Hasil untuk "Industrial hygiene. Industrial welfare"

Jian Yang, Wei Zhang, Jiajun Wu et al.

Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.

Keno Moenck, Adrian Philip Florea, Julian Koch et al.

Autonomous vision applications in production, intralogistics, or manufacturing environments require perception capabilities beyond a small, fixed set of classes. Recent open-vocabulary methods, leveraging 2D Vision-Language Foundation Models (VLFMs), target this task but often rely on class-agnostic segmentation models pre-trained on non-industrial datasets (e.g., household scenes). In this work, we first demonstrate that such models fail to generalize, performing poorly on common industrial objects. Therefore, we propose a training-free, open-vocabulary 3D perception pipeline that overcomes this limitation. Instead of using a pre-trained model to generate instance proposals, our method simply generates masks by merging pre-computed superpoints based on their semantic features. Following, we evaluate the domain-adapted VLFM "IndustrialCLIP" on a representative 3D industrial workshop scene for open-vocabulary querying. Our qualitative results demonstrate successful segmentation of industrial objects.

Naval Kishore Mehta, Arvind, Himanshu Kumar et al.

Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settings. To address this gap, we present a novel Multimodal Industrial Activity Monitoring (MIAM) dataset that captures realistic assembly and disassembly tasks, facilitating the evaluation of key meta-tasks such as action localization, object interaction, and engagement prediction. The dataset comprises multi-view RGB, depth, and Inertial Measurement Unit (IMU) data collected from 22 sessions, amounting to 290 minutes of untrimmed video, annotated in detail for task performance and operator behavior. Its distinctiveness lies in the integration of multiple data modalities and its emphasis on real-world, untrimmed industrial workflows-key for advancing research in human-robot collaboration and operator monitoring. Additionally, we propose a multimodal network that fuses RGB frames, IMU data, and skeleton sequences to predict engagement levels during industrial tasks. Our approach improves the accuracy of recognizing engagement states, providing a robust solution for monitoring operator performance in dynamic industrial environments. The dataset and code can be accessed from https://github.com/navalkishoremehta95/MIAM/.

Vyron Kampourakis, Christos Smiliotopoulos, Vasileios Gkioulos et al.

Traditional air gaps in industrial systems are disappearing as IT technologies permeate the OT domain, accelerating the integration of wireless solutions like Wi-Fi. Next-generation Wi-Fi standards (IEEE 802.11ax/be) meet performance demands for industrial use cases, yet their introduction raises significant security concerns. A critical knowledge gap exists regarding the empirical prevalence and security configuration of Wi-Fi in real-world industrial settings. This work addresses this by mining the global crowdsourced WiGLE database to provide a data-driven understanding. We create the first publicly available dataset of 1,087 high-confidence industrial Wi-Fi networks, examining key attributes such as SSID patterns, encryption methods, vendor types, and global distribution. Our findings reveal a growing adoption of Wi-Fi across industrial sectors but underscore alarming security deficiencies, including the continued use of weak or outdated security configurations that directly expose critical infrastructure. This research serves as a pivotal reference point, offering both a unique dataset and practical insights to guide future investigations into wireless security within industrial environments.

Siqi Wang, Chao Liang, Yunfan Gao et al.

Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://github.com/Tongji-KGLLM/IndustryScope.

Francesco Sanfedino, Paolo Iannelli, Daniel Alazard et al.

To overcome the innovation gap of the Guidance, Navigation and Control (GNC) design process between research and industrial practice a benchmark of industrial relevance has been developed and is presented. This initiative is driven as well by the necessity to train future GNC engineers and the GNC space community on a set of identified complex problems. It allows to demonstrate the relevance of state-of-the-art modeling, control and analysis algorithms for future industrial adoption. The modeling philosophy for robust control synthesis, analysis including the control architecture that enables the simulation of the mission, i.e. the acquisition of a high pointing space mission, are provided.

Bruno Santos, Rogério Luís C. Costa, Leonel Santos

Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations.

