Global time differences in road traffic injuries among children and adolescents between and 1990 and 2013: Regional and economical perspectives from global burden of diseases study by Uzma Rahim Khan (19794408)

“…</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Journal of Local and Global Health Science, title discontinued as of (2017)<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.5339/jlghs.2015.itma.62" target="_blank">https://dx.doi.org/10.5339/jlghs.2015.itma.62</a></p>…”

Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA by Muhammad Shehab (16904880)

Secrecy Performance of a RIS-Assisted Wireless Network: A Comprehensive Analysis Under Outdated CSI by Tasneem Alshamaseen (22392382)

“…<p dir="ltr">Reconfigurable intelligent surfaces have manifested notable merits in enhancing networks’ security from a physical layer perspective by leveraging smart genuine signal reflection using its reflective elements (REs) towards legitimate users. …”

Spatial, temporal, and demographic patterns in prevalence of chewing tobacco use in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Diseas... by Parkes J Kendrick (9714851)

“…</p><h3>Findings </h3><p dir="ltr">In 2019, 273·9 million (95% uncertainty interval 258·5 to 290·9) people aged 15 years and older used chewing tobacco, and the global age-standardised prevalence of chewing tobacco use was 4·72% (4·46 to 5·01). 228·2 million (213·6 to 244·7; 83·29% [82·15 to 84·42]) chewing tobacco users lived in the south Asia region. …”

Electric vehicles charging management using deep reinforcement learning considering vehicle-to-grid operation and battery degradation by Mostafa M., Shibl

“…In this paper, a deep RL-based EVs charging management solution is presented, while considering fast charging, conventional charging and V2G operation, in order to satisfy the requirements of the user and the utility. …”
Get full text
Get full text
Get full text

Electric vehicles charging management using deep reinforcement learning considering vehicle-to-grid operation and battery degradation by Mostafa M. Shibl (17821382)

“…In this paper, a deep RL-based EVs charging management solution is presented, while considering fast charging, conventional charging and V2G operation, in order to satisfy the requirements of the user and the utility. …”

Dispatchable capacity optimization strategy for battery swapping and charging station aggregators to participate in grid operations by Mingze Zhang (9947863)

“…</p><h2>Other Information</h2> <p> Published in: Energy Reports<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2023.07.022" target="_blank">https://dx.doi.org/10.1016/j.egyr.2023.07.022</a></p>…”

Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution by Kashif Bilal (16896357)

“…</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22031092" target="_blank">https://dx.doi.org/10.3390/s22031092</a></p>…”

Anabolic steroids-induced delirium by Mohamed Adil Shah Khoodoruth (14589828)

“…Laboratory results showed a decreased plasma testosterone level of 9.59 nmol/l (10.4–37.4 nmol/l). …”

Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers by Mostafa Shibl (18810412)

“…Due to its ability to use historical data to learn and identify patterns for making future decisions with minimal user intervention, ML has been utilized. ML models used in this paper are (1) Decision Tree (DT), (2) Random Forest (RF), (3) Support Vector Machine (SVM), (4) Naïve Bayes (NB), (5) K-Nearest Neighbors (KNN), (6) Deep Neural Networks (DNN), and (7) Long Short-Term Memory (LSTM). …”