Search Results (2,277)

The production of fiber-reinforced polymer composites using 3D printing technology offers significant potential and opportunities for industrial applications. However, current dimensional limitations in 3D printing necessitate the use of joining techniques to obtain larger components. Recently, innovative strategies such as friction stir spot welding (FSSW) have attracted considerable attention for joining polymer composites due to their ability to produce strong joints with relatively low heat input (solid-state welding). Nevertheless, it is important to understand how the fibers present in fiber-reinforced polymer composites influence material flow and welding performance during the FSSW process. In this study, glass fiber-reinforced polylactic acid (PLA-GF) composite samples produced using a 3D printer were joined by means of FSSW. Five different tool rotational speeds (900, 1200, 1500, 1800, and 2100 rpm) and three different plunge rates (10, 20, and 30 mm/min) were employed during the welding process. Mechanical tests were performed on the welded joints to investigate the relationship between the welding parameters and the resulting mechanical properties. In addition, microstructural analyses were conducted to examine the formation of welding defects. The results revealed that three distinct zones were formed in the material after the FSSW process: the stir zone, mixed zone, and shoulder zone. Defects were observed in the mixed zone of the samples exhibiting relatively lower mechanical properties. The highest tensile force was achieved at a plunge rate of 20 mm/min and a rotational speed of 900 rpm. The highest bending force, on the other hand, was obtained at a plunge rate of 30 mm/min and a tool rotational speed of 2100 rpm. Full article

Mathematical abilities are critical for the developmental outcomes of children with autism spectrum disorder (ASD). However, little is known about these abilities and their association with the approximate number system (ANS) in preschoolers with ASD beyond Western samples, including Chinese children. This cross-sectional study examined whether formal and informal mathematical abilities differed between children with and without ASD and assessed the extent to which these abilities were associated with ANS acuity. Participants included 47 children with ASD and 47 typically developing (TD) children aged 3–7 years. All children were assessed on measures of formal and informal mathematical abilities, ANS acuity, and non-verbal IQ. No significant group differences in mathematical abilities were found among children aged 3–5 years. However, among children aged 6–7 years, the ASD group showed significantly lower performance in mathematical abilities compared to their TD peers. ANS acuity was significantly correlated with both formal and informal mathematical abilities in the ASD group, but only with informal mathematical abilities in the TD group. Furthermore, ANS acuity accounted for 5.4% of the unique variance in formal mathematical abilities specifically within the ASD group. The patterns of mathematical abilities and their relationship with ANS acuity differ between preschoolers with and without ASD. These findings suggest a differential association between ANS and formal mathematics learning in children with ASD, highlighting implications for the design of early numeracy interventions. Full article

Polymer–graphene composites have emerged as an advantageous class of functional materials that combine the exceptional electrical, mechanical, and surface properties of graphene with the ability to be processed, modified, and made more flexible through polymers. Polymer–graphene composites have recently seen rapid growth in environmental applications, including water treatment, pollutant degradation, sensing, and energy–environment interfaces. This review critically examines recent advancements in polymer–graphene composites for catalysis (including photocatalysis, electrocatalysis, hydrogenation, and energy conversion) and environmental applications (such as water treatment, dye degradation, heavy-metal removal, and oil–water separation). There is considerable discussion about structure–property–performance relationships, catalytic and adsorption mechanisms, and the role of polymer matrices. Current challenges, scalability issues, and future research directions for sustainable, industrially viable polymer–graphene systems are highlighted. Full article

Remote sensing offers distinct advantages for lithological mapping, but its ability to detect underlying bedrock is limited in covered areas, whereas geochemical data are constrained by sparse sampling and low spatial resolution. To address these challenges, this study proposes a texture-guided adaptive data fusion framework combined with a Multi-scale Convolutional Neural Network (MCNN) for lithological mapping, using the Guyang area in Inner Mongolia as a case study. First, the non-linear relationships between geochemical components and remote sensing spatial textures are modeled to achieve complementary integration of heterogeneous multi-source data. Second, an MCNN model is constructed to extract multi-scale geological features, enabling improved discrimination of lithological units and more effective inference of concealed bedrock beneath Quaternary cover. Experimental results show that the proposed method overcomes the limitations of single data sources and achieves an overall accuracy (OA) of 0.95 on the fused dataset. Ablation experiments further demonstrate that the texture-guided fusion strategy significantly improves lithological identification performance. This study provides an effective framework for intelligent geological mapping and confirms the feasibility of inferring underlying bedrock in covered areas using multi-source surface information. Full article

