Hasil untuk "Industrial hygiene. Industrial welfare"

Xiaozhe Su, Yijun Yang, Heng Xiang et al.

Abstract Background With the escalating issue of polystyrene microplastic pollution, microplastic particles have been detected in human urine. While calcium oxalate (CaOx) crystals are well-established mediators of renal stone formation, the role of microplastics, particularly polystyrene nanoplastics (PS-NPs), in promoting CaOx kidney stone formation remains unclear. This study aims to explore whether PS-NPs interact with CaOx crystals to enhance renal tubular epithelial cell injury and facilitate the formation of kidney stones. Methods Clinical CaOx kidney stone samples were analyzed using pyrolysis—gas chromatography-mass spectrometry (Py-GC/MS), which detected microplastic components. In vitro, human renal proximal tubular epithelial cells (HK-2) were exposed to calcium oxalate monohydrate crystals (1.5 mM), or cells were pretreated with PS-NPs (100 nm, 0.1 mg/mL) for 24 h, followed by the addition of 1.5 mM CaOx for co-treatment. Comprehensive mechanistic assessments, including whole-transcriptome RNA sequencing, crystal adhesion assays, macrophage chemotaxis and polarization analysis, ferroptosis biomarker quantification, lipid peroxidation measurement, and mitochondrial ferrous ion accumulation, were conducted. In vivo, a rat model of CaOx nephrolithiasis was induced by ethylene glycol (EG) with concurrent exposure to PS-NPs (4 mg/Kg·Day) via drinking water. Results Clinical analysis confirmed the presence of PS-NPs and other microplastics in human CaOx kidney stones. In vitro, exposure to PS-NPs significantly altered the morphology of CaOx crystals, promoting aggregation and enhancing adhesion to renal tubular epithelial cells. Combined exposure to PS-NPs and CaOx crystals exacerbated HK-2 cell injury through upregulation of VCAM1, CXCL8-driven macrophage chemotaxis and M1 polarization, and ferroptosis induced by xCT/GPX4 suppression. Transcriptomic analysis revealed LRP6 downregulation as a central regulator in these pathological processes. Overexpression of low-density lipoprotein receptor-related protein 6 (LRP6) alleviated cell damage and attenuated inflammatory responses. In vivo, PS-NPs co-exposure exacerbated renal CaOx deposition, ferroptosis in renal tubular epithelial cells, and inflammatory responses in the rat model. Conclusion Our study identifies PS-NPs as novel lithogenic cofactors that promote CaOx nucleation, enhance crystal adhesion to renal tubular epithelial cells, and amplify inflammation and ferroptosis through LRP6 downregulation. This suggests that microplastic pollution may be an emerging environmental risk factor for kidney stone pathogenesis. Graphical abstract

Philippe Hartemann, Antoine Montiel

Throughout history, humans have sought to drink water that is good for their health, according to the knowledge of the time. Hippocrates’ definition of water quality, “good water should be clear, light, aerated, without any perceptible odor or taste, warm in winter and cold in summer”, remained virtually unchanged until 1887, when it was added that water should dissolve soap and foam well, be clear and colorless, have a pleasant taste, leave no large deposits after boiling, and cook vegetables and wash clothes well. This definition guided all treatments to remove the substances responsible for cloudiness, odor and discoloration, as well as the choice of resources: clear water and water with low mineral content. The discoveries by Pasteur and Koch led to the addition of microbiological criteria, like the absence of pathogens, and the definition of microbiological indicators. Throughout the 20th century, advances in scientific knowledge in microbiology, chemistry and toxicology led to major progress in treatment methods. These filtration and disinfection treatments are described here according to their historical implementation. Due to progress in numerous areas, e.g., both chemical and microbiological analytical detection limits, speed of information flow and origins of certain diseases that are discovered to be waterborne, the consumer is now exposed to anxiety-provoking news (microplastics, eternal pollutants (cf. per- and polyfluoroalkyl substances (PFASs)), drugs, pesticides residues, etc.). Thus, the consumer tends to lose confidence in tap or bottled water and turn to buying home purifiers. Drinking water treatment will continue to evolve with more sophisticated processes, as analytical progress enables us to expect further developments.

