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For the community, major manufacturing facilities pose a high fire risk. BLEVE is known as the sudden outflow of a superheated liquid resulting from the liquid's evaporation under pressure in the atmosphere. The reason for these sudden outflows is caused by fires around the tank, corrosion and overheating inside the tank. Before using the modeling methods in the literature, artificial neural networks were modeled with input data determined using the Levenberg-Marquardt algorithm, which is a multilayer sensor (MLP) (teacher learning) sort. The real values obtained from the measurement are compared to the network's outputs. The outputs of the real values, which produced, and of the outputs produced by the network using the real inputs are compared the compatibility. The feasibility of performance determination was investigated by using the neural network and curves formed from the resulting values. For BLEVE, the fireball explosion, a scenario was calculated. Artificial neural network results were generated for the outputs of the data on LPG gas explosion in tankers, which have 100, 150 and 200 m3 volumes. Input values in the generated artificial neural network model; BLEVE's expansion measurement distance values. Thermal radiation heat flux (W/m2) values were predicted from the network as an output corresponding to these inputs. In addition, graphics comparing the actual expected values with the values found by the network created with the Levenberg-Marquardt Algorithm were also added. [ABSTRACT FROM AUTHOR]

The adverse impact of occupational accidents and occupational diseases on the society have recently increased a lot since the industrial-oriented society is getting more and more widespread. Although attempts regarding occupational health and safety (OHS) increase in Turkey, they are still at an inadequate level. OHS experts prepare employees for a healthy and safer working environment, so that work accidents can be eliminated and employees can be healthy psychologically and physically. At the same time, the worksites can be prevented from being financially damaged and productivity can be improved. The aim of this study is to measure OHS service quality by the evaluations of OHS experts from various sectors and workplaces. In this way, the OHS performance is measured, the difference between perceptions and expectations of the OHS experts is determined and deficiencies are revealed. In the study, the OHS activities are examined in five dimensions using the SERVQUAL scale; tangibility, reliability, responsiveness, assurance and empathy. The questionnaire is carried out using a Likert-type five-point scale among 81 OHS experts from different sectors. In conclusion, SERVQUAL analysis is introduced as a useful tool for employers to better understand what they need to know about OHS services. [ABSTRACT FROM AUTHOR]

Occupational Health and Safety (OHS) is a basis to reduce occupational accidents in an acceptable level, and it covers employee health, safety, and welfare in the workplaces. Hospitals as the largest employer group in healthcare industry in Turkey face major hazards categorized as chemical, biological, physical, ergonomic, and psychosocial risks. Although Turkey demonstrates rapid economic growth, OHS practices have not been fully put into practice, and necessary attention has not been paid to the health industry. For this reason, this paper aims to assess risks for health staff, contribute for planning of health services, and enhance regulations. A case study was carried out in a leading hospital in Turkey using a two-stage fuzzy multi-criteria approach, which provides more consistency in decision-making process and gives an appropriate final rank of hazard types. Fuzzy Analytic Hierarchy Process (FAHP) is used in weighing five risk parameters, which are severity, occurrence, undetectability, sensitivity to maintenance non-execution, and sensitivity to personal protective equipment (PPE) non-utilization. The fuzzy VIKOR (FVIKOR) approach is then applied for prioritization of hazard types in each department of the hospital. On conclusion of the hazard control hierarchy, measures are overtaken for the hazards, and areas open for improvement are presented. [ABSTRACT FROM PUBLISHER]

Emergency departments (EDs) face high numbers of patient arrivals in comparison to other departments of hospitals because they provide non-stop service. Patient arrivals at these departments mostly do not appear in a steady state. Predicting existing uncertainty contributes to the future planning of these departments. Therefore, forecasting patient arrivals at emergency departments is crucial so as to make short and long term plans for physical capacity requirements, staffing, budgeting and arranging staff schedules. In this paper, variations in annual, monthly and daily ED arrivals are analyzed based on regression and neural network models with the aid of a collected data from a public hospital ED in Istanbul. The results show that ANNbased models have higher model accuracy values and lower values of absolute error in terms of forecasting the ED patient arrivals over the long and medium terms. The paper is also aimed to provide ED management and medical staff a useful guide for future planning of their emergency departments in the light of an accurate forecasting. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a discrete-event simulation (DES) model of an emergency department (ED) unit in a regional university hospital in Turkey. In this paper emergency department operations of the hospital were modeled, analyzed and improved. The goal of the study is to reduce patient average length of stay (LOS) and to improve patient throughput and utilization of locations and human resources (doctors, nurses, receptionists). Some alternative scenarios in an attempt to determine optimal staff level were evaluated. These alternative approaches illustrate that vital improvement in LOS and throughput can be obtained by minor changes in shift hours and number of resources. Considering future changes in patient demand a scenario which reduces LOS and improves throughput is available in the paper. Significance: The Key Performance Indicators (KPIs) to determine and improve system performance in healthcare emergency departments consist of to reduce patient average length of stay (LOS), to improve patient throughput and resource utilization rates. Alternative scenarios and optimal staff levels are enhanced within the scope of this study. [ABSTRACT FROM AUTHOR]