We investigate the transverse momentum spectra of identified particles at 7 TeV and 13 TeV in pp collisions in the framework of the blast wave model with Tsallis statistics (TBW). Based on experimental data by ALICE C...
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The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen ga...
The comparison on the performance of interdigitated electrode (IDE) graphite and carbon nanotube (CNT) using titanium dioxide-multiwall carbon nanotube (TiO2-MWCNT) composite as sensing materials to detect hydrogen gas is presented in this research work. Screen printing technique has been selected to deposited IDE graphite and CNT conductive pastes as the electrode thick film on a flexible substrate, which is continued by firing at 120°C each. TiO 2 -MWCNT paste was printed as a sensing layer on top of IDE, followed by firing at 350°C. FESEM was used to characterize the morphological analysis of the IDE film. The operating temperature was varied at 27°C, 50°C and 100°C and concentration varied from 100–1000 ppm for gas hydrogen. The gas sensor demonstrated as n-type gas characteristics where the response of the gas sensor produced increased current from the results. The results purpose when the sensor was exposed to hydrogen gas. Therefore, the ability of IDE graphite and CNT as an electrode to harvest the electron from sensing materials were successfully proven. The IDE CNT showed better response compare to IDE graphite, but both electrodes were able to sense 100 – 1000 ppm at the operating temperature of 27°C, 50°C and 100°C. The optimal of operating temperature for IDE graphite is 50°C and IDE CNT is at room temperature for the hydrogen gas detections in this research work.
Neurological conditions are a major source of movement disorders. Motion modelling and variability analysis have the potential to identify pathology but require profound data. We introduce a systematic dataset of 3D c...
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Cybersecurity is the biggest threat to the world economy. Nowadays, the internet is the main cause of increase the cybercrime and help the attacker to act to harm the victim system. The attacks and exploits are becomi...
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Machine learning method gives a learning technique that can be applied to extract information from data. Lots of researches are being conducted that involves machine learning techniques for medical diagnosis, predicti...
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This article has demonstrated the performance parameters optimization of the Giga passive optical networks (PONs) can enhanced with high transmission data rates with various modulation schemes. These modulation scheme...
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Swarm unmanned aerial vehicles (UAV) have been increasingly studied for delivering the contents in 6G networks. In this paper, we propose an energy-aware capacity and trajectory (ECT) optimisation solution for swarm U...
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ISBN:
(数字)9798331505073
ISBN:
(纸本)9798331505080
Swarm unmanned aerial vehicles (UAV) have been increasingly studied for delivering the contents in 6G networks. In this paper, we propose an energy-aware capacity and trajectory (ECT) optimisation solution for swarm UAV-assisted edge content delivery networks (eCDN). The ECT optimisation solution enables a swarm UAV system to cooperatively fly 1) to the optimal set of stops and serve the user equipments in different clusters at maximum content delivery capacity and 2) in the optimal trajectory planning at minimum distance, under the constraint of energy resource. However, the ECT optimisation problem is challenging due to multiple objectives with respect to different optimisation variable types and complicated constraints. Genetic algorithms (GA) are therefore modified to address the challenge of solving the so-called multi-objective optimisation problem feasibly. Simulation results are shown to demonstrate the feasibility of GA and the benefits of ECT method for swarm UAV-assisted eCDN.
We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational map is based on partial differential equations and, consequently, is computationally...
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We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational map is based on partial differential equations and, consequently, is computationally expensive to evaluate. A-optimality is a widely used and easy-to-interpret criterion for Bayesian experimental design. This criterion seeks the optimal experimental design by minimizing the expected conditional variance, which is also known as the expected posterior variance. This study presents a novel likelihood-free approach to the A-optimal experimental design that does not require sampling or integrating the Bayesian posterior distribution. Our proposed approach is developed based on two properties of the conditional expectation: the law of total variance and the property of orthogonal projection. The expected conditional variance is obtained via the variance of the conditional expectation using the law of total variance, and we take advantage of the orthogonal projection property to approximate the conditional expectation. We derive an asymptotic error estimation for the proposed estimator of the expected conditional variance and show that the intractability of the posterior distribution does not affect the performance of our approach. We use an artificial neural network (ANN) to approximate the nonlinear conditional expectation in the implementation of our method. We then extend our approach for dealing with the case that the domain of experimental design parameters is continuous by integrating the training process of the ANN into minimizing the expected conditional variance. Specifically, we propose a nonlocal approximation of the conditional expectation and apply transfer learning to reduce the number of evaluations of the observation model. Through numerical experiments, we demonstrate that our method greatly reduces the number of observation model evaluations compared with widely used importance sampling-based approaches. This reduction is c
The Jagiellonian Positron Emission Tomograph (J-PET) is a novel PET device that, in contrast to commercial PET scanners, is based on plastic scintillator strips. Modular J-PET is the latest prototype that consists of ...
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The nonextensive nature of black holes is one of the most intriguing discoveries. In fact, the black hole entropy is a nonextensive quantity that scales by its surface area at the event horizon. In our work, we extend...
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