Hasil untuk "Industrial hygiene. Industrial welfare"

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens et al.

Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

Yen-Chia Hsu, Despoina Touska

This report describes a dataset for industrial smoke segmentation, published on a figshare repository (https://doi.org/10.21942/uva.31847188). The dataset is licensed under CC BY 4.0.

Zeyi Liu, Shuang Liu, Jihai Min et al.

With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has become a key bottleneck for deploying predictive maintenance and autonomous inspection. Most public datasets remain limited by simulated data sources, single-modality sensing, or the absence of fine-grained object-level annotations, which prevents robust scene understanding and multimodal safety reasoning for industrial foundation models. To address these limitations, InspecSafe-V1 is released as the first multimodal benchmark dataset for industrial inspection safety assessment that is collected from routine operations of real inspection robots in real-world environments. InspecSafe-V1 covers five representative industrial scenarios, including tunnels, power facilities, sintering equipment, oil and gas petrochemical plants, and coal conveyor trestles. The dataset is constructed from 41 wheeled and rail-mounted inspection robots operating at 2,239 valid inspection sites, yielding 5,013 inspection instances. For each instance, pixel-level segmentation annotations are provided for key objects in visible-spectrum images. In addition, a semantic scene description and a corresponding safety level label are provided according to practical inspection tasks. Seven synchronized sensing modalities are further included, including infrared video, audio, depth point clouds, radar point clouds, gas measurements, temperature, and humidity, to support multimodal anomaly recognition, cross-modal fusion, and comprehensive safety assessment in industrial environments.

Jaroslava Dekkerová, Lucia Černáková

Verapamil (VER) is a calcium channel blocker used to treat cardiovascular diseases. However, some studies also suggest its antimicrobial potential. Changes in calcium abundance in yeasts can lead to decreased expression of transcription factors for genes related to morphology, resistance, and biofilm. Hyphal growth in <i>Candida albicans</i> is necessary for biofilm formation, especially in mixed biofilms with <i>Staphylococcus aureus</i>. This research studied the antibiofilm activity of VER in mixed biofilms of <i>C. albicans</i> SC5314 and <i>S. aureus</i> CCM3953. First, the minimal inhibitory concentration of VER was determined for single-species biofilms. Subsequently, a subinhibitory concentration of VER (1 mM) was tested on mixed biofilms. Biomass was reduced by 20% for <i>C. albicans</i> and 30% for <i>S. aureus</i>. The morphology of <i>C. albicans</i> was altered, and a decrease in <i>S. aureus</i> cells was also observed. qPCR was used to determine changes in <i>HWP1</i> and <i>ALS3</i> gene expression in biofilms formed w/wo VER. A decrease in the expression of both genes was observed. In vivo experiments with <i>Galleria mellonella</i> confirmed the antibiofilm activity of VER against mixed infections of <i>C. albicans</i> and <i>S. aureus.</i> These results suggest that VER regulates the morphology of <i>C. albicans</i>, resulting in changes in biofilm composition and the adhesion of <i>S. aureus</i>.

Wenting Zhang, Muhammad Daniyal Ghouri, Magda Blosi et al.

Abstract Background With the increasing integration of nanomaterials (NMs) into daily life, their technological advantages have become evident. However, their intricate interactions with biological systems introduce complexities that can lead to unpredictable toxicological outcomes. This study investigated the in vivo toxicokinetics and toxicodynamics of single- and multi-component NMs composed of silicon carbide (SiC), titanium dioxide (TiO2), and a SiC@TiO2 composite, along with a physical mixture of SiC and TiO2 in the same ratio as the composite. Rats were exposed to these materials via single intratracheal instillation, and biological responses were assessed over time (1 h to 28 days) to identify the no-observed-adverse-effect level (NOAEL). Results All NMs induced minimal structural alterations in lung tissue and prompted varying degrees of inflammatory cell infiltration. Over time, translocation from the lungs to secondary organs (heart, spleen, liver, kidney) was observed, with distinct distribution patterns between Si- and Ti-containing NMs. Bronchoalveolar lavage fluid analysis revealed a minimal to mild inflammatory response that evolved in a time-dependent manner, even at NOAEL exposure levels, suggesting delayed-onset biological effects. Conclusions SiC@TiO2 demonstrated a reduced pulmonary toxicological profile relative to its single-component counterparts, likely due to antagonistic effects between its constituents. These findings highlight the need to assess multicomponent nanomaterials as distinct entities and suggest that rational material design may help mitigate adverse biological effects, supporting safer nanotechnology development.

