Search Results (260)

Fostering mathematical thinking, communication, and creativity has become a central goal in mathematics education as these competencies are strongly linked to flexible problem solving and innovative engagement. Prior research has shown that students’ beliefs and dispositions play a crucial role in shaping their learning, strategy use, and persistence, yet limited evidence exists on how these constructs interrelate among pre-service elementary mathematics teachers. Addressing this gap, the present study examines the relationships among mathematical thinking perceptions, mathematical communication dispositions, and creative thinking dispositions. A correlational survey design was employed to test a hypothetical model developed within the framework of structural equation modeling (SEM). Data were collected from 615 pre-service teachers. Analyses involved descriptive statistics, correlations, and predictive algorithms via IBM SPSS Statistics 24, along with standardized regression coefficients and fit indices using AMOS. The results revealed that while perceptions of problem-solving and higher-order thinking predicted creative thinking dispositions both directly and indirectly, perceptions of reasoning did so only indirectly through mathematical communication. Mathematical communication dispositions had the strongest direct effect on creative thinking dispositions, underscoring their mediating role. These findings highlight the importance of fostering communication alongside creativity in teacher education, thereby equipping future teachers to promote creative thinking through cognitive, social, and representational processes. Full article

Objective: In the isokinetic literature, relatively limited attention has been paid to muscles of the wrist. Therefore, the objective of this study was to present an isokinetic profile of these muscles comprising the flexors (F); extensors (E); and ulnar (U) and radial (R) deviators. Method: The dominant-side F, E, U and R in 40 healthy participants (20 women and 20 men) were tested concentrically (Con) and eccentrically (Ecc) using a single speed of 90°/s. Results: Men were significantly stronger than women in both the Con and Ecc tests, as indicated by both the absolute (Nm) and the bodyweight-normalized (Nm/kgbw) representations. However, the bodyweight-normalized women/men strength ratio (78.6 ± 8.0%) was significantly higher than the absolute strength ratio (64.1 ± 6.6%). For both the Con and Ecc tests, and irrespective of the representation (absolute or normalized), the U was the strongest muscle group, followed successively by the F, R and E. This rank order was highly significant statistically. The eccentric/concentric strength ratios, E/C_F and E/C_U, were significantly higher in men than in women, with no remarkable inter-sex differences for E/C_E and for E/C_R. A correlational analysis, which included all pairs of basic isokinetic outcome parameters (e.g., the PM of F_con), was performed with respect to ‘sex’ using a nonparametric bootstrap procedure, revealing that men had significantly higher overall correlation coefficients compared to women. Conclusions: The consistency of the main findings with respect to both the sex of the participants and the various strength ratios supports the use of the current protocol. The observed strength order (U > F > R > E) may assist clinicians in setting preliminary return-to-function targets after wrist rehabilitation. Full article

Camelina (Camelina sativa) is an oilseed crop of increasing importance, valued not only for its adaptability to diverse environmental conditions and potential for sustainable agriculture but also for its economic advantages, including low input requirements and suitability for biofuel production and niche markets. This study examines the relationship between camelina yield and climatic variables—specifically temperature and precipitation—based on a ten-year field experiment conducted in Poland. To capture the temporal dynamics of weather conditions, Functional Data Analysis (FDA) was applied to daily temperature and precipitation data. The analysis revealed that yield variability was strongly influenced by the length of the vegetative period and specific weather patterns in April and July. Higher yields were recorded in years characterized by moderate spring temperatures, elevated temperatures in July, and evenly distributed rainfall during the early generative growth stages. The Maximal Information Coefficient ($\mathrm{M}\mathrm{I}\mathrm{C}$) confirmed the relevance of these variables, with the duration of the vegetative phase showing the strongest correlation with yield. Cluster analysis further distinguished high- and low-yield years based on functional weather profiles. The FDA-based approach provided clear, interpretable insights into climate–yield interactions and demonstrated greater effectiveness than traditional regression models in capturing complex, time-dependent relationships. These findings enhance our understanding of camelina’s response to climatic variability and support the development of predictive tools for resilient, climate-smart crop management. Full article

