Hasil untuk "Industrial medicine. Industrial hygiene"

Kasra Ekrad, Bjarne Johansson, Inés Alvarez Vadillo et al.

Interoperability across industrial automation systems is a cornerstone of Industry 4.0. To address this need, the OPC Unified Architecture (OPC UA) Publish-Subscribe (PubSub) model offers a promising mechanism for enabling efficient communication among heterogeneous devices. PubSub facilitates resource sharing and communication configuration between devices, but it lacks clear guidelines for mapping diverse industrial traffic types to appropriate PubSub configurations. This gap can lead to misconfigurations that degrade network performance and compromise real-time requirements. This paper proposes a set of guidelines for mapping industrial traffic types, based on their timing and quality-of-service specifications, to OPC UA PubSub configurations. The goal is to ensure predictable communication and support real-time performance in industrial networks. The proposed guidelines are evaluated through an industrial use case that demonstrates the impact of incorrect configuration on latency and throughput. The results underline the importance of traffic-aware PubSub configuration for achieving interoperability in Industry 4.0 systems.

Guillaume Barbalat, Ariane Guilbert, Lucie Adelaïde et al.

Abstract Background A number of negative developmental outcomes in response to extreme temperature have been documented. Yet, to our knowledge, environmental research has left the question of the effect of temperature on human neurodevelopment largely unexplored. Here, we aimed to investigate the effect of ambient temperature on linguistic development at the age of 2 years-old. Methods We used data from the prospective national French birth cohort ELFE (N = 12,163) and highly-resolved exposure models with daily temporal resolution and 200 m to 1 km spatial resolution. We investigated the effect of weekly averages of overall, daytime and night-time temperature in the prenatal (first 30 weeks of gestation) and postnatal (91 weeks after birth) period on vocabulary production scores from the MacArthur-Bates Communicative Development Inventories (MB-CDI) at 2 years-old. Exposure-response and lag-response relationships were modeled with confounder-adjusted distributed lag non-linear models. Results Scores at the MB-CDI decreased by 3.2% (relative risk (RR) 0.968, 95% confidence interval (CI): 0.939–0.998) following exposure to severe night-time heat of 15.6 °C (95th percentile) vs. 8.3 °C (median) throughout gestational weeks 14 to 19. In the postnatal period, scores at the MB-CDI decreased by 14.8% (RR 0.852; 95% CI: [0.756–0.96]) for severe overall heat of 21.9 °C (95th percentile) vs. 11.5 °C (median) throughout weeks 1 to 28. Consistent results were found for daytime and night-time heat. We observed positive effects of overall and night-time heat in the first few weeks of pregnancy. Night-time cold in the pre-natal period also resulted in improved scores at the MB-CDI. Adjusting our models for air pollutants (PM2.5, PM10 and NO2) tended to confirm these observations. Finally, there were no significant differences in temperature effects between boys and girls. Conclusion In this large cohort study, we showed a negative impact of hot temperatures during pregnancy and after birth on language acquisition. Positive associations observed in the first few weeks of pregnancy are likely the results of methodological artifacts. Positive associations with night-time cold during the prenatal period are likely truly protective, as colder temperatures may encourage staying indoors at a comfortable temperature. Policymakers should consider neurodevelopment impairments as a deleterious effect of climate change.

Yinuo Chen, Yiyang Nan, Lang Xu et al.