P. Vijaya Bharati, J. S. V. Siva Kumar, Sathish K Anumula et al.

Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning algorithms, predictive maintenance allows industrial systems to predict failures and optimize machines life. This paper presents the synergy between the Internet of Things and predictive maintenance in industrial engineering with an emphasis on the technologies, methodologies, as well as data analytics techniques, that constitute the integration. A systematic collection, processing, and predictive modeling of data is discussed. The outcomes emphasize greater operational efficiency, decreased downtime, and cost-saving, which makes a good argument as to why predictive maintenance should be implemented in contemporary industries.

Maryam Ahang, Todd Charter, Mostafa Abbasi et al.

Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artificial intelligence (AI) has emerged as a powerful tool for fault detection and diagnosis, attracting growing interest from both academia and industry. This paper provides a comprehensive overview of intelligent condition monitoring methods, with a particular emphasis on chemical plants and the widely used Tennessee Eastman Process (TEP) benchmark. State-of-the-art machine learning (ML) and deep learning (DL) algorithms are reviewed, highlighting their strengths, limitations, and applicability to industrial fault detection and diagnosis. Special attention is given to key challenges, including imbalanced and unlabeled data, and to strategies by which models can address these issues. Furthermore, comparative analyses of algorithm performance are presented to guide method selection in practical scenarios. This survey is intended to benefit both newcomers and experienced researchers by consolidating fundamental concepts, summarizing recent advances, and outlining open challenges and promising directions for intelligent condition monitoring in industrial plants.

Xiaomeng Zhu, Talha Bilal, Pär Mårtensson et al.

This paper is about effectively utilizing synthetic data for training deep neural networks for industrial parts classification, in particular, by taking into account the domain gap against real-world images. To this end, we introduce a synthetic dataset that may serve as a preliminary testbed for the Sim-to-Real challenge; it contains 17 objects of six industrial use cases, including isolated and assembled parts. A few subsets of objects exhibit large similarities in shape and albedo for reflecting challenging cases of industrial parts. All the sample images come with and without random backgrounds and post-processing for evaluating the importance of domain randomization. We call it Synthetic Industrial Parts dataset (SIP-17). We study the usefulness of SIP-17 through benchmarking the performance of five state-of-the-art deep network models, supervised and self-supervised, trained only on the synthetic data while testing them on real data. By analyzing the results, we deduce some insights on the feasibility and challenges of using synthetic data for industrial parts classification and for further developing larger-scale synthetic datasets. Our dataset and code are publicly available.

Madapu Amarlingam, Abhishek Wani, Adarsh NL

Federated Learning (FL) is the most widely adopted collaborative learning approach for training decentralized Machine Learning (ML) models by exchanging learning between clients without sharing the data and compromising privacy. However, since great data similarity or homogeneity is taken for granted in all FL tasks, FL is still not specifically designed for the industrial setting. Rarely this is the case in industrial data because there are differences in machine type, firmware version, operational conditions, environmental factors, and hence, data distribution. Albeit its popularity, it has been observed that FL performance degrades if the clients have heterogeneous data distributions. Therefore, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without any additional on-edge (clientlevel) computations and communications than standard FL and mitigates the shortcomings from data heterogeneity in industrial applications. Our approach enhances client-level model performance by allowing them to collaborate with similar clients and train more specialized or personalized models. Also, we propose an adaptive aggregation algorithm that extends the LICFL to Adaptive LICFL (ALICFL) for further improving the global model performance and speeding up the convergence. Through numerical experiments on real-time data, we demonstrate the efficacy of the proposed algorithms and compare the performance with existing approaches.

Aimira Baitieva, David Hurych, Victor Besnier et al.

Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.

Rohit Konda, Jordan Prescott, Vikas Chandan et al.