The relationship between energy consumption and economic growth remains a critical and complex issue in both economic and environmental research. This study investigates the disaggregated effects of primary energy sources on GDP growth across four country groups, including G20, OECD founding members (OECDf), all OECD members (OECDa), and a global subset (World), using data from the Our World in Data and World Bank. While prior studies often rely on aggregate energy use, this study investigates the disaggregated effects of primary energy sources on GDP growth across four country groups: G20, OECD founding members (OECDf), all OECD members (OECDa), and a global subset (World). To assess these relationships, both multiple linear regression and a multilayer feedforward neural network (MLP) model were employed. While the regression model exhibited low explanatory power across all groups, the MLP offered more accurate and flexible predictions by capturing nonlinear dynamics. The model exhibited high predictive performance, with Pearson correlation coefficients ranging from 0.80 to 0.94 and intraclass correlation coefficients exceeding 0.87 across all test datasets. Predictive accuracy was strongest in more homogenous and economically stable groups such as the G20 and OECDf, while wider confidence intervals in the OECDa and World datasets indicated increased variability, likely due to heterogeneous energy structures and data quality limitations—particularly for renewables prior to 2010. These findings highlight the effectiveness of machine learning in modeling complex energy–growth relationships and underscore the importance of accounting for energy source diversity and national context in empirical analyses. Full article

Background/Objectives: While skeletal facial asymmetry is commonly assessed using posteroanterior (PA) cephalometric radiographs, the association between skeletal measurements and volumetric soft tissue asymmetry remains unclear. This study aimed to identify which skeletal parameters are most strongly correlated with soft tissue asymmetry measured using three-dimensional (3D) imaging. Methods: Thirty-three Japanese patients (8 males and 25 females; mean age: 26.85 ± 12.13 years) undergoing orthodontic–orthognathic treatment were included. Three-dimensional facial surface data were acquired using the VECTRA^® H1 imaging system. Soft tissue asymmetry was quantified by calculating the volumetric difference between the original and mirrored 3D facial images, divided into three regions: whole face, midface, and lower face. PA cephalometric radiographs were traced, and 28 skeletal variables were measured. Pearson correlation coefficients were calculated between skeletal variables and asymmetry volumes and squared to obtain R² values. Results: The strongest correlation with whole facial soft tissue asymmetry was found for menton deviation from the midline (R² = 0.630). Similar trends were observed for the lower face. In contrast, only one skeletal variable showed a moderate correlation with midfacial asymmetry (maximum R² = 0.186), and skeletal parameters related to maxillary occlusal cant did not show significant associations. Conclusions: Volumetric soft tissue asymmetry is strongly associated with mandibular skeletal deviation, particularly menton displacement, whereas midfacial skeletal morphology may have a limited impact. Further studies including more patients with pronounced midfacial soft tissue asymmetry are warranted. Full article

Accurate real-time information about soil moisture (SM) at a large scale is essential for improving hydrological modeling, managing water resources, and monitoring extreme weather events. This study presents a framework using convolutional long short-term memory (ConvLSTM) network to produce short- (1, 3, and 7 days ahead) and mid-term (14 and 30 days ahead) forecasts of SM at surface (0–10 cm) and subsurface (10–40 and 40–100 cm) soil layers across the contiguous U.S. The model was trained with five-year period (2018–2022) datasets including Soil Moisture Active Passive (SMAP) level 3 ancillary covariables, North American Land Data Assimilation System phase 2 (NLDAS-2) SM product, shortwave infrared reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS), and terrain features (e.g., elevation, slope, curvature), as well as soil texture and bulk density maps from the Soil Landscape of the United States (SOLUS100) database. To develop and evaluate the model, the dataset was divided into three subsets: training (January 2018–January 2021), validation (2021), and testing (2022). The outputs were validated with observed in situ data from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN) soil moisture networks. The results indicated that the accuracy of SM forecasts decreased with increasing lead time, particularly in the surface (0–10 cm) and subsurface (10–40 cm) layers, where strong fluctuations driven by rainfall variability and evapotranspiration fluxes introduced greater uncertainty. Across all soil layers and lead times, the model achieved a median unbiased root mean square error (ubRMSE) of 0.04 cm³ cm⁻³ with a Pearson correlation coefficient of 0.61. Further, the performance of the model was evaluated with respect to both land cover and soil texture databases. Forecast accuracy was highest in coarse-textured soils, followed by medium- and fine-textured soils, likely because the greater penetration depth of microwave observations improves SM retrieval in sandy soils. Among land cover types, performance was strongest in grasslands and savannas and weakest in dense forests and shrublands, where dense vegetation attenuates the microwave signal and reduces SM estimation accuracy. These results demonstrate that the ConvLSTM framework provides skillful short- and mid-term forecasts of surface and subsurface soil moisture, offering valuable support for large-scale drought and flood monitoring. Full article

