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The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.

Feature extraction is an important part of data processing that provides a basis for more complicated tasks such as classification or clustering. Recently many approaches for signal feature extraction were created. However, plenty of proposed methods are based on convolutional neural networks. This class of models requires a high amount of computational power to train and deploy and large dataset. Our work introduces a novel feature extraction method that uses wavelet transform to provide additional information in the Independent Component Analysis mixing matrix. The goal of our work is to combine good performance with a low inference cost. We used the task of Electrocardiography (ECG) heartbeat classification to evaluate the usefulness of the proposed approach. Experiments were carried out with an MIT-BIH database with four target classes (Normal, Vestibular ectopic beats, Ventricular ectopic beats, and Fusion strikes). Several base wavelet functions with different classifiers were used in experiments. Best was selected with 5-fold cross-validation and Wilcoxon test with significance level 0.05. With the proposed method for feature extraction and multi-layer perceptron classifier, we obtained 95.81% BAC- score . Compared to other literature methods, our approach was better than most feature extraction methods except for convolutional neural networks. Further analysis indicates that our method performance is close to convolutional neural networks for classes with a limited number of learning examples. We also analyze the number of required operations at test time and argue that our method enables easy deployment in environments with limited computing power.

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.

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 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.

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.

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.

The paper proposes a new approach to the agglomeration of data in cluster analysis. The new approach assumes that sets of similar events are attributed to the cumulative probability of their occurrence at the same time. Such approaches will not be found in probability. Thanks to the mathematical theory of records fairly accurate classification of the object can be provided. This is the method which can be used in the cluster analysis by agglomeration. Figure 1 has been drawn for the purposes of better illustration of the problem. It shows the problem of classifying an object to one of the two classes: suitable or unsuitable for further use. Thanks to the merger of two classifiers: KNN algorithm (k nearest neighbours) and belief function a model was created, which is pretty strong as it seems to discriminate against space objects. It therefore seems reasonable to discriminate space of objects. The paper also shows a possibility of applying the proposed model to classification and the correlation between cytokines and features related to the occurrence of lymphocytic leukaemia. It therefore seems justified to carry out tests on this new method as regards various scientific problems.

Introduction The aim of this research study was to compare the situation concerning the use of microbiology testing, the epidemiology of healthcare-associated infection (HAI) and antimicrobial consumption (AMC) in Polish long-term care facilities (LTCFs) with other European countries, using the most recent findings available in the European databases. Furthermore, this study aimed to highlight several basic factors that contribute to the observable differences in AMC between countries participating in the HALT-3 study, especially the relationship with demographic indicators, as well as the health care resources utilization rates. Patients and methods The most recent HAIs in Long-Term care facilities Point Prevalence Survey (HALT PPS) was carried out in EU/EEA countries in 2016–2017, and in Poland it was carried out in April–June 2017 in 24 LTCFs. AMC data was collected with use of methodology of the Anatomical Therapeutic Chemical (ATC) classification system of the WHO. Results In total total in HALT-3 study on the day of the PPS, 5035 out of the 102,301 eligible residents received at least one antimicrobial agent, with prevalence of 4.9%, and in Poland 3.2%. The most common HAIs in the countries included into the study was urinary tract infection with relative frequency of 32%, in Poland it was skin infection, 30.4%. The respiratory tract infections, excluding pneumonia (PNU) were observed in 29.5% of residents in total, in Poland 17.4%, the prevalence rate of PNU were 1.4% and 5.4%, respectively. The lack of microbiological results of HAIs testing concerned the vast majority of all HAIs, 75.8% in total and 81.5% in Poland. The most frequently used antibacterial for systemic use were beta-lactams and the most frequently prescribed antimicrobial agent was ‘amoxicillin and enzyme inhibitor’. AMC was closely correlated with the age of the general population (65 years of age and more) and the availability of doctors in general population. Conclusions A significant problem observed in LTCFs was the empirical use of antibiotics and the scarcity of microbiological testing. In the studied Polish LTCFs, where the age of residents was low, also the AMC was found to be lower.

Background: The most important pathomechanism of Clostridioides difficile infections (CDI) is post-antibiotic intestinal dysbiosis. CDI affects both ambulatory and hospital patients. Aim: The objective of the study was to analyze the possibility of utilizing databases from the European Centre for Disease Prevention and Control subject to surveillance for the purpose of identifying areas that require intervention with respect to public health. Methods: The analysis encompassed data concerning CDI incidence and antibiotic consumption expressed as defined daily doses (DDD) and quality indicators for antimicrobial-consumption involving both ambulatory and hospital patients in 2016. Results: In 2016, in the European Union countries, total antibiotic consumption in hospital and outpatient treatment amounted to 20.4 DDD (SD 7.89, range 11.04–39.69); in ambulatory treatment using average of ten times more antibiotics than hospitals. In total, 44.9% of antibiotics used in outpatient procedures were broad-spectrum antibiotics. We have found a significant relationship between the quality of antibiotics and their consumption: The more broad-spectrum antibiotics prescribed, the higher the sales of antibiotics both in the community sector and in total. CDI incidence did not statistically significantly correlate with the remaining factors analyzed on a country-wide level. Conclusion: Antibiotic consumption and the CDI incidence may depend on many national variables associated with local systems of healthcare organization and financing. Their interpretation in international comparisons does not give clear-cut answers and requires caution.