Hasil untuk "Systems engineering"

Boyang Zhang, A. Korolj, B. Lai et al.

Baolin Guo, P. Ma

J. Mano, G. Silva, H. Azevedo et al.

The fields of tissue engineering and regenerative medicine aim at promoting the regeneration of tissues or replacing failing or malfunctioning organs, by means of combining a scaffold/support material, adequate cells and bioactive molecules. Different materials have been proposed to be used as both three-dimensional porous scaffolds and hydrogel matrices for distinct tissue engineering strategies. Among them, polymers of natural origin are one of the most attractive options, mainly due to their similarities with the extracellular matrix (ECM), chemical versatility as well as typically good biological performance. In this review, the most studied and promising and recently proposed naturally derived polymers that have been suggested for tissue engineering applications are described. Different classes of such type of polymers and their blends with synthetic polymers are analysed, with special focus on polysaccharides and proteins, the systems that are more inspired by the ECM. The adaptation of conventional methods or non-conventional processing techniques for processing scaffolds from natural origin based polymers is reviewed. The use of particles, membranes and injectable systems from such kind of materials is also overviewed, especially what concerns the present status of the research that should lead towards their final application. Finally, the biological performance of tissue engineering constructs based on natural-based polymers is discussed, using several examples for different clinically relevant applications.

K. Pohl, Günter Böckle, F. V. Linden

T. Ross

Eduardo Sontag

Eduard Enoiu, Jean Malm, Gregory Gay

We explore the concept of folklore within software engineering, drawing from folklore studies to define and characterize narratives, myths, rituals, humor, and informal knowledge that circulate within software development communities. Using a literature review and thematic analysis, we curated exemplar folklore items (e.g., beliefs about where defects occur, the 10x developer legend, and technical debt). We analyzed their narrative form, symbolic meaning, occupational relevance, and links to knowledge areas in software engineering. To ground these concepts in practice, we conducted semi-structured interviews with 12 industrial practitioners in Sweden to explore how such narratives are recognized or transmitted within their daily work and how they affect it. Synthesizing these results, we propose a working definition of software engineering folklore as informally transmitted, traditional, and emergent narratives and heuristics enacted within occupational folk groups that shape identity, values, and collective knowledge. We argue that making the concept of software engineering folklore explicit provides a foundation for subsequent ethnography and folklore studies and for reflective practice that can preserve context-effective heuristics while challenging unhelpful folklore.

H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

Daniel Cristóbal Andrade-Girón, Juana Sandivar-Rosas, William Joel Marin-Rodriguez et al.

Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, <i>F</i>1 score, precision, recall, Cohen’s Kappa, and area under the curve (<i>AUC</i>). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy ± SD: 93.36% ± 0.22; <i>F</i>1 score: 0.936; <i>AUC</i>: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy ± SD: 90.76% ± 0.18; <i>F</i>1 score: 0.916; <i>AUC</i>: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount.

Zhe Lv, Zhonghao Sun, Lei Wang et al.

With the accelerating global transition toward sustainable energy, the role of battery energy storage systems (ESSs) becomes increasingly prominent. This study employs the isothermal battery calorimetry (IBC) measurement method and computational fluid dynamics (CFD) simulation to develop a multi-domain thermal modeling framework for battery systems, spanning from individual cells to modules, clusters, and ultimately the container level. Experimental validation confirms the model’s accuracy, with the simulated maximum cell temperature of 36.2 °C showing only a 1.8 °C deviation from the measured value of 34.4 °C under real-world operating conditions. Furthermore, by integrating on-site calibrated thermodynamic parameters of the container, a battery system energy efficiency model is established. Combined with the battery aging engineering model, a coupled lifetime–energy efficiency model is constructed. Six different control strategies are simulated and analyzed to quantify the system’s comprehensive lifecycle benefits. The results demonstrate that the optimized control strategy enhances the overall energy storage station revenue by 2.63%, yielding an additional cumulative profit of CNY 13.676 million over the entire lifecycle. This research provides an effective simulation framework and decision-making basis for the thermal management optimization and economic evaluation of battery ESSs.

Kirill Djebko, Patrick Schurk, Tom Baumann et al.

Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the InnoCube 3U nanosatellite. Deploying complex Neural Networks (NNs) on resource-limited microcontrollers presents a significant challenge. To overcome this, we propose distilling a Multi-Layer Perceptron (MLP) trained with Deep Reinforcement Learning (DRL) for attitude control into a Kolmogorov–Arnold Network (KAN). We convert this numeric KAN into a symbolic KAN, where each edge represents a learnable mathematical function, and finally extract a concise symbolic formula. This symbolic representation dramatically reduces memory usage and computational complexity, making it ideal for pico- and nanosatellites. We evaluate and demonstrate the feasibility of this approach for inertial pointing with reaction wheels in simulation using a realistic model of the InnoCube satellite. Our results show that the highly compressed KANs successfully solve the attitude control problem, while reducing the required memory footprint and inference time on the InnoCube ADCS hardware by over an order of magnitude. Beyond attitude control, we believe symbolic KANs hold great potential in aerospace for neural network compression and interpretable, data-driven modeling and system identification in future space missions.

Jie Peng, Yuxi Zou, Hao Zhang et al.

Globally, recycled building materials have attracted much attention, but the ambiguity of the use of recycled building materials makes it difficult for the building material remanufacturer (BMR) to compete with the building material manufacturer (BMM). Brand building is an important strategic tool for enterprises to increase product competitiveness. From the new perspective of the supply chain, this paper aims to examine the decision-making behavior of enterprises under two scenarios of consumer ambiguity neutrality and ambiguity tolerance and to analyze the impact of ambiguity tolerance on the pricing decisions of building materials supply chains in a brand-building scenario. This paper constructs a building material supply chain game model consisting of the BMM and BMR, according to the cognitive–affective personality system (CAPS) theory and through the Stackelberg game. The main findings are as follows. (1) Strengthening brand building can mitigate the negative impact of ambiguity tolerance on new product pricing. The selling price of recycled building materials is positively related to ambiguity tolerance. (2) When the BMM has higher brand value, there is a U-shaped trend between profit and ambiguity tolerance at a cost coefficient above the threshold value of 0.61. (3) When the BMR has higher brand value, profit is negatively related to ambiguity tolerance at operational inefficiencies and cost coefficients below the threshold value of 0.45. Otherwise, profits and ambiguity tolerance follow a U-shaped trend. This paper not only expands the research on brand building and ambiguity tolerance but also provides theoretical guidance for enterprises to make effective decisions in response to consumers’ ambiguity psychology.

Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat et al.

The increasing adoption of electric vehicles (EVs) has led to the widespread implementation of battery swapping stations. However, ensuring fairness in battery pricing remains a significant challenge since variations in battery health and performance among swapped batteries can result in user dissatisfaction and operational inefficiencies. This paper introduces a novel approach to enhance fairness in battery swapping by integrating a machine learning-based real-time prediction model with a pricing strategy based on remaining useful life (RUL) estimation to address this issue. The proposed solution comprises a real-time RUL estimation system and a dynamic pricing mechanism that ensures fair pricing based on battery health and performance. This integrated approach aims to improve user satisfaction and the operational efficiency of swapping stations. The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. The results suggest that XGBoost provides the most suitable balance between accuracy and efficiency, making it an effective solution for real-world applications. Comparative analysis shows that the XGBoost model outperforms the second-best method (Random Forest) with a lower error (3.50 vs 3.79) while maintaining competitive computational efficiency (9.75 vs 8.52 seconds) and memory usage (2.12 vs 2.32 MB) when solving a typical numerical case study problem. The proposed approach has the potential to accelerate the adoption of electric vehicles and contribute to sustainability goals by promoting efficient battery utilization and fair pricing mechanisms.

Ziyou Li, Agnia Sergeyuk, Maliheh Izadi

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.

Marina Araújo, Júlia Araújo, Romeu Oliveira et al.

[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.

Adrien Banse, Giannis Delimpaltadakis, Luca Laurenti et al.

With the increasing ubiquity of safety-critical autonomous systems operating in uncertain environments, there is a need for mathematical methods for formal verification of stochastic models. Towards formally verifying properties of stochastic systems, methods based on discrete, finite Markov approximations -- abstractions -- thereof have surged in recent years. These are found in contexts where: either a) one only has partial, discrete observations of the underlying continuous stochastic process, or b) the original system is too complex to analyze, so one partitions the continuous state-space of the original system to construct a handleable, finite-state model thereof. In both cases, the abstraction is an approximation of the discrete stochastic process that arises precisely from the discretization of the underlying continuous process. The fact that the abstraction is Markov and the discrete process is not (even though the original one is) leads to approximation errors. Towards accounting for non-Markovianity, we introduce memory-dependent abstractions for stochastic systems, capturing dynamics with memory effects. Our contribution is twofold. First, we provide a formalism for memory-dependent abstractions based on transfer operators. Second, we quantify the approximation error by upper bounding the total variation distance between the true continuous state distribution and its discrete approximation.

