Hasil untuk "Industrial engineering. Management engineering"

Jay Lee, Fangji Wu, Wenyu Zhao et al.

W. Humphrey

R. Speece

Jiewu Leng, Guolei Ruan, P. Jiang et al.

Abstract Sustainability is a pressing need, as well as an engineering challenge, in the modern world. Developing smart technologies is a critical way to ensure that future manufacturing systems are sustainable. Blockchain is a next-generation development of information technology for realizing sustainability in businesses and industries. Much research on blockchain-empowered sustainable manufacturing in Industry 4.0 has been conducted from technical, commercial, organizational, and operational perspectives. This paper surveys how blockchain can overcome potential barriers to achieving sustainability from two perspectives, namely, the manufacturing system perspective and the product lifecycle management perspective. The survey first examines literature on these two perspectives, following which the state of research in blockchain-empowered sustainable manufacturing is presented, which sheds new light on urgent issues as part of the UN's Sustainable Development Goals. We found that blockchain-empowered transformation of a sustainable manufacturing paradigm is still in an early stage of the hype phase, proceeding toward full adoption. The survey ends with a discussion of challenges regarding techniques, social barriers, standards, and regulations with respect to blockchain-empowered manufacturing applications. The paper concludes with a discussion of challenges and social barriers that blockchain technology must overcome to demonstrate its sustainability in industrial and business spheres.

Jun Yong Eom, Daewon Seo

Identity-aware activity recognition is a key enabler for customized services. However, joint modeling of activity recognition and user identification from wireless signals remains underexplored. This work presents a dual-task graph model for millimeter-wave (mmWave) frequency-modulated continuous-wave (FMCW) radar point-cloud sequences. We construct directed graphs that capture a user’s spatial structure and motion over time. A shared graph neural backbone processes these graphs and produces node embeddings that encode local spatial features and short-term dynamics. Each task-specific head first aggregates node embeddings into a graph-level representation and then performs activity or identity classification. Experiments on two public datasets demonstrate that the proposed scheme achieves classification performance comparable to single-task baselines for both activity recognition and user identification while maintaining low-latency inference. Codes are available at https://github.com/junyongeom/mmActId/.

Jordi Cabot

There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems. New types of user interfaces, the need for intelligent components, sustainability concerns, etc. bring new challenges that we need to handle. In the last years, model-driven engineering (MDE), including its latest incarnation, i.e. low/no-code development, has been key to improving the quality and productivity of software development, but models themselves are becoming increasingly complex to specify and manage. At the same time, we are witnessing the growing popularity of vibe coding approaches that rely on Large Language Models (LLMs) to transform natural language descriptions into running code at the expense of potential code vulnerabilities, scalability issues and maintainability concerns. While many may think vibe coding will replace model-based engineering, in this paper we argue that, in fact, the two approaches can complement each other and provide altogether different development paths for different types of software systems, development scenarios, and user profiles. In this sense, we introduce the concept of \textit{vibe-driven model-based engineering} as a novel approach to integrate the best of both worlds (AI and MDE) to accelerate the development of reliable complex systems. We outline the key concepts of this new approach and highlight the opportunities and open challenges it presents for the future of software development.

M. Mojtahedi, A. M. F. Fard, R. Tavakkoli-Moghaddam et al.

Wan Ziyang

With the rapid development of deep learning, text-to-image generation technology has demonstrated significant application value in multiple domains, including content creation, automated design, and virtual reality. However, the quality of generated images is influenced by numerous factors, among which text input length and syntactic structure may play critical roles in generation efficiency and final image quality. This study aims to comprehensively investigate the impact of textual length and grammatical structures on text-to-image generation tasks, with the goal of optimizing the practical performance of the RAT-Diffusion model. Based on baseline methods for text-to-image generation in small-scale datasets, the paper designed and conducted a series of targeted experiments to evaluate the performance of short, medium-length, and long texts, as well as texts with varying syntactic structures, using quantitative metrics such as Fréchet Inception Distance (FID) and Inception Score (IS). The findings reveal that moderate text length and well-structured syntax enhance generation quality, while excessively long texts or overly complex grammatical structures may degrade output quality. These insights provide novel approaches for textual optimization, effectively improving the controllability and practicality of text-to-image generation, thereby offering valuable references for research and applications in related fields.

