Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

H. Beck, R. Hesse

Zeyu Zhang, Long Wan, Yong Yang et al.

Overcoming the strength-ductility trade-off dilemma is paramount for advanced materials engineering. Herein, we prepared 7075 aluminium alloys with superior strength and ductility via additive friction stir deposition (AFSD) and subsequent heat treatment. Compared with the commercial base material, the heat-treated 7075 aluminium alloy maintained a high ultimate tensile strength of 556 MPa, while the uniform elongation increased from 12.2% to 26.7%, exhibiting the highest strength-ductility synergy reported among commercial Al-Zn-Mg-Cu alloy systems. Grain boundary sliding was activated via the equiaxed grains to accommodate substantial plastic strain. This method provides a promising and cost-effective pathway for developing strength-ductility on Al-Zn-Mg-Cu alloys.

Swathi Muthyala Ramesh, Kristen M. Donnell

Frequency selective surfaces (FSSs) are arrays of conductive elements or apertures that exhibit frequency-dependent reflection and transmission properties. Their electromagnetic response is influenced by geometry and environmental conditions, making them attractive for wireless strain-sensing applications. However, temperature variations can produce frequency shifts similar to those caused by strain, reducing measurement accuracy. This work investigates the effects of intrinsic temperature compensation on two common FSS unit cell geometries&#x2014;loop and patch&#x2014;through comprehensive simulation analysis. The results show that loop-based cells offer superior thermal stability, while patch-based cells provide greater strain sensitivity, illustrating the tradeoff between thermal robustness and mechanical responsiveness. A patch-type FSS strain sensor was designed, fabricated, and characterized under varying temperature and strain. The sensor achieves a strain sensitivity of ~150 MHz per 1%<inline-formula> <tex-math notation="LaTeX">${\varepsilon }_{l}$ </tex-math></inline-formula>, while temperature-induced drift is limited to ~12 MHz over a 200&#x00B0;C range, confirming the effectiveness of the intrinsic compensation strategy. The results provide valuable insights for optimizing FSS-based sensor design in structural health monitoring applications and balancing thermal stability with mechanical sensitivity to ensure reliable performance in thermally dynamic environments.

H. Sinan Bank, Daniel R. Herber

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.

Haimo Joehri

This paper gives an overview about aspects of mechanical engineering of undulators. It is based mainly on two types that are used in the SwissFEL facility. The U15 Undulator is an example of an in-vacuum type and the UE38 is an APPLE-X type. It describes the frame, the adjustment of the magnets with flexible keepers and the adjustment of the whole device with eccentric movers.

Gahong Lee, Yunhui Jang, Yeojin Jeong et al.

Taofeek Olaidey Salaudeen, Nur Idora Abdul Razak, Syahrul Afzal Bin Che Abdullah et al.

Efficient edge caching is vital for meeting the stringent latency and reliability demands of Ultra-Reliable Low Latency Communication (URLLC) in 5G and beyond. This paper proposes CacheHitPredictor, a federated learning-based framework for predictive edge caching that preserves data privacy while handling client heterogeneity and dynamic user behavior. A URLLC scenario was simulated using CloudSimPlus to generate realistic user request traces. The resulting dataset was used to train a federated neural network model via the Flower framework, with varying Dirichlet factors to evaluate data heterogeneity. The trained model was deployed via a Flask REST API and integrated with the CloudSimPlus simulation to enable online cache decision-making. Experimental results demonstrate that the proposed model performs on par with LFU under skewed workloads, underscoring the practical potential of federated learning to enable adaptive and efficient caching in URLLC edge environments.

Vibha, Rajesh R. Pai, Sumith N.

