Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Chunxiang Qian, Wenxiang Du, Yudong Xie et al.

With the growing demand for large-scale infrastructure development in China—such as deep-sea, deep-underground, and urban subsurface projects—combined with the widespread use of general-purpose raw materials, there is an urgent need for more precise crack control technologies in concrete. This need stems from the imperative to reduce unnecessary material consumption and environmental impact caused by excessive safety margins. To address this, a set of governing equations that account for the mutual feedback between temperature and humidity was first proposed. A non-constant form of the diffusion coefficient was introduced, alongside latent heat terms and unsteady-state heat source terms, to establish a hygrothermal coupling model. This model was further enhanced by incorporating the effects of creep relaxation, reinforcement constraint, structural restraint, and thermal conduction characteristics of formwork, thereby forming a comprehensive multi-field coupling evaluation framework that encompasses the temperature field, moisture content field, strain field, and cracking index field. Subsequently, the proposed theoretical framework was applied to representative engineering scenarios, including large-scale concrete foundation slabs, bridge bearing platforms, large-area long-span side walls and prefabricated tunnel segments. The accuracy and reliability of the model were validated through comparisons between simulation results and field-monitored data. The results demonstrate that this method effectively overcomes the technical limitations of traditional concrete crack prediction models, particularly those relying on constant parameter assumptions and decoupled field interactions. It offers a practical and robust approach for engineering applications, providing a novel perspective for precision crack control in concrete and contributing to the broader goals of sustainability and resource efficiency.

YANG Hongwei, WANG Haifeng, YANG Jun, PU Renbin, YANG Can, JIN Chen

In order to enhance the long-term protective performance of water-based epoxy coatings，benzotriazole (BTAH) corrosion inhibitor was incorporated into hollow cerium oxide (CeO2) nanocontainers，resulting in the preparation of BTAH＠CeO2-doped epoxy coatings.The surface morphology，chemical composition and corrosion resistance of the epoxy/BTAH＠CeO2 composite coating were characterized using scanning electron microscopy (SEM)，X-ray photoelectron spectroscopy (XPS) and electrochemical impedance spectroscopy (EIS).The results showed that the loading amount of BTAH inhibitor in the CeO2 nanocontainers was 24.7%.The BTAH inhibitor was able to be rapidly released from the CeO2 nanocontainers，with a release amount reaching 92.6%after 8 h.The BTAH＠CeO2 particles were uniformly dispersed in the water-based epoxy resin and effectively filled the microscopic voids inside the coating.Electrochemical impedance testing results after corrosion in a 3.5%NaCl solution for 1 h indicated that the coating resistance of the epoxy/BTAH＠CeO2 composite coating was 16.7 times higher than that of pure epoxy coatings.After immersion in a 3.5%(mass fraction) NaCl solution for 30 d，the polarization resistance loss rate of the epoxy/BTAH＠CeO2 composite coating was only 10.6%compared to 1 h of immersion，demonstrating excellent long-term protective performance.

YUE Yan, LI Yu, ZHOU Xianxian et al.

[Purposes] Because of the high charging overpotential and lagging electrochemical reaction kinetics caused by the low electronic conductivity of Li2O2 in Li-O2 batteries, it is important to develop cathode catalysts with high activity. [Methods] By coating nitrogen-doped molybdenum disulfide ultra-thin nanosheets on the surface of nitrogen-doped carbon nanotubes, the N-MoS2/N-CNTs composite was prepared through hydrothermal method combined with ammonia annealing method. The morphology, surface element state, and Li-O2 battery electrochemical performance of N-MoS2/N-CNTs were characterized by X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, and electrochemical tests. [Results] The cathode obtains high initial charge/discharge capacity (7909/10015 mAh g-1), low charging overpotential, and high catalytic activity. Moreover, the performance of Li-O2 battery is further improved at large O2 mass transfer area. According to electrochemical reaction engineering, it is proposed that the possible initial discharge reaction interface is electrode/Li2O2 interface, and the charging reaction interface is electrode/electrolyte/Li2O2 interface. Three overpotential theories are used to explain the capacity and rate performance improvement mechanism of N-MoS2/N-CNTs cathode Li-O2 batteries, which is the decrease of electrochemical reaction overpotential (ηR) providing more space for the increase of concentration overpotential (ηC).

Ziad F. Doughan, Sari S. Itani

This paper introduces Rectified Discriminatory Delta-Adjust (RDDA), a novel methodology that enhances interpolation-based predictive modeling through adaptive sensitivity parameters. Building upon the foundational Delta-Adjust algorithm, RDDA addresses the limitations of fixed sensitivity parameters by incorporating three dynamic sensitivity estimation methodologies: Sensitivity Analysis (SA), Vector Calculus (VC), and Higher-Order Derivative Methods. The research establishes theoretical foundations for local-to-global emergence in inductive AI systems, proving that local inference mechanisms can reconstruct global information structures through the equivalence of local propagation and global entanglement views of shared information. We demonstrate that temporal ordering in datasets affects information flow profiles, with discriminatory coding revealing that data correlations are non-uniformly distributed across datasets. RDDA’s modular architecture allows plug-in sensitivity estimators to replace fixed parameters with query-adaptive, data-driven sensitivity metrics. Experimental validation across classification, regression, and interpolation tasks demonstrates the competitiveness of the RDDA framework. Its variants sometimes outperform the vanilla Delta-Adjust method. On interpolation benchmarks, RDDA matches the accuracy of dedicated methods like IDW and RBF, while on classification and regression tasks, it delivers performance comparable to established models including SVMs, KNNs, and Random Forests. The methodology preserves Delta-Adjust’s linear time core complexity while adding modular sensitivity estimation overhead, enabling practical deployment in data-driven modeling applications where local-to-global emergence is essential.

Carlos Arriaga, Alejandro Pozo, Alvaro Alonso

Massive online and hybrid events have become important during and after the COVID-19 pandemic. These events aim to replicate live experiences by maintaining interaction between the speakers and the audience. However, existing streaming and videoconferencing solutions fail to provide sufficient scalability and real-time interactions. Streaming technologies lack support for live interaction, whereas videoconferencing technologies scale enough. Events such as panel debates, conference presentations, and sessions with live interpretation require both the ability to support a large number of participants and real-time voice interactions. In this paper, we propose a new tree-based architecture that meets both requirements using videoconferencing technologies. This approach combines the scalability of streaming architectures with the low latency of videoconferencing technologies. The objective was to increase the maximum number of participants that videoconference providers can accept while maintaining live interactions. A prototype implementation of the proposed architecture was developed to test and validate it. Finally, this study provides valuable information for implementing and adapting the proposed architecture to various production environments.

Rashina Hoda

Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a wider range of socio-technical activities and concerns to make it work in practice. This paper contributes to the emerging visions by: (a) recommending an expansion of its scope beyond code, toward a 'whole of process' vision, grounding it in SE foundations and evolution and emerging agentic SE frameworks, (b) proposing a preliminary set of values and principles to guide community efforts, and (c) sharing guidance on designing and using well-defined vocabulary for agentic SE. It is hoped that these ideas will encourage collaborations and steer the SE community toward laying strong foundations of agentic SE so it is not limited to enabling coding acceleration but becomes the next process-level paradigm shift.

Shiruo Hu, Wenbo Shan, Yingjia Li et al.

Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought disasters. Featuring multiple engineering objectives, Hydro-SE is an inherently interdisciplinary domain that integrates scientific knowledge with engineering expertise. This integration necessitates extensive expert collaboration in decision-making, which poses difficulties for intelligence. With the rapid advancement of large language models (LLMs), their potential application in the Hydro-SE domain is being increasingly explored. However, the knowledge and application abilities of LLMs in Hydro-SE have not been sufficiently evaluated. To address this issue, we propose the Hydro-SE LLM evaluation benchmark (Hydro-SE Bench), which contains 4,000 multiple-choice questions. Hydro-SE Bench covers nine subfields and enables evaluation of LLMs in aspects of basic conceptual knowledge, engineering application ability, and reasoning and calculation ability. The evaluation results on Hydro-SE Bench show that the accuracy values vary among 0.74 to 0.80 for commercial LLMs, and among 0.41 to 0.68 for small-parameter LLMs. While LLMs perform well in subfields closely related to natural and physical sciences, they struggle with domain-specific knowledge such as industry standards and hydraulic structures. Model scaling mainly improves reasoning and calculation abilities, but there is still great potential for LLMs to better handle problems in practical engineering application. This study highlights the strengths and weaknesses of LLMs for Hydro-SE tasks, providing model developers with clear training targets and Hydro-SE researchers with practical guidance for applying LLMs.

E. A. Ryabykh, R. Maleev, A. Filin

The purpose of the study is to develop a methodology for the study of permanent magnet inductor generators used on mobile objects, as well as the selection of their effective parameters. The scientific novelty of this study is the development of a mathematical model of an inductor generator with permanent magnets in the Ansys Maxwell software and computing complex. The results obtained make it possible to introduce into the theoretical framework a new approach to the design of valve inductor generators with magnetoelectric excitation. The developed analytical data increases the design accuracy and improves the calculation methodology of inductor generators of the presented design. The conducted studies take into account the specifics of the generator's operation in a wide range of rotation speed, torque and load. Improving the technical and economic parameters of an inductor generator with permanent magnets and developing recommendations for research and design organizations is the main priority task in the theory of electrical engineering. To solve this problem, we used a list of CAD application programs and the Ansys Electronics Desktop software environment, which allowed us to calculate the mathematical model with high accuracy and obtain all the necessary output characteristics. The obtained mathematical modeling results were approximated using the classical magnetic field calculation method to confirm the adequacy of the developed model. The mathematical model makes it possible to simplify and speed up the process of selecting optimal weight and size parameters for the design of inductor generators of various capacities.

K. Nguyen, Andy Rivas, G. Delipei et al.

The last decade has seen the development and application of data-driven methods taking off in nuclear engineering research, aiming to improve the safety and reliability of nuclear power. This work focuses on developing a reinforcement learning-based control sequence optimization framework for advanced nuclear systems, which not only aims to enhance flexible operations, promoting the economics of advanced nuclear technology, but also prioritizing safety during normal operation. At its core, the framework allows the sequence of operational actions to be learned and optimized by an agent to facilitate smooth transitions between the modes of operations (i.e., load-following), while ensuring that all safety significant system parameters remain within their respective limits. To generate dynamic system responses, facilitate control strategy development, and demonstrate the effectiveness of the framework, a simulation environment of a pebble-bed high-temperature gas-cooled reactor was utilized. The soft actor-critic algorithm was adopted to train a reinforcement learning agent, which can generate control sequences to maneuver plant power output in the range between 100% and 50% of the nameplate power through sufficient training. It was shown in the performance validation that the agent successfully generated control actions that maintained electrical output within a tight tolerance of 0.5% from the demand while satisfying all safety constraints. During the mode transition, the agent can maintain the reactor outlet temperature within ±1.5 °C and steam pressure within 0.1 MPa of their setpoints, respectively, by dynamically adjusting control rod positions, control valve openings, and pump speeds. The results demonstrate the effectiveness of the optimization framework and the feasibility of reinforcement learning in designing control strategies for advanced reactor systems.

Jorge Buzzio-Garcia, Jaime Vergara, Santiago Rios-Guiral et al.

