Hasil untuk "Industrial hygiene. Industrial welfare"

Jaqueline Cruz Perdomo

Este editorial recoge la agremiación: en lo internacional, la cosecha; en lo nacional, la siembra y el abono. El 2025 brinda una nueva oportunidad para retomar los talentos de cada terapeuta ocupacional y entretejerlos comunitariamente, fortaleciendo así los saberes y las prácticas de la Terapia Ocupacional a nivel nacional y mundial. Se reconoce el trabajo de representación a nivel internacional, a través de dos miembros del Colegio Colombiano de Terapia Ocupacional, a partir del reciente cierre de sus períodos en las directivas de la Federación Mundial de Terapeutas Ocupacionales y la Confederación Latinoamericana de Terapeutas Ocupacionales. Se presentan además algunas proyecciones y perspectivas de trabajo del Colegio Colombiano de Terapia Ocupacional para el año 2025, particularmente, el XVII Congreso Colombiano de Terapia Ocupacional y el proyecto de modificación de la Ley de Terapia Ocupacional.

Livet Rocío Cristancho González, Jaqueline Cruz Perdomo, Diana Milena Ramírez Osorio et al.

Este editorial recoge la apuesta epistemológica y metodológica del XVII Congreso Colombiano de Terapia Ocupacional, que se realizará entre el 18 y el 20 de septiembre de 2025 en Bucaramanga, Santander. El Congreso se plantea como una plataforma de encuentro plural para fortalecer el quehacer profesional desde saberes y prácticas en diferentes territorios. Se proponen cuatro ejes temáticos a través de los cuales se busca reflejar e interpelar la historia, el crecimiento y el avance de la profesión, estos son: salud, atención primaria en salud y salud pública; tecnología, innovación y autonomía; historia, epistemología, acción política y prácticas emergentes; vida cotidiana en contextos rurales y urbanos. La apuesta metodológica, inspirada en el diálogo de saberes y en contraposición a formatos jerárquicos y estandarizados, involucra tres modalidades: presentación oral; imagen o representación visual; arte y otras formas de expresión cultural en la acción profesional. Se invita a profesionales, estudiantes y docentes a participar en el Congreso para seguir tejiendo una profesión viva, crítica y profundamente comprometida con las realidades sociales del país.

Dikshit Chauhan, Nitin Gupta, Anupam Yadav

Industrial and reliability optimization problems often involve complex constraints and require efficient, interpretable solutions. This paper presents AI-AEFA, an advanced parameter reconfiguration-based metaheuristic algorithm designed to address large-scale industrial and reliability-redundancy allocation problems. AI-AEFA enhances search space exploration and convergence efficiency through a novel log-sigmoid-based parameter adaptation and chaotic mapping mechanism. The algorithm is validated across twenty-eight IEEE CEC 2017 constrained benchmark problems, fifteen large-scale industrial optimization problems, and seven reliability-redundancy allocation problems, consistently outperforming state-of-the-art optimization techniques in terms of feasibility, computational efficiency, and convergence speed. The additional key contribution of this work is the integration of SHAP (Shapley Additive Explanations) to enhance the interpretability of AI-AEFA, providing insights into the impact of key parameters such as Coulomb's constant, charge, acceleration, and electrostatic force. This explainability feature enables a deeper understanding of decision-making within the AI-AEFA framework during the optimization processes. The findings confirm AI-AEFA as a robust, scalable, and interpretable optimization tool with significant real-world applications.

Dongwei Ji, Bingzhang Hu, Yi Zhou

Industrial anomaly detection (IAD) is critical for manufacturing quality control, but conventionally requires significant manual effort for various application scenarios. This paper introduces AutoIAD, a multi-agent collaboration framework, specifically designed for end-to-end automated development of industrial visual anomaly detection. AutoIAD leverages a Manager-Driven central agent to orchestrate specialized sub-agents (including Data Preparation, Data Loader, Model Designer, Trainer) and integrates a domain-specific knowledge base, which intelligently handles the entire pipeline using raw industrial image data to develop a trained anomaly detection model. We construct a comprehensive benchmark using MVTec AD datasets to evaluate AutoIAD across various LLM backends. Extensive experiments demonstrate that AutoIAD significantly outperforms existing general-purpose agentic collaboration frameworks and traditional AutoML frameworks in task completion rate and model performance (AUROC), while effectively mitigating issues like hallucination through iterative refinement. Ablation studies further confirm the crucial roles of the Manager central agent and the domain knowledge base module in producing robust and high-quality IAD solutions.

