يعد التعلم عن بعد أحد أساليب التدريس والتعلم التي تم تبنيها بعد جائحة كوفيد 19. مهمة جذب ومعرفة اهتمام المتعلمين إلى الفصل صعبة على الأساتذة. في هذا البحث، قمنا بإنشاء آلية لتقدير مستويات مشاركة الطلاب ومعرفة احاسيسهم حيث نستخدم البيانات المرئية من مقاطع الفيديو المرسلة من طرف الطلاب المشاركين في دورات التعلم نظرًا لوجود العديد من الطرق لقياس مستويات مشاركة الطلاب. نقوم بمعالجة هذه الفيديوهات لتحديد مقدار مشاركة الطلاب واكتشاف عواطفهم. لقد قمنا بإنشاء نظامنا وتجريبه، الذي مكننا من تقييم انتباه الطلاب وتحديد عواطفهم. توضح النتائج أنه من الممكن قياس المشاركة ومعرفة المشاعر استغلالها في اتخاذ قرارات في صالح نتائج الطلاب وتحسين طرق التعليم والتعلم. يمكن تطبيق هذه التقنية في سيناريوهات أخرى، مثل القيادة الذاتية والأمان مع تعديل بسيط.

State of charge estimation (SOC) is the most significant functionality of a vehicle's battery management system (BMS). The methods for this estimation are conventionally oriented towards model-based methods. As part of this paper, we introduce a first order equivalent circuit estimation approach known as the Thevenin model, along with an extended Kalman filter (EKF) approach to accurately estimate the SOC. We then deploy and simulate it in MATLAB by using a reference load profile from the new European driving cycle (NEDC). Afterwards, the simulation results are reviewed based on various initial noise values, and the results are compared to those of other EKF algorithms. According to the results, SOC estimation accuracy has significantly increased as a result of the improvements made. Specifically, the root-mean-square error decreased from 0.0068 to 0.0020.

<p>The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, therefore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version.</p>

Li-ion batteries (LiBs) are accurately estimated under varying operating conditions and external influences using extended Kalman filtering (EKF). Estimating the state of charge (SOC) is essential for enhancing battery efficiency, though complexities and unpredictability present obstacles. To address this issue, the paper proposes a second-order resistance-capacitance (RC) battery model and derives the EKF algorithm from it. The EKF approach is chosen for its ability to handle complex battery behaviors. Through extensive evaluation using a Simulink MATLAB program, the proposed EKF algorithm demonstrates remarkable accuracy and robustness in SOC estimation. The root mean square error (RMSE) analysis shows that SOC estimation errors range from only 0.30% to 2.47%, indicating substantial improvement over conventional methods. These results demonstrate the effectiveness of an EKF-based approach in overcoming external influences and providing precise SOC estimations to optimize battery management. In addition to enhancing battery performance, the results of the study may lead to the development of more reliable energy storage systems in the future. This will contribute to the wider adoption of LiBs in various applications.

Our goal is to set up a synthetic method to produce the hexaglycidyl amine tri-urea phosphoric acid (HGATUP), a novel hexafunctional prepolymer. We created this prepolymer by phosphorylating urea in two processes. Next, to generate the new prepolymer, we added epichlorohydrin. In addition, we focused on its microscopic characterization using FTIR and NMR. The novel HGATUP prepolymer was utilized as a corrosion inhibitor for E24 carbon steel in a 1.0M hydrochloric acid (HCl) solution. PDP, EIS, EFM, DFT, Monte Carlo (MC), and molecular dynamic (MD) simulations were used in this study. SEM/EDS and XPS studies were also used to investigate the surface morphology. These methods allowed us to examine the sample's surface characteristics and chemical composition, which added to our understanding of the adsorption along with corrosion prevention processes. The electrochemical tests' outcomes also showed that the concentration of HGATUP enhanced inhibitory performance. PDP results indicated that HGATUP at a concentration of 10-3M had a corrosion inhibition efficiency of 97.6% at 298K ambient temperature. The polarization curve confirmed that the HGATUP was a mixed type of inhibitor that could inhibit steel anodic and cathodic reactions. The Langmuir model predicted that an epoxy prepolymer protected the surface of E24 carbon steel. Calculated thermodynamic kinetic parameters were used to analyze the inhibitor adsorption processes.

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.

Corrosion is a pervasive issue with significant economic and safety implications across various industries. Nanoceramic-based coatings have emerged as a promising solution for corrosion protection due to their unique properties and mechanisms. This review aims to comprehensively examine the synthesis, mechanisms, and applications of nanoceramic-based coatings for corrosion protection. The review begins by highlighting the importance of corrosion protection and its impact on different industries. It introduces nanoceramic-based coatings as a potential solution to address this challenge. The objective is to provide a thorough understanding of the synthesis methods, mechanisms, and applications of these coatings. The fundamental principles of corrosion and different corrosion mechanisms are discussed, along with the limitations of traditional corrosion protection methods. The review emphasizes how nanoceramic-based coatings can overcome these limitations and provide superior corrosion resistance. Various synthesis methods, including sol-gel, electrodeposition, and physical vapor deposition, are described in detail, along with the factors influencing the synthesis process. Recent advancements and innovations in nanoceramic coating synthesis techniques are also highlighted. This looks at how coatings made with tiny ceramic particles protect against corrosion. It examines the importance of small-scale details like particle size, shape, and what the particles are made of. The formation of passive layers, self-healing mechanisms, and barrier properties of nanoceramic coatings are explained. The diverse applications of nanoceramic coatings for corrosion protection in industries such as automotive, aerospace, and marine are comprehensively discussed. Case studies and examples demonstrating the significant corrosion resistance and improved performance achieved with nanoceramic coatings are presented.

Abstract The high cost and complexity of Proton Exchange Membrane (PEM) electrolyzers pose substantial challenges for their integration and testing within smart grid emulators. Addressing this, our research offers two pivotal contributions. First, we introduce an innovative Equivalent Electrical Circuit (EEC) for PEM electrolyzers. This electrical model serves as an essential tool for evaluating the performance of PEM electrolyzers, especially within the framework of renewable energy systems in smart grids. Our second major contribution is the design and implementation of a hardware PEM electrolyzer emulator based on a DC/DC boost converter. Unlike conventional approaches, our emulator accurately reproduces the nonlinear polarization curve and dynamic behavior of PEM electrolyzers under a large range of operating conditions. By seamlessly integrating into a smart grid emulator, it provides an environment for efficient and cost-effective testing of PEM electrolyzers. Experimental results offer strong validation of the emulator’s ability to replicate the characteristics of the PEM electrolyzer, establishing it as a practical alternative for evaluating and improving smart grid emulators and their management strategies.