Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Duroyon Matthieu, Susini Patrick, Misdariis Nicolas et al.

Electric vehicles are now part of the everyday automotive landscape. The resulting sonic experience is a major challenge for driver comfort. Despite this challenge being known, no solution reaching general consensus has yet been proposed. This might be due to the lack of a common culture of the sound or the expected sonic target in electric vehicles, in opposition to what existed for thermal engine. This work proposes a decisive tool to enhance communication on sound description in the electric car cabin. Inspired by soundscape studies, the methodology consists in using a semi-structured questionnaire oriented toward sound description and judgment with 12 acousticians working on electric vehicles. A verbal analysis identifies 11 specific sound names describing this sonic environment. Definitions that include three levels of description: causal, reduced and hedonic as well as audio illustrations, are proposed for each sound name. The lexicon is validated by the same group of acousticians and available online.

Ji Yoon Park, Jun Hyuk Shin, Inpyo Hong et al.

Flexible electronics demand multifunctional human-machine interfaces (HMIs) and organic user interfaces (OUIs). Existing deformable displays often rely on mechanical wires or hinges, limiting their thinness and flexibility. Incorporating sound features typically requires extra components, complicating design. In this study, we developed a lightweight, multifunctional display with a multi-shape bendable design and integrated sound capabilities. Using asymmetrical strain engineering on poly(vinylidene fluoride) (PVDF), we achieved bidirectional and complex deformations through electrical signals, eliminating the need for mechanical hinges. The PVDF actuator enables simultaneous sound emission and intricate shape transformations through rapid actuation and vibration. This design maintains the thinness and flexibility of organic light-emitting diode (OLED) technology. By controlling strain through PVDF polarization and applied electric field, we realized varied shape transformations and integrated these functions into a practical 6-inch OLED display. This approach enhances the functionality of flexible displays, expanding possibilities for future applications in flexible electronics.

L. I. Gretchikhin

Electric currents arising in metal-semiconductor contact are represented as the sum of diffusion currents and drift currents. The use of various empirical formulas was proposed to determine their value. This approach to determining electric currents in metal-semiconductor diodes does not allow pinpointing experimentally obtained values of electric currents. In this regard, there was a problem in developing the theoretical foundations for the production of this type of equipment on a sufficiently sound theoretical basis, taking into account the latest achievements in electrical engineering and electronics. The theoretically calculated surface of triatomic molecules for silicon completely coincided with the experimental data obtained on a tunneling microscope. The process of applying a film made of germanium or silicon semiconductor to an aluminum metal base is considered. It is shown that the most optimal is the application of coatings by laser spraying. The concentration of free electrons in the conduction band of aluminum is determined, which occurs due to the ionization of negative ions and thermoautoelectronic emission of electrons from the metal under the influence of temperature and applied external voltage. A theory of the formation of electric currents of conduction and displacement has been developed. The conditions for the occurrence of an electric conduction current in columnar cavities on the aluminum surface and displacement currents in the supply wires are specified. It is shown how the conversion of conduction currents into a displacement current occurs at the boundary of the p–n junction.