Xinyi Wang, Shaukat Ali, Paolo Arcaini

In order to handle the increasing complexity of software systems, Artificial Intelligence (AI) has been applied to various areas of software engineering, including requirements engineering, coding, testing, and debugging. This has led to the emergence of AI for Software Engineering as a distinct research area within the field of software engineering. With the development of quantum computing, the field of Quantum AI (QAI) is arising, enhancing the performance of classical AI and holding significant potential for solving classical software engineering problems. Some initial applications of QAI in software engineering have already emerged, such as test case optimization. However, the path ahead remains open, offering ample opportunities to solve complex software engineering problems cost-effectively with QAI. To this end, this paper presents a roadmap towards the application of QAI in software engineering. Specifically, we consider two of the main categories of QAI, i.e., quantum optimization algorithms and quantum machine learning. For each software engineering phase, we discuss how these QAI approaches can address some of the tasks associated with that phase. Moreover, we provide an overview of some of the possible challenges that need to be addressed to make the application of QAI for software engineering successful.

Jayanaka L. Dantanarayana, Savini Kashmira, Thakee Nathees et al.

AI-Integrated programming is emerging as a foundational paradigm for building intelligent systems with large language models (LLMs). Recent approaches such as Meaning Typed Programming (MTP) automate prompt generation by leveraging the semantics already present in code. However, many real-world applications depend on contextual cues, developer intent, and domain-specific reasoning that extend beyond what static code semantics alone can express. To address this limitation, we introduce Semantic Engineering, a lightweight method for enriching program semantics so that LLM-based systems can more accurately reflect developer intent without requiring full manual prompt design. We present Semantic Context Annotations (SemTexts), a language-level mechanism that allows developers to embed natural-language context directly into program constructs. Integrated into the Jac programming language, Semantic Engineering extends MTP to incorporate these enriched semantics during prompt generation. We further introduce a benchmark suite designed to reflect realistic AI-Integrated application scenarios. Our evaluation shows that Semantic Engineering substantially improves prompt fidelity, achieving performance comparable to Prompt Engineering while requiring significantly less developer effort.

Huihang Qiu, Keisuke Obata, Zhicheng Yuan et al.

The green hydrogen economy is expected to play a crucial role in carbon neutrality, but industrial-scale water electrolysis requires improvements in efficiency, operation costs, and capital costs before broad deployment. Electrolysis operates at a high current density and involves the substantial formation of gaseous products from the electrode surfaces to the electrolyte, which may lead to additional resistance and a resulting loss of efficiency. A detailed clarification of the bubble departure phenomena against the electrode surface and the surrounding electrolytes is needed to further control bubbles in a water electrolyzer. This study clarifies how electrolyte properties affect the measured bubble detachment sizes from the comparisons with analytical expressions and dynamic simulations. Bubble behavior in various electrolyte solutions and operating conditions was described using various physical parameters. A quantitative relationship was then established to connect electrolyte properties and bubble departure diameters, which can help regulate the bubble management through electrolyte engineering.

Sijia Liu, Xuedong Wang, G. Guo et al.

The harm from mercury pollution to human health and the environment has long been known. In recent years, the combination of industrial activities and long-term atmospheric transport has resulted in a sustained increase in mercury concentrations in soils. However, soil remediation and mercury-contaminated soil management in China are still in its infancy, and there is ample space for the development of related research. We systematically reviewed several pertinent topics and found that soil mercury pollution around mines and industrial soil in China is the most serious. The highest mercury content is found in the soil around the Tongren mercury mine in Guizhou Province and the thermometer factories. The average content of soil mercury is similar to that of atmospheric mercury emission in China. Mercury content in soil gradually decreases from the southeast to the northwest. In order to repair the mercury-contaminated soil, solidification and stabilization technology have been developed in China and applied in the engineering of restoration. In the future, we will study more effective stabilizer materials and select plants highly rich in mercury, to develop low-cost and high-repair-rate remediation technology. China has also developed a series of policies, regulations, and regulatory documents to manage mercury pollution, such as the Agricultural Land Standard and the Construction Land Standard. Compared with other countries, the screening values for soil mercury in China are relatively low. China has also established control standards for methylmercury in soils of residential and industrial land. In addition, China has issued emission standards and control notices related to the mercury industry. However, there are still shortcomings in soil remediation technology and environmental management systems for mercury pollution in China. In the future, China will formulate standards according to local conditions and improve the responsibility mechanism, financial mechanism, and level of public participation.

