Hasil untuk "Industrial hygiene. Industrial welfare"

Michele Banfi, Rocco Felici, Stefano Baraldo et al.

This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets a recurrent bottleneck in tool-intensive workflows: expert know-how is effective but difficult to scale, and execution quality degrades when procedures are reconstructed ad hoc across operators and sessions. EBuddy operationalizes expert practice as a finite state machine (FSM) driven application that provides an interpretable decision frame at runtime (current state and admissible actions), so that spoken requests are interpreted within state-grounded constraints, while the system executes and monitors the corresponding tool interactions. Through modular workflow artifacts, EBuddy coordinates heterogeneous resources, including GUI-driven software and a collaborative robot, leveraging fully voice-based interaction through automatic speech recognition and intent understanding. An industrial pilot on impeller blade inspection and repair preparation for directed energy deposition (DED), realized by human-robot collaboration, shows substantial reductions in end-to-end process duration across onboarding, 3D scanning and processing, and repair program generation, while preserving repeatability and low operator burden.

Xiangyu Su, Juzhan Xu, Oliver van Kaick et al.

In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities, have shown potential in coordinating robots for simple household and manipulation tasks. However, in industrial scenarios, stricter sequential constraints and more complex dependencies within tasks present new challenges for LLMs. To address this, we propose IMR-LLM, a novel LLM-driven Industrial Multi-Robot task planning and program generation framework. Specifically, we utilize LLMs to assist in constructing disjunctive graphs and employ deterministic solving methods to obtain a feasible and efficient high-level task plan. Based on this, we use a process tree to guide LLMs to generate executable low-level programs. Additionally, we create IMR-Bench, a challenging benchmark that encompasses multi-robot industrial tasks across three levels of complexity. Experimental results indicate that our method significantly surpasses existing methods across all evaluation metrics.

Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang et al.

Standard Operating Procedures (SOPs) are essential for ensuring operational safety and consistency in industrial environments. However, retrieving and following these procedures presents unique challenges, such as rigid proprietary structures, condition-dependent relevance, and actionable execution requirement, which standard semantic-driven Retrieval-Augmented Generation (RAG) paradigms fail to address. Inspired by the Mixture-of-Experts (MoE) paradigm, we propose SOPRAG, a novel framework specifically designed to address the above pain points in SOP retrieval. SOPRAG replaces flat chunking with specialized Entity, Causal, and Flow graph experts to resolve industrial structural and logical complexities. To optimize and coordinate these experts, we propose a Procedure Card layer that prunes the search space to eliminate computational noise, and an LLM-Guided gating mechanism that dynamically weights these experts to align retrieval with operator intent. To address the scarcity of domain-specific data, we also introduce an automated, multi-agent workflow for benchmark construction. Extensive experiments across four industrial domains demonstrate that SOPRAG significantly outperforms strong lexical, dense, and graph-based RAG baselines in both retrieval accuracy and response utility, achieving perfect execution scores in real-world critical tasks.

Andrea Arcuri, Alexander Poth, Olsi Rrjolli et al.

REST APIs are widely used in industry, in all different kinds of domains. An example is Volkswagen AG, a German automobile manufacturer. Established testing approaches for REST APIs are time consuming, and require expertise from professional test engineers. Due to its cost and importance, in the scientific literature several approaches have been proposed to automatically test REST APIs. The open-source, search-based fuzzer EvoMaster is one of such tools proposed in the academic literature. However, how academic prototypes can be integrated in industry and have real impact to software engineering practice requires more investigation. In this paper, we report on our experience in using EvoMaster at Volkswagen AG, as an EvoMaster user from 2023 to 2026. We share our learnt lessons, and discuss several features needed to be implemented in EvoMaster to make its use in an industrial context successful. Feedback about value in industrial setups of EvoMaster was given from Volkswagen AG about 4 APIs. Additionally, a user study was conducted involving 11 testing specialists from 4 different companies. We further identify several real-world research challenges that still need to be solved.