Eric Rosen, Devesh K. Jha

We address the problem of teleoperating an industrial robot manipulator via a commercially available Virtual Reality (VR) interface. Previous works on VR teleoperation for robot manipulators focus primarily on collaborative or research robot platforms (whose dynamics and constraints differ from industrial robot arms), or only address tasks where the robot's dynamics are not as important (e.g: pick and place tasks). We investigate the usage of commercially available VR interfaces for effectively teleoeprating industrial robot manipulators in a variety of contact-rich manipulation tasks. We find that applying standard practices for VR control of robot arms is challenging for industrial platforms because torque and velocity control is not exposed, and position control is mediated through a black-box controller. To mitigate these problems, we propose a simplified filtering approach to process command signals to enable operators to effectively teleoperate industrial robot arms with VR interfaces in dexterous manipulation tasks. We hope our findings will help robot practitioners implement and setup effective VR teleoperation interfaces for robot manipulators. The proposed method is demonstrated on a variety of contact-rich manipulation tasks which can also involve very precise movement of the robot during execution (videos can be found at https://www.youtube.com/watch?v=OhkCB9mOaBc)

J. P. Edelen, M. J. Henderson, J. Einstein-Curtis et al.

Industrial particle accelerators inherently operate in much dirtier environments than typical research accelerators. This leads to an increase in noise both in the RF system and in other electronic systems. Combined with the fact that industrial accelerators are mass produced, there is less attention given to optimizing the performance of an individual system. As a result, industrial systems tend to under perform considering their hardware hardware capabilities. With the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging, improving the signal processing of these machines will increase the margin for the deployment of these systems. Our work is focusing on using machine learning techniques to reduce the noise of RF signals used for pulse-to-pulse feedback in industrial accelerators. We will review our algorithms, simulation results, and results working with measured data. We will then discuss next steps for deployment and testing on an industrial system.

Philippe Carvalho, Alexandre Durupt, Yves Grandvalet

The field of industrial defect detection using machine learning and deep learning is a subject of active research. Datasets, also called benchmarks, are used to compare and assess research results. There is a number of datasets in industrial visual inspection, of varying quality. Thus, it is a difficult task to determine which dataset to use. Generally speaking, datasets which include a testing set, with precise labeling and made in real-world conditions should be preferred. We propose a study of existing benchmarks to compare and expose their characteristics and their use-cases. A study of industrial metrics requirements, as well as testing procedures, will be presented and applied to the studied benchmarks. We discuss our findings by examining the current state of benchmarks for industrial visual inspection, and by exposing guidelines on the usage of benchmarks.

Bram van den Akker, Olivier Jeunen, Ying Li et al.

The bandit paradigm provides a unified modeling framework for problems that require decision-making under uncertainty. Because many business metrics can be viewed as rewards (a.k.a. utilities) that result from actions, bandit algorithms have seen a large and growing interest from industrial applications, such as search, recommendation and advertising. Indeed, with the bandit lens comes the promise of direct optimisation for the metrics we care about. Nevertheless, the road to successfully applying bandits in production is not an easy one. Even when the action space and rewards are well-defined, practitioners still need to make decisions regarding multi-arm or contextual approaches, on- or off-policy setups, delayed or immediate feedback, myopic or long-term optimisation, etc. To make matters worse, industrial platforms typically give rise to large action spaces in which existing approaches tend to break down. The research literature on these topics is broad and vast, but this can overwhelm practitioners, whose primary aim is to solve practical problems, and therefore need to decide on a specific instantiation or approach for each project. This tutorial will take a step towards filling that gap between the theory and practice of bandits. Our goal is to present a unified overview of the field and its existing terminology, concepts and algorithms -- with a focus on problems relevant to industry. We hope our industrial perspective will help future practitioners who wish to leverage the bandit paradigm for their application.

Bo Ying Su, Zhongqi Wei, James McCann et al.

Tactile skins made from textiles enhance robot-human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human-robot interaction in industrial environments, and can provide unique opportunities in robot control and learning, making them a promising technology for enhancing robot perception and interaction.

