Hasil untuk "Industrial engineering. Management engineering"

M. F. Manesh, M. Pellegrini, Giacomo Marzi et al.

Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the interconnectedness of machines and their ability to learn and share data autonomously. For this reason, the present article investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal six clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences.

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Tab, Zhang, Bjorn De Sutter et al.

Empirical research in reverse engineering and software protection is crucial for evaluating the efficacy of methods designed to protect software against unauthorized access and tampering. However, conducting such studies with professional reverse engineers presents significant challenges, including access to professionals and affordability. This paper explores the use of students as participants in empirical reverse engineering experiments, examining their suitability and the necessary training; the design of appropriate challenges; strategies for ensuring the rigor and validity of the research and its results; ways to maintain students' privacy, motivation, and voluntary participation; and data collection methods. We present a systematic literature review of existing reverse engineering experiments and user studies, a discussion of related work from the broader domain of software engineering that applies to reverse engineering experiments, an extensive discussion of our own experience running experiments ourselves in the context of a master-level software hacking and protection course, and recommendations based on this experience. Our findings aim to guide future empirical studies in RE, balancing practical constraints with the need for meaningful, reproducible results.

F. Abbas, Yiwei Zhou, Dylan O'Neill Rothenberg et al.

Plants produce an incredible variety of volatile organic compounds (VOCs) that assist the interactions with their environment, such as attracting pollinating insects and seed dispersers and defense against herbivores, pathogens, and parasites. Furthermore, VOCs have a significant economic impact on crop quality, as well as the beverage, food, perfume, cosmetics and pharmaceuticals industries. These VOCs are mainly classified as terpenoids, benzenoids/phenylpropanes, and fatty acid derivates. Fruits and vegetables are rich in minerals, vitamins, antioxidants, and dietary fiber, while aroma compounds play a major role in flavor and quality management of these horticultural commodities. Subtle shifts in aroma compounds can dramatically alter the flavor and texture of fruits and vegetables, altering their consumer appeal. Rapid innovations in -omics techniques have led to the isolation of genes encoding enzymes involved in the biosynthesis of several volatiles, which has aided to our comprehension of the regulatory molecular pathways involved in VOC production. The present review focuses on the significance of aroma volatiles to the flavor and aroma profile of horticultural crops and addresses the industrial applications of plant-derived volatile terpenoids, particularly in food and beverages, pharmaceuticals, cosmetics, and biofuel industries. Additionally, the methodological constraints and complexities that limit the transition from gene selection to host organisms and from laboratories to practical implementation are discussed, along with metabolic engineering’s potential for enhancing terpenoids volatile production at the industrial level.

Yuanchen Cheng, Zichen Zhang, Yuqing Liu et al.

Accurate estimation of tuna catch is crucial for effective pelagic fishery management and resource conservation. However, existing manual counting methods suffer from issues such as low accuracy and poor timeliness, highlighting the urgent need for an efficient and automated solution. This paper proposes an automatic tuna counting method based on the YOLOv8n-DMTNet target detection algorithm combined with the improved ByteTrack tracking algorithm. The method uses YOLOv8n as the base model, enhanced with detail-enhanced convolution and a multi-scale feature fusion pyramid network, which significantly improves detection accuracy in complex marine environments. Additionally, a dynamic, task-aligned detection head is introduced to optimize the synergy between classification and localization tasks. To further improve counting accuracy, the ByteTrack algorithm is employed for target tracking, and a region-specific counting method is designed to prevent double counting and omission due to occlusion and motion irregularities. Experimental results show that the improved YOLOv8n-DMTNet model achieves a 9.2% increase in mAP@0.5 and a 6.4% increase in mAP@0.5:0.95 compared to YOLOv8n in the tuna detection task, while reducing the number of parameters by 42.3% and computational complexity by 33.3%. The counting accuracy reaches 93.5%, and the method demonstrates superior performance in terms of accuracy, robustness, and computational resource efficiency, making it well-suited for resource-constrained fishing vessel environments. This approach provides reliable technical support for automated catch counting in pelagic fisheries.

Mahdi Yousefi Nejad, Karim Farajian, Hossein Jaleb

One of the major indicators in evaluating the performance of hospitals and their managers is the average length of stay of patients; given the importance of this indicator, the present study has examined the factors affecting the length of stay of hospitalized patients. This study was conducted with the aim of identifying the key factors affecting the length of stay of patients and providing practical solutions for improving the management of hospital beds. Data from 26,907 patients were analyzed using clustering models, clustering algorithms (K-Means) and association rules extraction (Apriori). The data consists of 10 numerical and discrete columns. The variables include 10 items, which are respectively: gender, marital status, hospitalization department, physician specialty, insurance, blood transfusion, surgery, type of discharge, age, and length of stay. The findings showed that the variables of surgery and blood transfusion have the greatest impact on the average length of stay in the hospital.

