This study aimed to assess the effect of distance learning on students' self-directed learning skills and motivation during the lockdown period of the COVID–19 pandemic. The study relies on a quantitative methodology. The data were collected using an administrative online survey distributed to 427 respondents with different majors (BBA) enrolled in the learning skills and scientific research skills course (obligatory course) in the second semester of the academic year 2020/2021 at The University of Jordan. Regression analysis was used to analyze the proposed hypotheses. The results showed that the independent variable (Distance Learning) positively influenced students' motivation and self-directed learning skills. The recommendations based on the outcomes of this research are useful for educational specialists to develop learning environments about the effects of distance learning on students’ self-directed learning skills and motivation. In terms of limitations, the analysis was performed in one university only; therefore, attention must be paid when generalizing the results.

This study aimed at examining the effect of using digital educational content designed by learning style (audio, visual, or kinesthetic) on the academic achievement and motivation towards learning among students of the Life Skills subject at Al al-Bayt University during COVID-19. The study sample consisted of two groups chosen randomly; one was the experimental group and comprised 55 students, while the other was the control group and comprised 50 students. An academic achievement test, consisting of 25 questions and a motivation scale of 25 items was prepared to measure the use of digital educational content based on learning style (audio, visual, or kinesthetic) in the academic achievement and motivation towards learning among Life Skills students. The validity and reliability of the study tools were determined. To answer the study questions, the means and standard deviations of the study sample were calculated, and a one-way analysis of variance (ANCOVA) was computed. The results showed a statistically significant difference between the mean scores of academic achievement and the scores on the motivation scale due to the variable of teaching method, in favor of the experimental group, which studied using digital educational content based on learning style (audio, visual, or kinesthetic). The researchers recommended generalizing the experience of using digital educational content based on learning styles (audio, visual, or kinesthetic) due to its impact on the academic achievement and motivation toward learning among students of the Life Skills subject at Al al-Bayt University during COVID-19 on various subjects in the university.

This paper presents a novel Hybrid Artificial Protozoa Optimizer with Differential Evolution (HPDE), combining the biologically inspired principles of the Artificial Protozoa Optimizer (APO) with the powerful optimization strategies of Differential Evolution (DE) to address complex and engineering design challenges. The HPDE algorithm is designed to balance exploration and exploitation features, utilizing innovative features such as autotrophic and heterotrophic foraging behaviors, dormancy, and reproduction processes alongside the DE strategy. The performance of HPDE was evaluated on the CEC2014 benchmark functions, and it was compared against two sets of state-of-the-art optimizers comprising 23 different algorithms. The results demonstrate HPDE’s good performance, outperforming competitors in 24 functions out of 30 from the first set and 23 functions from the second set. Additionally, HPDE has been successfully applied to a range of complex engineering design problems, including robot gripper optimization, welded beam design optimization, pressure vessel design optimization, spring design optimization, speed reducer design optimization, cantilever beam design optimization, and three-bar truss design optimization. The results consistently showcase HPDE’s good performance in solving these engineering problems when compared with the competing algorithms.

This paper proposes JADEGBO, a hybrid gradient-based metaheuristic for solving complex single- and multi-constraint engineering design problems as well as cost-sensitive security optimisation tasks. The method combines Adaptive Differential Evolution with Optional External Archive (JADE), which provides self-adaptive exploration through p-best mutation, an external archive, and success-based parameter learning, with the Gradient-Based Optimiser (GBO), which contributes Newton-inspired gradient search rules and a local escaping operator. In the proposed scheme, JADE is first employed to discover promising regions of the search space, after which GBO performs an intensified local refinement of the best individuals inherited from JADE. The performance of JADEGBO is assessed on the CEC2017 single-objective benchmark suite and compared against a broad set of classical and recent metaheuristics. Statistical indicators, convergence curves, box plots, histograms, sensitivity analyses, and scatter plots show that the hybrid typically attains the best or near-best mean fitness, exhibits low run-to-run variance, and maintains a favourable balance between exploration and exploitation across rotated, shifted, and composite landscapes. To demonstrate practical relevance, JADEGBO is further applied to the following four well-known constrained engineering design problems: welded beam, pressure vessel, speed reducer, and three-bar truss design. The algorithm consistently produces feasible high-quality designs and closely matches or improves upon the best reported results while keeping computation time competitive.

Many engineering design problems are formulated as constrained optimization tasks that are nonlinear and nonconvex, and often treated as black boxes. In such cases, metaheuristic algorithms are attractive because they can search complex design spaces without requiring gradient information. In this work, we propose an Enhanced Henry Gas Solubility Optimization (eHGSO) algorithm, which is an improved version of the physics-inspired HGSO method. The enhanced variant introduces six main contributions: (i) a more diverse, population-wide initialization strategy to cover the design space more thoroughly; (ii) adaptive temperature/pressure control parameters that automatically shift the search from global exploration to local refinement; (iii) an elitist archive with differential perturbation that accelerates exploitation around high-quality candidate designs; (iv) a simple combination of the global HGSO search moves with a lightweight gradient-free local search to refine promising solutions; (v) a constraint-handling mechanism that explicitly prioritizes feasible solutions while still allowing exploration near the constraint boundaries; and (vi) a complexity and ablation analysis that quantifies the impact of each mechanism and confirms that they introduce only modest computational overhead. We evaluate eHGSO on four classical constrained engineering design problems: the stepped cantilever beam, the tension/compression spring, the welded beam, and the three-bar truss. Its performance is compared with seventeen recent metaheuristic optimizers over multiple independent runs. eHGSO achieves the best average objective value on the cantilever, spring, and welded-beam problems and shares the best average result on the three-bar truss. Compared to the second-best method, the mean objective is improved by about $0.84\%$ for the cantilever beam and $0.35\%$ for the welded beam, while the spring and truss results are essentially equivalent at four significant figures. Convergence and robustness analyses show that eHGSO reaches high-quality solutions quickly and consistently. Overall, the proposed eHGSO algorithm appears to be a competitive and practical tool for constrained engineering design problems.