Metaphorical competence—the capacity to comprehend and produce metaphors in a second language (L2)—is essential for nuanced, accurate, and contextually appropriate English usage. Synthesizing 40 independent studies (N = 15,786), this meta-analysis quantified the relative contributions of cognitive, linguistic, and personal factors to L2 metaphorical competence. Effect sizes were derived from correlation coefficients and aggregated under random-effects models to account for between-study heterogeneity. Linguistic factors emerged as the dominant predictor (r = 0.421, 95% CI [0.34, 0.50]), primarily driven by vocabulary breadth/depth and reading proficiency. Cognitive factors exerted a moderate influence (r = 0.232, 95% CI [0.17, 0.30]), whereas personal variables such as gender yielded only a small effect (r = 0.216, 95% CI [0.15, 0.28]). Moderator analyses further revealed that L1 conceptual knowledge constitutes the strongest single predictor of L2 metaphor skills and highlighted distinct associations between receptive and productive metaphor abilities with linguistic versus cognitive aptitudes. The findings collectively point to lexico-semantic and literacy development as the main levers for boosting L2 metaphorical competence, with cognitive aptitudes and personal factors acting as secondary, yet important, modulators. Insight from this meta-analysis offers a robust foundation for evidence-based decisions in curriculum design, materials selection, and targeted pedagogical interventions. Full article

Background: Suicide remains a leading cause of death among youth, yet effective tools to predict suicide attempts (SA) in individuals under 18 are scarce. This study aims to develop machine learning (ML) models to predict SA in paediatric populations using Google Trends data. Methods: Relative Search Volumes (RSVs) from Google Trends were analysed for terms linked to suicide risk factors. Pearson Correlation Coefficients (PCC) identified terms strongly associated with SA rates. Based on these, several ML models were developed and evaluated, including Random Forest Regression, Support Vector Regression (SVR), XGBoost, and Linear Regression. Model performance was assessed using metrics such as PCC, mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results: Terms related to suicide prevention and symptoms, including psychiatrist and anxiety disorder, showed the strongest correlations with SA rates (PCC ≥ 0.90). Random Forest Regression emerged as the top-performing ML model (PCC = 0.953, MAPE = 20.12%, RMSE = 17.21), highlighting burnout, anxiety disorder, antidepressants, and psychiatrist as key predictors of SA. Other models’ scores were XGBoost (PCC = 0.446, MAPE = 22.57%, RMSE = 18.03), SVR (PCC = 0.833, MAPE = 42.23%, RMSE = 47.32) and Linear Regression (PCC = 0.947, MAPE = 23.64%, RMSE = 17.66). Conclusions: Google Trends–based ML models suggest potential utility for short-term prediction of youth SA. These preliminary findings support the utility of search data in identifying real-time suicide risk in paediatric populations. Full article

Background: Sense of coherence constitutes a significant personal resource that underpins the harmonious professional functioning of nurses employed in psychiatric inpatient wards. It serves as a protective factor, enabling effective coping with the psychophysical burden arising from a demanding and stress-inducing work environment, while also supporting the maintenance of a high level of job satisfaction. Regular assessment of the sense of coherence among psychiatric nursing staff is essential for the early identification of individuals at risk of developing occupational burnout. The aim of the present study was to determine the relationship between the level of sense of coherence and the degree of occupational burnout among nurses working in inpatient psychiatric units. Methods: The study employed a cross-sectional design and utilized standardized psychometric instruments, including The Sense of Coherence Questionnaire (SOC-29) to assess the level of coherence, and the Maslach Burnout Inventory (MBI) to measure occupational burnout. Additionally, a self-developed questionnaire was used to collect sociodemographic data. The research was conducted in five psychiatric hospitals in Poland between January and June 2023. The sample consisted of 555 nurses (449 women and 106 men) employed in inpatient psychiatric wards. Statistical analyses included descriptive statistics, Pearson’s correlation coefficients to examine relationships between variables, and multiple linear regression to identify predictors of burnout dimensions. Significance level set at p < 0.05. Results: The mean global sense of coherence score among psychiatric nurses was 124.68 (SD = 45.81), with manageability scoring highest among subscales (43.83, SD = 16.28). Average occupational burnout scores were emotional exhaustion 28.75 (SD = 16.39), depersonalization 13.55 (SD = 9.71), and reduced personal accomplishment 23.61 (SD = 11.11). Significant negative correlations were found between sense of coherence (and its components) and all burnout dimensions (p < 0.001). Manageability was the strongest predictor of lower emotional exhaustion (β = −0.73), depersonalization (β = −0.65), and reduced personal accomplishment (β = −0.65), while meaningfulness predicted depersonalization (β = 0.37, p = 0.012). These results indicate that higher sense of coherence, especially manageability, is linked to reduced burnout among psychiatric nurses. Conclusions: The study revealed significant negative associations between sense of coherence and all dimensions of occupational burnout, with manageability emerging as the strongest protective factor. Nurses with higher levels of sense of coherence reported lower emotional exhaustion, depersonalization, and reduced personal accomplishment. These findings highlight the importance of incorporating sense of coherence assessment into strategies for identifying individuals at increased risk of burnout. Full article