Challenges remain in developing a comprehensive understanding of spatial cognition, including gender and developmental differences, partly due to limitations of well-established spatial measures. Many traditional tasks face accessibility constraints and are not well suited for use across broad age ranges, populations, or ability levels. The present study introduced two game-based tasks, Q-bitz^® and Spot it!^®, designed to assess mental rotation and object location memory, respectively. We examined whether these game-based measures meaningfully complement established spatial tests, the Mental Rotation Test (MRT) and the Object Location Memory (OLM) task, across a wide age range (7–79 years, N = 114). Results indicated that MRT scores were strongly related to Q-bitz performance, whereas OLM scores were strongly related to Spot it! performance, supporting the convergent validity of the game-based tasks. Notably, gender-specific patterns emerged in the relationships among spatial measures, suggesting differences in spatial function. Age was associated with performance on speeded tasks (Q-bitz and Spot it!) but not with accuracy-based MRT or OLM performance. Together, these findings demonstrate that game-based assessments capture meaningful spatial constructs and reveal gender-specific patterns across the lifespan, providing a practical and ecologically valid approach for advancing research on spatial cognition. Full article

In this study, the rank of multifaceted activity of catechin (C), epicatechin (EC), epigallocatechin (EGC), epicatechin-3-gallate (ECG) and epigallocatechin-3-gallate (EGCG) was addressed. Their antioxidant activity was determined by the differential pulse voltammetry (DPV), whereas their ability to inhibit angiotensin-converting enzyme (ACE) activity, acetylcholinesterase activity (AChE), and formation of the advanced glycation end-products (AGEs) was performed in a model system to show their importance against hypertension, Alzheimer-type dementia, and diabetic’s complication, respectively. The order of the antioxidant potential of catechins in comparision to gallic acid (GA) was EGCG > ECG > EC > EGC ≈ C > GA, whereas the order of the ACE inhibitory activity was EGCG > ECG > EGC > EC > C, thus indicating the importance of the structure–activity relationship. The correlation between IC₅₀ for ACE inhibition of catechins and their antioxidant activity had the value r = −0.60. The order of the AChE enzyme inhibitory activity was EGCG ≈ EGC > ECG > EC > C, and the weak positive correlation between IC₅₀ and the first anodic peak potential (E_pa1) values was noted (r = 0.67). The ranking of the anti-AGE activities was EGCG ≈ ECG > EGC > EC > C, and a negative correlation between the inhibitory activity of catechins against AGE formation and their antioxidant activity was r = −0.82, whereas a positive correlation (r = 0.88) was noted between their first anodic peak potential (E_pa1) values. The provided results expand our knowledge on the multifaceted activity of catechins, indicating EGCG and ECG as the most active antioxidants against inhibition of ACE and AChE as well as towards AGE formation. Full article

To address the high labor intensity of weight handling and the low accuracy of testing outcomes in the 125% rated-load down-running braking test for high-speed elevators, this study proposes a numerical-model-driven evaluation method for elevator braking capability based on Dynamic Ant Colony Optimization–eXtreme Gradient Boosting (DACO-XGBoost). Firstly, to overcome the limited prediction accuracy caused by insufficient measured samples during braking analysis, vibration and noise effects are both considered, and thus an equivalent dynamic analysis is conducted for no-load up-running and 125% load down-running conditions. Based on this, a simulation-data generation approach was developed to produce loaded down-running braking samples from the no-load up-running operating condition. Secondly, by combining the simulated samples generated by the above model with a limited set of measured samples, an XGBoost model optimized by a dynamic ant colony algorithm was constructed, improving the model’s ability to fit the complex nonlinear relationships in the elevator braking process. This mitigates the constraints imposed by sample scarcity and enables accurate prediction of key braking-performance parameters. Experimental results demonstrate that the proposed DACO-XGBoost substantially improves prediction accuracy. For braking distance, it decreased from 7.5453 to 0.5661 (RMSE) and from 2.7452 to 0.0370 (MAE). For slip amount, it decreased from 60.0307 to 1.2200 (RMSE) and from 7.7401 to 0.8146 (MAE), respectively. Furthermore, after comparisons with RF, GA-RF, and PSO-RF, the effectiveness of the proposed method for quantitative evaluation of braking performance in high-speed elevators was verified. Full article

