Hasil untuk "Industrial engineering. Management engineering"

S. Krevor, Heleen de Coninck, S. Gasda et al.

Hermann Kopetz

Zhiwu Liang, Wichitpan Rongwong, Helei Liu et al.

Daniel Rangel-Martinez, K. Nigam, L. Ricardez‐Sandoval

Borna Naderi, Longsha Liu, Anita Ghandehari et al.

Abstract With EDs increasingly overburdened, Large Language Models (LLMs) may help streamline workflow and decision-making. We evaluated their emergency medicine knowledge and performance in simulated ED tasks. This two-part study first tested factual knowledge of 18 LLMs using a curated MedMCQA subset covering 12 ED chief complaints, assessing accuracy, precision, and recall. Five models (GPT-5, GPT-4, Claude 3.5, Claude 4, and LLaMA 3.1) were then evaluated on patient summaries, Emergency Severity Index scoring, investigative questioning, management planning, and differential diagnosis across 12 simulated ED cases presented through four sequential information levels. Physicians rated outputs for accuracy, safety, and clinical relevance, with performance differences analyzed statistically. LLaMA-4 Maverick achieved the highest factual accuracy(90.7%), followed by LLaMA-3.1-70B(90.1%). In clinical tasks, GPT-5 outperformed all models, (Level 2 onwards, p < 0.05), with performance stable or improving as complexity increased. Claude 3.5 ranked next, while Claude 4 performed slightly lower but stable with complexity. LLaMA-3.1 and GPT-4 ranked lowest and showed the greatest degradation. All models undertriaged except Claude 3.5, which initially overtriaged. GPT-5 demonstrated the strongest clinical reasoning and scalability with complexity, while LLaMA models excelled in factual recall. Findings suggest a generational leap in reasoning performance and support GPT-5 as a potential ED decision-support tool.

Noga Chemo, Yaniv Mordecai, Yoram Reich

We introduce a framework for Foundational Analysis of Safety Engineering Requirements (SAFER), a model-driven methodology supported by Generative AI to improve the generation and analysis of safety requirements for complex safety-critical systems. Safety requirements are often specified by multiple stakeholders with uncoordinated objectives, leading to gaps, duplications, and contradictions that jeopardize system safety and compliance. Existing approaches are largely informal and insufficient for addressing these challenges. SAFER enhances Model-Based Systems Engineering (MBSE) by consuming requirement specification models and generating the following results: (1) mapping requirements to system functions, (2) identifying functions with insufficient requirement specifications, (3) detecting duplicate requirements, and (4) identifying contradictions within requirement sets. SAFER provides structured analysis, reporting, and decision support for safety engineers. We demonstrate SAFER on an autonomous drone system, significantly improving the detection of requirement inconsistencies, enhancing both efficiency and reliability of the safety engineering process. We show that Generative AI must be augmented by formal models and queried systematically, to provide meaningful early-stage safety requirement specifications and robust safety architectures.

Weixing Zhang, Mario Herb, Martin Armbruster et al.

Despite Domain-Driven Design's proven value in managing complex business logic, a fundamental semantic expressiveness gap persists between generic modeling languages and tactical DDD patterns, causing continuous divergence between design intent and implementation. We envision a constraint-based tactical modeling environment that transforms abstract architectural principles into explicit, tool-enforced engineering constraints. At its core is a DDD-native metamodel where tactical patterns are first-class modeling primitives, coupled with a real-time constraint verification engine that prevents architectural violations during modeling, and bidirectional synchronization mechanisms that maintain model-code consistency through round-trip engineering. This approach aims to democratize tactical DDD by embedding expert-level architectural knowledge directly into modeling constraints, enabling small teams and junior developers to build complex business systems without sacrificing long-term maintainability. By lowering the technical barriers to DDD adoption, we envision transforming tactical DDD from an elite practice requiring continuous expert oversight into an accessible engineering discipline with tool-supported verification.

Pasan Peiris, Matthias Galster, Antonija Mitrovic et al.

Watching training videos passively leads to superficial learning. Adding gamification can increase engagement. We study how software engineering students and industry practitioners view gamifying video-based training. We conducted a survey with students and professionals. Students and professionals share similar perceptions toward video-based training in general and support combining gamification and video-based training. Our findings can inform the design of gamified training solutions for software engineers.

Greyce N. Schroeder, Charles Steinmetz, R. N. Rodrigues et al.