L. I. Gretchikhin

The volt-ampere characteristics of different diodes and zener diodes are obtained experimentally, and they differ significantly from experience to experience. It is unclear how to properly substantiate the reason for such differences. In this regard, a problem arose in developing the theoretical foundations for the production of this type of equipment on a sufficiently sound theoretical basis, taking into account the latest advances in electrical engineering and electronics. As a consequence, the process of formation of conduction currents and displacement currents in a metal–semiconductor contact is considered. Aluminum was used as a metal, and germanium and silicon were used as a semiconductor. With a direct applied external voltage, a theory for calculating the volt-ampere characteristics of germanium and silicon diodes has been developed. It is shown that the affinity energy of atoms of semi-conductor materials at the cathode increases slightly due to the coupling of negative ions with electric dipoles of atoms of the surface layer of aluminum molecules inside a columnar void, and an electric conduction current is formed by the movement of electrons from the cathode to the anode. The electron concentration due to the ionization of negative ions is determined not by the temperature of the diode itself, but by the reduced temperature of the electron gas inside the aluminum due to overcoming the contact potential difference at the p-n junction. A sequential accumulation of negative electron charge occurs at the anode, which determines the conversion of the conduction current into a displacement current, since the electron energy in this case does not exceed the energy of the aluminum work function of the crystal. At the reverse applied voltage, the affinity energy of the negative ions of the impurity atoms at the anode remains the same due to the ionization of the negative ions, taking into account an increase in the temperature of the electron gas at the p-n junction with an increase in the displacement current. An electric conduction current arises from an aluminum crystal acting as a cathode inside a columnar void due to thermoautoelectronic emission. The conduction current at the anode is converted into a displacement current, which enters the external electrical wires. The inner walls of the columnar void are a good dielectric and therefore they sufficiently conduct displacement current inside the columnar void. The conduction current in this case acts as an amplifier of the resulting electric current.

C. Mistrangelo, L. Bühler, S. Smolentsev et al.

Liquid metals and magnetic fields are used in many technical applications such as metallurgy, crystal growth and nuclear fusion reactors. When an electrically conducting fluid moves in a magnetic environment, electric currents and electromagnetic forces are generated that affect velocity and pressure losses in the flow. These magnetohydrodynamic (MHD) interactions have to be investigated to optimize the engineering processes. The characteristics of MHD flows depend on the geometrical configuration, the strength of the applied magnetic field, the electrical properties of fluid and structural materials and the thermal conditions. In the so-called blankets for fusion reactors, where liquid metals are used to breed the plasma fuel component tritium and to extract the generated heat, magneto-convective flows play a crucial role in determining heat and mass transfer. Therefore, the availability of numerical codes to simulate this type of flow is mandatory and their validation is a necessary step to guarantee the reliability of the results. For that reason, a benchmark problem has been defined to simulate liquid metal flows in a horizontal rectangular duct heated from below and exposed to a non-uniform magnetic field. Results obtained by five research groups using different codes are compared.

Kamal Singh, Muskan Verma, Vaishali Rathi et al.

Chandrashekhar Choudhary, A. D. Dhass, R. Krishna et al.

Thermoelectric Generators (TEGs) directly convert thermal energy into electrical energy, rendering them a promising sustainable energy source. Using thermoelectric materials, TEGs convert energy efficiently and environmentally, making them appropriate for many applications. TEG uses waste heat to generate power without moving components or intermediate processes, lowering maintenance and operational expenses. The figure of merit, which measures thermoelectric conversion efficiency, has improved dramatically in recent years due to thermoelectric material system advances. These advances allow TEGs to provide microwatts for small devices and several watts for industrial applications. For widespread use and expansion, wearable Internet of Things devices need tiny, inexpensive, and continuous power sources. Large-scale industrial systems can improve sustainability by efficiently recovering waste heat and transforming it into electricity. This study examines materials and designs for small electronics, high-level industrial applications, waste heat recovery systems, and renewable energy solutions. TEGs will help achieve energy efficiency and sustainability across sectors by blending modern thermoelectric materials with novel engineering.

Ran Zhang, Xiaoping Wu, Xude Zhang et al.

