Air pollution is one of the most challenging and complex problems of our time. This research presents the prediction of air pollutant values based on using an R program with linear regression. The research sample consists of obtained values of air pollutants such as sulphur dioxide (SO₂), particulate matter (PM10, PM2.5), carbon monoxide (CO), nitrite oxides (NO, NO₂, and NO_X), atmospheric data pressure (p), temperature (T), and relative humidity (rh). The research data were collected from the city of Belgrade air quality monitoring reports, published by the Environmental Protection Agency of the Republic of Serbia. The report data were transformed into a form suitable for processing by the R program and used to derive prediction functions based on linear regression upon pairs of air pollutants. In this paper, we describe the R program that was created to enable the correlation of air pollutants with linear regression, which results in functions that are used for the prediction of pollutant values. The correlation of pollutants is presented graphically with diagrams created within the R GUI environment. The predicted data were categorized according to air pollution standard ranges. It has been shown that the derived functions from linear regression enable predictions that are well correlated with the data obtained by automatic acquisition from air quality monitoring stations. The R program was created by using R language statements without any additional packages, and, therefore, it is suitable for multiple uses in a diversity of application domains with minor adjustments to appropriate data sets.

Balanced scorecard is one of most used methodologies for performance measurement in enterprises and non-profit organizations. It presents a general framework that has been applied more precisely by many scientists and practitioners. It has been adapted to particular performance measurement systems. These monitoring systems are based on collecting data and analysis of these data. This paper presents overview of implementing balanced scorecard in IT project management. Framework for software project success monitoring, based on PRINCE2 methodology and balanced scorecard is proposed. Key performance indicators as measures to be performed upon data collected during software project implementation are defined. This way, analytics to be performed upon data according to specified key performance indicators, needed for decision support is enabled.

One of the most important phases in information system development is data modeling. In higher education and in professional environment, the basic data models are ER model, relational model and object oriented model, i.e. class diagram. Since conceptual modeling is crucial data modeling activity, special concern on methods for conceptual modeling is given in this paper. Case study shows that application of separate conceptual data modeling methods produce different and incomplete models. This leads to the conclusion that it is important to use integration of these methods and apply them within the information system development process. Educational experiences of applying this approach at University of Novi Sad, Technical faculty “Mihajlo Pupin” in Zrenjanin are presented as description of class exercises and exams results.

Air pollution data processing is a relevant aspect of urban-life-quality monitoring. This study relates the values of air pollutants to meteorological factors with prediction models created with multiple linear regression (MLR) in the R program. Fragmented data were obtained in the years 2023/24 from the heaviest-urban-traffic location in Belgrade, Serbia. A Serbian-comparable list of air pollutants (PM₁₀, PM_2.5, SO₂, NO₂, CO, and O₃) was created according to an analysis of sensor availability at monitoring stations. With the prediction models and seasonally clustered data (spring and summer data clusters were incomplete), each of these pollutants (except O₃) was related to all measurable meteorological factors (atmospheric pressure, wind speed, relative humidity, and temperature). All independent (meteorological) factors showed a moderate level (Multiple R² < 0.7) of impact on the prediction of air pollutant values (dependent factors). The second group of prediction models related pollutant triplets, and fourteen of the created models reached R² > 0.7. The prediction model with the highest relevant R² value (CO~PM₁₀ + NO₂) was used for CO pollutant prediction and in-sample fit analysis. The obtained mean absolute error is ~0.13 mg/m³. This case study contributes to the examination of the role of meteorological factors in urban-traffic air-quality monitoring within a seasonal context.