Non-industrial Environments Research Articles

Peningkatan Kesadaran Kesehatan dan Keselamatan Kerja di Lingkungan GPIB Filadelfia Semarang

Occupational Safety and Health (OSH) is a crucial element in creating a safe, healthy, and productive work environment. This study aims to promote the importance of OSH implementation at the Protestant Church in Western Indonesia (GPIB) Filadelfia Congregation, Semarang. The methods employed in this community service activity include intensive coordination with partners, the development of OSH modules, the provision of basic OSH equipment, and the execution of awareness campaigns. The findings reveal that the partners require equipment such as fire extinguishers, evacuation signs, safety helmets, and fire blankets to support building renovation activities and daily operations. Additionally, the OSH module developed covers risk management, planning, and the implementation of procedures tailored to the partner's needs. The awareness campaigns successfully enhanced the partners' understanding of the importance of OSH and its practical application. This initiative is expected to serve as a model for OSH implementation in non-industrial environments to minimize workplace accidents and promote collective safety.

Exposure hazards of particles and volatile organic compounds emitted from material extrusion 3D printing: Consolidation of chamber study data

Ultrafine particles and volatile organic compounds (VOCs) have been detected from material extrusion 3D printing, which is widely used in non-industrial environments. This study consolidates data of 447 particle emission and 58 VOC emission evaluations from a chamber study using a standardized testing method with various 3D printing scenarios. The interquartile ranges of the observed emission rates were 109–1011 #/h for particles and 0.2–1.0 mg/h for total VOC. Print material contributed largely to the variations of particle and total VOC emissions and determined the most abundantly emitted VOCs. Printing conditions and filament specifications, included printer brand, print temperature and speed, build plate heating setup, filament brand, color and composite, also affected emissions and resulted in large variations observed in emission profiles. Multiple regression showed that particle emissions were more impacted by various print conditions than VOC emissions. According to indoor exposure modeling, personal and residential exposure scenarios were more likely to result in high exposure levels, often exceeding recommended exposure limits. Hazardous VOCs commonly emitted from 3D printing included aromatics, aldehydes, alcohols, ketones, esters and siloxanes, among which were various carcinogens, irritants and developmental and reproductive toxins. Therefore, 3D printing emits a complex mixture of ultrafine particles and various hazardous chemicals, exposure to which may exceed recommended exposure limits and potentially induce acute, chronic, or developmental health effects for users depending on exposure scenarios.

Towards a novel cyber physical control system framework: a deep learning driven use case

PurposeThe health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.Design/methodology/approachThe proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.FindingsThe analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.Originality/valueThis study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.

DESIGN AND IMPLEMENTATION OF A SMART HOME AUTOMATION SYSTEM

Objective: Due to the rapid development of various technologies and communication devices, the use of the Internet of Things (IoT) for automation has become increasingly common in non-industrial environments. It has integrated well into our day-to-day activities, leading us toward the use of smart home technology. Smart home systems are intelligent system that provide control to home appliances and also security systems. There are limited numbers of intelligent systems that address multiple aspects of the home automation, such as appliances control, security bridge detection, and reducing energy consumption and cost simultaneously. Hence, this research developed a system that solves these problems with an intelligent home automation system. Methods: The designed system was based on Arduino ATMEGA328P microcontroller, MQ2 sensor for gas detection, passive infrared (IR) sensor for motion detection, and flame sensor to detect fire outbreak. Arduino ATMEGA328P was used as a central controlling unit that controls the flow of system operations to achieved smart home automation system. Results: The system sends audible alarms through a buzzer to draw the user’s immediate attention. It also sends a warning message to the user’s mobile phone through the global system for mobile communication module. Conclusion: The system achieved a precision rate of 94.44% and provided a cost-effective platform for interconnecting a variety of devices and various sensors in a home through the IoT.

HBM4EU Chromates Study-Genotoxicity and Oxidative Stress Biomarkers in Workers Exposed to Hexavalent Chromium.

