Weld Joints Inspection Using Multisource Data and Image Fusion

The problem of inspecting weld joints is very complex, especially in critical parts of machines and vehicles. The welded joint is typically inspected visually, chemically or using radiography imaging. The flaw detection is a task for specialized personnel who analyze all the data on each stage of the inspection process separately. The inspection is prone to human error, and is labor intensive. In the stages of weld joint visual control geometrical measurements are performed, joint alignment, straightness, deformation, as well as the weld\u27s uniformity. Coloration my show the heat impact zone, and melted parts of the base material. Also during this stage the unwanted cracks, pores and other surface defects can be spotted. On the other side during the X-ray inspection other flaws can be discovered. Pores, cracks, lack of penetration and slag inclusions can be observed. The author’s goal was to develop a multisource data system of easier flaw detection, and possibly inspection process automation. The methods consisted of three image sources: X-ray, laser profilometer, and imaging camera. The proposed approach consists combining spatial information in the acquired data from all sources. A novel approach of data mixing is proposed to benefit from all the information. The signal form the profilometer enables geometrical information extraction. Deformation and alignment error assessment. The radiogram provides information about the hidden flaws. The color image gives information about texture and color of the surface as well as helps in combining multiple sources

A Deep Learning Approach for Automatic Detection of Qualitative Features of Lecturing

Artificial Intelligence in higher education opens new possibilities for improving the lecturing process, such as enriching didactic materials, helping in assessing students' works or even providing directions to the teachers on how to enhance the lectures. We follow this research path, and in this work, we explore how an academic lecture can be assessed automatically by quantitative features. First, we prepare a set of qualitative features based on teaching practices and then annotate the dataset of academic lecture videos collected for this purpose. We then show how these features could be detected automatically using machine learning and computer vision techniques. Our results show the potential usefulness of our work.Comment: 10 pages, 9 figure

The role of zinc in the pathogenesis and treatment of central nervous system (CNS) diseases. implications of zinc homeostasis for proper cns function

Zinc, the essential trace element, is known to play multiple biological functions in human organism. This metal is a component of many structural as well as regulatory and catalytic proteins. The precise regulation of zinc homeostasis is essential for central nervous system and for the whole organism. Zinc plays a significant role in the brain development and in the proper brain function at every stage of life. This article is a review of knowledge about the role of zinc in central nervous system (CNS) function. The influence of this biometal on etiopathogenesis, prevention and treatment of selected brain diseases and disorders was discussed. Zinc imbalance can result not only from insufficient dietary intake, but also from impaired activity of zinc transport proteins and zinc dependent regulation of metabolic pathways. It is known that some neurodegenerative processes are connected with zinc dyshomeostasis and it may influence the state of Alzheimerís disease, depression and ageing-connected loss of cognitive function. The exact role of zinc and zinc-binding proteins in CNS pathogenesis processes is being under intensive investigation. The appropriate zinc supplementation in brain diseases may help in the prevention as well as in the proper treatment of several brain dysfunctions

Selective catalytic reduction of NO with ammoniaat low temperature over Cu-promotedand N-modified activated carbon

Catalytic properties of activated carbons oxidized, treated with N-compounds, and promoted withcopper were studied in selective catalytic reduction NOXby ammonia (NH3-SCR). The modificationof the catalysts consisted of a series of steps (pre-oxidation of activated carbon, impregnation with urea,impregnation with copper). The physicochemical properties of the obtained samples were determinedusing X-ray diffraction, FT-IR spectroscopy, and low-temperature N2sorption. The modification withcopper improved the catalytic activity and stability of the catalysts. All the functionalized carbon dopedwith copper reached more than 90% of NO conversion and CO2did not exceed 240 ppm at 220◦C.The sample doped with 5 wt.% Cu had the maximum NO conversion of 98% at 300◦C. The maximumN2O concentration detected for the same sample was only 55 ppm, which confirmed its selectivity

Weld Joints Inspection Using Multisource Data and Image Fusion

The problem of inspecting weld joints is very complex, especially in critical parts of machines and vehicles. The welded joint is typically inspected visually, chemically or using radiography imaging. The flaw detection is a task for specialized personnel who analyze all the data on each stage of the inspection process separately. The inspection is prone to human error, and is labor intensive. In the stages of weld joint visual control geometrical measurements are performed, joint alignment, straightness, deformation, as well as the weld's uniformity. Coloration my show the heat impact zone, and melted parts of the base material. Also during this stage the unwanted cracks, pores and other surface defects can be spotted. On the other side during the X-ray inspection other flaws can be discovered. Pores, cracks, lack of penetration and slag inclusions can be observed. The author’s goal was to develop a multisource data system of easier flaw detection, and possibly inspection process automation. The methods consisted of three image sources: X-ray, laser profilometer, and imaging camera. The proposed approach consists combining spatial information in the acquired data from all sources. A novel approach of data mixing is proposed to benefit from all the information. The signal form the profilometer enables geometrical information extraction. Deformation and alignment error assessment. The radiogram provides information about the hidden flaws. The color image gives information about texture and color of the surface as well as helps in combining multiple sources.</p

Analysis of the Relationship between Estradiol and Follicle-Stimulating Hormone Concentrations and Polymorphisms of Apolipoprotein E and LeptinGenes in Women Post-Menopause

Background: Menopause is the permanent cessation of menstruation due to loss of ovarian follicular activity. A review of the available literature indicates that correlations between the changes that take place in a woman’s body after menopause and different genetic variants are still being sought. Methods: The study was conducted in 252 women who had completed physiological menopause. The women were divided into groups according to the time elapsed since menopause. The total concentrations of estradiol and follicle-stimulating hormone were determined by means of electrochemiluminescence. The apolipoprotein E (APOE) and lepitn (LEP) genotypes were determined by real-time PCR and polymerase chain reaction–restriction fragment length polymorphism, respectively. Results: We observed that people with the APOE3/E3 genotype entered menopause insignificantly later compared to other genotypes. Additionally, in the group of patients with the APOE3/E3 genotypes, differences in the E2 concentration were significantly related to the time since their last menstruation. There is no association found in the literature between these polymorphisms of the LEP gene and hormones. Conclusions: To date, attempts to formulate a model describing the association between E2 and FSH concentration with the polymorphisms of various genes of menopause in women have not been successful. This relationship is difficult to study because of the number of nongenetic factors. Environmental factors can explain variation in postmenopausal changes in hormone levels