Hasil untuk "Industrial engineering. Management engineering"

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.

Ehsan Abedini, Mohsen Nickray

Managing congestion effectively enables reliable and fast data transfer over networks. CUBIC delivers reliable results under normal circumstances but cannot adapt effectively to changing network scenarios. We introduce CUBIC-Learn, an RL approach for improving congestion control in CUBIC. The central idea is to use a Q-learning algorithm to adjust congestion window thresholds based on current data on packet loss, throughput, and latency. Simulations demonstrate more efficient and reliable congestion control when using CUBIC-Learn compared to standard CUBIC. CUBIC-Learn achieves a 47% reduction in packet loss, over a 59% increase in bandwidth utilization, approximately a 28% decrease in retransmissions, and 47% lower latency. In addition, CUBIC-Learn shows significant improvements in congestion window (cwnd) growth behavior, fairness among competing flows, and stability under heterogeneous traffic and network scenarios, including gigabit-scale bandwidth conditions. Statistical analysis further confirms the robustness of these gains, while the method introduces no additional computational overhead. Overall, CUBIC-Learn performs better than PCC, Reno, Tahoe, NewReno, and BBRv3 in most metrics. These findings suggest that RL can markedly improve congestion control in high-speed networks. [JJCIT 2025; 11(4.000): 466-483]

Dinh Minh Vu, Thanh Son Nguyen

This paper addresses the fundamental problem of seed selection in semi-supervised clustering, where the quality of initial seeds has a significant impact on clustering performance and stability. Existing methods often rely on randomly or heuristically selected seeds, which can propagate errors and increase dependence on expert labeling. To overcome these limitations, we propose FA-Seed, a flexible and adaptive model that integrates active querying with self-guided adaptation within the framework of fuzzy hyperboxes. FA-Seed partitions the data into hyperboxes, evaluates seed reliability through measures of membership and association density, and propagates labels with an emphasis on label purity. The model demonstrates strong adaptability to complex and ambiguous data distributions in which cluster boundaries are vague or overlapping. The main contributions of FA-Seed include: (1) automatic estimation and selection of candidate seeds that provide auxiliary supervision, (2) dynamic cluster expansion without retraining, (3) automatic detection and identification of structurally complex regions based on cluster characteristics, and (4) the ability to capture intrinsic cluster structures even when clusters vary in density and shape. Empirical evaluations on benchmark datasets, specifically the UCI and Computer Science collections, show that our approach consistently outperforms several state-of-the-art semi-supervised clustering methods.

Marta Miškufová, Martina Košíková, Petra Vašaničová et al.

The digital economy, driven by innovative technologies and artificial intelligence (AI), is transforming economic systems and increasing the demand for accurate assessments of digital competitiveness. This study addresses the inconsistencies in country rankings derived from global digital indices and aims to determine whether these rankings differ due to methodological variations. It also examines whether the rankings correlate significantly across different evaluation frameworks. The research focuses on 29 European countries and analyzes rankings from four widely recognized indices: the World Digital Competitiveness Ranking (WDCR), Network Readiness Index (NRI), AI Readiness Index (AIRI), and Digital Quality of Life Index (DQLI). To assess the consistency and variability in rankings from 2019 to 2024, the study applies Friedman’s ANOVA and Kendall’s coefficient of concordance. The results demonstrate strong correlations at the level of country rankings, indicating a high degree of consistency, but also confirm statistically significant differences in rankings among the indices, which reflect the diversity of their conceptual foundations. Countries such as Finland, the Netherlands, and Denmark consistently achieve top rankings, indicating convergence, while more variability is observed in indices like the DQLI. These findings highlight the importance of rank-based, multidimensional assessments in evaluating digital competitiveness. They support the use of such assessments as policy tools for monitoring progress, identifying gaps, and promoting inclusive digital development.

Sohaib Ahmed, Muhammad Zeeshan, David Parsons et al.

This article brings together the concepts of emerging technologies, game-based learning (GBL), and inquiry learning to conduct a research study undertaken in a maritime science museum. Over the last decade, the potential benefits of emerging technologies have enabled game-based inquiry activities in formal and informal pedagogical contexts. The use of ontologies has also grown significantly in representing learning content. In the science museum literature, there are a few applications found wherein ontologies are used for generating adaptive learning content. However, no study has been found in the literature that targets GBL for museum inquiry activities through emerging technologies using an ontology-driven approach. This paper outlines the results and analyses of research conducted on an ontology-driven GBL inquiry application, MUSEON. For evaluation purposes, the M3 evaluation framework was used and tested with 86 random visitors to explore visitors’ perspectives regarding the effectiveness of MUSEON. The results were encouraging as 71.6% of visitors were satisfied with their learning experiences in a game-based environment. Further, the experimental group performed well (74.6% score) in comparison with the control group (56.4% score) during inquiry learning activities about the maritime science museum exhibits.

