Visualization of Heliostat Field of Solar Thermal Tower Power Plant Using Virtual Reality (VR) Technologies

An important part of future global energy depends on the development of the solar industry. To date, we have noticed the shift from fossil fuels energy towards renewable energy. The past decade has shown significant progress in computer science, and CAD is increasingly used for design and development. Visualization of the data generated from the models in the CAD program plays an important role in the creation of state-of-the-art designs. An important limitation during the design phase is the visualization of three-dimensional geometry. This article attempts to illustrate the use of VR technologies in solar thermal power plant development. This article analyzes various strategies and methods for the visualization of CAD models in virtual reality. Android phone interfaces with a desktop computer, as well as head movement control strategies, are discussed. It is concluded that VR technologies can help with visualization, as well as in the development of the field of solar thermal power plants, having minimal design-related issues.

E-learning Facilities for Teaching Secondary School Physics: Awareness, Availability and Utilization

Secondary school physics contribute significantly to the technological development of a nation because it lays the foundation for further studies in physics. Physics is an abstract science subject that relies greatly on practical, and the crucial role played by the use of instructional materials especially, e-learning facilities in this 21st century cannot be over emphasized. This study investigates the awareness, availability and utilization of e-learning facilities among secondary school physics teachers. We adopt the descriptive survey design which involved 78 physics teachers, randomly selected from all the secondary schools in Ondo, Nigeria. A researcher designed checklist was used to collect data for this study. Three research questions were answered using descriptive statistics (frequency, percentage, mean, standard deviation) and the findings from this investigation revealed that physics teachers are aware of most of the e-learning facilities for teaching secondary school physics but only few of these facilities are readily available for teaching physics. This investigation also revealed that only one of these e-learning facilities (desktop computer) was utilized by secondary school physics teachers. This study conclude that e-learning facilities were not used in teaching secondary school physics in Ondo. Based on this findings, physics teachers are encouraged to use the few e-learning facilities that are available in secondary schools. Also, government education agencies and school management team are encouraged to further stress the importance of using the available e-learning facilities for teaching secondary school physics through in-service trainings and workshops.

Identifying protein sites contributing to vaccine escape via statistical comparisons of short-term molecular dynamics simulations

The identification of viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a molecular dynamic (MD) based approach that scales well to a desktop computer with a high-end modern graphics processor and enables the user to identify protein sites that are prone to vaccine escape in a viral antigen. We first implemented our MD pipeline to employ site-wise calculation of Kullback-Leibler divergence in atom fluctuation over replicate sets of short-term MD production runs to compare influenza hemagglutinin’s rapid motions in the presence and absence of three well-known neutralizing antibodies. Using this simple comparative method applied to motions of viral proteins, we successfully identified in silico all previously empirically confirmed sites of escape in hemagglutinin, predetermined via selection experiments and neutralization assays. After this validation of our computational approach, we identified potential hot spot residues in the receptor binding domain (RBD) of the SARS-CoV-2 virus in the presence of COVOX-222 and S2H97 antibodies. We identified sites in the antigen-antibody interface with strong dampening of fluctuation that may indicate potential antibody escape due to single mutations. Many of these sites were found to match known sites of mutations in SARS-CoV-2 variants of concern. The determination of single sites with large effect on antigen-antibody binding interfaces is crucial to discriminating neutral variants from potential escape variants. In summary, we provide a cheap, fast, and accurate in silico method to identify and quantify potential hot spots of functional evolution in antibody binding footprints.

Analysis of Athletes ' Views on Digital Games

The aim of this research is to determine athletes ' views on digital games and to determine their relationship to certain variables. 1521 (927 male, 594 female) athletes who continue their activities in different branches constitute the sample of the research. The research is in the screening model and has a descriptive quality. As a data collection tool, an online questionnaire was applied in which participants' demographic information, frequency of game play, the duration of game play with either mobile phone and desktop computer, and their digital game play status were surveyed. The data was analyzed using the SPSS statistical package program and the significance level was accepted as p<0.05. As a result, 60.9% of the research group were male, 35.5% were between the ages of 21 and 30, 59.2% were students, and 61.5% were undergraduates or graduates. 36.9% of participants were observed to play sports between 1-5 years, 27.5% were observed to play sports between 6-10 years, and 62.9% of them still continued their sports activities as athletes. In the study, it was determined that athletes played games in moderate frequency and preferred to play games at higher rates, daily, using their mobile phones. Participants expressed that they liked to play games and wanted to play again when they were successful in those games. Athletes stated that games are addictive, games with violent elements negatively affect people, and while excessive gameplay negatively affects social life, games also help them to relieve their boredom. They believe that games which are played with friends and family, etc. improved social skills, and they noted that games can be used for educational purposes, and that playing games helps them learn something new, but is mostly effective in filling out free time. Athletes also noted that they did not believe that playing digital games improved their athletic abilities specific to their sport in any way. A statistically significant difference was found when athletes' views on gender and digital games were examined. It was determined that there were statistically significant differences according to age, profession, year of playing sports, level in sports branch, frequency of playing games, duration of playing games with mobile phones, duration of playing games with computers, and that there were no statistically significant difference according to educational status. As a result, it was determined that the athletes in the research group played digital games with medium frequency and liked to play games. We believe that it will be useful for athletes to use digital games, the internet and social media to develop their skills in their branch of sports. Keywords: Digital game, sports and Digital Game, athletes and digital game.

