Providing an approach to analyze the risk of central oxygen tanks in hospitals during the COVID-19 pandemic.

The central oxygen unit of hospitals is considered a high-risk unit, requiring high safety standards to maintain the integrity of the system during the COVID-19 pandemic. The linear reasoning assumption of conventional risk analysis methods cannot adequately describe these modern systems, which are characterized by tight connections and complex interactions between technical, human, and organizational aspects. Therefore, this study presents a new and comprehensive approach to oxygen tanks in hospitals during the COVID-19 pandemic. In this study, trapezoidal fuzzy numbers were used to calculate failure rates. After determining the probability of basic events (BEs), intermediate events (IE), and top event (TE) with fuzzy logic and transferring it into Bayesian Network (BN), deductive and inductive reasoning, and sensitivity analysis were performed using RoV in GeNIe software. The results of the case study showed that the IE of "Human Error" had the highest probability of fuzzy fault tree (FFT) and the probability of oxygen leakage was lower using FBN than FFT. According to the results, BE16 (failure to use standard and updated instructions) and BE12 (defects in the inspection and testing program of tank devices) had the highest posterior probability, while based on the FFT results, BE4 (defects in the external coating system of the tank) and, BE3 (Corrosive environment (acidity state)) had the least probability. According to the sensitivity analysis, basic events 10, 11, and 16 were the most important in the oxygen leakage event with a very small difference, which was almost in line with the results of posterior FBN (FBNPO). Updating the existing guidelines, fixing defects in the inspection of all types of tank gauges, and testing related equipment can greatly help the reliability of these tanks. Root cause analysis of these events provides opportunities for prevention and emergency response in critical situations, such as the COVID-19 pandemic.

Optimized design and experimental validation of sound absorption coefficient performance in aluminium metal foam by spark plasma sintering.

Determining the structural properties of aluminum metal foam is essential to predicting its acoustic behavior. Acoustic models are presented that show the relationship between the morphology of the absorber and the sound absorption coefficient (SAC). Optimizing the parameters affecting the SAC can be the maximum theoretically SAC achieved at each frequency. In the previous article (https://doi.org/10.32604/sv.2021.09729) the parameters of porosity percentage (Ω), pore size (D) and pore opening size (d) were optimized by the genetic algorithm and Lu model. In this study, the optimal aluminum metal foam was synthesized using Spark Plasma Sintering (SPS), with the maximum temperature of 420 °C and final pressure of 20 MPa in samples with thicknesses of 5, 10, 15 and 20 mm in different frequencies from 1000 to 6300 Hz. The crystal structure and microstructure of samples were investigated using XRD and SEM. Optimized metal foam SAC (0.67, 0.9, 1 and 1) and experimental peak SAC (0.44, 0.67, 0.76 and 0.82) were compared with the optimized SAC in 5, 10, 15 and 20 mm thicknesses, respectively. The values of the coefficient of determination (R2) according to multiple linear regression (MLR) for the two optimized SAC and experimental in thicknesses of 5, 10, 15 and 20 mm were 0.90, 0.95, 0.96 and 0.90, respectively. The results of this study show that porous metal foam can have a high absorption coefficient in any desired thickness and frequency by using the optimal morphology.

A systematic review of consequence modeling studies of the process accidents in Iran from 2006 to 2022.

Analytical models and the prediction of accidents and their consequences are important tools for preventing accidents in the industry. Therefore, the present study was conducted to review process accident consequence modeling in Iran (2006-2022), helping improve incident modeling, and creating a context for preventing these accidents. In this study, the articles of 5 international Embase Medline/PubMed databases, ProQuest, Scopus, Google Scholar, and Web of Science, and four Iranian databases SID, MagIran, IranMedex, and IranDoc were examined using the PRISMA checklist. After reviewing the studies, 40 articles were included in the final analysis. The results showed that 25 studies used PHAST software, 11 studies used ALOHA software and 4 studies used ALOHA and PHAST software together. The highest number of studies was conducted in Imam Khomeini Port and Asaluyeh city, respectively. In addition, the number of studies published in Persian was more than in other studies. There was no similar agreement between the results of the two software in providing correct results at low concentrations. PHAST software also provided more accurate results than ALOHA over longer distances in stable and relatively stable weather conditions. The study and identification of hazards and scenarios in the studies that used PHAST software were more, more accurate and more coherent than in the studies that used ALOHA software. It is suggested that in future studies the same scenario can be compared with PHAST, ALOHA, and newer modeling software such as Fire Dynamics Simulator (FDS).

