses, MRI-defined atrophy (RR, 3.14) and mixed lesions (RR, 2.62) were associated with a higher risk of not achieving Engel I, whereas longer epilepsy duration was linked to a lower risk (RR, 0.97 per year). In adjusted models, predictors of not achieving Engel I included generalized tonic–clonic seizures (aRR, 1.96), atrophy (aRR, 2.98), mixed lesions (aRR, 2.45), and undergoing any advanced diagnostic test (aRR, 3.38). Longer epilepsy durations remained protective (aRR 0.95 per year). In modality-specific logistic models, fMRI use was associated with higher odds of Engel I (aOR 3.39), and MR spectroscopy was associated with lower odds (aOR 0.33). Conclusions: In this Central Asian single-center cohort, about two-thirds of adults achieved complete seizure freedom one year after resective surgery—comparable to international benchmarks. Advanced imaging modalities showed divergent associations with outcomes, likely reflecting confounding by indication. These findings support the feasibility of effective epilepsy surgery in a low-resource context and the value of targeted use of advanced imaging. Full article

We evaluated four candidate SNPs (GC-NPFFR2 rs137147462, GC-NPFFR2 rs109452259, BRCA1 rs134817801, and DGAT1 p.K232A) previously reported in relation to mastitis or milk production traits, using 10,729 test-day phenotypic records collected over 10 years from 269 Japanese Holstein cows (Bos taurus) enrolled in the national Dairy Herd Improvement (DHI) program. Linear mixed models were used to estimate genotypic effects on somatic cell score (SCS) and to test multiple inheritance models. To assess clinical relevance, mastitis severity was further analyzed using categories defined by somatic cell counts (SCC). Among the SNPs tested, GC-NPFFR2 rs137147462 showed the clearest and most consistent association with SCS under a recessive model, with GG cows exhibiting higher SCS throughout lactation. Ordinal logistic regression confirmed a higher probability of progression to severe mastitis in GG cows. DGAT1 p.K232A showed additive effects, with the A allele increasing milk yield while lowering fat and protein percentages. AA cows also showed higher SCS under a modest recessive effect. BRCA1 rs134817801 and GC-NPFFR2 rs109452259 had minimal effects. These findings support GC-NPFFR2 rs137147462 as a promising marker for mastitis resistance and indicate the importance of considering not only additive but also recessive genetic models in genomic selection strategies. Full article

Background and Objectives: Lifestyle and dietary behaviors are increasingly recognized as important factors influencing symptom management and quality of life (QoL) in patients with rheumatic diseases. However, evidence remains limited regarding how specific lifestyle patterns interact with sociodemographic and clinical variables to shape patient-reported outcomes. This study aimed to investigate the relationship between diet, lifestyle behaviors, and self-perceived QoL in a cohort of patients with rheumatic conditions. Methods: In this cross-sectional study, 350 adults with rheumatic diseases completed a structured questionnaire covering sociodemographic data, rheumatologic diagnosis and treatment, dietary behaviors, lifestyle factors (physical activity, sleep, smoking, alcohol), and QoL assessments (scales 1–10). Statistical analyses included descriptive measures, Chi-square tests, correlation analyses, logistic regression, and linear regression models to identify predictors of QoL. Results: The majority of participants were female (86.9%) and aged between 26 and 55 years. Urban patients were more likely to attribute a positive influence of diet on QoL, while rural participants reported stronger disease burden. Logistic regression showed that adherence to a special diet significantly increased the odds of reporting good QoL. Linear regression identified sleep quality (β = 0.42), perceived dietary influence (β = 0.29), and physical activity (β = 0.18) as independent predictors of QoL (adjusted R² = 0.47, all p < 0.001). Correlation analyses further revealed that disease burden negatively impacted emotional well-being and sleep, while dietary influence correlated positively with QoL. Conclusions: This study highlights the multidimensional role of diet and lifestyle in shaping QoL in patients with rheumatic diseases. Alongside pharmacological treatment, targeted lifestyle interventions focusing on nutrition, physical activity, and sleep hygiene may substantially improve patient outcomes. Future longitudinal studies are warranted to confirm these associations and explore causal mechanisms. Full article

Background: This paper develops a novel, interdisciplinary framework for optimizing high-speed rail (HSR) freight logistics hubs in the Ottawa–Quebec City corridor, addressing critical gaps in geospatial mismatches, static optimization limitations, and narrow sustainability scopes found in the existing literature. Methods: The research methodology integrates a hybrid graph neural network-reinforcement learning (GNN-RL) architecture that encodes 412 nodes into a dynamic graph with adaptive edge weights, fractal accessibility (α = 1.78) derived from fractional calculus (α = 0.75) to model non-linear urban growth patterns, and a multi-criteria sustainability evaluation framework embedding shadow pricing for externalities. Methodologically, the framework is validated through global sensitivity analysis and comparative testing against classical optimization models using real-world geospatial, operational, and economic datasets from the corridor. Results: Key findings demonstrate the framework’s superiority. Empirical results show an obvious reduction in emissions and lower logistics costs compared to classical models, with Pareto-optimal hubs identified. These hubs achieve the most GDP coverage of the corridor, reconciling economic efficiency with environmental resilience and social equity. Conclusions: This research establishes a replicable methodology for mid-latitude freight corridors, advancing low-carbon logistics through the integration of GNN-RL optimization, fractal spatial analysis, and sustainability assessment—bridging economic viability, environmental decarbonization, and social equity in HSR freight network design. Full article