ROA-CONS: Raccoon Optimization for Job Scheduling

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

Iterative Deepening Dynamically Improved Bounds Bidirectional Search

This paper presents a new bidirectional search algorithm to solve the shortest path problem. The new algorithm uses an iterative deepening technique with a consistent heuristic to improve lower bounds on path costs. The new algorithm contains a novel technique of filtering nodes to significantly reduce the memory requirements. Computational experiments on the pancake problem, sliding tile problem, and Rubik’s cube show that the new algorithm uses significantly less memory and executes faster than A* and other state-of-the-art bidirectional algorithms. Summary of Contribution: Quickly solving single-source shortest path problems on graphs is important for pathfinding applications and is a core problem in both artificial intelligence and operations research. This paper attempts to solve large problems that do not easily fit into the available memory of a desktop computer, such as finding the optimal shortest set of moves to solve a Rubik’s cube, and solve them faster than existing algorithms.

Real-time super-resolution mapping of locally anisotropic grain orientations for ultrasonic non-destructive evaluation of crystalline material

Estimating the spatially varying microstructures of heterogeneous and locally anisotropic media non-destructively is necessary for the accurate detection of flaws and reliable monitoring of manufacturing processes. Conventional algorithms used for solving this inverse problem come with significant computational cost, particularly in the case of high-dimensional, nonlinear tomographic problems, and are thus not suitable for near-real-time applications. In this paper, for the first time, we propose a framework which uses deep neural networks (DNNs) with full aperture, pitch-catch and pulse-echo transducer configurations, to reconstruct material maps of crystallographic orientation. We also present the first application of generative adversarial networks (GANs) to achieve super-resolution of ultrasonic tomographic images, providing a factor-four increase in image resolution and up to a 50% increase in structural similarity. The importance of including appropriate prior knowledge in the GAN training data set to increase inversion accuracy is demonstrated: known information about the material’s structure should be represented in the training data. We show that after a computationally expensive training process, the DNNs and GANs can be used in less than 1 second (0.9 s on a standard desktop computer) to provide a high-resolution map of the material’s grain orientations, addressing the challenge of significant computational cost faced by conventional tomography algorithms.

FPGA-based Neural Net for Failures Prediction in the Cold Forging Process

This paper presents and discusses the implementation of deep neural network for the purpose of failure prediction in the cold forging process. The implementation consists of an LSTM and a dense layer implemented on FPGA. The network was trained beforehand on Desktop Computer using Keras library for Python and the weights and the biases were embedded into the implementation. The implementation is executed using the DSP blocks, available via Vivado Design Suite, which are in compliance with the IEEE754 standard. The simulation of the network achieves 100% classification accuracy on the test data and high calculation speed.

Timelapse viability assay to detect division and death of primary multiple myeloma cells in response to drug treatments with single cell resolution

Heterogeneity in cancer cells and in the tumor microenvironment (TME) is considered to contribute to individual patient's clinical responses to different drugs at different points during therapy. However, there is a paucity of functional assays to analyze the heterogeneity in primary cell drug responses in the presence of patient's unique and varying TME components. Multiple myeloma (MM) is one example of a heterogenous cancer in genetic makeup, clinical manifestations, and therapy responses. To address the need to study cellular events and behaviors of drug-treated primary MM cells ex vivo in real time, we have developed a three-dimensional time-lapse drug sensitivity assay incorporating patient's own cell and soluble TME components and several new technologies. These include Java-based application of transport-of-intensity equation (TIE) on quantitative phase imaging (QPI), coupled with the use of Hoechst 33258, for noise reduction, label-free single-cell identification and robust quantification of cell division and death events, and open source JEX software package for objective image analysis and feasible data management of large timelapse experiments using a desktop computer. This time-lapse assay provides a new platform toward the development of a sensitive diagnostic tool which can model the tumor cell and TME heterogeneity to predict individual patient's therapeutic responses. New Discovery: As patient-to-patient heterogeneity is a fundamental barrier to MM clinical management, the development of this assay embraces heterogeneity to study cell death beyond survival endpoints and the technological advancements within the timelapse provides a quantitative and functional measure of the individual and combined influences of drugs and TME components within specific patient cells.

An Intelligent Wireless Charger Based on the Internet of Things

With the development of the times and the progress of science and technology, people continue to explore new possibilities; the leap from wired to wireless is one of them, breaking the limitations. Similar to wireless mice and wireless microphones, this paper aims to study an Internet of Things-based smart wireless charger that, by using mathematical analysis and wireless charging technology, requires experiments to include wireless charging stations, associated chip areas, and home gateways that are used in combination with them. In the experiment, the control circuit, transmit circuit, and reception circuit of wireless charger are studied, and the wireless charging system is analyzed and calculated in detail, and a wireless charger is designed. Electromagnetic field sensing, radio wave transmission, and resonance methods are applied to achieve the shortest charging time and the best quality in order to prevent the charger from heating and burning during charging. Experimental data show that this Internet of Things-based smart wireless charger can fully replace wired chargers for 99% of small appliances; the charging efficiency of large electrical appliances such as desktop computer is also 1.4 to 1.6 times increased. Experimental data show that this Internet of Things-based smart wireless charger can efficiently and conveniently charge electrical appliances; however it is relatively expensive; the required technology is relatively complex but can make charging more efficient. It can be seen that wireless charging has a major breakthrough in the concept of future charging.