Abstract Background Recent studies emphasize the significance of copper dyshomeostasis in neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, thereby highlighting the role of copper in neurotoxicity. Cuproptosis, a novel mechanism of copper-dependent cell death, remains underexplored, particularly concerning environmental pollutants like polystyrene nanoplastics (PS-NPs). While PS-NPs are recognized for inducing neurotoxicity through various forms of cell death, including apoptosis and ferroptosis, their potential to trigger neuronal cuproptosis has not yet been investigated. This study aims to determine whether exposure to PS-NPs induces neurotoxicity via cuproptosis and to explore the preliminary molecular mechanisms involved, thereby addressing this significant knowledge gap. Methods Seven-week-old male C57BL/6 mice were exposed to PS-NPs at dose of 12.5 mg/kg, and were co-treated with the antioxidant N-acetylcysteine (NAC). Complementary in vitro experiments were conducted using SH-SY5Y neuronal cells exposed to PS-NPs at a concentration of 0.75 mg/mL, with interventions that included the copper chelator tetrathiomolybdate (TTM), NAC, and the MAPK inhibitor PD98059. Results Exposure to PS-NPs significantly increased cerebral copper accumulation (P < 0.05) and induced cuproptosis, characterized by lipid-acylated DLAT oligomerization, dysregulation of cuproptosis regulators (FDX1, LIAS, HSP70), and mitochondrial damage. In murine models, PS-NPs elicited neurotoxicity, as evidenced by neuronal loss, decreased Nissl body density, impaired synaptic plasticity, and suppressed oxidative stress markers (GSH, SOD, Nrf2), alongside activation of the ERK-MAPK pathway, ultimately resulting in deficits in learning and memory. Treatment with NAC alleviated these adverse effects. In SH-SY5Y cells, exposure to PS-NPs resulted in reduced cell viability (p < 0.01), an effect that was mitigated by TTM. Furthermore, NAC and PD98059 were found to reverse elevated copper levels, cuproptosis markers, and mitochondrial anomalies (p < 0.05). Conclusion This study presents preliminary evidence indicating that PS-NPs may induce neuronal cuproptosis, potentially through the oxidative stress-mediated activation of the ERK-MAPK pathway, which contributes to cognitive dysfunction in mice. These findings provide insights into the potential mechanisms underlying PS-NPs neurotoxicity and highlight possible therapeutic targets, such as copper chelation or MAPK inhibition, for mitigating the neurological risks associated with nanoplastic exposure, pending further validation in human-relevant models.

Amaratou Mahamadou Saley, Thierry Moyaux, Aïcha Sekhari et al.

The convergence of the Internet of Things (IoT) and Industry 4.0 has significantly enhanced data-driven methodologies within the nuclear industry, notably enhancing safety and economic efficiency. This advancement challenges the precise prediction of future maintenance needs for assets, which is crucial for reducing downtime and operational costs. However, the effectiveness of data-driven methodologies in the nuclear sector requires extensive domain knowledge due to the complexity of the systems involved. Thus, this paper proposes a novel predictive maintenance methodology that combines data-driven techniques with domain knowledge from a nuclear equipment. The methodological originality of this paper is located on two levels: highlighting the limitations of purely data-driven approaches and demonstrating the importance of knowledge in enhancing the performance of the predictive models. The applicative novelty of this work lies in its use within a domain such as a nuclear industry, which is highly restricted and ultrasensitive due to security, economic and environmental concerns. A detailed real-world case study which compares the current state of equipment monitoring with two scenarios, demonstrate that the methodology significantly outperforms purely data-driven methods in failure prediction. While purely data-driven methods achieve only a modest performance with a prediction horizon limited to 3 h and a F1 score of 56.36%, the hybrid approach increases the prediction horizon to 24 h and achieves a higher F1 score of 93.12%.

Qiyu Chen, Huiyuan Luo, Haiming Yao et al.

Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS. The source code and the newly constructed dataset are publicly available at https://github.com/cqylunlun/CRAS.

Aylin Bayındır-Gümüş, Ebru Öztürk, Mihály Soós

Background. People live in a technological world, where social media is used very commonly. Social media has effects on eating behaviors, as in other aspects. For this reason, it is important to measure social media effect. Objective. This study aimed to adapt the Scale of Effects of Social Media on Eating Behaviour (SESMEB) that examines the effect of social media on eating behavior in Hungarian university students. Material and methods. The SESMEB was translated into the target language by taking various stages. The online questionnaire including general information, social media use, and the eighteen-item SESMEB was used to collect data. The scale was administered to the study group consisting of 213 Hungarian university students, and data from 203 of them were analyzed. Confirmatory factor analyses were performed to test construct validity, and the Cronbach alpha coefficient was calculated for the reliability of the scale in Hungarian. Results. Total correlation value was higher than 0.50 for all items of the scale. The fit indices were at an acceptable level or had a perfect fit. The t-values were significant at the level of 0.1 and ranged between 2.927 and 5.706. The Spearman–Brown coefficient was calculated at 0.894. The reliability coefficient of the scale was calculated to be 0.866. SESMEB scores were different according to spending time daily, sharing content, and using filters or Photoshop on social media (p < 0.05). Conclusions. Higher than 0.80 Cronbach’s alpha coefficient and other results show that Hungarian SESMEB is a valid and reliable tool. Therefore, Hungarian SESMEB will be useful for further studies to determine the impact of social media on eating behaviors.

