Mariusz Topolski

In literature relevant to this topic, attention is mainly paid to the qualitative and quantitative identification of defects in housing construction, and the factors that cause these defects. There is a research gap regarding the quantitative relationships between factors and defects, and the identification of factors that have a decisive impact on the occurrence of defects. The authors’ contribution to research regarding quality management in construction investments involves the identification of defects in residential buildings, identification of factors that generate construction defects occurring at various stages of the investment process, and also the assessment of their discriminatory power. This analysis used the results of technical inspections of buildings carried out in Poland in 2017–2020 in the Lower Silesia region. The study of the factors that influence quality in housing construction was carried out using the diagnostic survey method and the survey technique. Discriminant analysis was used for the calculations, with a number of influence factors being found. The following factors have the greatest discriminatory power: C1—a lack of internal control of design documentation before the start of the construction of the facility; C15—a lack of stability of the team (high staff turnover) that conducts contract tenders; C30—a lack of executive potential for preparing the facility for technical acceptance. Identifying the relationships between factors and quality, measured by the number and type of defects, will constitute the basis for developing procedures for conducting and controlling construction works and taking appropriate preventive actions in the form of employee training.

Ice phenomena occurring in water bodies are an important indicator of natural changes (e.g., climate change) and the possibilities for economic use of water bodies (e.g., using the ice cover); hence, there is a need to adopt new advanced statistical methods for the purpose of their analysis and assessment. Material for this study was collected for three winter seasons in 39 water bodies in the Silesian Upland (southern Poland). Nine variables were used in the analysis, of which three pertained to the features of the water bodies studied (surface area, mean depth, the amount of water retained), and six pertained patterns to of ice phenomena (average near-surface water temperature during ice phenomena, average and maximum ice thickness, the number of days with ice phenomena, the number of days with ice cover, and average thickness of the snow accumulated on ice). The centroid class principal component analysis (CCPCA) method was found to be the most precise of the five methods used in the study for classifying water bodies in terms of their ice regimes. It enabled the most accurate division of the group of water bodies covered by the study in terms of their ice regimes in conjunction with their morphometric features and hydrological types. The presented method of classifying water bodies using advanced statistical methods is an original proposal, which was used for the first time in limnological research and in the analysis of ice phenomena.

Background: Depression is a common problem in patients with cardiovascular diseases. Identifying a risk factor model of depression has been postulated. A model of the risk of depression would provide a better understanding of this disorder in this population. We sought to construct a model of the risk factors of depression in patients following cardiac surgery, with the use of machine learning. Methods and Measures: Two hundred and seventeen patients (65.4% men; mean age 65.14 years) were asked to complete the short form health survey-12 (SF-12v.2), three months after hospital discharge. Those at risk of depression were identified based on the SF-12 mental component summary (MCS). Centroid class principal component analysis (CCPCA) and the classification and regression tree (CART) were used to design a model. Results: A risk of depression was identified in 29.03% of patients. The following variables explained 82.53% of the variance in depression risk: vitality, limitation of activities due to emotional problems (role-emotional, RE), New York Heart Association (NYHA) class, and heart failure. Additionally, CART revealed that decreased vitality increased the risk of depression to 45.44% and an RE score > 68.75 increased it to 63.11%. In the group with an RE score < 68.75, the NYHA class increased the risk to 41.85%, and heart failure further increased it to 44.75%. Conclusion: Assessing fatigue and vitality can help health professionals with identifying patients at risk of depression. In addition, assessing functional status and dimensions of fatigue, as well as the impact of emotional state on daily functioning, can help determine effective intervention options.

Background: The study aimed to assess the severity of symptoms of anxiety and depression in children with previously diagnosed psychiatric disorders during the COVID-19 pandemic in Poland. Methods: Online questionnaires were used to investigate three groups of subjects: patients with a psychiatric diagnosis, primary school pupils, and children from children’s homes. A total of 167 children with their parents or guardians participated in the study. In addition to basic statistics, a multidimensional Centroid Class Principal Component Analysis (CCPCA) model was used. Results: It was found that the strongest fear of the coronavirus was experienced by children from children’s homes, while the most severe depressive symptoms and state anxiety were observed among patients diagnosed with psychiatric disorders. Parental care by assisting with school education and lack of close contact with other people (less than two metres) at parents/guardians’ work had the most potent protective effect in reducing the fear of COVID-19. Conclusions: There is a need for further research in children and adolescents to develop effective strategies for protecting their mental well-being when faced with social isolation or disease.

This article presents research that aims to identify waste reduction methods used in the construction industry in relation to the following materials: steel, concrete, masonry products, finishing products (i.e., ceramic, and stone tiles), and wood and the dependence between the use of these methods and the size of the construction company. The research is based on surveys conducted amongst construction site managers in Sharjah, United Arab Emirates. In the research, 13 methods of reducing construction waste were analyzed using Pearson’s independence test and the SPSS-26 software. Methods of reducing construction waste were identified. The study determined the frequency with which waste reduction methods in each material group were used, depending on the size of the company. Amongst the 13 methods analyzed, the ones which demonstrate a relationship between frequency of methods and size of the company were identified (for all groups of materials): the use of monitoring systems, reuse of materials within the construction, use of prefabricated elements, adequate storage, and engagement of subcontractors. In the case of the other tested methods, no such relationship was found.

Feature engineering is a branch of science that provides tools to support, for example, the preparation of feature spaces for a pattern recognition task. The present work focuses on the problem of feature extraction. The proposed model is based on the mechanisms of PCA principal component analysis. It fills a gap in the implementation of feature extraction by looking for spaces that best discriminate between classes. This was realized by rotating the features according to the centroids of the classes. In addition, a measure of their consistency was determined which allows precise estimation of the number of features for a particular component. Four experiments were conducted in this study. The first two were done on synthetic datasets, while the next two were conducted on ten real datasets. The synthetic data allowed to determine the characteristics depending on the percentage of informative features, the number of input features, the level of imbalance and the number of output components in the extraction task. The obtained results showed that the developed solution allows for a more precise extraction, thus increasing the quality of classification. Moreover, it was shown that the method based on class centroids allows to construct efficient ensembles of classifiers.