Sunhao Dai, Jiakai Tang, Jiahua Wu et al.

Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, which enriches raw input queries with contextual cues to better elicit model capabilities, and multi-step reasoning, which iteratively refines model outputs through intermediate reasoning paths. However, these two mechanisms and their potential to unlock substantial improvements remain largely underexplored in industrial ranking systems. In this paper, we propose OnePiece, a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models of industrial cascaded pipelines. OnePiece is built on a pure Transformer backbone and further introduces three key innovations: (1) structured context engineering, which augments interaction history with preference and scenario signals and unifies them into a structured tokenized input sequence for both retrieval and ranking; (2) block-wise latent reasoning, which equips the model with multi-step refinement of representations and scales reasoning bandwidth via block size; (3) progressive multi-task training, which leverages user feedback chains to effectively supervise reasoning steps during training. OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics, including over $+2\%$ GMV/UU and a $+2.90\%$ increase in advertising revenue.

Sajad Khatiri, Francisco Eli Vina Barrientos, Maximilian Wulf et al.

Ensuring robust robotic navigation in dynamic environments is a key challenge, as traditional testing methods often struggle to cover the full spectrum of operational requirements. This paper presents the industrial adoption of Surrealist, a simulation-based test generation framework originally for UAVs, now applied to the ANYmal quadrupedal robot for industrial inspection. Our method uses a search-based algorithm to automatically generate challenging obstacle avoidance scenarios, uncovering failures often missed by manual testing. In a pilot phase, generated test suites revealed critical weaknesses in one experimental algorithm (40.3% success rate) and served as an effective benchmark to prove the superior robustness of another (71.2% success rate). The framework was then integrated into the ANYbotics workflow for a six-month industrial evaluation, where it was used to test five proprietary algorithms. A formal survey confirmed its value, showing it enhances the development process, uncovers critical failures, provides objective benchmarks, and strengthens the overall verification pipeline.

TsaiChing Ni, ZhenQi Chen, YuanFu Yang

We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence. Additional details and resources can be found in this URL: https://ninaneon.github.io/projectpage/

Xiaoran Yang, Yuyang Du, Kexin Chen et al.

As Industry 4.0 progresses, flexible manufacturing has become a cornerstone of modern industrial systems, with equipment automation playing a pivotal role. However, existing control software for industrial equipment, typically reliant on graphical user interfaces (GUIs) that require human interactions such as mouse clicks or screen touches, poses significant barriers to the adoption of code-based equipment automation. Recently, Large Language Model-based General Computer Control (LLM-GCC) has emerged as a promising approach to automate GUI-based operations. However, industrial settings pose unique challenges, including visually diverse, domain-specific interfaces and mission-critical tasks demanding high precision. This paper introduces IndusGCC, the first dataset and benchmark tailored to LLM-GCC in industrial environments, encompassing 448 real-world tasks across seven domains, from robotic arm control to production line configuration. IndusGCC features multimodal human interaction data with the equipment software, providing robust supervision for GUI-level code generation. Additionally, we propose a novel evaluation framework with functional and structural metrics to assess LLM-generated control scripts. Experimental results on mainstream LLMs demonstrate both the potential of LLM-GCC and the challenges it faces, establishing a strong foundation for future research toward fully automated factories. Our data and code are publicly available at: \href{https://github.com/Golden-Arc/IndustrialLLM}{https://github.com/Golden-Arc/IndustrialLLM.

Yuxi Liu, Yunfeng Ma, Yi Tang et al.

Industrial surface defect detection (SDD) is critical for ensuring product quality and manufacturing reliability. Due to the diverse shapes and sizes of surface defects, SDD faces two main challenges: intraclass difference and interclass similarity. Existing methods primarily utilize manually designed models, which require extensive trial and error and often struggle to address both challenges effectively. To overcome this, we propose AutoNAD, an automated neural architecture design framework for SDD that jointly searches over convolutions, transformers, and multi-layer perceptrons. This hybrid design enables the model to capture both fine-grained local variations and long-range semantic context, addressing the two key challenges while reducing the cost of manual network design. To support efficient training of such a diverse search space, AutoNAD introduces a cross weight sharing strategy, which accelerates supernet convergence and improves subnet performance. Additionally, a searchable multi-level feature aggregation module (MFAM) is integrated to enhance multi-scale feature learning. Beyond detection accuracy, runtime efficiency is essential for industrial deployment. To this end, AutoNAD incorporates a latency-aware prior to guide the selection of efficient architectures. The effectiveness of AutoNAD is validated on three industrial defect datasets and further applied within a defect imaging and detection platform. Code is available at https://github.com/Yuxi104/AutoNAD.