Shareen Kate A. Gamiao, Marie Dale R. Soriano, Realyn Q. Salvador et al.

Background: In the Philippine basic education system, particularly in Technology and Livelihood Education (TLE) Cookery classes, teachers are at the forefront of promoting proper food hygiene. However, systemic challenges such as the absence of standardized policies, outdated training, and lack of resources hinder their effectiveness. To address this gap, this study aimed to assess the food hygiene knowledge and practices of cookery teachers and provides localized evidence to address the lack of division-level hygiene policies. Methods: A descriptive research design supported by qualitative interviews was employed. In total, 69 junior and senior high school cookery teachers from three school divisions in Ilocos Norte participated. A researcher-made survey questionnaire and an interview guide were used to gather data, which were analyzed using descriptive and inferential statistics. Participant testimonies were integrated to enrich the quantitative findings. Results showed high levels of food hygiene knowledge (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>x</mi></mrow><mo>¯</mo></mover><mo>=</mo></mrow></semantics></math></inline-formula> 3.48; Highly Competent) and practices (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>x</mi></mrow><mo>¯</mo></mover><mo>=</mo></mrow></semantics></math></inline-formula> 3.80; Highly Competent). Despite these strengths, notable gaps were identified in technical areas such as temperature control, cold storage, and specific aspects of personal hygiene. Conclusions: Interviews highlighted the need for a formal institutional hygiene policy to support teachers’ implementation of safe food-handling practices. All computed <i>p</i>-values were below 0.01, indicating significant correlations between demographic variables and both knowledge and practices. The correlation values ranged from r = 0.039 to r = 0.342, suggesting weak to moderate positive relationships and indicating that hygiene behaviors are influenced by multiple factors rather than demographics alone. Based on the findings, the study recommends institutionalizing the proposed policy brief, providing adequate resources, and implementing continuous professional development for Cookery teachers. The study’s scope is limited to Northern Philippines.

Lukas Schmidbauer, Carlos A. Riofrío, Florian Heinrich et al.

Real-world optimization problems must undergo a series of transformations before becoming solvable on current quantum hardware. Even for a fixed problem, the number of possible transformation paths -- from industry-relevant formulations through binary constrained linear programs (BILPs), to quadratic unconstrained binary optimization (QUBO), and finally to a hardware-executable representation -- is remarkably large. Each step introduces free parameters, such as Lagrange multipliers, encoding strategies, slack variables, rounding schemes or algorithmic choices -- making brute-force exploration of all paths intractable. In this work, we benchmark a representative subset of these transformation paths using a real-world industrial production planning problem with industry data: the optimization of work allocation in a press shop producing vehicle parts. We focus on QUBO reformulations and algorithmic parameters for both quantum annealing (QA) and the Linear Ramp Quantum Approximate Optimization Algorithm (LR-QAOA). Our goal is to identify a reduced set of effective configurations applicable to similar industrial settings. Our results show that QA on D-Wave hardware consistently produces near-optimal solutions, whereas LR-QAOA on IBM quantum devices struggles to reach comparable performance. Hence, the choice of hardware and solver strategy significantly impacts performance. The problem formulation and especially the penalization strategy determine the solution quality. Most importantly, mathematically-defined penalization strategies are equally successful as hand-picked penalty factors, paving the way for automated QUBO formulation. Moreover, we observe a strong correlation between simulated and quantum annealing performance metrics, offering a scalable proxy for predicting QA behavior on larger problem instances.

Jie Zhang, Changzai Pan, Kaiwen Wei et al.

Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information into reports remains a significant challenge for industrial applications. This task is plagued by two critical issues: 1) the complexity and diversity of tables lead to suboptimal reasoning outcomes; and 2) existing table benchmarks lack the capacity to adequately assess the practical application of this task. To fill this gap, we propose the table-to-report task and construct a bilingual benchmark named T2R-bench, where the key information flow from the tables to the reports for this task. The benchmark comprises 457 industrial tables, all derived from real-world scenarios and encompassing 19 industry domains as well as 4 types of industrial tables. Furthermore, we propose an evaluation criteria to fairly measure the quality of report generation. The experiments on 25 widely-used LLMs reveal that even state-of-the-art models like Deepseek-R1 only achieves performance with 62.71 overall score, indicating that LLMs still have room for improvement on T2R-bench.

Henning Femmer, Frank Houdek, Max Unterbusch et al.

Requirements quality is central to successful software and systems engineering. Empirical research on quality defects in natural language requirements relies heavily on datasets, ideally as realistic and representative as possible. However, such datasets are often inaccessible, small, or lack sufficient detail. This paper introduces QuRE (Quality in Requirements), a new dataset comprising 2,111 industrial requirements that have been annotated through a real-world review process. Previously used for over five years as part of an industrial contract, this dataset is now being released to the research community. In this work, we furthermore provide descriptive statistics on the dataset, including measures such as lexical diversity and readability, and compare it to existing requirements datasets and synthetically generated requirements. In contrast to synthetic datasets, QuRE is linguistically similar to existing ones. However, this dataset comes with a detailed context description, and its labels have been created and used systematically and extensively in an industrial context over a period of close to a decade. Our goal is to foster transparency, comparability, and empirical rigor by supporting the development of a common gold standard for requirements quality datasets. This, in turn, will enable more sound and collaborative research efforts in the field.

Annalena Daniels, Sebastian Kerz, Salman Bari et al.

Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw materials. Since devices may contain hazardous materials and their disassembly involves heavy manual labor, robotic disassembly is a promising venue. However, since devices may be damaged, dirty and unidentified, robotic disassembly is challenging since object models are unavailable or cannot be relied upon. This case study explores grasping strategies for industrial robotic disassembly of WEEE devices with uncertain vision data. We propose three grippers and appropriate tactile strategies for force-based manipulation that improves grasping robustness. For each proposed gripper, we develop corresponding strategies that can perform effectively in different grasping tasks and leverage the grippers design and unique strengths. Through experiments conducted in lab and factory settings for four different WEEE devices, we demonstrate how object uncertainty may be overcome by tactile sensing and compliant techniques, significantly increasing grasping success rates.

Todd A. Stueckle, Jake Jensen, Jayme P. Coyle et al.

Abstract Background Organomodified nanoclays (ONC), two-dimensional montmorillonite with organic coatings, are increasingly used to improve nanocomposite properties. However, little is known about pulmonary health risks along the nanoclay life cycle even with increased evidence of airborne particulate exposures in occupational environments. Recently, oropharyngeal aspiration exposure to pre- and post-incinerated ONC in mice caused low grade, persistent lung inflammation with a pro-fibrotic signaling response with unknown mode(s) of action. We hypothesized that the organic coating presence and incineration status of nanoclays determine the inflammatory cytokine secretary profile and cytotoxic response of macrophages. To test this hypothesis differentiated human macrophages (THP-1) were acutely exposed (0–20 µg/cm2) to pristine, uncoated nanoclay (CloisNa), an ONC (Clois30B), their incinerated byproducts (I-CloisNa and I-Clois30B), and crystalline silica (CS) followed by cytotoxicity and inflammatory endpoints. Macrophages were co-exposed to lipopolysaccharide (LPS) or LPS-free medium to assess the role of priming the NF-κB pathway in macrophage response to nanoclay treatment. Data were compared to inflammatory responses in male C57Bl/6J mice following 30 and 300 µg/mouse aspiration exposure to the same particles. Results In LPS-free media, CloisNa exposure caused mitochondrial depolarization while Clois30B exposure caused reduced macrophage viability, greater cytotoxicity, and significant damage-associated molecular patterns (IL-1α and ATP) release compared to CloisNa and unexposed controls. LPS priming with low CloisNa doses caused elevated cathepsin B/Caspage-1/IL-1β release while higher doses resulted in apoptosis. Clois30B exposure caused dose-dependent THP-1 cell pyroptosis evidenced by Cathepsin B and IL-1β release and Gasdermin D cleavage. Incineration ablated the cytotoxic and inflammatory effects of Clois30B while I-CloisNa still retained some mild inflammatory potential. Comparative analyses suggested that in vitro macrophage cell viability, inflammasome endpoints, and pro-inflammatory cytokine profiles significantly correlated to mouse bronchioalveolar lavage inflammation metrics including inflammatory cell recruitment. Conclusions Presence of organic coating and incineration status influenced inflammatory and cytotoxic responses following exposure to human macrophages. Clois30B, with a quaternary ammonium tallow coating, induced a robust cell membrane damage and pyroptosis effect which was eliminated after incineration. Conversely, incinerated nanoclay exposure primarily caused elevated inflammatory cytokine release from THP-1 cells. Collectively, pre-incinerated nanoclay displayed interaction with macrophage membrane components (molecular initiating event), increased pro-inflammatory mediators, and increased inflammatory cell recruitment (two key events) in the lung fibrosis adverse outcome pathway.

