Hasil untuk "Industrial hygiene. Industrial welfare"

Gulsun Akdemir Evrendilek

Seafood processing environments represent some of the most demanding hygienic settings in the global food sector. High humidity, variable temperatures, and heavy organic residues promote the persistence of <i>Listeria monocytogenes</i>, <i>Vibrio</i> spp., and <i>Salmonella</i> spp., making sanitation both critical and inherently complex. This review synthesizes recent advances in hygienic design, sanitation technologies, and workforce safety as interconnected elements of a single “hygiene continuum.” Building upon Codex, FDA, and European hygiene frameworks (2020–2024), the review examines how engineering design, Sanitation Standard Operating Procedures (SSOPs) and Good Manufacturing Practices (GMPs) systems, and occupational hygiene jointly determine microbial control, sustainability, and workforce well-being. Particular focus is given to biofilm dynamics, emerging disinfection technologies, and automation through cleaning-in-place (CIP) and cleaning-out-of-place (COP) systems. Recent trends—including digital monitoring, eco-efficient cleaning, and human-centered facility design—are discussed as drivers of next-generation hygiene management. Collectively, these insights demonstrate that hygienic performance in seafood processing is not a fixed endpoint but a living system linking design, management, and human behavior toward safe, sustainable, and resilient seafood production.

Mark R. Limon, Shaira Vita Mae G. Adviento, Chariza Mae B. Basamot et al.

<b>Background:</b> Hygiene and food-safety training is a critical public health strategy for preventing contamination and promoting safe food-handling practices in community settings. This study evaluated the long-term effectiveness of In-person, Online, and Hybrid instructional modes in enhancing hygiene and food-safety competencies among trainees in Ilocos Norte, Philippines. <b>Methods:</b> Using a longitudinal quasi-experimental design, performance was measured at 12, 24, and 36 months across four domains: Personal Health & Hygiene, Food Hazards, Cleaning and Sanitation, and Good Manufacturing Practices. A total of 384 students met all inclusion criteria and completed the full series of evaluations. Descriptive and inferential statistical analyses were employed. <b>Results:</b> Competency scores increased significantly over time in all instructional modes (<i>p</i> < 0.001). Hybrid learners demonstrated the highest early longitudinal gains at 12 months (mean score, <i>M</i> = 20.88), compared with In-person (<i>M</i> = 10.28) and Online (<i>M</i> = 10.57). At 36 months, Online learners achieved the highest performance (<i>M</i> = 19.50), indicating stronger long-term retention. Effect size analysis using eta squared (η²) showed large effects for Cleaning and Sanitation (η² = 0.196), Good Manufacturing Practices (η² = 0.115), and overall performance (η² = 0.138). Standardized Mean Change (SMC) indicated substantial improvement across modes, with Hybrid showing the greatest early change (SMC = 41.76 at 12 months) and Online exhibiting the strongest long-term improvement (SMC = 38.80 at 36 months). Training Efficiency Index (TEI) identified In-person instruction as most efficient (TEI = 30.55), followed by Online (29.49) and Hybrid (19.56). Linear Mixed-Effects Regression confirmed significant main effects of Time (β = 4.82, <i>p</i> < 0.001) and Mode (β = 3.97, <i>p</i> < 0.001), as well as a significant Time × Mode interaction (β = −1.42, <i>p</i> < 0.01). <b>Conclusions:</b> The findings indicate that Hybrid instruction supports rapid early competency gains, while Online instruction yields superior long-term mastery of hygiene and food-safety competencies. These results provide evidence-based guidance for optimizing hygiene training programs in community and public health contexts.

Leonard Kleinberger, Michael Gundall, Hans D. Schotten

Industrial 5G deployments using Time Division Duplex (TDD) networks face a critical challenge: existing schedulers rely on static configuration of Uplink (UL) to Downlink (DL) resource ratios, failing to adapt to dynamic asymmetric traffic demands. This limitation is particularly problematic in Industry 4.0 scenarios where traffic patterns exhibit significant asymmetry between directions and heterogeneous Quality of Service (QoS) requirements. We present FLEX, a novel QoS-aware scheduler that dynamically adjusts the UL/DL ratio in flexible TDD slots while respecting diverse QoS requirements. FLEX introduces DL buffer state estimation to prevent starvation of high-priority DL traffic, exploiting the deterministic nature of industrial traffic patterns for accurate predictions. Through extensive simulations of industrial scenarios using 5G LENA and ns-3, we demonstrate that FLEX achieves similar throughput compared to established scheduling while correctly enforcing QoS priorities in both traffic directions. For deterministic traffic patterns, FLEX maintains minimal latency overhead (less than 1 slot duration), making it particularly suitable for industrial automation applications.