Hayato Ishida, Amal Elsokary, Maria Aslam et al.

Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.

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.

Tomi Suomi, Petri Ihantola, Tommi Mikkonen et al.

Agile software development relies on self-organized teams, underlining the importance of individual responsibility. How developers take responsibility and build ownership are influenced by external factors such as architecture and development methods. This paper examines the existing literature on ownership in software engineering and in psychology, and argues that a more comprehensive view of ownership in software engineering has a great potential in improving software team's work. Initial positions on the issue are offered for discussion and to lay foundations for further research.

Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et al.

The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

Zhenpeng Chen, Chong Wang, Weisong Sun et al.

Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.

Krishna Ronanki, Simon Arvidsson, Johan Axell

The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.

A. Yu. Mikhaylov

The article presents an in-depth analysis of the formation and development of the capital market (stocks and bonds segment) in China. The factors determining the valuation of shares that are in circulation are highlighted. While the first factor identifies an assessment based on the company’s financial statements, the second factor considers current market conditions and investor sentiment. Examining historical data, it is interesting to note that from 2000 to 2006, the total market capitalization of stocks remained stable and amounted to a substantial 4 trillion yuan. However, in 2007 There was a significant shift when the market capitalization grew significantly and reached as much as 30 trillion yuan. This sudden growth can be explained by various reasons, such as increased investor confidence, favorable economic conditions and the introduction of progressive financial regulation. In addition, it is important to note that in the same year, the Shanghai Composite index (a key indicator of the Chinese stock market) reached an unprecedented value of 6,123.04 points. This indicator demonstrated the stability and potential of the Chinese capital market, and attracted both domestic and international investors. Thus, the analysis presented in the article reveals the intricacies of the Chinese capital market and the mechanisms of its assessment. This analysis establishes the importance of both book value and market value in determining the valuation of outstanding shares. In addition, historical trends have highlighted the resilience and vulnerability of the market to external shocks, as evidenced by significant fluctuations in market capitalization. In general, this study helps to understand the Chinese capital market and its evolution over time.

Iosif-Alin Beti, Paul-Corneliu Herghelegiu, Constantin-Florin Caruntu

The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles.

Jeanina-Biliana Ciurea , Venera-Cristina Manciu

Change management is a complex process by which organisations adapt to new market conditions, technologies, regulations or consumer needs. In the context of trade SMEs, changes can have a significant impact on long-term competitiveness and sustainability. This paper aims to analyse the management of change in trade SMEs, carrying out a comparative study between retail and traditional trade.

Dezhi Ran, Mengzhou Wu, Wei Yang et al.

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade, appealing for new theories and methodologies from the field of software engineering. In this paper, we outline our vision of introducing Foundation Model (FM) engineering, a strategic response to the anticipated FM crisis with principled engineering methodologies. FM engineering aims to mitigate potential issues in FM development and application through the introduction of declarative, automated, and unified programming interfaces for both data and model management, reducing the complexities involved in working with FMs by providing a more structured and intuitive process for developers. Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field.

Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee et al.

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.

Dimberu G. Atinafu, Beom Yeol Yun, Young Uk Kim et al.

The fundamental requirements for thermal comfort along with the unbalanced growth in the energy demand and consumption worldwide have triggered the development and innovation of advanced materials for high thermal‐management capabilities. However, continuous development remains a significant challenge in designing thermally robust materials for the efficient thermal management of industrial devices and manufacturing technologies. The notable achievements thus far in nanopolyhybrid design technologies include multiresponsive energy harvesting/conversion (e.g., light, magnetic, and electric), thermoregulation (including microclimate), energy saving in construction, as well as the miniaturization, integration, and intelligentization of electronic systems. These are achieved by integrating nanomaterials and polymers with desired engineering strategies. Herein, fundamental design approaches that consider diverse nanomaterials and the properties of nanopolyhybrids are introduced, and the emerging applications of hybrid composites such as personal and electronic thermal management and advanced medical applications are highlighted. Finally, current challenges and outlook for future trends and prospects are summarized to develop nanopolyhybrid materials.

Mobayode O. Akinsolu

Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a gray area for many engineering managers who are key players in the gravitation of manufacturing and industrial production toward the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via political, economic, social, and technological (PEST) considerations of the broad impact of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this article. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented.

Tala Talaei Khoei, Naima Kaabouch

Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised machine learning. However, these models require large labeled datasets for training and testing. Therefore, this paper compares the performance of supervised and unsupervised learning models in detecting cyber-attacks. The benchmark of CICDDOS 2019 was used to train, test, and validate the models. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, C-Support Vector Machine, Light Gradient Boosting, and Alex Neural Network. The unsupervised models are Principal Component Analysis, K-means, and Variational Autoencoder. The performance comparison is made in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, prediction time, training time per sample, and memory size. The results show that the Alex Neural Network model outperforms the other supervised models, while the Variational Autoencoder model has the best results compared to unsupervised models.