Rosidah Fidiyaningrum, Anita Dewi Prahastuti Sujoso, Reny Indrayani

Introduction: Mild Cognitive Impairment (MCI), according to several studies, has been discovered to be related to exposure to pesticides. Sukorambi Village is the largest vegetable producer village in Jember Regency and pesticides are used in the vegetable cultivation process. This study aimed to analyze the relationship between the age of vegetable farmers and the frequency of pesticide spraying with the incidence of MCI and to analyze differences in the incidence of MCI in farmers and their wives. Methods: This research is an analytical research with a cross-sectional design which was carried out in Sukorambi Village, from June to December 2022. The samples of this research are 142 people, obtained from a proportional stratified random sampling technique and represented groups of farmers in each hamlet. In this case, the research variables include age, frequency of pesticide spraying, as well as the incidence of MCI in vegetable farmers and their wives. Data were further collected through direct interviews, which were then analyzed through bivariate analysis using Spearman and paired t tests. Results: The results showed that the majority of vegetable farmers were above 55 years old and most of them sprayed pesticides for 3-4 and 5-6 times a month. Most farmers and their wives experience MCI. Conclusion: The farmer's age and the frequency of spraying pesticides are related to the incidence of MCI in farmers. There is a significant difference between the incidence of MCI in vegetable farmers and their wives, where MCI is more experienced by vegetable farmer wives.

Hugo Brouwer, Mojtaba Porbahaie, Sjef Boeren et al.

Abstract Background Micro- and nanoplastics (MNPs) represent one of the most widespread environmental pollutants of the twenty-first century to which all humans are orally exposed. Upon ingestion, MNPs pass harsh biochemical conditions within the gastrointestinal tract, causing a unique protein corona on the MNP surface. Little is known about the digestion-associated protein corona and its impact on the cellular uptake of MNPs. Here, we systematically studied the influence of gastrointestinal digestion on the cellular uptake of neutral and charged polystyrene MNPs using THP-1-derived macrophages. Results The protein corona composition was quantified using LC‒MS–MS-based proteomics, and the cellular uptake of MNPs was determined using flow cytometry and confocal microscopy. Gastrointestinal digestion resulted in a distinct protein corona on MNPs that was retained in serum-containing cell culture medium. Digestion increased the uptake of uncharged MNPs below 500 nm by 4.0–6.1-fold but did not affect the uptake of larger sized or charged MNPs. Forty proteins showed a good correlation between protein abundance and MNP uptake, including coagulation factors, apolipoproteins and vitronectin. Conclusion This study provides quantitative data on the presence of gastrointestinal proteins on MNPs and relates this to cellular uptake, underpinning the need to include the protein corona in hazard assessment of MNPs. Graphical abstract

Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo et al.

The increasing integration of renewable energy sources into power systems is intensifying the demand for greater flexibility among industrial electricity consumers. However, operational constraints, production requirements, and market dynamics pose significant challenges to achieving optimal flexibility. This paper presents an enhanced mixed integer linear programming (MILP) model that directly optimizes electricity consumption flexibility in manufacturing plants. Unlike previous approaches, the proposed model determines optimal transactions with both day-ahead and intraday continuous electricity markets, while ensuring production continuity and adhering to plant-specific operational constraints. The methodology is validated through annual simulations of two real world industrial configurations, cement manufacturing and steel production, using 2023 market data. Comparative results highlight that the steel plant achieved average electricity cost savings through flexibility of 0.41 euro/MWh, whereas the cement plant achieved 0.24 euro/MWh, reflecting differences in storage capacities, production rates, and operational flexibility. A comprehensive sensitivity analysis further identifies key parameters affecting flexibility potential, such as the production to demand ratio, storage capacity, and minimum operation periods. The findings offer valuable insights for industrial operators aiming to reduce energy costs, enhance operational flexibility, and support the decarbonization of electricity systems.