A study was conducted within the European Human Biomonitoring Initiative (HBM4EU) to characterize occupational exposure to Cr(VI). Herein we present the results of biomarkers of genotoxicity and oxidative stress, including micronucleus analysis in lymphocytes and reticulocytes, the comet assay in whole blood, and malondialdehyde and 8-oxo-2′-deoxyguanosine in urine. Workers from several Cr(VI)-related industrial activities and controls from industrial (within company) and non-industrial (outwith company) environments were included. The significantly increased genotoxicity (p = 0.03 for MN in lymphocytes and reticulocytes; p < 0.001 for comet assay data) and oxidative stress levels (p = 0.007 and p < 0.001 for MDA and 8-OHdG levels in pre-shift urine samples, respectively) that were detected in the exposed workers over the outwith company controls suggest that Cr(VI) exposure might still represent a health risk, particularly, for chrome painters and electrolytic bath platers, despite the low Cr exposure. The within-company controls displayed DNA and chromosomal damage levels that were comparable to those of the exposed group, highlighting the relevance of considering all industry workers as potentially exposed. The use of effect biomarkers proved their capacity to detect the early biological effects from low Cr(VI) exposure, and to contribute to identifying subgroups that are at higher risk. Overall, this study reinforces the need for further re-evaluation of the occupational exposure limit and better application of protection measures. However, it also raised some additional questions and unexplained inconsistencies that need follow-up studies to be clarified.

Emissions and Chemical Exposure Potentials from Stereolithography Vat Polymerization 3D Printing and Post-processing Units

Particles and volatile organic compounds (VOCs) have been detected emitting from material extrusion 3D printing, which is widely used in nonindustrial environments. However, vat polymerization 3D printing that is also commonly used has yet to be well-characterized for its emissions. In this study, we measured particle and VOC emission rates from stereolithography (SLA) 3D printing during print and post-processing wash and cure processes individually using a standardized testing method for 3D printer emissions in an exposure chamber. We observed minimal particle emissions and identified 30 to over 100 individual VOCs emitted from each operating phase, some of which accumulated after the printing ended. The total VOC emissions from SLA processes were higher than typical levels from material extrusion 3D printing, and the emission rate could be over 4 mg/h. Major VOCs emitted were associated with the resin and chemicals used in print and post-processing procedures, which included esters, alcohols, aldehydes, ketones, aromatics, and hydrocarbons. Emissions from post-processing units were lower than those from printing but also included chemicals with health concerns. The emitted mixture of sensitizers, carcinogens, irritants, and flammable chemicals may present a hazard for indoor air quality and human health. The estimated personal exposure to total VOC and some specific VOCs of concern to human health, like formaldehyde and naphthalene, exceeded the recommended indoor levels (e.g., California Office of Environmental Health Hazard Assessment), potentially causing irritation and other health impacts for 3D printer users.

Exhaled Carbon Dioxide as a Physiological Source of Deterioration of Indoor Air Quality in Non-Industrial Environments: Influence of Air Temperature

People are the main reason for the deterioration of indoor air quality (IAQ) due to the continuous physiological metabolism processes in their bodies, including respiration. We present results from an investigation of the influence of indoor air temperature on the concentration of exhaled carbon dioxide (CO2). The investigation was preconditioned by previous findings on the effect of air temperature on human metabolism. However, our literature survey showed a lack of studies on the influence of the indoor air temperature on the exhaled CO2 (or metabolic CO2), which leads to the novelty of our results. Our experiments had two phases: measurement in a university classroom with an installed heating, ventilation, and air-conditioning (HVAC) system during regular classes and measurement in a specially designed small climate chamber, where the time variations of the CO2 concentrations, together with some physiological parameters, were measured. Two indoor air temperatures were set: 23 °C and 27 °C. The results obtained and their respective analyses show the strong effect of the two air temperatures on the CO2 concentration due to exhalation. In the classroom, the CO2 concentration at 27 °C was higher by 6.2% than at 23 °C. In the climate chamber, the CO2 concentration at 27 °C was higher by 9.6% than at 23 °C. Physiological parameters (oxygen saturation pressure, pulse rate, end-tidal CO2, and respiration rate) and their dependence on the air temperature were also measured in the climate chamber, establishing an effect of the temperature on the pulse rate.

Low Level Carbon Dioxide Indoors—A Pollution Indicator or a Pollutant? A Health-Based Perspective

