Air quality during SARS-CoV-2 (COVID-19) lockdown in Sarajevo

The aim of this paper is to compare air quality in Sarajevo in March 2019 and March 2020 with outbreak of the novel coronavirus SARS-CoV-2 in Sarajevo and Bosnia and Herzegovina. First preventive and protective measures were issued at the end of second week of March, while on 21 March 2020 an order imposing complete ban of movement of citizens from late afternoon until early in the morning next day was issued. This was rare opportunity to compare air quality in Sarajevo having same causes of air pollution for one part of March 2019 and March 2020 and different causes of air pollution during the lockdown and ban of movement caused by SARS-CoV-2. Statistical hypothesis testing is used to compare values during the March 2019 and March 2020 before the lockdown (the first phase) and during the lockdown (the second phase). Complete and comprehensive analysis is performed for both phases of March 2019 and March 2020, before the lockdown and during the lockdown. It is shown that there are statistical evidences that during the lockdown period mean concentration values of O3 and NO2 are smaller than mean values during same period in March 2019, while mean concentration value of PM10 is greater than mean value during same period in March 2019. Also, statistical hypothesis testing is used to compare concentration of air pollutants before and during lockdown period in March 2020. It is shown that mean concentration values of PM10 and O3 are greater during lockdown period, while mean concentration value of NO2 before the lockdown in March 2020 is greater than during the lockdown period. Coefficients of correlation as the measure of the strength of linear association between air pollutants PM10, O3 and NO2 and meteorological parameters air temperature, humidity and pressure, wind speed and wind direction are calculated as well

Machine learning prediction and analysis of students’ academic performance

The aims of this research were to develop a machine learning prediction Decision Tree classification model and analyze the success of engineering students based on their performances during secondary school education. The success of students was analyzed and measured as a binomial response to whether students successfully finished the first and the second study years. The developed model examined general success, number of awards obtained at competitions, special awards, average grades in mathematics, physics, and one of the official state languages during secondary school as predictor variables. General success was defined by summing up students’ grade point averages (GPA) of each school year. The number of courses transferred from the first into the second study year and students’ GPA obtained during the first study year were added as predictor variables in the analysis and development of a prediction model for the student’s success during the second study year and their enrollment in the third study year. Data showed that majority of the students enrolled in the first study year were gymnasium or technical high school graduates. Developed machine learning prediction model showed that for the success of enrolled students in the first study year General Success of students during secondary school is the most important predictor variable, followed by mathematics and physics grades. However, for the success of the students enrolled in the second study year the most important predictor variable was number of the courses transferred from the first into the second study year, followed by students’ GPA obtained during the first study year and General Success. Machine learning Decision Tree classification modeling was shown to be an adequate tool for the prediction of the success of engineering students during the first and second study years

Analysis of the influence of cutting parameters on surface roughness in laser cutting of tungsten alloy using control charts

Rad predstavlja analizu utjecaja parametara rezanja na hrapavost površine tijekom CO2 rezanja laserom legure volframa uporabom nitrogena kao pomoćnog plina, zasnovanu na kontrolnim kartama izrađenim pristupom statističke kontrole procesa. Zavisna varijabla je hrapavost površine, dok su neovisne varijable snaga lasera i brzina rezanja. Kontrolna karta koja se koristi u ovom radu je karta promjenjivih srednjih vrijednosti, koja potrebne srednje vrijednosti i raspone uzoraka proračunava uporabom tri uzastopne pojedinačne mjerne vrijednosti. Uporabom kriterija koji se često koriste u metodama statističke kontrole podataka za provjeru da li je situacija "izvan kontrole" može se zaključiti da povećanje brzine rezanja vodi k pogoršanju kontrole procesa s manjom uporabljenom snagom lasera.The paper presents analysis of the influence of cutting parameters on surface roughness during CO2 laser cutting process of tungsten alloy by using nitrogen as assist gas, based on control charts made by statistical process control (SPC) approach. Dependent variable is surface roughness, while independent variables are laser power and cutting speed. The control chart used within this paper is a variation of the moving means chart of experimental data samples, that calculates mean and range values using the three consecutive individual values. Applying the criteria often used in the SPC methods for the assessment of "out of control" situations, it may be inferred that increasing the cutting speed leads to worsening of control status for the process with lower laser power used

Centroid – A Widely Misunderstood Concept In Facility Location Problems

The aim of this paper is to show, by use of a complete and exact mathematical model, that the centroid method is a widely misunderstood method in facility location problems and that it is, in fact, normally an inappropriate method to use for such problems. While numerous sources do describe the procedure as minimizing the total shipping cost when transportation costs are linearly proportional to the distances of travel, this study shows that these statements are not valid.  The misunderstanding regarding what the centroid method actually does results from an improper interpretation of the notion of the center of gravity.  In fact, the centroid method minimizes shipping costs only if transportation costs are proportional to the squares of distances traveled. 

Optimization of Thermal Insulation and Regression Analysis of Fuel Consumption

AbstractIn this paper analyses of the current state of the thermal insulation of walls without styrofoam and existing windows of Alipasino polje buildings in Sarajevo, Bosnia and Herzegovina, which is powered boiler K-5 through its substations, and current fuel consumption is performed. Research results lead to the conclusion that it is worth to consider insulation of buildings, i.e., simulation of adding styrofoam and new windows on the existing structure in order to reduce heat losses and thus reduce fuel consumption and gas emissions. Savings obtained by this simulation are over 30%. Surface area of buildings subject to the installation of the insulation is obtained on the basis of projects from the district heating system Toplane Sarajevo. Styrofoam thickness is determined by optimizing reduction of the payback period. Prediction of fuel consumption was evaluated for the existing and projected future depending on outside temperature