Browsing by Author "Alghamdi, Ali"

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Medication Use and Cognitive Outcomes in Older Adults
(Saudi Digital Library, 2023) Alghamdi, Ali; Hak, Eelko; van Munster, Barbara
Abstract: This thesis investigated whether existing medications could be repurposed to help prevent or delay Alzheimer’s disease (AD), the most common form of dementia worldwide. Because current Alzheimer’s treatments only relieve symptoms and do not stop disease progression, there is an urgent need for new preventive strategies. Developing entirely new drugs is costly and time-consuming, so this research focused on “drug repurposing,” which means finding new uses for already approved medicines. The research used large healthcare databases in the Netherlands, including the IADB.nl prescription database and the Lifelines population cohort. Several medication groups were studied, including cardiovascular drugs, respiratory drugs, and antiviral medications. The thesis also examined patterns of polypharmacy, defined as the use of multiple medications, in older adults before an Alzheimer’s diagnosis. The findings showed that people later diagnosed with Alzheimer’s disease used more medications overall and were more frequently exposed to drugs that may negatively affect cognition. Studies on beta-blockers and beta-agonists showed mixed and inconclusive results regarding cognitive protection. However, the strongest findings involved antiherpetic drugs such as valacyclovir, acyclovir, and famciclovir. These medications were consistently associated with a lower risk of later Alzheimer’s treatment initiation. Evidence from herpes zoster vaccination studies also suggested a reduced risk of dementia. Overall, the thesis supports the idea that some existing medications, particularly antiviral therapies, may have potential in Alzheimer’s disease prevention and deserve further clinical investigation.
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MSc Dissertation Report
(Sheffield Hallam University, 2024-09-05) Alghamdi, Ali; Shobayo, Olamilekan
The present study assesses how Big Data technology has affected healthcare with regard to telemedicine and demographic disparities among Medicare beneficiaries. This research has discovered large demographic variations in the use of telehealth across many demographic variables, alluding to the fact that it is oriented toward women, urban residents, and non-Hispanic whites more than their counterparts. It is demographic insight that allows one to understand the need for targeted interventions to close the gap in telehealth access and use. Among the important recommendations based on the findings are those that aim at improving the enhancement of digital literacy and access among older adults. This is attained through the development of user-friendly telehealth platforms, community-based training in telehealth, and support systems made up of family members and caregivers. It is also critical to invest more in broadband infrastructure, alter policies for reimbursement parity, and implement mobile health units across rural areas in order to reduce the urban-rural disparities with regard to telehealth usage. This involves the promotion of gender-inclusive strategies, mainly through the reduction of racial and ethnic disparities related to telehealth adoption. The techniques in achieving this include targeted outreach programs, training of healthcare providers to promote the use of telehealth in men with prostate cancer, and culturally competent programs that have the capacity to take care of various racial and ethnic groups. Further monitoring and richer data analysis may continue to shed light on the barriers to telehealth use across demographic groups, further ensuring there is equal access to care.
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Resilience enhancement of post-disaster power distribution systems using Deep Reinforcement Learning
(Saudi Digital Library, 2026) Alotaibi, Raed; Zohdy, Mohamed; Kaur, Amanpreet; Alghamdi, Ali; Edwards, William; Al-Salman, Zeina
Weather-driven extreme events are placing growing stress on aging distribution infrastructure and increasingly threaten continuity of service for critical loads during prolonged outages. Microgrids can enhance resilience by transitioning to islanded operations and supplying prioritized loads with local distributed energy resources (DERs); however, post-disaster restoration remains challenging because operators must make coupled discrete–continuous decisions under tight resource and operating constraints. This dissertation addressed this challenge by developing a parameterized deep reinforcement learning controller, PDQN-CLR, that targets priority-weighted restoration while enforcing operational feasibility under scarcity. The PDQN-CLR modeled restoration as a hybrid action in which a discrete operational category was selected and paired with a continuous parameter vector specifying the DER real and reactive power setpoints. The approach was evaluated in a closed-loop OpenDSS environment using the IEEE 123-node feeder configured as an islanded microgrid with five grid-forming battery energy storage systems and 17 prioritized critical loads over a 72-step (36-hour) horizon with a 30-minute decision interval. Snapshot power-flow evaluation was performed at each step. Uncertainty was represented through capacity-factor derating under sufficient and scarce regimes, and each episode randomized the fault scenario, derating level, and initial state of charge. This dissertation also introduced (i) an uncertainty-aware evaluation protocol based on capacity-factor derating; (ii) a three-tier priority-weighted reward to encode the critical-load hierarchy; and (iii) a DER-aware load service rule that reduced voltage-only overstatement in islanded operation. With sufficient resources, PDQN-CLR achieved a mean PCS of 0.94 versus 0.80 for a Greedy baseline, while maintaining a low constraint violation score (CVS) of approximately 0.009 in both sufficient and scarce regimes. The baseline produced substantially larger violation magnitudes (CVS ≈ 0.388–0.667), indicating more severe and/or more frequent exceedances of the constraints. These results indicate that parameterized deep reinforcement learning can improve priority-weighted restoration when capacity is available and preserve feasibility as a primary outcome when scarcity limits achievable restoration.
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