This study proposes a semantic pipeline designed to generate domain-oriented and contextually relevant hypotheses by analyzing existing literature on mHealth applications in India. Using a corpus of mHealth texts, the framework extracts hidden semantics through TF-IDF, topic modeling, and contextual mapping with domain ontologies. It then employs prompt-based interactions with large language models (LLMs) to systematically generate and validate hypotheses aligned with identified topic-concept relationships. The results demonstrate the framework&#x2019;s effectiveness in producing high-quality, structured hypotheses, as validated by expert ratings ranging from 4.2 to 4.6. Most hypotheses were found to be plausible or highly plausible, with low semantic redundancy indicating diversity across topics, except in stakeholder-related areas which showed moderate overlap. Although the inclusion of semantic augmentation increased processing time, it significantly enhanced interpretability and validity. The high lexical density observed (up to 0.90) further reflects the linguistic flexibility of the generated hypotheses. This approach underscores the potential of computational methods in automating hypothesis generation and enabling data-driven discoveries in the mHealth domain.

Ana Arauzo

This lecture presents an overview of the basic concepts and fundamentals of Engineering Materials within the framework of accelerator applications. After a short introduction, main concepts relative to the structure of matter are reviewed, like crystalline structures, defects and dislocations, phase diagrams and transformations. The microscopic description is correlated with physical properties of materials, focusing in metallurgical aspects like deformation and strengthening. Main groups of materials are addressed and described, namely, metals and alloys, ceramics, polymers, composite materials, and advanced materials, where brush-strokes of tangible applications in particle accelerators and detectors are given. Deterioration aspects of materials are also presented, like corrosion in metals and degradation in plastics.

C. -J. Yang

I show a way to tune photo-nuclear cross section effectively and therefore achieve nuclear transitions "on demand". The method is based on combinatorial enhancement of multiphoton processes under intense conditions. Taking advantage of recent advances in high-power laser systems (HPLS) and nuclear structure calculations, efficient control of nuclear transitions up to E4 in multipolarity can be reached today. The same idea can be extended to the search for rare transitions and hidden states, which applies to the $γ$-beams generated from conventional sources as well.

Nathasha F Almeda

Boolean Algebra stands among the most widely recognized technique used especially in computer science and electrical engineering. This branch largely relates to subjects such as digital logic design, circuit optimization, and computational problems resolution. This is basically the drive behind the present assignment: To develop an Algebraic Boolean calculator capable of accepting any standard user input for analysing and simplifying Boolean expressions. Two salient features of this calculator are the Simplification Mode and the Solving Mode. In the Simplification mode, the input is a Boolean expression, and the output is the possible simplified forms of equivalent expressions. This, in turn, is helpful in optimizing digital circuits because the reduction would lessen the required number of logic gates, thus making the design more efficient. In contrast, the Solving mode will evaluate Boolean expressions with variable values as conferred by the user. It performs logical operations to crunch numbers at an extremely fast speed. This mode will, therefore, be used to assess a particular circuit/function, make a truth table, and implement real-time Boolean evaluation. Hence, two modes combined make this Boolean Algebraic Calculator an utmost evaluator to digital electronics, logical circuit designing, and computation logic students, scholars, and practitioners. It enhances the productivity of Boolean function analysis, says "as easy as ABC" to solving problems and building up to a viable design option for logical systems.

