Hasil untuk "Industrial medicine. Industrial hygiene"

Hasan Tarik Akbaba, Efe Bozkir, Anna Puhl et al.

Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

Pietro Chiavassa, Stefano Scanzio, Gianluca Cena

Wi-Fi is currently considered one of the most promising solutions for interconnecting mobile equipment (e.g., autonomous mobile robots and active exoskeletons) in industrial environments. However, relability requirements imposed by the industrial context, such as ensuring bounded transmission latency, are a major challenge for over-the-air communication. One of the aspects of Wi-Fi technology that greatly affects the probability of a packet reaching its destination is the selection of the appropriate transmission rate. Rate adaptation algorithms are in charge of this operation, but their design and implementation are not regulated by the IEEE 802.11 standard. One of the most popular solutions, available as open source, is Minstrel, which is the default choice for the Linux Kernel. In this paper, Minstrel performance is evaluated for both static and mobility scenarios. Our analysis focuses on metrics of interest for industrial contexts, i.e., latency and packet loss ratio, and serves as a preliminary evaluation for the future development of enhanced rate adaptation algorithms based on centralized digital twins.

Changheon Han, Yun Seok Kang, Yuseop Sim et al.

Deep learning-based machine listening is broadening the scope of industrial acoustic analysis for applications like anomaly detection and predictive maintenance, thereby improving manufacturing efficiency and reliability. Nevertheless, its reliance on large, task-specific annotated datasets for every new task limits widespread implementation on shop floors. While emerging sound foundation models aim to alleviate data dependency, they are too large and computationally expensive, requiring cloud infrastructure or high-end hardware that is impractical for on-site, real-time deployment. We address this gap with LISTEN (Lightweight Industrial Sound-representable Transformer for Edge Notification), a kilobyte-sized industrial sound foundation model. Using knowledge distillation, LISTEN runs in real-time on low-cost edge devices. On benchmark downstream tasks, it performs nearly identically to its much larger parent model, even when fine-tuned with minimal datasets and training resource. Beyond the model itself, we demonstrate its real-world utility by integrating LISTEN into a complete machine monitoring framework on an edge device with an Industrial Internet of Things (IIoT) sensor and system, validating its performance and generalization capabilities on a live manufacturing shop floor.

Marcos Kalinowski, Lucas Romao, Ariane Rodrigues et al.

Lean R&D has been used at PUC-Rio to foster industry-academia collaboration in innovation projects across multiple sectors. This industrial experience paper describes recent experiences and evaluation results from applying Lean R&D in partnership with Petrobras in the oil and gas sector and Americanas in retail. The findings highlight Lean R&D's effectiveness in transforming ideas into meaningful business outcomes. Based on responses from 57 participants - including team members, managers, and sponsors - the assessment indicates that stakeholders find the structured phases of Lean R&D well-suited to innovation projects and endorse the approach. Although acknowledging that successful collaboration relies on various factors, this industrial experience positions Lean R&D as a promising framework for industry-academia projects focused on achieving rapid, impactful results for industry partners.

Muhammad Junaid Asif, Abdul Rehman, Asim Mehmood et al.

This research proposes an extensive technique for monitoring and controlling the industrial parameters using Internet of Things (IoT) technology based on wireless communication. We proposed a system based on NRF transceivers to establish a strong Wireless Sensor Network (WSN), enabling transfer of real-time data from multiple sensors to a central setup that is driven by ARDUINO microcontrollers. Different key parameters, crucial for industrial setup such as temperature, humidity, soil moisture and fire detection, are monitored and displayed on an LCD screen, enabling factory administration to oversee the industrial operations remotely over the internet. Our proposed system bypasses the need for physical presence for monitoring by addressing the shortcomings of conventional wired communication systems. Other than monitoring, there is an additional feature to remotely control these parameters by controlling the speed of DC motors through online commands. Given the rising incidence of industrial fires over the worldwide between 2020 and 2024 due to an array of hazards, this system with dual functionality boosts the overall operational efficiency and safety. This overall integration of IoT and Wireless Sensor Network (WSN) reduces the potential risks linked with physical monitoring, providing rapid responses in emergency scenarios, including the activation of firefighting equipment. The results show that innovations in wireless communication perform an integral part in industrial process automation and safety, paving the way to more intelligent and responsive operating environments. Overall, this study highlights the potential for change of IoT-enabled systems to revolutionize monitoring and control in a variety of industrial applications, resulting in increased productivity and safety.

Jinyun Gao, Lijun Cai, Tingting Lin et al.