Background: Ovarian cancer is often diagnosed at advanced stages due to the absence of specific early symptoms, resulting in high mortality rates. This study aims to develop a robust and interpretable machine learning (ML) model for the early detection of ovarian cancer, enhancing its transparency through the use of the Contrastive Explanation Method (CEM), an advanced technique within the field of explainable artificial intelligence (XAI). Methods: An open-access dataset of 349 patients with ovarian cancer or benign ovarian tumors was used. To improve reliability, the dataset was augmented via bootstrap resampling. A three-layer deep neural network was trained on normalized demographic, biochemical, and tumor marker features. Model performance was measured using accuracy, sensitivity, specificity, F1-score, and the Matthews correlation coefficient. CEM was used to explain the model’s classification results, showing which factors push the model toward “Cancer” or “No Cancer” decisions. Results: The model achieved high diagnostic performance, with an accuracy of 95%, sensitivity of 96.2%, and specificity of 93.5%. CEM analysis identified lymphocyte count (CEM value: 1.36), red blood cell count (1.18), plateletcrit (0.036), and platelet count (0.384) as the strongest positive contributors to the “Cancer” classification, with lymphocyte count demonstrating the highest positive relevance, underscoring its critical role in cancer detection. In contrast, age (change from −0.13 to +0.23) and HE4 (change from −0.43 to −0.05) emerged as key factors in reversing classifications, requiring substantial hypothetical increases to shift classification toward the “No Cancer” class. Among benign cases, a significant reduction in RBC count emerged as the strongest determinant driving a shift in classification. Overall, CEM effectively explained both the primary features influencing the model’s classification results and the magnitude of changes necessary to alter its outputs. Conclusions: Using CEM with ML allowed clear and trustworthy detection of early ovarian cancer. This combined approach shows the promise of XAI in assisting clinicians in making decisions in gynecologic oncology. Full article

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies. Full article

Background/Objectives: Quantitative intravoxel incoherent motion (IVIM) imaging, incorporating both diffusion- and perfusion-derived metrics, offers a promising non-invasive approach for assessing tissue microstructure and clinical disability in multiple sclerosis (MS). This study aimed to investigate the correlation and predictive values of the IVIM apparent diffusion coefficient (ADC), true diffusion coefficient (D), and perfusion-derived pseudo-diffusion coefficient (D*) and perfusion fraction (f) parameters with disability status, measured using the Expanded Disability Status Scale (EDSS), in relapsing–remitting MS patients. Methods: This cross-sectional study retrospectively analyzed MRI data from 197 MS patients. Quantitative IVIM parameters were extracted from scans obtained using a 1.5 T MRI scanner. Clinical data were also obtained, including age, disease duration, number of relapses, disease-modifying therapy (DMT) status, and need for mobility assistance. Bivariate analyses were conducted to compare mean values across subgroups. Pearson correlation was used to examine associations between EDSS score and imaging/clinical variables. Multiple linear regression was applied to identify independent predictors of EDSS score. Results: The bivariate analyses revealed that ADC, D, D*, and EDSS values were higher in patients over 50 years old, those with a longer disease duration, and those who required mobility assistance. f was higher in females and DMT-treated patients, but it had no effect on EDSS score. Patients with longer disease duration and limited mobility had a higher number of MS lesions and relapses. EDSS score exhibited positive Pearson correlations with ADC, D, D*, the number of MS lesions, and the number of relapses (p-value < 0.001). In the multivariate regression analysis, only the number of MS lesions and relapses emerged as independent predictors of EDSS score (p-value < 0.001). Other variables, including ADC, D, D*, f, age, and disease duration, were not independently associated with EDSS score (p-value > 0.05). Conclusions: This study demonstrates the utility of IVIM parameters in detecting microstructural alterations associated with MS impairment. Despite relapse frequency and lesion count being the strongest predictors of EDSS score, IVIM metrics showed meaningful clinical correlations. The findings support combining IVIM biomarkers with clinical data for better disability assessment. Full article