M. Stadnik, Andrii Shtuts, Roman Lypnytskyi et al.

The article examines the application of mechatronic principles using scraper conveyors as an example, which are among the key mechanisms for transporting bulk materials in the agro-industrial sector and industry. The authors propose the structure of a scraper conveyor as a mechatronic module that integrates mechanical, electromechanical and electronic components, which ensures high reliability, productivity and flexibility of the system as a whole. Special attention is paid to the use of two-speed induction motors, which makes it possible to implement a staged start-up of the conveyor: first at low speed for smooth acceleration and reduction of inrush currents, and then at the rated high speed to provide the required throughput and optimize energy consumption. This approach significantly reduces overloads of the mechanisms, increases the energy efficiency and service life of the equipment, and also reduces the likelihood of emergency situations. The paper provides a detailed analysis of functional control schemes for the conveyor drive and start-up algorithms, including adaptive solutions that take into account variable load, material type and operating conditions. The interaction between the electronic control system and the mechanical components of the conveyor is analyzed, which ensures optimal load distribution on the gearbox, traction elements and drive mechanisms, and also enables in-depth diagnostics of the equipment condition and planning of preventive maintenance. Such integration increases the productivity, reliability and durability of the scraper conveyor and contributes to the efficient use of energy resources with minimal operator involvement. The abstract emphasizes the interdisciplinary nature of the research: the combination of knowledge in power supply, electrical technologies, automation, electronics, digital circuitry, information technologies and application software makes it possible to create a comprehensive mechatronic control system that can be implemented in modern agro-industrial and industrial production processes. The proposed solution demonstrates the possibility of integrating automated and information-controlled components into transport systems to increase their adaptability, energy efficiency and operational safety. The obtained results may be useful for engineers, researchers, developers and technical specialists involved in the design, implementation and operation of automated bulk-material conveying systems in agriculture, the agro-industrial sector and industry in general. They provide practical recommendations on the integration of mechanical, electromechanical and electronic components within a single mechatronic module, as well as on the optimization of start-up algorithms and control of conveyor drives. In addition, the research findings can be used to improve the energy efficiency, productivity and reliability of transport systems, reduce maintenance costs and extend equipment lifetime. They are also of considerable value to university lecturers and students who study mechatronics, automated power systems, modern methods of controlling electrical equipment, as well as digital and information technologies in industry. Using the obtained results in the educational process makes it possible to develop practical competencies in the design of integrated automated systems, in the analysis of their dynamic characteristics, and in the development of algorithms for adaptive control and equipment diagnostics. In addition, the results may be useful for research groups and scientific laboratories involved in improving transport equipment, implementing intelligent control systems, and optimizing energy consumption in production processes. The interdisciplinary approach makes it possible to combine knowledge of power supply, electrical technologies, automation, electronics, digital circuit design, information technologies and application software, which contributes to increasing the efficiency and innovation potential of industrial and agro-industrial systems. The research results not only enhance the scientific and technical level of knowledge in the field of mechatronics and automation, but also create a practical foundation for the development of modern, integrated and highly efficient automated material transportation systems.

Yuhui Ren, Jiale Su, Jiahan Ke et al.

Germanium (Ge) has long been regarded as a promising channel material, owing to its superior carrier mobility and highly tunable electronic band structure. The new generation of low-power electronics is approaching the formation of fully depleted (FD) transistors on Si-on-insulator (SOl) and Ge-on-insulator (GOl) substrates. In this work, we present a full process of a novel FDGOI transistor formed on a strained GOI with low defect density. This scalable and industry-compatible approach enables the formation of uniform 50 nm thick Ge layers by using spinning wet etch with ultrasmooth surfaces (RMS roughness = 0.262 nm) and a low etch-pit density of ~105 cm−2. Electrical measurements reveal excellent carrier transport properties, with back-gate (BG) transistors achieving mobilities of 550–600 cm2/V·s, while front-gate (FG) devices exhibit sharp switching behavior and steep subthreshold slopes, yielding ION/IOFF ratios up to 105. Temperature-dependent measurements further demonstrate a pronounced enhancement of device performance: the ION/IOFF ratio increases to 106, the subthreshold swing (SS) decreases from 179 mV/dec at room temperature to 137 mV/dec at 120 K, and the threshold-voltage shift with temperature is as low as 1.87 mV/K across the range of 30–300 K. Such behavior highlights the potential of band-gap engineering for precise threshold-voltage control. Taken together, these results establish GOI as a CMOS-compatible material platform and provide a solid technological basis for the development of next-generation low-power transistors beyond conventional CMOS scaling.

Ayesha Kausar

To meet today’s technological demands, nanofibrous materials (especially nanocomposite nanofibers) have been noticed more competent of sustaining anticorrosion, structural integrity, and radiation protection, as compared to traditional polymers or nanocomposites. Along these lines, this innovative review article strategically highlights the scientific merit of multifunctional polymer nanofibers and polymer nanocomposite nanofibers for radiation shielding purposes. Accordingly, distinct sections of this manuscript systematically address fundamentals, corrosion protection, and nuclear/electromagnetic shielding capabilities of nanofibrous materials. In this regard, design, fabrication, physical features, and performance aspects of electrospun nanocomposite nanofibers (reinforced with nanocarbons and inorganic nanoparticles) have been argued. Notably, microstructural continuity, electrical/electrochemical characteristics, percolation features, robustness, barrier effects, and self healing properties of nanocomposite nanofibers revealed technical worth for high end anticorrosion applications. Additionally, electron conductivity, dielectric/magnetic permittivity, mechanical/heat stability, and interfacial compatibility of nanocomposite nanofibers depicted next level radiation shielding competence. Consequently, radiation shielding nanofibrous materials with notable mechanical and corrosion protection characteristics attained utmost importance for futuristic real-world industries (aeronautical engineering, defense, electronics, energy devices). Despite the research progress so far, future industrial deployments of radiation shielding multifunctional nanocomposite nanofibers strongly rely on using sustainable green materials, optimized manufacturing methods/parameters, material reproducibility, degradation/biodegradation, and final life cycle assessments.