Tim Langer, Matthias Widra, Volkhard Beyer

In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontroller. After a short review on TinyML in industrial process monitoring, the creation of the novel MillingVibes dataset is described. The feasibility of a TinyML system for structure-integrated process quality monitoring could be shown by the development of an 8-bit-quantized convolutional neural network (CNN) model with 12.59kiB parameter storage. A test accuracy of 100.0% could be reached at 15.4ms inference time and 1.462mJ per quantized CNN inference on an ARM Cortex M4F microcontroller, serving as a reference for future TinyML process monitoring solutions.

Shuxin Lin, Dhaval Patel, Christodoulos Constantinides

Small Language Models (SLMs) are becoming increasingly popular in specialized fields, such as industrial applications, due to their efficiency, lower computational requirements, and ability to be fine-tuned for domain-specific tasks, enabling accurate and cost-effective solutions. However, performing complex reasoning using SLMs in specialized fields such as Industry 4.0 remains challenging. In this paper, we propose a knowledge distillation framework for industrial asset health, which transfers reasoning capabilities via Chain-of-Thought (CoT) distillation from Large Language Models (LLMs) to smaller, more efficient models (SLMs). We discuss the advantages and the process of distilling LLMs using multi-choice question answering (MCQA) prompts to enhance reasoning and refine decision-making. We also perform in-context learning to verify the quality of the generated knowledge and benchmark the performance of fine-tuned SLMs with generated knowledge against widely used LLMs. The results show that the fine-tuned SLMs with CoT reasoning outperform the base models by a significant margin, narrowing the gap to their LLM counterparts. Our code is open-sourced at: https://github.com/IBM/FailureSensorIQ.

Waqar Muhammad Ashraf, Vivek Dua, Ramit Debnath

Machine learning and optimisation techniques (MLOPT) hold significant potential to accelerate the decarbonisation of industrial systems by enabling data-driven operational improvements. However, the practical application of MLOPT in industrial settings is often hindered by a lack of domain compliance and system-specific consistency, resulting in suboptimal solutions with limited real-world applicability. To address this challenge, we propose a novel human-in-the-loop (HITL) constraint-based optimisation framework that integrates domain expertise with data-driven methods, ensuring solutions are both technically sound and operationally feasible. We demonstrate the efficacy of this framework through a case study focused on enhancing the thermal efficiency and reducing the turbine heat rate of a 660 MW supercritical coal-fired power plant. By embedding domain knowledge as constraints within the optimisation process, our approach yields solutions that align with the plant's operational patterns and are seamlessly integrated into its control systems. Empirical validation confirms a mean improvement in thermal efficiency of 0.64\% and a mean reduction in turbine heat rate of 93 kJ/kWh. Scaling our analysis to 59 global coal power plants with comparable capacity and fuel type, we estimate a cumulative lifetime reduction of 156.4 million tons of carbon emissions. These results underscore the transformative potential of our HITL-MLOPT framework in delivering domain-compliant, implementable solutions for industrial decarbonisation, offering a scalable pathway to mitigate the environmental impact of coal-based power generation worldwide.

Jingqi Wu, Hanxi Li, Lin Yuanbo Wu et al.

Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during production, leveraging them usually requires pixel-level annotations, limiting scalability. To address this, we propose ADClick, an Interactive Image Segmentation (IIS) algorithm for industrial anomaly detection. ADClick generates pixel-wise anomaly annotations from only a few user clicks and a brief textual description, enabling precise and efficient labeling that significantly improves AD model performance (e.g., AP = 96.1\% on MVTec AD). We further introduce ADClick-Seg, a cross-modal framework that aligns visual features and textual prompts via a prototype-based approach for anomaly detection and localization. By combining pixel-level priors with language-guided cues, ADClick-Seg achieves state-of-the-art results on the challenging ``Multi-class'' AD task (AP = 80.0\%, PRO = 97.5\%, Pixel-AUROC = 99.1\% on MVTec AD).

