Hasil untuk "Industrial hygiene. Industrial welfare"

Yiliu He, Tianle Li, Binghao Ji et al.

Optimization modeling via mixed-integer linear programming (MILP) is fundamental to industrial planning and scheduling, yet translating natural-language requirements into solver-executable models and maintaining them under evolving business rules remains highly expertise-intensive. While large language models (LLMs) offer promising avenues for automation, existing methods often suffer from low data efficiency, limited solver-level validity, and poor scalability to industrial-scale problems. To address these challenges, we present EvoOpt-LLM, a unified LLM-based framework supporting the full lifecycle of industrial optimization modeling, including automated model construction, dynamic business-constraint injection, and end-to-end variable pruning. Built on a 7B-parameter LLM and adapted via parameter-efficient LoRA fine-tuning, EvoOpt-LLM achieves a generation rate of 91% and an executability rate of 65.9% with only 3,000 training samples, with critical performance gains emerging under 1,500 samples. The constraint injection module reliably augments existing MILP models while preserving original objectives, and the variable pruning module enhances computational efficiency, achieving an F1 score of ~0.56 on medium-sized LP models with only 400 samples. EvoOpt-LLM demonstrates a practical, data-efficient approach to industrial optimization modeling, reducing reliance on expert intervention while improving adaptability and solver efficiency.

Sandro Kresina, Nataša Ivančić Jokić, Vlatka Sotošek et al.

<b>Background:</b> Human papillomavirus (HPV) infection is the most common sexually transmitted infection worldwide and a major cause of several cancers. HPV vaccination is the most effective measure of preventing HPV infection, but knowledge and attitudes towards HPV vaccination are inconsistent. <b>Methods:</b> This study aimed to assess the knowledge, attitudes, and vaccination status related to HPV among university students in both healthcare and non-healthcare fields. A cross-sectional online survey was conducted among 769 full-time students, including 362 healthcare and 407 non-healthcare students. Validated questionnaires assessed HPV knowledge, vaccination attitudes, sociodemographic characteristics, and vaccination status. <b>Results:</b> Healthcare students scored significantly higher on HPV knowledge and had more positive attitudes towards vaccination compared to non-healthcare students. Vaccination rates were similar in both groups. Higher HPV knowledge was significantly correlated with reduced vaccine hesitancy and more positive vaccination attitudes. Binary logistic regression indicated that being younger, having more positive attitudes toward vaccination, and possessing greater knowledge about HPV were each associated with a significantly higher likelihood of HPV vaccination. <b>Conclusions:</b> In conclusion, targeted educational interventions are necessary to enhance HPV vaccination acceptance, especially among non-healthcare students. Universities represent crucial settings for promoting health literacy and increasing HPV vaccination coverage to effectively prevent HPV-related cancers among young adults.

Samira Abdelrahman, Hossam Farag, Gilberto Berardinelli

Emerging 6G industrial networks envision autonomous in-X subnetworks to support efficient and cost-effective short range, localized connectivity for autonomous control operations. Supporting timely transmission of event-driven, critical control traffic is challenging in such networks is challenging due to limited radio resources, dynamic device activity, and high mobility. In this paper, we propose a distributed, learning-based random access protocol that establishes implicit inter-subnetwork coordination to minimize the collision probability and improves timely delivery. Each subnetwork independently learns and selects access configurations based on a contention signature signal broadcast by a central access point, enabling adaptive, collision-aware access under dynamic traffic and mobility conditions. The proposed approach features lightweight neural models and online training, making it suitable for deployment in constrained industrial subnetworks. Simulation results show that our method significantly improves the probability of timely packet delivery compared to baseline methods, particularly in dense and high-load scenarios. For instance, our proposed method achieves 21% gain in the probability of timely packet delivery compared to a classical Multi-Armed Bandit (MAB) for an industrial setting of 60 subnetworks and 5 radio channels.

Bastien Muraccioli, Mathieu Celerier, Mehdi Benallegue et al.

Physical Human-Robot Interaction (pHRI) is critical for implementing Industry 5.0, which focuses on human-centric approaches. However, few studies explore the practical alignment of pHRI to industrial-grade performance. This paper introduces a versatile control framework designed to bridge this gap by incorporating the torque-based control modes: compliance control, null-space compliance, and dual compliance, all in static and dynamic scenarios. Thanks to our second-order Quadratic Programming (QP) formulation, strict kinematic and collision constraints are integrated into the system as safety features, and a weighted hierarchy guarantees singularity-robust task tracking performance. The framework is implemented on a Kinova Gen3 collaborative robot (cobot) equipped with a Bota force/torque sensor. A DualShock 4 game controller is attached to the robot's end-effector to demonstrate the framework's capabilities. This setup enables seamless dynamic switching between the modes, and real-time adjustments of parameters, such as transitioning between position and torque control or selecting a more robust custom-developed low-level torque controller over the default one. Built on the open-source robotic control software mc_rtc, our framework ensures reproducibility for both research and industrial deployment, this framework demonstrates a step toward industrial-grade performance and repeatability, showcasing its potential as a robust pHRI control system for industrial environments.

