Search Results (1,348)

This study combines replicated experimentation and machine learning to characterize how user-controllable fused deposition modeling (FDM) parameters relate to local surface quality in complex ULTEM 1010 components. A surgical guide geometry was evaluated across 18 printing conditions incorporating the infill pattern, infill density, body thickness, raster angle, part orientation, and annealing. Three independently printed specimens per condition were measured at two locations, yielding 108 observations across five areal roughness metrics: Sa, Sz, Sq, Ssk, and Sku. An exploratory analysis of variance with false discovery rate correction identified orientation associations with Sa, Sq, Ssk, and Sku, and a body thickness association with Ssk in the cleaned measurements. Descriptive results associated the −XY orientation with a lower Sa and Sq at the measured regions, identifying a candidate placement for subsequent process trials. Evaluating multiple roughness metrics captured both the surface height magnitude and height distribution, providing a broader characterization than average roughness alone. Artificial neural network, random forest, and Gaussian process regression models were assessed using condition-grouped validation, which kept all replicates and paired measurement sites together and showed a limited generalization to unseen printing conditions. The study provides replicated evidence connecting industrially accessible printing settings with local areal surface characteristics. Its findings support prioritizing the orientation in process refinement, assessing the surface quality at functionally relevant locations, and validating predictive models on independent printing conditions before using them for parameter selection. Full article

The Industry 4.0 transition of construction requires evidence linking digital maturity to sustainability outcomes in operations management. Yet Building Information Modelling (BIM) maturity is still assessed through a priori stage models rather than the adoption patterns companies actually exhibit, and its co-occurrence with perceived sustainability performance is uncharacterised beyond single-country samples. This paper derives a typology of lifecycle BIM-use maturity in Central European construction companies and characterises how the types differ in perceived sustainability performance. Four objectives are pursued: the indicators’ dimensional structure and reliability; derivation of the typology by unsupervised learning and the support for its boundaries; each type’s sustainability profile, national composition, and incremental information beyond country and firm scale; and delimitation of the BIM–sustainability association. A structured questionnaire administered to 199 companies in Croatia, Slovakia and Slovenia over five years yielded six lifecycle BIM-use items and three perceived-sustainability items (recycling, waste, CO₂), which were analysed by reliability assessment, principal component analysis, and k-means and Ward clustering with country-stratified checks. Two dimensions—adoption intensity and an end-of-life-versus-design orientation—accounted for 78.1% of the variance. The 199 independently surveyed companies exhibit only seven distinct response profiles, which bounds the typology’s resolution; no internal validity index showed an interior optimum, so the four-type solution is chosen rather than validated. The types differ systematically in perceived sustainability; the gradient holds within Croatia (with one inversion) and Slovakia but reverses in Slovenia. The typology is exploratory and perception-based—a precursor to objective lifecycle assessment rather than a substitute: the BIM-use and sustainability items are empirically proximate and failed a common-method-variance check, so their relation is descriptive co-occurrence. One result is independent of that caveat: substantial end-of-life BIM use appears in only one of the seven profiles, so the model data that circular workflows require are largely absent. Full article

Background/Objectives: Alexithymia may constrain emotion regulation, but its relevance may differ across personality configurations. This study examined whether narcissistic Admiration and Rivalry are embedded in distinct early maladaptive schema and alexithymia profiles. Methods: A non-clinical adult sample (N = 311) completed the Narcissistic Admiration and Rivalry Questionnaire, the Young Schema Questionnaire–Short Form 3, and the Toronto Alexithymia Scale–20. Analyses examined 18 schemas and three alexithymia components—Difficulty Identifying Feelings (DIF), Difficulty Describing Feelings (DDF), and Externally Oriented Thinking (EOT)—using zero-order correlations, within-domain regressions, hierarchical regressions, and structural equation models. Results: Admiration was associated mainly with Approval/Recognition Seeking and Unrelenting Standards and with lower Social Isolation/Alienation, Defectiveness/Shame, Failure, Subjugation, Emotional Inhibition, and Insufficient Self-Control. Rivalry showed a threat-related profile involving Defectiveness/Shame, Subjugation, Emotional Inhibition, Negativity/Pessimism, and Insufficient Self-Control. Entitlement/Grandiosity was positively associated with both dimensions and did not reliably differentiate them. Alexithymia was essentially unrelated to Admiration and did not improve its schema model. In contrast, adding DIF, DDF, and EOT increased explained variance in Rivalry by 5.5%, a small-to-moderate increment; DIF and EOT made independent contributions, whereas DDF did not. A parsimonious Rivalry structural model reproduced these associations but showed mixed global fit. Conclusions: These cross-sectional findings indicate a modest, dimension-specific contribution of alexithymic processing to Rivalry rather than a general association with narcissistic self-regulation. The EOT findings require particular caution because of the subscale’s limited reliability. Longitudinal and experimental studies are needed to test the proposed vulnerability–stress process. Full article