In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-based datasets. This study seeks to bridge this gap by introducing a novel dataset for intrusion detection in Software-Defined Networking named SDNFlow. The dataset, derived from OpenFlow statistics gathered from real traffic, integrates a comprehensive range of network activities. An empirical evaluation leveraging diverse Machine and deep Learning algorithms was performed. Namely, Logistic regression, decision tree, random forest, K-nearest neighbors, Support Vector Machines, and Multilayer Perceptron were tested getting pretty good results with a precision average of 98% to 99% in binary classification and from 97% to 99% in multiclass classification depending of the attack, we highlight the efficacy of K-Nearest Neighbors (KNN) for traffic classification, particularly in detecting DDoS attacks and port scanning. The dataset is valuable for evaluating intrusion detection systems within SDN environments and deepening the understanding of traffic patterns in Software Defined Networks.

Francesco Toscano, Costanza Fiorentino, Nicola Capece et al.

Digital Precision Agriculture (DPA) is a comprehensive approach to agronomic management that utilizes advanced technologies, such as sensor data analysis and automation, to optimize crop productivity, enhance farm income, and minimize environmental impacts. DPA encompasses various agricultural domains, including pest control, pest management, fertilization, irrigation management, sowing, transplanting, crop health monitoring, yield forecasting, harvesting, and post-harvest stages. Among the enabling technologies for DPA, Unmanned Aerial Vehicles (UAVs) have gained significant attention and market growth. The advancements in control systems, robotics, electronics, and artificial intelligence have led to the development of sophisticated agricultural drones. UAVs offer advantages such as versatility, quick and accurate remote sensing capabilities, and high-quality imaging at affordable prices. Furthermore, the miniaturization of sensors and advancements in nanotechnology enable UAVs to perform multiple operations simultaneously without compromising flight autonomy. However, various variables, including aircraft mass, payload capacity, size, battery characteristics, flight autonomy, cost, and environmental conditions, impact the performance and applicability of UAV systems in agriculture. The economic considerations involve the purchase of drones, equipment, and the expertise of trained pilots for flight management and data processing. Payload capacity, flight range, and financial factors influence agriculture’s choice and implementation of UAVs. The research and patent trends show the growing interest in UAVs for agricultural applications. This paper provides a general review of UAV types, construction architectures, and their diverse applications in agriculture until 2022.

Dong Wang, Wenzhe Chen, Jiali Chen et al.

In individuals afflicted with hemophilia, characterized by a deficiency of coagulation factor VIII (FVIII), the occurrence of spontaneous recurrent intra-articular hemorrhage precipitates the emergence of hemophilic arthropathy (HA). Although clotting factor replacement therapy reduces joint bleeding clinically, clotting factors need to be injected frequently due to the rapid diffusion of the drug. Hence, a novel drug delivery approach may be developed to improve the drug therapy. Platelet-derived extracellular vesicles (PEVs) are known to possess anti-inflammatory and hemostatic properties and could be used as a potential HA therapy. In this study, we constructed a PEV-LS@FVIII nanotherapeutic system by combining thioketal (TK), liposomes (LS), and FVIII to form the LS@FVIII complexes, and then hybridizing PEV with LS@FVIII. Our results demonstrated that PEV-LS@FVIII could efficiently facilitate FVIII delivery and specifically target the injured knee joint. Both in vitro and in vivo studies showed a reduction in the M1 phenotype of macrophages and an enhancement of the M2 phenotype, compared to FVIII free control. Furthermore, PEV-LS@FVIII appeared to alleviate HA-induced cartilage damage. In conclusion, our findings demonstrate that PEV-LS@FVIII could delay the progression of HA by targeting bleeding joints, modulating macrophage polarization to suppress inflammation, and mitigating cartilage damage.

James S. Wheaton, Daniel R. Herber

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. We explore the application of MBSE to requirements engineering by extending the Model-Based Structured Requirement SysML Profile to comply with the INCOSE Guide to Writing Requirements while conforming to the ISO/IEC/IEEE 29148 standard requirement statement patterns. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition, verification & validation. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to assess its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support in the system architecture modeling software.

