Purpose The aim of this study is to identify burnout prevalence among ophthalmology residents and the predisposing factors associated with higher levels of burnout. Methods A cross-sectional study was conducted on all ophthalmology residents in Saudi Arabia using Maslach Burnout Inventory in January 2018. Associations between Emotional Exhaustion scores and other continuous variables were evaluated using Spearman’s correlation coefficients. Logistic regression model was constructed, and results were reported as odds ratios with 95% confidence intervals. The significance level was set at p < 0.05. Results A total of 117 residents responded to the survey with a 70% response rate. The response rate was above 65% for each training programs by region. 41% of ophthalmology residents scored a positive burnout result on the common subscales (Emotional Exhaustion and/or Depersonalization). Further sub-analysis of data showed positive Spearman’s correlation with number of call days per month and EE subscale ( r 0.195). Multivariate logistic regression of the sample yielded significant results with satisfaction with work/life balance and choosing medicine again as a graduate level major p ≤ 0.05. The regression model also showed the Southern program had significantly higher burnout on the common subscales p ≤ 0.05. Conclusions Prevalence of burnout among ophthalmology residents was lower when compared to plastic surgery and otolaryngology residents in Saudi Arabia. Work hours and on call days were associated with higher burnout. Actions must be taken to ensure that all training programs implement work hour limitations. Special attention should be given to the Southern region program due to its significantly higher levels of burnout.

Background Application of Artificial Intelligence (AI) and the use of agent-based systems in the healthcare system have attracted various researchers to improve the efficiency and utility in the Electronic Health Records (EHR). Nowadays, one of the most important and creative developments is the integration of AI and Blockchain that is, Distributed Ledger Technology (DLT) to enable better and decentralized governance. Privacy and security is a critical piece in EHR implementation and/or adoption. Health records are updated every time a patient visits a doctor as they contain important information about the health and wellbeing of the patient and describes the history of care received during the past and to date. Therefore, such records are critical to research, hospitals, emergency rooms, healthcare laboratories, and even health insurance providers. Methods In this article, a platform employing the AI and the use of multi-agent based systems along with the DLT technology for privacy preservation is proposed. The emphasis of security and privacy is highlighted during the process of collecting, managing and distributing EHR data. Results This article aims to ensure privacy, integrity and security metrics of the electronic health records are met when such copies are not only immutable but also distributed. The findings of this work will help guide the development of further techniques using the combination of AI and multi-agent based systems backed by DLT technology for secure and effective handling EHR data. This proposed architecture uses various AI-based intelligent based agents and blockchain for providing privacy and security in EHR. Future enhancement in this work can be the addition of the biometric based systems for improved security.

Repowering a wind farm enhances its ability to generate electricity, allowing it to better utilize areas with high mean wind speeds. Pakistan’s present energy dilemma is a serious impediment to its economic development. The usage of a diesel generator as a dependable backup power source raises the cost of energy per kWh and increases environmental emissions. To minimize environmental emissions, grid-connected wind farms enhance the percentage of wind energy in the electricity system. These wind generators’ effects, on the other hand, are augmented by the absorption of greater quantities of reactive electricity from the grid. According to respective grid codes, integration of commercial onshore Large-Scale Wind Farms (LSWF) into a national grid is fraught with technical problems and inter-farm wake effects, which primarily ensure power quality while degrading overall system operation and limiting the optimal use of attainable wind resources. The goal of this study is to examine and estimate the techno-economic influence of large-scale wind farms linked to poor transmission systems in Pakistan, contemplating the inter-farm wake effect and reactive power diminution and compensating using a range of voltage-ampere reactive (VAR) devices. This study presents a partial repowering technique to address active power deficits produced by the wake effect by raising hub height by 20 m, which contributed to recovering the active power deficit to 48% and so reduced the effects of upstream wind farms. Simulations were conducted for several scenarios on an actual test system modeled in MATLAB for comparative study using capacitor banks and different flexible alternating current transmission system (FACTS) devices. Using the SAM (System Advisor Model) and RETscreen, a complete technical, economic, and environmental study was done based on energy fed into the grid, payback time, net present value (NPV), and greenhouse gases (GHG) emission reduction. The studies suggest that the unified power flow controller (UPFC) is the optimum compensating device via comparison analysis as it improved the power handling capabilities of the power system. Our best-case scenario includes UPFC with hub height augmentation, demonstrating that it is technically, fiscally, and environmentally viable. Over the course of its lifespan, the planned system has the potential to save 1,011,957 tCO2, resulting in a greener environment. When the energy generated annually by a current wake-affected system is compared to our best-recommended scenario, a recovered shortfall of 4.851% is seen, with improved system stability. This modest investment in repowering boosts energy production due to wake effects, resulting in increased NPV, revenue, and fewer CO2 footprints.

With the increasing number of cybercrimes, the digital forensics team has no choice but to implement more robust and resilient evidence-handling mechanisms. The capturing of digital evidence, which is a tangible and probative piece of information that can be presented in court and used in trial, is very challenging due to its volatility and improper handling procedures. When computer systems get compromised, digital forensics comes into play to analyze, discover, extract, and preserve all relevant evidence. Therefore, it is imperative to maintain efficient evidence management to guarantee the credibility and admissibility of digital evidence in a court of law. A critical component of this process is to utilize an adequate chain of custody (CoC) approach to preserve the evidence in its original state from compromise and/or contamination. In this paper, a practical and secure CustodyBlock (CB) model using private blockchain protocol and smart contracts to support the control, transfer, analysis, and preservation monitoring is proposed. The smart contracts in CB are utilized to enhance the model automation process for better and more secure evidence preservation and handling. A further research direction in terms of implementing blockchain-based evidence management ecosystems, and the implications on other different areas, are discussed.