Hasil untuk "Industrial hygiene. Industrial welfare"

Yunhui Liu, Pengyu Qiu, Yu Xing et al.

Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. Despite its significance in industrial applications such as fraud detection and user segmentation, a significant chasm persists between academic research and real-world deployment. Current evaluation protocols suffer from the small-scale, high-homophily citation datasets, non-scalable full-batch training paradigms, and a reliance on supervised metrics that fail to reflect performance in label-scarce environments. To bridge these gaps, we present PyAGC, a comprehensive, production-ready benchmark and library designed to stress-test AGC methods across diverse scales and structural properties. We unify existing methodologies into a modular Encode-Cluster-Optimize framework and, for the first time, provide memory-efficient, mini-batch implementations for a wide array of state-of-the-art AGC algorithms. Our benchmark curates 12 diverse datasets, ranging from 2.7K to 111M nodes, specifically incorporating industrial graphs with complex tabular features and low homophily. Furthermore, we advocate for a holistic evaluation protocol that mandates unsupervised structural metrics and efficiency profiling alongside traditional supervised metrics. Battle-tested in high-stakes industrial workflows at Ant Group, this benchmark offers the community a robust, reproducible, and scalable platform to advance AGC research towards realistic deployment. The code and resources are publicly available via GitHub (https://github.com/Cloudy1225/PyAGC), PyPI (https://pypi.org/project/pyagc), and Documentation (https://pyagc.readthedocs.io).

Shintia Yunita Arini

Diego Pollini, Bruna V. Guterres, Rodrigo S. Guerra et al.

The integration of Large Language Models (LLMs), such as GPT, in industrial robotics enhances operational efficiency and human-robot collaboration. However, the computational complexity and size of these models often provide latency problems in request and response times. This study explores the integration of the ChatGPT natural language model with the Robot Operating System 2 (ROS 2) to mitigate interaction latency and improve robotic system control within a simulated Gazebo environment. We present an architecture that integrates these technologies without requiring a middleware transport platform, detailing how a simulated mobile robot responds to text and voice commands. Experimental results demonstrate that this integration improves execution speed, usability, and accessibility of the human-robot interaction by decreasing the communication latency by 7.01\% on average. Such improvements facilitate smoother, real-time robot operations, which are crucial for industrial automation and precision tasks.

Katharina Ledebur. Ladislav Bartuska, Klaus Friesenbichler, Peter Klimek

The automotive industry is undergoing transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends blur the boundaries between the automotive sector and other industries. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms' ability to leverage cross-industry diversification contributes to competitive advantage. We develop a country-level product space covering all industries and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts that are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. We examine this relationship across industrial sectors to establish patterns of path dependency, diversification and capability formation, and then focus on the EV transition. New strengths in vehicles and aluminium products in the EU are expected to generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade, compared to only 1.6 and 4.5 new strengths in already diversified China. Countries such as South Korea, China, the US and Canada show strong potential for diversification into EV-related products, while established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation depends on regions' ability to mobilize existing industrial capabilities, particularly in sectors such as machinery and electronic equipment.

Muzakkiruddin Ahmed Mohammed, John R. Talburt, Leon Claasssens et al.

Industrial part specification extraction from unstructured text remains a persistent challenge in manufacturing, procurement, and maintenance, where manual processing is both time-consuming and error-prone. This paper introduces a retrieval-augmented multi-LLM ensemble framework that orchestrates nine state-of-the-art Large Language Models (LLMs) within a structured three-phase pipeline. RAGsemble addresses key limitations of single-model systems by combining the complementary strengths of model families including Gemini (2.0, 2.5, 1.5), OpenAI (GPT-4o, o4-mini), Mistral Large, and Gemma (1B, 4B, 3n-e4b), while grounding outputs in factual data using FAISS-based semantic retrieval. The system architecture consists of three stages: (1) parallel extraction by diverse LLMs, (2) targeted research augmentation leveraging high-performing models, and (3) intelligent synthesis with conflict resolution and confidence-aware scoring. RAG integration provides real-time access to structured part databases, enabling the system to validate, refine, and enrich outputs through similarity-based reference retrieval. Experimental results using real industrial datasets demonstrate significant gains in extraction accuracy, technical completeness, and structured output quality compared to leading single-LLM baselines. Key contributions include a scalable ensemble architecture for industrial domains, seamless RAG integration throughout the pipeline, comprehensive quality assessment mechanisms, and a production-ready solution suitable for deployment in knowledge-intensive manufacturing environments.

Jingzhi Gong, Rafail Giavrimis, Paul Brookes et al.