Daniel Lindenschmitt, Paul Seehofer, Marius Schmitz et al.

This paper presents the application of Dynamic Spectrum Management (DSM) for future 6G industrial networks, establishing an efficient controller for the Networks-in-Network (NiN) concept. The proposed architecture integrates nomadic as well as static sub-networks (SNs with diverse Quality of Service (QoS) requirements within the coverage area of an overlayer network, managed by a centralized spectrum manager (SM). Control plane connectivity between the SNs and the DSM is ensured by the self-organizing KIRA routing protocol. The demonstrated system enables scalable, zero-touch connectivity and supports nomadic SNs through seamless discovery and reconfiguration. SNs are implemented for modular Industrial Internet of Things (IIoT) scenarios, as well as for mission-critical control loops and for logistics or nomadic behavior. The DSM framework dynamically adapts spectrum allocation to meet real-time demands while ensuring reliable operation. The demonstration highlights the potential of DSM and NiNs to support flexible, dense, and heterogeneous wireless deployments in reconfigurable manufacturing environments.

Rosmery Baryolo Gómez, Damian Valdés Santiago, Arlene Oramas Viera

Introducción: El estudio de las emociones en el ambiente de trabajo es un tema actual y pertinente en relación con la prevención de los problemas de la salud mental en el trabajo y la gestión de los factores psicosociales laborales. Objetivos: Determinar la validez y confiabilidad de la escala de afectividad positiva y negativa y describir el ambiente emocional en un centro de control de tráfico aéreo. Métodos: Se realizó un estudio descriptivo con un diseño transversal. Se aplicó el instrumento a nivel personal y organizacional. Se emplearon medidas de tendencia central, medidas de asociación, coeficientes de correlación y análisis factorial con el método de extracción de componentes principales con rotación Varimax y normalización Kaiser. Resultados: La consistencia interna de las dos escalas en ambos niveles fue superior a 0,70. En un rango de valores entre 10 y 50, la afectividad positiva a niveles personal y ambiental están cercanas (mediana 38 y 37 respectivamente) la negativa coincide (mediana 13). En la conciencia emocional personal se forman 6 factores con autovalores que explican el 67,586 % de la varianza total. En la conciencia emocional ambiental 4 componentes explican el 68,829 % de la varianza. Conclusiones: Predominan las emociones positivas a nivel personal: activo, atento, alerta y decidido, en cuanto al nivel ambiental coinciden, excepto alerta. Las emociones menos percibidas en ambos niveles son: avergonzado, culpable y temeroso. Los resultados apuntan a la pertinencia de ambos niveles para caracterizar el ambiente emocional en una organización laboral Introduction: The study of emotions in the work environment is a current and pertinent issue in relation to the prevention of mental health problems at work and the management of occupational psychosocial factors. Objectives: Determine the validity and reliability of the positive and negative affectivity scale and describe the emotional environment in the Air Traffic Control Center. Methods: A descriptive study with a cross-sectional design was carried out. The positive and negative affectivity scale was applied at a personal and organizational level. Measures of central tendency, measures of association, correlation coefficients, and factor analysis were used with the principal component extraction method with Varimax rotation and Kaiser normalization. Results: The internal consistency of the two scales at both levels was higher than 0.70. In a range of values between 10 and 50, the positive affectivity at personal and environmental levels are close (median 38 and 37 respectively), the negative coincides (median 13). In personal emotional awareness, 6 factors are formed with eigenvalues that explain 67.586% of the total variance. In environmental emotional awareness, 4 components explain 68.829% of the variance. Conclusions: Positive emotions predominate on a personal level: active, attentive, alert and determined, regarding the environmental level they coincide except alert. The least perceived emotions in both levels are: ashamed, guilty and afraid. The results point to the relevance of both levels to characterize the emotional environment in a work organization

Rui Pimentel de Figueiredo, Stefan Nordborg Eriksen, Ignacio Rodriguez et al.

