Hasil untuk "Industrial hygiene. Industrial welfare"

Shuai Wang, Yinan Yu, Earl Barr et al.

Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI coding assistants like GitHub Copilot, this process remains inefficient; individual coding tasks are semi-automated, but the workflow connecting domain knowledge to implementation is not. Developers and experts still lack a shared view, resulting in repeated coordination, clarification rounds, and error-prone handoffs. We address this gap through a graph-based workflow optimization approach that progressively replaces manual coordination with LLM-powered services, enabling incremental adoption without disrupting established practices. We evaluate our approach on \texttt{spapi}, a production in-vehicle API system at Volvo Group involving 192 endpoints, 420 properties, and 776 CAN signals across six functional domains. The automated workflow achieves 93.7\% F1 score while reducing per-API development time from approximately 5 hours to under 7 minutes, saving an estimated 979 engineering hours. In production, the system received high satisfaction from both domain experts and developers, with all participants reporting full satisfaction with communication efficiency.

Zhuonan Wang, Zhenxuan Fan, Siwen Tan et al.

As industrial manufacturing scales, automating fine-grained product image analysis has become critical for quality control. However, existing approaches are hindered by limited dataset coverage and poor model generalization across diverse and complex anomaly patterns. To address these challenges, we introduce MAU-Set, a comprehensive dataset for Multi-type industrial Anomaly Understanding. It spans multiple industrial domains and features a hierarchical task structure, ranging from binary classification to complex reasoning. Alongside this dataset, we establish a rigorous evaluation protocol to facilitate fair and comprehensive model assessment. Building upon this foundation, we further present MAU-GPT, a domain-adapted multimodal large model specifically designed for industrial anomaly understanding. It incorporates a novel AMoE-LoRA mechanism that unifies anomaly-aware and generalist experts adaptation, enhancing both understanding and reasoning across diverse defect classes. Extensive experiments show that MAU-GPT consistently outperforms prior state-of-the-art methods across all domains, demonstrating strong potential for scalable and automated industrial inspection.

Chang Guo, Alison Buckley, Sarah Robertson et al.

Abstract Background Understanding the impacts of inhaled insoluble nanomaterials as they are encountered in the environment and workplace, in injured lungs remains limited, particularly with respect to their role in the progression or mitigation of lung pathology. While some studies suggest potential protective effects of cerium(IV) oxide nanoparticles (CeO2NPs) under certain conditions, their influence during active disease processes is unclear. This study builds on prior work to investigate the effects of CeO2NP aerosols on bleomycin-induced pulmonary injury and active disease processes. Method To establish conditions of active pulmonary disease processes, bleomycin was used in both animal and airway epithelium models. Male Sprague-Dawley rats were intratracheally instilled with bleomycin or saline (control) followed by nose-only inhalation exposure to CeO2NP aerosols (diameter of ~ 43 nm) or control for 3 h per day for 4 days per week for one or two weeks. At three days postexposure, the animals were sacrificed for analysis of bronchoalveolar lavage (BAL) fluid, lung histopathology and global mRNA expression. Comparative in vitro studies were conducted to investigate biological responses at the cellular level, using 3D human small airway epithelium cultures (SmallAir™) exposed to CeO2NP aerosols (with a diameter of ~ 86 nm) at the air-liquid-interface at deposition doses comparable to those received in vivo in the small airway. Results In vivo, bleomycin treatment resulted in an increase in total BAL cells and fibrotic staining, and significant induction of inflammatory and oxidative stress, as shown by mRNA sequencing analysis. One week of exposure to CeO2NPs modified these responses by attenuating fibrotic staining and reducing the expression of genes associated with lung function, inflammation and epithelial-mesenchymal transition (EMT). In vitro, CeO2NP exposure modulated some bleomycin-induced cellular responses, although these models do not fully capture the complexity of whole body and tissue systems, highlighting limitations and considerations for future in vitro exposure studies. Conclusions In this study, inhaled CeO2NPs modulated lung injury responses in the context of active disease, with both potential protective effects and adverse outcomes. These findings demonstrate that the timing of CeO2NP exposure relative to disease progression is critical and highlight the need for hazard assessment frameworks to consider context-dependent effects, particularly in the presence of pre-existing lung injury.

