Hasil untuk "Industrial hygiene. Industrial welfare"

Lin Liu, Rita Machacy, Simi Kuniyilh

The Industrial Internet of Things (IIoT) integrates intelligent sensing, communication, and analytics into industrial environments, including manufacturing, energy, and critical infrastructure. While IIoT enables predictive maintenance and cross-site optimization of modern industrial control systems, such as those in manufacturing and energy, it also introduces significant privacy and confidentiality risks due to the sensitivity of operational data. Contrastive learning, a self-supervised representation learning paradigm, has recently emerged as a promising approach for privacy-preserving analytics by reducing reliance on labeled data and raw data sharing. Although contrastive learning-based privacy-preserving techniques have been explored in the Internet of Things (IoT) domain, this paper offers a comprehensive review of these techniques specifically for privacy preservation in Industrial Internet of Things (IIoT) systems. It emphasizes the unique characteristics of industrial data, system architectures, and various application scenarios. Additionally, the paper discusses solutions and open challenges and outlines future research directions.

Junfeng Jiao, Saleh Afroogh, Kevin Chen et al.

The rise of Generative AI (GAI) and Large Language Models (LLMs) has transformed industrial landscapes, offering unprecedented opportunities for efficiency and innovation while raising critical ethical, regulatory, and operational challenges. This study conducts a text-based analysis of 160 guidelines and policy statements across fourteen industrial sectors, utilizing systematic methods and text-mining techniques to evaluate the governance of these technologies. By examining global directives, industry practices, and sector-specific policies, the paper highlights the complexities of balancing innovation with ethical accountability and equitable access. The findings provide actionable insights and recommendations for fostering responsible, transparent, and safe integration of GAI and LLMs in diverse industry contexts.

Despina Tomkou, George Fatouros, Andreas Andreou et al.

This paper introduces a novel integration of Retrieval-Augmented Generation (RAG) enhanced Large Language Models (LLMs) with Extended Reality (XR) technologies to address knowledge transfer challenges in industrial environments. The proposed system embeds domain-specific industrial knowledge into XR environments through a natural language interface, enabling hands-free, context-aware expert guidance for workers. We present the architecture of the proposed system consisting of an LLM Chat Engine with dynamic tool orchestration and an XR application featuring voice-driven interaction. Performance evaluation of various chunking strategies, embedding models, and vector databases reveals that semantic chunking, balanced embedding models, and efficient vector stores deliver optimal performance for industrial knowledge retrieval. The system's potential is demonstrated through early implementation in multiple industrial use cases, including robotic assembly, smart infrastructure maintenance, and aerospace component servicing. Results indicate potential for enhancing training efficiency, remote assistance capabilities, and operational guidance in alignment with Industry 5.0's human-centric and resilient approach to industrial development.

Ruiyang Ma, Tianhao Wei, Jiaxi Zhang et al.

As hardware design complexity increases, hardware fuzzing emerges as a promising tool for automating the verification process. However, a significant gap still exists before it can be applied in industry. This paper aims to summarize the current progress of hardware fuzzing from an industry-use perspective and propose solutions to bridge the gap between hardware fuzzing and industrial verification. First, we review recent hardware fuzzing methods and analyze their compatibilities with industrial verification. We establish criteria to assess whether a hardware fuzzing approach is compatible. Second, we examine whether current verification tools can efficiently support hardware fuzzing. We identify the bottlenecks in hardware fuzzing performance caused by insufficient support from the industrial environment. To overcome the bottlenecks, we propose a prototype, HwFuzzEnv, providing the necessary support for hardware fuzzing. With this prototype, the previous hardware fuzzing method can achieve a several hundred times speedup in industrial settings. Our work could serve as a reference for EDA companies, encouraging them to enhance their tools to support hardware fuzzing efficiently in industrial verification.

Riccardo Fontana, Mattia Buratto, Anna Caproni et al.

