Abstract Purpose: Mechanical thrombectomy (MT) is the gold standard for treating acute ischemic stroke. However, challenges such as operator radiation exposure, reliance on operator experience, and limited treatment access remain. Although autonomous robotics could mitigate some of these limitations, current research lacks benchmarking of reinforcement learning (RL) algorithms for MT. This study aims to evaluate the performance of Deep Deterministic Policy Gradient, Twin Delayed Deep Deterministic Policy Gradient, Soft Actor-Critic, and Proximal Policy Optimization for MT. Methods: Simulated endovascular interventions based on the open-source stEVE platform were employed to train and evaluate RL algorithms. We simulated navigation of a guidewire from the descending aorta to the supra-aortic arteries, a key phase in MT. The impact of tuning hyperparameters, such as learning rate and network size, was explored. Optimized hyperparameters were used for assessment on an MT benchmark. Results: Before tuning, Deep Deterministic Policy Gradient had the highest success rate at 80% with a procedure time of 6.87 s when navigating to the supra-aortic arteries. After tuning, Proximal Policy Optimization achieved the highest success rate at 84% with a procedure time of 5.08 s. On the MT benchmark, Twin Delayed Deep Deterministic Policy Gradient recorded the highest success rate at 68% with a procedure time of 214.05 s. Conclusion: This work advances autonomous endovascular navigation by establishing a benchmark for MT. The results emphasize the importance of hyperparameter tuning on the performance of RL algorithms. Future research should extend this benchmark to identify the most effective RL algorithm.

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Melanoma is the most deadly type of skin cancer, and its morbidity and fatality rates are rising globally. Melanoma has significant heterogeneity and a high potential for metastasis, which limits the effectiveness of currently available treatments, which were limited to chemotherapy, radiation, and surgery for several years. The creation of novel treatment classes, including immune checkpoint and small molecule kinase inhibitors, has been made possible by advances in our understanding of the pathophysiological mechanisms underlying the illness. Effectiveness is still far from ideal, despite the undeniable improvements in patients' quality of life and survival rates. The primary obstacles are a few negative side effects and resistance mechanisms. As a result, numerous clinical trials looking into novel medications and/or combinations have been prompted by the quest for better alternatives. Drugs' poor stability, quick metabolism, and limited water solubility restrict the clinical potential and medicinal applications of certain substances. Therefore, the investigation of nanotechnology-based approaches is being investigated as the foundation for the widespread use of various nanosystem types in melanoma treatment. The difficulties in comprehending the mechanisms that increase the effectiveness of these nanosystems will be the main focus of future research

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• Page : pp.5478-5489
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PV panels has been proven to be a good renewable energy source. However, excessive heat of the panel can lead to degradation of the longevity of the panel. Crucial determinants impacting the effectiveness of PV panels encompass the type of materials, temperature, and the level of solar radiation received. Thus, cooling is required to overcome this issue which leads to improved electrical efficiency and lifespan of the panel. This study provides an overview of different techniques that can be employed to mitigate the adverse effects of elevated temperatures while simultaneously improving the performance of photovoltaic solar panels operating above the recommended temperature of the Standard Test Conditions (STC). The objective of this review is to enhance comprehension of the mentioned technologies in order to decrease the surface temperature of the PV module. Cooling methods that are reviewed are heat exchanger, nanofluids and phase change material (PCM). The review and classification of many research publications is conducted based on their specific focus, contribution, and the sort of technology employed to facilitate the cooling of photovoltaic panels. Each of these systems is exemplified with precise schematics and extensively examined and compared. Moreover, this work presents a novel categorization system for the cooling techniques employed in photovoltaic panels, providing useful direction for future investigations and enhancing efficiency. The findings of this review shows that heat exchanger with higher flowrate has better higher temperature improvement. Moreover, different heat exchanger pipes shapes resulted in different cooling efficiency outcome. Hybrid nanofluids shows higher temperature drops compared to nanofluid with water. Addition of porous material to PCM resulted in a lower melting point thus cooling occurs faster compared to regular PCM.

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