Ekky Imanjaya, Cynthia MF Pangabean

Introduction: As stipulated in the Indonesian Labor Law, every worker is entitled to work safety and health protection, including the film industry. This research focuses on two articles in the Health, Safety, and Environment (HSE) regulations and the Law of Film Year 2009. However, the Indonesian film industry has not officially implemented these laws. There have been several cases of HSE, which caused death or fatal injuries to film workers, without applying the regulations. Other HSE issues include the cases where only a few film producers gave insurance to the film workers, applied proper risk assessment, or provided first aid kits. The paper will overview HSE in the Indonesian film industry by mapping out the problems and potential solutions. Methods: By having in-depth interviews with key persons in the field, such as the workers and film producers, this research aims to map out such issues and answering why and how the laws on work health and safety are not implemented in the Indonesian film industry. Result: This research has resulted in maps of problems and recommendations for policymakers, film workers, and related institutes concerning HSE and the rights of film workers, including of the lack awareness of film workers on HSE and HSE-related curriculum in film education, as well as the need for stronger film associations and union. Conclusion: HSE in the Indonesian film industry must be evaluated to be more effective. Some factors to be reviewed include law enforcement in contracts, health insurance, the collaboration of various parties, HSE-related knowledge in the curriculum in Indonesian film education, and the application of Work Competency Standards (SKKNI) to all film professional associations.

Marcela Gonçalves dos Santos, Sylvain Hallé, Fábio Petrillo

Industrial robotic systems (IRS) are increasingly deployed in diverse environments, where failures can result in severe accidents and costly downtime. Ensuring the reliability of the software controlling these systems is therefore critical. Mutation testing, a technique widely used in software engineering, evaluates the effectiveness of test suites by introducing small faults, or mutants, into the code. However, traditional mutation operators are poorly suited to robotic programs, which involve message-based commands and interactions with the physical world. This paper explores the adaptation of mutation testing to IRS by defining domain-specific mutation operators that capture the semantics of robot actions and sensor readings. We propose a methodology for generating meaningful mutants at the level of high-level read and write operations, including movement, gripper actions, and sensor noise injection. An empirical study on a pick-and-place scenario demonstrates that our approach produces more informative mutants and reduces the number of invalid or equivalent cases compared to conventional operators. Results highlight the potential of mutation testing to enhance test suite quality and contribute to safer, more reliable industrial robotic systems.

Aydin Homay

Design is a fundamental aspect of engineering, enabling the creation of products, systems, and organizations to meet societal and/or business needs. However, the absence of a scientific foundation in design often results in subjective decision-making, reducing both efficiency and innovation. This challenge is particularly evident in the software industry and, by extension, in the domain of industrial control and automation systems (iCAS). In this study, first we review the existing design definitions within the software industry, challenge prevailing misconceptions about design, review design definition in the field of design theory and address key questions such as: When does design begin? How can design be defined scientifically? What constitutes good design? and the difference between design and design language by relying on advancements in the field of design theory. We also evaluate the distinction between ad-hoc and systematic design approaches, and present arguments on how to balance complementary operational concerns while resolving conflicting evolutionary concerns.

Pablo Otálora, Sigurd Skogestad, José Luis Guzmán et al.

The industrial production of microalgae is an important and sustainable process, but its actual competitiveness is closely related to its optimization. The biological nature of the process hinders this task, mainly due to the high nonlinearity of the process along with its changing nature, features that make its modeling, control and optimization remarkably challenging. This paper presents an economic optimization framework aiming to enhance the operation of such systems. An Economic Model Predictive Controller is proposed, centralizing the decision making and achieving the theoretical optimal operation. Different scenarios with changing climate conditions are presented, and a comparison with the typical, non-optimized industrial process operation is established. The obtained results achieve economic optimization and dynamic stability of the process, while providing some insight into the priorities during process operation at industrial level, and justifying the use of optimal controllers over traditional operation.

