scholarly journals The Impact of Heat Change Intensity on the Prevalence of Work-Related Accidents in Isfahan Steel Industry; A Time-Series Approach

2020 ◽  
Author(s):  
Hadi Alimoradi ◽  
Mahsa Nazari ◽  
Mohammad Javad Zare Sakhvidi
Keyword(s):  
Time Series ◽  
Heat Stress ◽  
Wind Speed ◽  
Steel Industry ◽  
Thermal Stresses ◽  
Meteorological Data ◽  
Daily Basis ◽  
Work Related ◽  
Change Intensity ◽  
The Impact

In the steel industries, workers are exposed to heat and ambient thermal stresses on a daily basis, leading to discomfort and limited performance. In this study, the main purpose is to investigate the effect of climate heat stress on the rate of accidents in the workplace for workers for 5 consecutive years. The data of this study were received without any sampling through the HSE Center for Steel Industry and meteorological data from 2015 to 2019 from Isfahan Meteorological station. The daily number of casualties among workers in the steel industry during 2015-2019 by adjusting seasonal patterns, months, effects of the day of the week and other meteorological factors on the average daily temperature using the studied model has a decreasing effect. Eviews software (version 8) was used to model and investigate the relationship between events and meteorological variables. The mean temperature was at least 40.2-2 and at most 70.34 ° C, respectively. In the time-series study for the main model, the number of accidents shows a direct relationship with the average temperature and wind speed. Climatic indices of humidity and rainfall have the least impact on accidents compared to temperature and wind speed. A strong correlation was shown between the increase in average ambient temperature and the rate of accidents over the past 5 years. Given the fundamental differences in studies of environmental exposure and wind speed over heat stress, further analysis in workers should be considered.

scholarly journals MATERIAL AND THERMAL ANALYSIS OF LASER SINTERTED PRODUCTS

2013 ◽  
Vol 7 (1) ◽  
pp. 15-19 ◽  
Author(s):  
Radovan Hudák ◽  
Martin Šarik ◽  
Róbert Dadej ◽  
Jozef Živčák ◽  
Daniela Harachová
Keyword(s):  
Thermal Analysis ◽  
Heat Stress ◽  
Thermal Stresses ◽  
Processing Parameters ◽  
Sintering Process ◽  
Input Processing ◽  
Thermal Simulations ◽  
The Impact ◽  
Selection Of

Abstract Thermal analysis of laser processes can be used to predict thermal stresses and consequently deformation in a completed part. Analysis of temperature is also the basic for feedback of laser processing parameters in manufacturing. The quality of laser sintered parts greatly depends on proper selection of the input processing parameters, material properties and support creation. In order to relatively big heat stress in the built part during sintering process, the thermal simulation and thermal analysis, which could help better understand and solve the issue of parts deformations is very important. Main aim of presented work is to prepare input parameters for thermal simulations by the use of RadTherm software (Thermoanalytics Inc., USA), directly during the sintering process and after the process and find out the impact of the heat stress on a final shape and size of the prototype. Subsequently, an annealing process of constructed products after DMLS could be simulated and specified.

scholarly journals Time Series Modeling of Air Pollution and Its Association with Season and Climate Variables in Istanbul, Turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

scholarly journals An Investigation Towards The Suitability Of Vector Autoregressive Approach On Modeling Meteorological Data

2015 ◽  
Vol 9 (11) ◽  
pp. 89 ◽  
Author(s):  
Siti Mariam Norrulashikin
Keyword(s):  
Time Series ◽  
Meteorological Data ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Suitable Model ◽  
Multiple Time ◽  
Vector Autoregressive ◽  
Modeling Strategy ◽  
Vector Time Series ◽  
The Impact

In most meteorological problems, two or more variables evolve over time. These variables not only haverelationships with each other, but also depend on each other. Although in many situations the interest was onmodelling single variable as a vector time series without considering the impact other variables have on it. Thevector autoregression (VAR) approach to multiple time series analysis are potentially useful in many types ofsituations which involve the building of models for discrete multivariate time series. This approach has 4important stages of the process that are data pre-processing, model identification, parameter estimation, andmodel adequacy checking. In this research, VAR modeling strategy was applied in modeling three variables ofmeteorological variables, which include temperature, wind speed and rainfall data. All data are monthly data,taken from the Kuala Krai station from January 1985 to December 2009. Two models were suggested byinformation criterion procedures, however VAR (3) model is the most suitable model for the data sets based onthe model adequacy checking and accuracy testing.

Influence of climate change on thermal stresses in concrete box girders

2021 ◽  
Author(s):  
Saad Saad ◽  
Rashid Bashir ◽  
Stavroula Pantazopoulou
Keyword(s):  
Climate Change ◽  
Temperature Distribution ◽  
Thermal Stresses ◽  
Structural Model ◽  
Meteorological Data ◽  
Structural Response ◽  
Box Girders ◽  
Box Girder ◽  
Compressive Stresses ◽  
The Impact