With modern populations in developed countries spending approximately 90% of their time indoors, and with carbon dioxide (CO2) concentrations inside being able to accumulate to much greater concentrations than outdoors, it is important to identify the health effects associated with the exposure to low-level CO2 concentrations (&lt;5000 ppm) typically seen in indoor environments in buildings (non-industrial environments). Although other reviews have summarised the effects of CO2 exposure on health, none have considered the individual study designs of investigations and factored that into the level of confidence with which CO2 and health effects can be associated, nor commented on how the reported health effects of exposure correspond to existing guideline concentrations. This investigation aimed to (a) evaluate the reported health effects and physiological responses associated with exposure to less than 5000 parts per million (ppm) of CO2 and (b) to assess the CO2 guideline and limit concentrations in the context of (a). Of the 51 human investigations assessed, many did not account for confounding factors, the prior health of participants or cross-over effects. Although there is some evidence linking CO2 exposures with health outcomes, such as reductions in cognitive performance or sick building syndrome (SBS) symptoms, much of the evidence is conflicting. Therefore, given the shortcomings in study designs and conflicting results, it is difficult to say with confidence whether low-level CO2 exposures indoors can be linked to health outcomes. To improve the epidemiological value of future investigations linking CO2 with health, studies should aim to control or measure confounding variables, collect comprehensive accounts of participants’ prior health and avoid cross-over effects. Although it is difficult to link CO2 itself with health effects at exposures less than 5000 ppm, the existing guideline concentrations (usually reported for 8 h, for schools and offices), which suggest that CO2 levels &lt;1000 ppm represent good indoor air quality and &lt;1500 ppm are acceptable for the general population, appear consistent with the current research.

A Model for the Evaluation of Vocs Abatement by Potted Plants in Indoor Environments

Phytoremediation of indoor air represents an innovative, cost-effective and eco-friendly strategy to control indoor air quality that could integrate more traditional methods (e.g. ventilation and source control), especially in non-industrial environments. Since the end of the ‘80s, it is known that simple potted plants used as passive biofilters can remove Volatile Organic Compounds (VOCs) from indoor air even at low concentrations. In the last thirty years, many studies were carried out to quantify the removal rates of specific plant species towards different VOCs and the removal efficiencies that can be obtained introducing plants in a closed environment. Nevertheless, there are no well-assessed methodologies yet.Considering all the factors that influence indoor air quality is crucial to comprehensively assess potted plants' removal capacity in closed environments. Therefore, basic indoor air models have been expanded and integrated with plants' abatement effect in order to develop an original indoor air model suitable for this purpose. The model consists in a mass balance where VOC concentration depends on the volume considered, on the outdoor pollutant concentration and on the ventilation rate (Air Change per Hour, ACH). At the same time potential indoor sources, and removal rate (sink) due to potted plants are taken into account in the model. The novel depletion term introduced is function of the plant's leaf area and of the VOC removal rate per unit of leaf area. The latter is species-specific and must be derived from experimental studies.A real-existing copy shop, a non-industrial environment of particular interest, was chosen as a case study. The model was first calibrated on the basis of available measured concentrations without plants and then applied to estimate the removal efficiency obtainable with a defined leaf area of Zamioculcas Zamiifolia on ethylbenzene, xylenes and styrene concentrations (contaminants typically emitted by copiers). The estimated removal efficiencies (0.23 - 4.14% range) show that the significance of plants' removal contribution is strongly related to ventilation, but also show that the model is able to capture main features involved in idoor air quality.It can be concluded that the proposed model wants to be a step forward towards the rational use of potted plants in indoor air mitigation interventions. After defining the VOC to address and the plant species to use, it allows the evaluation of the impact of inserting a given leaf area in a defined indoor environment and the determination of the leaf area necessary to obtain a specific abatement efficiency. Moreover, it could also guide the necessary process of standardisation of the protocols in the field, especially for the experimentations in real environments.

A new CUSUM control chart under uncertainty with applications in petroleum and meteorology.

In these last few decades, control charts have received a growing interest because of the important role they play by improving the quality of the products and services in industrial and non-industrial environments. Most of the existing control charts are based on the assumption of certainty and accuracy. However, in real-life applications, such as weather forecasting and stock prices, operators are not always certain about the accuracy of an observed data. To efficiently monitor such processes, this paper proposes a new cumulative sum (CUSUM) chart under the assumption of uncertainty using the neutrosophic statistic (NS). The performance of the new chart is investigated in terms of the neutrosophic run length properties using the Monte Carlo simulations approach. The efficiency of the proposed neutrosophic CUSUM (NCUSUM) chart is also compared to the one of the classical CUSUM chart. It is observed that the NCUSUM chart has very interesting properties compared to the classical CUSUM chart. The application and implementation of the NCUSUM chart are provided using simulated, petroleum and meteorological data.