Background: Treatment adherence and outcomes for drug-resistant tuberculosis (DR-TB) continue to be subpar in rural South Africa, where structural health system limitations, comorbid conditions, and diverse resistance patterns make clinical management more challenging. This study aimed to assess how demographic, clinical, and programmatic factors, including a Community Engagement–Clinical Governance (CE–CG) implementation period, affect DR-TB treatment outcomes using explanatory predictive modeling. Methods: A retrospective cohort study was conducted using routine program data from 694 DR-TB patients. A complete-case analysis was performed for multivariable modeling (n = 282). Logistic regression and decision tree models were used to examine the relationships between treatment success and selected predictors, including age, sex, treatment regimen, resistance phenotype, comorbidities, and the CE–CG implementation period. Model discrimination and performance were evaluated using receiver operating characteristic (ROC) curves, pseudo-R² statistics, likelihood ratio tests, and multicollinearity diagnostics. Results: The cohort had a mean age of 40.7 years, and 58.8% of patients were male. Overall treatment success was 59.9%. Severe resistance phenotypes were rare (1.7%) but clinically significant. Comparative analysis showed no notable demographic or outcome differences between included and excluded patients, indicating minimal selection bias. In adjusted models, treatment initiation during the CE–CG implementation period was significantly linked to lower odds of treatment success (adjusted odds ratio [aOR] = 0.443; 95% CI: 0.240–0.818; p = 0.009). Severe resistance phenotypes were strongly negatively associated with treatment success (aOR = 0.303; p = 0.056). Logistic regression models had limited discriminatory ability (AUC: 0.523–0.548), while the decision tree model showed modest improvement (AUC: 0.626). Overall, the model’s explanatory power was limited (pseudo-R² = 0.029), although no evidence of multicollinearity was found. Conclusions: Programmatic implementation periods and resistance severity were important factors associated with treatment outcomes in this rural DR-TB cohort. Although model discrimination was modest and explanatory power was limited, the findings provide useful insights into structural and programmatic vulnerabilities that affect treatment success in real-world settings. Strengthening clinical governance, improving routine program documentation, and incorporating more granular adherence, social, and governance indicators into routine data systems may improve both program evaluation and future predictive modeling. Full article

Classrooms in severe cold regions face the dual challenge of ensuring high-quality daylighting while minimizing heating energy consumption. To address this challenge, this study develops a data-driven workflow that integrates building performance simulation, multi-objective optimization and a classification-based surrogate model, aiming to explore integrated improvements in daylighting and heating energy consumption in university classrooms. The results show that: (1) multi-objective optimization significantly enhances overall performance. Daylighting performance improves, with Spatial Daylight Autonomy (sDA) and Useful Daylight Illuminance (UDI) increasing by 0.15 and 10.67%, respectively, and Daylight Glare Probability (DGP) decreasing by 16.35%. Meanwhile, Heating Energy Consumption (E_h) is reduced by 6.20 kWh/m²; (2) SHAP analysis further identifies classroom depth, height, and glazing option as key design parameters influencing integrated daylight–thermal performance; (3) the MLP classification model achieves stable predictive accuracy, with accuracy, recall, and F1-score exceeding 0.95, demonstrating strong generalization ability. This study provides quantitative insights into the relationship between spatial parameters and daylight–thermal performance, offering researchers a method for rapidly evaluating design schemes at the early design stage. Full article

Background: Sprint performance, including acceleration, maximal velocity and deceleration, is crucial for athletic success in field and court-based sports; however, deceleration remains understudied despite its role in change of direction (COD) and match performance. Methods: This study addressed this gap by comparing eccentric metrics from countermovement jumps (CMJ), drop jumps (DJ) and the Nordic hamstring exercise (NHE) to 30 m sprint and deceleration ability in 28 university athletes (Age: 20 ± 1 years; Mass: 68 ± 9 kg; Height:166 ± 6 cm). Correlations were analysed with Pearson’s r for normal data and Spearman’s r for non-normal data. Results: Significant negative correlations were found between the CMJ and DJ heights and the modified reactive strength index (RSI_MOD), as well as the reactive strength index (RSI) with sprint time (r = −0.54 to −0.83, p < 0.05), while positive correlations were obtained with sprint velocity (r = 0.57 to 0.83, p < 0.05). The eccentric mean forces from CMJs and DJs were positively correlated with sprint time and deceleration momentum (r = 0.62 to 0.84, p < 0.05). However, there were no significant correlations between NHE eccentric force and any sprint or deceleration metrics. The CMJ and DJ heights, RSI and eccentric mean forces strongly predicted sprint time, velocity, and momentum, but not deceleration performance, highlighting the role of explosive power and reactive strength. The NHE eccentric force had no significant relationships with sprint or deceleration metrics. Conclusions: These results highlight that CMJ and DJ are effective predictors of sprint performance, while deceleration efficiency may rely on other biomechanical factors. Full article