Theofanis P. Raptis, Andrea Passarella, Marco Conti

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Xuanming Zhang

Industrial carbon emissions are a major driver of climate change, yet modeling these emissions is challenging due to multicollinearity among factors and complex interdependencies across sectors and time. We propose a novel graph-based deep learning framework DGL to analyze and forecast industrial CO_2 emissions, addressing high feature correlation and capturing industrial-temporal interdependencies. Unlike traditional regression or clustering methods, our approach leverages a Graph Neural Network (GNN) with attention mechanisms to model relationships between industries (or regions) and a temporal transformer to learn long-range patterns. We evaluate our framework on public global industry emissions dataset derived from EDGAR v8.0, spanning multiple countries and sectors. The proposed model achieves superior predictive performance - reducing error by over 15% compared to baseline deep models - while maintaining interpretability via attention weights and causal analysis. We believe that we are the first Graph-Temporal architecture that resolves multicollinearity by structurally encoding feature relationships, along with integration of causal inference to identify true drivers of emissions, improving transparency and fairness. We also stand a demonstration of policy relevance, showing how model insights can guide sector-specific decarbonization strategies aligned with sustainable development goals. Based on the above, we show high-emission "hotspots" and suggest equitable intervention plans, illustrating the potential of state-of-the-art AI graph learning to advance climate action, offering a powerful tool for policymakers and industry stakeholders to achieve carbon reduction targets.

Mikhail Bychkov, Matey Yordanov, Andrei Kuchma

This paper introduces AURA, a novel hybrid spatiotemporal-chromatic framework designed for robust, real-time detection and classification of industrial smoke emissions. The framework addresses critical limitations of current monitoring systems, which often lack the specificity to distinguish smoke types and struggle with environmental variability. AURA leverages both the dynamic movement patterns and the distinct color characteristics of industrial smoke to provide enhanced accuracy and reduced false positives. This framework aims to significantly improve environmental compliance, operational safety, and public health outcomes by enabling precise, automated monitoring of industrial emissions.

Zongyun Zhang, Jiacheng Ruan, Xian Gao et al.

Industrial Anomaly Detection (IAD) is critical to ensure product quality during manufacturing. Although existing zero-shot defect segmentation and detection methods have shown effectiveness, they cannot provide detailed descriptions of the defects. Furthermore, the application of large multi-modal models in IAD remains in its infancy, facing challenges in balancing question-answering (QA) performance and mask-based grounding capabilities, often owing to overfitting during the fine-tuning process. To address these challenges, we propose a novel approach that introduces a dedicated multi-modal defect localization module to decouple the dialog functionality from the core feature extraction. This decoupling is achieved through independent optimization objectives and tailored learning strategies. Additionally, we contribute to the first multi-modal industrial anomaly detection training dataset, named Defect Detection Question Answering (DDQA), encompassing a wide range of defect types and industrial scenarios. Unlike conventional datasets that rely on GPT-generated data, DDQA ensures authenticity and reliability and offers a robust foundation for model training. Experimental results demonstrate that our proposed method, Explainable Industrial Anomaly Detection Assistant (EIAD), achieves outstanding performance in defect detection and localization tasks. It not only significantly enhances accuracy but also improves interpretability. These advancements highlight the potential of EIAD for practical applications in industrial settings.

Henna Jäntti, Steffi Jonk, Mireia Gómez Budia et al.

Abstract Background Air pollution is recognized as an emerging environmental risk factor for neurological diseases. Large-scale epidemiological studies associate traffic-related particulate matter (PM) with impaired cognitive functions and increased incidence of neurodegenerative diseases such as Alzheimer’s disease. Inhaled components of PM may directly invade the brain via the olfactory route, or act through peripheral system responses resulting in inflammation and oxidative stress in the brain. Microglia are the immune cells of the brain implicated in the progression of neurodegenerative diseases. However, it remains unknown how PM affects live human microglia. Results Here we show that two different PMs derived from exhausts of cars running on EN590 diesel or compressed natural gas (CNG) alter the function of human microglia-like cells in vitro. We exposed human induced pluripotent stem cell (iPSC)-derived microglia-like cells (iMGLs) to traffic related PMs and explored their functional responses. Lower concentrations of PMs ranging between 10 and 100 µg ml−1 increased microglial survival whereas higher concentrations became toxic over time. Both tested pollutants impaired microglial phagocytosis and increased secretion of a few proinflammatory cytokines with distinct patterns, compared to lipopolysaccharide induced responses. iMGLs showed pollutant dependent responses to production of reactive oxygen species (ROS) with CNG inducing and EN590 reducing ROS production. Conclusions Our study indicates that traffic-related air pollutants alter the function of human microglia and warrant further studies to determine whether these changes contribute to adverse effects in the brain and on cognition over time. This study demonstrates human iPSC-microglia as a valuable tool to study functional microglial responses to environmental agents.