Statistical Model Checking of Human-Robot Interaction Scenarios

Robots are soon going to be deployed in non-industrial environments. Before society can take such a step, it is necessary to endow complex robotic systems with mechanisms that make them reliable enough to operate in situations where the human factor is predominant. This calls for the development of robotic frameworks that can soundly guarantee that a collection of properties are verified at all times during operation. While developing a mission plan, robots should take into account factors such as human physiology. In this paper, we present an example of how a robotic application that involves human interaction can be modeled through hybrid automata, and analyzed by using statistical model-checking. We exploit statistical techniques to determine the probability with which some properties are verified, thus easing the state-space explosion problem. The analysis is performed using the Uppaal tool. In addition, we used Uppaal to run simulations that allowed us to show non-trivial time dynamics that describe the behavior of the real system, including human-related variables. Overall, this process allows developers to gain useful insights into their application and to make decisions about how to improve it to balance efficiency and user satisfaction.

Comparison of biological markers in aerosol-weighed workplaces

Airborne particulates present a potential hazard to health in a variety of indoor workplaces, from offices to the manufacturing floor. Dust and aerosols are two of the most common occupational risk factors in the workplace worldwide. It is very important to understand when it is sufficient to describe dust exposure only by given mass concentration that dust particles could be in the nanosized range in the workplace air even for brief exposures, such as welding aerosols. The main objective was to assess total dust and nanoparticle concentrations in the selected workplaces and to evaluate their impact on workers’ health.This project describes nanoparticle concentrations and their potential impact on workers’ health considering the exposure levels in three workplaces. Industrial and non-industrial environments have been included in the project. The office environment was used as an indicator for the background level of comparison with the metalworking and woodworking industries for measurements of dust particles including the nanoscale particles (> 100 nm). Fraction size of the samples according to a dispersity of > 10 μm, 0.25–10 μm, < 0.25 μm, and 1–100 nm was analysed in order to describe the particles’ chemical composition at the different stages.The worst working conditions (the smallest particles, 12 nm), the highest total dust concentrations, and the lowest difference between total inhalable particle and nanoparticle concentrations were detected among woodworkers. These findings provide a basis on how to evaluate nanoparticle exposure and its impact on workers’ health because the highest immune cell counts and relatively high IL-6 expression were observed among woodworkers compared with the other groups.The data show a negative tendency of nanoparticle exposure concentrations on workers’ health based on the increase of inflammatory processes and damage to airway epithelial cell functionality.

A Motion-Planning System for a Domestic Service Robot

Service robots are intended to help humans in non-industrial environments such as houses or offices. To accomplish their goal, service robots must have several skills such as object recognition and manipulation, face detection and recognition, speech recognition and synthesis, task planning and, one of the most important, navigation in dynamic environments. This paper describes a fully implemented motion-planning system which comprehends from motion and path planning algorithms to spatial representation and behavior-based active navigation. The proposed system is implemented in Justina, a domestic service robot whose design is based on the ViRBot, an architecture to operate virtual and real robots that encompasses serveral layers of abstraction, from low-level control to symbolic planning. We evaluated our proposal both in simulated and real environments and compared it to classical implementations. For the tests, we used maps obtained from real environments (the Biorobotics Laboratory and the Robocup@Home arena) and maps generated from obstacles with random positions and shapes. Several parameters were used for comparison: the total traveled distance, the number of collisions, the number of reached goal points and the average execution speed. Our proposal performed significantly better both in real and simulated tests. Finally, we show our results in the context of the RoboCup@Home competition, where the system was successfully tested.

Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture

Real-time asset tracking in indoor mass production manufacturing environments can reduce losses associated with pausing a production line to locate an asset. Complemented by monitored contextual information, e.g. machine power usage, it can provide smart information, such as which components have been machined by a worn or damaged tool. Although sensor based Internet of Things (IoT) positioning has been developed, there are still key challenges when benchmarked approaches concentrate on precision, using computationally expensive filtering and iterative statistical or heuristic algorithms, as a trade-off for timeliness and scalability. Precise but high-cost hardware systems and invasive infrastructures of wired devices also pose implementation issues in the Industrial IoT (IIoT). Wireless, self-powered sensors are integrated in this paper, using a novel, communication-economical RSSI/ToF ranging method in a proposed semantic IIoT architecture. Annotated data collection ensures accessibility, scalable knowledge discovery and flexibility to changes in consumer and business requirements. Deployed at a working indoor industrial facility the system demonstrated comparable RMS ranging accuracy (ToF 6m and RSSI 5.1m with 40m range) to existing systems tested in non-industrial environments and a 12.6–13.8m mean positioning accuracy.

