Background: Rheumatoid arthritis (RA) is a chronic, systematic inflammatory disorder that may affect many tissues and organs, but principally attacks synovial joints and it is a common rheumatic disease with many subtypes. Nuclear Magnetic resonance (1H NMR) spectrometers with high sensitivity, resolution and dynamic range has permitted the rapid, simultaneous investigation of complex mixtures of endogenous or exogenous components present in biological materials. Metabonomics is the systematic study of chemical finger print resulted from cell reactions and could be used as a new biomarker for early disease diagnosis. In the present investigation, we studied serum metabolic profile in rheumatoid arthritis (RA) in order to find out the metabolic finger print Pattern of the disease.Materials and methods: In our metabonomics study serum samples were collected from 16 patients with active RA, and from equal number of healthy subjects. They were evaluated during a one-year follow-up with the assessment of disease activity and 1H NMR spectroscopy of sera samples. In all the cases, the presence of active rheumatoid arthritis was shown by an increase in the T1 values of the synovium of the joints. We specified and classified all metabolites using PCA, PLSDA chemometrics methods. Chenomx (Trail Version) and ProMetab codes in Matlab software environments were used for our data analysis. Results were compared with the NMR metabolite data bank (www.metabolomics.ca). Anti-CCP, ANA and urea were also analyzed by ElISA and colorimetric methods respectively.Results: The most changes identified in this study were in the biosynthesis pathways of steroid hormones, biotin, fatty acids, amino acids (Leucine, Valin and isoleucine) and also linoleic acid.Conclusion: In rheumatoid arthritis disease, the activation of the immune system consumes larg amounts of energy. The main donor of free energy in cells is ATP, which is generated by both glycolysis and oxidative phosphorylation. Changes in amino acids and free fatty acids biosynthesis pathways confirm the high energy utilization. In this disease, the increase in free fatty acid metabolism leads to production of Acetyl CoA and ketone bodies. Since there are many diseases subtype in rheumatoid arthritis, more sensitive diagnostic method is required. The result of our investigation suggests that metabolome profiling method could be used as a new biomarker for early diagnosis of rheumatoid arthritis disease.

In this paper, a novel Patch Geodesic Derivative Pattern (PGDP) describing the texture map of a face through its shape data is proposed. Geodesic adjusted textures are encoded into derivative Patterns for similarity measurement between two 3D images with different pose and expression variations. An extensive experimental investigation is conducted using the publicly available Bosphorus and BU-3DFE databases covering face Recognition under pose and expression changes. The performance of the proposed method is compared with the performance of the state-of-the-art benchmark approaches. The encouraging experimental results demonstrate that the proposed method is a new solution for 3D face Recognition in single model databases.

Background and Objective Landform refers to any physical feature of the surface with a recognizable structure and shape. Landform elements and structural forms of the terrain surface could, directly and indirectly, drive many other environmental variables. Numerical representation of the surface and uneven Pattern of the earth is a common topic in geographical, geomorphological, geological, and geophysical hazard mapping as well as sea-bed exploration. The combination of the earth and computer science with mathematics and geomorphometric engineering interacts with discrete and continuous landforms. Geomorphometry dates back to about 150 years ago and the work of Alexander von Humboldt and geomorphologists, and today with the revolution in computer science and especially digital computer models is developing rapidly. Detection and classification of landforms are of interest to GIS developers, geoscientists, and geomorphometry researchers. In this way, the desired work units are extracted with higher speed and accuracy and used in the form of vector and raster maps. Existing approaches are mainly based on height, terrain derivative, gradient, curvature, flow direction, slope position, morphometric indices, and the like. Also, less attention has been paid to the challenge of matching the diagnostic scale with the Landform scale, and most models have this shortcoming. On the other hand, less attention has been paid to the possibility of vectorization output results and also to the analysis of sensitivity and temporal response algorithms to machine processing. In this research, we attempted to recover and resolve the mentioned shortcoming and problems in the previous works. In this research, using basic algorithms of raster analysis and coding, new methods and algorithms for the automatic detection of landforms have been developed. Focal raster analysis is also emphasized and the moving window technique is used to implement the algorithms. Facing the scale challenge, sensitivity analysis, and the response algorithms to input changes as well as accuracy assessment are other aspects that have been addressed in this research. Materials and Methods In this study, the Digital Surface Model (DSM) published by the Japan Space Agency in May and October 2015 with a horizontal resolution of about 30 meters was used to work on the topography of the region. These data are obtained from ALOS satellite images. This database is based on DSM data (5m network version) 3D topography, one of the most accurate elevation data on a global scale. The digital elevation model was transformed into a matrix structure using a Python coding environment. Then, raster analysis was implemented using the moving window technique. The moving window algorithm was coded in a way that the dimensions of the moving window could be freely determined and changed. In proportion to the size of the moving window, some adaptive algorithms are implemented to automatically correct and organize the edge effect in proportion to the size of the moving window. In this study, automatic landform detection was performed using spatial analysis of kernel Patterns in the raster grid of digital elevation models and the results were presented in the form of three algorithms applied in the detection of topographic peaks and ridges. These algorithms include Multilevel Mean Summit Recognition Algorithm (MLMSR), Complex Multilevel Summit Recognition Algorithm (CMLSR), and Single Point Summit Recognition (SPSR). Each of these three algorithms was first conceptually designed and then coded and executed using the Python programming language. In the next step, the sources of error and specific scenarios of the algorithms were examined. The sensitivity of each algorithm related to the dimensions of the moving window, the resolution, and the size of the raster file, was evaluated, and finally, the accuracy and validation of the three models, using reference layers that were manually prepared and plotted, were assessed. All the procedures were designed in a way that could easily be implemented in an official software and were completely compatible with the structure of machinery processing. Also, being automatic and working on different platforms where one of our priorities. Results and Discussion In the automatic detection of peaks and ridges using a digital terrain model, kernel spatial Pattern analysis was used. In this regard, three proposed algorithms in this field were designed, coded, and executed. The output results of each of the algorithms were presented in the form of a raster and vector data model. Accuracy and sensitivity assessments were performed by considering changes in moving window size, resolution, and raster grid size (row x column) for each of the algorithms. The MLMSR algorithm tends to be in a more binary result in the lower dimensions of the moving window, while the CMLSR and SPSR algorithms do not. In all algorithms, increasing the size of the moving window causes a more generalization ratio. CMLSR and SPSR algorithms are more suitable for cartographic and visual purposes due to the higher degree of grading in the results. Regarding the temporal performance (Runtime) or sensitivity to input changes, the SPSR algorithm performs better. This is especially important when the input file size (number of rows and columns) is large. According to the results of validation and accuracy evaluation, MLMSR and SPSR had better performance than, the CMLSR algorithm. Python programming language has been widely used in the design and implementation of all algorithms, as well as in the field of sensitivity evaluation and validation. Totally more than 500 lines of codes were done for this purpose. All algorithms are automated and are able to execute and store results in raster and vector format using machine processing. Conclusion The results show that the MLMSR algorithm in smaller dimensions of the moving window is tending to more binary results, which is problematic in some graphical and cartographic applications, but the CMLSR and SPSR algorithms showed more gradual trends in their outputs and so, they performed better in this respect. Researchers who intend to study and develop in this field are advised to focus on adaptive algorithms and optimize the dimensions of the moving window in relation to the volume of input information and so, in this way, they increase the flexibility of algorithms in relation to input changes.