This study presents a methodology for balancing surface integrity, densification, and hardness in selective laser melting (SLM) of 316L stainless steel. While SLM offers unparalleled geometric freedom, achieving optimal performance is often hindered by trade-offs among process parameters. To address this, a high-fidelity predictive and optimization framework was established by integrating response surface methodology (RSM) with Taguchi-based grey relational analysis (GRA). A Taguchi L₉ orthogonal array with analysis of variance (ANOVA) was implemented to identify the discrete influences of laser power, scanning speed, hatch spacing, and scanning pattern. Reduced quadratic RSM models were then developed to capture nonlinear interactions. Statistical validation yielded coefficients of determination (R²) of 85.6% for surface roughness and 80.7% for hardness, with a mean absolute error within 2.5% of average response values. Interactive response surface analysis revealed that surface roughness improves monotonically with energy density, whereas hardness exhibits a convex behavior governed by the synergy between laser power and scanning speed. Multi-objective optimization through GRA identified the optimal parameter set as 180 W laser power, 500 mm/s scanning speed, 0.08 mm hatch spacing, and a spiral scanning pattern. Validation experiments produced a balanced profile of 8.45 μm surface roughness, 0.79% porosity, and 207.8 HV hardness, closely matching analytical predictions. Compared to single-objective optimization, the integrated GRA approach effectively reconciled contradictory optimization trajectories, providing a robust strategy for fabricating high-performance 316L components for demanding industrial and biomedical applications. Full article

This work evaluates a hybrid harmonic and data-driven approach for short-term sea-level forecasting in the Venice lagoon. The observed sea level is decomposed into an astronomical component, estimated through harmonic analysis, and a non-astronomical component, modelled using autoregressive (ARX) and neural network autoregressive (NNARX) methods with (and without) exogenous covariates. The forecasting experiment uses a rolling-origin out-of-sample design over 120 hourly horizons, with 2023 as the test period. The results show that the harmonic model explains approximately 66% of the total variance, providing a stable astronomical benchmark. Data-driven models substantially improve upon this benchmark, especially when wind components, atmospheric pressure, and the MoSE activation records are included as covariates. ARX and NNARX achieve the highest explained variance and the lowest forecast errors across most horizons. However, they are statistically similar over the full sample, while NNARX provides significant gains over several medium and long horizons under high-water conditions. Full article

Background: The Academic Resilience Scale-30 (ARS-30) was originally developed to assess cognitive, emotional, and behavioral responses following negative academic evaluation. Because structured performance appraisal has recently become an established component of the Greek public healthcare system, the present study examined whether this evaluative response framework could be meaningfully adapted to occupational performance evaluation. Methods: Test–retest reliability and descriptive analyses were conducted in an initial sample of 140 public healthcare employees, while EFA, internal-consistency analyses, and CFA were conducted in a separate larger sample of 483 employees. Results: Test–retest reliability was very high to excellent across items (ICC = 0.904–1.000). Internal consistency was acceptable for the total score (α = 0.799), good for Performance-Oriented Coping (α = 0.889), and acceptable for Negative Emotional Reactions/Passive Coping (α = 0.767). Positive Reappraisal/Growth Orientation (α = 0.705) and Acute Negative Emotional Reaction (α = 0.707) demonstrated marginally acceptable internal consistency and should therefore be interpreted cautiously. Item 23 was reverse-coded based on its semantic and conceptual direction. Exploratory factor analysis identified a preliminary four-component solution explaining 42.17% of the variance. However, the rotated solution included cross-loadings, and confirmatory factor analysis did not provide adequate support for the prespecified four-factor structure (CFI = 0.661, TLI = 0.629, RMSEA = 0.105, and SRMR = 0.105). Conclusions: Although some reliability findings were encouraging, the hypothesized four-factor structure was not supported by CFA and cannot currently be considered structurally validated. Further psychometric evaluation and model development are therefore required. Full article

