The subject of the study is the improvement of the quality of moulding sand preparation. An exploration research performed on the data concerning moulding sand quality parameters was described. The aim of the research was to find relationships between various factors determining the properties of moulding sands and, based on the results obtained, build models predicting the sand moisture content with the induction of classification and regression trees. A two-match prediction approach was demonstrated and its effectiveness in evaluating the moulding sand moisture content was discussed. The knowledge in the form of rules acquired in this way can be used in the creation of knowledge bases for systems supporting decisions in the diagnostics of the moulding sand rebonding process. Formalized knowledge also facilitates further processing of the measurement data.

Background and Objectives: Poor sleep quality in patients with obstructive sleep apnea (OSA) may be associated with different clinical and polysomnographic features. The aim of this study was to identify features associated with poor sleep quality in OSA patients. Materials and Methods: This was a cross-sectional study enrolling patients with OSA confirmed by polysomnography (PSG). In addition to gathering clinical data, patients were assessed using the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Clinical Global Impression Scale. Univariate and multivariable analyses were performed to identify factors associated with an increased risk of poor sleep quality in this population. Results: Among 505 enrolled patients (mean age of 57.1 years, 69.7% male) poor quality of sleep (PSQI score ≥ 5) was confirmed in 68.9% of them. Multivariable analysis revealed the following factors associated with poor sleep quality: chronic heart failure (OR 3.111; 95% CI, 1.083–8.941, p = 0.035), male sex (OR 0.396; 95% CI, 0.199–0.787, p = 0.008), total ESS score (OR 1.193; 95% CI, 1.124–1.266, p < 0.001), minimal saturation during sleep (OR 1.034; 95% CI, 1.002–1.066, p = 0.036), and N3 percentage of total sleep time (OR 1.110; 95% CI, 1.027–1.200, p = 0.009). Conclusions: Our study suggests that both the female sex and coexistence of heart failure are independent risk factors for poor sleep quality. Moreover, we hypothesize that nocturnal hypoxia may lead to a misperception of sleep quality and may explain the counterintuitive association between a higher proportion of deep sleep and poor sleep quality.

The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis.