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The COVID-19 pandemic significantly affected all sectors, including education--schools were affected by widespread lockdowns, which necessitated the adoption of online learning platforms. Using a mixed-methods research methodology incorporating questionnaires and interviews, researchers in the current study examined the impact of e-learning on high school students' English language learning, particularly their spoken skills, in Kuwait. The researchers studied a sample of 60 participants for the quantitative analysis and 18 students for the qualitative analysis. All were high school students in Kuwait enrolled in English classes. The study's results revealed significant challenges associated with e-learning, including low acceptance rates among students. Most students disagreed that online learning is a perfect learning tool, suggesting that e-learning fails to promote critical thinking skills and facilitate learning. E-learning also affects learners' capabilities to express their feelings and ideas. The interviews showed that e-learning failed to improve the students' English language mastery. Some of the challenges we noted include technical hitches and the inability to deploy teaching strategies used successfully in physical classes. Overall, the results indicate that students disliked online learning in Kuwait. In conclusion, e-learning is a significant opportunity for students to improve their learning, but it must be effectively used to encourage students' uptake. It is necessary to assess schools' preparedness to implement it as well as to design complementary programs and strategies to ensure students gain mastery of the English language.

Purpose: Artificial intelligence (AI) is constantly evolving and is poised to significantly transform the world, affecting nearly every sector and aspect of society. As AI continues to evolve, it is expected to create a more dynamic, efficient and personalized education system, supporting lifelong learning and adapting to the needs and pace of each student. In this research, we focus on testing the model of AI acceptance in higher education (HE) through human interaction-based factors including attitudes, competencies and openness to experience. Perceived benefits were our expectation to enhance AI acceptance in HE. Design/methodology/approach: To test the model, we collected data from Arab HE institutions by spreading an online questionnaire. The sample consisted of 1,152 of teaching staff and students in Arab region, which were selected randomly. Partial least squares structural equation modeling (PLS-SEM) was employed to determine the interrelated dependence of relationships among variables. Furthermore, processing analysis was conducted to ensure the reliability and validity of questionnaires, multicollinearity and factor loading, in which the items were tested one more time to ensure their validity after translation into Arab language. Findings: Results reveal that adopted attitude, digital competency and openness to experience have positive and significant relationship with both perceived benefits and AI acceptance in HE in the Arab region. The results also demonstrate the indirect impact of digital factors on AI acceptance in existence of perceived benefits, which was important in the validation of the model. Originality/value: The research contributes to AI acceptance theory and research by providing evidence of AI acceptance in the Arab region. As generative AI applications continue to expand and change, the way we accept and interact with them will be different. This model could be adopted by authorities to facilitate the acceptance of AI in Arab HE institutions.

Despite the routine use of high-fidelity patient simulation (HFPS) in health care disciplines, pharmacy residents' involvement in HFPS is limited. To evaluate pharmacy residents' self-reported confidence in their clinical skills dealing with acute medical scenarios. Three separate HFPS sessions (stroke, Advanced cardiovascular life support [ACLS], and acetaminophen poisoning) were conducted for pharmacy residents at a designated simulation center using a high-fidelity mannequin. The scenarios were facilitated by experienced physicians, nurses, and clinical pharmacists. A questionnaire that addressed 5 clinical skills was administered to the residents pre- and post-HFPS and 6 months afterward. The primary outcome was an assessment of residents' confidence in acute medical scenarios. Fourteen pharmacy residents participated (mean age of 27.7 years; 71.4% female; 57% have [greater than or equal to]3 years of work experience as a pharmacist). Around 85.6%, 78.6%, and 64.3% of the residents had encountered stroke, ACLS, and acetaminophen poisoning cases, respectively, before the HFPS. After the HFPS sessions, the residents felt significantly more confident in making decisions during an emergency in a timely manner (P = 0.006 for stroke; P = 0.02 for poisoning), providing recommendations to the health care team (P = 0.006 for stroke; P = 0.024 for ACLS), and providing optimal patient care during a stressful situation (P = 0.02 for stroke and poisoning). We found no significant difference between post-simulation and 6 months post-simulation scores in most of the domains in all scenarios, indicating residents' confidence was maintained at 6 months. HFPS is a valuable active learning tool for enhancing pharmacy residents' confidence in managing an emergency situation.

Abstract Cryptococcal meningitis is a high-mortality infection. Adding 5-fluorocytosine (5-FC) to its treatment improves outcomes, but resistance to 5-FC presents a significant challenge. We conducted whole-genome sequencing on seven C. neoformans isolates with varying 5-FC susceptibility, along with proteomic and in silico analyses. Our findings indicate that mutations in genes of the pyrimidine salvage pathway are responsible for 5-FC resistance. Specifically, we identified an E64G missense mutation in the FUR1 gene, a large deletion in the FCY1 gene, and a point mutation in FCY1 leading to a truncated protein. The proteomic data indicated that these mutations resulted in the absence or reduction of crucial enzymes in resistant isolates. Genetic transformations confirmed the association between these mutations and 5-FC resistance. Resistance to 5-FC can develop during treatment and is closely tied to mutations in key metabolic enzymes. Understanding in vivo resistance development is crucial for combating resistance and enhancing patient outcomes.

