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Cardiovascular problems have emerged as a significant concern, adversely impacting individuals across all age groups. Several recent research studies have used Machine learning (ML) techniques to design decision-making systems for the tremendous data in the medical sector. Although these works obtained promising results, most of the studies focused on small datasets. Since the size of the dataset affects algorithm performance, this study used two datasets, such as Kaggle’s heart disease dataset of over 70,000 records and UCI’s heart disease dataset of 1025 records. In addition to the old features the Pulse Pressure (PP), the Body Mass Index (BMI), and the Mean Arterial Pressure (MAP), three new features are introduced to improve the results. This paper proposes the TLV (Two-Layer Voting) model, which is an ensemble method of hard and soft voting. As part of layer 1, features are shortlisted by soft and hard voting using three statistical methods, including the ANOVA f-test, Chi-squared test, and Mutual Information. In layer 2, soft voting and hard voting performance are compared, which incorporates Multi-Layer Perceptron, Decision Tree, Support Vector Classifier, and Random Forest algorithms. Classification algorithms are hyper-tuned using the GridSearchCV method in the second layer. Using UCI’s heart disease dataset and Kaggle’s CVD dataset, the proposed TLV methodology with soft voting provided the highest accuracy of 99.03% and 88.09%, respectively. The proposed model significantly outperforms existing CAD disease prediction studies.

The main aim of the paper was to investigate factors influencing flight delays of a European airline. Besides the identification and analysis of those factors the paper offers possible suggestions on how to eliminate the delays. The research is based on data acquired over the period of time spanning from June to September in 2008 – 2014. Analysis of contingency tables, including Pearson’s chi‑squared test, has been used for data processing. The dependencies have been presented in graphical form by using correspondence maps. The proportion of delayed flights reaches approx. 50 % during nearly the entire monitored period only in September the proportion drops to 45 %. Flight delays are most frequently caused by delays of previous flights of the same plane. These previous delayed flights are the main culprit of long delays and the frequency of delay occurrence caused by this reason increases significantly during the day. Longer delays of flights appear also due to technical maintenance or aircraft defects. On the contrary other factors such as operational control and crew duty norms, air traffic control and airport limitations tend to cause rather shorter delays of flights with the air traffic control encountering more problems with coordination of flights in the early morning. The supply and service companies also manage to eliminate long delays.

This paper examines the effect of weather conditions on truck type distribution using combined nonparametric chi-squared and binomial probability statistical tests. Influence of the winter conditions on truck type distribution is investigated in this paper by classifying trucks into single-unit trucks, single-trailer, and multi-trailer units. The investigation is based on 5 years Weigh-In-Motion (WIM) traffic data collected from Alberta provincial highway network in Canada. The WIM data is collected from six WIM sites located on Highway 2, Highway 2A, Highway 3, Highway 16 and Highway 44. The objective of this study is to investigate the association of three truck type distribution with month and season depending on weather conditions by means of nonparametric statistical test. The statistical results indicate that the variation of truck type distribution is influenced by type of highway facility, such as regional commuter roads and rural long distance highways. The season of the year (winter and non-winter) may also affect the truck type distribution on some types of roads. Findings of this study can benefit highway agencies in developing programs and policies related to efficient monitoring of truck traffic and maintaining highway network throughout the year.

In order to find out the human factors leading to coal mine general accidents, the paper counts the coal mine accidents occurred in 2019. Based on the investigation reports, HFACS is used to carry out the statistics and analysis of 43 coal mine general accidents. Then the relationships between different human factors, which are involved in the HFACS-PEFE framework, are identified by conducting chi-square test and odds ratio analysis. Four routes of coal mine general accidents are obtained. Among them, the route comprised of organizational process loopholes → invalid correction → individual mental state → habitual violation is the easiest way to lead to general accidents in coal mines. Finally, according to the analysis results, the measures to prevent coal mine general accidents are proposed.