Jan Kortstegge, Volker Krömker

The aim of this study was to determine the prevalence of <i>Streptococcus</i> (<i>Sc.</i>) <i>agalactiae</i>, <i>Prototheca</i> spp., <i>Staphylococcus</i> (<i>S</i>.) <i>aureus</i>, and especially methicillin-resistant <i>S. aureus</i> as well as <i>Myco-plasmopsis</i> (<i>M</i>.) spp. and <i>M. bovis</i> in bulk tank milk (BTM) on dairy farms in Lower Saxony, Germany. BTM samples were collected in January 2023 from 208 selected dairy farms. The samples were quantitatively culturally analyzed for <i>S. aureus</i> and <i>Prototheca</i> spp. Presumptive <i>S. aureus</i> colonies were further confirmed by MALDI-TOF. Presumptive <i>Prototheca</i> spp. colonies were confirmed by light microscopy. <i>Sc. agalactiae</i> and <i>Mycoplasmopsis</i> spp. were detected by real-time polymerase chain reaction (rtPCR). <i>Sc. agalactiae</i> was detected in two herds (1% (Confidence Interval 95% (CI) 0.3–3.4)). <i>S. aureus</i> was confirmed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) in 38 herds (18.3% (CI 13.6–24.1)), assuming a threshold of >10 cfu/mL milk. A total of 154 isolates identified as <i>S. aureus</i> by MALDI-TOF were transferred to agar with added oxacillin for resistance testing, of which 19 isolates (12.3% (CI 8–18.5)) showed growth. The 19 isolates came from eight different farms (3.8% (2–7.4)). <i>Prototheca</i> spp. were identified in 13 herds (6.3% (CI 3.7–10.4)). <i>Mycoplasmopsis</i> spp. were detected by PCR in 18 herds (8.7% (CI 5.5–13.3)). Of these, <i>M. bovis</i> was present in three herds (1.4% (0.5–4.2)). The herd prevalence of <i>Sc. agalactiae</i> in BTM appears to be at low levels in the sampled area. The prevalence of <i>Mycoplasmopsis</i> spp. in the herds was higher than expected compared to previous studies. It is interesting to note that the percentage of <i>M. bovis</i> in the total <i>Mycoplasmopsis</i> spp. was only 16.7%.

Da Xu, Danqing Zhang, Guangyu Yang et al.

Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys). Despite growing research efforts at the intersection of these fields, the integration of GAI into industrial Recsys remains in its infancy, largely due to the intricate nature of modern industrial Recsys infrastructure, operations, and product sophistication. Drawing upon our experiences in successfully integrating GAI into several major social and e-commerce platforms, this survey aims to comprehensively examine the underlying system and AI foundations, solution frameworks, connections to key research advancements, as well as summarize the practical insights and challenges encountered in the endeavor to integrate GAI into industrial Recsys. As pioneering work in this domain, we hope outline the representative developments of relevant fields, shed lights on practical GAI adoptions in the industry, and motivate future research.

Nico Baumgart, Markus Lange-Hegermann, Mike Mücke

In industrial manufacturing, deploying deep learning models for visual inspection is mostly hindered by the high and often intractable cost of collecting and annotating large-scale training datasets. While image synthesis from 3D CAD models is a common solution, the individual techniques of domain and rendering randomization to create rich synthetic training datasets have been well studied mainly in simple domains. Hence, their effectiveness on complex industrial tasks with densely arranged and similar objects remains unclear. In this paper, we investigate the sim-to-real generalization performance of standard object detectors on the complex industrial application of terminal strip object detection, carefully combining randomization and domain knowledge. We describe step-by-step the creation of our image synthesis pipeline that achieves high realism with minimal implementation effort and explain how this approach could be transferred to other industrial settings. Moreover, we created a dataset comprising 30.000 synthetic images and 300 manually annotated real images of terminal strips, which is publicly available for reference and future research. To provide a baseline as a lower bound of the expectable performance in these challenging industrial parts detection tasks, we show the sim-to-real generalization performance of standard object detectors on our dataset based on a fully synthetic training. While all considered models behave similarly, the transformer-based DINO model achieves the best score with 98.40 % mean average precision on the real test set, demonstrating that our pipeline enables high quality detections in complex industrial environments from existing CAD data and with a manageable image synthesis effort.