Winnie Koskei, Peterson Warutere, Bernard Awuonda

Hairdressers are exposed to awkward posture, prolonged standing, long working hours and chemical hazards capable of causing adverse health effects. The present study aimed to evaluate hairdressers' safety and health risks. The study adopted a descriptive cross-sectional and analytical design. Systematic random sampling was used to select salons and hairdressers. Closed and open-ended questionnaires were distributed to 286 hairdressers who consented to participate in the study. An observation checklist, WISHA caution checklist, thermometer, light meter and noise level meter were used to collect data in the sampled salon. Data were analyzed descriptively and with regression analysis. It was found that the average space for salons was 8.79 m2, and 68.5% of hairdressers work for long hours (11-12 hours). It was established that 5.48% of salons have an adequate amount of light and that 8.22% of salons have high temperatures. Aprons were the most used personal protective equipment by hairdressers. Manual handling of salon equipment and awkward posture cause musculoskeletal disorders among hairdressers. Their odd ratios impacting the health and safety of hairdressers were 2.706 and 2.728, respectively. The study reveals that hairdressing salon designs, space, lighting, and temperatures affect the health and safety of hairdressers. The hours off work and minimal or no breaks also have negative impacts on the health and safety of hairdressers

Vishal Verma, Vinod Reddy, Jaiprakash Ravi

The increasing need for causal analysis in large-scale industrial datasets necessitates the development of efficient and scalable causal algorithms for real-world applications. This paper addresses the challenge of scaling causal algorithms in the context of conducting causal analysis on extensive datasets commonly encountered in industrial settings. Our proposed solution involves enhancing the scalability of causal algorithm libraries, such as EconML, by leveraging the parallelism capabilities offered by the distributed computing framework Ray. We explore the potential of parallelizing key iterative steps within causal algorithms to significantly reduce overall runtime, supported by a case study that examines the impact on estimation times and costs. Through this approach, we aim to provide a more effective solution for implementing causal analysis in large-scale industrial applications.

Xuanchao Ma, Yanlin Jiang, Hongyan Liu et al.

Recent years have witnessed a broader range of applications of image processing technologies in multiple industrial processes, such as smoke detection, security monitoring, and workpiece inspection. Different kinds of distortion types and levels must be introduced into an image during the processes of acquisition, compression, transmission, storage, and display, which might heavily degrade the image quality and thus strongly reduce the final display effect and clarity. To verify the reliability of existing image quality assessment methods, we establish a new industrial process image database (IPID), which contains 3000 distorted images generated by applying different levels of distortion types to each of the 50 source images. We conduct the subjective test on the aforementioned 3000 images to collect their subjective quality ratings in a well-suited laboratory environment. Finally, we perform comparison experiments on IPID database to investigate the performance of some objective image quality assessment algorithms. The experimental results show that the state-of-the-art image quality assessment methods have difficulty in predicting the quality of images that contain multiple distortion types.