Growing concern for environmental sustainability has resulted in the implementation of sanitization methods that respect ecological principles. This research evaluates a “green” sanitizing protocol that uses CAM (Minimum Environmental Criteria)-compliant products against a traditional protocol within two ASL Roma 1 facilities. The study performed a Life Cycle Assessment (LCA) following ISO 14040, ISO 14044, and ISO 14067 standards to measure greenhouse gases emissions. Microbiological sampling was conducted according to established protocols across three different risk zones utilizing contact plates and surface swabs. The Life Cycle Assessment showed that CO₂ emissions reduced by 49.6% to 53.3% at different sites due to reduced energy use together with concentrated detergents and improved washing cycles. Microbiological testing revealed notable decreases in contamination rates across both cleaning systems yet demonstrated the “green” system achieved superior results specifically within high-risk zones. The “green” protocol matched traditional cleaning methods hygienically but delivered significant environmental advantages which positions it as a sustainable hospital cleaning solution.

Miltiadis A. Makrygiannakis, Angeliki Anna Gkinosati, Sotirios Kalfas et al.

Fluoride mouthwashes are often recommended by dental professionals due to their proven benefits for oral hygiene. However, it is vital to acknowledge that these products may have undesirable effects on orthodontic treatment outcomes, particularly by altering the biomechanical properties of orthodontic devices and their components. To gain a comprehensive understanding of this potential issue, an extensive and systematic search was conducted across seven distinct databases. PRISMA extension for scoping reviews (PRISMA ScR) guidelines were followed. Following a detailed evaluation and careful scrutiny of the available evidence, a total of seven relevant studies met the inclusion criteria and were incorporated into the current scoping review. Findings indicated that regular intraoral use of fluoride-containing mouthwashes could lead to heightened corrosion and greater release of metal ions from stainless-steel brackets and nickel–titanium (NiTi) archwires. Additionally, the mechanical properties and structural integrity of titanium–molybdenum alloy (TMA) wires were negatively influenced by exposure to fluoride mouthwashes. Although existing evidence highlights these potential drawbacks, there remains a clear necessity for additional comprehensive research. Given the possibility that fluoride mouthwashes could adversely influence orthodontic treatment effectiveness, orthodontists and dental clinicians must exercise cautious judgment and deliberate consideration when prescribing fluoride-based mouthwashes for patients undergoing orthodontic therapy.

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.

Sotiris Michaelides, Stefan Lenz, Thomas Vogt et al.

The industrial landscape is undergoing a significant transformation, moving away from traditional wired fieldbus networks to cutting-edge 5G mobile networks. This transition, extending from local applications to company-wide use and spanning multiple factories, is driven by the promise of low-latency communication and seamless connectivity for various devices in industrial settings. However, besides these tremendous benefits, the integration of 5G as the communication infrastructure in industrial networks introduces a new set of risks and threats to the security of industrial systems. The inherent complexity of 5G systems poses unique challenges for ensuring a secure integration, surpassing those encountered with any technology previously utilized in industrial networks. Most importantly, the distinct characteristics of industrial networks, such as real-time operation, required safety guarantees, and high availability requirements, further complicate this task. As the industrial transition from wired to wireless networks is a relatively new concept, a lack of guidance and recommendations on securely integrating 5G renders many industrial systems vulnerable and exposed to threats associated with 5G. To address this situation, in this paper, we summarize the state-of-the-art and derive a set of recommendations for the secure integration of 5G into industrial networks based on a thorough analysis of the research landscape. Furthermore, we identify opportunities to utilize 5G to enhance security and indicate remaining challenges, identifying future academic directions.

Linda Kölbel, Markus Hornsteiner, Stefan Schönig

In industry, the networking and automation of machines through the Internet of Things (IoT) continues to increase, leading to greater digitalization of production processes. Traditionally, business and production processes are controlled, optimized and monitored using business process management methods that require process discovery. However, these methods cannot be fully applied to industrial production processes. Nevertheless, processes in the industry must also be monitored and discovered for this purpose. The aim of this paper is to develop an approach for process discovery methods and to adapt existing process discovery methods for application to industrial processes. The adaptations of classic discovery methods are presented as universally applicable guidelines specifically for the Industrial Internet of Things (IIoT). In order to create an optimal process model based on process evaluation, different methods are combined into a standardized discovery approach that is both efficient and cost-effective.

Xi Jiang, Jian Li, Hanqiu Deng et al.

In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. To bridge this gap, we present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial Anomaly Detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset with 39,672 questions for 8,366 industrial images. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs. The commercial models performed the best, with the average accuracy of GPT-4o models reaching 74.9%. However, this result falls far short of industrial requirements. Our analysis reveals that current MLLMs still have significant room for improvement in answering questions related to industrial anomalies and defects. We further explore two training-free performance enhancement strategies to help models improve in industrial scenarios, highlighting their promising potential for future research.

