مطالب

Through advanced analytical models such as machine learning (ML) and, conceptually, Large Language Models (LLMs), this study explores how Industrial Internet of Things (IIoT) applications can transform educational experiences in the context of smart steel production. To mitigate the shortage of authentic industrial datasets for research, we developed an industry-validated IIoT educational dataset drawn from three months of operational records at a steel plant and enriched with domain-specific annotations—most notably distinct operational phases. Building on this foundation, we propose an IIoT framework for intelligent steel manufacturing that merges ML-driven predictive analytics (employing Lasso regression to optimize energy use) with LLM-based contextualization of data streams within IIoT environments. At its core, this architecture delivers real-time process monitoring alongside adaptive learning modules, effectively simulating the dynamics of a smart factory. By promoting human–machine collaboration and mirroring quality-control workflows, the framework bridges the divide between theoretical instruction and hands-on industrial practice. A key feature is an interactive decision-support dashboard: this interface presents ML model outcomes and elucidates IIoT measurements—such as metallization levels and H 2 /CO ratios—through dynamic visualizations and scenario-based simulations that invite risk-free exploration of energy-optimization strategies. Such tools empower learners to grasp the intricate multivariate dependencies that govern steel manufacturing processes. Our implementation of the Lasso regression model resulted in a 9% reduction in energy consumption and stabilization of metallization levels. Overall, these findings underscore how embedding advanced analytics within IIoT education can cultivate a more engaging, practice-oriented learning environment that aligns closely with real-world industrial operations.

Objective: This study aimed to investigate the relationship between parental overcontrol and adolescent social withdrawal, with fear of negative and positive evaluation as potential mediating variables. Methods: The research employed a descriptive correlational design and included a sample of 551 Nigerian adolescents selected based on the Morgan and Krejcie table. Data were collected using standardized self-report instruments: the Psychological Control Scale–Youth Self-Report (PCS-YSR) for parental overcontrol, the Brief Fear of Negative Evaluation Scale (BFNE), the Fear of Positive Evaluation Scale (FPES), and the Withdrawn/Depressed subscale of the Youth Self-Report (YSR). Descriptive statistics and Pearson correlation coefficients were calculated using SPSS-27, and Structural Equation Modeling (SEM) was conducted via AMOS-21 to examine direct and indirect pathways among the variables. Findings: Pearson correlation analysis showed that parental overcontrol was significantly associated with fear of negative evaluation (r = .51, p < .001), fear of positive evaluation (r = .47, p < .001), and social withdrawal (r = .42, p < .001). SEM results confirmed that the structural model had good fit indices (χ² = 146.23, df = 84, χ²/df = 1.74, CFI = 0.97, RMSEA = 0.041). Parental overcontrol had significant direct effects on fear of negative evaluation (β = .51, p < .001), fear of positive evaluation (β = .48, p < .001), and social withdrawal (β = .27, p < .001). Both fears significantly predicted social withdrawal (FNE: β = .45, p < .001; FPE: β = .38, p < .001). Indirect effects of parental overcontrol on social withdrawal via FNE (B = 0.19, p < .001) and FPE (B = 0.14, p < .001) were also significant. Conclusion: These findings highlight that fear of evaluation—both negative and positive—serves as a psychological mechanism linking parental overcontrol to social withdrawal in adolescents, emphasizing the importance of addressing cognitive-affective vulnerabilities in preventive and therapeutic interventions.

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.