Lv Tian, Yixuan Wen, Jingmiao Li et al.

BackgroundPatients with cancer may experience physiological and psychological adverse reactions, such as fatigue, pain, anxiety, and depression, which seriously affect their quality of life. Research has shown that remote interventions based on apps or miniprograms may help improve the physiological and mental health of patients with cancer. However, due to the limited number of relevant studies, the impact of web-based interventions in cancer management remains unclear. ObjectiveWe aimed to determine the efficacy of interventions based on apps, miniprograms, or other web-based tools on the physiological (body pain and fatigue) and psychological (anxiety and depression) states and the quality of life of patients with cancer. MethodsWe conducted electronic literature searches in PubMed, Scopus, Web of Science, the Cochrane Library, CINAHL, and EMBASE databases. The search period spanned from the inception of each database to October 15, 2024. Two researchers independently conducted literature retrieval and data extraction. The risk of bias was assessed with the Cochrane risk-of-bias tool, and the quality of evidence was assessed according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). All statistical analyses were performed using Review Manager version 5.4. ResultsA total of 36 randomized controlled trials were included. The remote web-based interventions significantly improved the pain intensity (n=14, 39% studies; standardized mean difference [SMD] –0.39, 95% CI –0.64 to –0.14; I2=82%; GRADE rating=low) and fatigue status (n=11, 31% studies; SMD –0.52, 95% CI –0.95 to –0.09; I2=95%; GRADE rating=low) in patients with cancer. Regarding psychology, the results indicated that the remote web-based interventions significantly improved the anxiety (n=14, 39% studies; SMD –0.60, 95% CI –0.90 to –0.30; I2=91%; GRADE rating=low) and depressive state (n=10, 28% studies; SMD –0.36, 95% CI –0.58 to –0.14; I2=81%; GRADE rating=low) of patients with cancer. For quality of life, the results showed that the remote web-based interventions had a significant positive impact on the quality of life of patients with cancer (n=26, 72% studies; SMD 0.63, 95% CI 0.39-0.87; I2=92%; GRADE rating=low). ConclusionsThe remote web-based interventions were effective in reducing the intensity of physiological pain, relieving fatigue, improving depression and anxiety, and improving the quality of life of patients with cancer. However, due to the low certainty of evidence, more rigorous randomized controlled trials are needed to validate these findings further. Trial RegistrationPROSPERO CRD42024611768; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024611768

Albert Ntumba Nkongolo, Yae Olatoundji Gaba, Kafunda Katalay Pierre et al.

This study explores the application of natural language processing (NLP) techniques in financial time series forecasting, specifically in predicting stock prices. Historical stock price data and textual data from financial news articles and social media sources were collected, and TextBlob was used to obtain sentiment indices from the textual data. A hybrid model combining NLP techniques with LSTM (Long Short-Term Memory) neural networks was developed, and the methodology involved preprocessing and analyzing textual data using sentiment analysis with TextBlob and integrating the sentiment indices with historical stock price data for forecasting with LSTM. The LSTM model achieved a performance of 89.6 percent precision and outperformed traditional time series forecasting models in terms of accuracy and reliability. The results demonstrate that incorporating sentiment indices obtained through NLP significantly enhances the predictive performance of stock price forecasting models, and the study highlights the potential of NLP techniques, particularly sentiment analysis with TextBlob, in conjunction with LSTM neural networks, to improve the accuracy of financial time series forecasting, specifically in predicting stock prices.   Studi ini mengeksplorasi penerapan teknik pemrosesan bahasa alami (Natural Language Processing/NLP) dalam peramalan deret waktu keuangan, khususnya untuk memprediksi harga saham. Data harga saham historis dan data tekstual dari artikel berita keuangan serta sumber media sosial dikumpulkan, dan TextBlob digunakan untuk memperoleh indeks sentimen dari data tekstual tersebut. Sebuah model hibrida yang menggabungkan teknik NLP dengan jaringan saraf LSTM (Long Short-Term Memory) dikembangkan, dan metodologinya melibatkan praproses dan analisis data tekstual menggunakan analisis sentimen dengan TextBlob, serta integrasi indeks sentimen dengan data harga saham historis untuk peramalan menggunakan LSTM. Model LSTM ini mencapai kinerja dengan tingkat ketepatan (precision) sebesar 89,6 persen dan mengungguli model peramalan deret waktu tradisional dalam hal akurasi dan keandalan. Hasilnya menunjukkan bahwa penggabungan indeks sentimen yang diperoleh melalui NLP secara signifikan meningkatkan kinerja prediktif model peramalan harga saham, dan studi ini menekankan potensi teknik NLP, khususnya analisis sentimen dengan TextBlob, dalam kombinasi dengan jaringan saraf LSTM, untuk meningkatkan akurasi peramalan deret waktu keuangan, khususnya dalam memprediksi harga saham.

