PROCJENA HRAPAVOSTI POVRŠINE PRIRODNIH STIJENSKIH PUKOTINA TEMELJENA NA TEHNICI NENADZIRANOG PREPOZNAVANJA UZORAKA POMOĆU 2D PROFILA

The stability of a jointed rock mass is generally controlled by its shear strength that significantly depends on surface roughness. So far, different methods have been presented for determining surface roughness using 2D profiles. In this study, a new method based on the unsupervised pattern recognition technique using a combination of statistical, geostatistical, directional, and spectral methods for the quantification of the surface roughness will be proposed. To reach this goal, more than 10,000 profiles gathered from 92 surfaces of natural rock joints were scanned. The samples were collected from limestone cores of the Lar Dam located in the Mazandaran Province, Iran. After introducing a new spectral index, determined from the fast Fourier transform for measuring the unevenness of rough profiles, statistical, geostatistical, directional, and spectral features revealing waviness and unevenness of the 2D profiles were extracted, and a representative vector and profile for each surface were introduced through the weighted mean and median of the profile features. Principal component analysis (PCA) was utilized for finding the direction of the maximum variance of information. Then, clustering of the 92 samples was performed via K-means, and the silhouette measure was used in order to find the optimal number of clusters resulted in the creation of 13 clusters. To verify the procedure, a sample was selected in each cluster, and direct shear tests were performed on the samples. Comparing the experiments and the clustering results shows they are in good agreement. Thus, the method is an efficient tool for the quantitative recognition of surface roughness considering the waviness and unevenness of a surface.Stabilnost raspucane stijenske mase općenito se kontrolira posmičnom čvrstoćom koja značajno ovisi o hrapavosti površine. Do sada su prikazane različite metode za određivanje hrapavosti površine pomoću 2D profila. U ovom radu predlaže se nova metoda koja se temelji na tehnici nenadziranog prepoznavanja uzoraka kombinacijom statističkih, geostatističkih, usmjerenih i spektralnih metoda za kvantifikaciju hrapavosti površine. Kako bi se postigao taj cilj, skenirano je više od 10.000 profila prikupljenih s 92 površine prirodnih stijenskih pukotina. Uzorci su prikupljeni iz vapnenačkih jezgri brane Lar koja se nalazi u pokrajini Mazandaran u Iranu. Nakon uvođenja novog spektralnog indeksa, određenog Fourierovom transformacijom za mjerenje neravnina hrapavih profila, izvučene su statističke, geostatističke, usmjerene i spektralne značajke koje opisuju valovitost i neravnine 2D profila, a reprezentativni vektor i profil za svaku površinu uvedeni su kroz ponderiranu aritmetičku sredinu i medijan značajki profila. Analiza glavnih komponenti (PCA) korištena je za pronalaženje smjera najvećeg odstupanja informacija. Zatim je grupiranje 92 uzorka provedeno putem metode K-sredina, a mjera siluete korištena je kako bi se pronašao optimalan broj grupa, a to je rezultiralo stvaranjem 13 grupa. Za provjeru postupka odabran je uzorak u svakoj grupi, a na tim uzorcima provedena su ispitivanja izravnog smicanja. Usporedba rezultata ispitivanja i grupiranja pokazala je dobro slaganje, stoga je ova metoda učinkovit alat za kvantitativno utvrđivanje hrapavosti s obzirom na valovitost i neravnine površine

Association between Cognitive function and metabolic syndrome using Montreal Cognitive Assessment Test.

Background and Purpose: The increased risk for cognitive defects in individuals affected bymetabolic syndrome especially in those patients with cardiovascular disorders is now claimed.We aimed to assess the relationship between cognitive performance and the various componentsof metabolic syndrome.Methods: One hundred and eighteen consecutive individuals aged 30 to 86 years were includedinto this cross-sectional survey. The metabolic syndrome and its definitive components weredefined according to the definition described in the Framingham Heart Study by NCEP ATP IIIcriteria. The Montreal Cognitive Assessment (MOCA) questionnaire was employed to cognitivescreening.Results: Those patients with metabolic syndrome had significantly lower mean MOCA scorecompare to the group without metabolic syndrome (19.11 ± 5.49 versus 21.28 ± 4.56, p =0.021). Among all cognition sub domains, the mean attention score was significantly lower inthe group with metabolic syndrome than in another group. In a multivariate linear regressionmodel adjusting sex and age variables showed that the presence of metabolic syndrome couldeffectively predict cognitive impairment (beta = -2.202, SE = -0.214, p = 0.013).Conclusion: The presence of metabolic syndrome can be mainly related to damaging cognitionespecially impairing the power of attention