Aida Navas

Este artículo responde a la invitación de la Editora y el Comité Editorial de la Revista Ocupación Humana, para la publicación del número especial de celebración de los 50 años de fundación de la organización científica y gremial de las y los terapeutas ocupacionales de Colombia. Recoge las memorias, análisis y reflexiones a futuro de la terapeuta ocupacional Aida Navas, quien tuvo a su cargo la presidencia de la Asociación Colombiana de Terapia Ocupacional -ACTO en los periodos 1980 a 1982, 1999 a 2001, 2001 a 2003 y 2010 a 2012, y del Consejo Directivo Nacional del Colegio Colombiano de Terapia Ocupacional entre 2012 y 2014. Actualmente es vicepresidenta del Consejo Directivo para el periodo 2022-2024. Este y los demás textos de expresidentas que hacen parte de este número especial están llenos de la fortaleza, energía y proyección de sus autoras y de nuestra profesión. Resultan, entonces, en un importante y significativo testimonio histórico de lo que hemos construido y de la inmensa y poderosa tarea que tiene el Colegio Colombiano de Terapia Ocupacional para continuar construyendo y respondiendo a los retos del ser ocupacional – personales, colectivos y sociales –, del país, la región y el mundo. 

Erin Long, Carley Schwartz, Christopher Carlsten

Abstract Diesel exhaust (DE) is a major component of air pollution in urban centers. Controlled human exposure (CHE) experiments are commonly used to investigate the acute effects of DE inhalation specifically and also as a paradigm for investigating responses to traffic-related air pollution (TRAP) more generally. Given the critical role this model plays in our understanding of TRAP’s health effects mechanistically and in support of associated policy and regulation, we review the methodology of CHE to DE (CHE–DE) in detail to distill critical elements so that the results of these studies can be understood in context. From 104 eligible publications, we identified 79 CHE–DE studies and extracted information on DE generation, exposure session characteristics, pollutant and particulate composition of exposures, and participant demographics. Virtually all studies had a crossover design, and most studies involved a single DE exposure per participant. Exposure sessions were typically 1 or 2 h in duration, with participants alternating between exercise and rest. Most CHE–DE targeted a PM concentration of 300 μg/m3. There was a wide range in commonly measured co-pollutants including nitrogen oxides, carbon monoxide, and total organic compounds. Reporting of detailed parameters of aerosol composition, including particle diameter, was inconsistent between studies, and older studies from a given lab were often cited in lieu of repeating measurements for new experiments. There was a male predominance in participants, and over half of studies involved healthy participants only. Other populations studied include those with asthma, atopy, or metabolic syndrome. Standardization in reporting exposure conditions, potentially using current versions of engines with modern emissions control technology, will allow for more valid comparisons between studies of CHE–DE, while recognizing that diesel engines in much of the world remain old and heterogeneous. Inclusion of female participants as well as populations more susceptible to TRAP will broaden the applicability of results from CHE–DE studies.

Maria Krantz, Alexander Windmann, Rene Heesch et al.

The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.

Ziwei Wan, Zhen Gao, Marco Di Renzo et al.

The fourth industrial revolution, i.e., Industry 4.0, is evolving all around the globe. In this article, we introduce the landscape of Industry 4.0 and beyond empowered by the seamless collaboration of communication technology (CT), information technology (IT), and operation technology (OT), i.e., CIOT collaboration. Specifically, CIOT collaboration is regarded as a main improvement of Industry 4.0 compared to the previous industrial revolutions. We commence by reviewing the previous three industrial revolutions and we argue that the key feature of Industry 4.0 is the CIOT collaboration. More particularly, CT domain supports ubiquitous connectivity of the industrial elements and further bridges the physical world and the cyber world, which is a pivotal prerequisite. Then, we present the potential impacts of CIOT collaboration on typical industrial use cases with the objective of creating a more intelligent and human-friendly industry. Furthermore, the technical challenges of paving the way for the CIOT collaboration with an emphasis on the CT domain are discussed. Finally, we shed light on a roadmap for Industry 4.0 and beyond. The salient steps to be taken in the future CIOT collaboration are highlighted, which may be expected to expedite the paradigm shift towards the next industrial revolution.

A. M. M. Todd, R. Agustsson, D. L. Bruhwiler et al.

The purpose of this white paper is to discuss the importance of having a world class domestic industrial vendor base, capable of supporting the needs of the particle accelerator facilities, and the necessary steps to support and develop such a base in the United States. The paper focuses on economic, regulatory, and policy-driven barriers and hurdles, which presently limit the depth and scope of broader industrial participation in US accelerator facilities. It discusses the international competition landscape and proposes steps to improve the strength and vitality of US industry.