L. Nilsson, Fredric Bauer, Max Åhman et al.

ABSTRACT The target of zero emissions sets a new standard for industry and industrial policy. Industrial policy in the twenty-first century must aim to achieve zero emissions in the energy and emissions intensive industries. Sectors such as steel, cement, and chemicals have so far largely been sheltered from the effects of climate policy. A major shift is needed, from contemporary industrial policy that mainly protects industry to policy strategies that transform the industry. For this purpose, we draw on a wide range of literatures including engineering, economics, policy, governance, and innovation studies to propose a comprehensive industrial policy framework. The policy framework relies on six pillars: directionality, knowledge creation and innovation, creating and reshaping markets, building capacity for governance and change, international coherence, and sensitivity to socio-economic implications of phase-outs. Complementary solutions relying on technological, organizational, and behavioural change must be pursued in parallel and throughout whole value chains. Current policy is limited to supporting mainly some options, e.g. energy efficiency and recycling, with some regions also adopting carbon pricing, although most often exempting the energy and emissions intensive industries. An extended range of options, such as demand management, materials efficiency, and electrification, must also be pursued to reach zero emissions. New policy research and evaluation approaches are needed to support and assess progress as these industries have hitherto largely been overlooked in domestic climate policy as well as international negotiations. Key policy insights Energy and emission intensive industries can no longer be complacent about the necessity of zero greenhouse gas (GHG) emissions. Zero emissions require profound technology and organizational changes across whole material value chains, from primary production to reduced demand, recycling and end-of-life of metals, cement, plastics, and other materials. New climate and industrial policies are necessary to transform basic materials industries, which are so far relatively sheltered from climate mitigation. It is important to complement technology R&D with the reshaping of markets and strengthened governance capacities in this emerging policy domain. Industrial transformation can be expected to take centre stage in future international climate policy and negotiations.

Rodrigo Laigner, Yongluan Zhou, M. V. Salles et al.

Microservices have become a popular architectural style for data-driven applications, given their ability to functionally decompose an application into small and autonomous services to achieve scalability, strong isolation, and specialization of database systems to the workloads and data formats of each service. Despite the accelerating industrial adoption of this architectural style, an investigation of the state of the practice and challenges practitioners face regarding data management in microservices is lacking. To bridge this gap, we conducted a systematic literature review of representative articles reporting the adoption of microservices, we analyzed a set of popular open-source microservice applications, and we conducted an online survey to cross-validate the findings of the previous steps with the perceptions and experiences of over 120 experienced practitioners and researchers. Through this process, we were able to categorize the state of practice of data management in microservices and observe several foundational challenges that cannot be solved by software engineering practices alone, but rather require system-level support to alleviate the burden imposed on practitioners. We discuss the shortcomings of state-of-the-art database systems regarding microservices and we conclude by devising a set of features for microservice-oriented database systems.

Hamed Taherdoost

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration.

Nikhil Vijay Jagtap, Niklas Reinisch, Rasul Abdusalamov et al.

Open die forging is one of the oldest manufacturing methods known to remove defects in the ingot resulting from the casting process. The improved properties of the final component are highly dependent on the strain distribution. Although sinusoidal equations and empirical formulations have been already used to estimate the strain, they have been applied only to the core of the workpiece. In this work, a novel approach is presented to model the equivalent strain distribution in 2D cross-sections, in the direction of the press, of open die forged components using neural networks. The proposed method efficiently combines a parametric sinusoidal function with a neural network to learn the complex relationships between the process parameters and the resulting local strain. The neural network is trained on a dataset of finite element (FE) simulations of rectangular geometries that cover a wide range of aspect ratios, bite ratios, and height reductions. The presented methodology with near real-time prediction capabilities shows good agreement with FE results. Moreover, the parametric function captures the characteristic pattern of the strain distribution and reveals certain physical relationships affecting the deformation of the material. These patterns are later examined by analyzing the parameters identified in the parametric sinusoidal function.

Jingwen Yang, Ruohua Zhou

Whisper speaker recognition (WSR) has received extensive attention from researchers in recent years, and it plays an important role in medical, judicial, and other fields. Among them, the establishment of a whisper dataset is very important for the study of WSR. However, the existing whisper dataset suffers from the problems of a small number of speakers, short speech duration, and lack of neutral speech with the same-text as the whispered speech in the same dataset. To address this issue, we present Whisper40, a multi-person Chinese WSR dataset containing same-text neutral speech spanning around 655.90 min sourced from volunteers. In addition, we use the current state-of-the-art speaker recognition model to build a WSR baseline system and combine the idea of transfer learning for pre-training the speaker recognition model using neutral speech datasets and transfer the empirical knowledge of specific network layers to the WSR system. The Whisper40 and CHAINs datasets are then used to fine-tune the model with transferred specific layers. The experimental results show that the Whisper40 dataset is practical, and the time delay neural network (TDNN) model performs well in both the same/cross-scene experiments. The equal error rate (EER) of Chinese WSR after transfer learning is reduced by 27.62% in comparison.

