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Currently, fast and efficient computing is driving the new disruptive technologies required by modern healthcare systems. A simulation model is presented that studies how virtualization affects the performance of task offloading in smart health-care environments. Virtualization technology is suitable for task-offloading processes. A simulation-based framework can be used to examine how virtualization overhead influences task offloading efficiency in smart healthcare environments. Therefore, the proposed model uses virtualization to manage resources more effectively and make the system more reliable. Important factors such as how long a task takes to finish, how much energy it uses, how much data it can handle, and how well it can grow when more tasks are added must be considered for smart healthcare. The results were compared with those of a system that did not use virtualization; thus, we can clearly observe how virtualization changes the overall performance. The findings show that better resource use, lower energy consumption, and improved fault recovery are possible, which makes the proposed system suitable for real healthcare applications. In the future, this work can be extended by adding predictive analytics, improving machine-learning–based scheduling, using multiple cloud platforms, and making virtualization even more energy-efficient.

This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1–3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

In this comprehensive investigation, we evaluate the efficacy of the Fenton process in degrading basic fuchsin (BF), a resistant dye. Our primary focus is on the utilization of readily available, environmentally benign, and cost-effective reagents for the degradation process. Furthermore, we delve into various operational parameters, including the quantity of sodium percarbonate (SPC), pH levels, and the dimensions of waste iron bars, to optimize the treatment efficiency. In the course of our research, we employed an initial SPC concentration of 0.5 mM, a pH level of 3, a waste iron bar measuring 3.5 cm in length and 0.4 cm in diameter, and a processing time of 10 min. Our findings reveal the successful elimination of the BF dye, even when subjected to treatment with diverse salts and surfactants under elevated temperatures and acidic conditions (pH below 3). This underscores the robustness of the Fenton process in purifying wastewater contaminated with dye compounds. The outcomes of our study not only demonstrate the efficiency of the Fenton process but highlight its adaptability to address dye contamination challenges across various industries. Critically, this research pioneers the application of waste iron bars as a source of iron in the Fenton reaction, introducing a novel, sustainable approach that enhances the environmental and economic viability of the process. This innovative use of recycled materials as catalysts represents a significant advancement in sustainable chemical engineering practices. Graphical abstract

The purpose of this study was to describe in-service Saudi Arabian art education teachers’ (a) philosophies of education before and after an issues-based art education (IBAE) workshop, (b) perceptions of the IBAE workshop and its products, (c) perception of the importance of IBAE approach, reasons, topics, and challenges after the workshop, and (d) perception of IBAE within the Saudi Arabian context. A mixed-methods approach was followed. The qualitative portion of the study utilized a post-workshop questionnaire and reflective essay completed by 37 participants, and personal teaching journal protocol and focus group discussion from 18 participants who attended the workshop and implemented the IBAE lesson in their classrooms. Analysis of the data confirmed that art teachers’ perspectives towards IBAE positively increased after attending the IBAE workshop. Specifically, in the quantitative findings, participants indicated a positive attitude toward the teaching philosophy of social reconstruction after the IBAE workshop. These results demonstrate that the tenets of social reconstruction align with the IBAE approach. In addition, the quantitative data suggests that teacher participants’ overall perception of the importance of addressing social issues in the art curriculum was positive, with 91.9% of respondents agreeing. Overall the qualitative findings, indicated a positive attitude toward the IBAE workshop. Further, the analysis confirmed that these teachers had positive perspectives toward incorporating social issues in their art education again in their classroom of the IBAE approach especially within the Saudi Arabian context.