Hasil untuk "Industrial hygiene. Industrial welfare"

Ruomu Tan, Martin W Hoffmann

The integration of artificial intelligence (AI) into the industrial sector has not only driven innovation but also expanded the ethical landscape, necessitating a reevaluation of principles governing technology and its applications and awareness in research and development of industrial AI solutions. This chapter explores how AI-empowered industrial innovation inherently intersects with ethics, as advancements in AI introduce new challenges related to transparency, accountability, and fairness. In the chapter, we then examine the ethical aspects of several examples of AI manifestation in industrial use cases and associated factors such as ethical practices in the research and development process and data sharing. With the progress of ethical industrial AI solutions, we emphasize the importance of embedding ethical principles into industrial AI systems and its potential to inspire technological breakthroughs and foster trust among stakeholders. This chapter also offers actionable insights to guide industrial research and development toward a future where AI serves as an enabler for ethical and responsible industrial progress as well as a more inclusive industrial ecosystem.

Shinichi Mae, Ryousuke Yamada, Hirokatsu Kataoka

Pre-training on real-image datasets has been widely proven effective for improving instance segmentation. However, industrial applications face two key challenges: (1) legal and ethical restrictions, such as ImageNet's prohibition of commercial use, and (2) limited transferability due to the domain gap between web images and industrial imagery. Even recent vision foundation models, including the segment anything model (SAM), show notable performance degradation in industrial settings. These challenges raise critical questions: Can we build a vision foundation model for industrial applications without relying on real images or manual annotations? And can such models outperform even fine-tuned SAM on industrial datasets? To address these questions, we propose the Instance Core Segmentation Dataset (InsCore), a synthetic pre-training dataset based on formula-driven supervised learning (FDSL). InsCore generates fully annotated instance segmentation images that reflect key characteristics of industrial data, including complex occlusions, dense hierarchical masks, and diverse non-rigid shapes, distinct from typical web imagery. Unlike previous methods, InsCore requires neither real images nor human annotations. Experiments on five industrial datasets show that models pre-trained with InsCore outperform those trained on COCO and ImageNet-21k, as well as fine-tuned SAM, achieving an average improvement of 6.2 points in instance segmentation performance. This result is achieved using only 100k synthetic images, more than 100 times fewer than the 11 million images in SAM's SA-1B dataset, demonstrating the data efficiency of our approach. These findings position InsCore as a practical and license-free vision foundation model for industrial applications.

Jay Lee, Hanqi Su

Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.

Hang Yu, Yuzhou Lai, Li Zhang et al.

The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE), driving a paradigm shift in both academic research and industrial practice. While top-tier SE venues continue to show sustained or emerging focus on areas like automated testing and program repair, with researchers worldwide reporting continuous performance gains, the alignment of these academic advances with real industrial needs remains unclear. To bridge this gap, we first conduct a systematic analysis of 1,367 papers published in FSE, ASE, and ICSE between 2022 and 2025, identifying key research topics, commonly used benchmarks, industrial relevance, and open-source availability. We then carry out an empirical survey across 17 organizations, collecting 282 responses on six prominent topics, i.e., program analysis, automated testing, code generation/completion, issue resolution, pre-trained code models, and dependency management, through structured questionnaires. By contrasting academic capabilities with industrial feedback, we derive seven critical implications, highlighting under-addressed challenges in software requirements and architecture, the reliability and explainability of intelligent SE approaches, input assumptions in academic research, practical evaluation tensions, and ethical considerations. This study aims to refocus academic attention on these important yet under-explored problems and to guide future SE research toward greater industrial impact.

Lu Xia, Kaiqi Zhao, Sunil Kadam et al.