S. Shen, Z. Lin, W. Liu et al.

Industrial dashboards, commonly deployed by organizations such as enterprises and governments, are increasingly crucial in data communication and decision-making support across various domains. Designing an industrial dashboard prototype is particularly challenging due to its visual complexity, which can include data visualization, layout configuration, embellishments, and animations. Additionally, in real-world industrial settings, designers often encounter numerous constraints. For instance, when companies negotiate collaborations with clients and determine design plans, they typically need to demo design prototypes and iterate on them based on mock data quickly. Such a task is very common and crucial during the ideation stage, as it not only helps save developmental costs but also avoids data-related issues such as lengthy data handover periods. However, existing authoring tools of dashboards are mostly not tailored to such prototyping needs, and motivated by these gaps, we propose DashChat, an interactive system that leverages large language models (LLMs) to generate industrial dashboard design prototypes from natural language. We collaborated closely with designers from the industry and derived the requirements based on their practical experience. First, by analyzing 114 high-quality industrial dashboards, we summarized their common design patterns and inject the identified ones into LLMs as reference. Next, we built a multi-agent pipeline powered by LLMs to understand textual requirements from users and generate practical, aesthetic prototypes. Besides, functionally distinct, parallel-operating agents are created to enable efficient generation. Then, we developed a user-friendly interface that supports text-based interaction for generating and modifying prototypes. Two user studies demonstrated that our system is both effective and efficient in supporting design prototyping.

Mengyang Zhao, Teng Fu, Haiyang Yu et al.

Few-Shot Industrial Anomaly Detection (FS-IAD) has important applications in automating industrial quality inspection. Recently, some FS-IAD methods based on Large Vision-Language Models (LVLMs) have been proposed with some achievements through prompt learning or fine-tuning. However, existing LVLMs focus on general tasks but lack basic industrial knowledge and reasoning capabilities related to FS-IAD, making these methods far from specialized human quality inspectors. To address these challenges, we propose a unified framework, IADGPT, designed to perform FS-IAD in a human-like manner, while also handling associated localization and reasoning tasks, even for diverse and novel industrial products. To this end, we introduce a three-stage progressive training strategy inspired by humans. Specifically, the first two stages gradually guide IADGPT in acquiring fundamental industrial knowledge and discrepancy awareness. In the third stage, we design an in-context learning-based training paradigm, enabling IADGPT to leverage a few-shot image as the exemplars for improved generalization to novel products. In addition, we design a strategy that enables IADGPT to output image-level and pixel-level anomaly scores using the logits output and the attention map, respectively, in conjunction with the language output to accomplish anomaly reasoning. To support our training, we present a new dataset comprising 100K images across 400 diverse industrial product categories with extensive attribute-level textual annotations. Experiments indicate IADGPT achieves considerable performance gains in anomaly detection and demonstrates competitiveness in anomaly localization and reasoning. We will release our dataset in camera-ready.

Joseph Le Bihan, Thomas Lapi, José Halloy

This study proposes a new model based on a classic S-curve that describes deployment and stabilization at maximum capacity. In addition, the model extends to the post-growth plateau, where technological capacity is renewed according to the distribution of equipment lifespans. We obtain two qualitatively different results. In the case of "fast" deployment, characterized by a short deployment time in relation to the average equipment lifetime, production is subject to significant oscillations. In the case of "slow" deployment, production increases monotonically until it reaches a renewal plateau. These results are counterintuitively validated by two case studies: nuclear power plants as a fast deployment and smartphones as a slow deployment. These results are important for long-term industrial planning, as they enable us to anticipate future business cycles. Our study demonstrates that business cycles can originate endogenously from industrial dynamics of installation and renewal, contrasting with traditional views attributing fluctuations to exogenous macroeconomic factors. These endogenous cycles interact with broader trends, potentially being modulated, amplified, or attenuated by macroeconomic conditions. This dynamic of deployment and renewal is relevant for long-life infrastructure technologies, such as those supporting the renewable energy sector and has major policy implications for industry players.

Rinieri Lorenzo, Gori Giacomo, Melis Andrea et al.