There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

Nikolai West, Jochen Deuse

This paper presents a comprehensive collection of industrial screw driving datasets designed to advance research in manufacturing process monitoring and quality control. The collection comprises six distinct datasets with over 34,000 individual screw driving operations conducted under controlled experimental conditions, capturing the multifaceted nature of screw driving processes in plastic components. Each dataset systematically investigates specific aspects: natural thread degradation patterns through repeated use (s01), variations in surface friction conditions including contamination and surface treatments (s02), diverse assembly faults with up to 27 error types (s03-s04), and fabrication parameter variations in both upper and lower workpieces through modified injection molding settings (s05-s06). We detail the standardized experimental setup used across all datasets, including hardware specifications, process phases, and data acquisition methods. The hierarchical data model preserves the temporal and operational structure of screw driving processes, facilitating both exploratory analysis and the development of machine learning models. To maximize accessibility, we provide dual access pathways: raw data through Zenodo with a persistent DOI, and a purpose-built Python library (PyScrew) that offers consistent interfaces for data loading, preprocessing, and integration with common analysis workflows. These datasets serve diverse research applications including anomaly detection, predictive maintenance, quality control system development, feature extraction methodology evaluation, and classification of specific error conditions. By addressing the scarcity of standardized, comprehensive datasets in industrial manufacturing, this collection enables reproducible research and fair comparison of analytical approaches in an area of growing importance for industrial automation.

Junfeng Jiao, Saleh Afroogh, Kevin Chen et al.

This paper introduces IGGA, a dataset of 160 industry guidelines and policy statements for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in industry and workplace settings, collected from official company websites, and trustworthy news sources. The dataset contains 104,565 words and serves as a valuable resource for natural language processing tasks commonly applied in requirements engineering, such as model synthesis, abstraction identification, and document structure assessment. Additionally, IGGA can be further annotated to function as a benchmark for various tasks, including ambiguity detection, requirements categorization, and the identification of equivalent requirements. Our methodologically rigorous approach ensured a thorough examination, with a selection of reputable and influential companies that represent a diverse range of global institutions across six continents. The dataset captures perspectives from fourteen industry sectors, including technology, finance, and both public and private institutions, offering a broad spectrum of insights into the integration of GAIs and LLMs in industry.

MohammadNoor Injadat

The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number of network related attacks due to the increased number of potential attack surfaces. Industrial IoT (IIoT) devices are prone to various network related attacks that can have severe consequences on the manufacturing process as well as on the safety of the workers in the manufacturing plant. One promising solution that has emerged in recent years for attack detection is Machine learning (ML). More specifically, ensemble learning models have shown great promise in improving the performance of the underlying ML models. Accordingly, this paper proposes a framework based on the combined use of Bayesian Optimization-Gaussian Process (BO-GP) with an ensemble tree-based learning model to improve the performance of intrusion and attack detection in IIoT environments. The proposed framework's performance is evaluated using the Windows 10 dataset collected by the Cyber Range and IoT labs at University of New South Wales. Experimental results illustrate the improvement in detection accuracy, precision, and F-score when compared to standard tree and ensemble tree models.

Tita Alissa Bach, Aleksandar Babic, Narae Park et al.

The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.

Jun-ichi Akagi, Yasuko Mizuta, Hirotoshi Akane et al.

Abstract Background Though titanium dioxide (TiO2) is generally considered to have a low impact in the human body, the safety of TiO2 containing nanosized particles (NPs) has attracted attention. We found that the toxicity of silver NPs markedly varied depending on their particle size, as silver NPs with a diameter of 10 nm exhibited fatal toxicity in female BALB/c mice, unlike those with diameters of 60 and 100 nm. Therefore, the toxicological effects of the smallest available TiO2 NPs with a crystallite size of 6 nm were examined in male and female F344/DuCrlCrlj rats by repeated oral administration of 10, 100, and 1000 mg/kg bw/day (5/sex/group) for 28 days and of 100, 300, and 1000 mg/kg bw/day (10/sex/group) for 90 days. Results In both 28- and 90-day studies, no mortality was observed in any group, and no treatment-related adverse effects were observed in body weight, urinalysis, hematology, serum biochemistry, or organ weight. Histopathological examination revealed TiO2 particles as depositions of yellowish-brown material. The particles observed in the gastrointestinal lumen were also found in the nasal cavity, epithelium, and stromal tissue in the 28-day study. In addition, they were observed in Peyer's patches in the ileum, cervical lymph nodes, mediastinal lymph nodes, bronchus-associated lymphoid tissue, and trachea in the 90-day study. Notably, no adverse biological responses, such as inflammation or tissue injury, were observed around the deposits. Titanium concentration analysis in the liver, kidneys, and spleen revealed that TiO2 NPs were barely absorbed and accumulated in these tissues. Immunohistochemical analysis of colonic crypts showed no extension of the proliferative cell zone or preneoplastic cytoplasmic/nuclear translocation of β-catenin either in the male or female 1000 mg/kg bw/day group. Regarding genotoxicity, no significant increase in micronucleated or γ-H2AX positive hepatocytes was observed. Additionally, the induction of γ-H2AX was not observed at the deposition sites of yellowish-brown materials. Conclusions No effects were observed after repeated oral administration of TiO2 with a crystallite size of 6 nm at up to 1000 mg/kg bw/day regarding general toxicity, accumulation of titanium in the liver, kidneys, and spleen, abnormality of colonic crypts, and induction of DNA strand breaks and chromosomal aberrations.

