Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

S. Rich, R. Wood, C. Majidi

A. Liu, Huihui Zhu, Sai Bai et al.

Ha Eun Kang, Seong-Do Kim, Young Soo Yoon et al.

Nickel-rich layered oxide cathodes, typified by compositions such as LiNi₁₋ₓ₋ᵧCoₓMnᵧO₂ (NCM) have garnered significant attention as high-energy-density candidates for next-generation lithium-ion batteries. However, their widespread deployment is hindered by a confluence of structural degradation, surface instability, and poor interfacial compatibility under high voltage cycling. To address these multifaceted limitations, this review comprehensively examines recent advances in surface coating and bulk doping strategies, which have emerged as pivotal approaches for enhancing the electrochemical stability and longevity of Ni-rich cathodes. Surface coatings including oxides, phosphates, and fluorides have been shown to effectively mitigate electrolyte-induced parasitic reactions and reinforce cathode–electrolyte interfaces. Simultaneously, elemental doping at transition-metal, lithium, and oxygen sites offer promising pathways to suppress cation disorder, stabilize layered frameworks, and facilitate Li⁺ transport. Emphasis is placed on site-specific doping mechanisms, the role of multi-site (co-)doping, and their synergistic interplay with surface modification layers. By synthesizing recent findings, this review delineates how the judicious integration of coating and doping techniques can enable the rational design of Ni-rich cathodes with enhanced structural integrity, rate capability, and cycle life.

Anjana M S, Aryadevi Remanidevi Devidas, Maneesha Vinodini Ramesh

Electrical energy plays a pivotal role in modern society by powering homes, industries, and transportation systems. However, the production of electricity is associated with significant carbon emissions, primarily from fossil fuel-based power generation, and there is 1.1% rise in carbon emissions by 2023 compared to 2022. Mitigating carbon emissions from electrical energy is a critical global challenge that requires a multifaceted approach. Transitioning to cleaner energy sources and improving energy efficiency are essential steps to reduce the environmental impact of electricity generation. Energy management is crucial to reduce energy consumption effectively. So this study proposes a Multi-Model Energy Management System (MEnMS) integrated with a Fractal Internet of Things (IoT) architecture to address enhanced energy management by reducing energy usage, and carbon footprint. The study conducts a detailed energy consumption analysis across distinct cases. From the analysis, it can be seen that an average of 25% of energy can be saved with MEnMS without IoT energy overhead. Key observations include, EnMS with IoT devices and automation offers smartness, they do not lead to a significant reduction in energy consumption. Moreover, these IoT devices and centralized learning consume more energy. However, integrating IoT devices with distributed learning and multiple models significantly reduces energy consumption as well as the carbon footprint. The analysis reveals that the MEnMS system outperforms alternative approaches, particularly at higher occupancy levels, establishing itself as the most efficient energy management solution. At an occupancy level of 25 users, it achieves an impressive 8% reduction in energy consumption compared to the Traditional System, showcasing its unique capability to scale energy savings as occupancy increases. This innovative system combines advanced local processing with EQC optimization, providing a cutting-edge approach to sustainable energy management in high-occupancy scenarios. Furthermore, the algorithms driving occupant-centric automation and the indoor localization method demonstrate remarkable performance, achieving an efficiency of 92% and an accuracy of 90%, respectively. Therefore, the MEnMs framework can be used to monitor energy usage thereby reducing energy consumption, which results in a low-carbon footprint. By tracking the activity, the occupants get a clear understanding of their carbon footprint and they can make adjustment to reduce carbon emissions.

Junzhe Hu, Chuanwen Luo, Yi Hong et al.

Recently, the Internet of Things (IoT) has played an important role in many fields. Nevertheless, the fast and uneven energy consumption of IoT Devices (IoTDs) significantly limits the lifetime of IoT networks. One of the effective solutions is to deploy Laser Static Chargers (LSCs) to power IoTDs. However, deploying LSCs to cover all IoTDs will consume enormous costs. To prolong the lifetime of IoT and reduce the deployment costs of LSCs, in this paper, we first propose a novel IoT network named Self-organizing Power Transfer IoT with Laser Static Chargers (SPTIoT-LSC), where IoTDs are equipped with laser transmission and reception modules allowing energy transfer between IoTDs, and several LSCs are deployed into the network to charge IoTDs. Based on SPTIoT-LSC, we study the Minimizing Laser Chargers Coverage(MLCC) problem, which aims to minimize the number of LSCs deployed in SPTIoT-LSC while enabling all IoTDs to work continuously. Then we prove its NP-hardness. To solve the problem, we propose two sub-algorithms: the Layered Charging Scheduling Strategy (LCSS) algorithm and Deploy Chargers based on the Multi-agent deep deterministic policy gradient (DCM) algorithm to maximize the working time of IoTDs with given LSCs and corresponding positions and deploy given LSCs in SPTIoT-LSC, respectively. Based on the above sub-algorithms, we propose an approximation algorithm to solve the MLCC problem. Finally, extensive experiments are proposed to verify the efficiency of the proposed algorithm and the superiority of SPTIoT-LSC.