The community's resilience in the face of natural hazards relies heavily on the rapid and efficient restoration of electric power networks, which plays a critical role in emergency response, economic recovery, and the functionality of essential lifeline and social infrastructure systems. Leveraging the recent data revolution, the digital twin (DT) concept emerges as a promising tool to enhance the effectiveness of post-disaster recovery efforts. This paper introduces a novel framework for post-hurricane electric power restoration using a hybrid DT approach that combines physics-based and data-driven models by utilizing a dynamic Bayesian network. By capturing the complexities of power system dynamics and incorporating the road network's influence, the framework offers a comprehensive methodology to guide real-time power restoration efforts in post-disaster scenarios. A discrete event simulation is conducted to demonstrate the proposed framework's efficacy. The study showcases how the electric power restoration DT can be monitored and updated in real-time, reflecting changing conditions and facilitating adaptive decision-making. Furthermore, it demonstrates the framework's flexibility to allow decision-makers to prioritize essential, residential, and business facilities and compare different restoration plans and their potential effect on the community.

Non-coding RNAs (ncRNAs) are a diverse group of functional RNA molecules that lack the ability to code for proteins. Despite missing this traditional role, ncRNAs have emerged as crucial regulators of various biological processes and have been implicated in the development and progression of many diseases, including cancer. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two prominent classes of ncRNAs that have emerged as key players in cancer pathophysiology. In particular, miR-21 has been reported to exhibit oncogenic roles in various forms of human cancer, including prostate, breast, lung, and colorectal cancer. In this context, miR-21 overexpression is closely associated with tumor proliferation, growth, invasion, angiogenesis, and chemoresistance, whereas miR-21 inactivation is linked to the regression of most tumor-related processes. Accordingly, miR-21 is a crucial modulator of various canonical oncogenic pathways such as PTEN/PI3K/Akt, Wnt/β-catenin, STAT, p53, MMP2, and MMP9. Moreover, interplays between lncRNA and miRNA further complicate the regulatory mechanisms underlying tumor development and progression. In this regard, several lncRNAs have been found to interact with miR-21 and, by functioning as competitive endogenous RNAs (ceRNAs) or miRNA sponges, can modulate cancer tumorigenesis. This work presents and discusses recent findings highlighting the roles and pathophysiological implications of the miR-21-lncRNA regulatory axis in cancer occurrence, development, and progression. The data collected indicate that specific lncRNAs, such as MEG3, CASC2, and GAS5, are strongly associated with miR-21 in various types of cancer, including gastric, cervical, lung, and glioma. Indeed, these lncRNAs are well-known tumor suppressors and are commonly downregulated in different types of tumors. Conversely, by modulating various mechanisms and oncogenic signaling pathways, their overexpression has been linked with preventing tumor formation and development. This review highlights the significance of these regulatory pathways in cancer and their potential for use in cancer therapy as diagnostic and prognostic markers.

Abstract Cybertwin (CT) is an innovative network structure that digitally simulates humans and items in a virtual environment, significantly influencing Cybertwin instances more than regular VMs. Cybertwin-driven networks, combined with Mobile Edge Computing (MEC), provide practical options for transmitting IoT-enabled data. This research introduces a hybrid methodology integrating deep learning with Cybertwin-driven resource allocation to enhance energy-efficient workload offloading and resource management in MEC networks. Offloading work is essential in MEC networks since several applications require significant resources. The Cybertwin-driven approach considers user mobility, virtualization, processing power, load migrations, and resource demand as crucial elements in the decision-making process for offloading. The model optimizes job allocation between on-premises and distant execution using a task-offloading strategy to reduce the operating burden on the MEC network. The model uses a hybrid partitioning approach and a cost function to optimize resource allocation efficiently. This cost function accounts for energy consumption and service delays associated with job assignment, execution, and fulfilment. The model calculates the cost of several segmentation and offloading procedures and chooses the lowest cost to enhance energy efficiency and performance. The approach employs a deep learning architecture called “CNN-LSTM-TL” to accomplish energy-efficient task offloading, utilizing pre-trained transfer learning models. Batch normalization is used to speed up model training and improve its robustness. The model is trained and assessed using an extensive mobile edge computing public dataset. The experimental findings confirm the efficacy of the proposed methodology, indicating a 20% decrease in energy usage compared to conventional methods while achieving comparable or superior performance levels. Simulation studies emphasize the advantages of incorporating Cybertwin-driven insights into resource allocation and workload-offloading techniques. This research enhances energy-efficient and resource-aware MEC networks by incorporating Cybertwin-driven techniques.

Abstract Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

The future electric loads in the Kingdom of Saudi Arabia (KSA) are increasing significantly, particularly in the Eastern Province of the KSA. These high-rise loads are primarily driven by the operational needs of the Saudi Arabian oil company Aramco, including oil refineries, and the infrastructures of the Saudi Basic Industries Corporation (SABIC) factories. This study aims to construct a nuclear power plant in that area to supplement and support the baseload currently covered by conventional generation units powered by fossil fuels within the Saudi Electricity Company (SEC) operations. The objective function is to minimize the operational costs of the power systems to the greatest extent possible. This paper describes a case study conducted using the IBM CPLEX Optimizer software to compare the operational costs of KSA’s power systems for a 24-h period. Two scenarios were considered and addressed: the first scenario without the inclusion of a nuclear power plant (NPP) and the second scenario with the inclusion of the NPP. The unit commitment problem was modeled for both scenarios. The obtained results revealed that the second scenario, which involved the penetration of the NPP, offered an optimal economic solution for operating KSA’s power systems. By employing the CPLEX Optimizer software and analyzing the unit commitment problem, this study provides valuable insights into the economic advantages of integrating the NPP into the power systems of the Kingdom of Saudi Arabia. The NPP shows viability in terms of minimizing the operational costs to 32.10 $/MWh compared to the first scenario where the operational costs were 42.10 $/MWh and resulted in almost 24% reduction in operational costs. In addition, the NPP is deemed as an optimal technology to contribute to the net zero goal by 2060, where it can reduce the reliance on fossil fuel power plants and contribute to the reduction of CO2 emissions.