Paired electrolysis at industrial current densities offers an energy-efficient and sustainable alternative to thermocatalytic chemical synthesis by leveraging anodic and cathodic valorization. However, its industrial feasibility remains constrained by system integration, including reactor assembly, asymmetric electron transfer kinetics, membrane selection, mass transport limitations, and techno-economic bottlenecks. Addressing these challenges requires an engineering-driven approach that integrates reactor architecture, electrode-electrolyte interactions, reaction pairing, and process optimization. Here, we discuss scale-specific electrochemical reactor assembly strategies, transitioning from half-cell research to full-scale stack validation. We develop reaction pairing frameworks that align electrocatalyst design with electrochemical kinetics, enhancing efficiency and selectivity under industrial operating conditions. We also establish application-dependent key performance indicators (KPIs) and benchmark propylene oxidation coupled with hydrogen evolution reaction (HER) or oxygen reduction reaction (ORR) against existing industrial routes to evaluate process viability. Finally, we propose hybrid integration models that embed paired electrolysis into existing industrial workflows, overcoming adoption barriers.

Xiaoli Ji, Yanping Li, Shuyi Gu et al.

Abstract Background Tin mine dust (MD), a by-product of tin mining and rock drilling, is a significant contributor to miners’ pneumoconiosis. This aerosolized dust is a complex mixture of mineral components, including potentially toxic heavy metals such as arsenic, which may contribute to the development of pneumoconiosis and lung cancer. This study investigates the inhalation toxicity of tin MD samples on pulmonary cells using an Air-Liquid Interface (ALI) exposure model. Results MD-A was characterized by high arsenic content, exceeding 30%. In contrast, the elemental composition of MD-B and MD-C was predominantly composed of calcium, magnesium, and aluminum. In the toxicity study, key toxicological endpoints (cell viability, cytotoxicity, pro-inflammatory markers, and cell barrier function) were systematically assessed, and real-time monitoring of the cell-delivered MD particles (MD-A, MD-B, MD-C, and silica) concentrations was achieved using QCM. MD-A significantly enhanced the proliferation ability of 16HBE and Calu-3 cells compared to other particulate matters, indicating arsenic-containing MD promotes cell proliferation. MD-A resulted in an increase in IL-1β mRNA expression in 16HBE cells; elevations in IL-1β, IL-6, IL-8, TNF-α, and CCL2 mRNA were observed in Calu-3 cells. Additionally, treatment with four different particles significantly increased the mRNA expression of MUC5AC in both cell types. Immunofluorescence staining demonstrated alterations in the typical morphology of epithelial cells exposed to arsenic-containing MD and silica particles. In this study, it was shown that four types of particles delivered via suspension to the same in vitro model can induce differing levels of cytotoxicity and proinflammatory responses. The differences in results underscore the specific effects of the inherent physicochemical attributes of particles on biological interactions. Conclusions Under identical particle size conditions, in vitro studies on inhalation toxicity reveal that the chemical composition of particulate matter causes varying degrees of toxic damage to cells. This study utilizes an advanced in vitro method to assess the inhalation hazards of tin MD particles by integrating the ALICE system. The chemical complexity of tin MD, particularly its significant arsenic content, requires special attention and thorough evaluation.

Simon Liebl, Leah Lathrop, Ulrich Raithel et al.

As part of the Internet of Things, industrial devices are now also connected to cloud services. However, the connection to the Internet increases the risks for Industrial Control Systems. Therefore, a threat analysis is essential for these devices. In this paper, we examine Industrial Internet of Things devices, identify and rank different sources of threats and describe common threats and vulnerabilities. Finally, we recommend a procedure to carry out a threat analysis on these devices.

Xiao Xia, Dan Zhang, Zibo Liao et al.

The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown significant progress in generating general 3D scenes from textual descriptions, generating industrial scenes with LLMs poses a unique challenge due to their demand for precise measurements and positioning, requiring complex planning over spatial arrangement. To address this challenge, we introduce SceneGenAgent, an LLM-based agent for generating industrial scenes through C# code. SceneGenAgent ensures precise layout planning through a structured and calculable format, layout verification, and iterative refinement to meet the quantitative requirements of industrial scenarios. Experiment results demonstrate that LLMs powered by SceneGenAgent exceed their original performance, reaching up to 81.0% success rate in real-world industrial scene generation tasks and effectively meeting most scene generation requirements. To further enhance accessibility, we construct SceneInstruct, a dataset designed for fine-tuning open-source LLMs to integrate into SceneGenAgent. Experiments show that fine-tuning open-source LLMs on SceneInstruct yields significant performance improvements, with Llama3.1-70B approaching the capabilities of GPT-4o. Our code and data are available at https://github.com/THUDM/SceneGenAgent .