Débora Grandón Valenzuela

El artículo aborda el carácter histórico y político del tiempo, por considerarle un articulador de la existencia humana, a partir de una lectura crítica de la vida cotidiana. La reproducción de la temporalidad está mediada por el trabajo, el que, analizado desde una perspectiva feminista-marxista, permite reconocer desigualdades en la experiencia del tiempo de hombres y mujeres, basadas en la división sexual del trabajo. Para profundizar en este fenómeno se presentan resultados derivados de una investigación cualitativa que buscó analizar la experiencia del tiempo cotidiano de mujeres que realizan el trabajo de cuidados de personas adultas con discapacidad en Santiago de Chile. Se realizaron entrevistas semiestructuradas y observaciones participantes en los contextos cotidianos de ocho mujeres, reconociendo que su experiencia del tiempo depende de otras personas, que está densificada por la continua realización simultánea de trabajos no remunerados y que no distingue tiempos libres, de ocio ni por fuera del cuidado. Se concluye reconociendo que las desigualdades de género también producen desigualdades en la experiencia del tiempo, lo que insta a avanzar en políticas sociales que reconozcan el cuidado como un derecho social para que las mujeres puedan construir experiencias de sentido, azar, demora y libertad.

Adinda Kusumawardhani, Hendra Djamalus, Kartika Dani Lestari

Introduction: Musculoskeletal Disorders (MSDs) symptoms are experienced by 1.71 billion of the human population and are characterized by persistent pain that decreases the ability to work in almost all types of occupations, including laboratory workers. The various stages of work in laboratory can cause complaints due to repetitive motions, manual handling, static and awkward posture, as well as long-duration of work. Therefore, this study aimed to determine risk level of work ergonomic and MSDs symptoms among laboratory workers. Methods: This study used a cross-sectional design involving 71 laboratory workers who were observed from 8 to 22 June 2022. The respondents were categorized into three Similar Exposure Group (SEG), namely administrative officers, analysts, and field workers. Risk level of MSDs symptoms and work ergonomic of each SEG was measured using the instrument of SNI 9011-2021, while individual factor was estimated through the questionnaire. Results: Out of the 71 respondents, the majority were males, aged <35 years, and had <5 years of work experience. The survey revealed that half of workers experienced MSDs symptoms with a high-risk level in analysts and field workers, particularly in the lower back. The highest MSDs symptoms in all SEG were neck, lower back, upper back, and right shoulder. Conclusion: Ergonomic risk level in laboratory was dangerous for analysts and field workers, and required further assessment by administrative officers. To reduce risk level of work ergonomic, particularly for analysts and field workers, engineering control and the use of manual handling equipment can be implemented.

Gaye Ezgi Yılmaz, Ilgım Göktürk, Mamajan Ovezova et al.

Microbial colonization on various surfaces is a serious problem. Biofilms from these microbes pose serious health and economic threats. In addition, the recent global pandemic has also attracted great interest in the latest techniques and technology for antimicrobial surface coatings. Incorporating antimicrobial nanocompounds into materials to prevent microbial adhesion or kill microorganisms has become an increasingly challenging strategy. Recently, many studies have been conducted on the preparation of nanomaterials with antimicrobial properties against diseases caused by pathogens. Despite tremendous efforts to produce antibacterial materials, there is little systematic research on antimicrobial coatings. In this article, we set out to provide a comprehensive overview of nanomaterials-based antimicrobial coatings that can be used to stop the spread of contamination to surfaces. Typically, surfaces can be simple deposits of nanomaterials, embedded nanomaterials, as well as nanotubes, nanowires, nanocolumns, nanofibers, nanoneedles, and bio-inspired structures.