Chentao Shen, Ding Pan, Mingyu Mei et al.

Visual pose tracking is playing an increasingly vital role in industrial contexts in recent years. However, the pose tracking for industrial metal objects remains a challenging task especially in the real world-environments, due to the reflection characteristic of metal objects. To address this issue, we propose a novel 6DoF pose tracking method based on active control points. The method uses image control points to generate edge feature for optimization actively instead of 6DoF pose-based rendering, and serve them as optimization variables. We also introduce an optimal control point regression method to improve robustness. The proposed tracking method performs effectively in both dataset evaluation and real world tasks, providing a viable solution for real-time tracking of industrial metal objects. Our source code is made publicly available at: https://github.com/tomatoma00/ACPTracking.

Ting-Yen Wang

This study introduces a novel framework for estimating industrial heterogeneity by integrating maximum entropy (ME) estimation of production functions with Zonotope-based measures. Traditional production function estimations often rely on restrictive parametric models, failing to capture firm behavior under uncertainty. This research addresses these limitations by applying Hang K. Ryu's ME method to estimate production functions using World Bank Enterprise Survey (WBES) data from Bangladesh, Colombia, Egypt, and India. The study normalizes entropy values to quantify heterogeneity and compares these measures with a Zonotope-based Gini index. Results demonstrate the ME method's superiority in capturing nuanced, functional heterogeneity often missed by traditional techniques. Furthermore, the study incorporates a "Tangent Against Input Axes" method to dynamically assess technical change within industries. By integrating information theory with production economics, this unified framework quantifies structural and functional differences across industries using firm-level data, advancing both methodological and empirical understanding of heterogeneity. A numerical simulation confirms the ME regression functions can approximate actual industrial heterogeneity. The research also highlights the superior ability of the ME method to provide a precise and economically meaningful measure of industry heterogeneity, particularly for longitudinal analyses.

Muhammad Aqeel, Shakiba Sharifi, Marco Cristani et al.

Anomaly detection is fundamental for ensuring quality control and operational efficiency in industrial environments, yet conventional approaches face significant challenges when training data contains mislabeled samples-a common occurrence in real-world scenarios. This paper presents RAD, a robust anomaly detection framework that integrates Normalizing Flows with Model-Agnostic Meta-Learning to address the critical challenge of label noise in industrial settings. Our approach employs a bi-level optimization strategy where meta-learning enables rapid adaptation to varying noise conditions, while uncertainty quantification guides adaptive L2 regularization to maintain model stability. The framework incorporates multiscale feature processing through pretrained feature extractors and leverages the precise likelihood estimation capabilities of Normalizing Flows for robust anomaly scoring. Comprehensive evaluation on MVTec-AD and KSDD2 datasets demonstrates superior performance, achieving I-AUROC scores of 95.4% and 94.6% respectively under clean conditions, while maintaining robust detection capabilities above 86.8% and 92.1% even when 50% of training samples are mislabeled. The results highlight RAD's exceptional resilience to noisy training conditions and its ability to detect subtle anomalies across diverse industrial scenarios, making it a practical solution for real-world anomaly detection applications where perfect data curation is challenging.

Dachel Martínez Asanza, Anuli Njoku, Snehal Baviskar et al.

Background: There is a need to develop comprehensive guidelines to encourage the promotion of oral hygiene care among older adults and to assist caregivers in this endeavor, taking into consideration the specific challenges that arise from aging, comorbidities and caregiving. Methods: This review was conducted by searching across relevant literature from meta-databases including Academic Google, PubMed, Scielo and Scopus for studies published from 2020 to 2024. PRISMA guidelines were followed. We included articles that described oral hygiene methods, caregiver education and mechanization status of older adults. Common themes, best practices, and gaps in current guidelines were tracked using extracted and analyzed data. Results: The review revealed multiple factors affecting the oral hygiene of older adults, with themes relating to physical impairment, cognitive dysfunction, and caregiver involvement. Highlighted between the approaches are individualized therapy for oral hygiene, caregiver education, and the use of technology to improve adherence to oral hygiene. Barriers like dental care access, underlying medical conditions complicating dental treatments, and cost considerations were identified. Conclusions: The findings emphasize the necessity of clear recommendations that can help caregivers and advance dental care for older adults.

