Search Results (16,902)

Background/Objectives: The Emotion Regulation Questionnaire (ERQ) is a 10-item self-report measure of two emotion regulation strategies, cognitive reappraisal (CR) and expressive suppression (ES). This study aimed to (1) examine the latent structure of the Polish version of the ERQ, and (2) use it to explore different profiles of emotion regulation strategy use and their links with mental health outcomes. Methods: Our sample was 1197 Polish-speaking adults from the general community in Poland. Results: A factor analysis showed that the ERQ had strong factorial validity, with an intended two-factor structure (CR and ES factors) that was invariant across gender, age, and education categories, as well as across different levels of psychopathology symptoms and alexithymia. Our latent profile analysis extracted four emotion regulation profiles (subtypes): a Mainly Reappraisal profile (high CR, low ES), a Mainly Suppression profile (low CR with high ES), a Generally Low Regulation profile (low CR, low ES), and a Generally High Regulation profile (high CR, high ES). People with the Mainly Reappraisal profile had the best mental health outcomes, whereas people with the Mainly Suppression profile had the poorest mental health outcomes. Conclusions: Conceptually, these findings support the process model of emotion regulation, illustrating the differential affective outcomes of various emotion regulation strategies. Our results highlight the importance of considering individual differences in strategy use patterns, including combinations of strategies within an emotion regulation profile. The Polish version of the ERQ appears to be a robust measure of these key emotion regulation processes across a variety of demographic groups. To facilitate its use, including score interpretations in clinical practice, we present Polish percentile rank norms for the ERQ. Full article

Band selection is an effective way to reduce redundant information, while preserving the physical properties of hyperspectral images (HSI). However, most band selection methods merely consider the relevance and separability between pairs of bands and ignore those for different ground objects. To solve these issues, we propose a Unique Pixel extraction and Adaptive Neighbor Clustering (UPANC) band selection method in this theoretical study. First, in consideration of the characteristics of HSI data and tasks, unique pixels are obtained with a low-rank representation, where the importance of bands is analyzed from both spectral and spatial perspectives. Second, an adaptive neighbor clustering method is designed based on the unique pixels, which groups bands into several clusters through optimizing the graph structure under label smoothness. With support vector machines (SVM) as the classifier, the UPANC method achieved good performance, where the overall accuracy scores were 89.05%, 82.62%, and 92.07% on the Houston, IndianPines, and Pavia University datasets, respectively. The experimental results illustrated the advantages of the UPANC method, which could select optimal bands to enhance the performance in land cover observation. Full article

With the acceleration of global aging, outdoor environments, especially urban green space’s planning and design, play a crucial role in not only promoting physical health but also significantly increasing the opportunities for social interactions for older adults. In recent years, the study of age-friendly outdoor environments has attracted increasing attention, with digital methods emerging as essential tools due to their precision and versatility. In this research, three parks in the Nankai District, Tianjin, are taken as the subject of a case study to explore the spatial factors that may exert influence on the behavior distribution of older adults in summery urban parks’ planning and design. With the behavior data of the older adults in the park collected using an Insta360 camera every hour (from 8 a.m. to 15 p.m.), the three parks are divided into a total of 49 areas for further analysis. Additionally, the visual indexes of the spatial syntax are analyzed with Depthmap 10, the sunlight conditions are analyzed with the Tangent model, and some other spatial factors, such as the green space ratio and the hard ground ratio, are calculated according to the semantic segmentation of the 360-degree panoramic view photo from the center of every area. SPSS and Gradient Boosting Decision Trees (GBDTs) are used to reveal not only the correlations between the sunlight conditions and the behavior distribution of behavior of the older adults, but also the importance ranking of spatial factors. Furthermore, some improvement strategies are proposed for spatial facility configuration, park furniture arrangement, rational hardscape planning, as well as greening and landscape design. By exploring how to improve the spatial planning and design of summery urban green space for older adults, this research provides guidance on the creation of urban green spaces in extremely hot weather that are not only visually appealing but also socially equitable and environmentally sustainable. Full article