With the rapid development of information technology, personalized education has become a key direction for improving the quality of online learning and optimizing individualized learning paths. However, accurately recommending appropriate courses and exercises for diverse learners remains a significant challenge. Existing recommendation methods often struggle with effectively modeling learner interests, addressing the cold-start problem, and dynamically adapting recommendation strategies to meet personalized needs. To address these limitations, this paper proposes RL-TBTNet, a novel teaching optimization recommendation framework that integrates a bidirectional Transformer, BERT, and reinforcement learning (DQN). The model first vectorizes user behavior data, learning content, and knowledge base information. Transformer layers are employed for feature encoding, while BERT extracts deep semantic representations to form individualized feature vectors. These features are then fused via Transformer-based processing to predict optimal learning content. In addition, a DQN-based reinforcement learning module models dynamic shifts in user interests, enabling adaptive refinement of learning trajectories over time. Experimental evaluations on public datasets show that RL-TBTNet outperforms existing Transformer-based methods such as BST in terms of key metrics like HR and NDCG, particularly excelling in cold-start scenarios. Ablation studies further confirm the effectiveness of semantic enhancement through BERT and reinforcement-driven optimization. These results demonstrate the framework’s potential as a robust and adaptive solution for personalized educational content recommendation, offering both practical value and theoretical insights for the development of intelligent education systems.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or interpretable for software practitioners. These scales fail to account for the diverse, role-specific, and domain-bound expressions of empathy in SE, such as understanding a non-technical user's frustrations or another practitioner's technical constraints, which differ substantially from empathy in clinical or everyday contexts. To address this gap, we developed and validated two domain-specific empathy scales: EmpathiSEr-P, assessing empathy among practitioners, and EmpathiSEr-U, capturing practitioner empathy towards users. Grounded in a practitioner-informed conceptual framework, the scales encompass three dimensions of empathy: cognitive empathy, affective empathy, and empathic responses. We followed a rigorous, multi-phase methodology, including expert evaluation, cognitive interviews, and two practitioner surveys. The resulting instruments represent the first psychometrically validated empathy scales tailored to SE, offering researchers and practitioners a tool for assessing empathy and designing empathy-enhancing interventions in software teams and user interactions.

Dinesh Eswararaj, Ajay Babu Nellipudi, Vandana Kollati

The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparative analysis of Delta Parquet, Iceberg, and Hudi using real-world time-series automotive telemetry data with fields such as vehicle ID, timestamp, location, and event metrics. The evaluation considers modeling strategies, partitioning, CDC support, query performance, scalability, data consistency, and ecosystem maturity. Key findings show Delta Parquet provides strong ML readiness and governance, Iceberg delivers high performance for batch analytics and cloud-native workloads, while Hudi is optimized for real-time ingestion and incremental processing. Each format exhibits tradeoffs in query efficiency, time-travel, and update semantics. The study offers insights for selecting or combining formats to support fleet management, predictive maintenance, and route optimization. Using structured datasets and realistic queries, the results provide practical guidance for scaling data pipelines and integrating machine learning models in automotive applications.

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri

Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.

Jonathan Ebube Jibunor, Olamotse Roland Igbape

Abstract: This report presents the results of research conducted at Auchi, Edo State, Nigeria, to evaluate the effectiveness of UA741-based op-amp configurations in enhancing student learning. The study utilized the instrumentation module developed by Jibunor et al. (2025) in their work titled "Design and Development of an LM741 Instrumentation Module for Enhancing the Teaching of Basic Analog Circuit Principles in Physics and Electrical Engineering" [International Journal of Research and Scientific Innovation (IJRSI), [https://rsisinternational.org/journals/ijrsi/articles/design-and-development-of-an-lm741-instrumentation-module-for-enhancing-the-teaching-of-basic-analog-circuit-principles-in-physics-and-electrical-engineering/]]. Traditional electronics education often struggles to connect theory with practical application, making it hard for students to understand op-amps in circuit design. "This study involved 40 second-year students from the Physics and Electrical Engineering programs, divided into two groups of 20. One group served as the experimental group, assessing the UA741 module through six configurations: voltage follower, inverting amplifier, non-inverting amplifier, summing amplifier, difference amplifier, and integrator, while the other group functioned as the control group. Knowledge tests were given at the end of the lessons, based on Bloom’s taxonomy, covering knowledge, comprehension, application, analysis, synthesis, and evaluation. Results showed that using the UA741 module significantly improved both the quantity and quality of student achievement compared to the traditional "chalk-and-talk" method. Data analysis focused on performance characteristics like voltage gain, waveform shapes, and input-output relationships, confirming expected behaviors. These findings highlight the effectiveness of hands-on experimentation in enhancing student understanding of electrical and electronic concepts, thus improving teaching effectiveness.