Lijun Cai, Tingting Lin, Chufen Zhuang et al.

ObjectiveThe present study intends to observe the cleaning effect of different detection methods in cleaning silicone tubes used in phacoemulsification.MethodsA total of 100 silicone tubes were selected randomly after surgery. The silicone tubes were retained for ≤2 hours after surgery, and then washed with a high-pressure water gun at a flow rate of 12~14 ml/s. Adenosine Triphosphate (ATP) detection and quantitation of residual protein were performed on the samples before cleaning and after washing for 30 s, 40 s, and 50 s, respectively, including the sample surface and the water after cleaning.ResultsAccording to the results before and after cleaning the silicone tube, there are significant differences in three methods of quantitation of residual protein, ATP detection in water sample, and ATP detection in sample surface (c2=8.6, P<0.05), while having no difference between the three methods after washing for 30 s, 40 s and 50 s, respectively (c2=4.918 and 5.571, P>0.05). A comparison of the means of ATP detection in water samples showed significant differences between rinses 30 s/40 s and 30 s/50 s. (Z=-7.45 and -0.08, P<0.05); pairwise contrast of ATP detection in sample surface for rinsing 30 s/40 s, 40 s/50 s, and 30 s/50 s showed significant differences (Z=3.64, 14.92, and 25.86, P<0.05). The quantitation of residual protein in silicone tubes showed pass rates of 84%, 100%, and 100% for 30 s, 40 s, and 50 s, respectively.ConclusionQuantitation of residual protein, ATP detection in water sample, and ATP detection in sample surface are available for monitoring the cleaning quality of silicone tube. The tube should be cleaned at a 12~14 ml/s flow rate and a washing time of ≥50 s.

Madona Tito-Betancur, Renzo Felipe Carranza Esteban, Calixto Tapullima-Mori et al.

Introducción: La salud financiera, determinada en buena parte por el salario, está estrechamente relacionada a la salud global del individuo y su familia. Por ello se tuvo como objetivo evaluar la producción científica sobre salud financiera en la base de datos Scopus: periodo 2011 - 2022. Método: Scoping review en la que se analizaron manuscritos publicados en revistas indexadas en la base de datos Scopus entre los años 2011 - 2022. Para la búsqueda se utilizó descriptores tales como financial obligations, financial inclusion, family economy, financial education, financial literacy, financial wellness y financial stress. Se realizó una síntesis narrativa. Resultados: Se incluyeron 6 940 manuscritos, de los cuales el 82,0% eran artículos originales. Se observó un crecimiento constante del número de artículos a lo largo del periodo de estudio, especialmente a partir de 2016, con un incremento del 860% en 2022 (n = 1429) respecto a 2011 (n=165). Estados Unidos fue el país con mayor producción científica. Las revistas con mayor número de publicaciones fueron Sustainability (Suiza) y el Journal of Financial Counseling and Planning (EEUU). Entre los descriptores de mayor impacto se encuentran la inclusión financiera a través del uso de la tecnología, estrés financiero, educación financiera y salud financiera. Conclusiones: La investigación sobre salud financiera ha tenido un aumento significativo. El nuevo conocimiento sobre el tema es impulsado por autores e instituciones de Estados Unidos en su mayoría, y finalmente, se evidencian tendencias de estudio relacionadas a la inclusión y educación financiera.

Eric Guiffo Kaigom

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.

Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera et al.