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs). Methods: We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.0.0 software, from segmented primary tumors. The supervised strategy (SL) employed an HMLS–PCA connected with six classifiers on both HRFs and DRFs. The SSL strategy expanded the datasets by adding 408 pseudo-labeled HNCa cases (labeled by the Random Forest algorithm) to 199 LCa cases, using the same HMLS techniques. Furthermore, principal component analysis (PCA) linked with four survival prediction algorithms were utilized in the survival hazard ratio analysis. Results: The SSL strategy outperformed the SL method (p << 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). Additionally, PCA linked with Component-wise Gradient Boosting Survival Analysis on both HRFs and DRFs, as extracted from CT, had an average C-index of 0.80, with a log rank p-value << 0.001, confirmed by external testing. Conclusions: Shifting from HRFs and SL to DRFs and SSL strategies, particularly in contexts with limited data points, enabling CT or PET alone, can significantly achieve high predictive performance. Full article

This paper presents a holistic assessment framework for the impacts of water distribution pipe breaks to promote environmentally sustainable and socially resilient cities. This framework considers social, environmental, and economic vulnerabilities as well as probabilities associated with pipe failure. The integration of these features provides a comprehensive approach to understanding infrastructure risks. Taking the city of Vancouver as a case study, the social vulnerability index (SVI) is obtained following the application of a cross-correlation matrix and principal component analysis (PCA) to identify the most influential among 33 selected variables from the 2021 census of the Canadian population. The Environmental Vulnerability Index (EVI) is evaluated by considering the park and floodplain areas. The Economic Vulnerability Index (ECI) is derived from the replacement cost of pipes. These indices offer valuable insights into the spatial distribution of vulnerabilities (consequences) across urban areas. Subsequently, the Consequence of Failure (COF) is computed by aggregating the three vulnerabilities with equal weights. Pipe probability of failure (POF) is evaluated by a Weibull model calibrated on real break data as a function of pipe age. This approach enables a dynamic evaluation of pipe deterioration over time. Risk is finally assessed by combining COF and POF for prioritizing pipe replacement and rehabilitation, with the final objective of mitigating the adverse impacts of infrastructure failure. The findings show the significant impact of ethnicity, socioeconomic indices, and education on the social vulnerability index. Moreover, the areas close to English Bay and Fraser River are more environmentally vulnerable. The pipes with high economic vulnerability are primarily concrete pipes, due to their expensive replacement costs. Finally, the risk framework resulting from the vulnerabilities and pipe break probabilities is used to rank the Vancouver City water distribution network pipes. This ranking system highlights critical areas requiring different levels of attention for infrastructure improvements. All the pipes and corresponding risks are illustrated in Vancouver maps, highlighting that the pipes associated with a very high level of risk are mostly in the south and north of Vancouver. Full article

The present study was conducted to analyze the correlation between the milk fat content of Binglangjiang buffaloes and their microbial and host metabolites. The 10 buffaloes with the highest milk fat content (HF, 5.60 ± 0.61%) and the 10 with the lowest milk fat content (LF, 1.49 ± 0.13%) were selected. Their rumen fluid and plasma were collected for rumen microbiota and metabolome analysis. The results showed that the rumen bacteria abundance of Synergistota, Quinella, Selenomonas, and Fretibacterium was significantly higher in the HF buffaloes. The abundance of 14 rumen fungi, including Candida, Talaromyces, Cyrenella, and Stilbella, was significantly higher in the HF buffaloes. The analysis of the metabolites in the rumen and plasma showed that several metabolites differed between the HF and LF buffaloes. A total of 68 and 42 differential metabolites were identified in the rumen and plasma, respectively. By clustering these differential metabolites, most of those clustered in the HF group were lipid and lipid-like molecules such as secoeremopetasitolide B, lucidenic acid J LysoPE (0:0/18:2 (9Z, 12Z)), and 5-tetradecenoic acid. Spearman’s rank correlations showed that Quinella, Fretibacterium, Selenomonas, Cyrenella, and Stilbella were significantly positively correlated with the metabolites of the lipids and lipid-like molecules in the rumen and plasma. The results suggest that rumen microbiota such as Quinella, Fretibacterium, Selenomonas, and Cyrenella may regulate milk fat synthesis by influencing the lipid metabolites in the rumen and plasma. In addition, the combined analysis of the rumen microbiota and host metabolites may provide a fundamental understanding of the role of the microbiota and host in regulating milk fat synthesis. Full article