Vegard Sæter Grytting, Magne Refsnes, Johan Øvrevik et al.

Abstract Background Respirable stone- and mineral particles may be a major constituent in occupational and ambient air pollution and represent a possible health hazard. However, with exception of quartz and asbestos, little is known about the toxic properties of mineral particles. In the present study, the pro-inflammatory and cytotoxic responses to six stone particle samples of different composition and with diameter below 10 μm were assessed in human bronchial epithelial cells (HBEC3-KT), THP-1 macrophages and a HBEC3-KT/THP-1 co-culture. Moreover, particle-induced lysis of human erythrocytes was assessed to determine the ability of the particles to lyse biological membranes. Finally, the role of the NLRP3 inflammasome was assessed using a NLRP3-specific inhibitor and detection of ASC oligomers and cleaved caspase-1 and IL-1β. A reference sample of pure α-quartz was included for comparison. Results Several stone particle samples induced a concentration-dependent increase in cytotoxicity and secretion of the pro-inflammatory cytokines CXCL8, IL-1α, IL-1β and TNFα. In HBEC3-KT, quartzite and anorthosite were the most cytotoxic stone particle samples and induced the highest levels of cytokines. Quartzite and anorthosite were also the most cytotoxic samples in THP-1 macrophages, while anorthosite and hornfels induced the highest cytokine responses. In comparison, few significant differences between particle samples were detected in the co-culture. Adjusting responses for differences in surface area concentrations did not fully account for the differences between particle samples. Moreover, the stone particles had low hemolytic potential, indicating that the effects were not driven by membrane lysis. Pre-incubation with a NLRP3-specific inhibitor reduced stone particle-induced cytokine responses in THP-1 macrophages, but not in HBEC3-KT cells, suggesting that the effects are mediated through different mechanisms in epithelial cells and macrophages. Particle exposure also induced an increase in ASC oligomers and cleaved caspase-1 and IL-1β in THP-1 macrophages, confirming the involvement of the NLRP3 inflammasome. Conclusions The present study indicates that stone particles induce cytotoxicity and pro-inflammatory responses in human bronchial epithelial cells and macrophages, acting through NLRP3-independent and -dependent mechanisms, respectively. Moreover, some particle samples induced cytotoxicity and cytokine release to a similar or greater extent than α-quartz. Thus, these minerals warrant further attention in future research.

Marcela G. dos Santos, Fabio Petrillo

Industrial robots are important machines applied in numerous modern industries that execute repetitive tasks with high accuracy, replacing or supporting dangerous jobs. In this kind of system, with increased complexity in which cost is related to the time the system keeps working, the system must operate with a minimum number of failures. In other words, a quality aspect important in industry is reliability. We hypothesize that Automated Acceptance Testing improves reliability for industrial robot program. We present the research question, the motivation for this study, our hypothesis and future research efforts.

Katherine Sierra Gil, Antonio Torres Valle

Los procesos de hemodiálisis son una alternativa de sobrevida para los pacientes con insuficiencia renal crónica, sin embargo, por su proceder invasivo resultan una de las prácticas médicas con mayores riesgos para los mismos. También resulta riesgosa la práctica para los trabajadores ocupacionalmente expuestos, dados los peligros biológicos por el manejo de fluidos y la existencia de patógenos, con los que estos se ven involucrados. El método de “matriz de riesgo tridimensional” es utilizado comúnmente como herramienta para establecer prioridades en la gestión del riesgo de muchas prácticas médicas con radiaciones ionizantes, a partir del análisis combinado de los escenarios de riesgo que pueden aparecer en esta práctica. El objetivo de este estudio es evaluar, utilizando de manera novedosa este método, el riesgo biológico en una sala de hemodiálisis. Para ello se aplicó el código SECURE-MR-FMEA, el cual informatiza el método de matriz de riesgo. Se detectó que el riesgo en este proceso es muy alto debido, esencialmente, a la no existencia de local para el almacenamiento de desechos biológicos peligrosos. Otros contribuyentes importantes son el uso inadecuado de los medios de protección individual y buenas prácticas y procedimientos.