O. Korotenko, E. Filimonov, I. Martynov

Introduction. In the coal industry, 78.7% of employees work in places with harmful working conditions, which play a leading role in the development of not only professional, but also industrial diseases, the leading of which are diseases of the cardiovascular system. The identification of preclinical systolic dysfunction of the left ventricle and the assessment of the role of traditional and professionally determined risk factors for the development of systolic dysfunction of the left ventricle in workers of the main professions of the coal industry is of scientific interest. The study aims to assess the risk factors for the development of heart failure with a preserved left ventricular ejection fraction in workers of the main professions of the coal industry. Materials and methods. The study included 101 employees of the main professions of the coal industry and 80 employees of the paramilitary mine rescue unit. The subjects had no somatic pathology, which could lead to structural and functional changes of the heart. The scientists performed echocardiographic and ultrasound examinations of the main arteries according to standard methods and assessed the generally accepted risk factors for the development of cardiovascular pathology (smoking, abdominal obesity, body mass index, total cholesterol, triglycerides, high and low density lipoprotein cholesterol, glycated hemoglobin). Results. The researchers revealed systolic dysfunction of the left ventricle significantly in miners more often in the form of a decrease in longitudinal deformation (27.7% of miners versus 7.6% of paramilitary rescuers, p=0.0005), while its average value is also significantly lower in miners and has a value below the established norm (–17.2±0.044 and –19.3±0.03, p=0.0005). The authors found no significant differences in the frequency of commonly accepted risk factors: abdominal obesity, smoking, atherosclerosis of the main arteries, dyslipidemia and the level of glycated hemoglobin in miners and workers of the mine rescue unit, as well as in miners, depending on the index of longitudinal deformation of the left ventricle. Conclusion. The obtained results indicate the need for a prenosological diagnosis of systolic dysfunction of the left ventricle, studying it in dynamics and simultaneously expanding the search for risk factors, which will allow early prevention of this complication in workers in harmful working conditions. Ethics. The study was conducted in compliance with the standards of the Bioethical Committee of the Research Institute of Complex Problems of Hygiene and Occupational Diseases, established in accordance with the Helsinki Declaration of the World Association "Ethical Principles of Scientific Medical Research with Human Participation" as amended in 2013 and the "Rules of Good Clinical Practice" approved by Order of the Ministry of Health of the Russian Federation dated 01.04.2016 No. 200n. The subjects signed an informed consent to participate in the study.

Alegna Cruz Ruiz, Marta Martínez Rodríguez, Yoanna María Hernández Gil et al.

Introducción: Las organizaciones laborales desde una perspectiva positiva, ponderan el bienestar de sus trabajadores para alcanzar el éxito. Contar con colaboradores y grupos de trabajo con capacidades de resiliencia y comprometidos con su organización, debe ser la brújula que guíe a las organizaciones que no solo busquen sobrevivir, sino hacer de ese espacio un lugar de crecimiento personal, grupal y organizacional. Compromiso y resiliencia son dos temas que han ido ganando interés en el ámbito laboral, en particular, desde la Psicología positiva. Objetivo: Caracterizar las variables resiliencia organizacional y compromiso organizacional en una instalación hotelera. Métodos: Metodología cuantitativa, diseño no experimental de tipo transversal con un alcance exploratorio y descriptivo. Se aplicó el cuestionario de compromiso organizacional de Schaufeli (2003) y el cuestionario de resiliencia organizacional Salanova y otros (2012). Resultados: Las puntuaciones más altas en cuanto al compromiso organizacional se obtuvieron en las dimensiones vigor y dedicación (4,75 y 4,70, respectivamente); la más baja también se obtuvo en la dimensión absorción (4,52). En cuanto a la resiliencia organizacional, los resultados obtenidos permiten considerarla como una organización resiliente (medias entre 4 y 5). Conclusiones: En cuanto al compromiso organizacional, este se caracteriza por el vigor y la dedicación. Se obtuvo un nivel alto de resiliencia organizacional; sin embargo, algunos indicadores como la ausencia de solvencia económica suficiente para enfrentar dificultades en la organización y la participación de los trabajadores en la toma de decisiones organizacionales, pudieran menoscabar la resiliencia organizacional en el hotel Introduction: Labor organizations, from a positive perspective, weigh the well-being of their workers to achieve success. Having collaborators and work groups, with resilience capabilities and committed to their organization, should be the compass that guides organizations that not only seek to survive, but make that space a place of personal, group and organizational growth. Commitment and resilience are two topics that have been gaining interest in the workplace, in particular, from positive psychology. Objective: To characterize the variables organizational resilience and organizational commitment in a hotel facility. Methods: A quantitative methodology and a non-experimental cross-sectional design were applied, with an exploratory and descriptive scope. The organizational commitment questionnaire of Schaufeli (2003) and the organizational resilience questionnaire of Salanova et al. (2012) were applied. Results: The highest scores regarding organizational commitment were obtained in the energy and dedication dimensions (4.75 and 4.70, respectively); the lowest was also obtained in the absorption dimension (4.52). Regarding organizational resilience, the results obtained allow it to be considered as a resilient organization (averages between 4 and 5). Conclusions: Regarding organizational commitment, it is characterized by energy and dedication. A high level of organizational resilience was obtained. However, some indicators such as the lack of sufficient economic solvency to face difficulties in the organization and the participation of workers in organizational decision-making, could undermine organizational resilience in the hotel