The OPC UA protocol is becoming the de facto standard for Industry 4.0 machine-to-machine communication. It stands out as one of the few industrial protocols that provide robust security features designed to prevent attackers from manipulating and damaging critical infrastructures. However, prior works showed that significant challenges still exists to set up secure OPC UA deployments in practice, mainly caused by the complexity of certificate management in industrial scenarios and the inconsistent implementation of security features across industrial OPC UA devices. In this paper, we present Pk-IOTA, an automated solution designed to secure OPC UA communications by integrating programmable data plane switches for in-network certificate validation and leveraging the IOTA Tangle for decen- tralized certificate distribution. Our evaluation is performed on a physical testbed representing a real-world industrial scenario and shows that Pk-IOTA introduces a minimal overhead while providing a scalable and tamper-proof mechanism for OPC UA certificate management.

Qi Liu, Kai Zheng, Rui Huang et al.

Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS benchmark datasets primarily focus on the exposure space, where novel RS algorithms are trained and evaluated. However, when these algorithms transition to real world industrial RS, they face a critical challenge of handling unexposed items which are a significantly larger space than the exposed one. This discrepancy profoundly impacts their practical performance. Additionally, these algorithms often overlook the intricate interplay between multiple RS stages, resulting in suboptimal overall system performance. To address this issue, we introduce RecFlow, an industrial full flow recommendation dataset designed to bridge the gap between offline RS benchmarks and the real online environment. Unlike existing datasets, RecFlow includes samples not only from the exposure space but also unexposed items filtered at each stage of the RS funnel. Our dataset comprises 38M interactions from 42K users across nearly 9M items with additional 1.9B stage samples collected from 9.3M online requests over 37 days and spanning 6 stages. Leveraging the RecFlow dataset, we conduct courageous exploration experiments, showcasing its potential in designing new algorithms to enhance effectiveness by incorporating stage-specific samples. Some of these algorithms have already been deployed online, consistently yielding significant gains. We propose RecFlow as the first comprehensive benchmark dataset for the RS community, supporting research on designing algorithms at any stage, study of selection bias, debiased algorithms, multi-stage consistency and optimality, multi-task recommendation, and user behavior modeling. The RecFlow dataset, along with the corresponding source code, is available at https://github.com/RecFlow-ICLR/RecFlow.

Mazen Alamir, Raphaël Dion

In this paper, a new model-free anomaly detection framework is proposed for time-series induced by industrial dynamical systems.The framework lies in the category of conventional approaches which enable appealing features such as a learning with reduced amount of training data, a high potential for explainability as well as a compatibility with incremental learning mechanism to incorporate operator feedback after an alarm is raised and analyzed. Although these are crucial features towards acceptance of data-driven solutions by industry, they are rarely considered in the comparisons that generally almost exclusively focus on performance metrics. Moreover, the features engineering step involved in the proposed framework is inspired by the time-series being implicitly governed by physical laws as it is generally the case in industrial time-series. Two examples are given to assess the efficiency of the proposed approach.

M. Gundall, J. Schneider, H. D. Schotten et al.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the "fourth industrial revolution", referred to as "Industrie 4.0". Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).

Md. Ripul Kabir, Mst. Taslima Khatun Toru, Golam Faruk Faruk

This study aimed to find the factors affecting the fruit vendor’s safety measures and sanitation facilities during COVID-19 in Bangladesh. A quantitative research design was used to conduct this study. Random sampling techniques were executed to collect data from 416 fruit vendors through a field survey. The study reveals that 87% of the fruit vendors took safety measures during COVID-19. Most of them had better sanitation facilities at home compared to their workplaces. Their safety measures and sanitation facilities were associated with socioeconomic backgrounds. Age, type of house, place of business, and receiving financial support influenced fruit vendors’ safety measures. Sanitation facilities at home were affected by their sex, schooling, types of houses, and working hours.  Types of houses, family income, and business duration impacted the workplace's sanitation facilities. Their socio-demographic and economic situations affected fruit vendors' safety measures and sanitation facilities during COVID-19. Fear of death and government market mechanisms also might have played a significant role in measuring the different steps against Coronavirus infection. Health and hygiene rules prescribed by the authorities should be followed strictly to avoid dire situations during the pandemic. Both government and non-government initiatives should be given the utmost priority to create sanitation awareness, whether about personal cleanliness or workplace hygiene and paired with financial support.

Dongxu Lei, Xiaotian Lin, Xinghu Yu et al.

In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development.