Quantitative indicators of street lighting with mood, fatigue, mental workload and sleepiness in car drivers: Using generalized structural equation modeling.

Inadequate lighting will be associated with some degree of perceptual error such as sleepiness. The main purpose of this study was to investigate the interactions between mood, fatigue, mental workload, and sleepiness and their relationship with quantitative indicators of street lighting in passenger car drivers. The present study was a cross-sectional study that was performed on 270 drivers of passenger cars. The quantitative indices of lighting studied were illuminance, luminance, uniformity, and disability glare which were calculated using the Hagner device (EC1-L) and according to EN 13201 standard. Alertness and mood indices, fatigue scale (SAMN-PERELLI), mental workload (NASA-TLX), positive and negative affect schedule (PANAS) were used. Generalized structural equation modeling (GSEM) was used to investigate the relationship between mood, fatigue, mental workload, and drivers' sleepiness. Data analysis was performed in version 26 of SPSS software and version 14 of Stata software There is a significant relationship between illuminance and mood (P < 0.001). There is a significant relationship between the degree of disability glare on the streets and the mood (P = 0.006). There is a significant relationship between fatigue score and mood (P < 0.001) so that with increasing one unit in fatigue scale, mood score decreases by 0.669 units (P < 0.001). Finally, it can be assured that lighting interventions can be done as an effective way to increase alertness and reduce fatigue and the mental workload of drivers with the aim of reducing night traffic accidents.

Effect of anthropometric and demographic factors on musculoskeletal disorders in nurses' aides.

BACKGROUND: Nurses' aides usually face various stressors, making them prone to musculoskeletal disorders (MSDs). OBJECTIVE: This study evaluates the effect of ergonomic and anthropometric indices, postural risk, and demographic variables on MSDs in nurses' aides. METHODS: Demographic variables, anthropometric dimensions, postural risk level (RL), and related percentiles of 75 nurses' aides were examined using Quick Exposure Check (QEC) software, caliper, and body map questionnaire. Pearson correlation coefficient, univariate and multivariate tests were used to analyze the data. RESULTS: The results showed that the RL of QEC in both groups of males and females was 73.67±22.34 (RL = 4) and 65.34±18.38 (RL = 3), respectively. The level of MSDs in the lumbar, thigh, and leg areas was higher than in other areas. Also, increasing age and work experience, and BMI were significantly associated with increasing disorders in the hands, wrists, and shoulders, respectively (P < 0.05). The results showed that an increase in some anthropometric indices such as body height, buttock, knee, popliteal height, abdomen depth, standing grip access limit, sitting grip access limit increased disorders in the hands and wrists. Also, MSDs were predicted in different areas of the nurses' aides' bodies using regression models, which was significant in the hands, wrists, elbows, legs, and shoulders (P < 0.05). CONCLUSIONS: The results showed that there was a significant difference between male and female nurses in most aspects of anthropometry and the risk level of QEC. Therefore, it is necessary to pay attention to anthropometric dimensions, and demographic diversity to design tools and workstations.

Relationship between musculoskeletal disorders and anthropometric indices among bus drivers in Zahedan city.

INTRODUCTION: Most work-related musculoskeletal disorders (MSDs) are associated with improper postures and poorly designed workstations. This study is an attempt to examine the prevalence and severity of MSDs as well as anthropometric dimensions among city bus drivers. METHODOLOGY: This cross-sectional study was carried out on 60 male bus drivers. A body discomfort chart was used to evaluate MSDs. Spearman correlation was employed to examine the relationship between body size and the severity of discomfort. RESULTS: Data analysis showed that discomfort reported in the lower back (33.3%), upper back (18.3%) and knee (15%) was severe and there was a direct and significant correlation between the body mass index and the severity of discomfort in the shoulder, arm and hip (p < 0.05). However, the results showed that eye sitting height and sitting height had significant inverse relationships with discomfort in the ankle and elbow (p < 0.05). CONCLUSION: The results of the study can be used to address ergonomic risk factors and reduce their associated disorders. The findings of the study can be used in training programs to educate drivers how to decrease the risk associated with work-related MSDs by adopting appropriate behavior strategies.