Hasil untuk "Engineering design"

Yonatan Uziel, Natan Orlov, Loay Atamneh et al.

Chemical monitoring of pollutants and hazardous materials in water supply systems traditionally depends on centralized laboratories, advanced instrumentation, and trained personnel, limiting accessibility and preventing real-time, on-site analysis. This work presents an alternative cost-effective, field-deployable approach that uses genetically engineered bioluminescent bioreporters, encapsulated in self-sufficient alginate capsules and integrated with an optoelectronic detection circuit, to detect and quantify target materials in water. We have developed a scalable single-channel prototype featuring four sensing tracks—two for sample measurement, one for clean water, and one for a standard reference solution. The latter employs the standard ratio (SR) method to ensure robust quantification, compensating for batch variability and environmental effects. System characterization showed high uniformity across tracks. Validation with nalidixic acid (NA) demonstrated reliable quantitative performance, with a blind test estimation of 5.6 mg/L for a true concentration of 5 mg/L, well within the calibration error range. Additional sensitivity testing confirmed detection of mitomycin C (MMC) at concentrations as low as 50 µg/L. Overall, the results highlight the potential of bacterial chemical sensing as a practical and scalable tool for real-time, in situ water quality monitoring networks.

Shangkai Liu, Kai Tong, Fengmin Chen et al.

The reliability of high-strength bolted connections is critical to the safety of large-scale engineering structures. This study proposes a non-contact quantitative method for detecting bolt loosening based on the self-magnetic flux leakage (SMFL) effect. Systematic experiments were carried out on M14-12.9 bolts, using nine independent specimens tested under six torque levels, to reveal the intrinsic relationship between bolt preload and the “magnetic valley” feature of the surface leakage field. For quantitative evaluation, the absolute value of the differential peak magnetic field, |ΔPMF|, is defined as the core feature parameter. The results show that, in the reference specimen group, |ΔPMF| exhibits a pronounced linear relationship with the applied torque (<i>R</i>² > 0.96), and the corresponding linear regression parameters display good consistency across the nine specimens (RSD ≈ 4%). Comparative tests on two additional bolt specifications clarify how bolt strength grade and geometric size influence the detection sensitivity and linearity. To address lift-off effects, measurements on a representative specimen at four lift-off heights were used to construct a simplified bivariate linear compensation model, which significantly reduces lift-off-induced bias within the working range <i>h</i> = 10–16 mm. Finally, a hierarchical diagnostic scheme for bolt loosening that incorporates lift-off compensation is established on the basis of |ΔPMF|, providing a feasible approach for rapid assessment of bolt loosening under complex service conditions.

Jaromir Hunia, Jaromir Hunia, Jaromir Tomasik et al.

The recent advancement of mRNA technology has opened new therapeutic avenues for treating hematologic malignancies, offering innovative approaches to enhance existing immunotherapies. This review examines the expanding role of in vitro transcribed (IVT)-mRNA-based platforms in hemato-oncology, focusing on key areas: monoclonal antibody production, bispecific antibody development, and CAR-T cell engineering. Unlike conventional biologics, mRNA allows for in vivo expression of therapeutic proteins, reducing manufacturing complexity and expanding access through scalable, cell-free synthesis. IVT-mRNA-encoded monoclonal and bispecific antibodies can overcome limitations such as short half-life and the need for continuous infusion, while enabling innovations like Fc silencing, protease-activated masking, and combinatorial immunotherapies. In CAR-T cell therapy, IVT-mRNA provides transient, safer alternatives to viral vector-based approaches and facilitates emerging strategies such as in vivo CAR programming and IVT-mRNA vaccine-like boosters. Despite these advantages, challenges remain, including delivery precision, durability of therapeutic effects, and limited clinical trial success. Beyond therapeutic mechanisms, the integration of bioinformatics and AI in IVT-mRNA design is accelerating the development of personalized and efficient cancer treatments. Overall, mRNA technology is redefining immunotherapy in hematology and holds the potential to broaden access to advanced treatments globally.