Zhanna A. Kostoreva, S. Syrodoy, A. A. Omarov

Relevance. Today, increasing attention of the world community is being paid to the problems of ecology and global warming. Objective prerequisites are emerging for more active introduction of renewable energy sources and energy carriers into thermal and electrical energy production cycle. But non-traditional energy sources, such as wind generators and solar panels, have a number of significant disadvantages, the main one of which is that the stability of renewable energy sources depends significantly on weather conditions and time of day. For this reason, the governments of many countries are already discussing new programs for the development of the economy energy sector based on the large-scale construction of nuclear and thermal power plants. This global problem can be solved by switching to wider use of coal as an energy source. Coal thermal power plants provide stable production of thermal and electrical energy, unlike renewable energy sources. But coal is a “dirty” fuel – when it is burned, it produces significant amounts of anthropogenic emissions, such as carbon dioxide, as well as sulfur and nitrogen oxides. Aim. Experimental studies of the conditions and characteristics of ignition (thermal preparation times) of humidified coal of several fairly common and widely used brands and the amount of nitrogen oxide in their combustion products. Object. Humidified coal of four grades (lean, long-flame, anthracite and brown). Method. To establish the main characteristics and conditions of coal fuel ignition and combustion under high-temperature heating conditions, a special experimental stand was used. Results. The results of experimental studies are presented to substantiate the possibility of using four grades of humidified coal as the main fuel in thermal power engineering. The authors have established nitrogen oxide sequestration in the combustion products of the latter in a small vicinity of a humidified coal particle in comparison with dry coal under high temperature conditions. The experimental studies established as well an insignificant (increase of no more than 11%) influence of additional humidity on the thermal preparation times of single coal particles for the four studied grades of coal.

Md Jawaid Alam, H. Sidhwa, S. Giriprasad

Aluminum Gallium Nitride/Gallium Nitride (AlGaN/GaN) High Electron Mobility Transistors (HEMTs) have gained significant attention for their superior electrical properties, making them promising candidates for high-frequency and high-power applications. The performance of AlGaN/GaN HEMTs is significantly influenced by the properties of the contact materials and interface states. This research article presents a comprehensive review and analysis of the effect of contact material choices and interface states on the performance of AlGaN/GaN HEMTs. Various theoretical models, and simulation studies are discussed to elucidate the underlying mechanisms governing the electrical characteristics of AlGaN/GaN HEMTs. The findings underscore the critical role of contact materials and interface engineering in optimizing device performance and reliability for next-generation electronics.

Ijsrem Journal

Abstract—Experience the future of fitness with our innovative automated power generating wall-mounted punching bag. This advanced system revolutionizes traditional workouts by incorporating customizable punch combinations, LED-guided targets, and real-time performance tracking. Users can seamlessly adjust punch settings with a simple knob, while dynamic LED lights guide them through targeted combinations, enhancing training effectiveness and refining technique. Embedded sensors accurately measure punching force and track workout progress, providing insightful feedback via a user-friendly mobile application. Detailed analytics and calorie expenditure data empower users to monitor their fitness journey effectively, while the bag harnesses kinetic energy to contribute to sustainable power generation. This interdisciplinary project merges cutting-edge technology with principles of electrical, electronics, and mechanical engineering, showcasing ingenuity and versatility. Join us in redefining fitness with a sustainable, interactive, and informative workout experience tailored to the needs of enthusiasts and athletes alike.

Nurul Husniyah Abas, Mohd Zainal Abidin Ab Kadir, N. Azis et al.