Teng Zhong, Yinglei Teng, Shijun Ma et al.

The advent of Industrial Internet of Things (IIoT) has imposed more stringent requirements on industrial software in terms of communication delay, scalability, and maintainability. Microservice architecture (MSA), a novel software architecture that has emerged from cloud computing and DevOps, presents itself as the most promising solution due to its independently deployable and loosely coupled nature. Currently, practitioners are inclined to migrate industrial legacy systems to MSA, despite numerous challenges it presents. In this paper, we propose an automated microservice decomposition method for extracting microservice candidates based on spectral graph theory to address the problems associated with manual extraction, which is time-consuming, labor intensive, and highly subjective. The method is divided into three steps. Firstly, static and dynamic analysis tools are employed to extract dependency information of the legacy system. Subsequently, information is transformed into a graph structure that captures inter-class structure and performance relationships in legacy systems. Finally, graph-based clustering algorithm is utilized to identify potential microservice candidates that conform to the principles of high cohesion and low coupling. Comparative experiments with state of-the-art methods demonstrate the significant advantages of our proposed method in terms of performance metrics. Moreover, Practice show that our method can yield favorable results even without the involvement of domain experts.

Chaihan Rungsirisangratana, Nawiya Nuntapanich, Patima Pinsuwannabud et al.

Introduction: Dry eye symptoms are the common ocular complaints that are found at the ophthalmologic outpatient services. This research’s main purposes were to study the risk factors associated with dry eye symptoms and to evaluate the severity of dry eye among Visual Display Terminal (VDT) users. Methods: This study was a descriptive observational study involving 104 VDT users in 3 branches of the Social Security Offices and the Bureau of Labor Protection and Welfare in Samutprakarn province, Thailand. The study instruments used were: (1) questionnaires associated with VDT use and dry eye symptoms that were evaluated by using the Ocular Surface Disease Index (OSDI) and (2) Lux meter for desk-brightness and the angle of gaze measurement during VDT use. Data were analyzed using a Chi-square test and multiple logistic regression. Results: The results found that VDT users had severe dry eye symptoms, accounting for 51.9%, and experienced moderate and mild dry eye symptoms in the same number, which was 24.0%. In addition, dry eye symptoms were related to VDT use for 5-7 hours/day with statistically significant value. Other VDT use factors, including the desk-brightness or the angle of gaze during VDT use, were related to severe dry eye symptoms with no statistically significant difference. Conclusion: Based on the findings, VDT users should use VDT no more than 5 hours/day in order to reduce VDT-related dry eye symptoms. these factors were not statistically significant for the occurrence of severe dry eye symptoms.

Mark Eisen, Santosh Shukla, Dave Cavalcanti et al.

We consider the problem of controlling a series of industrial systems, such as industrial robotics, in a factory environment over a shared wireless channel leveraging edge computing capabilities. The wireless control system model supports the offloading of computational intensive functions, such as perception workloads, to an edge server. However, wireless communications is prone to packet loss and latency and can lead to instability or task failure if the link is not kept sufficiently reliable. Because maintaining high reliability and low latency at all times prohibits scalability due to resource limitations, we propose a communication-control co-design paradigm that varies the network quality of service (QoS) and resulting control actions to the dynamic needs of each plant. We further propose a modular learning framework to solve the complex learning task without knowledge of plant or communication models in a series of learning steps and demonstrate its effectiveness in learning resource-efficient co-design policies in a robotic conveyor belt task.

Markus Voggenreiter, Ulrich Schöpp

Integrating security activities into the software development lifecycle to detect security flaws is essential for any project. These activities produce reports that must be managed and looped back to project stakeholders like developers to enable security improvements. This so-called Feedback Loop is a crucial part of any project and is required by various industrial security standards and models. However, the operation of this loop presents a variety of challenges. These challenges range from ensuring that feedback data is of sufficient quality over providing different stakeholders with the information they need to the enormous effort to manage the reports. In this paper, we propose a novel approach for treating findings from security activity reports as belief in a Knowledge Base (KB). By utilizing continuous logical inferences, we derive information necessary for practitioners and address existing challenges in the industry. This approach is currently evaluated in industrial DevOps projects, using data from continuous security testing.

