Multi-Objective Task Scheduling Approach for Fog Computing

Despite the remarkable work conducted to improve fog computing applications’ efficiency, the task scheduling problem in such an environment is still a big challenge. Optimizing the task scheduling in these applications, i.e. critical healthcare applications, smart cities, and transportation is urgent to save energy, improve the quality of service, reduce the carbon emission rate, and improve the flow time. As proposed in much recent work, dealing with this problem as a single objective problem did not get the desired results. As a result, this paper presents a new multi-objective approach based on integrating the marine predator’s algorithm with the polynomial mutation mechanism (MHMPA) for task scheduling in fog computing environments. In the proposed algorithm, a trade-off between the makespan and the carbon emission ratio based on the Pareto optimality is produced. An external archive is utilized to store the non-dominated solutions generated from the optimization process. Also, another improved version based on the marine predator’s algorithm (MIMPA) by using the Cauchy distribution instead of the Gaussian distribution with the levy Flight to increase the algorithm’s convergence with avoiding stuck into local minima as possible is investigated in this manuscript. The experimental outcomes proved the superiority of the MIMPA over the standard one under various performance metrics. However, the MIMPA couldn’t overcome the MHMPA even after integrating the polynomial mutation strategy with the improved version. Furthermore, several well-known robust multi-objective optimization algorithms are used to test the efficacy of the proposed method. The experiment outcomes show that MHMPA could achieve better outcomes for the various employed performance metrics: Flow time, carbon emission rate, energy, and makespan with an improvement percentage of 414, 27257.46, 64151, and 2 for those metrics, respectively, compared to the second-best compared algorithm

Operating of Gasoline Engine Using Naphtha and Octane Boosters from Waste as Fuel Additives

Fuel quality is an important indicator for the suitability of alternative fuel for the utilization in internal combustion (IC) engines. In this paper, light naphtha and fusel oil have been introduced as fuel additives for local low octane gasoline to operate a spark ignition (SI) engine. Investigated fuel samples have been prepared based on volume and denoted as GN10 (90% local gasoline and 10% naphtha), GF10 (90% local gasoline and 10% fusel oil), and GN5F5 (90% local gasoline, 5% naphtha and 5% fusel oil) in addition to G100 (Pure local gasoline). Engine tests have been conducted to evaluate engine performance and exhaust emissions at increasing speed and constant wide throttle opening (WTO). The study results reveal varying engine performance obtained with GN10 and GF10 with increasing engine speed compared to local gasoline fuel (G). Moreover, GN5F5 shows higher brake power, lower brake specific fuel consumption, and higher brake thermal efficiency compared to other investigated fuel samples over the whole engine speed. The higher CO and CO2 emissions were obtained with GN10 and GF10, respectively, over the entire engine speed and the minimum CO emissions observed with GN5F5. Moreover, the higher NOx emission was observed with pure local gasoline while the lowest was observed with GF10. On the other hand, GN5F5 shows slightly higher NOx emissions than GF10, which is lower than GN10 and gasoline. Accordingly, GN5F5 shows better engine performance and exhaust emissions, which can enhance the local low gasoline fuel quality using the locally available fuel additives. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This project has been funded by King Saud University, Riyadh, Saudi Arabia under project number RSP‐2021/167

Photovoltaic Thermal Collectors Integrated with Phase Change Materials: A Comprehensive Analysis

The target of the current study was to review and analyze the research activities of previous studies on cooling techniques for thermal photovoltaic (PV) systems using phase-change materials. These materials have the ability to absorb and release certain amounts of potential heat energy by changing their state from phase to phase (solid–liquid) within a small temperature range. These materials have been used to regulate and lower the temperature, increase the efficiency, and extend the life of solar cells. A host of improvements have been made to phase-changing materials through the combined utilization of phase-change materials and fins in addition to nanoscale fluids to enhance electrical efficiency. When using PCMs, the thermal, electrical, and overall efficiency improved by 26.87%, 17.33%, and 40.59%, respectively. The addition of nanomaterials increased phase-change materials’ specific heat capacity and thermal conductivity, thus reducing the plate temperature and increasing the electrical efficiency. It was found that using of nanoparticles together with a microcapsule had better performance in terms of energy efficiency. Studies indicated that variable phase materials were not used because of their high cost and lack of stable operational design. Therefore, the effect of phase-change materials on PV/thermal (PVT) system performance needs further investigation and study. © by the authors. Licensee MDPI, Basel, Switzerland.Funding: This project is funded by King Saud University, Riyadh, Saudi Arabia

Enhancement of the Evaporation and Condensation Processes of a Solar Still with an Ultrasound Cotton Tent and a Thermoelectric Cooling Chamber

