In this study, Al-doped Cu2-xAlxZnSnS4 (x = 0%, 1%, 2%, 3%, and 4%) nanoparticles have forged using a simple hydrothermal method. The effect of Al doping on the structural, morphological, and ...thermoelectric properties have investigated and it has found that Al occupies the Cu site and acts as an acceptor dopant. As compared to un-doped Cu2ZnSnS4, the electrical conductivity and Seebeck coefficient improved with the increase in Al doping concentration leads to a noticeable improvement in power factor. Meanwhile, increased grain size with the doping of higher radius atoms causes the improvement in the mobility of the charge carriers and hence electrical conductivity (27.31–36.1 S/cm). The Seebeck coefficient value has also improved by the energy filtering effect at the grain boundaries. As a result, the Al-doped sample (4%) has achieved maximum power factor value (1.82 × 10−7 Wm−1K−2), which is roughly 35% higher than pure CZTS.
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Applications of Quantum Computing in Health Sector Arshad, Muhammad Waqas; Murtza, Iqbal; Arshad, Muhammad Abdullah
Journal of Data Science and Intelligent Systems,
02/2023, Volume:
1, Issue:
1
Journal Article
The purpose of this paper is to provide an overview of the current state of quantum computing in the health sector and to explore its potential future applications. Quantum computing has the ...potential to revolutionize a wide range of industries, including healthcare, by greatly enhancing the speed and accuracy of various tasks such as drug discovery, personalized medicine, and medical imaging. A literature review of the existing literature on the use of quantum computing in the health sector is conducted, revealing the various applications of quantum computing in the health sector and the current state of research in this area. The paper concludes that while the technology is still in its early stages of development, quantum computing has the potential to revolutionize the health sector, however, further studies are needed to fully understand the implications of quantum computing in healthcare.
Drones are unmanned aerial vehicles (UAV) utilized for a broad range of functions, including delivery, aerial surveillance, traffic monitoring, architecture monitoring, and even War-field. Drones ...confront significant obstacles while navigating independently in complex and highly dynamic environments. Moreover, the targeted objects within a dynamic environment have irregular morphology, occlusion, and minor contrast variation with the background. In this regard, a novel deep Convolutional Neural Network(CNN) based data-driven strategy is proposed for drone navigation in the complex and dynamic environment. The proposed Drone Split-Transform-and-Merge Region-and-Edge (Drone-STM-RENet) CNN is comprised of convolutional blocks where each block methodically implements region and edge operations to preserve a diverse set of targeted properties at multi-levels, especially in the congested environment. In each block, the systematic implementation of the average and max-pooling operations can deal with the region homogeneity and edge properties. Additionally, these convolutional blocks are merged at a multi-level to learn texture variation that efficiently discriminates the target from the background and helps obstacle avoidance. Finally, the Drone-STM-RENet generates steering angle and collision probability for each input image to control the drone moving while avoiding hindrances and allowing the UAV to spot risky situations and respond quickly, respectively. The proposed Drone-STM-RENet has been validated on two urban cars and bicycles datasets: udacity and collision-sequence, and achieved considerable performance in terms of explained variance (0.99), recall (95.47%), accuracy (96.26%), and F-score (91.95%). The promising performance of Drone-STM-RENet on urban road datasets suggests that the proposed model is generalizable and can be deployed for real-time autonomous drones navigation and real-world flights.