New technologies such as Machine Learning (ML) gave great potential for evaluating industry workflows and automatically generating key performance indicators (KPIs). However, despite established standards for measuring the efficiency of industrial machinery, there is no precise equivalent for workers' productivity, which would be highly desirable given the lack of a skilled workforce for the next generation of industry workflows. Therefore, an ML solution combining data from manufacturing processes and workers' performance for that goal is required. Additionally, in recent times intense effort has been devoted to explainable ML approaches that can automatically explain their decisions to a human operator, thus increasing their trustworthiness. We propose to apply explainable ML solutions to differentiate between expert and inexpert workers in industrial workflows, which we validate at a quality assessment industrial workstation. Regarding the methodology used, input data are captured by a manufacturing machine and stored in a NoSQL database. Data are processed to engineer features used in automatic classification and to compute workers' KPIs to predict their level of expertise (with all classification metrics exceeding 90 %). These KPIs, and the relevant features in the decisions are textually explained by natural language expansion on an explainability dashboard. These automatic explanations made it possible to infer knowledge from expert workers for inexpert workers. The latter illustrates the interest of research in self-explainable ML for automatically generating insights to improve productivity in industrial workflows.

Carlos Alberto Durigan Junior, André Taue Saito, Daniel Reed Bergmann et al.

The main objective of this paper is to Identify which macroe conomic factors and industrial indexes influenced the total Brazilian banking spread between March 2011 and March 2015. This paper considers subclassification of industrial activities in Brazil. Monthly time series data were used in multivariate linear regression models using Eviews (7.0). Eighteen variables were considered as candidates to be determinants. Variables which positively influenced bank spread are; Default, IPIs (Industrial Production Indexes) for capital goods, intermediate goods, du rable consumer goods, semi-durable and non-durable goods, the Selic, GDP, unemployment rate and EMBI +. Variables which influence negatively are; Consumer and general consumer goods IPIs, IPCA, the balance of the loan portfolio and the retail sales index. A p-value of 05% was considered. The main conclusion of this work is that the progress of industry, job creation and consumption can reduce bank spread. Keywords: Credit. Bank spread. Macroeconomics. Industrial Production Indexes. Finance.

Haiming Yao, Yunkang Cao, Wei Luo et al.

Image anomaly detection plays a pivotal role in industrial inspection. Traditional approaches often demand distinct models for specific categories, resulting in substantial deployment costs. This raises concerns about multi-class anomaly detection, where a unified model is developed for multiple classes. However, applying conventional methods, particularly reconstruction-based models, directly to multi-class scenarios encounters challenges such as identical shortcut learning, hindering effective discrimination between normal and abnormal instances. To tackle this issue, our study introduces the Prior Normality Prompt Transformer (PNPT) method for multi-class image anomaly detection. PNPT strategically incorporates normal semantics prompting to mitigate the "identical mapping" problem. This entails integrating a prior normality prompt into the reconstruction process, yielding a dual-stream model. This innovative architecture combines normal prior semantics with abnormal samples, enabling dual-stream reconstruction grounded in both prior knowledge and intrinsic sample characteristics. PNPT comprises four essential modules: Class-Specific Normality Prompting Pool (CS-NPP), Hierarchical Patch Embedding (HPE), Semantic Alignment Coupling Encoding (SACE), and Contextual Semantic Conditional Decoding (CSCD). Experimental validation on diverse benchmark datasets and real-world industrial applications highlights PNPT's superior performance in multi-class industrial anomaly detection.

Mykhailo Koshil, Tilman Wegener, Detlef Mentrup et al.