Atif Hussain, Rana Rizwan

This paper argues for the strategic treatment of artificial intelligence as a key industry within broader industrial policy framework of Pakistan, underscoring the importance of aligning it with national goals such as economic resilience and preservation of autonomy. The paper starts with defining industrial policy as a set of targeted government interventions to shape specific sectors for strategic outcomes and argues for its application to AI in Pakistan due to its huge potential, the risks of unregulated adoption, and prevailing market inefficiencies. The paper conceptualizes AI as a layered ecosystem, comprising foundational infrastructure, core computing, development platforms, and service and product layers, supported by education, government policy, and research and development. The analysis highlights that AI sector of Pakistan is predominantly service oriented, with limited product innovation and dependence on foreign technologies, posing risks to economic independence, national security, and employment. To address these challenges, the paper recommends educational reforms, support for local AI product development, initiatives for indigenous cloud and hardware capabilities, and public-private collaborations on foundational models. Additionally, it advocates for public procurement policies and infrastructure incentives to foster local solutions and reduce reliance on foreign providers. This strategy aims to position Pakistan as a competitive, autonomous player in the global AI ecosystem.

Wahdah Dhiyaul Akrimah, Irlangga Wisnu Wardana, Abdul Rohim Tualeka

Introduction: Non-operational employees of the Terminal Jamrud Surabaya are faced with various work-related issues while performing their duties which can be stressful due to work demands, relationships with many parties, and workloads. This study analyzes the relationship between mental workload and work factors (role ambiguity, role conflict, job insecurity, and interpersonal conflict) with occupational stress for non-operational office employees at Terminal Jamrud Surabaya. Methods: This study was a descriptive observational study with a cross-sectional design. The sampling technique used in this study is total sampling. The sample for this study were all employees of the Terminal Jamrud Surabaya office, up to 30 people. Primary data were collected using a questionnaire to assess stress levels and work factors variables. The mental workload was measured using the NASA - Task Load Index questionnaire. Results: The employees mostly have a mental workload, role ambiguity, role conflict, and interpersonal conflict in the moderate category, while the majority of office employees have job insecurity at a low level. Meanwhile, the stress levels felt by employees were mainly in the moderate category. The strong relationship between mental workload with occupational stress is 0.634, while work factors with stress levels consist of role ambiguity (r=0.523), role conflict (r=0.468), job insecurity (r=0.075), and interpersonal conflict (r=0.445). Conclusion: Variables that have a strong relationship are mental workload variables with stress levels in non-operational office employees at Terminal Jamrud Surabaya, PT Pelabuhan Indonesia III.

Rolando Rodríguez Puga, Yasnier Dueñas Rodríguez, Yoánderson Pérez Díaz et al.

Introducción: Regularmente, los fumadores tienen mayor riesgo de enfermar por COVID-19 y desarrollar complicaciones. Objetivo: Determinar algunas características clínico-epidemiológicas de la COVID-19 en trabajadores fumadores y no fumadores del Hospital Pediátrico de Camagüey, durante el año 2021. Métodos: Se realizó un estudio descriptivo, transversal, comparativo. El universo quedó conformado por 86 pacientes confirmados, divididos en dos grupos de 43, asignando a uno los fumadores y al otro los no fumadores, con encuesta epidemiológica confeccionada y el resultado de PCR positivo. Las variables estudiadas incluyeron: grupos etarios y sexo, síntomas referidos, lugar de atención médica, estadía hospitalaria, enfermedades asociadas, principales complicaciones y estado al egreso. Resultados: Predominó el grupo etario de 30-39 años (31,3 %) tanto en no fumadores (17,4 %) como en fumadores (13,9 %), mayoritariamente del sexo femenino (56,9 %). Sobresalieron los síntomas respiratorios como la congestión (35,0 %) y secreción nasal (25,6 %) a predominio de fumadores, fue necesario el internamiento del mayor número de estos últimos en centros mejor equipados (16,3 %) y en hospitales (18,6 %), donde permanecieron más de 5 días (81,4 %), por presentar además hipertensión arterial (13,9 %) y diabetes mellitus (8,2 %), desarrollando neumonía (12,8 %) y bronconeumonía (12,8 %) como complicaciones más frecuentes en fumadores. Conclusiones: Se concluye que la COVID-19 predominó en féminas entre 30 y 39 años que tuvieron como síntoma predominante la secreción nasal, permanecieron más días hospitalizados por presentar procesos inflamatorios pulmonares. Todos los pacientes egresaron mejorados o curados. Introduction: Smokers are usually within the higher risk group to suffer from COVID-19 and resulting complications. Objective: To determine some clinical-epidemiological characteristics of COVID-19 in smokers and non-smokers workers of the Pediatric Hospital of Camagüey, during the year 2021. Methods: A descriptive, cross-sectional, comparative study was carried out. The universe consisted of 86 confirmed patients, divided into two groups of 43, assigning smokers and non-smokers to one group and non-smokers to the other, with an epidemiological survey and positive PCR result. Variables studied included: age groups and gender, referred symptoms, place of medical care, hospital stay, associated diseases, main complications, and medical condition when discharged. Results: The age group 30-39 years (31.3%) predominated in both non-smokers (17.4%) and smokers (13.9%), mostly female (56.9%), with respiratory symptoms such as congestion (35.0%) and nasal secretion (25.6%) predominating in smokers, being necessary the hospitalization of the greater number of the latter in better-equipped centers (16.3%) and in hospitals (18.6%), where they admitted for more than 5 days (81.4%), for also presenting arterial hypertension (13.9%) and diabetes mellitus (8.2%), developing pneumonia (12.8%) and bronchopneumonia (12.8%) as the more frequent complications in smokers. Conclusions: It is concluded that COVID-19 predominated in females, between 30 and 39 years old, who had nasal secretion as the predominant symptom, remained more days hospitalized for presenting pulmonary inflammatory processes, discharging the totality of patients improved or cured.