Lolitta Ammann, Jorge Martinez-Gil, Michael Mayr et al.

Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time series data, specifically tailored for industrial applications. Our framework addresses the complexities inherent in industrial datasets, transforming them into knowledge graphs that improve decision-making, process optimization, and knowledge discovery. Additionally, it employs Granger causality to identify key attributes that can inform the design of predictive models. To illustrate the practical utility of our approach, we also present a motivating use case demonstrating the benefits of our framework in a real-world industrial scenario. Further, we demonstrate how the automated conversion of time series data into knowledge graphs can identify causal influences or dependencies between important process parameters.

Zi Wang, Xingcheng Xu, Yanqing Yang et al.

We propose a deep learning framework, DL-opt, designed to efficiently solve for optimal policies in quantifiable general equilibrium trade models. DL-opt integrates (i) a nested fixed point (NFXP) formulation of the optimization problem, (ii) automatic implicit differentiation to enhance gradient descent for solving unilateral optimal policies, and (iii) a best-response dynamics approach for finding Nash equilibria. Utilizing DL-opt, we solve for non-cooperative tariffs and industrial subsidies across 7 economies and 44 sectors, incorporating sectoral external economies of scale. Our quantitative analysis reveals significant sectoral heterogeneity in Nash policies: Nash industrial subsidies increase with scale elasticities, whereas Nash tariffs decrease with trade elasticities. Moreover, we show that global dual competition, involving both tariffs and industrial subsidies, results in lower tariffs and higher welfare outcomes compared to a global tariff war. These findings highlight the importance of considering sectoral heterogeneity and policy combinations in understanding global economic competition.

Gabriele Valvano, Antonino Agostino, Giovanni De Magistris et al.

Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments. Generative AI offers opportunities to enlarge small industrial datasets artificially, thus enabling the usage of state-of-the-art supervised approaches in the industry. Unfortunately, also good generative models need a lot of data to train, while industrial datasets are often tiny. Here, we propose a new approach for reusing general-purpose pre-trained generative models on industrial data, ultimately allowing the generation of self-labelled defective images. First, we let the model learn the new concept, entailing the novel data distribution. Then, we force it to learn to condition the generative process, producing industrial images that satisfy well-defined topological characteristics and show defects with a given geometry and location. To highlight the advantage of our approach, we use the synthetic dataset to optimise a crack segmentor for a real industrial use case. When the available data is small, we observe considerable performance increase under several metrics, showing the method's potential in production environments.

Kaira Devi, Priskila Hananingrum, Y. Denny A. Wahyudiono

Introduction:Work stress can occur in many professions, including nursing, which is inseparable from individual characteristics. Inpatient is one of the units at Ploso Regional Public Hospital, Jombang, which has time-consuming work that requires observation on an ongoing basis. This study aimed to understand the relationship between individual characteristics, such as age, gender, marital status, working period, and personality type, with the level of work stress experienced by the inpatient installation unit nurses at Ploso Regional Public Hospital, Jombang. Methods: Observational descriptive study was applied with a cross-sectional design. Age, gender, marital status, working period, and personality type were the independent variables used in this study, while the dependent variable was work stress. The sample used was the total accessible population of nurses in the inpatient unit with 33 respondents. The data collection method used was a general questionnaire for personal variables (age, gender, marital status, working period), Personality Type Questionnaire for personality type, and Health and Safety Executive (HSE) Questionnaire for work stress. Data were analyzed using chi-square correlation and spearman correlation test. Results: In the inpatient installation unit, most nurses were male between the ages of 24-37, had a working period of less than five years, were married, and had type A personality. The individual characteristics which had a moderate relationship with work stress were age (ρ = 0.419), marital status (ρ = 0.461), and working period (ρ = 0.359). Gender (ρ = 0.246) and personality type (ρ = 0.179) had a weak relationship with work stress. Conclusion: Age, marital status, and working period had a moderate relationship with work stress, while gender and personality type had a weak relationship.