<p>The purpose of this study is to investigate the impact of climate change on the thermal and structural response of concrete box girders. An advanced finite element platform was used to model a concrete box girder and analyze the additional thermal stresses that result from climate change. Meteorological data for future climate scenarios in Toronto, Canada was used as input in a thermal model to simulate the temperature distribution within the bridge deck. The temperature distribution was then used as input in a structural model of the bridge, to determine the resulting thermal stresses. The results show increases in tensile and compressive stresses as well as increased bridge movements. This study highlights the importance of explicitly considering climate change to achieve more robust bridge codes, particularly when it comes to thermal effects.</p>

scholarly journals Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

2018 ◽  
Vol 22 (6) ◽  
pp. 900-906 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Atmospheric Pressure ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Total Solar Radiation ◽  
Proposed Model ◽  
Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

scholarly journals DO RECRUITERS IN SERBIA FACE BURNOUT? THE IMPACT OF DEMOGRAPHIC FACTORS, DEADLINES AND WORK PRESSURE ON BURNOUT PRESENCE

TEME ◽  
2021 ◽  
pp. 1351
Author(s):  
Tatjana Ivanović ◽  
Sonja Ivančević ◽  
Tanja Trajković ◽  
Milica Maričić
Keyword(s):  
Work Experience ◽  
Quantitative Research ◽  
Daily Basis ◽  
Burnout Syndrome ◽  
Work Related ◽  
Work Pressure ◽  
Contemporary Work ◽  
Related Stress ◽  
Training Programmes ◽  
The Impact

Burnout syndrome represents one of the most serious disorders in contemporary work environment. One of the professions that did not receive much attention in the scientific research of burnout is that of recruiters, even though literature shows that recruiters face work-related stress on a daily basis, which can often lead to burnout among this group of employees. The aim of this paper is to attempt to identify whether and to what extent burnout is present among recruiters in Serbia (both in-house recruiters and those employed in recruiting companies and agencies) and to reveal the determinants which influence the possibility of burnout occurrence among this group of workers. The most frequently examined variables affecting burnout in other professions were analyzed: age, work experience, marital status, strict deadlines and work pressure. Copenhagen Burnout Inventory (CBI) was used to measure the level of burnout. The results obtained by a quantitative research using questionnaires conducted among 100 recruiters in Serbia have shown that recruiters in Serbia face burnout to an extent (overall burnout, individual, work-related and client-related burnout), while all examined variables (except employee’s age) were found to have statistically significant impact on burnout presence among recruiters. What adds value to this paper is the fact that the amount of burnout studies conducted in Serbia in general is scarce and mostly focused on helping professions. The research has a practical purpose to help companies and human resource departments create appropriate burnout prevention training programmes targeted for recruiters.

scholarly journals Analysis of temperature, air humidity and wind conditions for the needs of outdoor thermal comfort

2018 ◽  
Vol 44 ◽  
pp. 00028 ◽  
Author(s):  
Ewelina Dec ◽  
Bożena Babiarz ◽  
Robert Sekret
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Thermal Comfort ◽  
Meteorological Data ◽  
Daily Basis ◽  
Outdoor Thermal Comfort ◽  
Maximum Daily Temperature ◽  
Hot Days ◽  
Average Relative Humidity ◽  
The Relationship

On the thermal comfort of a man staying outdoor during the summer affect mostly meteorological factors, physical activity and the type of clothing. The work analyzed external air parameters, such as: temperature, relative humidity and wind speed, occuring in years 1997‒2016. Meteorological data recorded at the RzeszÓw-Jasionka station allowed to determine, among others, the occurrence of maximum daily and hourly temperatures of outdoor, the daytime and hourly air relative humidity, the hourly wind speed, as well as the relationship between these parameters. In recent years, it has been observed the increase of the number of hot and very hot days which indicates a warming of the climate. The duration of series of days with maximum daily temperature above 30°C is also prolonged, which is not comfortable for a person staying outside. During summer, during hot and very hot days, the average relative humidity remained below 70%. The daily course of this factor was characterized by the opposite tendency with respect to temperature. The wind speed in the summer season varies from 0 to 6 m/s. On a daily basis, the increase in wind speed occurred in the afternoon hours which is consistent with the temperature characteristics. The occurrence of wind during the hottest hours causes a pleasant cooling of the organism.

scholarly journals The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations

2020 ◽  
Vol 12 (8) ◽  
pp. 3431
Author(s):  
Markus Gross ◽  
Vanesa Magar ◽  
Alfredo Peña
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Power ◽  
Power Density ◽  
Estimation Error ◽  
Weather Prediction ◽  
Numerical Data ◽  
Data Driven ◽  
Wind Power Density ◽  
The Impact

The Wind Power Density (WPD) is widely used for wind resource characterization. However, there is a significant level of uncertainty associated with its estimation. Here, we analyze the effect of sampling frequencies, averaging periods, and the length of time series on the WPD estimation. We perform this analysis using four approaches. First, we analytically evaluate the impact of assuming that the WPD can simply be computed from the cube of the mean wind speed. Second, the wind speed time series from two meteorological stations are used to assess the effect of sampling and averaging on the WPD. Third, we use numerical weather prediction model outputs and observational data to demonstrate that the error in the WPD estimate is also dependent on the length of the time series. Finally, artificial time series are generated to control the characteristics of the wind speed distribution, and we analyze the sensitivity of the WPD to variations of these characteristics. The WPD estimation error is expressed mathematically using a numerical-data-driven model. This numerical-data-driven model can then be used to predict the WPD estimation errors at other sites. We demonstrate that substantial errors can be introduced by choosing too short time series. Furthermore, averaging leads to an underestimation of the WPD. The error introduced by sampling is strongly site-dependent.

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