Abdulrahman M. Abdulghani, Azizol Abdullah, A. R. Rahiman et al.

Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability.

Fernando Ayach, Vitor Lameirão, Raul Leão et al.

Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.

Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser et al.

With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.

Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae et al.

Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.

Allysson Allex Araújo, Marcos Kalinowski, Matheus Paixao et al.

[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

Catalin Anghel, M. Craciun, Emilia Pecheanu et al.

Large language models (LLMs) are increasingly integrated into educational contexts, particularly for the automated assessment of problem-solving and reasoning tasks. Their capacity to generate answers and explanatory feedback at scale makes them attractive for engineering education, but inconsistency, bias, and limited reproducibility remain major concerns. To address these limitations, this paper reports on the application of CourseEvalAI, a rubric-guided evaluation framework designed to ensure transparency and comparability in automated scoring.The framework was applied to a dataset derived from a university-level course in artificial intelligence. Two configurations of the Mistral-7B model were investigated: the baseline version and a LoRA-adapted variant fine-tuned on course-specific data. Model outputs, consisting of answers and explanations, were evaluated by GPT-4 using rubric-based criteria across technical, argumentative, and explanation dimensions. Prior work has shown that GPT-4 achieves results comparable to human evaluators, supporting its use as an expert proxy in this context.Experimental validation demonstrates that CourseEvalAI enables fine-grained analysis of model behavior, detecting scoring drift, rubric-specific improvements, and inter-model performance differences. By integrating structured rubrics, expert evaluation, and graph-based storage, the framework enhances transparency and reproducibility. The approach shows direct applicability in electrical and electronics engineering, automation, and computer science, offering a robust methodology for reliable and interpretable automated assessment.

Chunping Fan

Abstract: From both historical and logical perspectives, we trace and analyze the condensation process of the spirit in engineering. By drawing on Merton 􀆳 s definition and analysis of the scientific spirit, we attempt to provide a definition of the engineering spirit and distill its core and framework. It is pointed out that the term “ craftsman spirit ” in the Chinese context is a comprehensive expression of related foreign consciousness that has undergone the domestication of the Chinese language. It has its specific group of carriers and cannot be simply understood or transferred to the concept of engineering spirit. The spirit of engineering has been historically iterated and refined under the nourishment of the spirit of craftsmanship, the spirit of science, the spirit of entrepreneurship, the spirit of business and the spirit of humanity, as well as in the development of the engineering profession and engineering professional organizations and the growth of the group of engineers. Combining Merton 􀆳 s explanation and construction of the scientific spirit, based on the ASME Code of Ethics of Engineers, which was first established in 1976 and last revised in 2012, And take the IEEE Code of Ethics of the highly representative engineering professional organization, the Institute of Electrical and Electronics Engineers (IEEE), and the release of the “ Code of Engineering Ethics of the Chinese Chemical Society ” by the Chinese Chemical Society on February 24, 2021 as examples to demonstrate that the scientific spirit is based on epistemology and takes “ truth ” as its core; The engineering spirit is based on Highlights

M. Koç, Ismail Esen, M. Eroğlu

This study explores a new nanoplate design's thermal and mechanical properties, including an auxetic core with a negative Poisson ratio. The core is between face plates made of barium–cobalt, which possess magnetoelectroelastic properties. The analysis centers on the parameters θ, β1, and β2 to clarify their influence on the nanoplate's performance. The evaluations of the nanoplate's thermal, electrical, and magnetic properties showcase its remarkable versatility and sensitivity. Incorporating magnetoelectroelastic face plates improves the multifunctionality of the nanoplate, making it a highly promising option for use in smart technologies. The findings offer valuable insights into the distinctive features of auxetic core structures, significantly enhancing the comprehension of these materials. This research emphasizes the potential for creating groundbreaking applications in fields like aerospace engineering and advanced electronics, where versatile and adaptable materials play a vital role. This study contributes to the broader knowledge of auxetic materials and their practical implementation in cutting‐edge technological solutions by exploring the interplay between thermal, mechanical, and magnetoelectroelastic properties.

V. Ya. Tsvetkov

The development of society is accompanied by an increase in the complexity of management objects and management mechanisms. To counteract the growth of complexity, new management models and methods should be introduced. New methods include semasiological management which uses a model approach and induction principle. It borrows the ideas of semasiology from linguistics and forms management decisions on the basis of application of information management units. Despite the fact that this complicates the preliminary process of preparing for management, it also gives an advantage in the comparability of different management decisions and technologies. Semasiological management allows, when reconfiguring management, not to create management models anew, but to modernise them by replacing management information units or forming new combinations of these units. Semasiological management is related to onomasiological information modeling and requires its use. In addition, it can be used in automated management, smart management, and digital twin management. Semasiological management requires special organisation and specific training, such as a special management language. The research proposes a variant of semasiological management which is based on the application of the theory of information units.