ObjectiveThe present study aims to observe the cleaning effect of three methods on the phacoemulsification (phaco) silicone tube in ophthalmology.MethodsA total of 142 phaco silicone tubes taken within 2 hours after surgery were divided in three groups randomly. Manual cleaning was applied in Group A (43 cases), the method for cleaning and drying devices for batch cleaning of ophthalmic class A lumen was applied in Group B (46 cases), and the method for cleaning with a negative-pressure vacuum cleaner was applied in Group C (53 cases). The cleaning time was recorded and the cleaning effect was evaluated by three methods, namely, adenosinetriphosphate (ATP) bioluminescence assay, magnifying glass with light, and air gun.ResultsThe qualified rates of manual cleaning, the method for cleaning and drying devices for batch cleaning of ophthalmic class A lumen, and the method for cleaning with a negative-pressure vacuum cleaner were 95.3%, 94.3%, and 95.7%, respectively, and the difference was not statistically significant (P>0.05). The average cleaning duration was 76 seconds per tube, 29.3 seconds per tube, and 69.46 seconds per tube, respectively.ConclusionAll three cleaning methods are available in the clinic, of which the method for cleaning and drying devices for batch cleaning of ophthalmic class A lumen has the highest efficiency.

Anna M. Krol, Marvin Erdmann, Ewan Munro et al.

In this paper, we use open-source tools to perform quantum resource estimation to assess the requirements for industry-relevant quantum computation. Our analysis uses the problem of industrial shift scheduling in manufacturing and the Quantum Industrial Shift Scheduling algorithm. We base our figures of merit on current technology, as well as theoretical high-fidelity scenarios for superconducting qubit platforms. We find that the execution time of gate and measurement operations determines the overall computational runtime more strongly than the system error rates. Moreover, achieving a quantum speedup would not only require low system error rates ($10^{-6}$ or better), but also measurement operations with an execution time below 10ns. This rules out the possibility of near-term quantum advantage for this use case, and suggests that significant technological or algorithmic progress will be needed before such an advantage can be achieved.

Christian W. Frey

Industrial production processes, especially in the pharmaceutical industry, are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial processes. Addressing the limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios with unbalanced data sets and highly correlated process variables, HULS combines existing unsupervised learning techniques to address these challenges. To evaluate the performance of the HULS concept, comparative experiments are performed based on a laboratory batch

Joanna Frąckiewicz, Zuzanna Sawejko, Anna Ciecierska et al.

Background. Meat and fish contain easily digestible whole protein, B vitamins and numerous minerals, such as zinc, phosphorus and iron, thanks to which these products have a high nutritional value. Objective. The aim of the study was to assess the frequency of consumption of meat and fish in young adults depending on gender. Material and Methods. Data was collected from 200 respondents aged 19-30 using online survey questionnaire. The questionnaire was divided into three parts. The first part contained questions about sociodemographic and anthropometric data, the second part contained a question regarding the self-assessment of the diet. Whereas, the third part of the questionnaire concerned the frequency of consumption of meat and fish. Statistical analysis of the results was performed using Statistica 13.3 software and statistical significance was assumed at the p≤0.05 level. Results. Meat consumption was declared by 86.5% of the respondents (83% of women and 90% of men), usually 5-6 times a week (20%). Gender statistically significantly differentiated the frequency of meat consumption. Men significantly more often consumed total meat (p=0.002), red meat (p=0.001) and poultry (p=0.004) compared to women. Fish was eaten by 85% of the respondents, and 39% only 1-3 times a month. Respondents preferred oily fish. There were no statistically significant differences in the consumption of fish by men and women. Conclusions. Considering the complexity of the relationship between men and women’s meat and fish consumption and health, research is needed to clarify the amounts of meat and fish consumed, the degrees and how they are processed, and the reasons for eating or not eating them. This can be helpful in directions for nutritional education.

Juan Ma, Yin Wan, Lingmin Song et al.

Abstract Background Nanoplastics (NPs) are omnipresent in our lives as a new type of pollution with a tiny size. It can enter organisms from the environment, accumulate in the body, and be passed down the food chain. Inflammatory bowel disease (IBD) is a nonspecific intestinal inflammatory disease that is recurrent and prevalent in the population. Given that the intestinal features of colitis may affect the behavior and toxicity of NPs, it is imperative to clarify the risk and toxicity mechanisms of NPs in colitis models. Methods and results In this study, mice were subjected to three cycles of 5-day dextran sulfate sodium (DSS) exposures, with a break of 7 to 11 days between each cycle. After the first cycle of DSS exposure, the mice were fed gavagely with water containing 100 nm polystyrene nanobeads (PS-NPs, at concentrations of 1 mg/kg·BW, 5 mg/kg·BW and 25 mg/kg·BW, respectively) for 28 consecutive days. The results demonstrated that cyclic administration of DSS induced chronic inflammation in mice, while the standard drug “5-aminosalicylic acid (5-ASA)” treatment partially improved colitis manifestations. PS-NPs exacerbated intestinal inflammation in mice with chronic colitis by activating the MAPK signaling pathway. Furthermore, PS-NPs aggravated inflammation, oxidative stress, as well as hepatic lipid metabolism disturbance in the liver of mice with chronic colitis. Conclusion PS-NPs exacerbate intestinal inflammation and injury in mice with chronic colitis. This finding highlights chronically ill populations’ susceptibility to environmental hazards, which urgent more research and risk assessment studies.