N. Szekely, S. Salcu, M. Bojan et al.

The paper presents a dedicated teaching platform primarily for studying the principles of switching power MOSFET transistors, in the field of Electrical Engineering. The proposed platform is dedicated to practical implementation, measurements and switching analysis of MOSFET transistors for different control regimes and various types of loads. It is designed to study the influence of the control circuit and the load over the switching performance of the power MOSFET transistor. For a better analysis, the influences of the circuit’s path length can also be observed. The platform is used in undergraduate laboratory studies specific to power electronics, which are designed to provide support to theoretical lectures and problem-solving exercises.

Bo Li, Zidie Song, Yuxin Shen et al.

Demand for materials resistant to extreme environments is growing in aerospace, electronics, electrical engineering, and automotive manufacturing industries. Traditional epoxy resins struggle to meet high-temperature requirements, whereas bismaleimide (BMI) resin offers both excellent heat resistance and good processability. This study focuses on the novel QY8911-II BMI resin and its composites with CCF800H and CCM40J carbon fibers. Performance evaluation confirmed their excellent and stable flexural strength and interlaminar shear strength both at room and elevated temperatures, along with reliable molding processes. Furthermore, honeycomb sandwich structures with skins made of CCF800H/QY8911-IIand CCM40J/QY8911-II composites were fabricated and evaluated. Key findings are as follows:(1) Both sandwich structures exhibited good process stability; (2) The CCF800H/QY8911-II structure demonstrated higher strength thantheCCM40J/QY8911-II structure at both room and elevated temperatures, while the CCM40J/QY8911-II structure possessed higher room-temperature modulus. The conservation rate of mechanical properties of CCM40J/QY8911-IIcomposite material is superior to that of CCF800H/QY8911-II; (3) Interface tension (pull-off) tests indicated that both co-curing (pre-embedded inserts) and secondary bonding (post-bonded inserts) methods performed stably, with the skin material exerting minimal influence. Specifically, the pull-off failure load strength of pre-embedded inserts was approximately twice that of post-bonded inserts.

Ashok Bindra

This feature provides a summary of IEEE Energy Conversion Congress and Expo (ECCE) 2024, which was held in Phoenix, AZ, 20–24 October. Besides covering the four provocative plenary talks by distinguished industry and academic world leaders in some details, the article also investigates special sessions on emerging technologies and other important industry-oriented topics. This includes luminaries special session to honor the contributions of two esteemed scholars in the field of power electronics: Dr. John Kassakian, Professor Emeritus of Electrical Engineering, The Massachusetts Institute of Technology (MIT), USA and Distinguished Professor Hirofumi (Hiro) Akagi, Tokyo Institute of Technology, Japan. In addition, the multifaceted technical program was filled with educational tutorials, student demos, timely and topical poster papers, and novel technical sessions on next-generation power and energy conversion technologies. Plus, it uncovers latest in GaN products displayed on the exhibit floor.

K. Umetani, Masataka Ishihara, Eiji Hiraki

At Okayama University, the network engineering and energy electronics courses share the same student experiment class on electrical engineering. However, many experiments in this class were conventionally designed based on a single perspective of either network engineering or energy electronics engineering, which has easily demotivated students from the other field. This paper seeks a solution to this problem by designing a new experiment covering both perspectives equally. The analog modulation and demodulation technology was adopted as the experiment topic. This experiment was designed to integrate these two fields of engineering naturally. In the field of network engineering, this experiment teaches the basic principles of frequency multiplex communication by observing the frequency spectrum of the modulated waveform. Meanwhile, in the field of energy electronics engineering, this experiment teaches the basic circuit design of the LC resonating circuit for demodulating the signal. This experiment was conducted in the student experiment class at Okayama University, Japan, in 2023 and 2024.