Sabrina Nurul Faiza, Kresna Febriyanto

Introduction: Noise is an unwelcome sound that disrupts workers. Noise is present in every workplace, including ship engine noise. Continuous noise exposure can result in health issues, including hearing loss. Noise can cause stress on traditional boat workers because being continuously exposed to noise causes an uncomfortable feeling in the work environment. This uncomfortable feeling can trigger stress on ferry boat workers. This study aims to determine the relationship between noise and work stress on ferry boat workers at the Pier of Kampung Baru Tengah, Balikpapan. Methods: This study used a quantitative approach with a cross-sectional research design with 44 respondents. The instruments used a Sound Level Meter to measure Noise Level and Dass 21 Questionnaire with an interview method to measure Job Stress. Results: As many as 35 respondents were exposed to noise caused by traditional boat engines, and more than 50% of workers did not experience work stress (normal). The results of this study indicated a relationship between noise and work stress in traditional boat workers. Conclusion: The direction of the association between noise and work stress was positive but low, meaning that, as noise levels rise, so does the risk of workplace stress.

Gisele Tais Roldão de Souza, Rachel Maciel Monteiro, Lucas Lazarini Bim et al.

The disinfection procedures aim to reduce the microbial load, but there are doubts about the risks of contamination spreading into the lumens of devices, such as three-way stopcocks (3-WS). This study aimed at an in vitro evaluation of the antibacterial procedure of manual friction of 3-WS intentionally contaminated and to determine the solution dispersion into the lumens. Laboratory experiments were developed in two steps: evaluation of bacterial spread through intentional contamination with <i>Staphylococcus aureus</i> and <i>Pseudomonas aeruginosa</i>, and alcohol dispersion into the 3-WS lumens. After manual friction of the 3-WS with saline solution at 0.85% (<i>w</i>/<i>v</i>) [control group], <i>S. aureus</i> and <i>P. aeruginosa</i> were isolated in the lumens of 55.6% and 27.8% of the devices, respectively. However, after the disinfection of the 3-WS with ethyl alcohol at 70% (<i>w</i>/<i>v</i>), there was no bacterial contamination in the lumens of the 3-WS. On the other hand, the solution dispersion (dye) into the lumens was evidenced by two different techniques: Durham tubes (5.6%) and swabs (46.3%). The manual friction of the 3-WS with ethyl alcohol at 70% demonstrated antibacterial efficacy, but it refers to reflections on the risk of solution diffusion into the venous network and the inherent clinical practice situations and patient safety.

Bin Han, Mohammad Asif Habibi, Bjoern Richerzhagen et al.

Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, identifying the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios.

Nala Astari Pramesti, Shintia Yunita Arini

Introduction: Asosiasi untuk Demokrasi dan Kesejahteraan Sosial (Ademos) was founded as a form of anxiety towards rural communities whose majority of the population does not receive enough attention and access to economic development. One of the empowerment programs is a program to improve the quality of the Bojonegoro batik craftsmen. Workers can work more than 8 hours a day in a sitting and bending position for long periods of time. This study aimed to determine the correlation between the length of work and exercise routines on musculoskeletal complaints among batik craftsmen of Ademos, Bojonegoro Regency. Methods: This research was a descriptive analytic study using a cross-sectional design. The research was conducted in July-August 2020 on Ademos batik craftsmen in Bojonegoro Regency. The total population of the study was 42 batik craftsmen who were selected using a total sampling technique. The variables studied included tenure, exercise routines, and musculoskeletal complaints. Data collection was carried out through questionnaire sheets, observations and the Nordic Body Map Questionnaire. Results: The results of the study found that there was a significant correlation between working period and musculoskeletal complaints (p=0.032) experienced by Batik craftsmen of Ademos Bojonegoro. On the other hand, there was no correlation found between exercise routines and musculoskeletal complaints (p=0.361) on Batik craftsmen of Ademos Bojonegoro. Conclusion: The significant factor causing musculoskeletal complaints in Ademos Bojonegoro Batik craftsmen was the working period factor.