Corrosion, a naturally occurring process leading to the deterioration of metallic materials, demands diligent detection for quality control and the preservation of metal-based objects, especially within industrial contexts. Traditional techniques for corrosion identification, including ultrasonic testing, radio-graphic testing, and magnetic flux leakage, necessitate the deployment of expensive and bulky equipment on-site for effective data acquisition. An unexplored alternative involves employing lightweight, conventional camera systems, and state-of-the-art computer vision methods for its identification. In this work, we propose a complete system for semi-automated corrosion identification and mapping in industrial environments. We leverage recent advances in LiDAR-based methods for localization and mapping, with vision-based semantic segmentation deep learning techniques, in order to build semantic-geometric maps of industrial environments. Unlike previous corrosion identification systems available in the literature, our designed multi-modal system is low-cost, portable, semi-autonomous and allows collecting large datasets by untrained personnel. A set of experiments in an indoor laboratory environment, demonstrate quantitatively the high accuracy of the employed LiDAR based 3D mapping and localization system, with less then $0.05m$ and 0.02m average absolute and relative pose errors. Also, our data-driven semantic segmentation model, achieves around 70\% precision when trained with our pixel-wise manually annotated dataset.

Zhicheng Wang, Wensheng Liang, Ruiyan Zhuang et al.

Action recognition (AR) in industrial environments -- particularly for identifying actions and operational gestures -- faces persistent challenges due to high deployment costs, poor cross-scenario generalization, and limited real-time performance. To address these issues, we propose a low-cost real-time framework for industrial action recognition using foundation models, denoted as LRIAR, to enhance recognition accuracy and transferability while minimizing human annotation and computational overhead. The proposed framework constructs an automatically labeled dataset by coupling Grounding DINO with the pretrained BLIP-2 image encoder, enabling efficient and scalable action labeling. Leveraging the constructed dataset, we train YOLOv5 for real-time action detection, and a Vision Transformer (ViT) classifier is deceloped via LoRA-based fine-tuning for action classification. Extensive experiments conducted in real-world industrial settings validate the effectiveness of LRIAR, demonstrating consistent improvements over state-of-the-art methods in recognition accuracy, scenario generalization, and deployment efficiency.

Daniel Barros, Paula Fraga-Lamas, Tiago M. Fernandez-Carames et al.

The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images.

Alejandro Mata Ali, Iñigo Perez Delgado, Aitor Moreno Fdez. de Leceta

In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in order to determine their potential scalability.

Polly McLean, William Mueller, Ilse Gosens et al.

Abstract Background Toxicity assessment for regulatory purposes is starting to move away from traditional in vivo methods and towards new approach methodologies (NAM) such as high-throughput in vitro models and computational tools. For materials with limited hazard information, utilising quantitative Adverse Outcome Pathways (AOPs) in a testing strategy involving NAM can produce information relevant for risk assessment. The aim of this work was to determine the feasibility of linking in vitro endpoints to in vivo events, and moreover to key events associated with the onset of a chosen adverse outcome to aid in the development of NAM testing strategies. To do this, we focussed on the adverse outcome pathway (AOP) relating to the onset of pulmonary fibrosis. Results We extracted in vivo and in vitro dose–response information for particles known to induce this pulmonary fibrosis (crystalline silica, specifically α-quartz). To test the in vivo–in vitro extrapolation (IVIVE) determined for crystalline silica, cerium dioxide nanoparticles (nano-CeO2) were used as a case study allowing us to evaluate our findings with a less studied substance. The IVIVE methodology outlined in this paper is formed of five steps, which can be more generally summarised into two categories (i) aligning the in vivo and in vitro dosimetry, (ii) comparing the dose–response curves and derivation of conversion factors. Conclusion Our analysis shows promising results with regards to correlation of in vitro cytokine secretion to in vivo acute pulmonary inflammation assessed by polymorphonuclear leukocyte influx, most notable is the potential of using IL-6 and IL-1β cytokine secretion from simple in vitro submerged models as a screening tool to assess the likelihood of lung inflammation at an early stage in product development, hence allowing a more targeted investigation using either a smaller, more targeted in vivo study or in the future a more complex in vitro protocol. This paper also highlights the strengths and limitations as well as the current difficulties in performing IVIVE assessment and suggestions for overcoming these issues.