Amirah Abd Rahman, Kamilia Kamardin, Yoshihide Yamada et al.

In radio wave hyperthermia therapy, array antenna configuration was mainly studied to generate a small spot at the diseased part. Array antennas have the flexibility in controlling radiation performance, such as spot positions, by using their numerous radiating elements. However, the flexibility is achieved at the expense of antenna structure complexity. On the other hand, a lens antenna can concentrate radio waves into a small spot by forming a lens shape. The simplicity of a lens antenna structure lends itself to easy handling in a practical application. Moreover, the frequency independence of the lens antenna allows for a more flexible selection of hyperthermia therapy frequencies. Therefore, the lens antenna is selected as a focusing antenna in this paper. The lens shaping method and the temperature increment measurement are the main contents of this paper. The designed lens has a diameter of 30 cm, a focusing distance of 30 cm, and a working frequency of 2.45 GHz. A thin lens design method is applied to reduce lens weight. Firstly, the focusing ability of the designed lens is ensured by comparing the spot size results of electromagnetic (EM) simulation with its theoretical value. A spot size of 1.77 cm is obtained in both cases. Next, the temperature increment is examined by EM simulations. The temperature at the 2 cm tumor was increased to 41 °C from the human body temperature of 37 °C by an input power of 10 Watts (W). For the temperature increment measurement, a tumor within human body phantom is utilized and the available input power is reduced to 4 W. The tumor temperature increased from 21.5 °C of room temperature to 24.4 °C, which was captured by a thermal imaging camera. As a result, the functionality of the lens antenna for hyperthermia therapy is verified.

Balint Bicski, Adrian Pekar

This paper presents a comprehensive study on the identification and analysis of Service Degradation (SD) events within a university dormitory network, leveraging LAN data to develop a robust methodology applicable to diverse networking environments. Employing statistical techniques, such as Interquartile Range (IQR) and Z-score analyses, we detect significant deviations in network performance&#x2014;specifically, extreme delays and jitter&#x2014;that indicate potential SD. The methodology was rigorously validated in various settings, demonstrating minimal deviations in results and reinforcing the approach&#x2019;s consistency and reliability. Initial tests conducted in a university dormitory environment suggest the model&#x2019;s potential applicability in both residential and enterprise networks, thus broadening its utility. By refining the detection and understanding of SD indicators, this research contributes systematic methodological applications and a valuable annotated dataset to the field. This groundwork enables network administrators to enhance service quality preemptively, offering significant implications for future research and practical applications in network management.

Jasman Pardede, Desita Nurrohmah

Background: Hepatitis is a contagious inflammatory disease of the liver and is a public health problem because it is easily transmitted. The main factors causing hepatitis are viral infections, disease complications, alcohol, autoimmune diseases, and drug effects. Some hepatitis variants such as B, C, and D can also cause liver cancer if left untreated. Objective: This research aims to determine the effect of Backward Elimination feature selection on the performance of hepatitis disease identification compared to cases where Backward Elimination is not applied. Methods: XGBoost classification, capable of handling machine learning problems, was utilized. Additionally, Backward Elimination was used as a featured selection to increase accuracy by reducing the number of less important features in the data classification process. Results: The results for training XGBoost model with Backward Elimination, and applying Random Search for hyperparameter optimization, achieved an accuracy of 98.958% at 0.64 seconds. This performance was better than using Bayesian search, which produced the same accuracy of 98.958% but required a longer training time of 0.70 seconds. Conclusion: The use of features obtained from Backward Elimination process as well as the use of feature average values for missing value treatment, produced an accuracy of 98.958%.the precision in training XGBoost model with hyperparameter Bayesian search achieved accuracy, recall, and F1 score of 98.934%, 98.934%, and 98.934%, respectively. Consequently, the use of Backward Elimination in XGBoost model led to faster training, improved accuracy, and decreased overfitting.   Keywords: Hepatitis, Backward Elimination, XGBoost, Bayesian Search, Random Search

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.