Higher prevalence of metabolic syndrome among male employees of a gas refinery than in their counterparts in nonindustrial environments

Background: Occupation and working conditions may affect the risk of developing metabolic syndrome (MetS), an important risk factor for diabetes and cardiovascular disease (CVD). Objective: To investigate the prevalence of MetS and its risk factors in employees in an industrial workplace and compare them with those in employees from a nonindustrial setting. Methods: Male employees (n = 757) from a gas-refinery and 2700 adult men from the general population of whom 750 were nonindustrial employees (Khorasan province, Iran), were evaluated for CVD risk factors, including those used to define MetS. Individuals were matched for age and educational attainment, and 670 industrial and681 nonindustrial employees were included in the final analysis. International Diabetes Federation (IDF) and Adult Treatment Panel (ATP) III criteria were used for diagnosis of MetS. We compared MetS and its risk factors between the two groups. Results: There were more gas refinery employees with a body mass index > 30 kg/m2, abdominal obesity, and a high fasting blood glucose level than nonindustrial employees (P Conclusions: The prevalence of MetS among male gas refinery employees was higher than for male nonindustrial employees. Keywords: Cardiovascular disease, industrial employees, metabolic syndrome, obesity

Optimizing Time-of-Arrival Localization Solutions for Challenging Industrial Environments

Since Global Positioning System technologies cannot be used indoors, a significant amount of research focuses on developing radio-frequency-based alternatives for indoor localization. Unfortunately, most of the suggested solutions for indoor localization consist of theoretical work or have been evaluated in non-industrial environments, typically office spaces. To evaluate the influence of industrial environments on localization accuracy, in this paper, a time-of-arrival (ToA) approach was used to determine the stationary locations of a robot inside the w-iLab.t II testbed, an open industrial-like environment containing several metal obstacles. The ToA method utilizes the measured propagation time of a radio wave between a sender and receiver to estimate their corresponding distance. This paper evaluates several industrial-related deployment aspects that influence location accuracy and describes how their negative impact can be reduced, resulting in an almost 50% accuracy improvement in industrial environments.

Robot transparency, trust and utility

ABSTRACTAs robot reasoning becomes more complex, debugging becomes increasingly hard based solely on observable behaviour, even for robot designers and technical specialists. Similarly, non-specialist users have difficulty creating useful mental models of robot reasoning from observations of robot behaviour. The EPSRC Principles of Robotics mandate that our artefacts should be transparent, but what does this mean in practice, and how does transparency affect both trust and utility? We investigate this relationship in the literature and find it to be complex, particularly in nonindustrial environments where, depending on the application and purpose of the robot, transparency may have a wider range of effects on trust and utility. We outline our programme of research to support our assertion that it is nevertheless possible to create transparent agents that are emotionally engaging despite having a transparent machine nature.

Human responses to carbon dioxide, a follow-up study at recommended exposure limits in non-industrial environments

To extend the results of a previous study on the effects of carbon dioxide (CO2) and bioeffluents on humans, the new study reported in this paper was carried out. The purpose of this study was to examine, whether exposure to CO2 at 5000 ppm would cause sensory discomfort, evoke acute health symptoms, reduce the performance of cognitive tasks, or result in changes in physiological responses. The outdoor air supply rate was set high enough in a low-emission stainless-steel climate chamber to create a reference condition with CO2 at 500 ppm when subjects were present, and chemically pure CO2 was added to the supply air to create an exposure condition with CO2 at 5000 ppm (the measured exposure level was ca. 4900 ppm). Ten healthy college-age students were exposed twice to each of the two conditions for 2.5 h in a design balanced for order of presentation. The raised CO2 concentration had no effect on perceived air quality or physiological responses except for end-tidal CO2 (ETCO2), which increased more (to 5.3 kPa) than it was in the reference condition (5.1 kPa). Other results indicate additionally that a 2.5-h exposure to CO2 up to 5000 ppm did not increase intensity of health symptoms reported by healthy young individuals and their performance of simple or moderately difficult cognitive tests and some tasks resembling office work. These results accord well with the current occupational exposure limit recommendation for CO2 and with many other reports published in the literature.

0191 Are indoor concentrations of airborne mould spores in non-industrial environments sufficiently high to cause hypersensitivity pneumonitis?

ObjectivesAntigen exposure is the only diagnostic criteria specific for hypersensitivity pneumonitis (HP) compared with other interstitial lung diseases. Indoor mould exposure in non-industrial environments has been claimed to cause HP,...

Is noise exposure in non-industrial work environments associated with increased sickness absence?

Is noise exposure in non-industrial work environments associated with increased sickness absence?