Tatiana Areas da Cruz, André Pereira dos Santos, Jéssica Fernanda Corrêa Cordeiro et al.

In social cognitive theory, self-efficacy refers to the belief of a person in their own capacity to successfully perform certain tasks or behaviors. This study measured the self-efficacy of health professionals in hand hygiene (HH) and glove usage (GU) during the COVID-19 pandemic. It was an observational Brazilian multicenter study with a cross-sectional design with an online application of an instrument measuring the self-efficacy of health professionals in HH and GU. Health professionals (<i>n</i> = 193) participated in this study: 96 (49.7%) were nursing professionals, 38 (20.2%) were dental professionals, 21 (10.9%) were physicians, 10 (5.2%) were pharmacists, and 27 (14.0%) were other health professionals. Regarding the instrument applied, the maximum score (100 points) was achieved by 167 (86.5%) participants on Question 2 (confidence in regular routine behaviors), and the lowest scores achieved were 0, 10, 30, 40, and 50 points, referring to 18 (9.3%) participants, on Question 14 (the influence of management’s conduct related to practices). A total of 64.1% dental professionals, 57.1% of physicians, 39.6% of nurses, 20.0% of pharmacists, and 55.6% of other health professionals were classified as having self-efficacy. There was only a significant association between being a dental professional and having self-efficacy regarding HH and GU during the COVID-19 pandemic in relation to other health professional categories.

Rizqy Kartika Sari, Y. Denny A. Wahyudiono, Bachtiar Chahyadhi et al.

Introduction: Welding is one of the activities in the manufacture of gamelan gong which has the potential for causing photokeratitis in workers. Photokeratitis can occur as a result of acute exposure to UV rays in the eyes of workers. Risk assessment was used to determine the magnitude of the risk of several factors causing photokeratitis. The purpose of the study was to analyze the risk of photokeratitis among the welders of gamelan gongs in Ponorogo Regency based on the concept of epidemiology. Methods: The research design was cross sectional which was carried out during the Covid-19 pandemic in May 2021. The population of this study was welders making gamelan gongs in Ponorogo, Indonesia. There were six respondents selected using the non-probability sampling technique. Data was collected by interviews, discussions, and observations. The variables, namely host, agent, and environment, were identified as risk factors, then risk analysis was carried out using the semi-quantitative technique by taking into account the level of frequency and severity. The risk evaluation was completed using the ALARP concept. Results: The causative factors of photokeratitis found within the host category were age and working period, which was considered moderate risk, and PPE use behavior, which was considered high risk. In the agent category, the intensity of UV exposure was considered high-risk. The environmental factors, namely working time, exposure distance, and welding location were considered moderate risk. Conclusion: The factors of photokeratitis still exist, thus continuous control efforts are needed.

Ángel Luis Perales Gómez, Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez et al.

Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues such as cyberattacks affecting industrial processes. Federated Learning (FL) combined with time-series analysis is a promising cyberattack detection mechanism proposed in the literature. However, the fact of having a single point of failure and network bottleneck are critical challenges that need to be tackled. Thus, this article explores the benefits of the Decentralized Federated Learning (DFL) in terms of cyberattack detection and resource consumption. The work presents TemporalFED, a software module for detecting anomalies in industrial environments using FL paradigms and time series. TemporalFED incorporates three components: Time Series Conversion, Feature Engineering, and Time Series Stationary Conversion. To evaluate TemporalFED, it was deployed on Fedstellar, a DFL framework. Then, a pool of experiments measured the detection performance and resource consumption in a chemical gas industrial environment with different time-series configurations, FL paradigms, and topologies. The results showcase the superiority of the configuration utilizing DFL and Semi-Decentralized Federated Learning (SDFL) paradigms, along with a fully connected topology, which achieved the best performance in anomaly detection. Regarding resource consumption, the configuration without feature engineering employed less bandwidth, CPU, and RAM than other configurations.

Federico Rollo, Gennaro Raiola, Nikolaos Tsagarakis et al.

Robots with a high level of autonomy are increasingly requested by smart industries. A way to reduce the workers' stress and effort is to optimize the working environment by taking advantage of autonomous collaborative robots. A typical task for Human-Robot Collaboration (HRC) which improves the working setup in an industrial environment is the \textit{"bring me an object please"} where the user asks the collaborator to search for an object while he/she is focused on something else. As often happens, science fiction is ahead of the times, indeed, in the \textit{Iron Man} movie, the robot \textit{Dum-E} helps its creator, \textit{Tony Stark}, to create its famous armours. The ability of the robot to comprehend the semantics of the environment and engage with it is valuable for the human execution of more intricate tasks. In this work, we reproduce this operation to enable a mobile robot with manipulation and grasping capabilities to leverage its geometric and semantic understanding of the environment for the execution of the \textit{Bring Me} action, thereby assisting a worker autonomously. Results are provided to validate the proposed workflow in a simulated environment populated with objects and people. This framework aims to take a step forward in assistive robotics autonomy for industries and domestic environments.

Ernestina Solórzano Álvarez, Antonio Torres Valle, Mayra Ramos et al.

Resumen Introducción: La percepción del riesgo biológico ha ido ganando terreno como mecanismo regulador de la seguridad laboral en instalaciones de salud, ya que está asociada a la ocurrencia y prevención de accidentes laborales. Para las diferentes instituciones de salud es fundamental conocer este fenómeno, favoreciendo así la implementación de estrategias de prevención, que permitan que el trabajador realice una correcta valoración del peligro y, entre otros factores, contribuir a la disminución de accidentes laborales. Objetivos: Compilar estudios realizados sobre percepción del riesgo biológico en el personal de la salud en instalaciones pertenecientes al sector y analizar el comportamiento de las variables relacionadas con la percepción del riesgo biológico. Métodos: Se realizó un estudio explicativo transversal que aplicó el método de evaluación de la percepción del riesgo en el personal biológicamente expuesto, de siete instituciones hospitalarias y el programa RISKPERCEP. Resultados: Las variables familiaridad, comprensión del riesgo, demanda laboral, voluntariedad de exposición al riesgo, pánico y beneficios no fueron percibidas adecuadamente por parte del personal y resulta un problema a resolver que pudiera relacionarlas con la realización de actos inseguros que guardaran alguna relación con accidentes laborales. Conclusión: Existe una alta subestimación del riesgo por el personal expuesto que labora en las áreas estudiadas. Abstract Introduction: Biological risk perception has been gaining ground as a regulatory mechanism for occupational safety in health facilities, since it is associated with the occurrence and prevention of occupational accidents. For the different health institutions, it is essential to know this phenomenon, thus favoring the implementation of prevention strategies that allow the worker to make a correct assessment of the danger and, among other factors, contribute to the reduction of occupational accidents. Objectives: To gather studies about biological risk perception among the health personnel from facilities belonging to this sector and to analyze the behavior of the variables related to biological risk perception. Methods: A cross-sectional and explanatory study was carried out using the risk perception assessment method in biologically exposed personnel from seven hospital institutions and the RISKPERCEP program. Results: The variables familiarity, understanding of risk, occupational demand, willingness to risk exposure, panic and benefits were not adequately perceived by the personnel, a problem to be solved that could be related to doing unsafe activities somehow related to occupational accidents. Conclusion: There is a high underestimation of risk by the exposed personnel working in the areas studied.