As the leading cause of dementia worldwide, Alzheimer’s Disease (AD) has prompted significant interest in developing Deep Learning (DL) approaches for its classification. However, it currently remains unclear whether these models rely on established biological indicators. This work compares a novel DL model using structural connectivity (namely, BC-GCN-SE adapted from functional connectivity tasks) with an established model using structural magnetic resonance imaging (MRI) scans (namely, ResNet18). Unlike most studies primarily focusing on performance, our work places explainability at the forefront. Specifically, we define a novel Explainable Artificial Intelligence (XAI) metric, based on gradient-weighted class activation mapping. Its aim is quantitatively measuring how effectively these models fare against established AD biomarkers in their decision-making. The XAI assessment was conducted across 132 brain parcels. Results were compared to AD-relevant regions to measure adherence to domain knowledge. Then, differences in explainability patterns between the two models were assessed to explore the insights offered by each piece of data (i.e., MRI vs. connectivity). Classification performance was satisfactory in terms of both the median true positive (ResNet18: 0.817, BC-GCN-SE: 0.703) and true negative rates (ResNet18: 0.816; BC-GCN-SE: 0.738). Statistical tests (p < 0.05) and ranking of the 15% most relevant parcels revealed the involvement of target areas: the medial temporal lobe for ResNet18 and the default mode network for BC-GCN-SE. Additionally, our findings suggest that different imaging modalities provide complementary information to DL models. This lays the foundation for bioengineering advancements in developing more comprehensive and trustworthy DL models, potentially enhancing their applicability as diagnostic support tools for neurodegenerative diseases. Full article

This paper analyzes the surface quality characteristics, such as arithmetic average roughness (Ra), maximum peak-to-valley height (Rt), and average peak-to-valley height (Rz), in hard turning of AISI 52100 steel using a (TiN/TiCN/Al₂O₃) coated carbide insert under a minimal cutting fluid environment (MCFA). MCFA, a sustainable high-velocity pulsed jet technique, reduces harmful effects on human health and the environment while improving machining performance. Taguchi’s L27 orthogonal array was used to conduct the experiments. The findings showed that surface roughness increases with feed rate, identified as the most influential parameter, while the depth of cut shows a negligible effect. The main effects plot of signal-to-noise (S/N) ratios for the combined response of Ra, Rt, and Rz revealed the optimal cutting conditions: cutting speed of 140 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.3 mm. Feed rate ranked highest in influence, followed by cutting speed and depth of cut. The lower values of surface roughness parameters were observed in the ranges of Ra ≈ 0.248–0.309 µm, Rt ≈ 2.013–2.186 µm, and Rz ≈ 1.566 µm at a feed rate of 0.05–0.07 mm/rev. MCFA-assisted hard turning reduces surface roughness by 35–40% compared to dry hard turning and 10% to 24% when compared to the MQL technique. Moreover, this study emphasizes the significant environmental benefits of MCFA, as it incorporates minimal eco-friendly cutting fluids that minimize ecological impact while enhancing surface finish. Full article