The accurate delineation of mega-environments (MEs) and the rigorous evaluation of test locations are critical for optimizing regional variety trial schemes, particularly when addressing unbalanced datasets from multi-year, multi-location wheat (Triticum aestivum L.) trials. This study aimed at refining the regional wheat trial framework in the Huanghuai Winter Wheat Region (HWWR) of China using an integrated BLUP-GGE biplot approach, which combines best linear unbiased prediction (BLUP) values with genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to account for temporal variability and experimental error. We systematically evaluated the BLUP-GGE biplot approach, focusing on its goodness of fit and its ability to resolve inter-location relationships. We further assessed test location representativeness, discriminating ability, and overall desirability via the BLUP-GGE biplot, and contrasted ME delineation outcomes between the traditional “which-won-where” polygon method and the test location clustering-based approach. The BLUP-GGE biplot explained 72.9% of total phenotypic variation, with all location vectors displaying positive correlations (maximum angle = 88.8°), confirming the ecological homogeneity of the target region and yielding robust evaluation results. Based on the ideal tester view, Puyang was identified as the most desirable location, followed by Zhumadian, Shangqiu, and Huixian, while Lianyungang and Suqian exhibited relatively poor comprehensive performance. MEs delineated by the “which-won-where” method showed strong inter-ME correlations and insufficient differentiation, whereas the location clustering-based method markedly enhanced inter-ME discrimination (maximum vector angle > 60°), stably partitioning the HWWR into three distinct MEs with clear cultivar–ME interaction patterns: ME1 (Lianyungang, Suqian, Fuyang, Suzhou, Guoyang, Huixian, Huai’an, Xinmaqiao, Huayin, and Yangling), ME2 (Luoyang, Xinxiang, Zhumadian, Shangqiu, Puyang, and Luohe), and ME3 (Baoji, Xuzhou, Yuanyang, Sheyang, and Xingyang). This study confirms the superiority of the BLUP-GGE biplot for analyzing unbalanced multi-year multi-environment trial data and validates a robust clustering strategy for ME delineation. The findings provide a scientific basis for optimizing wheat regional trial systems and facilitating precise cultivar deployment in the HWWR, and offer a reference for analogous studies on other crops or ecological regions. Full article

While it seems sensible that human-centred artificial intelligence (AI) means centring “human behaviour and experience,” it cannot be any other way. AI, I argue, is usefully seen as a relationship between technology and humans where it appears that artefacts can perform, to a greater or lesser extent, human cognitive labour. This is evinced using examples that juxtapose technology with cognition, inter alia: abacus versus mental arithmetic; alarm clock versus knocker-upper; camera versus vision; and sweatshop versus tailor. Using novel definitions and analyses, sociotechnical relationships can be seen as varying types of: displacement (harmful), enhancement (beneficial), and/or replacement (neutral) of human cognitive labour. Ultimately, all AI implicates human cognition; no matter what. Obfuscation of cognition in the AI context—from clocks to artificial neural networks—results in distortion, in slowing critical engagement, perverting cognitive science, and indeed in limiting our ability to truly centre humans and humanity in the engineering of AI systems. To even begin to de-fetishise AI, we must look the human-in-the-loop in the eyes. Full article

Turning impairment is a major contributor to falls in Parkinson’s disease (PD), yet the mechanisms linking cortical dysfunction to altered motor behavior remain unclear. In particular, it is unknown whether disrupted cortical communication impairs turning by altering muscle coordination. This study investigates a novel mechanistic pathway: whether muscle synergy complexity mediates the relationship between cortical network connectivity and turning performance in PD. Specifically, electroencephalography (EEG) and electromyography (EMG) were recorded from 12 individuals with PD and 12 age-matched healthy controls during a 180° turning task. Directed cortical connectivity, muscle synergy complexity, and spatiotemporal turning performance were quantified. Mediation analysis was used to determine whether cortical influences on behavior operate indirectly through neuromuscular coordination. Compared to controls, individuals with PD performed slower turns with shorter stride lengths and reduced synergy complexity (p < 0.05), alongside altered frontal cortical connectivity (p < 0.05). Across participants, higher synergy complexity was associated with faster, longer strides (p < 0.04). Cortical connectivity strength strongly predicted synergy complexity (R² = 0.66, p < 0.001) and exerted a significant indirect effect on turning performance (β = 0.312; 95% CI [0.072, 0.605]; p = 0.008). In PD, reliance on this indirect pathway increased with disease severity and poorer turning ability (r > 0.57, p < 0.03). This work establishes how muscle synergy complexity significantly mediates the relationship between cortical connectivity and turning performance in PD. Our findings provide evidence of a cortical–neuromuscular–behavioral pathway underlying turning deficits, highlighting coordination as a key target for neurorehabilitation. Full article