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Juan M. Torres Zanardi, Ana Scarabino, Federico Bacchi et al.

This study presents the numerical models used for the simulation of the large viscoplastic deformations that aluminium undergoes during the extrusion process in order to obtain industrial profiles. This study also gives examples of results obtained by the Computational Fluid Dynamics Group of the UNLP Faculty of Engineering, in collaboration with the company Madexa S.A., dedicated to the manufacturing of dies for this type of processes. The equations that model the process, the difficulties associated with its numerical resolution and the advantages that the simulation work represent for the company are also presented in this study.

Zhen Xu, Lanning Wei, Huan Zhao et al.

Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in modeling graph structured data and many variants of GNN architectures have been proposed. However, much human effort is often needed to tune the architecture depending on different datasets. Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search. To understand GNN practitioners' automated solutions, we organized AutoGraph Challenge at KDD Cup 2020, emphasizing on automated graph neural networks for node classification. We received top solutions especially from industrial tech companies like Meituan, Alibaba and Twitter, which are already open sourced on Github. After detailed comparisons with solutions from academia, we quantify the gaps between academia and industry on modeling scope, effectiveness and efficiency, and show that (1) academia AutoML for Graph solutions focus on GNN architecture search while industrial solutions, especially the winning ones in the KDD Cup, tend to obtain an overall solution (2) by neural architecture search only, academia solutions achieve on average 97.3% accuracy of industrial solutions (3) academia solutions are cheap to obtain with several GPU hours while industrial solutions take a few months' labors. Academic solutions also contain much fewer parameters.

R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat et al.

Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning. This opens up an opportunity to use modern machine learning tools on large-scale nonlinear monitoring and control problems. This article provides a survey of recent results with applications in the process industry.

Shintia Yunita Arini

Introduction: In the recent years, woven bag companies have taken a step forward from traditional labor intensive work practices to technological assistance which is operated by workers. Nevertheless, the increased production capability and capacity with assistance of the machineries has been known to cause significant Occupational Safety and Health concerns as having been reported in various previous studies. Therefore, this research aimed to determine the relationship between perception of exposure to hazards and OSH incidents taking into consideration the work shift of the operators. Methods: This research was an analytical observational study with cross-sectional design. There was a total of 67 operators being the population of this study, 53 of whom were recruited as respondents using simple random sampling. The instruments that were used in this research were questionnaires about individual characterization, perception of work environment and work incidents. Variable testing was performed using contingency coefficient. Results: There was a relationship between the complaint towards the noise and the work incident in the morning and afternoon shift. Based on the measurement of the noise, the value was high in the morning of 100dBA, while in the afternoon the value was 91dBA and at night the value was 92 dBA. For the variable of dry temperature, there was a relationship between the complaint towards the dry temperature and the work incident, which showed that in the morning, the dry temperature was 33.1oC. Conclusion: High noise and dry temperature exposure value had a relationship with the work incident on the operators of a Woven Bag Company. Keywords: dry temperature, noise, shift worker, work incident

Yuan Xia, Xulong Zhang, Dejun Sun et al.