To optimize the varietal structure of fruit crops in arid environments and screen apricot-plum varieties suitable for cultivation in the Aksu region, a systematic agronomic evaluation was conducted to comprehensively analyze the phenotypic diversity and fruit quality traits of seven apricot-plum cultivars in Aksu, Xinjiang. In 2025, seven-year-old trees cultivated under identical and standardized agronomic conditions were selected as experimental materials. Key agronomic parameters, including flowering and fruiting phenology, floral and foliar morphological traits, fruit appearance properties, and internal nutritional profiles (soluble solids, soluble sugars, titratable acids, vitamin C, flavonoids, and total phenolics) were systematically measured. Correlation analysis and principal component analysis (PCA) were applied to quantitatively integrate these multi-dimensional datasets, identify key factors driving agronomic variation, and evaluate varietal adaptability to the arid climate. The results revealed a high degree of phenotypic diversity and significant variations in agronomic performance among the seven cultivars. Specifically, ‘Weihou’ recorded the highest single-fruit weight (111.27 g), indicating superior physical development, whereas ‘Konglongdan’ achieved the maximum soluble solid content (21.43%) and vitamin C level (96.41 mg/100 g), exhibiting exceptional nutritional quality. Despite these outstanding individual traits in specific cultivars, evaluating overall adaptability requires a holistic approach. PCA effectively captured 93.84% of the total variance through five extracted principal components, successfully modeling the interrelationships among vegetative vigor, reproductive morphology, and fruit chemical profiles. Based on the comprehensive agronomic scores, the varieties ranked as follows: ‘Fengweihuanghou’ > ‘Weihou’ > ‘Hongtianerong’ > ‘Konglongdan’ > ‘Weidi’ > ‘WeiWang’ > ‘Fengweimeigui’. Our findings identify ‘Fengweihuanghou’ as the most superior cultivar, with optimal agronomic adaptability and fruit quality balance under arid conditions. This study suggests that ‘Fengweihuanghou’ is a highly promising cultivar for large-scale cultivation in the Aksu region, providing a valuable reference for optimizing the local crop varietal structure, although further multi-year and multi-location validations are warranted. Full article

Background: Physical activity is widely recognized as an important determinant of health and cognitive development; however, the association between specific components of motor performance and academic achievement remains incompletely understood. This cross-sectional study investigated the relationships between self-reported physical activity, self-administered motor tests, and self-reported academic achievement in lower secondary school students. Methods: A cross-sectional observational study was conducted involving 113 lower secondary school students (mean age: 12 years; 66 males and 47 females). Self-reported weekly physical activity and self-reported Grade Point Average (GPA) were collected using an online questionnaire. Following standardized familiarization during physical education classes, students self-administered the Stork Balance Stand Test, Plate Tapping Test, and Illinois Agility Test, whereas choice reaction performance was assessed individually under standardized conditions using the FITLIGHT Trainer system. Pearson’s correlation analyses, one-way ANOVA, and multiple linear regression were performed. Results: Weekly physical activity showed a strong positive correlation with GPA (r = 0.654, FDR-adjusted p < 0.001) and significant FDR-adjusted associations with upper-limb speed and choice reaction performance, whereas its associations with balance and agility did not remain statistically significant after correction for multiple comparisons. In the multiple regression analysis, weekly physical activity was the only variable independently associated with GPA (β = 0.639, p < 0.001), whereas balance, upper-limb speed, choice reaction performance, and agility were not independently associated with academic achievement after adjustment for age, sex, and school grade. The regression model explained 57.3% of the variance in GPA (adjusted R² = 0.535; p < 0.001). Conclusions: In this sample of lower secondary school students, self-reported weekly physical activity was independently associated with self-reported academic achievement, whereas the assessed motor performance measures were not independently associated with GPA after adjustment for demographic variables. Given the cross-sectional design and reliance on self-reported measures, these findings should be interpreted as associative rather than causal. Further longitudinal studies using objective measures of physical activity and validated school-based motor assessment protocols are warranted. Full article