Meng Wu, Feng Xu, Geetha Subramaniam et al.

Hematopoietic stem cell transplantation (HSCT) is a crucial treatment option for hematological disorders. This study aims to design and develop a blockchain-based system tailored explicitly for HSCT typing and evaluate its performance and capabilities. The system, built on Hyperledger Fabric 2.4.6 and integrated with LevelDB and MySQL databases, implements features such as data on-chaining, access control, information retrieval, and matching through chaincode. It ensures accurate and efficient human leukocyte antigen (HLA) typing while protecting patient and hospital data security. Tests demonstrate the system’s ability to safeguard data security and reliability effectively. Performance metrics, including block size, CPU utilization, network throughput, and response latency, were evaluated on Ubuntu 20.04.2 operating systems using VMware Workstation 16 Pro and Docker containerization. By leveraging blockchain’s traceability and immutability, the system achieves data security, reliability, and verifiability, enabling faster and more accurate typing and improved healthcare efficiency. This innovative approach optimizes user experience, maintains data integrity, and ensures privacy. Future integration with existing databases could enable secure data sharing among healthcare institutions, allowing rapid verification of data ownership and unified authentication. This study contributes a significant advancement in hematologic disease treatment and research, offering practical implications for improving HSCT processes.

Madhava Krishna, Bhagesh Gaur, Arsh Verma et al.

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama's results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entry-level software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.

Haochen Li, Jonathan Leung, Zhiqi Shen

Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering methods, but also aims to highlight the limitation of designing prompts based on an anthropomorphic assumption that expects LLMs to think like humans. From our review of 50 representative studies, we demonstrate that a goal-oriented prompt formulation, which guides LLMs to follow established human logical thinking, significantly improves the performance of LLMs. Furthermore, We introduce a novel taxonomy that categorizes goal-oriented prompting methods into five interconnected stages and we demonstrate the broad applicability of our framework. With four future directions proposed, we hope to further emphasize the power and potential of goal-oriented prompt engineering in all fields.

Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan et al.

Machine learning (ML) based bearing fault detection is an emerging application of Artificial Intelligence (AI) that has proven its utility in effectively classifying various faults for timely measures. There are myriad studies dedicated to the effective classification of bearing faults under different conditions and experimental settings. In this study, we proposed a weighted voting ensemble (WVE) of three low-computation custom-designed convolutional neural networks (CNNs) to classify bearing faults at 48 KHz. Some of the recent studies have exploited 1-d time-series signals and time-frequency based 2-d transformations for bearing fault classification. However, 1-d signals lack contextual information and higher-dimensional interpretations whereas time-frequency based transformations provide a more appropriate, visually perceivable and explainable representation of the time and frequency changes. Therefore in this study, a scalogram based representation of the signals is leveraged for classification using the CNN. Furthermore, the class imbalance is a significant challenge that affects the modelling behavior and possibly create biases. This study provides a novel density and distance hybrid over-sampling approach namely Density-Aware SMOTE(DA-SMOTE) built upon the SMOTE methodology for a more refined representation of synthetic samples within the minority class distribution. The experimentation procedures were carried out before and after the oversampling and it was observed that the balanced dataset acquired much better accuracy then the imbalanced dataset. This is evident by the fact that the highest validation accuracy for the proposed ensemble method (WVCNN) reached at 0-HP and 1-HP reached 99.28% and 99.13% while for the over-sampled dataset the accuracy soared to 99.71% and 99.87% for 0 and 1-HP respectively. The performance was evaluated for other metrics apart from the accuracy to assess the model’s performance in terms of chance occurrences and the class wise performance.

Jun LUO, Chi LIU, Binglei WANG

The communication network based on quantum key distribution (QKD) has the ability to realize “perfect secrecy”.At present, it cannot meet the needs of large-scale applications and needs to combine classical cryptography in practical applications.Firstly, the functional architecture model of the telecom-operators cryptography application system integrating quantum key distribution was proposed, and then the hierarchy, the core network elements, the functional modules, and the interface relationships of the model were described.Furthermore, the framework of the application system was given, and the main components of the framework were introduced.Finally, the typical application scenarios and workflow were illustrated.

Luiz Fernando Capretz, Abdul Rehman Gilal

Software testing is one of the crucial supporting processes of the software life cycle. Unfortunately for the software industry, the role is stigmatized, partly due to misperception and partly due to treatment of the role. The present study aims to analyze the situation to explore what restricts computer science and software engineering students from taking up a testing career in the software industry. To conduct this study, we surveyed 88 Pakistani students taking computer science or software engineering degrees. The results showed that the present study supports previous work into the unpopularity of testing compared to other software life cycle roles. Furthermore, the findings of our study showed that the role of tester has become a social role, with as many social connotations as technical implications.