Yuliya Preger

The increasing voltage and energy density of lithium-ion (Li-ion) batteries have led to their widespread adoption in applications including consumer electronics, electric vehicles, and grid-tied energy storage systems (ESS). However, as energy density and installation sizes grow, so do concerns regarding safety, particularly the risks of thermal runaway and catastrophic fires. To better understand the risks and consequences associated with Li-ion battery failures, numerous studies have investigated their responses to various abuse scenarios. Additionally, established standards and certifications impose stringent requirements for battery safety testing. These tests are normally conducted with fresh batteries; however, given the expected lifetime of Li-ion batteries, it is critical to understand the safety profile of aged cells. This presentation explores the different degradation pathways of Li-ion cells cycled for more than seven years. We will also examine critical changes in the thermal, electrical, and mechanical abuse responses of these aged Li-ion cells compared to uncycled ones. By understanding how the safety profiles of aged cells differ from those of new cells, we can identify what diagnostics to prioritize and enable the design of more effective battery failure mitigation systems. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2024-17030A.

S. Nivithan, A.Abhishek, P. Thanapal et al.

This study highlights the necessity of employing extremely potent software tools for both teaching and mastering mathematically complex theory subjects. In the field of Electrical Engineering, there are courses like Engineering Electromagnetics, Signals & Systems, Control Systems, Digital Signal Processing, Digital Image Processing, Antennas & Wave Propagation, etc., where students learn very little because teachers must spend a lot of time in the classroom explaining mathematical principles. Since lecturing alone doesn't aid in the appropriate interpretation of the material, learners perceive it as being extremely difficult and unconnected. A robust graphical data flow programming environment with an extensive library of functions and tool sets in both simulation and real-time mode is one of the many such software tools that are currently accessible. With the use of these technologies, students from non-electronics streams can complete courses with a strong mathematical foundation more quickly and easily. This paper uses Orange Data Mining software tools, ChatGPT, Google Classroom, Matlab, and the Whatsapp platform to teach digital signal processing more effectively. Educators may focus more on practice because of this kind of research, which encourages students to learn for themselves.

R. Basu, Chinara Kuldip, Mahesh Kumar et al.

Power electronic systems are essential in modern electrical engineering, playing a critical role in various applications. Designing and developing hardware prototypes for these systems presents significant challenges for engineers. This paper proposes a universal framework to streamline the design and development of power electronics hardware. It also establishes a relationship between converter frequency and system clock frequency in real-time processes. The framework is validated through real-time simulation and controller hardware-in-the-loop testing using DSP and Opal RT systems. This allows the designer to examine the system behavior and controller performance prior to the physical prototype development by performing mathematically intensive computations on high-speed real-time processors.

Hongming Peng, Siyu Xiang, Mingju Chen et al.