Suman Lata Tripathi, Neeraj Nayan Prakash, Inung Wijayanto

Abstract Low power consumption and high speed are always concerns of IC designers. Increased transistor density increases the power density per unit chip and is a major parameter of chip reliability. Nanosheet transistors are a hybrid of multichannel, similar to MOSFET, and vertically aligned like FinFET, which is a suitable choice due to its reduced self-heating effects and high current drive capabilities. In this work, the heterojunction nanosheets demonstrate high switching current and good channel control due to multichannel and multigate features. The heterojunction nanosheet MOSFET is analyzed for electric field, channel potential, drain current, leakage current, threshold voltage, transconductance, subthreshold performance, and device switching current ratio. The analysis also explores the possibility of CMOS logic with the designed n & p-channel heterojunction nanosheet MOSFET at a 5 nm technology node. All the designs and simulations are performed using TCAD software, 2D and 3D visuals, and simulation data generation. The results show a very low leakage current and a good value of the Ion/Ioff current ratio. The designed n- and p-channel devices are also compared for compatibility in a CMOS logic circuit by implementing inverters, universal gate layouts, and verifying the functionality of the logic circuit.

Ghina Bani Azifah, Teuku Faisal Fathani, Hendy Setiawan

Abstract Background There were more than 700 earthquakes with a magnitude of more than 5.0 over the past 100 years in the Special Region of Yogyakarta, Indonesia. Due to the high intensity of seismic activities, it is essential to perform seismic hazard analysis by considering local site effects. Therefore, this study aimed to analyze the peak ground acceleration (PGA) value based on the earthquake scenario of May 27, 2006, with a magnitude of 6.3, which occurred on the eastern side of the Opak Fault. Methods The study was conducted in the southern part of the Progo River, the Special Region of Yogyakarta, using 31 boreholes and 18 microtremor measurement points. The analysis was carried out using four methods: Kanai (In: Proceeding of Japan Earthquake Engineering Symposium 1–4, 1966) equation using microtremor data, deterministic equations with Ground Motion Prediction Equations Next Generations Attenuation West 2 (GMPE NGA West 2), Kanno et al (Bull Seismol Soc Am 96:879–897, 2006) attenuation equation, and probabilistic method referring to the Indonesian Seismic code. Results Results indicated that the highest value of PGA was obtained using the deterministic GMPE NGA West 2 weighted attenuation equation, which varied from 0.475 to 0.549 g. Meanwhile, Kanno et al (Bull Seismol Soc Am 96:879–897, 2006) attenuation equation resulted in values ranging from 0.266 to 0.394 g. In contrast, PGA values obtained through microtremor measurement resulted in a smaller value, in the range of 0.126–0.214 g. Probabilistic analysis in the study area produces values ranging from 0.373 to 0.450 g. Conclusion The location on the central side of the Progo River shows a lower PGA value than the other sides. PGA values will tend to be higher at locations near the earthquake source. The low PGA value that resulted from microtremor analysis was due to the consideration of local site effects in determining earthquake parameters in the study area. Determining the seismic hazard analysis method in infrastructure planning requires a comprehensive analysis by considering various parameters, such as the planning and design objectives, the location proximity to earthquake sources, historical seismic conditions, and the presence of the local site effects.

Bang Zhu, Wenkai Cheng, Shiting Wen et al.

Abstract The application of the $$\Phi$$ -OTDR (Phase-Optical Time Domain Reflectometry) system in real-time monitoring of power grid infrastructure has been proven effective in identifying and classifying various anomalies, such as digging, watering, and shaking. However, previous deep learning-based methods for $$\Phi$$ -OTDR event classification are primarily designed for balanced classification problems, where the number of abnormal and normal event samples is relatively equal. In practical scenarios, the data for abnormal events are often much smaller than those for normal events (noise), resulting in a long-tailed distribution problem that poses significant challenges for accurate classification. To address this long-tailed imbalance issue in the practical application of $$\Phi$$ -OTDR data, we introduce the Controllable Diffusion (ConDiff) framework, which aims to generate high-quality synthetic samples for abnormal situations. The ConDiff framework is composed of three essential components: Feedback-guided $$\Phi$$ -OTDR Augmenter, the High-Quality Sample Selection module, and the Dynamic Threshold Adjustment module. The Feedback-guided $$\Phi$$ -OTDR Augmenter utilizes diffusion model to generate synthetic samples that simulate abnormal events. The High-Quality Sample Selection module evaluates the quality of the generated synthetic samples and selects high-Quality samples. The Dynamic Threshold Adjustment module provides real-time feedback to dynamically control the sample generation process of Feedback-guided $$\Phi$$ -OTDR Augmenter. Compared to current state-of-the-art baselines, our proposed ConDiff framework achieves a notable improvement in classification accuracy, with an increase ranging from 3.7% to 7.2% in the BJTU-OTDR-LT dataset. This improvement demonstrates the effectiveness of the proposed ConDiff framework in addressing the long-tailed imbalance problem in $$\Phi$$ -OTDR event classification. The code will be released upon acceptance.