The widespread use of industrial refrigeration systems across various sectors contribute significantly to global energy consumption, highlighting substantial opportunities for energy conservation through intelligent control design. As such, this work focuses on control algorithm design in industrial refrigeration that minimize operational costs and provide efficient heat extraction. By adopting tools from inventory control, we characterize the structure of these optimal control policies, exploring the impact of different energy cost-rate structures such as time-of-use (TOU) pricing and peak pricing. While classical threshold policies are optimal under TOU costs, introducing peak pricing challenges their optimality, emphasizing the need for carefully designed control strategies in the presence of significant peak costs. We provide theoretical findings and simulation studies on this phenomenon, offering insights for more efficient industrial refrigeration management.

Putri Ayuni Alayyannur, Muhammad Malik Al Hakim, Rr. Sri Rejeki Eviyanti Puspita Sari

Introduction: Every workplace must make an occupational health effort to avoid health problems. Many workers underestimate the risks of the job and, therefore, do not use safety equipment even when available. The most often reported case of occupational skin illnesses, contact dermatitis, accounts for more than 85% of all cases. This study was conducted to occupational dermatitis and its relationship to personal protective equipment (PPE) use. Methods: The literature search was carried out in April 2021. The research sources were taken from several databases with the keywords dermatitis, occupational health, and personal protective equipment. The Google Scholar database found 17,710 articles, ScienceDirect found 1,264 articles, ProQuest found 888 articles, and PubMed found 452 articles. Of the entire database, only 36 articles met the inclusion criteria. Results: This literature review shows that dermatitis is experienced by workers in various sectors including health workers, hairdressers, scavengers, farmers, fishermen, manufacturing industry workers, printing workers, and construction workers. The use of PPE can reduce the risk of dermatitis. However, in some conditions, the use of PPE has no effect or can even cause dermatitis due to irritation and allergies to the ingredients contained in the PPE. The limitation of this research is that the articles that are the source of this review are only from 2016–2021.Conclusion: Dermatitis still occurs in various occupational sectors. The risk of dermatitis can be decreased by using PPE; however, it can also cause the occurrence of dermatitis itself.

Yusuke Kato, Ryo Okumura, Tadahiro Taniguchi

The process of industrial box-packing, which involves the accurate placement of multiple objects, requires high-accuracy positioning and sequential actions. When a robot is tasked with placing an object at a specific location with high accuracy, it is important not only to have information about the location of the object to be placed, but also the posture of the object grasped by the robotic hand. Often, industrial box-packing requires the sequential placement of identically shaped objects into a single box. The robot's action should be determined by the same learned model. In factories, new kinds of products often appear and there is a need for a model that can easily adapt to them. Therefore, it should be easy to collect data to train the model. In this study, we designed a robotic system to automate real-world industrial tasks, employing a vision-based learning control model. We propose in-hand-view-sensitive Newtonian variational autoencoder (ihVS-NVAE), which employs an RGB camera to obtain in-hand postures of objects. We demonstrate that our model, trained for a single object-placement task, can handle sequential tasks without additional training. To evaluate efficacy of the proposed model, we employed a real robot to perform sequential industrial box-packing of multiple objects. Results showed that the proposed model achieved a 100% success rate in industrial box-packing tasks, thereby outperforming the state-of-the-art and conventional approaches, underscoring its superior effectiveness and potential in industrial tasks.

Eric Wagner, David Heye, Martin Serror et al.

Industrial control systems increasingly rely on middlebox functionality such as intrusion detection or in-network processing. However, traditional end-to-end security protocols interfere with the necessary access to in-flight data. While recent work on middlebox-aware end-to-end security protocols for the traditional Internet promises to address the dilemma between end-to-end security guarantees and middleboxes, the current state-of-the-art lacks critical features for industrial communication. Most importantly, industrial settings require fine-grained access control for middleboxes to truly operate in a least-privilege mode. Likewise, advanced applications even require that middleboxes can inject specific messages (e.g., emergency shutdowns). Meanwhile, industrial scenarios often expose tight latency and bandwidth constraints not found in the traditional Internet. As the current state-of-the-art misses critical features, we propose Middlebox-aware DTLS (Madtls), a middlebox-aware end-to-end security protocol specifically tailored to the needs of industrial networks. Madtls provides bit-level read and write access control of middleboxes to communicated data with minimal bandwidth and processing overhead, even on constrained hardware.