Road segmentation is an important task in the field of semantic segmentation, and the Deeplabv3+ algorithm, which is commonly used for road segmentation, has shortcomings, such as numerous parameters and a tendency to lose detailed information. Therefore, this paper proposes DCN-Deeplabv3+, an improved road segmentation algorithm with dual attention modules based on the Deeplabv3+ network, aiming to reduce the model parameters and computation while improving the segmentation accuracy. <xref ref-type="disp-formula" rid="deqn1">(1)</xref> MobileNetV2 is used as the backbone network to reduce model parameters and memory consumption. <xref ref-type="disp-formula" rid="deqn2">(2)</xref> DenseASPP+SP is used for multi-scale information fusion to obtain a larger sensory field for improved model performance. <xref ref-type="disp-formula" rid="deqn3">(3)</xref> The deep learning model&#x2019;s understanding of the spatial structure of the input data is enhanced by using CA (coordinate attention) to improve the model&#x2019;s performance in dealing with spatial structure-related tasks. <xref ref-type="disp-formula" rid="deqn4">(4)</xref> The neural attention mechanism (NAM) is applied to better focus on key regions in the image, thereby improving the accuracy of target detection. The experimental results show that mIoU and mPA are improved by 1.20% and 2.30% on the PASCAL VOC 2012 dataset, mIoU and mPA are improved by 3.15% and 3.90% on the Cityscapes dataset, respectively. It can be concluded that the method proposed in this paper outperforms the baseline method and has excellent segmentation accuracy on roads.

Toni Utech, Henning Kruppa, Anya Vollpracht et al.

Biological materials found in nacre or glass sponges reveal specific layered organic and inorganic structures known for their fracture toughness caused by crack deflection and bridging. This work aims to bio-mimic the brick-and-mortar (BnM) structure from nacre and the layer-by-layer (LbL) structure from the glass sponge filaments to incorporate these effects into the contact zone of carbon fiber-reinforced concrete (CFRC) in order to prevent brittle composite failure. To build up BnM- and LbL-structure materials such as nanoclays, sodium water glass, and polymer dispersions were selected since they are well-established low-cost materials in building. Nanoclays were analyzed regarding their size, dispersibility and exfoliation. Montmorillonite (MMT) was used to be mixed with polymers to produce self-assembled BnM-structured films and coatings. Also, LbL-structures were formed by alternating layers of sodium water glass and polymer. Scanning electron microscopy and energy-dispersive X-ray spectroscopy were used to verify the morphology. The MMT-containing coatings demonstrated enhanced nucleation potential when exposed to cementitious eluate. Micromechanical pull-out tests on single carbon fibers with BnM- and LbL-coatings embedded in concrete demonstrate the potential to increase composite toughness. The successful implementation of the bio-inspired structures using affordable materials lays the groundwork for their scalability and integration into composite structures for building.

Siripen Pongpaichet, Boonyapat Sukosit, Chitchaya Duangtanawat et al.

Crimes result in not only loss to individuals but also hinder national economic growth. While crime rates have been reported to decrease in developed countries, underdeveloped and developing nations still suffer from prevalent crimes, especially those undergoing rapid expansion of urbanization. The ability to monitor and assess trends of different types of crimes at both regional and national levels could assist local police and national-level policymakers in proactively devising means to prevent and address the root causes of criminal incidents. Furthermore, such a system could prove useful to individuals seeking to evaluate criminal activity for purposes of travel, investment, and relocation decisions. Recent literature has opted to utilize online news articles as a reliable and timely source for information on crime activity. However, most of the crime monitoring systems fueled by such news sources merely classified crimes into different types and visualized individual crimes on the map using extracted geolocations, lacking crucial information for stakeholders to make relevant, informed decisions. To better serve the unique needs of the target user groups, this paper proposes a novel comprehensive crime visualization system that mines relevant information from large-scale online news articles. The system features automatic crime-type classification and metadata extraction from news articles. The crime classification and metadata schemes are designed to serve the need for information from law enforcement and policymakers, as well as general users. Novel interactive spatiotemporal designs are integrated into the system with the ability to assess the severity and intensity of crimes in each region through the novel Criminometer index. The system is designed to be generalized for implementation in different countries with diverse prevalent crime types and languages composing the news articles, owing to the use of deep learning cross-lingual language models. The experiment results reveal that the proposed system yielded 86&#x0025;, 51&#x0025;, and 67&#x0025; F1 in crime type classification, metadata extraction, and closed-form metadata extraction tasks, respectively. Additionally, the results of the system usability tests indicated a notable level of contentment among the target user groups. The findings not only offer insights into the possible applications of interactive spatiotemporal crime visualization tools for proactive policymaking and predictive policing but also serve as a foundation for future research that utilizes online news articles for intelligent monitoring of real-world phenomena.