The increase in the number of people living after an oncological diagnosis requires the use of instruments capable of assessing both general dimensions and specific aspects of quality of life. The study aimed to evaluate the psychometric properties of the Romanian version of the Quality of Life in Adult Cancer Survivors (QLACS) questionnaire, analyze the relationships between its scores, age, and residence environment, and identify distinct quality of life profiles. Methods: A cross-sectional study was conducted in 224 adult women with a confirmed diagnosis of breast cancer. The internal consistency of the 12 QLACS domains was assessed by Cronbach’s alpha coefficient. The structure of the instrument was explored by principal component analysis with Oblimin rotation. Differences according to residence were analyzed by the Mann–Whitney test, and the relationships with age by the Spearman coefficient. Multivariable regression models including age and residential environment were fitted for all 12 QLACS domains. Quality of life profiles were identified by hierarchical classification and k-means analysis. Results: Cronbach’s alpha coefficients ranged from 0.832 to 0.925, indicating good internal consistency across the 12 QLACS domains. Principal component analysis retained 11 components, explaining 73.738% of the total variance, providing preliminary exploratory support for the multidimensional structure of the instrument. Most domains were clearly delineated, whereas Cognitive problems partially overlapped with Recurrence distress. Urban residence was associated with higher Cognitive problems scores (r = 0.17), whereas rural residence was associated with higher Family distress scores (r = 0.29). Age was negatively correlated with Negative feelings (rho =−0.200, p = 0.003). A weak exploratory association was also observed with Recurrence distress (rho = 0.135, p = 0.043), although the corresponding multivariable model was not statistically significant. Cluster analysis identified three exploratory quality-of-life profiles, and residential environment was associated with cluster membership, although the association was small (Cramer’s V = 0.188). Conclusions: The Romanian QLACS showed good internal consistency and preliminary exploratory structural evidence in women with breast cancer. Further studies are needed to confirm its psychometric properties. Full article

This study presents a rapid design-and-validation framework incorporating resistance optimization for 3D-printed bridge pier geometries. A full-factorial experimental campaign comprising 16 reduced-scale solid pier sections is first conducted by systematically varying upstream fairing length and downstream fishtail length while maintaining constant maximum transverse width and cross-sectional area. The specimens are fabricated using fused deposition modeling (FDM) 3D printing with polyethylene terephthalate glycol-modified (PETG) material and tested in controlled towing experiments driven by a field-oriented control (FOC) motor, with motor torque signals recorded as a proxy for hydrodynamic resistance. The raw data are processed through steady-state trimming, null-test bias correction, and one-dimensional Kalman filtering, after which a root-mean-square (RMS) resistance metric is computed for each geometry. A key finding from the two-way analysis of variance (ANOVA) analysis reveals that fairing length exerts the dominant influence on resistance, followed by the fairing-fishtail interaction, whereas fishtail length alone plays a secondary role. The experimental ranking identifies S13, combining a short fairing with a long fishtail, as the optimal geometry, achieving a 33.6% reduction in RMS torque relative to the circular baseline. Bootstrap resampling confirms that the low-resistance cluster is a robust geometry family rather than a statistically fragile optimum. Then, independent COMSOL Multiphysics 6.3 (COMSOL) topology optimization and transient flow-field simulations are employed as morphology-level validation tools, with the optimized outline converging toward a streamlined profile qualitatively consistent with the experimental findings. The drag decomposition further indicates that pressure drag constitutes the dominant component, suggesting that shape-induced pressure redistribution is the primary mechanism underlying resistance reduction. The proposed framework thus provides a physically grounded, low-cost intermediate step between computational shape generation and detailed engineering validation for resistance-optimized bridge-pier sections, and it can be readily extended to a broader range of pier cross-sections or other hydraulic structures. Full article