Maria Elena Widman Valencia, Damaris Estrella Castillo, Lizzette Gómez De Regil

Eating and swallowing disorders are prevalent among children with neuromotor disabilities, significantly impacting their overall quality of life. The COVID-19 pandemic exacerbated the challenges by restricting access to health care, underscoring the necessity for innovative solutions with caregiver involvement. This study investigated the effectiveness of a distance learning educational model in oral-motor therapy for primary caregivers of children with neuromotor impairments in Mérida, Mexico. The quasi-experimental pretest-posttest design included thirty primary caregivers of children aged 2 to 12 with feeding and swallowing disorders from seven institutions. Twenty-three participants completed the program. The program encompassed theoretical sessions on various aspects of oral motor therapy and practical sessions focusing on hands-on training. Results revealed substantial enhancements in theoretical knowledge and practical competencies among caregivers, with competence levels exceeding 80% in all evaluated activities. Despite these positive outcomes, the study acknowledges limitations such as a small sample size and the absence of a control group. Addressing these constraints through future research endeavors will bolster the evidence supporting the effectiveness of this innovative caregiver-centric approach. Ultimately, integrating caregivers into the care team is imperative for improving the quality of life for children with neuromotor disabilities and effectively managing eating and swallowing disorders.

Shusen Wang

Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS engineers who are diligently working to improve their key performance indicators, such as retention and duration. The experiences shared in this paper have been tested in some real industrial RSs and are likely to be generalized to other RSs as well. Most contents in this paper are industry experience without publicly available references.

Diego Vallarino

Using as a central instrument a new database, resulting from a compilation of historical administrative records, which covers the period 1974-2010, we can have new evidence on how industrial companies used tax benefits, and claim that these are decisive for the investment decision of the Uruguayan industrial companies during that period. The aforementioned findings served as a raw material to also affirm that the incentives to increase investment are factors that positively influence the level of economic activity and exports, and negatively on the unemployment rate.

Dianhui Wang, Matthew J. Felicetti

Real-time predictive modelling with desired accuracy is highly expected in industrial artificial intelligence (IAI), where neural networks play a key role. Neural networks in IAI require powerful, high-performance computing devices to operate a large number of floating point data. Based on stochastic configuration networks (SCNs), this paper proposes a new randomized learner model, termed stochastic configuration machines (SCMs), to stress effective modelling and data size saving that are useful and valuable for industrial applications. Compared to SCNs and random vector functional-link (RVFL) nets with binarized implementation, the model storage of SCMs can be significantly compressed while retaining favourable prediction performance. Besides the architecture of the SCM learner model and its learning algorithm, as an important part of this contribution, we also provide a theoretical basis on the learning capacity of SCMs by analysing the model's complexity. Experimental studies are carried out over some benchmark datasets and three industrial applications. The results demonstrate that SCM has great potential for dealing with industrial data analytics.

Tianyu Zhang, Gang Wang, Chuanyu Xue et al.

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications.

Man Zhang, Andrea Arcuri, Yonggang Li et al.

With several microservice architectures comprising of thousands of web services, used to serve 630 million customers, companies like Meituan face several challenges in the verification and validation of their software. This paper reports on our experience of integrating EvoMaster (a search-based white-box fuzzer) in the testing processes at Meituan over almost 2 years. Two user studies were carried out in 2021 and in 2023 to evaluate two versions of EvoMaster, respectively, in tackling the test generation for industrial web services which are parts of a large e-commerce microservice system. The two user studies involve in total 321,131 lines of code from five APIs and 27 industrial participants at Meituan. Questionnaires and interviews were carried out in both user studies with employees at Meituan. The two user studies demonstrate clear advantages of EvoMaster (i.e., code coverage and fault detection) and the urgent need to have such a fuzzer in industrial microservices testing. To study how these results could generalize, a follow up user study was done in 2024 with five engineers in the five different companies. Our results show that, besides their clear usefulness, there are still many critical challenges that the research community needs to investigate to improve performance further.

Dafeng Zhu, Bo Yang, Zhaojian Wang et al.