Kashif Ishaq, Fatima Khan

The possibility of block chain innovation revolutionizing business operations and interpersonal interactions in Industry 4.0 is becoming more widely acknowledged. Industry 4.0 and the Industrial Internet of Things (IoT) are among the new application fields. As a result, the purpose of this article is to investigate the block chain applications that are already being used in IoT and Industry 4.0. In particular, it looks at current research trends in various IoT applications, addressing problems, concerns, and potential future uses of integrating block chain technology. This article also includes a thorough discussion of the key elements of block chain databases, including Merkle trees, transaction management, sharding, long-term memory, and short-term memory. In order to do this, more than 46 pertinent primary research that have been published in reputable journals have been chosen for additional examination. The workflow of a block chain network utilizing IoT is also demonstrated, demonstrating how IoT devices communicate with one another and how they contribute to the network's overall operation. The taxonomy diagram below serves to illustrate the contribution.

Maya-Liliana Avramescu, Christian Potiszil, Tak Kunihiro et al.

Abstract Background Asbestos is a fibrous mineral that was widely used in the past. However, asbestos inhalation is associated with an aggressive type of cancer known as malignant mesothelioma (MM). After inhalation, an iron-rich coat forms around the asbestos fibres, together the coat and fibre are termed an “asbestos ferruginous body” (AFB). AFBs are the main features associated with asbestos-induced MM. Whilst several studies have investigated the external morphology of AFBs, none have characterised the internal morphology. Here, cross-sections of multiple AFBs from two smokers and two non-smokers are compared to investigate the effects of smoking on the onset and growth of AFBs. Morphological and chemical observations of AFBs were undertaken by transmission electron microscopy, energy dispersive x-ray spectroscopy and selected area diffraction. Results The AFBs of all patients were composed of concentric layers of 2-line or 6-line ferrihydrite, with small spherical features being observed on the outside of the AFBs and within the cross-sections. The spherical components are of a similar size to Fe-rich inclusions found within macrophages from mice injected with asbestos fibres in a previous study. As such, the spherical components composing the AFBs may result from the deposition of Fe-rich inclusions during frustrated phagocytosis. The AFBs were also variable in terms of their Fe, P and Ca abundances, with some layers recording higher Fe concentrations (dense layers), whilst others lower Fe concentrations (porous layers). Furthermore, smokers were found to have smaller and overall denser AFBs than non-smokers. Conclusions The AFBs of smokers and non-smokers show differences in their morphology, indicating they grew in lung environments that experienced disparate conditions. Both the asbestos fibres of smokers and non-smokers were likely subjected to frustrated phagocytosis and accreted mucopolysaccharides, resulting in Fe accumulation and AFB formation. However, smokers’ AFBs experienced a more uniform Fe-supply within the lung environment compared to non-smokers, likely due to Fe complexation from cigarette smoke, yielding denser, smaller and more Fe-rich AFBs. Moreover, the lack of any non-ferrihydrite Fe phases in the AFBs may indicate that the ferritin shell was intact, and that ROS may not be the main driver for the onset of MM.

Bian Shabri Putri Irwanto, Meirina Ernawati, Indriati Paskarini et al.

Introduction: Fires in the workplace can have consequences that adversely affect many parties, both for companies of the workers and the wider community, including institutions such as hospitals. In this research, hospitals are considered to be at high risk of causing fatalities in the event of a fire. The purpose of this research is to evaluate the prevention and control of fire in dr. R. Koesma Hospital Tuban based on the regulation of Minister of Health No.66 of 2016 about Occupational Health and Safety of Hospital. Method: This research is observational research. Data collection was done by interview and observation. The assessment of the evaluation of fire prevention and control is done by using a scoring formula made independently. Result: The evaluation is done on the identification of fire and explosion risk areas as well as on the mapping of high-risk areas of fire and explosion in dr. R. Koesma Hospital based on regulation of Minister of Health No.66 of 2016. The evaluation results on both aspects are 4% out of 6%. The evaluation result of the risk reduction of fire and explosion hazards shows a score of 15% out of 18%. The evaluation result of fire control is 22%. The evaluation result of the fire simulation shows a score of 38% out of 48%. Conclusion: This research concludes that the evaluation results of the fire prevention and control system in dr. R. Koesma Hospital based on regulation of Minister of Health No.66 of 2016 show a score of 83%.

Yuri Chervonyi, Praneet Dutta, Piotr Trochim et al.

We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) applications and balances simplicity with simulation fidelity and interpretability. The model's fidelity is evaluated against real world data from a large scale cooling system. This is followed by a case study illustrating how the model can be used for RL research. For this, we develop an industrial task suite that allows specifying different problem settings and levels of complexity, and use it to evaluate the performance of different RL algorithms.