T. Tsarenko, L. Korniienko

In Ukraine zoonoses are a permanent threat to human health, some of them are bacterial diseases associated with farm animals. Complete avoidance of outbreaks of bacterial zoonoses is not possible but it is appropriate to study them to reduce the risks of transmission of zoonosis pathogens from industrial farms to the human population and the environment. The article highlights the results of a literature review on the potential role of industrial livestock farms in the spread of major bacterial zoonoses in Ukraine. About half of all of the country’s farmed animals are kept on farms using industrial technology; more than half of the establishments are classified as medium and large. The technology of keeping animals on such farms contributes to the development of diseases of obligate hosts caused by fecal bacteria. The systematic search and selection of literary sources, which are relevant to the topic of the study were carried out. The vast majority of analyzed publications are published in Ukrainian in local peer-reviewed scientific journals. An analysis of open-access official statistics from the state authorities of Ukraine was also conducted. The authors analyzed statistics and scientific papers published over the last 10–15 years discussing the outbreaks of food-borne zoonoses among humans and the studying their pathogens (Campylobacter spp., Salmonella spp., Escherichia coli (STEC strains), Listeria spp.) on industrial livestock farms. The main source of Campylobacter spp. and Salmonella spp. distribution are industrial poultry, including broilers and chickens, respectively. The STEC strains E. coli carriers are various types of farm animals, including cattle and pigs. The majority of infections documented in Ukraine are cases of salmonellosis in humans and animals. Despite reports of a significant prevalence of campylobacteriosis, colibacillosis and listeriosis in livestock farms, their association with outbreaks of food-borne zoonoses in humans remains poorly understood. The concept of an industrial livestock farm involves a permanent presence of a risk of outbreaks of bacterial zoonoses and their rapid spreading to the human population. This is due to concentrated maintenance of animals, standardized feeding, the priority of achieving the highest productivity of animals and economic indicators. Under such conditions, disturbance of hygienic norms and technologies significantly increases the risk of bacterial zoonoses on industrial farms. It is important to enforce the continuous control of the level of microbial pollution of farms, animal health, hygiene of milk production and processing, meat, eggs, etc. Farms have a negative impact on the ecological welfare of the surrounding territories. The problem of spread of antibiotic-resistant strains of bacterial zoonoses is a very serious one. Efforts for the formation of a national system of epidemiological supervision over bacterial zoonoses, comprising epidemiological, epizootological, ecological, microbiological, serological and molecular genetic monitoring, as well as the development on this basis of effective prophylactic and anti-epidemic measures are relevant and necessary.

María Luisa Ellis Yard, Dinora García Martín, Maylin Phillips Ellis

Introducción: Un entorno laboral saludable favorece la productividad y la convivencia de los trabajadores, mejora el bienestar de sus colaboradores y las relaciones interpersonales, mientras que el espacio de trabajo que no cumple con estas características favorece en los involucrados la acumulación de estrés y, posteriormente, consecuencias en la salud. Objetivo: Reflejar las características que identifican el mobbing o acoso laboral y su repercusión en la salud mental del trabajador, la familia y la comunidad. Material y método: Se realizó una revisión bibliográfica en fuentes de información y bases de datos de reconocido prestigio nacional e internacional. Se utilizaron 41 citas bibliográficas con actualización del 78 % entre 2017-2020. A partir de esta información, se elaboró el presente artículo. Análisis e integración de la información: El acoso psicológico en el trabajo o mobbing se considera como el deliberado y continuado maltrato verbal y modal que recibe un trabajador en su desempeño, por parte de uno o varios compañeros de trabajo, que pudieran desestabilizarlo y afectarlo emocionalmente y hacer disminuir su capacidad laboral hasta solicitar la baja laboral. Conclusiones: El mobbing o acoso psicológico laboral afecta a la víctima, la familia y la institución. Identificar el problema como tal, informarse sobre este, pedir asesoramiento psicológico y de profesionales afines a esta problemática es una de las medidas a tomar para su control y prevención desde la atención primaria de salud. Introduction: A healthy work environment favors workers´ productivity and coexistence, improves the well-being of its collaborators and interpersonal relationships, while a workspace that doesn’t meet these characteristics favors the accumulation of stress in those involved and, subsequently, health consequences. Objective: To describe the characteristics those identify mobbing or workplace harassment and its repercussion on the mental health of the worker, the family and the community. Material and method: A bibliographic review was carried out in information sources and databases of recognized national and international prestige. 41 bibliographic citations were used with an update of 78% between 2017 and 2020. From them the present article was prepared. Analysis and integration of information: Psychological harassment at work or mobbing is considered as the deliberate and continuous verbal and modal abuse that a worker receives in his performance, by one or more co-workers that could destabilize him and affect him emotionally and reduce their work capacity until they request sick leave. Conclusions: Mobbing or psychological harassment at work affects the victim, the family, and the institution, identifying the problem as such, finding out about it, requesting psychological and professional advice to address this problem is one of the measures to be taken carried out for its control and prevention from primary health care.