Karenzha Iftinan, Angga Mardro Raharjo, Dini Agustina et al.

Introduction: Tinea unguium is a nail infection caused by dermatophytes, characterized by nail discoloration, thickening, and brittleness. Farmers, often exposed to prolonged wet conditions, are at higher risk of this infection. The use of appropriate footwear is recommended to mitigate this risk. However, inadequate personal protective equipment (PPE) usage makes farmers more susceptible to Tinea unguium. Research on Tinea unguium among farmers in Lojejer Village is limited, with differing result on the link between wearing footwear as PPE and the occurance of Tinea unguium. This study aimed to investigate the relationship between the use of footwear as PPE and the incidence of Tinea unguium among farmers in Lojejer Village. Methods: This study uses a cross-sectional observational design. It involved 98 respondents determined using Slovin’s formula. Data were collected through interviews and nail samples, which were subjected to fungal culture on sabouraud dextrose agar (SDA) medium and microscopic examination with lactophenol cotton blue staining. Fisher’s Exact tests were used for analysis. Results: Results revealed that seven farmers (7.14%) were affected by Tinea unguium, predominantly males (85.8%) aged 45–64 years (57.1%). Trichophyton rubrum was identified as the primary dermatophyte, with Aspergillus sp. as a contaminant. Statistical analysis showed no significant relationship between footwear usage, personal hygiene, or footwear hygiene and Tinea unguium (p-values > 0.05). Conclusion: The study found no significant association between footwear use, hpersonal hygiene, and footwear hygiene with Tinea unguium incidence among farmers in Lojejer Village

Silvia García-Méndez, Francisco de Arriba-Pérez, María del Carmen Somoza-López

Artificial Intelligence (AI) can potentially transform the industry, enhancing the production process and minimizing manual, repetitive tasks. Accordingly, the synergy between high-performance computing and powerful mathematical models enables the application of sophisticated data analysis procedures like Machine Learning. However, challenges exist regarding effective, efficient, and flexible processing to generate valuable knowledge. Consequently, this work comprehensively describes industrial challenges where AI can be exploited, focusing on the dairy industry. The conclusions presented can help researchers apply novel approaches for cattle monitoring and farmers by proposing advanced technological solutions to their needs.

Karthik Muthineni, Alexander Artemenko, Josep Vidal et al.

The precise and accurate indoor positioning using cellular communication technology remains to be a prerequisite for several industrial applications, including the emergence of a new topic of Integrated Sensing and Communication (ISAC). However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in a heavy multipath dominant industrial scenario challenge the wireless signal propagation, leading to abnormal estimation errors (outliers) in the signal measurements taken at the receiver. In this paper, we investigate the iterative positioning scheme that is robust to the outliers in the Time of Arrival (ToA) measurements. The Iteratively Reweighted Least Squares (IRLS) positioning scheme formulated on the Least Squares (LS) is implemented to reject the outlier measurements and reweight the available ToA samples based on their confidence. Our positioning scheme is validated under 5G frequency bands, including the C-band (3.7 GHz) and the mmWave-band (26.8 GHz) in a Ray-Tracing enabled industrial scenario with different emulation setups.

Nicolò Oreste Pinciroli Vago, Francesca Forbicini, Piero Fraternali

Non-neural Machine Learning (ML) and Deep Learning (DL) models are often used to predict system failures in the context of industrial maintenance. However, only a few researches jointly assess the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three data sets concerning the operation of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. The problem is formulated as a binary classification task that assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared using multivariate telemetry time series. The results indicate that, in the considered scenarios, the dimension of the prediction windows plays a crucial role and highlight the effectiveness of DL approaches at classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches at classifying similar and repetitive patterns preceding a failure.

Budi Aswin, Willia Novita Eka Rini, Fajrina Hidayati

Introduction: Ergonomic work hazards are potential hazards that can negatively affect the health of farmers. One of the ergonomic hazards that farmers often experience is fatigue. This study aims to analyze the effectiveness of innovative ergonomic models and the preparation of balanced calorie needs in preventing work fatigue in rice farmers. Methods: The type of research used is a randomized controlled trial (RCT) design, which is the most powerful design to evaluate the intervention used, namely the effectiveness of innovative ergonomic models and the preparation of balanced calorie needs in preventing occupational fatigue in rice farmers. The population in this study were all farmers in Pudak Village, Kumpeh Ulu Subdistrict, Muaro Jambi Regency, totaling 238 people. The number of research samples was 68 farmers has taken using simple random sampling technique. Data were analyzed to determine the effectiveness of innovative ergonomic models using the ANOVA test with (α = 0.05). Result: There was a difference in the effectiveness of innovative ergonomic models in preventing work fatigue between at least two groups of rice farmers. Conclusion: the provision of stretching and snacks coupled with rest periods is most effective in preventing occupational fatigue. As for occupational fatigue, the provision of stretching, snacks, and rest time and the provision of simple education on the hazards of work ergonomics are effective in preventing occupational fatigue in rice farmers.