Background: Life satisfaction is a key component of subjective well-being in later life and may be associated with psychological, economic, family, functional, and food-related factors. Objective: This study aimed to identify factors associated with life satisfaction among older adults, considering sociodemographic characteristics, food insecurity, physical activity, and psychological factors. Methods: A quantitative, observational, analytical, cross-sectional study was conducted with 200 noninstitutionalized older adults from Sogamoso, Colombia. Self-report instruments assessed life satisfaction, food insecurity, physical activity, depression, anxiety, and perceived stress. Data were analyzed using descriptive statistics, group comparison tests, Pearson correlations, and multiple linear regression. Results: Higher perceived stress (B = −0.762, p < 0.001), perceived insufficient income (B = −3.380, p < 0.001), self-employment (B = −2.153, p = 0.009), other sources of income (B = −2.569, p = 0.017), and technical education (B = −4.744, p = 0.003) were associated with lower life satisfaction. Conversely, combined engagement in strength and flexibility exercises (B = 3.203, p < 0.001), having two children (B = 3.498, p = 0.002), and having three or more children (B = 2.618, p = 0.008) were associated with higher life satisfaction. Primary education was not significantly associated with life satisfaction after adjustment (p = 0.093). Food insecurity was associated with life satisfaction in the bivariate analysis but did not remain statistically significant in the adjusted model. The regression model explained 48.7% of the variance in life satisfaction (R² = 0.487; adjusted R² = 0.457). Conclusions: Life satisfaction among older adults was associated with psychological, economic, family, and functional factors. The findings highlight the relevance of perceived stress, economic security, functional physical activity, and family resources when addressing subjective well-being in later life. Full article

High-altitude lakes are increasingly exposed to nutrient enrichment, organic loading and wastewater-derived contamination, while predictive performance can be overstated when temporally ordered observations are randomly partitioned or target-defining measurements are reused as predictors. We analyzed 177 consecutive monthly water-quality records from the Inner Bay of Lake Titicaca, Peru (January 2011–September 2025), integrating Carlson’s composite trophic state index (CTSI), Hamed–Rao modified Mann–Kendall tests, block-bootstrap Sen slopes, Pettitt change point detection with 12-month block permutation, nutrient stoichiometry, correlation, principal component analysis, leakage-aware contemporaneous classification and one-month-ahead forecasting. Total phosphorus was treated as elemental P and phosphate as PO₄. The bay remained chronically hypereutrophic (mean CTSI 74.08 ± 4.07; 83.1% of months). BOD₅ increased by 0.547 mg L⁻¹ yr⁻¹, chlorophyll-a by 3.254 mg m⁻³ yr⁻¹, total suspended solids by 0.752 mg L⁻¹ yr⁻¹ and conductivity by 13.13 µS cm⁻¹ yr⁻¹, whereas total phosphorus declined by 0.085 mg P L⁻¹ yr⁻¹. Block-supported shifts occurred in chlorophyll-a in November 2016 (21.81 to 54.18 mg m⁻³) and BOD₅ in June 2018 (6.54 to 11.77 mg L⁻¹). After removing target-defining variables and preserving temporal order, the best trophic-state classifier had balanced accuracy 0.575 and MCC 0.257. Organic pollution classification had a balanced accuracy of 0.624 but a sensitivity of only 0.271, whereas the fecal-indicator model was unstable (MCC 0.130; ROC-AUC 0.460). Persistence was the best BOD₅ forecast (RMSE 4.193 mg L⁻¹; R² 0.390). The best CTSI model explained only 4.8% of future variance, and all thermotolerant coliform forecasts had negative out-of-time R². Persistent ecological degradation was therefore evident, but monthly observations alone were insufficient for deployment-ready early warning. Higher-frequency sensing, hydrometeorological and wastewater load covariates, spatial replication, direct microbiological measurements and prospective validation are required. Full article