This paper details a case study on the implementation of dynamic line rating (DLR) to enhance the ampacity rating of Malaysia’s grid. Utilizing heat balance equations endorsed by the Institute of Electrical and Electronics Engineering (IEEE 738) and the International Council on Large Electric Systems (CIGRE technical brochure 601), the ampacity rating of a Zebra-type aluminum cable steel reinforced (ACSR) conductor on a 275 kV transmission line has been assessed. Real-time weather conditions and conductor temperatures, measured hourly by the DLR sensor over the course of a year, were incorporated into the ampacity calculation to determine the available margin. The weather parameters were analyzed based on the monsoon seasons. A comparative analysis between various methods outlined in the standards and the estimated ampacity rating derived from both standards is presented. According to both standards, the findings indicate that DLR surpasses static line rating (SLR), highlighting the presence of untapped ampacity for grid optimization. Remarkably, CIGRE TB 601 exhibits a higher ampacity rating margin than the IEEE 738 standard, with a percentage difference of 16.20%. The study concludes that the conductor is underutilized and proposes optimization through the integration of real-time weather conditions data into the heat balance equations.

Noor Aisyah Ahmad Shah, Norli Abdullah, Siti Hasnawati Jamal et al.

The advancement of sensor technologies, optical computing, communication, and signal processing with ultra-broadband at GHz-THz bandwidths relies heavily on high-performance electro-optic (EO) modulators. Significant advancements have been made in silicon-organic hybrid (SOH) and plasmonic-organic hybrid (POH) technologies over the past ten years, enabling devices with higher bandwidth, improved energy efficiency, reduced footprints, and significantly lessen the π-voltage-length product. Modern SOH and POH technologies take advantage of the fundamental of EO activity of organic chromophores as well as the improved electrical and optical field overlap that can be achieved in nanophotonic devices. To achieve ground-breaking performance, synergistic innovation is required from the engineering of devices to the logical design of organic EO materials. It is acknowledged that the forthcoming of information technology depends on the chip-scale integration of electronics and photonics as well as the use of the greatest features of plasmonics, photonics, and electronics to accomplish this goal. However, there are still many obstacles to overcome, such as matching the sizes of the electrical and photonic circuits, accomplishing low-loss transitions across the three fields of electronics, photonics, and plasmonics, and creating and integrating novel materials. This review concentrates on the advancement and innovations of material design of different structures of organic dipolar chromophores that highlight the importanceand influence of their structures on electrical and EO properties.

Salami Sulaimon Abiodun, Oyebamiji Hammed Lasisi, Oluwaseun Joel Olasunkanmi et al.

This project designs and implements a solar-powered spy security system with motion detection capability. The system integrates a solar panel, charge controller, battery, PIR sensor, 555 timer IC, buzzer, WiFi module, and a spy camera. The solar panel recharges the battery, providing a sustainable power source. The PIR sensor detects motion, triggering the 555 timer IC to activate the buzzer and spy camera. The system captures images or records videos only when motion is detected, conserving energy and memory space. The spy camera connects to a smart device via WiFi, enabling remote viewing of captured footage. Testing demonstrated the system's effectiveness as a reliable and efficient security solution. This project addresses the limitations of traditional CCTV cameras, providing a more efficient and sustainable solution for home security. The design leverages principles of electrical and electronics engineering, including circuit design, electronics, and control systems. Future enhancements can include integrating Artificial Intelligence to improve security and reduce false alarms.

Bengisu Yalçinkaya, Mohamed Benzaghta, R. B. Coruk et al.

Introductory courses regarding radar technologies are very popular in the undergraduate curriculum of many electrical and electronics engineering departments. Hands-on experience is an essential part for understanding the theoretical concepts given in lectures. In most cases, it is not affordable for universities to acquire experimental radar systems, especially those in developing countries. This paper presents a detailed description of a cost-effective, easy-to-deploy radar system laboratory sessions and measures the educational effectiveness of the proposed material. The provided radar models can be used in teaching undergraduate students the working principles of frequency modulated continuous wave (FMCW) radar systems, as well as assisting graduate students in their research activities. The effectiveness of the laboratory sessions is measured thoroughly via qualitative and quantitative methods based on the proposed learning process and students’ success. The results show that the lab sessions have increased the students' understanding of the topics covered within the course, and the students' general perception is positive.