This study proposes a complete data-processing framework for Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) echo signals. It investigates the feasibility of classifying target and background noise using machine learning. Four feature processing schemes were first compared, among which the PNT strategy (Principal Component Analysis without tail features) was identified as the most effective and adopted for subsequent analysis. Based on this framework, nine models derived from six baseline algorithms—Decision Trees (DTs), Support Vector Machines (SVMs), Backpropagation Neural Networks (NN-BPs), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and k-Nearest Neighbors (KNN)—were systematically assessed under Monte Carlo simulations with varying echo signal-to-noise ratio (ESNR) and statistical frame number (SFN) conditions. Model performance was evaluated using eight metrics: accuracy, precision, recall, FPR, FNR, F1-score, Kappa coefficient, and relative change percentage (RCP). Monte Carlo simulations were employed to generate datasets, and Principal Component Analysis (PCA) was applied for feature extraction in the machine learning training process. The results show that LDA achieves the shortest training time (0.38 s at SFN = 20,000), DT maintains stable accuracy (0.7171–0.8247) across different SFNs, and NN-BP models perform optimally under low-SNR conditions. Specifically, NN-BP-3 achieves the highest test accuracy of 0.9213 at SFN = 20,000, while NN-BP-2 records the highest training accuracy of 0.9137. Regarding stability, NN-BP-3 exhibits the smallest RCP value (0.0111), whereas SVM-3 yields the largest (0.1937) at the same frame count. In conclusion, NN-BP-based models demonstrate clear advantages in classifying sky-background noise. Building on this, we design a ResNet based on NN-BP, which achieves further accuracy gains over the best baseline at 400, 2000, and 20,000 frames—12.5% (400), 9.16% (2000), and 2.79% (20,000)—clearly demonstrating the advantage of NN-BP for GM-APD LiDAR signal classification. This research thus establishes a novel framework for GM-APD LiDAR signal classification, provides the first systematic comparison of multiple machine learning models, and highlights the trade-off between accuracy and computational efficiency. The findings confirm the feasibility of applying machine learning to GM-APD data and offer practical guidance for balancing detection performance with real-time requirements in field applications. Full article

Maternal tobacco smoke exposure is associated with impaired fetal growth and long-term disease risk (DOHaD, Developmental Origins of Health and Disease). Whether placental steroid hormones are independently altered remains a matter of debate. We quantified six placental steroids (estradiol, estriol, estrone, progesterone, testosterone, and pregnanediol) using HPLC–Corona CAD in 70 deliveries (C = 30; PS = 20; AS = 20). Distributional differences were assessed with Kruskal–Wallis and pairwise Mann–Whitney tests with Benjamini–Hochberg (BH) control. Adjusted associations used log-linear OLS with HC3 robust SE: Model A (gestational age, maternal BMI, newborn sex) and Model B (Model A + birth weight), reported as percent change vs. controls, computed as (exp(β) − 1) × 100 with 95% CI. Secondary analyses tested (i) multiclass logistic classification of C/PS/AS from the steroid panel (5-fold stratified CV) and (ii) prediction of birth weight (OLS and 2-component PLS). All six steroids differed by group (BH-adjusted p ranging from 9.18 × 10⁻¹² to 6.66 × 10⁻⁸). In Model A, AS vs. C showed lower estrogens/progestins (estradiol, −46.2%; estriol, −24.7%; estrone, −25.9%; progesterone, −28.2%; pregnanediol, −31.4%) and higher testosterone (+40.8%); these effects persisted in Model B after adjusting for birth weight. The panel classified C/PS/AS with 0.900 cross-validated accuracy (weighted OvR AUC 0.994). Hormones poorly predicted birth weight (PLS CV R² = −0.777). Maternal active and passive smoking is associated with a coherent and independent disruption of placental steroidogenesis. A targeted placental steroid panel offers biologically meaningful early markers relevant to DOHaD. Full article

Background/Objectives: The identification of a decedent through skeletal analysis is dependent on accurate estimation of demographic characteristics, including biological sex. The most well-known sex estimation technique using the pubic bone is the Phenice method. In 2012, it was revised by Klales and colleagues and a logistic regression equation to predict sex was applied. Later, a program that estimates sex from Klales’ scoring with a random forest model, MorphoPASSE, was developed by Klales. Methods: Here we compare the accuracy of the original and revised methods, along with MorphoPASSE, using a contemporary sample of Northern Italians with documented sex. We further test the assertions by Phenice that his method is easy to employ for new observers and that ambiguity can be applied when characteristics do not morphologically fit into the categories of the method. Accuracy, error, bias, sensitivity, and specificity were calculated for each approach, along with McNemar’s tests for paired data, which compared documented sex and estimated sex. A linear weighted Cohen’s Kappa measured the differences in scoring between a new observer and an experienced observer. Results: Phenice’s method achieved higher accuracy (97%) than the Klales method and MorphoPASSE (86% each), as well as higher sensitivity and specificity, and lower error and bias. All McNemar’s tests conducted were not significant. The new observer demonstrated a similar accuracy (93%) to the experienced observer (97%). Furthermore, comparisons of Phenice’s scoring with ambiguity indicate its superior performance for capturing variation over the Klales method and MorphoPASSE. Conclusions: Phenice’s method is recommended in forensic anthropology and bioarchaeological contexts, particularly in Milan. Full article