Fraxinus spp. is one of the most important salt-alkali resistant tree species in the Yellow River region of China. However, the limited number of superior families and individuals, as well as the lack of a well-established parent selection system for hybrid breeding, have seriously constrained the improvement of seed orchards and the construction of advanced breeding populations. To address these issues, this study investigated 22 full-sib families of Fraxinus spp., using SSR molecular markers to calculate the genetic distance (GD) between parents. Combined with combining ability analysis, the study aimed to predict heterosis in offspring growth traits and select superior families and individuals through multi-trait comprehensive evaluation. The results showed the following: (1) Tree height (TH), diameter at breast height (DBH), and volume index (VI) exhibited extremely significant differences among families, indicating rich variation and strong selection potential. (2) The phenotypic and genotypic coefficients of variation for TH, DBH, and VI ranged from 4.34% to 16.04% and 5.10% to 17.73%, respectively. Family heritability was relatively high, ranging from 0.724 to 0.818, suggesting that growth is under strong genetic control. (3) The observed and expected heterozygosity of 15 parents were 0.557 and 0.410, respectively, indicating a moderate level of heterozygosity. Nei’s genetic diversity index and Shannon’s information index were 0.488 and 0.670, respectively, indicating relatively high genetic diversity. GD between parents ranged from 0.155 to 0.723. (4) Correlation analysis revealed significant or highly significant positive correlations between family heterosis and growth traits, combining ability, and GD, with specific combining ability (SCA) showing the strongest predictive power. Regression analysis further demonstrated significant linear correlations between GD and heterosis of TH and VI, and between SCA and heterosis of TH, DBH, and VI, establishing a GD threshold (≤0.723) and SCA-based co-selection strategy. In addition, four superior Fraxinus families and 11 elite individuals were selected. Their genetic gains for TH, DBH, and VI reached 2.28%, 3.30%, and 9.96% (family selection), and 1.98%, 2.11%, and 4.00% (individual selection), respectively. By integrating genetic distance (GD) and quantitative genetic combining ability (SCA), this study established a quantifiable prediction model and proposed the “GD–SCA dual-index parent selection method”, offering a new paradigm for genetic improvement in tree breeding. Full article

To establish a systematic approach for evaluating heat tolerance at the seedling stage in broccoli, we investigated 14 representative cultivars cultivated in China. Physiological indicators such as electrical conductivity, malondialdehyde, proline, and chlorophyll content were measured before and after heat stress, alongside phenotypic scoring of heat injury, to characterize the differential thermotolerance among genotypes. The results indicated that significant differences (p < 0.05) in heat tolerance at the seedling stage were observed among different broccoli cultivars after heat stress treatment at 40 °C. Among them, Yanxiu exhibited the strongest heat tolerance, followed by Meiqing, Naihanyouxiu, Meiao 7172, Feicui 5, Guowang 11, Zheqing 80, Zhongqing 15, Zhongqing 318, Zhongqing 319, and Qianghan. Lvxiong 90, Zhongqing 11, and Zhongqing 16 were the least heat tolerant. Pearson’s correlation analysis demonstrated that the seedling heat tolerance of different broccoli cultivars was significantly negatively correlated with electrical conductivity, with a correlation coefficient of 0.542 (p < 0.05). In this study, a rapid and robust method for determining the heat resistance response of broccoli was established, providing a scientific basis and technical support for the identification of the heat resistance of new broccoli cultivars and the selection and breeding of heat-tolerant cultivars in China. Full article

Background: Several physical qualities have been linked to countermovement jump (CMJ) performance. However, the relative importance of each of these factors is unclear. (1) Objectives: The present systematic review sought to evaluate the characteristics associated with CMJ performance in adult team-sport athletes. (2) Methods: A comprehensive search of three databases and the grey literature yielded 18 articles that met the inclusion criteria. Pearson’s correlation coefficient was used to assess statistically significant relationships and interpreted as negligible (0.00–0.10), weak (0.10–0.39), moderate (0.40–0.69), strong (0.70–0.89), and very strong (0.90–1.00). (3) Results: Eighteen articles remained eligible, with an average quality score of 76% ± 14 on the Joanna Briggs Institute critical appraisal index. The strongest correlations reported included time-to-bottom, time-to-peak force, knee extension peak power at 180 °/s, and squat jump height. (4) Conclusions: The conclusions drawn from this study suggest that, to maximize CMJ performance, priority should be given to movement biomechanics and lower-body power whilst considering individual braking-phase strategies. These findings may inform training programs aimed not only at enhancing athletic performance but also at reducing injury risks associated with poor jumping mechanics in team sports. Full article