Sathish Krishna Anumula, Sivaramkumar Ponnarangan, Faizal Nujumudeen et al.

A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a suggested way to evaluate intelligent robotic systems in industry, we give an overview of how their use impacts productivity, safety and operational costs. Experience and case studies confirm the benefits this area brings and the problems that have yet to be solved. The findings indicate that intelligent robotics involves more than a technology change; it introduces important new methods in engineering.

Alex Gu, Naman Jain, Wen-Ding Li et al.

AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.

Oana-Elena Chelariu, Ionuț Minea, Corneliu Iațu

The multidisciplinary approach outlined in this paper aims to assess the correlation between geophysical and social vulnerability for disaster risk reduction. To achieve this, two multi-criteria methods based on Geographic Information Systems (GIS) techniques have been integrated to assess the risk to natural hazards in the North-East Development Region. The Analytical Hierarchy Process (AHP) was utilized to conduct a comprehensive hazard analysis, including floods, landslides, and earthquakes. The Principal Component Analysis (PCA) method was applied to examine the spatial distribution of social vulnerability at the Local Administrative Unit (LAU level), utilizing 24 variables to generate 6 principal components. Based on these results a Regional Risk Index (RRI) was developed using the bivariate method, combining the multi-hazard distribution with social vulnerability. The results showed that the north-eastern part of the region presents the highest risk, encompassing 10.68% of administrative units. These results can contribute to the design of risk reduction programs and policies.

Rob Eerdekens, Jo Zelis, Herman ter Horst et al.

Abstract BackgroundDue to aging of the population, the prevalence of aortic valve stenosis will increase drastically in upcoming years. Consequently, transcatheter aortic valve implantation (TAVI) procedures will also expand worldwide. Optimal selection of patients who benefit with improved symptoms and prognoses is key, since TAVI is not without its risks. Currently, we are not able to adequately predict functional outcomes after TAVI. Quality of life measurement tools and traditional functional assessment tests do not always agree and can depend on factors unrelated to heart disease. Activity tracking using wearable devices might provide a more comprehensive assessment. ObjectiveThis study aimed to identify objective parameters (eg, change in heart rate) associated with improvement after TAVI for severe aortic stenosis from a wearable device. MethodsIn total, 100 patients undergoing routine TAVI wore a Philips Health Watch device for 1 week before and after the procedure. Watch data were analyzed offline—before TAVI for 97 patients and after TAVI for 75 patients. ResultsParameters such as the total number of steps and activity time did not change, in contrast to improvements in the 6-minute walking test (6MWT) and physical limitation domain of the transformed WHOQOL-BREF questionnaire. ConclusionsThese findings, in an older TAVI population, show that watch-based parameters, such as the number of steps, do not change after TAVI, unlike traditional 6MWT and QoL assessments. Basic wearable device parameters might be less appropriate for measuring treatment effects from TAVI.

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera et al.

New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.

Sascha Stribick, Rebecca Pahmeyer

Cutting tools undergo constant development to meet the demands of higher cutting speeds, difficult-to-cut materials and ecological considerations. One way to improve cutting tools involves transitioning from soldering to adhesive bonding in the manufacturing process. However, there is limited research comparing adhesively bonded tools with soldered tools in woodcutting applications. This paper presents a comparison between adhesively bonded and soldered tools in the cutting of medium-density fiberboards over a cutting distance of 1000 m. The results indicate that adhesively bonded tools are well-suited for machining medium-density fiberboards. Additionally, the cutting-edge radii exhibit a slower increase and the tool temperatures are higher compared to soldered tools. Future research could optimize the damping effect through the precise design of the bonding area. Additionally, investigating a cooling concept for the machining process could help minimize ageing effects.