Testing multivariate normality is an ever-lasting interest in the goodness-of-fit area since the classical Pearson’s chi-squared test. Among the numerous approaches in the construction of tests for multivariate normality, normal characterization is one of the common approaches, which can be divided into the necessary and sufficient characterization and necessary-only characterization. We construct a test for multivariate normality by combining the necessary-only characterization and the idea of statistical representative points in this paper. The main idea is to transform a high-dimensional sample into a one-dimensional one through the necessary normal characterization and then employ the representative-point-based Pearson’s chi-squared test. A limited Monte Carlo study shows a considerable power improvement of the representative-point-based chi-square test over the traditional one. An illustrative example is given to show the supplemental function of the new test when used together with existing ones in the literature.

One of the negative trends in current nutrition is an increased consumption of refined foods with a low content of useful nutrients and antioxidants and an abundance of oxidation accelerators. Fruit paste and jelly have a relatively low energy value, compared to other confectionery products. Along with this fact, they also have gel- ling agents and fruit raw materials in their composition, which can classify them as diet food. This paper presents a comprehensive approach to developing a technology for producing fruit jelly with an improved carbohydrate profile. For that, we used viburnum and orange puree, a valuable natural plant material, as a source of carbohydrates, and fructose, as a sugar substitute. The qualimetric model created from the tasting data was used to select the optimal proportions of the main ingredients, viburnum and orange puree, as 25:75 and 75:25. The comparative assessment of antioxidant capacity (AOC) showed that the sample with the 75:25 ratio of viburnum to orange puree had an AOC of 22.33 μmol TEq/g. It was twice as high as AOC of the 25:75 ratio sample. With a glycaemic index of 29.2, this fruit jelly can be recommended as a diet food with preventative properties.

In 1939 N.I. Ermolaeva published the results of an experiment which repeated parts of Mendel's classical experiments. On the basis of her experiment she concluded that Mendel's principle that self-pollination of hybrid plants gave rise to segregation proportions 3:1 was false. The great probability theorist A.N. Kolmogorov reviewed Ermolaeva's data using a test, now referred to as Kolmogorov's, or Kolmogorov-Smirnov, test, which he had proposed in 1933. He found, contrary to Ermolaeva, that her results clearly confirmed Mendel's principle. This paper shows that there were methodological flaws in Kolmogorov's statistical analysis and presents a substantially adjusted approach, which confirms his conclusions. Some historical commentary on the Lysenko-era background is given, to illuminate the relationship of the disciplines of genetics and statistics in the struggle against the prevailing politically-correct pseudoscience in the Soviet Union. There is a Brazilian connection through the person of Th. Dobzhansky.

Abstract Background The evaluation of associations between genotypes and diseases in a case-control framework plays an important role in genetic epidemiology. This paper focuses on the evaluation of the homogeneity of both genotypic and allelic frequencies. The traditional test that is used to check allelic homogeneity is known to be valid only under Hardy-Weinberg equilibrium, a property that may not hold in practice. Results We first describe the flaws of the traditional (chi-squared) tests for both allelic and genotypic homogeneity. Besides the known problem of the allelic procedure, we show that whenever these tests are used, an incoherence may arise: sometimes the genotypic homogeneity hypothesis is not rejected, but the allelic hypothesis is. As we argue, this is logically impossible. Some methods that were recently proposed implicitly rely on the idea that this does not happen. In an attempt to correct this incoherence, we describe an alternative frequentist approach that is appropriate even when Hardy-Weinberg equilibrium does not hold. It is then shown that the problem remains and is intrinsic of frequentist procedures. Finally, we introduce the Full Bayesian Significance Test to test both hypotheses and prove that the incoherence cannot happen with these new tests. To illustrate this, all five tests are applied to real and simulated datasets. Using the celebrated power analysis, we show that the Bayesian method is comparable to the frequentist one and has the advantage of being coherent. Conclusions Contrary to more traditional approaches, the Full Bayesian Significance Test for association studies provides a simple, coherent and powerful tool for detecting associations.