Ulfiatul Azizah, Reny Indrayani, Ragil Ismi Hartanti

Introduction: Safety riding is a behavior to minimize the level of danger as well as safety and security in riding that accordance with laws and regulations system in our country. Safety riding is very important, especially for online motorcycle taxi drivers who have a high risk of having a traffic accident at work. Gojek is one of the largest online transportation companies in Indonesia. Method: This research was conducted on Gojek driver partners in three sub-districts of the Jember urban areas Sumbersari, Patrang and Kaliwates. A study that aims to analyze factors related to safety riding on the Gojek Rider community in the urban area of Jember Regency. This study is a quantitative study using an observational analytical research type with a cross-sectional research design with a sample of 75 drivers. Data collection used interview instrument adopted from previous research, observations and documentations. Analysis of the correlation data used the Chi-Square test. Result: This research the majority of Gojek drivers had 2 years of service (72%), good riding knowledge (68%), mobile phone usages usage on the road without pulling over (74,7%), moderate work fatigue (64%), roadworthy vehicles (82.7%) and unsafe riding (61.3%). Conclusion: There is no correlation between length of service and safety riding. There is a correlation between driving knowledge, work fatigue, cell phone use and vehicle factors.

Hassan Ali, Muhammad Shahbaz Khan, Maha Driss et al.

In the era of Industrial IoT (IIoT) and Industry 4.0, ensuring secure data transmission has become a critical concern. Among other data types, images are widely transmitted and utilized across various IIoT applications, ranging from sensor-generated visual data and real-time remote monitoring to quality control in production lines. The encryption of these images is essential for maintaining operational integrity, data confidentiality, and seamless integration with analytics platforms. This paper addresses these critical concerns by proposing a robust image encryption algorithm tailored for IIoT and Cyber-Physical Systems (CPS). The algorithm combines Rule-30 cellular automata with chaotic scrambling and substitution. The Rule 30 cellular automata serves as an efficient mechanism for generating pseudo-random sequences that enable fast encryption and decryption cycles suitable for real-time sensor data in industrial settings. Most importantly, it induces non-linearity in the encryption algorithm. Furthermore, to increase the chaotic range and keyspace of the algorithm, which is vital for security in distributed industrial networks, a hybrid chaotic map, i.e., logistic-sine map is utilized. Extensive security analysis has been carried out to validate the efficacy of the proposed algorithm. Results indicate that our algorithm achieves close-to-ideal values, with an entropy of 7.99 and a correlation of 0.002. This enhances the algorithm's resilience against potential cyber-attacks in the industrial domain.

Gerrit Bredeck, Jochen Dobner, Burkhard Stahlmecke et al.

Abstract Background Epidemiological studies have related desert dust events to increased respiratory morbidity and mortality. Although the Sahara is the largest source of desert dust, Saharan dust (SD) has been barely examined in toxicological studies. Here, we aimed to assess the NLRP3 inflammasome-caspase-1-pathway-dependent pro-inflammatory potency of SD in comparison to crystalline silica (DQ12 quartz) in an advanced air-liquid interface (ALI) co-culture model. Therefore, we exposed ALI co-cultures of alveolar epithelial A549 cells and macrophage-like differentiated THP-1 cells to 10, 21, and 31 µg/cm² SD and DQ12 for 24 h using a Vitrocell Cloud system. Additionally, we exposed ALI co-cultures containing caspase (CASP)1 −/− and NLRP3 −/− THP-1 cells to SD. Results Characterization of nebulized DQ12 and SD revealed that over 90% of agglomerates of both dusts were smaller than 2.5 μm. Characterization of the ALI co-culture model revealed that it produced surfactant protein C and that THP-1 cells remained viable at the ALI. Moreover, wild type, CASP1 −/−, and NLRP3 −/− THP-1 cells had comparable levels of the surface receptors cluster of differentiation 14 (CD14), toll-like receptor 2 (TLR2), and TLR4. Exposing ALI co-cultures to non-cytotoxic doses of DQ12 and SD did not induce oxidative stress marker gene expression. SD but not DQ12 upregulated gene expressions of interleukin 1 Beta (IL1B), IL6, and IL8 as well as releases of IL-1β, IL-6, IL-8, and tumor necrosis factor α (TNFα). Exposing wild type, CASP1 −/−, and NLRP3 −/− co-cultures to SD induced IL1B gene expression in all co-cultures whereas IL-1β release was only induced in wild type co-cultures. In CASP1 −/− and NLRP3 −/− co-cultures, IL-6, IL-8, and TNFα releases were also reduced. Conclusions Since surfactants can decrease the toxicity of poorly soluble particles, the higher potency of SD than DQ12 in this surfactant-producing ALI model emphasizes the importance of readily soluble SD components such as microbial compounds. The higher potency of SD than DQ12 also renders SD a potential alternative particulate positive control for studies addressing acute inflammatory effects. The high pro-inflammatory potency depending on NLRP3, CASP-1, and IL-1β suggests that SD causes acute lung injury which may explain desert dust event-related increased respiratory morbidity and mortality.