Muhammad Zohaib

The Continuous software engineering is a collaborative software development environment which offers the continues development and deployment of quality software project within short time. The Continuous software engineering practices are not yet mature enough, and the software organizations hesitate to adopt it. This study aims: (1) to explore the Continuous software engineering challenges by conducting systematic literature review (SLR) and to get the insight of industry experts via questionnaire survey study; (2) to prioritize the investigated challenges using fuzzy analytical hierarchy process (FAHP). The study findings provides the set of critical challenges faced by the software organizations while adopting Continuous software engineering and a prioritization based taxonomy of the Continuous software engineering challenges. The application of FAHP is novel in this research area as it assists in addressing the vagueness of practitioners concerning the influencing factors of Continuous software engineering. We believe that the finding of this study will serve as a body of knowledge for real world practitioners and researchers to revise and develop the new strategies for the successful implementation of Continuous software engineering practices in the software industry.

Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza et al.

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.

Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real et al.

Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token \textit{dataset} to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.

Oluwatosin Ogundare, Gustavo Quiros Araya, Yassine Qamsane

Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as 'No Code' or 'Low Code' alongside the mainstream success of statistical learning theory or the so-called machine learning is redefining the way in which we structure programs for the digital machine to execute. A principal focus of 'No Code' is deriving executable programs directly from a set of requirement documents or any other documentation that defines consumer or customer expectation. We present a method for generating Function Block Diagram (FBD) programs as either the intermediate or final artifact that can be executed by a target system from a set of requirement documents using a constrained selection algorithm that draws from the top line of an associated recommender system. The results presented demonstrate that this type of No-code generative model is a viable option for industrial process design.

Neil A. Ernst, Maria Teresa Baldassarre

Registered reports are scientific publications which begin the publication process by first having the detailed research protocol, including key research questions, reviewed and approved by peers. Subsequent analysis and results are published with minimal additional review, even if there was no clear support for the underlying hypothesis, as long as the approved protocol is followed. Registered reports can prevent several questionable research practices and give early feedback on research designs. In software engineering research, registered reports were first introduced in the International Conference on Mining Software Repositories (MSR) in 2020. They are now established in three conferences and two pre-eminent journals, including Empirical Software Engineering. We explain the motivation for registered reports, outline the way they have been implemented in software engineering, and outline some ongoing challenges for addressing high quality software engineering research.

Ahmad Shabani, Hmid Pirayesh, Saeed Rajaeepour et al.

Purpose: The purpose of this study is to investigate the relationship between knowledge management and organizational learning among public librarians in Fars province. Method: This study is an applied study which was conducted using the descriptive-correlational method. The statistical population included all librarians of public libraries in Fars province in 1400 (322 people), from whom 175 librarians were selected as the research sample using simple random sampling. In order to collect people's opinions, Bukowitz and Williams (1999) standard knowledge management questionnaire and organizational learning questionnaire based on Neefe theory (2001) were used. In order to analyze the data, SPSS software version 22. and descriptive statistical methods such as frequency, percentage, mean, standard deviation, and in inferential statistics, Pearson correlation test and multiple linear regression were used. Findings: The results of correlation coefficient showed that there was a significant relationship between knowledge management (KM) and organizational learning (OL) among the librarians of public libraries in Fars province. Furthermore, among KM’s components, knowledge production and consolidation and sharing had the most correlation with OL (respectively 0.900 and 0/898 correlation coefficients). Moreover, the result of multiple linear regression showed that there is a 100-percent common variance between KM and OL. The production and consolidation component is the strongest predictor for OL (Beta=0.197). This research showed that knowledge production and consolidation component (acquisition of required skills in accordance with organizational goals) and knowledge sharing (transfer and sharing of effective individual and organizational experiences) have the greatest relationship and impact on the promotion and improvement of OL. Originality/value: The issue of knowledge management has been an issue of concern in public library literature in recent years. However, the relationship between knowledge management and organizational learning has not been studied much; In addition, examining this issue among the community of public library librarians of Fars province has been the focus of attention in the present study. The results of this research can pave the way for library managers to try to improve knowledge and knowledge management that has led to organizational learning by planning, appropriate policies, and providing standards.

Johan Linåker, Björn Regnell, Hussan Munir

In recent years Open Innovation (OI) has gained much attention and made firms aware that they need to consider the open environment surrounding them. To facilitate this shift Requirements Engineering (RE) needs to be adapted in order to manage the increase and complexity of new requirements sources as well as networks of stakeholders. In response we build on and advance an earlier proposed software engineering framework for fostering OI, focusing on stakeholder management, when to open up, and prioritization and release planning. Literature in open source RE is contrasted against recent findings of OI in software engineering to establish a current view of the area. Based on the synthesized findings we propose a research agenda within the areas under focus, along with a framing-model to help researchers frame and break down their research questions to consider the different angles implied by the OI model.