Asini Kumar Baliarsingh, Sangram Keshori Mohapatra , Pabitra Mohan Dash

This article introduces hybrid Arithmetic Optimization Algorithm (AOA) and Local Unimodal Sampling (hAOA-LUS)-based fractional order (FO) proportional derivative (PD) cascaded with one plus proportional integral (1+PI) controller for automatic generation control of power system with renewable energy sources (RES) and electric vehicle (EV). The control-area 1 has thermal, hydro, gas, and wind power generators and the same true for control area 2, which uses thermal, hydro, gas, and solar energy sources. Initially, Proportional-Integral-Derivative (PID) controllers are taken into consideration and it has been demonstrated that hAOA-LUS outperforms as compared to the AOA, Particle Swarm Optimization, and Generic Algorithm (GA). The assessing of overshoots, undershoots, and different integral errors of frequency and tie-line power deviations after step load perturbations in each area allows for performance comparison of the proposed power system. In the next stage, EVs are considered in each area and the controller parameters are optimized by hAOA-LUS techniques in the presence of RES and EV. A comparative analysis is carried out by hAOA-LUS-tuned FO PD(1+PI) controller with PID as well integer ordered PD(1+PI) for various cases so as to validate the superiority of the anticipated controller. The results from MATLAB and OPAL-RT are compared in order to verify the authenticate feasibility of method.

Felix E. B. Brettner, Stefanie Gier, Annika Haessler et al.

Abstract Inflammation plays a critical role in the pathophysiology of many diseases, and dysregulation of the involved signaling cascades often culminates in uncontrollable disease progression and, ultimately, chronic manifestation. Addressing these disorders requires balancing inflammation control while preserving essential immune functions. Cyclodextrins (CDs), particularly β-CD, have gained attention as biocompatible biomaterials with intrinsic anti-inflammatory properties, and chemical modification of their backbone offers a promising strategy to enhance their physicochemical properties, adaptability, and therapeutic potential. This study evaluated and characterized the immunomodulatory effects of amphiphilic CD derivatives, which self-assemble into nanoparticles, compared to soluble parent β-CD. In a human macrophage model, CD nanoparticles demonstrated superior anti-inflammatory activity, with derivative-specific effects tied to their physicochemical properties, surpassing the soluble β-CD control. Alongside the downregulation of key pro-inflammatory markers, significant reductions in inflammasome activation and changes in lipid profiles were observed. The findings of this study underscore the potential of cyclodextrin-based nanoparticles as versatile biomaterials for treating the complex pathophysiology of various acute and chronic inflammation-associated disorders.

Peiyao Guo, Shahab Dehghan, Vladimir Terzija et al.

With the increasing share of wind power in the energy sector, many countries start to cut back supporting policies for wind power and shift towards market-oriented schemes, challenging the profitability of wind farms. Energy storage offers a flexible solution to enhance their profitability. This work explores different wind-related storage investment modes, including 1) direct ownership, 2) cooperative, and 3) competitive modes in a market-based environment. For the direct ownership mode, a bilevel single-leader-single–follower Stackelberg game model is proposed, where wind farms invest in and operate storage facilities strategically to maximize their profits in the upper level, while the lower-level problem represents the system operator’ s market-clearing process. A cooperative game framework is presented for the cooperative mode, that wind farms and storage investors agree on a profit allocation rule, i.e., Shapley value or Nucleolus to collaborate in investing and bidding as a coalition. The competitive mode is interpreted as a multi-leader-single-follower Stackelberg game, describing an independent investor investing in and operating storage facilities in competition with wind farms. Case studies conducted on a 6-bus and the IEEE 30-bus test systems demonstrate that storage facilities directly invested in by wind farms are the best option for maximizing their profits, resulting in up to an 8.7% increase. The cooperative option provides a suboptimal increase of up to 3.1%, diversifying the costs and risks associated with storage investments. In contrast, the competitive mode can diminish wind farms’ profitability, with up to a 30.6% decrease in profits.

Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan et al.

Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.