In this paper, an experimental investigation study was conducted to show the effect of enhancing the evaporation and condensation processes inside a modified solar still by placing ultrasonic humidifiers inside a cotton mesh tent in the basin water and by installing a cooling chamber with thermoelectric elements on top of the solar still. Various parameters were recorded every hour, such as temperatures at different points within the solar still, the weather conditions (e.g., solar irradiance intensity, ambient air temperature, and wind speed), the yield of distilled water, and thermal efficiency on 29 July 2021 at the Ural Federal University (Russia). The production cost of distilled water from modified and traditional solar stills was also estimated. The experimental results showed that the productivity of the modified solar still increased by 124% compared with the traditional solar still, and the highest thermal efficiency was recorded at 2:00 p.m. (approximately 95.8% and 35.6% for modified and traditional solar stills, respectively). Finally, the productivity cost of distillate water (1 L) was approximately 0.040 and 0.042 $/L for the modified and traditional solar stills, respectively. The current work has contributed to increasing solar still productivity by applying simple and new technologies with the lowest possible capital and operational costs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI21C1831) and the Soonchunhyang University Research Fund

Utilization of additive from waste products with gasoline fuel to operate spark ignition engine

Impacts of blending fusel oil with gasoline on fuel combustion have been investigated experimentally in the current research to evaluate engine performance improvement and exhaust emission. Tested fuel include F10, F20 (10% and 20% of fusel oil by volume) and pure gasoline as baseline fuel have been used to operate 4-cylinder SI engine at increasing engine speed and constant throttle valve of 45%. The present results reveal a shorter combustion duration and better engine performance with F10 over engine speeds with maximum value of 33.9% for the engine brake thermal efficiency. The lowest BSFC of 251 g/kW h was recorded at 3500 rpm engine speed also with F10. All blended fuel have almost similar COVIMEP. Less NOx emission was measured with F10 at 4500 engine speed compared to gasoline. However, CO emissions reduced while higher CO2 was observed with introducing fusel oil in the blend. Moreover, HC emission increased an average by 11% over speed range and the highest value was achieved with 10% fusel oil addition compared to 20% and pure gasoline. Accordingly, higher oxygen content of fusel oil and octane number contribute to improve combustion of fuel mixture. © 2022, The Author(s).King Saud University, KSUThis Project is funded by King Saud University, Riyadh, Saudi Arabia

Speed Control of a Multi-Motor System Based on Fuzzy Neural Model Reference Method

The direct-current (DC) motor has been widely utilized in many industrial applications, such as a multi-motor system, due to its excellent speed control features regardless of its greater maintenance costs. A synchronous regulator is utilized to verify the response of the speed control. The motor speed can be improved utilizing artificial intelligence techniques, for example fuzzy neural networks (FNNs). These networks can be learned and predicted, and they are useful when dealing with nonlinear systems or when severe turbulence occurs. This work aims to design an FNN based on a model reference controller for separately excited DC motor drive systems, which will be applied in a multi-machine system with two DC motors. The MATLAB/Simulink software package has been used to implement the FNMR and investigate the performance of the multi-DC motor. moreover, the online training based on the backpropagation algorithm has been utilized. The obtained results were good for improving the speed response, synchronizing the motors, and applying load during the work of the motors compared to the traditional PI control method. Finally, the multi-motor system that was controlled by the proposed method has been improved where its speed was not affected by the disturbance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Taif University, TU: TURSP-2020/211Funding: This research was funded by Taif University, project number (TURSP-2020/211), Taif University, Taif, Saudi Arabia

A novel approach for detecting deep fake videos using graph neural network

Abstract Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs

Blockchain-enabled K-harmonic framework for industrial IoT-based systems

Abstract Industrial Internet of Things (IIoT)-based systems have become an important part of industry consortium systems because of their rapid growth and wide-ranging application. Various physical objects that are interconnected in the IIoT network communicate with each other and simplify the process of decision-making by observing and analyzing the surrounding environment. While making such intelligent decisions, devices need to transfer and communicate data with each other. However, as devices involved in IIoT networks grow and the methods of connections diversify, the traditional security frameworks face many shortcomings, including vulnerabilities to attack, lags in data, sharing data, and lack of proper authentication. Blockchain technology has the potential to empower safe data distribution of big data generated by the IIoT. Prevailing data-sharing methods in blockchain only concentrate on the data interchanging among parties, not on the efficiency in sharing, and storing. Hence an element-based K-harmonic means clustering algorithm (CA) is proposed for the effective sharing of data among the entities along with an algorithm named underweight data block (UDB) for overcoming the obstacle of storage space. The performance metrics considered for the evaluation of the proposed framework are the sum of squared error (SSE), time complexity with respect to different m values, and storage complexity with CPU utilization. The results have experimented with MATLAB 2018a simulation environment. The proposed model has better sharing, and storing based on blockchain technology, which is appropriate IIoT