Dwuetapowa metoda eksploracji danych pozyskiwanych z obrazów cyfrowych

The main aim of this work is to develop a two-step method of extracting knowledge from digital images. The method integrates the analysis of digital images directed at the extraction of quantitative and qualitative characteristics and knowledge extraction methods. The proposed method let to conduct a new kind of exploratory research aimed at extracting knowledge from digital images. This method allows to create rule set knowledge bases for decision support systems. Analysis of a coherent series of images allows the extraction of interesting qualitative and quantitative features. Put their further exploratory analysis may allow for the discovery of regularities occurring, generalizations, connections and relationships. The paper presents the research literature on both the available algorithms analysis and processing images as well as methods of exploration of knowledge. Realized computer program implements the proposed method, while providing the environment of its experimental verification. Checks on the correctness of the system were carried out on real data: computer tomographic images of the surface of the tooth, dermatoscopic skin cancer images and microscopic images of Friction Stir Welding joints. The proposed method of data mining of digital images was divided into two steps. The first step uses the selected methods of image analysis, focused on the extraction of quantitative and qualitative characteristics of objects presented on images. The basic input data for the selected methods are a series of images depicting the analyzed objects. At the step of extraction of characteristics: • Determined their number, type, the names of the attributes and names or ranges of features. • Establish a set of graphical transformations to be carried images to their standardization and to obtain the required characteristics. The result of this stage are data table - information system. These data are subjected to pre-processing, covering the processing of missing, outliers, digitization data. Pre-processed data make decision table. In the table determined attribute decision-making and conditional attributes. The decision table is input for the second step of the proposed method. This step involves data mining, ended generating decision rules. This process is preceded by an analysis of the consistency of the two available methods: qualitative and quantitative. Data mining is based on an approach based on rough set theory. The result of exploratory research are the decision rules generated by various methods. Cross validation allows to carry out the quality of the method. At each stage it is possible to support the use of domain experts who are using a dedicated system could verify the results obtained with regard to input images. The last element of the method is the ability to use established knowledge base for the implementation of subject decision support system. This is done by inference forward module. The inference can be used for both practical and experimental verification of the received rule base, as well as to achieve ready to implement the user system. The proposed method can maximize the automation capabilities of acquiring knowledge of the images, while allowing for the use of knowledge and competence domain experts

NON-INVASIVE ANALYSIS AND VISUALIZATION OF OBJECTS FROM FSW AlSi9Mg/2017A ALUMINUM ALLOY JOINTS

Friction stir welding (FSW) technology allows for the welding of materials that differ in their chemical compositions, microstructures, and properties. When selecting the parameters, it is necessary to analyze the structure of a weld and eliminate the defects. The use of X-ray microtomography creates the possibility of precise imaging of a joint structure – especially when it contains defects (voids). The tests were carried out on an FSW joint made with both 2017A and AlSi9Mg aluminum alloys. The macrostructure was cross-sectional, and a defect was found in the middle part of the joint and on the advancing side. We used a conventional method of observation light microscopy. Then, an analysis was performed using X-ray microtomography, which revealed the shape of the defect in 3D. Individual cross-sections were extracted, which enabled us to measure the geometric quantities. A dependence was found for the shape of the defect on the welding parameters and the variability of its construction. The usefulness of this advanced material imaging technology for the analysis of FSW welds has been confirmed

X-ray microtomography analysis of the aluminum alloy composite reinforced by SiC after Friction Stir Processing

Despite many years of using friction stir processing (FSP), there are many unexplained aspects concerning the processes which appear during FSP: determining the direction of flow and mixing of the materials and the degree of mixing and microstructure fragmentation in specific areas. This paper presents the impact of FSP on the micro- and macrostructure of the composite with hypo-eutectic Si matrix reinforced by SiC particles. The analysis of the structure of the processed area in FSP in the relation to the microstructure of the base material has been made using x-ray microtomography. The results of these studies have been juxtaposed with studies using microscopic methods (light microscopy and SEM). The microtomography images revealed an additional separation on the advancing side and the weld nugget, where on the basis of a 3D reconstruction a layer microstructure on the direction of linear movement of the tool has been demonstrated. The analyses have revealed a limited flow of the material above the weld nugget. The main advantages of the research method applied were the possibility to show the invisible or barely visible elements of the microstructure using standard test methods and the ability to analyze the microstructure changes uninterruptedly in different directions in the volume of the material

Named Entity Recognition -- Is there a glass ceiling?

Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study reveals the weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, for training processes and for checking a model's quality and stability. Presented results are based on the CoNLL 2003 data set for the English language. A new enriched semantic annotation of errors for this data set and new diagnostic data sets are attached in the supplementary materials.Comment: Accepted to CoNLL 201

Patophysiology of nonalcoholic fatty liver disease

Clinically, NAFLD is the most common cause of asymptomatic increases in transaminases. NAFLD is also currently the most common liver disorder in developed countries, affecting 24% of the world's population. The prevalence of NAFLD in European societies is estimated in the range of 17-46% and shows an upward trend with the increasing incidence of obesity and type II diabetes. NAFLD occurs in about 7% of people without excess weight, however, they are usually people with impaired insulin sensitivity, leading a sedentary lifestyle, having an increased cardiovascular risk, with higher levels of hepatic lipids as a result of reduced fat accumulation and reduced mitochondrial activity in adipose tissue and increased de novo hepatic lipogenesis

Major causes of insuline resistance

Causes of insulin resistance can be divided into innate and secondary. There are many reasons for insulin resistance, including genetic mutations like insulin receptor mutations, hormonal and pharmacological or immunological. However insulin resistance is most commonly associated with obesity. Obesity is associated with a low-grade inflammation of white adipose tissue (WAT) resulting from chronic activation of the innate immune system and which can subsequently lead to insulin resistance, impaired glucose tolerance and diabetes

Analysis of Selected Physicochemical Properties of Commercial Apple Juices

The paper presents the comparison of quality of six different commercial apple juices produced in Poland. The apple juices came from two different Polish companies. From each manufacturer three various juices were selected: two cloudy and clarified one produced as a mixture of different apple varieties. The following properties were evaluated: density, soluble solid content, viscosity, total phenolic contents, phenolic acid, and antiradical activity. The obtained results showed that three from fourth cloudy juices (M1Sz, M1A and M2A) differed from clarified juices. The highest differences were noticed for total phenolic contents, phenolic acids, antiradical activity and viscosity. The values of these properties ranged between 60.72–103.6 mg GAE·100-1 mL-1 for phenolic contents, 78.3–90.9% for antiradical activity and 2.68–5.79 mPa·s for viscosity. The remaining cloudy juice (M2Sz) was more similar to the clarified ones. The strong correlation between total phenolic content and antiradical activity of apple juices was found. The high correlation coefficient obtained between these features indicates that both producers do not apply excessive amounts of vitamin C during the production of cloudy apple juices. There was also a strong interrelation between the density of juice and solid soluble content. However, no correlation was found between the solid soluble content and the viscosity of the tested juices

The role of the gynaecologist in the promotion and maintenance of oral health during pregnancy

Objectives: The aim of the study was to assess routine dental examination attendance of pregnant women and a possible impact of gynaecological referrals on the attendance rate. Material and methods: An electronic survey was conducted that was inclusive of women up to 5 years following delivery. The questions related to socio-demographic data, the course of pregnancy and childbirth, and visits to dental office during pregnancy. For statistical analysis, the authors utilized the chi-square test, Spearman’s rank correlation coefficient and odds ratios. A significance level of 0.05 has been assumed. Results: A total of 3455 questionnaires were analyzed encompassing women aged 13.1–45.4 years. The respondents were on average 1.78 ± 1.44 years after childbirth. The population comprises of women in 59.1% from large cities, in 74.8% with higher education and in 41% with good socio-economic status. A total of 62.3% of women from the study population have visited a dentist for a routine dental examination. Gynaecologists have given a simple referral to a dentist to 17.6% of all women. 45.9% of them were further requested to provide back the feedback of their dental consultation. Dental appoint­ments were upheld by 87.3% of referred women and by 56.9% of those without a referral (OR = 5.20 (4.05–6.67); p < 0.001). Among those who were referred, dental appointments were upheld in 91.7% of cases when further asked to provide oral health feedback and in 83.5% of cases in absence of such further request (OR = 2.19 (1.3–3.66); p = 0.003). Conclusions: It was determined that referrals from a gynaecologist, and associated oral health feedback requests increase the frequency of abiding to dental appointments during pregnancy. As such, it is necessary to increase the involvement of gynaecologists in the promotion and maintenance of perinatal oral health

Paraoxonase 1 as an important antiatherogenic agent

The inverse relationship between High Density Lipoproteins (HDL) level and risk of ischaemic heart disease was proved by many epidemiological studies. Although the main mechanism of antiatherogenic activity of HDL is a reverse transport of cholesterol from peripheral tissues to the liver, HDL additionally carries some antioxidative enzymes like Paraoxonase 1 (PON1) which protects LDL and HDL lipoproteins from oxidative modification. A lot of antiatherogenic activities of HDL depends on PON1 activity