Background: Renal dysfunction is common in patients hospitalized with acute decompensated heart failure (ADHF) and represents a key component of cardiorenal syndrome. However, the relationships between renal impairment, cardiorenal biomarkers, and echocardiographic markers of myocardial function remain incompletely characterized in ADHF populations. Methods: We conducted a cross-sectional analysis of 144 consecutive patients hospitalized with ADHF. Renal dysfunction was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m². Clinical, laboratory, and echocardiographic parameters were compared according to renal function. Correlation analyses, multivariable logistic regression, and receiver operating characteristic (ROC) curve analyses were performed to evaluate associations between renal dysfunction, cardiorenal biomarkers, and myocardial functional indices. Results: Patients with renal dysfunction were older (p = 0.002) and more frequently had diabetes mellitus (p = 0.006). Echocardiographic evaluation demonstrated significantly lower systolic mitral annular velocity (S′) (p < 0.001) and higher E/e′ ratios (p < 0.001) in patients with renal dysfunction, whereas left ventricular ejection fraction (p = 0.133) and global longitudinal strain (GLS) (p = 0.121) were similar between groups. Log-transformed NT-proBNP and albuminuria were significantly correlated with S′, GLS, and E/e′ (all p < 0.001). In multivariable analysis adjusted for clinically relevant confounders, chronic kidney disease (OR 8.16, 95% CI 2.13–31.34; p = 0.002) and the E/e′ ratio (OR 2.01, 95% CI 1.52–2.66; p < 0.001) remained independently associated with renal dysfunction. ROC analysis showed that E/e′ had the strongest ability to distinguish between patients with and without renal dysfunction (AUC 0.887, 95% CI 0.834–0.941; p < 0.001). Conclusions: Renal dysfunction in ADHF is associated with echocardiographic markers reflecting impaired longitudinal myocardial function and elevated filling pressure, with E/e′ emerging as the strongest echocardiographic correlate. The integration of echocardiographic parameters with cardiorenal biomarkers may improve the characterization of the cardiorenal profile in patients hospitalized with ADHF. Full article

The growing number of on-orbit spacecraft and the increasing volume of telemetry data have made intelligent anomaly detection in multi-channel telemetry essential for mission operations. Current spacecraft anomaly detection methods primarily rely on statistical models or time-series deep learning approaches, which often fail to explicitly model spatiotemporal dependencies across multiple telemetry channels. This shortcoming limits their ability to capture the dynamically evolving and intricately coupled relationships between variables. To overcome this limitation, a Progressive Spatiotemporal Graph (PSTG) model is proposed for anomaly detection in multi-channel spacecraft telemetry. PSTG employs a multi-scale patch embedding module to extract hierarchical semantic features from multi-channel time series, effectively reducing the dimensionality of the spatiotemporal graph. It constructs a sparse adjacency matrix using a multi-head attention mechanism that integrates intra-channel temporal dynamics, inter-channel spatial correlations, and cross-channel spatiotemporal interactions. An improved multi-head graph attention network then captures pairwise dependencies among nodes within the adjacency matrix. As a result, PSTG encodes rich spatiotemporal representations derived from intricate variable interactions, enabling accurate, real-time prediction of multi-channel telemetry. Furthermore, a dynamic thresholding mechanism is incorporated into PSTG to perform online anomaly detection based on prediction residuals. Extensive experiments on real-world spacecraft telemetry data collected over 84 months show that PSTG outperforms eleven state-of-the-art benchmark methods in almost all cases across multiple evaluation metrics. Finally, visualizations of the learned adjacency and attention matrices are presented to interpret the spatiotemporal modeling process, providing operators with actionable insights into the detected anomalies and facilitating root cause analysis. Full article