The multi-energy management framework of industrial parks advocates energy conversion and scheduling, which takes full advantage of the compensation and temporal availability of multiple energy. However, how to exploit elastic loads and compensate inelastic loads to match multiple generators and storage is still a key problem under the uncertainty of demand and supply. To solve the issue, the energy management problem is constructed as a stochastic optimization problem. The optimization aims are to minimize the time-averaged energy cost and improve the energy efficiency while respecting the energy constraints. To achieve the distributed implementation in real time without knowing any priori knowledge of underlying stochastic process, a distributed stochastic gradient algorithm based on dual decomposition and a fast scheme are proposed. The numerical results based on real data show that the industrial park, by adopting the proposed algorithm, can achieve social welfare maximization asymptotically.

Rizqi Supramulyana Putra, Tya Nisvi Rahmadhani, Sho`im Hidayat

Introduction: Occupational stress is a body response that is present in the form of physiological, psychological, and behavioral responses to stressors in the work environment. If this condition is not handled properly, it will have a negative impact on workers, and it can reduce the level of health and cause several diseases. The purpose of this research was to determine the risk of occupational stress among nurses in the Emergency Room at Sosodoro Djatikoesoemodan Hospital and what factors were related to the risk of occupational stress. Methods: This research is a descriptive observational study with respondents consisting of 26 nurses in the Emergency Room at Sosodoro Djatikoesoemo Hospital. Data collection was done using questionnaires to measure the level of occupational stress risk of nurses, which included variables of individual worker’s characteristics and job characteristics. The data analysis in this study used the Spearman correlation. Results: The results showed that 15.4% of nurses had a low level of occupational stress risk, 69.2% had a moderate level of occupational stress risk, and 15.4% had a high level of occupational stress risk. Moreover, there was a significant relationship between social support and workload factors on the risk of occupational stress. Conclusion: Most of the nurses experienced moderate occupational stress. Gender, personality type, workload and mental demands were factors that were related to the risk of occupational stress and could increase the risk of occupational stress. Meanwhile, interpersonal conflict and job control were elements that were not related to the risk of occupational stress. Keywords: emergency room Sododoro Djatikoesomo hospital, nurses, risk of occupational stress

Alejandro Guajardo Córdoba

Lucía Rodríguez Guzmán, Arlene Oramas Viera

Introducción: La labor docente es reconocida como altamente estresante por las demandas de las tareas y las condiciones en que se realizan. Aunque menos estudiada y con otras particularidades, las academias universitarias no se excluyen y el estrés afecta negativamente el desempeño de los profesores, su salud y bienestar. Objetivos: Identificar las condiciones de trabajo que devienen en fuentes de estrés en los trabajadores académicos de una universidad mexicana. Material y método: A una muestra de 120 profesores académicos mexicanos se le aplicó de forma digital la escala de Estrés en docentes, de Travers y Cooper, la cual fue adaptada previamente y objeto de estudio en otras poblaciones de docentes en Guanajuato, México. Resultados: Las principales fuentes de estrés se relacionan con el salario, el volumen de trabajo, las presiones de los superiores y la falta de recursos. Se destaca la vulnerabilidad de las féminas al predominar estas en el grupo que más vivencia estos estresares, y las diferencias entre los campus universitarios evidencian el papel que desempeñan las variables organizacionales. Conclusiones: Las fuentes de estrés identificadas constituyen peligros psicosociales laborales que son esenciales para realizar acciones de intervención a diferentes niveles, individuales y organizacionales. Introduction: The educational work is known as a high stressing activity due to the task requested and to the conditions in which these are developed. Although university academies are less studied and they have other peculiarities, they cannot be excluded and stress affects the performance of professors, their health, and wellness. Objective: The purpose was to identify working conditions that become the source of stress in higher education professors of a Mexican university. Material and methods: It was applied digitally, to a sample of 120 Mexican academic professors, the Travers and Cooper Stress Scale for academicians, which was previously adapted and object of study in other selections of professors in Guanajuato, Mexico. Results: The main sources of stress were related to salary, work volume, exerted pressure by superiors, and lack of resources. Women are the most vulnerable, since they are prevalent in the group that experiences these stressors the most, and differences between university campuses show the role played by organizational variables. Conclusions: It is concluded that identified sources of stress represent occupational psychosocial dangers to the academic sector, which must be essential to implementing health and welfare prevention programs and carrying out intervention actions in different levels, individually and organizational