Smart grids are complex cyber-physical systems that incorporate smart devices’ communication capabilities into the grid to enable remote management and the control of power systems. However, this integration reveals numerous SCADA system flaws, which could compromise security goals and pose severe cyber threats to the smart grid. In conventional works, various attack detection methodologies are developed to strengthen the security of smart grid SCADA systems. However, they have several issues with complexity, slow training speed, time consumption, and inaccurate prediction outcomes. The purpose of this work is to develop a novel security framework for protecting smart grid SCADA systems against harmful network vulnerabilities or intrusions. Therefore, the proposed work is motivated to develop an intelligent meta-heuristic-based Artificial Intelligence (AI) mechanism for securing IoT-SCADA systems. The proposed framework includes the stages of dataset normalization, Zaire Ebola Search Optimization (ZESO), and Deep Random Kernel Forest Classification (DRKFC). First, the original benchmarking datasets are normalized based on content characterization and category transformation during preprocessing. After that, the ZESO algorithm is deployed to select the most relevant features for increasing the training speed and accuracy of attack detection. Moreover, the DRKFC technique accurately categorizes the normal and attacking data flows based on the optimized feature set. During the evaluation, the performance of the proposed ZESO-DRKFC method is validated and compared in terms of accuracy, detection rate, f1-score, and false acceptance rate. According to the results, it is observed that the ZESO-DRKFC mechanism outperforms other techniques with high accuracy (99%) by precisely spotting intrusions in the smart grid systems.

Background: Individuals with dry eye disease (DED) may experience ocular symptoms, affecting their quality of life. DED is multifactorial and is related to age, gender, and other factors. While a study may contain examination results of DED symptoms in elderly populations, where electronic device use has contributed to DED, disease occurrence in younger populations, such as university students, remains unclear. Purpose: We evaluated DED frequency and risk factors for health science university students using electronic devices during online courses. Methods: This study is a cross-sectional study using a previously validated DED questionnaire Computer Vision Syndrome Questionnaire (CVSQ), which contains three parts: student demographics, electronic device information, and DED symptoms. We conducted an observational cross-sectional study of 359 health science students (aged 18 and above) at King Saud Bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia, between October 2022 and January 2023. We collected data on electronic device variables and DED symptoms in this cohort. Results: We recruited 359 male (29.2%) and female (70.8%) health science students. Participant ages ranged from 18–20 (62.4%) to 21–27 (37.6%) years old. Participants were asked 24 questions, which gathered information on electronic device type, screen time in online classes, occupational characteristics (smoking status), and associated eye symptoms, such as pain, redness, itchiness, dryness, and heavy eyelids. Based on most responses, the Tablet was the most used and preferred device (85%), with continuous use of the device during university courses/ activities (85.8%). The correlation of the survey parameters was only significantly related to gender and study years with DED symptoms. The highest eye symptom intensity levels (always to severe) indicated burning (6.7%) and dryness (8.6%) sensations. Lastly, a significant association was identified between DED symptoms and screen time, gender, and smoking status. Conclusion: DED symptoms were commonly reported in university students who used electronic devices. Our study provides valuable insights into electronic device usage duration impacted the ocular health of females and younger students, aiding in preventing dry eye disease risks and maintaining good eye health.

Purpose- In today's rapidly evolving business landscape, organizations are increasingly recognizing the significance of leveraging HR analytics to drive data-driven decision-making and enhance HR outcomes. However, the successful implementation of HR analytics hinges upon a careful consideration of various factors. This paper aims to explore and analyze the key factors that influence the successful implementation of HR analytics, drawing on a comprehensive literature review and expert insights. Methodology- A systematic literature review is conducted to examine the previous published articles in the HR analytics domain. The Emerald database revealed 37 articles that passed the inclusion criteria and quality assessment. Findings- The study identifies several critical factors that play a pivotal role in the successful implementation of HR analytics. Leadership support and organizational culture emerge as paramount drivers, as they foster a data-driven decision-making culture, emphasizing the value of evidence-based HR practices. Conclusion- These factors facilitate the integration of analytics into HR processes and ensure the commitment necessary to overcome implementation challenges. Keywords: HR analytics, leadership support, data quality, data integration, technology infrastructure, ethical considerations. JEL Codes: M50, M52

Background: Healthcare chatbots have emerged as a promising application of artificial intelligence in healthcare, offering potential benefits in accessibility, efficiency, and patient engagement. However, despite their growing adoption, limited research has examined the factors that determine their success from the user’s perspective. Objective: This study aimed to evaluate the success of a health chatbot service by applying the updated DeLone and McLean Information Systems Success Model augmented with a trust construct, examining the effects of information quality, system quality, service quality, and trust on intention to use, user satisfaction, and net benefits. Methods: An online survey design was employed, utilizing a structured questionnaire with 28 items measuring seven constructs on a seven-point Likert scale. Data were collected electronically from residents of Saudi Arabia between July and September 2024 using convenience sampling. Eligible participants were adults aged 18 years or older who had previously used the health chatbot service. A total of 321 valid responses were obtained. Partial Least Squares Structural Equation (PLS-SEM) was conducted using SmartPLS 3.3 software for measurement and structural model analysis. Results: The measurement model demonstrated acceptable reliability and validity, with composite reliability values exceeding 0.90 and average variance extracted values above 0.70 for all constructs. Structural model analysis supported eight of ten hypotheses. Trust exhibited the strongest effect on intention to use (β = 0.359, p < 0.001), followed by system quality (β = 0.234, p < 0.001) and information quality (β = 0.147, p < 0.01). Intention to use significantly predicted user satisfaction (β = 0.620, p < 0.001) and net benefits (β = 0.278, p < 0.001). User satisfaction demonstrated a strong positive effect on net benefits (β = 0.610, p < 0.001). The model explained 67.6% of the variance in intention to use, 72.7% in user satisfaction, and 71.4% in net benefits. Conclusions: Trust emerged as the most influential factor affecting intention to use the healthcare chatbot service, underscoring its critical role in user acceptance of health chatbot services. Information quality, system quality, and service quality exerted small to moderate effects on behavioral outcomes. These findings suggest that healthcare organizations deploying chatbot services should prioritize building user trust alongside ensuring high system and information quality to maximize user satisfaction and realized net benefits.