The automotive industry is responsible for about 20% of greenhouse gas emissions in Europe, and it is under notable pressure to meet the reduction targets set by the European Union for the next decades. In this context, lightweighting is a very effective design strategy for which materials selection plays a key role. One of the main challenges of lightweighting is selecting materials with enhanced structural properties but a reduced weight in comparison with traditional solutions. The spectrum of available materials is very large, and the choice needs to be carefully evaluated based on multiple factors, such as mechanical behavior, raw materials cost, the availability of manufacturing processes, and environmental impact. This article presents an innovative methodology for materials selection in the lightweight automotive field based on the Ashby approach for mechanical performance coefficients as an initial filtering criterion. Following this preliminary screening, this study adopts the VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) MCDA (Multi-Criteria Decision Analysis) technique to rank feasible design solutions based on case study boundary conditions. The evaluation criterion of different design options encompasses crucial factors, such as mechanical properties, cost considerations, and environmental impact measures. The method is finally validated by the application of a redesign case study, a motor bracket of an electric commercial car. Full article

We elaborate on the brane configuration that gives rise to a QCD-like gauge theory that confines at low energies and becomes scale invariant at the highest energies. In the limit where the rank of the gauge group is large, a gravitational description emerges. For the confined phase, we obtain a vector meson spectrum and demonstrate how a certain choice of parameters can lead to quantitative agreement with empirical data. Full article

Outlier values and rankings are important for emphasizing data distribution variability, which improves the accuracy and effectiveness of variance estimations. To enhance the estimation of finite population variance in a two-phase sampling framework, this study presents an improved class of double exponential-type estimators by utilizing the outlier values and ranks of an auxiliary variable. A theoretical analysis is conducted to derive the biases and mean squared errors (MSEs) of these estimators using first-order approximations. A comprehensive simulation study is then performed to analyze the performance of the proposed estimators. The results clearly show that the new estimators provide more precise estimates, achieving a higher percentage relative efficiency (PRE) across all simulated scenarios. Furthermore, three data sets are analyzed to further confirm the efficiency of the proposed estimators as compared to other existing estimators. These results emphasize the potential of the proposed class of estimators to optimize variance estimation techniques, making it a more cost-effective and accurate choice for researchers using two-phase sampling in a variety of domains. Full article

Background: AI-driven mental health solutions offer transformative potential for improving mental healthcare outcomes, but identifying the most effective approaches remains a challenge. This study addresses this gap by evaluating and prioritizing AI-driven mental health alternatives based on key criteria, including feasibility of implementation, cost-effectiveness, scalability, ethical compliance, user satisfaction, and impact on clinical outcomes. Methods: A fuzzy multi-criteria decision-making (MCDM) model, consisting of fuzzy TOPSIS and fuzzy ARAS, was employed to rank the alternatives, while a hybridization of the two methods was used to address discrepancies between the methods, each emphasizing distinct evaluative aspect. Results: Fuzzy TOPSIS, focusing on closeness to the ideal solution, ranked personalization of care (A5) as the top alternative with a closeness coefficient of 0.50, followed by user engagement (A2) at 0.45. Fuzzy ARAS, which evaluates cumulative performance, also ranked A5 the highest, with an overall performance rating of Si = 0.90 and utility degree Qi = 0.92. Combining both methods provided a balanced assessment, with A5 retaining its top position due to high scores in user satisfaction and clinical outcomes. Conclusions: This result underscores the importance of personalization and engagement in optimizing AI-driven mental health solutions, suggesting that tailored, user-focused approaches are pivotal for maximizing treatment success and user adherence. Full article

The elicitation of broadly neutralizing antibodies (bnAbs) is a major goal of vaccine design for highly mutable pathogens, such as influenza, HIV, and coronavirus. Although many rational vaccine design strategies for eliciting bnAbs have been devised, their efficacies need to be evaluated in preclinical animal models and in clinical trials. To improve outcomes for such vaccines, it would be useful to develop methods that can predict vaccine efficacies against arbitrary pathogen variants. As a step in this direction, here, we describe a simple biologically motivated model of antibody reactivity elicited by nanoparticle-based vaccines using only antigen amino acid sequences, parametrized with a small sample of experimental antibody binding data from influenza or SARS-CoV-2 nanoparticle vaccinations. Results: The model is able to recapitulate the experimental data to within experimental uncertainty, is relatively insensitive to the choice of the parametrization/training set, and provides qualitative predictions about the antigenic epitopes exploited by the vaccine, which are testable by experiment. For the mosaic nanoparticle vaccines considered here, model results suggest indirectly that the sera obtained from vaccinated mice contain bnAbs, rather than simply different strain-specific Abs. Although the present model was motivated by nanoparticle vaccines, we also apply it to a mutlivalent mRNA flu vaccination study, and demonstrate good recapitulation of experimental results. This suggests that the model formalism is, in principle, sufficiently flexible to accommodate different vaccination strategies. Finally, we show how the model could be used to rank the efficacies of vaccines with different antigen compositions. Conclusion: Overall, this study suggests that simple models of vaccine efficacy parametrized with modest amounts of experimental data could be used to compare the effectiveness of designed vaccines. Full article