Steven Landgraf, Markus Hillemann, Moritz Aberle et al.

In many industrial processes, such as power generation, chemical production, and waste management, accurately monitoring industrial burner flame characteristics is crucial for safe and efficient operation. A key step involves separating the flames from the background through binary segmentation. Decades of machine vision research have produced a wide range of possible solutions, from traditional image processing to traditional machine learning and modern deep learning methods. In this work, we present a comparative study of multiple segmentation approaches, namely Global Thresholding, Region Growing, Support Vector Machines, Random Forest, Multilayer Perceptron, U-Net, and DeepLabV3+, that are evaluated on a public benchmark dataset of industrial burner flames. We provide helpful insights and guidance for researchers and practitioners aiming to select an appropriate approach for the binary segmentation of industrial burner flames and beyond. For the highest accuracy, deep learning is the leading approach, while for fast and simple solutions, traditional image processing techniques remain a viable option.

Ilja Zakharov, Evgeny Novikov, Ilya Shchepetkov

Automatic software verification tools help to find hard-to-detect faults in programs checked against specified requirements non-interactively. Besides, they can prove program correctness formally under certain assumptions. These capabilities are vital for verification of critical industrial programs like operating system kernels and embedded software. However, such programs can contain hundreds or thousands of KLOC that prevent obtaining valuable verification results in any reasonable time when checking non-trivial requirements. Also, existing tools do not provide widely adopted means for environment modeling, specification of requirements, verification of many versions and configurations of target programs, and expert assessment of verification results. In this paper, we present the Klever software verification framework, designed to reduce the effort of applying automatic software verification tools to large and critical industrial C programs.