Bernardo Prieto Muñoz

Introducción: El riesgo biológico es la posibilidad de que un trabajador sufra un daño como consecuencia de la exposición o contacto con agentes biológicos durante la realización de su actividad laboral. Cada persona tiene una susceptibilidad individual, lo que explica que algunos trabajadores expuestos enfermen cuando entran en contacto con ellos y otros no lo hacen. Objetivo: Evaluar el riesgo biológico del personal sanitario en un laboratorio de Anatomía Patológica de un hospital de la provincia de Valencia, España. Métodos: Estudio observacional, transversal, una investigación cualitativa de tipo evaluativa. El método utilizado es el conocido como BIOGAVAL-NEO, en su última edición de 2018. Se entrevistó a 13 trabajadores técnicos superiores en Anatomía. El puesto de trabajo evaluado es ocupado por trabajadores que realizan las mismas funciones y poseen la misma categoría profesional, en específico para estudiar el riesgo de contraer carbunco por el Bacillus anthracis. Resultados: El riesgo biológico en el laboratorio objeto de estudio es de tres, para el agente biológico Bacillus anthracis. Comparado con el valor del nivel de acción biológica (NAB) que es ocho, resulta menor. Se encuentran establecidas las medidas higiénicas y de corrección del daño y de la transmisión, por lo que no hay requerimientos adicionales. Conclusión: Se verificó que son suficientes las medidas higiénicas previamente adoptadas para la seguridad biológica de los trabajadores del Laboratorio, los que realizan sus funciones laborales con seguridad Introduction: The biological risk is the possibility that a worker suffers harm like consequence of the exposure or contact with biological agents during the realization of his labor activity. Each person has an individual susceptibility, that explains that some people make ill when they go in in contact with determinate biological agent, whereas others no. Objective: The aim of this work is to evaluate the biological risk of the sanitary personnel in a Laboratory of Pathological Anatomy of a hospital of the province of Valencia (Spain). Methods: The work is an observational studio, transversal, evaluative research, using variable and qualitative technicians of collection of the information. The method used is the known as BIOGAVAL-NEO, in his last edition of 2018. It has spent to the practice by means of interviews to 13 workers Technical in Pathological Anatomy of the Laboratory of a Hospital of the province of Valencia (Spain). Specifically, it has studied the risk to contract carbuncle by the Bacillus anthracis. Result: The biological risk in the laboratory for the Bacillus anthracis agent is of three. It´s results minor compared with the value of the Level of Biological Action that it is eight. They find established the measures, hygienic and of correction of the harm and of the transmission; by what there are not additional requests. Conclusion: The main conclusion obtained after the work is that if they adopt all the preventive sanitary measures the biological risk is small

Alicia Trujllo Rojas

Este artículo responde a la invitación de la Editora y el Comité Editorial de la Revista Ocupación Humana, para la publicación del número especial de celebración de los 50 años de fundación de la organización científica y gremial de las y los terapeutas ocupacionales de Colombia. Recoge las memorias, análisis y reflexiones a futuro de la profesora Alicia Trujillo Rojas, quien tuvo a su cargo la primera presidencia de la Asociación Colombiana de Terapia Ocupacional -ACTO, entre 1972 y 1973, y fue  reelegida para los años 1973 a 1974. Este y los demás textos de expresidentas que hacen parte de este número especial están llenos de la fortaleza, energía y proyección de sus autoras y de nuestra profesión. Resultan, entonces, en un importante y significativo testimonio histórico de lo que hemos construido y de la inmensa y poderosa tarea que tiene el Colegio Colombiano de Terapia Ocupacional para continuar construyendo y respondiendo a los retos del ser ocupacional – personales, colectivos y sociales –, del país, la región y el mundo.