Abstract Background This study aims to investigate the effects of water soluble particulate matter (WSPM) on the viability and protein expression profile of human lung adenocarcinoma cell A549 in the Bayou Obo rare earth mining area, and explore the influence of WSPM on the A549 cell cycle. Results It was found that WSPM can inhibit the viability of A549 cells and induce cell arrest in the G2/M phase. Compared with controls, exposure to WSPM10 and WSPM2.5 induced 134 and 116 proteins to be differentially expressed in A549 cells, respectively. In addition, 33 and 31 differentially expressed proteins were further confirmed, and was consistent with the proteomic analysis. The most prominent enrichment in ribosome-associated proteins were presented. When RPL6, RPL13, or RPL18A gene expression was inhibited, A549 cells were arrested in the G1 phase, affecting the expression of Cyclin D1, p21, RB1, Cyclin A2, Cyclin B1, CDC25A, CDK2, CHEK2 and E 2 F 1 . Furthermore, the La3+, Ce3+, Nd3+ and F- in WSPM also inhibited the viability of A549 cells. After 24 h of exposure to 2 mM of NaF, A549 cells were also arrested in the G2/M phase, while the other three compounds did not have this effect. These four compounds affected the cell cycle regulatory factors in A549 cells, mainly focusing on effecting the expression of CDK2, CDK4, RB1, ATM, TP53 and MDM2 genes. These results are consistent with the those from WSPM exposure. Conclusions These results revealed that WSPM from rare earth mines decreased the viability of A549 cells, and induced cell cycle G2/M phase arrest, and even apoptosis, which may be independent of the NF-κB/MYD88 pathway, and be perceived by the TLR4 receptor. The dysfunction of the cell cycle is correlated to the down-expression of ribosomal proteins (RPs). However, it is not the direct reason for the A549 cell arrest in the G2/M phase. La3+, Ce3+, and F- are probably the main toxic substances in WSPM, and may be regulate the A549 cell cycle by affecting the expression of genes, such as MDM2, RB1, ATM, TP53, E 2 F 1 , CDK2 and CDK4. These results indicate the importance for further research into the relationship between APM and lung cancer.

Nathaly Berrío García, Germán Fernando Vieco Gómez

Introducción: Los estilos de vida constituyen uno de los principales determinantes sociales de la salud. Un componente del estilo de vida en la modernidad es el trabajo asalariado. Pasamos un 30 % de nuestra vida en el puesto de trabajo. El trabajo puede ser fuente de desarrollo o de enfermedad y muerte prematura. En cualquiera de los dos escenarios, el autocuidado de la salud laboral tiene un papel fundamental, y se encuentra a medio camino entre la promoción de la salud y la prevención de los accidentes y las enfermedades de origen ocupacional. Desarrollo: El autocuidado no ha sido una estrategia sostenible y sostenida en el marco del Sistema de Salud Colombiano (SSC) ni en el Sistema General de Seguridad y Salud en el Trabajo, los cuales han obstaculizado el desarrollo de una política de salud realmente integral que asuma, de manera complementaria, la especificidad de la promoción y la prevención. Por tanto, el objetivo de este ensayo es describir el carácter vinculante del autocuidado de la salud entre la promoción de la salud y la prevención de la enfermedad en el ámbito laboral, de modo que trascienda el enfoque preventivo del modelo sustentado por el SSC. Conclusiones: El autocuidado es un recurso sanitario primordial del sistema de atención en salud, que se concibe como un conjunto de medidas que toman los trabajadores para mejorar su propia salud y el bienestar en el seno de sus labores productivas cotidianas. Introduction: Lifestyles is one of the main social determinants of health. A component of the modern lifestyle is wage labor. We spend 30 % of our life in the workplace. Work can be a source of the development of illness and premature death. In either of the two scenarios, occupational health self-care plays a fundamental role, and it is halfway between promoting health and preventing accidents and occupational diseases. Development: Self-care has not been a sustainable and sustained strategy within the framework of the Colombian Health System (CHS) or in the General System of Safety and Health at Work, which has hindered the development of a health policy truly comprehensive that assumes, in a complementary way, the specificity of promotion and prevention. Therefore, the objective of this essay is to describe the binding nature of health self-care between health promotion and disease prevention in the workplace, so that it transcends the preventive approach of the model supported by the CHS. Conclusions: Self-care is a primary health resource of the health care system, which is conceived as a set of measures that workers take to improve their own health and well-being within their daily production work.