University of Maribor

About: University of Maribor is a education organization based out in Maribor, Slovenia. It is known for research contribution in the topics: Population & KEKB. The organization has 3987 authors who have published 13077 publications receiving 258339 citations. The organization is also known as: Univerza v Mariboru.

Sustainability performance evaluation in industry by composite sustainability index

Abstract: The growing importance of sustainable development as a policy objective has initiated a debate about those suitable frameworks and tools useful for policy makers when making a sustainable decision. Composite indicators (CIs) aggregate multidimensional issues into one index, thus providing comprehensive information. However, it is frequently argued that CIs are too subjective, as their results undesirably depend on the normalization method, a specific weighting scheme, and the aggregation method of sub-indicators. This article applies different combinations of normalization, weighting, and aggregation methods for the assessment of an industrial case study, with the aim of determining the best scheme for constructing CIs. The applied methodology gradually aggregates sustainable development indicators into sustainability sub-indices and, finally, to a composite sustainability index. The normalization methods included in this analysis are: minimum–maximum, distance to a reference, and the percentages of annual differences over consecutive years. Equal weightings, the ‘benefit of the doubt’ approach, and budget allocation process were used for determining the weights of individual indicators and sustainability sub-indices. The linear, geometric, and non-compensatory multi-criteria approaches (NCMCs) were used as aggregation methods. The NCMC is modified to fit the two-level aggregation, then to sub-indices, and finally to a composite sustainable index. Also, a penalty criterion is introduced into the evaluation process with the aim of motivating the company to move towards sustainable development. The results are analyzed by variance-based sensitivity analysis. According to the results the recommended scheme for CIs’ construction is: distance to a reference–benefit of the doubt–linear aggregation.

Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Abstract: Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models.

Natural light controls and guides in buildings. Energy saving for electrical lighting, reduction of cooling load

Abstract: The residential sector is responsible for approximately a quarter of energy consumption in Europe. This consumption, together with that of other buildings, mainly from the tertiary sector, makes up 40% of total energy consumption and 36% of CO2 emissions. Artificial lighting makes up 14% of electrical consumption in the European Union and 19% worldwide. Through the use of well-designed natural lighting, controlled by technologies or systems which guarantee accessibility from all areas inside buildings, energy consumption for lighting and air conditioning can be kept to a minimum. The authors of this article carried out a state of the art on the technologies or control systems of natural light in buildings, concentrating on those control methods which not only protect the occupants from direct solar glare but also maximize natural light penetration in buildings based on the occupants׳ preferences, whilst allowing for a reduction in electrical consumption for lighting and cooling. All of the control and/or natural light guidance systems and/or strategies guarantee the penetration of daylight into the building, thus reducing the electrical energy consumption for lighting and cooling. At the same time they improve the thermal and visual comfort of the users of the buildings. However various studies have also brought to light certain disadvantages to these systems.

Dalitz analysis of the three-body charmless decays B+→K+π+π- and B+→K+K+K-

Abstract: We report results on the Dalitz analysis of three-body charmless B+ -> K+ pi(+) pi(-) and B+-> K+ K+ K- decays based on a 140 fb(-1) data sample collected with the Belle detector. Measurements of branching fractions for quasi-two-body decays to scalar-pseudoscalar states: B+ -> f(0)(980)K+, B+ -> K-0(*)(1430)(0)pi(+), and to vector-pseudoscalar states: B+ -> K-*(892)(0)pi(+), B+ -> rho(0)K(+), B+ -> phi K+ are presented. Upper limits on decays to some pseudoscalar-tensor final states are reported. We also report the measurement of the B+ -> chi(c0)K(+) branching fraction in two chi(c0) decays channels: chi(c0) -> pi(+)pi(-) and chi(c0) -> K+K-.