Aleida Fernández Moreno

Se presenta un sencillo caso hipotético sobre la presencia y la puntualidad de profesionales en los servicios de salud y, derivadas de este, algunas consideraciones sobre la ética en Terapia Ocupacional. La autora invita a pensar en el tema y a realizar una revisión ética desde tres aristas: la ley profesional vigente, los principios de la bioética y la deliberación ética. En las dos primeras no se logra resolver el caso, la salida será planteada desde la deliberación ética. La Terapia Ocupacional colombiana necesita la renovación de su ley profesional, empezar la formación en ética desde los primeros semestres y generar espacios interdisciplinares de discusión, especialmente a nivel de las prácticas profesionales.

Carly V. Goodman, Rivka Green, Allya DaCosta et al.

Abstract Background Early life exposure to lead, mercury, polychlorinated biphenyls (PCBs), polybromide diphenyl ethers (PBDEs), organophosphate pesticides (OPPs), and phthalates have been associated with lowered IQ in children. In some studies, these neurotoxicants impact males and females differently. We aimed to examine the sex-specific effects of exposure to developmental neurotoxicants on intelligence (IQ) in a systematic review and meta-analysis. Method We screened abstracts published in PsychINFO and PubMed before December 31st, 2021, for empirical studies of six neurotoxicants (lead, mercury, PCBs, PBDEs, OPPs, and phthalates) that (1) used an individualized biomarker; (2) measured exposure during the prenatal period or before age six; and (3) provided effect estimates on general, nonverbal, and/or verbal IQ by sex. We assessed each study for risk of bias and evaluated the certainty of the evidence using Navigation Guide. We performed separate random effect meta-analyses by sex and timing of exposure with subgroup analyses by neurotoxicant. Results Fifty-one studies were included in the systematic review and 20 in the meta-analysis. Prenatal exposure to developmental neurotoxicants was associated with decreased general and nonverbal IQ in males, especially for lead. No significant effects were found for verbal IQ, or postnatal lead exposure and general IQ. Due to the limited number of studies, we were unable to analyze postnatal effects of any of the other neurotoxicants. Conclusion During fetal development, males may be more vulnerable than females to general and nonverbal intellectual deficits from neurotoxic exposures, especially from lead. More research is needed to examine the nuanced sex-specific effects found for postnatal exposure to toxic chemicals.

Savvas Papaioannou, Andrew Markham, Niki Trigoni

To date, the majority of positioning systems have been designed to operate within environments that have long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker's mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e. new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.

Oluwatosin Ogundare, Gustavo Quiros Araya, Ioannis Akrotirianakis et al.

This paper proposes a study of the resilience and efficiency of automatically generated industrial automation and control systems using Large Language Models (LLMs). The approach involves modeling the system using percolation theory to estimate its resilience and formulating the design problem as an optimization problem subject to constraints. Techniques from stochastic optimization and regret analysis are used to find a near-optimal solution with provable regret bounds. The study aims to provide insights into the effectiveness and reliability of automatically generated systems in industrial automation and control, and to identify potential areas for improvement in their design and implementation.

Olav Lamberts, Konrad Wolsing, Eric Wagner et al.

Industrial systems are increasingly threatened by cyberattacks with potentially disastrous consequences. To counter such attacks, industrial intrusion detection systems strive to timely uncover even the most sophisticated breaches. Due to its criticality for society, this fast-growing field attracts researchers from diverse backgrounds, resulting in 130 new detection approaches in 2021 alone. This huge momentum facilitates the exploration of diverse promising paths but likewise risks fragmenting the research landscape and burying promising progress. Consequently, it needs sound and comprehensible evaluations to mitigate this risk and catalyze efforts into sustainable scientific progress with real-world applicability. In this paper, we therefore systematically analyze the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. Our analysis of 609 publications shows that the rapid growth of this research field has positive and negative consequences. While we observe an increased use of public datasets, publications still only evaluate 1.3 datasets on average, and frequently used benchmarking metrics are ambiguous. At the same time, the adoption of newly developed benchmarking metrics sees little advancement. Finally, our systematic analysis enables us to provide actionable recommendations for all actors involved and thus bring the entire research field forward.