Kirsi K. Siivola, Michael J. Burgum, Blanca Suárez-Merino et al.

Abstract The number of publications in the field of nanogenotoxicology and the amount of genotoxicity data on nanomaterials (NMs) in several databases generated by European Union (EU) funded projects have increased during the last decade. In parallel, large research efforts have contributed to both our understanding of key physico-chemical (PC) parameters regarding NM characterization as well as the limitations of toxicological assays originally designed for soluble chemicals. Hence, it is becoming increasingly clear that not all of these data are reliable or relevant from the regulatory perspective. The aim of this systematic review is to investigate the extent of studies on genotoxicity of NMs that can be considered reliable and relevant by current standards and bring focus to what is needed for a study to be useful from the regulatory point of view. Due to the vast number of studies available, we chose to limit our search to two large groups, which have raised substantial interest in recent years: nanofibers (including nanotubes) and metal-containing nanoparticles. Focusing on peer-reviewed publications, we evaluated the completeness of PC characterization of the tested NMs, documentation of the model system, study design, and results according to the quality assessment approach developed in the EU FP-7 GUIDEnano project. Further, building on recently published recommendations for best practices in nanogenotoxicology research, we created a set of criteria that address assay-specific reliability and relevance for risk assessment purposes. Articles were then reviewed, the qualifying publications discussed, and the most common shortcomings in NM genotoxicity studies highlighted. Moreover, several EU projects under the FP7 and H2020 framework set the aim to collectively feed the information they produced into the eNanoMapper database. As a result, and over the years, the eNanoMapper database has been extended with data of various quality depending on the existing knowledge at the time of entry. These activities are highly relevant since negative results are often not published. Here, we have reviewed the NanoInformaTIX instance under the eNanoMapper database, which hosts data from nine EU initiatives. We evaluated the data quality and the feasibility of use of the data from a regulatory perspective for each experimental entry.

Karl Löwenmark, Cees Taal, Stephan Schnabel et al.

In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions and work-orders written by domain experts are increasingly digitised in modern condition monitoring systems, for example in the context of rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severities exists as technical language annotations in industrial datasets. Furthermore, recent advances in natural language processing enable weakly supervised model optimisation using natural language annotations, most notably in the form of natural language supervision (NLS). This creates a timely opportunity to develop technical language supervision (TLS) solutions for IFD systems grounded in industrial data, for example as a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample generalisation. We surveyed the literature and identify a considerable improvement in the maturity of NLS over the last two years, facilitating applications beyond natural language; a rapid development of weak supervision methods; and transfer learning as a current trend in IFD which can benefit from these developments. Finally we describe a general framework for TLS and implement a TLS case study based on SentenceBERT and contrastive learning based zero-shot inference on annotated industry data.

Hui Zhou, Changyang She, Yansha Deng et al.

Industrial Internet of Things (IIoT) revolutionizes the future manufacturing facilities by integrating the Internet of Things technologies into industrial settings. With the deployment of massive IIoT devices, it is difficult for the wireless network to support the ubiquitous connections with diverse quality-of-service (QoS) requirements. Although machine learning is regarded as a powerful data-driven tool to optimize wireless network, how to apply machine learning to deal with the massive IIoT problems with unique characteristics remains unsolved. In this paper, we first summarize the QoS requirements of the typical massive non-critical and critical IIoT use cases. We then identify unique characteristics in the massive IIoT scenario, and the corresponding machine learning solutions with its limitations and potential research directions. We further present the existing machine learning solutions for individual layer and cross-layer problems in massive IIoT. Last but not the least, we present a case study of massive access problem based on deep neural network and deep reinforcement learning techniques, respectively, to validate the effectiveness of machine learning in massive IIoT scenario.

Mary Gulumian, Flemming R. Cassee

Abstract Safe-by-Design (SbD) has been put forward as a concept to assure that only safe nanomaterials will reach the market and that safety aspects have already been considered in a very early stage of the innovation process. In practice, several laboratory test have been proposed to screen newly developed nanomaterials and nano-enabled products to assess their hazardous nature. These tests need to have sufficient predictive power for possible adverse effects on human health, not only due to acute (peak) exposures, but also for long-term (low dose) exposures as these materials may accumulate over time in organs and tissues.