The objective of this study was to assess the relationships between physicochemical and microbiological properties of raw milk and the use of multivariate statistical analysis such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and stepwise discriminant analysis (SDA) for Pattern Recognition and classification it. In this study, 48 raw milk samples were collected from some dairy herds of Mashhad. Samples were analyzed for the microbiological and physicochemical properties. PCA, HCA and SDA were applied to estimate the usefulness of the physicochemical and microbiological parameters for the differentiation and classification of raw milk using. The results of PCA shown the seven principal components explained 93.65% of total system variance. The PCA method permits a good classification between raw milk samples on the basis of the first three PCs. HCA classified physicochemical and microbiological properties of raw milk into three main groups that confirmed the correlation between the studied variables obtained by PCA. Using SDA it was determined which variables best classified the raw milk samples according to their quality. Finally, the classification functions allowed the correct classification of 91.7% of the raw milk samples. Due to the direct effect of raw milk quality on dairy products quality and consumer health, the quality of raw milk has special importance in the dairy industry. Therefore, classification of raw milk based on the quality characteristics will help to determine the price of raw milk and to produce high quality dairy products.

In industrial rotatory machines, different forces in rotor bearings are generated due to various impaired mechanical sources, namely bearing misalignment and nonhomogeneous mass distribution (unbalance). By precisely analyzing and diagnosing the produced Patterns of bearing forces, one can determine the unbalance parameters such as quantities of masses, their distance from the rotational axis, and characteristics of corresponding parallel planes. Consequently, it will be possible to formulate pragmatic protocols according to which the maintenance engineers of rotatory systems will pinpoint properties of problematic imbalance masses and then straightforwardly balance them. In the procedure of conducting this research, several exemplary imbalance masses are deployed on a rotatory mechanical shaft and the equations of motion and forces in perfectly aligned rigid bearings are extracted. Then, by applying a neural network-oriented system the Patterns of bearing forces are recognized and the characteristics of the nominal masses including magnitudes, distances from the rotational axis, angles as well as the unbalance type are determined. The accuracy of predicting 8 variables of balancing masses was 41% and after eliminating the redundant overlaps from principal components, the accuracy of predicted 5 variables of balancing masses significantly increased to 95%. Also, by implementing another comprehensive neural network system, it was shown that by exerting two separate balancing masses, the applicability of this method in balancing any faulty systems with dynamic unbalance is possible.

Introduction: Academic faculty members need to emphasize ethical principles in order to improve higher education, with the understanding of this importance, the current research was conducted with the aim of designing the ethical model of academic faculty members in Iran's higher education. Material & Methods: This research is a type of qualitative study that uses foundational data theory. The participants, including experts and faculty members of the country's public universities, who were responsible for the promotion of academic members, were selected as key informants through a targeted sampling method of 45 people. Data were collected through semi-structured interviews. The reliability and validity of the data were obtained from the two methods of reviewing the participants and recoding by experts. Data were analyzed by open, central and selective coding. Results: The results showed that the moral model of faculty members in Iran's higher education is explained in 10 main categories and 27 subcategories. Major and core categories in the form of 6 dimensions of causal conditions including (individual factors, organizational factors), core (cultural-educational, educational ethics, research ethics, scientific-executive), background conditions (higher education policies, moral activism), intervening conditions (environmental factors), strategies (micro level-university, macro level-higher education) and consequences (individual and organizational) were elevated to a higher abstract level and finally the research paradigm model was presented. Conclusion: The regulations for the promotion of academic staff members in Iran require a detailed revision of the content with an ethical and qualitative approach in order to make the indicators of the promotion regulations efficient and effective.