Hanxian Huang, Jishen Zhao

The increasing adoption of WebAssembly (Wasm) for performance-critical and security-sensitive tasks drives the demand for WebAssembly program comprehension and reverse engineering. Recent studies have introduced machine learning (ML)-based WebAssembly reverse engineering tools. Yet, the generalization of task-specific ML solutions remains challenging, because their effectiveness hinges on the availability of an ample supply of high-quality task-specific labeled data. Moreover, previous works overlook the high-level semantics present in source code and its documentation. Acknowledging the abundance of available source code with documentation, which can be compiled into WebAssembly, we propose to learn representations of them concurrently and harness their mutual relationships for effective WebAssembly reverse engineering. In this paper, we present WasmRev, the first multi-modal pre-trained language model for WebAssembly reverse engineering. WasmRev is pre-trained using self-supervised learning on a large-scale multi-modal corpus encompassing source code, code documentation and the compiled WebAssembly, without requiring labeled data. WasmRev incorporates three tailored multi-modal pre-training tasks to capture various characteristics of WebAssembly and cross-modal relationships. WasmRev is only trained once to produce general-purpose representations that can broadly support WebAssembly reverse engineering tasks through few-shot fine-tuning with much less labeled data, improving data efficiency. We fine-tune WasmRev onto three important reverse engineering tasks: type recovery, function purpose identification and WebAssembly summarization. Our results show that WasmRev pre-trained on the corpus of multi-modal samples establishes a robust foundation for these tasks, achieving high task accuracy and outperforming the state-of-the-art ML methods for WebAssembly reverse engineering.

Christof Tinnes, Alisa Welter, Sven Apel

Modeling structure and behavior of software systems plays a crucial role in the industrial practice of software engineering. As with other software engineering artifacts, software models are subject to evolution. Supporting modelers in evolving software models with recommendations for model completions is still an open problem, though. In this paper, we explore the potential of large language models for this task. In particular, we propose an approach, RAMC, leveraging large language models, model histories, and retrieval-augmented generation for model completion. Through experiments on three datasets, including an industrial application, one public open-source community dataset, and one controlled collection of simulated model repositories, we evaluate the potential of large language models for model completion with RAMC. We found that large language models are indeed a promising technology for supporting software model evolution (62.30% semantically correct completions on real-world industrial data and up to 86.19% type-correct completions). The general inference capabilities of large language models are particularly useful when dealing with concepts for which there are few, noisy, or no examples at all.

Vikram Poria, Anuj Rana, A. Kumari et al.

Simple Summary Chitin is a polysaccharide that forms the outer layer of many organisms, and it is widely used in industry. Chitinases are enzymes that can break down chitin into monomeric molecules and are used in the agro-industrial sectors. Because chitin is the key structural component of marine (mollusks, crustaceans, and marine invertebrates) and other species (algae, fungi, and insects), chitinases can be employed in the marine waste management and biocontrol of pathogenic fungi and harmful insects. Chitinase also has uses in the food industry, cosmetics, medicine, waste management, crop protection, and the production of single-cell proteins, among others. This study includes detailed information on the characterization, sources, and uses of chitinases in several areas. Abstract Chitinases are a large and diversified category of enzymes that break down chitin, the world’s second most prevalent polymer after cellulose. GH18 is the most studied family of chitinases, even though chitinolytic enzymes come from a variety of glycosyl hydrolase (GH) families. Most of the distinct GH families, as well as the unique structural and catalytic features of various chitinolytic enzymes, have been thoroughly explored to demonstrate their use in the development of tailor-made chitinases by protein engineering. Although chitin-degrading enzymes may be found in plants and other organisms, such as arthropods, mollusks, protozoans, and nematodes, microbial chitinases are a promising and sustainable option for industrial production. Despite this, the inducible nature, low titer, high production expenses, and susceptibility to severe environments are barriers to upscaling microbial chitinase production. The goal of this study is to address all of the elements that influence microbial fermentation for chitinase production, as well as the purifying procedures for attaining high-quality yield and purity.