The digital twin (DT) is a virtual representation of a physical object, which has been proposed as one of the key concepts for Industry 4.0. The DT provides a virtual representation of products along their lifecycle that enables the prediction and optimization of the behavior of a production system and its components. A methodology design using model-driven engineering (MDE) is proposed that strives toward being both flexible and generic. This approach is presented at two levels: first, a DT is modeled as a composition of basic components that provide basic functionalities, such as identification, storage, communication, security, data management, human–machine interface (HMI), and simulation; second, an aggregated DT is defined as a hierarchical composition of other DTs. A generic reference architecture based on these concepts and a concrete implementation methodology are proposed using AutomationML. This methodology follows an MDE approach that supports most of the DT features currently proposed in the literature. A case study has been developed, the proposed ideas are being evaluated with industrial case studies, and some of the preliminary results are described in this article. With the case study, it is possible to verify that the proposed methodology supports the creation and the deployment process of a DT.

L. Salvatore, Nunzia Gallo, M. L. Natali et al.

In the last two decades, marine collagen has attracted great scientific and industrial interest as a 'blue resource', with potential for use in various health-related sectors, such as food, medicine, pharmaceutics and cosmetics. In particular, the large availability of polluting by-products from the fish processing industry has been the key factor driving the research towards the conversion of these low cost by-products (e.g. fish skin and scales) into collagen-based products with high added value and low environmental impact. After addressing the extraction of collagen from aquatic sources and its physicochemical properties, this review focuses on the use of marine collagen and its derivatives (e.g. gelatin and peptides) in different healthcare sectors. Particular attention is given to the bioactive properties of marine collagen that are being explored in preclinical and clinical studies, and pave the way to an increased demand for this biomaterial in the next future. In this context, in addition to the use of native collagen for the development of tissue engineering or wound healing devices, particularly relevant is the use of gelatin and peptides for the development of dietary supplements and nutraceuticals, specifically directed to weight management and glycemic control. The marine collagen market is also briefly discussed to highlight the opportunities and the most profitable areas of interest.

P. Brauner, M. Dalibor, M. Jarke et al.

The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production, two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI. Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure. Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

Allen Love, Omar Alejandro Valdez Pastrana, Saeed Behseresht et al.

Metal additive manufacturing (AM) techniques such Direct Energy Deposition (DED), Powder Bed Fusion (PBF), and Wire Arc Additive Manufacturing (WAAM) enable the production of complex metal components built at rapid rates. Because of the complexity of the process, including high thermal gradients, residual stress, and parameter optimization, these techniques pose significant challenges necessitating the need for advanced computational modeling. A powerful technique to reduce or, in some cases, eliminate these challenges at a much lower cost compared to trial-and-error experiments, is Finite Element Analysis (FEA). This study provides a comprehensive review of the FEA techniques being used and developed to model metal AM processes focusing on the thermal, mechanical, and coupled thermo-mechanical models in DED, PBF, and WAAM. Key topics include heat transfer, residual stress and distortion prediction, microstructure evolution and parameter optimization. Recent advancements in FEA have improved the accuracy of AM process simulations, reducing the need for costly experimental testing, though there is still room for improvement and further development of FEA in metal AM. This review serves as a foundation for future work in the metal AM modeling field, enabling the development of optimized process parameters, defect reduction strategies and improved computational methodologies for high-fidelity simulations.

Funso Kehinde Ariyo, Ayooluwa Peter Adeagbo, Oludamilare Bode Adewuyi et al.

Abstract Integrating distributed generators (DGs) into electrical power networks remains a significant area of research which has several technical and economic benefits for optimum performance, especially at the distribution level. This study presents a model of power distribution networks with renewable distributed generation (DG) units. It adapted Loss Sensitivity Factor (LSF) and Constriction Coefficient Particle Swarm Optimization (CCPSO) technique to ascertain the optimal placement and sizing of the DG units. The numerical analysis is executed on the Imalefalafia 32-bus network which is a real distribution network in Nigeria and the commonly known standard IEEE 33-bus network. The result shows that the scenario where cost minimization with loss reduction and voltage stability improvement concurrently performs better than other scenarios for different numbers of allocated DGs. With 3 DGs incorporation, the real and reactive power loss reduction of 62.46% and 62.32%, respectively, were achieved in scenario three compared to 14.06% and 14.46% in scenario two and 61.48% and 61.46% in scenario one for IEEE 33-bus RDN while for Imalefalafia 32-bus, the real and reactive power loss were reduced by 71.07% and 71.13% were achieved in scenario three compared to 43.95% and 43.99% in scenario two and 55.87% and 55.92% in scenario one. It was also observed that the voltage at the worst performing bus improves significantly better for scenario 3 for all numbers of DGs compared to the other scenarios.