S. V. Raikova, S. S. Raykin, N. Komleva et al.

Introduction. The relevance of the study is determined by the continuous high prevalence of smoking tobacco products among the working population. The aim is to assess the prevalence of smoking tobacco use among the working population of the Saratov region. Materials and methods. In frames of the cross-sectional study, four hundred fifty three employees of various industrial enterprises of the Saratov region who underwent periodic medical examination at the occupational diseases clinic of the Saratov Hygiene Center of the Federal State Budgetary Institution “MRC (Medical Research Center) of Medical and Preventive Technologies for Public Health Risk Management”, were surveyed, including 280 men (63.8%) and 173 women (38.2%). The average age was 50.5 years. The results of the study were statistically processed using the software Statistica 10. The nonparametric Mann-Whitney method was used to compare two independent samples. Results. The prevalence of tobacco smoking was 38.6% among men and 13.3% among women. More than half of those who used tobacco products belonged to the group of “trained” smokers - 66.7% of men and 56.5% of women, the majority of employees (79.4%) smoked more than 10 cigarettes a day. The main reason for giving up smoking was the state of health (65.2%). 8.7% and 9.9% of non-smoking respondents were found to be exposed to secondhand tobacco smoke at home and at work respectively. The frequency of use of other types of smoking products has been studied. Limitations. The study has regional (Saratov region) and professional (employees of industrial enterprises) limitations. Conclusion. Despite the complex of anti-smoking measures people of working age remain highly committed to the use of tobacco smoking products. Smoking of tobacco products and electronic means of heating tobacco is important to take into account when developing and carrying out preventive measures, including during periodic medical examinations of the able-bodied population.

Yajie Cui, Zhaoxiang Liu, Shiguo Lian

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.

Wei Sun, Jia Li, Xiaoliang Li et al.

Abstract Background Alumina nanoparticles (aluminaNPs), which are widely used in a range of daily and medical fields, have been shown to penetrate blood-brain barrier, and distribute and accumulate in different brain areas. Although oral treatment of aluminaNPs induces hippocampus-dependent learning and memory impairments, characteristic effects and exact mechanisms have not been fully elucidated. Here, male adult rats received a single bilateral infusion of aluminaNPs (10 or 20 µg/kg of body weight) into the hippocampal region, and their behavioral performance and neural function were assessed. Results The results indicated that the intra-hippocampus infusions at both doses of aluminaNPs did not cause spatial learning inability but memory deficit in the water maze task. This impairment was attributed to the effects of aluminaNP on memory consolidation phase through activation of proBDNF/RhoA pathway. Inhibition of the increased proBDNF by hippocampal infusions of p75NTR antagonist could effectively rescue the memory impairment. Incubation of aluminaNPs exaggerated GluN2B-dependent LTD induction with no effects on LTD expression in hippocampal slices. AluminaNP could also depress the amplitude of NMDA-GluN2B EPSCs. Meanwhile, increased reactive oxygen specie production was reduced by blocking proBDNF-p75NTR pathway in the hippocampal homogenates. Furthermore, the neuronal correlate of memory behavior was drastically weakened in the aluminaNP-infused groups. The dysfunction of synaptic and neuronal could be obviously mitigated by blocking proBDNF receptor p75NTR, implying the involvement of proBDNF signaling in aluminaNP-impaired memory process. Conclusions Taken together, our findings provide the first evidence that the accumulation of aluminaNPs in the hippocampus exaggeratedly activates proBDNF signaling, which leads to neural and memory impairments.