Visual inspection, or industrial anomaly detection, is one of the most common quality control types in manufacturing. The task is to identify the presence of an anomaly given an image, e.g., a missing component on an image of a circuit board, for subsequent manual inspection. While industrial anomaly detection has seen a surge in recent years, most anomaly detection methods still utilize knowledge only from normal samples, failing to leverage the information from the frequently available anomalous samples. Additionally, they heavily rely on very general feature extractors pre-trained on common image classification datasets. In this paper, we address these shortcomings and propose the new anomaly detection system AnomalousPatchCore~(APC) based on a feature extractor fine-tuned with normal and anomalous in-domain samples and a subsequent memory bank for identifying unusual features. To fine-tune the feature extractor in APC, we propose three auxiliary tasks that address the different aspects of anomaly detection~(classification vs. localization) and mitigate the effect of the imbalance between normal and anomalous samples. Our extensive evaluation on the MVTec dataset shows that APC outperforms state-of-the-art systems in detecting anomalies, which is especially important in industrial anomaly detection given the subsequent manual inspection. In detailed ablation studies, we further investigate the properties of our APC.

Julia R. Varshavsky, Swati D. G. Rayasam, Jennifer B. Sass et al.

Abstract A key element of risk assessment is accounting for the full range of variability in response to environmental exposures. Default dose-response methods typically assume a 10-fold difference in response to chemical exposures between average (healthy) and susceptible humans, despite evidence of wider variability. Experts and authoritative bodies support using advanced techniques to better account for human variability due to factors such as in utero or early life exposure and exposure to multiple environmental, social, and economic stressors. This review describes: 1) sources of human variability and susceptibility in dose-response assessment, 2) existing US frameworks for addressing response variability in risk assessment; 3) key scientific inadequacies necessitating updated methods; 4) improved approaches and opportunities for better use of science; and 5) specific and quantitative recommendations to address evidence and policy needs. Current default adjustment factors do not sufficiently capture human variability in dose-response and thus are inadequate to protect the entire population. Susceptible groups are not appropriately protected under current regulatory guidelines. Emerging tools and data sources that better account for human variability and susceptibility include probabilistic methods, genetically diverse in vivo and in vitro models, and the use of human data to capture underlying risk and/or assess combined effects from chemical and non-chemical stressors. We recommend using updated methods and data to improve consideration of human variability and susceptibility in risk assessment, including the use of increased default human variability factors and separate adjustment factors for capturing age/life stage of development and exposure to multiple chemical and non-chemical stressors. Updated methods would result in greater transparency and protection for susceptible groups, including children, infants, people who are pregnant or nursing, people with disabilities, and those burdened by additional environmental exposures and/or social factors such as poverty and racism.

Martina Gažarová, Petra Lenártová, Lucia Struharňanská

Background. Nutri-score is a useful and comprehensible system of extended nutrition labeling of food, which is intended to provide the consumer with simple guidance in choosing food products, taking into account the consumer’s healthy diet. In several countries, in addition to the mandatory nutritional value of food indicated on the product packaging, the use of the so-called food traffic lights, which, based on a simple graphic display, make it easier for consumers to concentrate on choosing healthier food options. Objective. The aim of the work was to evaluate the nutritional composition of gluten-free food products based on the nutritional data indicated on the packaging of these products in order to find out how useful the use of Front-of-Pack labeling (FOPL) Nutri-score will be in distinguishing the nutritional value of products. Material and Methods. We analyzed 206 randomly selected gluten-free food products obtained from commercial retail chains (semi-finished products, other bakery products, biscuits, flour mixtures, porridges, pasta, muesli, snacks, confectionery, etc.) intended for celiacs. Based on the obtained data, we evaluated the composition of the products using a modified algorithm for calculating the Nutri-score. Results. We found that gluten-free products are a very rich source of energy, especially fats, carbohydrates and sugars, while the proportion of fiber and protein is very low. More than one third of the products had a nutritional score of category A or B, which are healthier variants, but over 40% of the analyzed products already fell into categories D or E. We found the lowest average energy value in the case of products classified in category B, the lowest average fat content and saturated fatty acids were found in products labeled A, the highest sugar content was found in products labeled D and E, the highest average protein content in products labeled A. The highest average salt content was found in products labeled C, fiber in products labeled B and A. Conclusions. Nutritional profiling can significantly contribute to several health-beneficial decisions, especially when choosing and buying healthier food options, including gluten-free foods.

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.

Yuting Sun, Tong Chen, Quoc Viet Hung Nguyen et al.