Balaram Puli

This paper explores Site Reliability Engineering (SRE), a modern approach to maintaining scalable and reliable software systems. It presents observations on how structured SRE processes improve operational efficiency, reduce system downtime, and simplify maintenance. Drawing from real-world implementations, the study outlines key techniques in automation, monitoring, incident management, and deployment strategies. The work also highlights how these practices can be tailored to different environments, offering practical insights for engineers aiming to improve service reliability.

Vincenzo De Martino, Mohammad Amin Zadenoori, Xavier Franch et al.

Language Models are increasingly applied in software engineering, yet their inference raises growing environmental concerns. Prior work has examined hardware choices and prompt length, but little attention has been paid to linguistic complexity as a sustainability factor. This paper introduces Green Prompt Engineering, framing linguistic complexity as a design dimension that can influence energy consumption and performance. We conduct an empirical study on requirement classification using open-source Small Language Models, varying the readability of prompts. Our results reveal that readability affects environmental sustainability and performance, exposing trade-offs between them. For practitioners, simpler prompts can reduce energy costs without a significant F1-score loss; for researchers, it opens a path toward guidelines and studies on sustainable prompt design within the Green AI agenda.

Markus Borg, Martin Larsson, Philip Breid et al.

Maintainable source code is essential for sustainable development in any software organization. Unfortunately, many studies show that maintainability often receives less attention than its importance warrants. We argue that requirements engineering can address this gap the problem by fostering discussions and setting appropriate targets in a responsible manner. In this preliminary work, we conducted an exploratory study of industry practices related to requirements engineering for maintainability. Our findings confirm previous studies: maintainability remains a second-class quality concern. Explicit requirements often make sweeping references to coding conventions. Tools providing maintainability proxies are common but typically only used in implicit requirements related to engineering practices. To address this, we propose QUPER-MAn, a maintainability adaption of the QUPER model, which was originally developed to help organizations set targets for performance requirements. Developed using a design science approach, QUPER-MAn, integrates maintainability benchmarks and supports target setting. We posit that it can shift maintainability from an overlooked development consequence to an actively managed goal driven by informed and responsible engineering decisions.

Davis Byamugisha, Francis Kamuganga, Adones Rukundo et al.

Effective treatment of cancer requires early diagnosis which involves the patient's awareness of the early signs and symptoms, leading to a consultation with a health provider, who would then promptly refer the patient for confirmation of the diagnosis and thereafter treatment. However, this is not always the case because of delays arising from limited skilled manpower and health information management systems that are neither integrated nor organized in their design hence leading to information gap among care groups. Existing methods focus on using accumulated data to support decision making, enhancing the sharing of secondary data while others exclude some critical stakeholders like patient caretakers and administrators thus, leaving an information gap that creates delays and miscommunication during case management. We however notice some similarities between cancer treatment and software engineering information management especially when progress history needs to be maintained (versioning). We analyze the similarities and propose a model for information sharing among cancer care groups using the software engineering principles approach. We model for reducing delays and improving coordination among care groups in cancer case management. Model design was guided by software engineering principles adopted in GitHub version control system for bug fixing in open-source code projects. Any-Logic simulation software was used to mimic the model realism in a virtual environment. Results show that bug resolution principles from software engineering and GitHub version control system can be adopted to coordinate collaboration and information sharing among care groups in a cancer case management environment while involving all stakeholders to improve care treatment outcomes, ensure early diagnosis and increase patient's survival chances.

Oleksandr Polozov, Sumit Gulwani

: Traditionally alarms are designed on the basis of empirical guidelines rather than on a sound scientific framework rooted in a theoretical foundation for process and control system design. This paper proposes scientific principles and a methodology for design of alarms based on a functional modeling technique (MFM) which represents a process in terms of its goals, functions and operating requirements. The reasoning capabilities of MFM enable identification of operational situations which threaten to generate an alarm and derivation of potential response scenarios. The design methodology can be applied to any engineering system which can be modeled by MFM. The methodology provides a set of alarms which can facilitate event interpretation and operator support for abnormal situation management. The proposed design methodology provides the information content of the alarms, but does not deal with alarm presentation or display design issues. A hydraulically powered grinding process is employed as an industrially relevant system to show the applicability of the proposed design methodology with promising results.

A. Nath, Sandeep S. Udmale, S. Singh