Amir Eshaghi Chaleshtori, Abdollah aghaie

In the Engineering discipline, predictive maintenance techniques play an essential role in improving system safety and reliability of industrial machines. Due to the adoption of crucial and emerging detection techniques and big data analytics tools, data fusion approaches are gaining popularity. This article thoroughly reviews the recent progress of data fusion techniques in predictive maintenance, focusing on their applications in machinery fault diagnosis. In this review, the primary objective is to classify existing literature and to report the latest research and directions to help researchers and professionals to acquire a clear understanding of the thematic area. This paper first summarizes fundamental data-fusion strategies for fault diagnosis. Then, a comprehensive investigation of the different levels of data fusion was conducted on fault diagnosis of industrial machines. In conclusion, a discussion of data fusion-based fault diagnosis challenges, opportunities, and future trends are presented.

Emmanouil Samanis, Joseph Gardiner, Awais Rashid

Asset scanning and discovery is the first and foremost step for organizations to understand what assets they have and what to protect. There is currently a plethora of free and commercial asset scanning tools specializing in identifying assets in industrial control systems (ICS). However, there is little information available on their comparative capabilities and how their respective features contrast. Nor is it clear to what depth of scanning these tools can reach and whether they are fit-for-purpose in a scaled industrial network architecture. We provide the first systematic feature comparison of free-to-use asset scanning tools on the basis of an ICS scanning taxonomy that we propose. Based on the taxonomy, we investigate scanning depths reached by the tools' features and validate our investigation through experimentation on Siemens, Schneider Electric, and Allen Bradley devices in a testbed environment.

Ashutosh Tewari, Antonio R. Paiva

Identifying and mitigating safety risks is paramount in a number of industries. In addition to guidelines and best practices, many industries already have safety management systems (SMSs) designed to monitor and reinforce good safety behaviors. The analytic capabilities to analyze the data acquired through such systems, however, are still lacking in terms of their ability to robustly quantify risks posed by various occupational hazards. Moreover, best practices and modern SMSs are unable to account for dynamically evolving environments/behavioral characteristics commonly found in many industrial settings. This article proposes a method to address these issues by enabling continuous and quantitative assessment of safety risks in a data-driven manner. The backbone of our method is an intuitive hierarchical probabilistic model that explains sparse and noisy safety data collected by a typical SMS. A fully Bayesian approach is developed to calibrate this model from safety data in an online fashion. Thereafter, the calibrated model holds necessary information that serves to characterize risk posed by different safety hazards. Additionally, the proposed model can be leveraged for automated decision making, for instance solving resource allocation problems -- targeted towards risk mitigation -- that are often encountered in resource-constrained industrial environments. The methodology is rigorously validated on a simulated test-bed and its scalability is demonstrated on real data from large maintenance projects at a petrochemical plant.

Zhenghan Wang

Although the current information revolution is still unfolding, the next industrial revolution is already rearing its head. A second quantum revolution based on quantum technology will power this new industrial revolution with quantum computers as its engines. The development of quantum computing will turn quantum theory into quantum technology, hence release the power of quantum phenomena, and exponentially accelerate the progress of science and technology. Building a large-scale quantum computing is at the juncture of science and engineering. Even if large-scale quantum computers become reality, they cannot make the conventional computers obsolete soon. Building a large-scale quantum computer is a daunting complex engineering problem to integrate ultra-low temperature with room temperature and micro-world with macro-world. We have built hundreds of physical qubits already but are still working on logical and topological qubits. Since physical qubits cannot tolerate errors, they cannot be used to perform long precise calculations to solve practically useful problems yet.