Iryna S. Protsak, Yevhenii M. Morozov

This review summarizes the fundamental concepts, recent advancements, and emerging trends in the field of stimuli-responsive hydrogels. While numerous reviews exist on this topic, the field continues to evolve dynamically, and certain research directions are often overlooked. To address this, we classify stimuli-responsive hydrogels based on their response mechanisms and provide an in-depth discussion of key properties and mechanisms, including swelling kinetics, mechanical properties, and biocompatibility/biodegradability. We then explore hydrogel design, synthesis, and structural engineering, followed by an overview of applications that are relatively well established from a scientific perspective, including biomedical uses (biosensing, drug delivery, wound healing, and tissue engineering), environmental applications (heavy metal and phosphate removal from the environment and polluted water), and soft robotics and actuation. Additionally, we highlight emerging and unconventional applications such as local micro-thermometers and cell mechanotransduction. This review concludes with a discussion of current challenges and future prospects in the field, aiming to inspire further innovations and advancements in stimuli-responsive hydrogel research and applications to bring them closer to the societal needs.

Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan

The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems as part of the emerging Software Engineering 3.0 era. Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution. This process involves dynamic optimization of cognitive architectures and their constituents (e.g., prompts, foundation model configurations, and system parameters) while finding the optimal trade-off between several objectives, such as accuracy, cost, and latency. This paper outlines the architecture of Compiler.next and positions it as a cornerstone in democratizing software development by lowering the technical barrier for non-experts, enabling scalable, adaptable, and reliable AI-powered software. We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search heuristics, reproducibility, and interoperability between compilers. Our vision lays the groundwork for fully automated, search-driven software development, fostering faster innovation and more efficient AI-driven systems.

Timo Kehrer, Robert Haines, Guido Juckeland et al.

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Haibo Wang, Zhuolin Xu, Shin Hwei Tan

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Xingang Guo, Yaxin Li, Xiangyi Kong et al.

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).

Sekip Sadiye Yasar, Osman Komut, Mehmet Yasar et al.

This study aimed to determine the risk level of noise, which is an important physical risk, in small and medium-sized furniture industry enterprises. The noise levels of the circular sawing machines, edge banding machines, and mitre cutting machines, which are among the main processing machines of the sector, were measured. The study was carried out in 32 furniture businesses. The possible risks of noise on the operators of the machines in question and other employees were evaluated. Noise level measurements were made with the help of TESTO 815 measuring device. Dunnett’s T3 test was used to detect differences in noise levels for machine operators and other employees. It was determined that the edge banding machine does not pose an occupational health and safety risk in terms of noise risk factors. However, the mitre cutting machine and the circular sawing machine pose a risk for the machine operator in active production by creating noise above the established exposure limit value. The mitre cutting machine carries the same risk for the machine operator when it is in operation but in passive production. The results revealed the need for personal protective equipment for machine operators for mitre cutting and circular sawing machine.

Chenghao Yu, Renjie Chen, Jinli Chen et al.

Advancements in tissue engineering are crucial for successfully healing tendon-bone connections, especially in situations like anterior cruciate ligament (ACL) restoration. This study presents a new and innovative three-dimensional scaffold, reinforced with nanofibers, that is specifically intended for acellular tendon complexes. The scaffold consists of a distinct layered arrangement comprising an acellular tendon core, a middle layer of polyurethane/type I collagen (PU/Col I) yarn, and an outside layer of poly (L-lactic acid)/bioactive glass (PLLA/BG) nanofiber membrane. Every layer is designed to fulfill specific yet harmonious purposes. The acellular tendon core is a solid structural base and a favorable environment for tendon cell functions, resulting in considerable tensile strength. The central PU/Col I yarn layer is vital in promoting the tendinogenic differentiation of stem cells derived from tendons and increasing the expression of critical tendinogenic factors. The external PLLA/BG nanofiber membrane fosters the process of bone marrow mesenchymal stem cells differentiating into bone cells and enhances the expression of markers associated with bone formation. Our scaffold's biocompatibility and multi-functional design were confirmed through extensive in vivo evaluations, such as histological staining and biomechanical analyses. These assessments combined showed notable enhancements in ACL repair and healing. This study emphasizes the promise of multi-layered nanofiber scaffolds in orthopedic tissue engineering and also introduces new possibilities for the creation of improved materials for regenerating the tendon-bone interface.

