درخت حوزه‌های تخصصی

This research work aims to present a robust deep learning framework by devising a deep learning-based ensemble method of YOLOv8 and EfficientNet. The suggested model is evaluated on the dataset collected from Kaggle, comprising 10,000 high-definition images of stems, leaves, and cut fruits of banana and papaya. These images are captured under different lighting conditions and thus expanded to 80,000 images. Authors have proposed an ensemble model comprising YoloV8 and EfficientNet as base deep learning models to enhance prediction and classification performance. Here, authors combine the merits of both models, i.e., speed of YoloV8 and the accuracy of EfficientNet, by putting a majority voting method in place. The final forecast is determined by majority voting, and EfficientNet is given higher significance in the situation of a tie owing to its enhanced accuracy. The proposed model presents a robust solution for agricultural disease management and demonstrates significant improvements in the detection of diseases in papaya and banana, opening avenues for its widespread employment in real life.

Objective: This study aimed to investigate the predictive roles of co-parenting quality and parenting stress in relation to child behavioral adjustment among parents in Morocco. Methods: A correlational descriptive design was employed involving a sample of 396 parents selected based on Krejcie and Morgan’s sampling table. Participants completed three standardized instruments: the Child Behavior Checklist (CBCL) to assess child behavioral problems, the Parenting Stress Index–Short Form (PSI-SF) to measure perceived parenting stress, and the Co-parenting Relationship Scale (CRS) to evaluate co-parenting quality. Data were analyzed using SPSS version 27. Pearson correlation coefficients were computed to examine the bivariate associations between the dependent variable and each independent variable. Multiple linear regression was then conducted to evaluate the joint predictive power of parenting stress and co-parenting quality on child behavioral adjustment, with all assumptions for regression analysis thoroughly checked and confirmed. Findings: Results showed a significant positive correlation between parenting stress and child behavioral problems (r = .56, p < .01), and a significant negative correlation between co-parenting quality and child behavioral problems (r = –.42, p < .01). Multiple regression analysis revealed that parenting stress (β = .48, t = 8.60, p < .01) and co-parenting quality (β = –.31, t = –6.75, p < .01) were both significant predictors of child behavioral adjustment. The overall model was significant (F(2, 393) = 129.72, p < .01), explaining 40% of the variance in child behavioral outcomes (R² = .40, Adjusted R² = .39). Conclusion: The findings highlight the critical roles of both relational and psychological parenting factors in child development. Interventions aiming to reduce parenting stress and enhance co-parenting quality may be effective in improving behavioral outcomes in children, especially in non-Western cultural contexts like Morocco.

Objective: This study aimed to examine the direct and indirect effects of parental smartphone addiction on child behavioral problems, with parenting quality as a mediating variable. Methods and Materials: A descriptive correlational design was employed with a sample of 410 parents from Malaysia, determined according to the Morgan and Krejcie sample size table. Participants were recruited through schools and community centers and completed standardized measures assessing smartphone addiction, parenting quality, and child behavioral problems. Data were analyzed using SPSS-27 for descriptive statistics and Pearson correlations, and AMOS-21 for structural equation modeling (SEM). Model fit indices, including χ²/df, GFI, AGFI, CFI, TLI, and RMSEA, were used to assess model adequacy. Findings: Descriptive statistics revealed moderate levels of parental smartphone addiction (M = 32.47, SD = 6.81), relatively high parenting quality (M = 74.26, SD = 9.54), and mild to moderate child behavioral problems (M = 18.63, SD = 5.72). Correlation analyses showed that parental smartphone addiction was positively correlated with child behavioral problems (r = .36, p < .001) and negatively correlated with parenting quality (r = –.41, p < .001). Parenting quality was negatively correlated with child behavioral problems (r = –.44, p < .001). SEM confirmed partial mediation: parental smartphone addiction negatively predicted parenting quality (β = –.39, p < .001), which in turn negatively predicted child behavioral problems (β = –.41, p < .001). The direct path from parental smartphone addiction to child behavioral problems remained significant (β = .25, p = .001), while the indirect effect through parenting quality was also significant (β = .16, p < .001). Conclusion: The study highlights that parental smartphone addiction increases child behavioral problems both directly and indirectly through reduced parenting quality. Interventions should target reducing parental smartphone addiction and enhancing parenting practices to safeguard children’s behavioral health.

In the Iranian academic setting, the current investigation aimed to identify the assessment literacy (AL) components that experienced and novice English for Specific Purposes (ESP) teachers with and without TEFL (teaching English as a foreign language) backgrounds comprehended. We designed an explanatory sequential mixed-methods study, and selected 100 PhD male and female ESP educators from various branches of Islamic Azad University via criterion sampling in order to complete the teacher assessment literacy scale (TALS). The standard deviation as well as mean of the TALS were computed. The mean scores of the TEFL/non-TEFL and novice/experienced teachers on the seven TALS components were then independently compared using two multivariate analyses of variances (MANOVA). After administering the scale to 100 ESP teachers and analyzing the results, 20 of the instructors were chosen via convenience sampling for observation during the study's qualitative phase. Afterwards, the qualitative information was collected through the use of the observation notes. Employing axial/open coding and content analysis of themes, all of the notes were classified and examined. The results showed that in terms of knowledge of assessment literacy components, instructors with TEFL backgrounds understand much better than teachers without TEFL backgrounds. Similarly, the means of all TALS components were greater for inexperienced teachers than for experienced ones. Furthermore, the results of the qualitative phase demonstrated that the novice TEFL teachers performed AL components more effectively than their non-TEFL counterparts as well as the experienced TEFL/non-TEFL instructors.