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the memory constraints of MCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to efficiently facilitate onboard inference of CNNs for real-time anomaly detection. First, we conduct a comprehensive analysis of depthwise separable CNNs and regular CNNs for anomaly detection and find that the depthwise separable convolution operation can reduce the model size by 50-90% compared with the traditional CNNs. Then, to reduce the peak memory consumption of CNNs, we explore two complementary strategies, in-place, and patch-by-patch memory rescheduling, and integrate them into a unified framework. The in-place method decreases the peak memory of the depthwise convolution by sparing a temporary buffer to transfer the activation results, while the patch-by-patch method further reduces the peak memory of layer-wise execution by slicing the input data into corresponding receptive fields and executing in order. Furthermore, by adjusting the dimension of convolution filters, these strategies apply to both univariate time series and multidomain time series features. Extensive experiments on real-world industrial datasets show that our framework can reduce peak memory consumption by 2-5x with negligible computation overhead.

Ryan J. McNeilly, Jennifer A. Schwanekamp, Logan S. Hyder et al.

Abstract Background Since the introduction of copper based, lead-free frangible (LFF) ammunition to Air Force small arms firing ranges, instructors have reported symptoms including chest tightness, respiratory irritation, and metallic taste. These symptoms have been reported despite measurements determining that instructor exposure does not exceed established occupational exposure limits (OELs). The disconnect between reported symptoms and exposure limits may be due to a limited understanding of LFF firing byproducts and subsequent health effects. A comprehensive characterization of exposure to instructors was completed, including ventilation system evaluation, personal monitoring, symptom tracking, and biomarker analysis, at both a partially enclosed and fully enclosed range. Results Instructors reported symptoms more frequently after M4 rifle classes compared to classes firing only the M9 pistol. Ventilation measurements demonstrated that airflow velocities at the firing line were highly variable and often outside established standards at both ranges. Personal breathing zone air monitoring showed exposure to carbon monoxide, ultrafine particulate, and metals. In general, exposure to instructors was higher at the partially enclosed range compared to the fully enclosed range. Copper measured in the breathing zone of instructors, on rare occasions, approached OELs for copper fume (0.1 mg/m3). Peak carbon monoxide concentrations were 4–5 times higher at the partially enclosed range compared to the enclosed range and occasionally exceeded the ceiling limit (125 ppm). Biological monitoring showed that lung function was maintained in instructors despite respiratory symptoms. However, urinary oxidative stress biomarkers and urinary copper measurements were increased in instructors compared to control groups. Conclusions Consistent with prior work, this study demonstrates that symptoms still occurred despite exposures below OELs. Routine monitoring of symptoms, urinary metals, and oxidative stress biomarkers can help identify instructors who are particularly affected by exposures. These results can assist in guiding protective measures to reduce exposure and protect instructor health. Further, a longitudinal study is needed to determine the long-term health consequences of LFF firing emissions exposure.