C. Muehlberger, F. Patty

Rohit Gupta, Sieglinde Kranz, Nikolaus Regnat et al.

Domain-Specific Languages (DSLs) help practitioners in contributing solutions to challenges of specific domains. The efficient development of user-friendly DSLs suitable for industrial practitioners with little expertise in modelling still is challenging. For such practitioners, who often do not model on a daily basis, there is a need to foster reduction of repetitive modelling tasks and providing simplified visual representations of DSL parts. For industrial language engineers, there is no methodical support for providing such guidelines or documentation as part of reusable language modules. Previous research either addresses the reuse of languages or guidelines for modelling. For the efficient industrial deployment of DSLs, their combination is essential: the efficient engineering of DSLs from reusable modules that feature integrated documentation and guidelines for industrial practitioners. To solve these challenges, we propose a systematic approach for the industrial engineering of DSLs based on the concept of reusable DSL Building Blocks, which rests on several years of experience in the industrial engineering of DSLs and their deployment to various organizations. We investigated our approach via focus group methods consisting of five participants from industry and research qualitatively. Ultimately, DSL Building Blocks support industrial language engineers in developing better usable DSLs and industrial practitioners in more efficiently achieving their modelling.

Tomasa María Esther Linares Fernández, Jesús Salvador Hernández Romero

Nunca pudimos imaginar que el escribir una página se tornara tan difícil, agobiante. Pero es que se trataba de hacerlo comentando otra partida física, siempre inesperada, siempre dolorosa. Se nos ha ido, este 22 de marzo, Pedro, Almirall, el profesor, el científico, el orador, el autor, el directivo, el consejero, el oportunamente crítico, el amigo, el familiar. Y es que todos, en Cuba y en una buena parte del mundo, en América, porque no fue solo en América Latina, lo conocieron, aprendieron de él, lo respetaron y lo admiraron. Nació un día que posteriormente se convertiría en una fecha gloriosa: un 26 de julio, en el año 1949. En su niñez y juventud recibió de sus padres una educación que lo hizo honrarlos y respetarlos mientras los tuvo en vida, y posteriormente, en el caso de la madre muy recientemente, apenas dos meses atrás. Psicólogo, en toda la extensión de la palabra, estamos seguros de que mucho antes de su graduación en 1974, en la entonces Escuela de Psicología de la Facultad de Ciencias de la Universidad de La Habana. Su extensa y diversa producción científica, en Cuba y en el extranjero, alcanzó y sobrepasó el centenar de contribuciones, tanto publicaciones como actividades docentes; dirigió y participó en más de 40 proyectos de investigación, muchos de ellos asociados a programas ramales y nacionales, y fue prolífico ponente en eventos científicos. Su presencia constante le otorgó un sello distintivo a las actividades institucionales: Forum de Ciencia y Técnica, jornadas por aniversarios, simposios y congresos. Imposible que no olvidemos señalar algo destacado y ejemplar que haya hecho; igual de imposible resultará que su ejemplo y recuerdo no permanezcan entre nosotros; Almirall, Pedro, llegó, estableció y deja un legado: fue quien introdujo en Cuba, para que quedara para siempre, la Psicología dentro de la Salud Ocupacional.

Jaehyuk Park, Morgan R. Frank, Lijun Sun et al.

Categorization is an essential component for us to understand the world for ourselves and to communicate it collectively. It is therefore important to recognize that classification system are not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented. Out-of-date classification systems would potentially limit further understanding of the current economy because things constantly change. Here, we develop an occupation-based classification system for the US labor economy, called industrial topics, that satisfy adaptability and representability. By leveraging the distributions of occupations across the US urban areas, we identify industrial topics - clusters of occupations based on their co-existence pattern. Industrial topics indicate the mechanisms under the systematic allocation of different occupations. Considering the densely connected occupations as an industrial topic, our approach characterizes regional economies by their topical composition. Unlike the existing survey-based top-down approach, our method provides timely information about the underlying structure of the regional economy, which is critical for policymakers and business leaders, especially in our fast-changing economy.