LIN Yan, JIN Tingyu, YANG Yuchao

Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, economy, coordination and operability, and the defects of ant colony optimization algorithm in dealing with mixed pipeline layout conditions are improved. A spatial state transition strategy for optimizing feasible solution search, a pheromone diffusion mechanism for improving pheromone inspiration effect and accelerating algorithm convergence are proposed, and a multi-ant colony co-evolution mechanism is designed for mixed pipeline layout conditions. Based on the secondary development technology, the application of this method in the third-party design software is realized, and verified by a nuclear primary pipeline layout project. The results show that the pheromone Gaussian diffusion multi ant colony optimization (PG-MACO) algorithm has a better performance and layout effect than the traditional ant colony algorithm. The routing efficiency is improved by 58.38%, the convergence algebra is shortened by 43.24%, the pipeline length is shortened by 33.88%, and the number of pipeline bends is reduced by 41.67%, which verifies the effectiveness and engineering practicability of the proposed method.

Yvonne Dittrich, Helen Sharp, Cleidson de Souza

Ethnography has become one of the established methods for empirical research on software engineering. Although there is a wide variety of introductory books available, there has been no material targeting software engineering students particularly, until now. In this chapter we provide an introduction to teaching and learning ethnography for faculty teaching ethnography to software engineering graduate students and for the students themselves of such courses. The contents of the chapter focuses on what we think is the core basic knowledge for newbies to ethnography as a research method. We complement the text with proposals for exercises, tips for teaching, and pitfalls that we and our students have experienced. The chapter is designed to support part of a course on empirical software engineering and provides pointers and literature for further reading.

Ranim Khojah, Mazen Mohamad, Philipp Leitner et al.

Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case that relies on the production of text, including software engineering. Currently there is much debate, but little empirical evidence, regarding the practical usefulness of LLM-based tools such as ChatGPT for engineers in industry. We conduct an observational study of 24 professional software engineers who have been using ChatGPT over a period of one week in their jobs, and qualitatively analyse their dialogues with the chatbot as well as their overall experience (as captured by an exit survey). We find that, rather than expecting ChatGPT to generate ready-to-use software artifacts (e.g., code), practitioners more often use ChatGPT to receive guidance on how to solve their tasks or learn about a topic in more abstract terms. We also propose a theoretical framework for how (i) purpose of the interaction, (ii) internal factors (e.g., the user's personality), and (iii) external factors (e.g., company policy) together shape the experience (in terms of perceived usefulness and trust). We envision that our framework can be used by future research to further the academic discussion on LLM usage by software engineering practitioners, and to serve as a reference point for the design of future empirical LLM research in this domain.

Oleksandr Kosenkov, Michael Unterkalmsteiner, Jannik Fischbach et al.

Context: Regulatory acts are a challenging source when eliciting, interpreting, and analyzing requirements. Requirements engineers often need to involve legal experts who, however, may often not be available. This raises the need for approaches to regulatory Requirements Engineering (RE) covering and integrating both legal and engineering perspectives. Problem: Regulatory RE approaches need to capture and reflect both the elementary concepts and relationships from a legal perspective and their seamless transition to concepts used to specify software requirements. No existing approach considers explicating and managing legal domain knowledge and engineering-legal coordination. Method: We conducted focus group sessions with legal researchers to identify the core challenges to establishing a regulatory RE approach. Based on our findings, we developed a candidate solution and conducted a first conceptual validation to assess its feasibility. Results: We introduce the first version of our Artifact Model for Regulatory Requirements Engineering (AM4RRE) and its conceptual foundation. It provides a blueprint for applying legal (modelling) concepts and well-established RE concepts. Our initial results suggest that artifact-centric RE can be applied to managing legal domain knowledge and engineering-legal coordination. Conclusions: The focus groups that served as a basis for building our model and the results from the expert validation both strengthen our confidence that we already provide a valuable basis for systematically integrating legal concepts into RE. This overcomes contemporary challenges to regulatory RE and serves as a basis for exposure to critical discussions in the community before continuing with the development of tool-supported extensions and large-scale empirical evaluations in practice.