Vega García López

Tras una primera “Introducción conceptual” bajo un paradigma de trabajo-salud que integra todos los elementos que explican su interconexión (condiciones de empleo, servicios sanitarios, prevención, daños a la salud, causalidad, responsabilidad…) los autores y autoras nos conducen al complejo mundo de la salud laboral desde la visión clásica de los riesgos laborales y los daños hasta una visión holística que aborda los distintos dispositivos del Sistema de Salud y los condicionantes sociales del empleo. Todo abordado con un ENFOQUE DE SALUD PÚBLICA que busca la salud y bienestar de la población trabajadora. Aunque la perspectiva de la salud laboral en el Sistema Público de Salud ya se describía en nuestro país en la LGS’86 (Ley General de Salud 14/1986) y LGSP’11 (Ley General de Salud Pública 33/2011), todavía está insuficientemente desarrollada, y tal como se enfoca en el libro, es necesario considerar los riesgos laborales como determinantes de salud e imprescindible la coordinación con los Servicios de Salud Laboral.  Recorriendo la publicación(1), se aborda la PREVENCION de RIESGOS, desde los más evidentes, de seguridad que causan la patología traumática aguda, hasta otros más silentes como los químicos, biológicos o físicos, de los que cuesta tomar conciencia por sus consecuencias a más largo plazo (ej. Cáncer laboral, hipoacusia…) e incluye los de naturaleza psicosocial que son los que producen mayor merma en la percepción global de la salud. Advierte de la existencia de trabajadores ESPECIALMENTE SENSIBLES a los riesgos que normativamente establecen unos límites permisibles no válidos para ellos (estado biológico, embarazo, edad límite…). Asimismo, recuerda la necesaria PARTICIPACION del personal trabajador, legalmente protegida y fundamental en la implicación en la prevención de riesgos laborales.  También, reflexiona sobre la VIGILANCIA DE LA SALUD, creyendo necesario conceptualizar los Criterios de Aptitud y reconocimientos iniciales, sobre todo. Considera que es necesario tomar conciencia de los daños, más allá de los legalmente reconocidos (lesiones por accidentes de trabajo y enfermedades profesionales) aquellos relacionados con el trabajo y que, con frecuencia, se atienden en el Sistema Público de Salud (ej. Sucesos centinela) y advierte de la necesidad de revisar a la luz de la evidencia científica los PROTOCOLOS de vigilancia de la salud y los aspectos éticos que aseguren el respeto a la confidencialidad , dignidad y voluntariedad del trabajador. Incluye, además, el Sistema Público de Salud como complemento a los Servicios que tienen encomendada la Vigilancia de la Salud, para la detección precoz de la patología laboral, su consecuente notificación y protección a través de los sistemas de aseguramiento de las lesiones por accidentes de trabajo y enfermedades profesionales, y las ENCUESTAS de condiciones de trabajo y de salud que reflejan la percepción que los trabajadores.  Reserva espacio, además, para la prevención de la INCAPACIDAD laboral que, aunque cuenta con un sistema garantista de subsidio, lamentablemente retira a la población trabajadora del mundo laboral precozmente.  Destacar, el reservado para la Salud MENTAL en ambas vertientes, agravamiento de problemas personales en ambientes hostiles y los problemas generados por el trabajo (desde estrés postraumático hasta ideaciones suicidas), sin olvidar los problemas de reconocimiento y la estigmatización social y laboral que provoca. Así considera necesaria la concienciación y abordaje en coordinación con el sistema público de salud.  Además, enumera los trabajadores VULNERABLES: por el trabajo INFORMAL, no protegido y muy frecuentes en trabajadoras domésticas (de cuidados y mujeres)  Inmigrantes: mayor dificultad de acceso al mercado laboral, peores condiciones, y mayor accidentabilidad. Colectivos ESPECIALES: trabajadoras domésticas y del Sector Agrario. Dedica un extenso capítulo al CANCER laboral (en el que existe evidencia de asociación exposición y neoplasia) y PROFESIONAL, el legalmente reconocido y varía según los países. Desafía a estimar la fracción atribuible real del origen laboral, ya que si para los 14 principales cancerígenos laborales es del 3,9%, queda mucho recorrido para la investigación y adopción de medidas que eviten su infradeclaración (registros de expuestos, sistemas de reconocimiento e indemnización..). Reflexiona de como la pandemia COVID ha visibilizado a los trabajadores esenciales, fundamentalmente sanitarios, el entorno laboral como lugar de especial riesgo y el papel de los SPRL en el control de la salud de los trabajadores. Concluye con la consideración de la PROMOCION de la Salud como otra extensión de la salud laboral que contribuirá al bienestar y salud en el futuro del trabajo Concepto NIOSH (Total Workers Health) y con el cambio en la naturaleza del trabajo y del empleo (teletrabajo, automatización de tareas, coworking, empleo flexible…), el cambio de la población laboral (envejecimiento, diversidad…) que exige, además, un ENFOQUE ampliado de la salud laboral como “Salud INTEGRAL “ del trabajador.  En resumen, se trata de una lectura imprescindible para quienes trabajan en salud laboral en estos tiempos de “crisis” que exigen cambio de paradigma… guiado por expertos.

Wen Wu, Peng Yang, Weiting Zhang et al.

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this paper, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.