G. Sioutas, K. Evangelou, Antonios Vlachavas et al.

Domestic rabbits are commercially farmed for their meat whilst gastrointestinal diseases can hinder their production. Passalurus ambiguus and Eimeria spp. are two common rabbit intestinal parasites that can cause diarrhoea, among other symptoms, and in severe cases, death. C. guttulatus is a commensal yeast of the rabbits’ stomach that is considered apathogenic but can worsen symptoms in rabbits suffering from coccidiosis. In the present case report, we describe an outbreak of deaths in three different age groups (A: lactating does, B: 58 days old and C: 80 days old) in an industrial rabbit farm in Greece. Symptoms included depression, diarrhoea, inappetence, weight loss, dehydration and ruffled furs. Using a faecal flotation technique, sick rabbits were found to be moderately to heavily infected with P. ambiguus, Eimeria spp. and C. guttulatus. Treatment with fenbendazole and oregano oil combined with hygiene control measures successfully controlled the infections and resolved clinical symptoms. A faecal flotation method or other reliable diagnostic technique should be used regularly in industrial rabbit farms to screen for gastrointestinal parasitic infections. Early diagnosis and control will help to maintain production levels and, therefore, limit financial losses for the farmer while ensuring animal welfare.

E. Sans, F. Tuyttens, C. Taconeli et al.

We compared closed- and open-sided industrial houses with respect to the welfare of broiler chickens in southern Brazil. Ten flocks from each design were evaluated and measures divided into the following categories: i) bird health: contact dermatitis on the breast and abdominal areas, bird soiling, foot-pad dermatitis, hock burn, lameness, fractures, bruising, scratches, dead on arrival, diseases; ii) environmental measurements: relative humidity, temperature, air velocity, ammonia (NH3), carbon dioxide (CO2), light intensity, litter moisture; iii) behaviour: bird behaviour, touch test; and iv) affective states: qualitative behaviour assessment. Closed-sided houses showed worse contact dermatitis on the breast and abdominal areas, lower exploratory behaviour prevalence, higher NH3 (11.2 [± 6.8] vs 7.5 [± 3.9] ppm) and CO2 (1,124.9 [± 561.5] vs 841.0 [± 158.0] ppm), lower light intensity (6.9 [± 6.3] vs 274.2 [± 241.9] lux), while open-sided houses had a higher prevalence for scratches and panting behaviour, and lower air velocity (2.1 [± 0.7] vs 1.1 [± 1.0] m s–1). Stocking densities of 13.9 (± 0.4) and 12.0 (± 0.3) per m2 for closed- and open-sided houses, respectively, likely influenced some results. All values shown are means (± SD). Even though open-sided houses presented fewer animal welfare restrictions (according to five indicators as opposed to three for closed-sided houses), both revealed important welfare problems, evidenced by poor environmental indicators, behavioural restrictions and injuries.

J. J. Hayer, D. Nysar, C. Heinemann et al.

Consumers, industrial stakeholders, and the legislature demand a stronger focus on animal welfare of all livestock at the farm level by using suitable indicators in self-assessments. In order to deduce farms' animal welfare status reliably, factors that influence indicators' results need to be identified. Hence, this study aimed to apply possible animal welfare indicators for unweaned dairy calves on conventional dairy farms with early cow-calf separation and evaluate influencing factors such as age and sex of calves or climatic conditions on the applied indicators' results. An animal welfare assessment using seven resource-based and 14 animal-based indicators was conducted at 42 typical Western German dairy farms (844 calves) in 2018 and 2019 by two observers. The effect of influencing factors was calculated by binary and ordinal logistic regressions and expressed as odds ratios. Although every unweaned calf was assessed during the farm visits, most farms had relatively few unweaned calves (average number of calves ± standard deviation = 20.1 ± 6.7 calves), with six farms having not more than ten calves. The small sample sizes question the usage of those indicators to compare between farms and to set thresholds at farm level. Only one assessed indicator (cleanliness core body) was not statistically affected by the evaluated influencing factors. Calf age was identified as the most decisive factor, as it affected 16 of 21 evaluated indicators and calf age distribution on-farm varied greatly. Climatic conditions (ambient temperature and rainfall) influenced resource-based indicators such as access to concentrate and water or the cleanliness of feeding implements and bedding as well as animal-based cleanliness indicators and the occurrence of health-related impairments such as coughing and diarrhea. The authors found differences between calves on farms assessed by the different observers in resource-based hygiene indicators but also in animal-based indicators like hyperthermia or hypothermia, highlighting the need for further evaluation of quality criteria in dairy calf welfare assessments. Nevertheless, animal welfare assessments by farmers themselves could be useful tools to sensitize farmers to animal welfare and thereby improve calves' welfare.