Nancy Rubí Estrada Ledesma, José Guadalupe Salazar Estrada

Resumen Introducción: La pandemia por el SARS-CoV-2 puso a prue-ba al sistema de salud en México. La rápida conversión a “hospitales COVID” y las dificultades que ya enfrentaban las instituciones de salud pública, como la saturación de servicios de salud, la falta de estructura hospitalaria y equipo de protec-ción personal, incidieron negativamente en las condiciones de trabajo y la salud de los médicos de primera atención en áreas COVID. Objetivo: Identificar qué factores incidieron en las condiciones de trabajo y la salud de los médicos que laboraron en áreas COVID en instituciones de salud pública de México. Métodos: Se realizó una revisión bibliográfica, se consultaron seis diferentes bases de datos, se incluyeron lecturas en torno a la discusión salud ocupacional de los médicos en áreas CO-VID, y se seleccionaron nueve artículos. Resultados: Las condiciones de trabajo se describen como precarias: falta de equipos de protección personal, jornadas laborales de más de diez horas y ausencia de contrato. En cuanto a la salud, se encontró que este grupo de profesionistas padece estrés, ansiedad, miedo y angustia. Conclusiones: La participación y cooperación de los médicos, las autoridades sanitarias, los sectores gubernamentales, las instituciones y asociaciones involucradas, las políticas públicas, el sistema de salud y la sociedad pueden sumar esfuerzos para reducir el riesgo de infecciones por la COVID-19, así como la tasa de contagios y comorbilidades. Todas estas acciones permitirán disminuir los niveles de estrés y la sobrecarga de trabajo de los profesionistas de la salud. Abstract Introduction: The SARS-CoV-2 pandemic put the health system in Mexico to the test. The rapid conversion to "COVID hospitals" and the difficulties already faced by public health institutions, such as the saturation of health services, lack of hospital structure and personal protective equipment, had a negative impact on the working conditions and health of pri-mary care physicians in COVID areas. Objective: To identify the factors that affected the working conditions and health of physicians who worked in COVID areas in public health institutions in Mexico. Methods: A bibliographic review was carried out, six different databases were consulted, readings on the discussion of occupational health of physicians in COVID areas were in-cluded, and nine articles were selected. Results: The working conditions were described as precarious: lack of personal protective equipment, working hours of more than ten hours, and absence of a contract. In terms of health, this group of professionals was found to suffer from stress, anxiety, fear and anguish. Conclusions: The participation and cooperation of physicians, health authorities, governmental sectors, involved institutions and associations, the involvement of public policies, the health system and the society can join efforts to reduce the risk of COVID-19 infections, as well as the rate of contagion and comorbidities. All these actions will make it possible to reduce the stress levels and work overload of health professionals. médicos; COVID-19; precariedad laboral; salud laboral physicians; COVID-19; occupational precarious-ness; occupational health

Ni Made Truly Pinanti Sastra, Indri H. Susilowati

Introduction: Corrosion inhibitor (CI) is injected as carbon steel pipe corrosion protection with sulfur-containing substances in the product. One type of them is thioglycolic acid (TGA). Besides having benefits in maintaining pipe integrity, TGA can be decomposed to HS (hydrogen sulfide) due to changes in ambient temperature during storage, such as direct sunlight exposure on the field. This irritant gas can pose a risk to the health of chemical workers. Therefore, this study aims to measure the concentration of H2S in a CI product containing TGA. Method: The data were collected from an oil and gas company measurement report on 12 CI drums with 1-3%w of TGA content by using a glass tube detector. Measurements were performed by varying the measurement distance (0 and 10 cm from the mouth of the drum), observing the condition of the inflated drum surface, and determining the existence of internal pressure. Results: All samples were contained H2S, and the inflated drums had higher H2S content than those that were not inflated up to more than 200 ppm in the drum bore. At this concentration, workers can experience pulmonary edema significantly prolonged exposure. Biological monitoring can be done by analyzing thiosulfate content in urine and blood after exposure or routine examination at the end of the work shift. Conclusion: CI with TGA content has the potential of high H2S concentration, and it requires risk control such as engineering control, administration control, and PPE application to minimize the health impact of H2S exposure to the workers.

Vira Nalia Maharani, Novrikasari Novrikasari, Desheila Andarini et al.

Introduction: Fire cases in Indonesia continue to increase every year. Based on data from the Regional Disaster Management Agency (BPBD) of South Sumatra, there were 116 cases of fires during 2019 in South Sumatra. An office building is a building that serves as a place for people to do office activities. Office buildings that have been relatively safeare actually faced with various risks of emergency hazards such as fires, earthquakes, floods and others. Ogan Ilir Police Station has experienced a life-threatening fire and losses, and therefore life facilities are needed according to the existing SNI. The purpose of this study is to analyze life-saving facilities and fire management facilities in Ogan Ilir Police Station, South Sumatera. Methods: This research used a qualitative descriptive method. Descriptive research was conducted by evaluating the fire protection system in accordance with the national standard in reference to the Regulation of the Minister of Public Works. Sources of information were obtained from key informants and other informants. Results: The suitability of the fire protection system at Ogan Ilir Police Station with the standards of the Minister of Public Works No. 26/PRT/M/2008 for the system of life-saving has complied with the requirements. Meanwhile, fire fighting facilities such as fire extinguishers have been installed, but there are some small elements that are not in accordance with the requirements. Conclusion: The fire protection system at Ogan Ilir Police Station, South Sumatera has not complied with the requirements of the Minister of Public Works No. 26/PRT/M/2008.