Maximilian Pöschl, David Jungwirth, Daniela Haluza

The broad variety of measures that governments worldwide took against the COVID-19 pandemic led to restrictions in our everyday life, including the practice of sports such as rowing. This study aimed to examine changes in the daily life of rowers and their rowing engagement. We distributed an online questionnaire in German among rowers in 2021. In total, 234 (48.7% females, mean age 45.01 years, SD 16.94) participants met the inclusion criteria. We found that the amount of time spent rowing was significantly lower during the COVID-19 crisis. Additionally, we detected a notable shift in the rowing landscape, with a marked increase in home-based training and a complete cessation of rowing activities. Moreover, the life satisfaction of both female and male rowers witnessed a significant decline during the pandemic when compared to before. The present findings showed that the pandemic led to far-reaching changes in sports activities among rowers. Most rowers had to deal with negative effects not only on their rowing engagement, but also on life satisfaction. In view of future pandemics, it becomes crucial to prioritize and ensure the continuity of active sports engagement, including that of rowers, in a safe and secure manner.

Wolfram M. Brück, Alain Savary, Martine Baudin et al.

Good hand hygiene has proven to be essential in reducing the uncontrolled spread of human pathogens. Cold atmospheric plasma (CAP) may provide an alternative to disinfecting hands with ethanol-based handrubs when handwashing facilities are unavailable. CAP can be safely applied to the skin if the energy is well controlled. In this study, radio frequency (RF) and direct current (DC) plasma sources were built with a pin-to-mesh electrodes configuration inside a fused silica tube with a 5 mm inner diameter. Microbiological assays based on EN 13697:2015+A1:2019 using <i>Escherichia coli</i> DSM 682 or <i>Staphylococcus epidermidis</i> DSM 20044 were used to examine the antimicrobial effect of various plasma conditions. Metal and silicone disks that model skin were used as inoculation matrices. The prototype air RF CAP achieved significant disinfection in the MHz range on stainless steel and silicone substrates. This is equivalent to half the performance of direct current CAP, which is only effective on conductive substrates. Using only electricity and air CAP could, with further optimization to increase its efficacy, replace or complement current hand disinfection methods, and mitigate the economic burden of public health crises in the future.

Soumyakanti Pan, Darpan Das, Gurumurthy Ramachandran et al.

A series of experiments in stationary and moving passenger rail cars were conducted to measure removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols, and the air changes per hour provided by existing and modified air handling systems. Such methods for exposure assessments are customarily based on mechanistic models derived from physical laws of particle movement that are deterministic and do not account for measurement errors inherent in data collection. The resulting analysis compromises on reliably learning about mechanistic factors such as ventilation rates, aerosol generation rates and filtration efficiencies from field measurements. This manuscript develops a Bayesian state space modeling framework that synthesizes information from the mechanistic system as well as the field data. We derive a stochastic model from finite difference approximations of differential equations explaining particle concentrations. Our inferential framework trains the mechanistic system using the field measurements from the chamber experiments and delivers reliable estimates of the underlying physical process with fully model-based uncertainty quantification. Our application falls within the realm of Bayesian "melding" of mechanistic and statistical models and is of significant relevance to industrial hygienists and public health researchers working on assessment of exposure to viral aerosols in rail car fleets.