Predicting the height of the water-conducting fracture zone (WCFZ) is crucial for preventing water inrush and ensuring safe underground mining operations. In this study, we propose a novel model combining CatBoost, XGBoost, and AdaBoost with SSA, HHO, and LEA. Key stratum data (DK, TK) and dip mining intensity data were integrated into the existing parameters for WCFZ height prediction. The main influence angle tangent, derived from the probability integral method, replaces the hard rock ratio coefficient. A total of 104 field datasets with eight input parameters were used, with WCFZ height as the dependent variable. The model was validated using five-fold cross-validation and evaluated with root mean square error ($RMSE$), mean absolute error ($MAE$), $R^2$, and mean relative error ($MRE$). The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) was applied to rank the models. The CAT-HHO model demonstrated the best performance. Using this model, predictions of WCFZ height under varying dip mining intensities showed an approximately linear relationship. SHAP analysis identified mining thickness as the most influential factor. Removing key stratum data from models significantly reduced prediction accuracy. The results highlight the model’s ability to improve WCFZ height prediction, offering insights for water inrush prevention in coal mining operations and providing guidance for applying machine learning to similar challenges. Full article

Background: Diabetes affects over 460 million people worldwide and represents a growing public health challenge driven largely by dietary and lifestyle factors. While Type 2 diabetes (T2D) is more prevalent, Type 1 diabetes (T1D) presents unique therapeutic challenges, particularly in younger individuals. Advances in diabetes management, such as continuous glucose monitoring (CGM), insulin pumps (IP), and, more recently, smart multiple dose injection (MDI) pens, have significantly enhanced glycemic control and improved patients’ quality of life. Aim: This study aims to evaluate the baseline characteristics of patients switching from MDI therapy to the Medtronic Smart MDI system [composed of a smart insulin pen (InPenTM) and a connected CGM Medtronic SimpleraTM sensor] and to assess its impact on glycemic outcomes over different time periods (14, 30, and 90 days). Methods: A retrospective observational study was conducted among adults with T1D who initiated Medtronic Smart MDI therapy. Participants were enrolled voluntarily at the Diabetes and Nutrition Clinic in Ast Fermo, Marche Region, Italy. Glycemic parameters were monitored using CGM data and analyzed with descriptive statistics, including mean, standard deviation (SD), and interquartile range (IQR). Comparisons across time periods were performed using the Wilcoxon signed-rank test, with statistical significance set at p < 0.05. Results: This study included 21 participants with a mean age of 51.5 years, a mean BMI of 24.7, and a mean duration of T1D of 21.9 years. The transition from a traditional MDI system to the Smart MDI system resulted in significant improvements in key glycemic parameters: mean Sensor Glucose (SG) decreased from 171.0 mg/dL to 153.5 mg/dL (p = 0.035), Time In Range (TIR) increased from 58.0% to 64.4% (p = 0.005), and time above range (TAR; >180 mg/dL) decreased from 39.0% to 34.2% (p = 0.015). No significant differences were observed in the time below range (TBR). Conclusions: The transition to the Medtronic Smart MDI system significantly enhanced glycemic control by lowering mean glucose levels and increasing TIR. These findings highlight its efficacy in improving hyperglycemia management while maintaining a stable risk of hypoglycemia. Full article