Siddhartha Paul Tiwari

Soybean is the most globalized, traded and processed crop commodity. USA, Argentina and Brazil continue to be the top three producers and exporters of soybean and soymeal. Indian soyindustry has also made a mark in the national and global arena. While soymeal, soyoil, lecithin and other soy-derivatives stand to be driven up by commerce, the soyfoods for human health and nutrition need to be further promoted. The changing habitat of commerce in soyderivatives necessitates a shift in strategy, technological tools and policy environment to make Indian soybean industry continue to thrive in the new industrial era. Terms of trade for soyfarming and soy-industry could be further improved. Present trends, volatilities, slowdowns, challenges faced and associated desiderata are accordingly spelt out in the present article.

Xian Tao, Xinyi Gong, Xin Zhang et al.

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization algorithms have become more widely used in industrial inspection tasks. This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of anomaly localization, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this paper provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial anomaly localization and who wish to apply it to the localization of anomalies in other fields.

Tim Schreiter, Lucas Morillo-Mendez, Ravi T. Chadalavada et al.

Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, and efficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robots in industrial environments are not usually anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here.

Chao Dong, Xiaoxiong Xiong, Qiulin Xue et al.

Network architecture design is very important for the optimization of industrial networks. The type of network architecture can be divided into small-scale network and large-scale network according to its scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, its structure often has regular topology. For large-scale networks, the existing research mainly focuses on the random characteristics of network nodes and edges. Recently, popular models include random networks, small-world networks and scale-free networks. Starting from the scale of network, this survey summarizes and analyzes the network modeling methods based on graph theory and the practical application in industrial scenarios. Furthermore, this survey proposes a novel network performance metric - system entropy. From the perspective of mathematical properties, the analysis of its non-negativity, monotonicity and concave-convexity is given. The advantage of system entropy is that it can cover the existing regular network, random network, small-world network and scale-free network, and has strong generality. The simulation results show that this metric can realize the comparison of various industrial networks under different models.

Emilio Di Ianni, Johanna Samulin Erdem, Peter Møller et al.

Abstract Background Multi-walled carbon nanotubes (MWCNT) have received attention due to extraordinary properties, resulting in concerns for occupational health and safety. Costs and ethical concerns of animal testing drive a need for in vitro models with predictive power in respiratory toxicity. The aim of this study was to assess pro-inflammatory response (Interleukin-8 expression, IL-8) and genotoxicity (DNA strand breaks) caused by MWCNT with different physicochemical properties in different pulmonary cell models and correlate these to previously published in vivo data. Seven MWCNT were selected; two long/thick (NRCWE-006/Mitsui-7 and NM-401), two short/thin (NM-400 and NM-403), a pristine (NRCWE-040) and two surface modified; hydroxylated (NRCWE-041) and carboxylated (NRCWE-042). Carbon black Printex90 (CB) was included as benchmark material. Human alveolar epithelial cells (A549) and monocyte-derived macrophages (THP-1a) were exposed to nanomaterials (NM) in submerged conditions, and two materials (NM-400 and NM-401) in co-cultures of A549/THP-1a and lung fibroblasts (WI-38) in an air-liquid interface (ALI) system. Effective doses were quantified by thermo-gravimetric-mass spectrometry analysis (TGA-MS). To compare genotoxicity in vitro and in vivo, we developed a scoring system based on a categorization of effects into standard deviation (SD) units (< 1, 1, 2, 3 or 4 standard deviation increases) for the increasing genotoxicity. Results Effective doses were shown to be 25 to 53%, and 21 to 57% of the doses administered to A549 and THP-1a, respectively. In submerged conditions (A549 and THP-1a cells), all NM induced dose-dependent IL-8 expression. NM-401 and NRCWE-006 caused the strongest pro-inflammatory response. In the ALI-exposed co-culture, only NM-401 caused increased IL-8 expression, and no DNA strand breaks were observed. Strong correlations were found between in vitro and in vivo inflammation when doses were normalized by surface area (also proxy for diameter and length). Significantly increased DNA damage was found for all MWCNT in THP-1a cells, and for short MWCNT in A549 cells. A concordance in genotoxicity of 83% was obtained between THP-1a cells and broncho-alveolar lavaged (BAL) cells. Conclusion This study shows correlations of pro-inflammatory potential in A549 and THP-1a cells with neutrophil influx in mice, and concordance in genotoxic response between THP-1a cells and BAL cells, for seven MWCNT.