Background: Reliable judging is essential to the fairness of elite boxing, yet inter-judge reliability under the current World Boxing 10-Point Must Scoring System remains largely unexplored. This study aimed to evaluate inter-judge reliability across rounds and weight categories at the 2025 World Boxing Championships. Methods: A retrospective observational analysis included 94 men’s bouts in the 60 kg (n = 42), 75 kg (n = 23), and 90+ kg (n = 29) categories. Judge-level round scores were analysed using linear mixed-effects variance-component models, with bout-round target and judge identity specified as random effects. Reliability was estimated for individual judge ratings (R_single) and an aggregate measurement based on five judge ratings (R_panel(5)). Pairwise differences across rounds and weight categories were evaluated using bout-level bootstrap procedures with Holm adjustment for multiple testing. Results: Across all bouts, R_single was 0.617 (95% CI: 0.521–0.693) in Round 1, 0.634 (95% CI: 0.539–0.709) in Round 2, and 0.687 (95% CI: 0.597–0.754) in Round 3. The corresponding R_panel(5) estimates were consistently higher at 0.889 (95% CI: 0.845–0.919), 0.897 (95% CI: 0.854–0.924), and 0.916 (95% CI: 0.881–0.939), respectively. No statistically significant differences in reliability were detected across rounds or between weight categories after Holm adjustment. Overall, 98.2% of judge-round scores were 10:9 and 68.9% of bouts decided by judges’ scores ended in unanimous 5:0 decisions. Conclusions: Reliability was consistently higher when five judge ratings were aggregated than when individual judge ratings were considered. No statistically significant evidence of systematic differences in reliability across rounds or between the examined weight categories was detected. Full article

This research analyzes the association between the quality of virtual learning environments and students’ digital literacy in higher education. From a hierarchical perspective, virtual learning environment quality was defined as a second-order emergent construct integrating technological usability and accessibility with pedagogical mediation. Similarly, student digital literacy was conceived as a second-order emergent construct encompassing information and digital resource management, digital content production, digital communication, collaboration and citizenship, as well as problem-solving and digital autonomy. The study followed a quantitative, non-experimental and cross-sectional design. Data were obtained from 342 university students enrolled in the 2026-I academic term and examined through partial least squares structural equation modeling, applying a two-stage procedure in ADANCO. First-order constructs were estimated as reflective Mode A consistent variables, whose standardized latent scores served as indicators for second-order Mode B emergent constructs. Findings confirmed significant external weights and adequate collinearity levels across all second-order components. The structural model showed a positive and statistically significant association between virtual learning environment quality and student digital literacy (β = 0.795, p < 0.001), with VLEQ accounting for 63.2% of the variance in SDL within the estimated model. Overall, the results suggest that higher perceived digital literacy is associated not only with platform access, but also with pedagogically mediated environments that support interaction, autonomy, and responsible digital engagement. Full article

The deep-sea tunnels at the Sanshan Island Gold Mine are subjected to extreme conditions characterized by high stress and complex erosion resulting from high mineralization. Under these conditions, conventional shotcrete is prone to performance degradation and insufficient durability, posing a threat to the long-term safety of the tunnels. At the same time, mine water is difficult to recycle on-site. To address these engineering challenges, this study utilized fly ash (FA), S105-grade ground granulated blast furnace slag (GGBS), polypropylene coarse fiber (PPCF), and hydroxypropyl methylcellulose (HPMC) as modifying components and employed the response surface method (RSM) to optimize the mix design of mine water-blended shotcrete. The study selected compressive strength, direct shear strength, and chloride ion electrical flux at 6 h as response indicators and constructed a quadratic polynomial regression model. Analysis of variance and goodness-of-fit tests indicated that the model possessed good significance and reliability of fit. Based on this model, the optimal mix design was determined: an FA/GGBS blend ratio of 3:7, a cement replacement rate of 20%, a PPCF content of 3.3%, and an HPMC content of 0.18%. Performance testing showed that the optimal mixture achieved a compressive strength of 25.24 MPa, a direct shear strength of 8.08 MPa, and a chloride ion electrical flux of 778 C after 6 h. Compared to the control group, its peak compressive strength decreased by only 9.98%, while its residual strength increased significantly; direct shear strength increased by 18.1%, and electrical flux decreased by 33.8%. This indicates that the material’s mechanical load-bearing capacity, deformation coordination, and corrosion resistance have been enhanced in a synergistic manner. Field industrial trials have verified that this modified concrete possesses excellent ductile yield characteristics, can effectively suppress water seepage in mine tunnels, is capable of withstanding extreme underground operating conditions, and enables the efficient reuse of mine water resources. Full article