Problems of thermal conduction for the composite environs are very important in the high temperature thermo physics, and it is connected with necessity of calculation of multiple coating for the different devices.\nStudying of the laminated superconducting materials, for example, “sandwiches” (dielectric-metal-dielectric), is closely associated with research of the boundary interface and surface layers.In a non-linear case, we have considered the problem with a small parameter and discussed an issue of applicability of some aspects of a theory of bifurcation of the nonlinear equations\' solutions[1;3].One of the physical statement leads to the boundary problem of thermal conduction of the multilayer materials and studying of the solutions complying with the thermal contact conditions and nonlinear boundary conditions of Stefan-Boltzmann [2].

In the introduction of this study some aspects are highlighted on the importance and usefulness of simple linear regression econometric model in an economic entity decision making. A case study is presened below on the correlation between the number of employees within a company and the amount of contributions the company must transfer to the security and social protection budgets for each employee.\nThe conclusions focused on the results obtained in this case study which confirm the hypothesis of a connection between the employees number and the contributions amount paid to the security and social protection budgets. From this point of view this case study’s main aim was achieved.

Every individual is provided with access to plenty of information with the help of\nWorld Wide Web, but it becomes progressively more difficult to discover the significant\npieces of information. Study in web mining tries to tackle this problem by applying data\nmining techniques to Web data and documents.The data available on the web is so\nheterogeneous and huge that it becomes a crucial factor to extract this accessible data to make\nit pertinent to a particular problem. Web mining uses data mining techniques to extract\nknowledge from web sources. This paper focuses on detecting and extracting templates from\nweb pages that are heterogeneous in nature by means of an algorithm. Locality sensitive\nhashing finds the similarity between the input web documents and provides good\nperformance compared to Minimum Description Length (MDL) principle and hash cluster\nprocess in terms of execution time.

We investigated genetic variation in branch and needles traits of Pinus silvestris L. clonal seed orchard from Taşköprü-Kastamonu. Collected from 30 clones x 7 graft x 5 branch and 10 needles from each branch were studied. In this study, genetic diversity structure in a clonal seed garden was determined according to 16 branch and needle characteristics. Results of study show that NW2, NL1, NL2 and NW1 are strongly inherited characteristics. According to the results of SAS analysis, the variation between the clones is averaged at 19.2 % and the variation within the clones is averaged at 22.72 %. The variation between the clones are range from 6.93 % (NN1) to 41.0 % (NL2) and the variation within the clones are range from 58.02 % (NL2) to 92.08 (NN1). Results of this study show that, genetic variation within clones is higher than among clones for all characters.

Breast cancer is the most frequently diagnosed cancer in women, and the main cause of cancer related deaths. Mortality can be significantly reduced by detecting the disease at early stages, then through proper medication and therapies. Microcalcifications are tiny calcium deposits that show up as fine white specks on digital mammogram images. Detection of clustered microcalcifications in mammograms is an indicator for early diagnosis of breast cancer. In this paper, a novel method is proposed for the earlier detection of breast cancer. In this proposed technique, initially the suspicious regions containing microcalcifications in digital mammograms are extracted and then they are classified into benign or malignant categories. The performance of the classification mainly depends on the selection and usage of precise features. The visual content features as well as the biological features are extracted for the microcalcifications. The extracted visual content features are subjected to two phase clustering process for obtaining the feature combination in order to achieve better performance. The proposed microcalcifications classification technique is performed in two sections namely learning section and classification section. Here, the learning of extracted features as well as the classification is done by using the SVM classifier. The proposed approach is tested and its results are analyzed to visualize the performance.

In this article we discuss the break point existence in the monthly precipitation series collected between January 1965 and December 2005 at two meteorological stations situated on the Romanian Black Sea Littoral. Performing the segmentation procedure of\nHubert, the mDP algorithm and the BP procedure, August and October 2005 have been determined as break points for Mangalia series. Since there is no enough data after October 2005, the model for the precipitation evolution has been designed for the period\nbefore August 2005. The segmentation procedure of Hubert, the mDP algorithm and the BP procedure, on the one hand, and the Pettitt test, one the other hand, provided different change points for Sulina series. Therefore, a Box-Cox transformation has been performed to obtain the data normality, which is a requisite for the Buishand, Lee &\nHeghinian and Barry & Hartighan tests. Since for the transformed series, all the tests gave the same change point (August 1982), alternative models have been built for it, for the periods until and after it. Both approaches (parametrical and nonparametric)\nproposed by us, suggest the same trend for the precipitation evolution at the studied stations.

It is an easily noticed fact that a new generation of residents has been establishing new habitation structures all over Romania. This also applies to the south-eastern Oradea Suburban Area, in north-western Romania. The analysed suburban landscapes bring out open attitudes in former city dwellers, strongly biased pro-landscape (78.52% of all interviewees). The landscape criterion ranks second in reasons for relocation, indicating that local nature meets the expectations of the new residents. Indubitable spiritual benefits are also involved, the new residents’ perception of local landscapes being dominated by responses like beauty, repose, naturalness. However, the new residents do not have a narrowed-down, specialised definition in their minds when expressing opinions on local landscape physiognomy in detail, and on outstanding features that render local landscapes attractive. Even the landscape management interventions of new residents and of local authorities revolve around land estate categories and tailored urbanistic requisitions. Consequently, the configuration of neo-landscapes with a distinct suburban identity emerges. The major directions of this case study may serve as groundwork for further studies on the issue of landscape as subject matter in attracting city dwellers to suburban locations.

Cancer classification has become an active area of research in the biomedical domain. With the increase in the number of cancer victims, an efficient technique for cancer classification is necessary to reduce the death rate of the cancer patients. Several researches are being done to improve the accuracy of cancer classification and also to reduce the convergence time of the algorithms. Cancer classification results in better diagnosis which reduces the death rate of the cancer patients. Moreover, cancer classification plays a vital role in the discovery of drug in the field of medical sciences. In this research, efficient neural network techniques and statistical ranking techniques are used along with able learning algorithms for providing significant cancer classification. The primary objective is to propose efficient cancer classification techniques which provide reliable and significant classification accuracy. The proposed cancer classification approaches have two steps. In the first step, all genes in the training dataset are ranked using a scoring scheme. From this step, the genes with the highest ranks are identified. In the second step, the classification capability of all simple two gene combinations among the genes selected is tested using a neural network algorithm. In this paper uses three proficient classifiers such as Back Propagation Algorithm, Fuzzy Neural Networks and Adaptive Neuro Fuzzy Inference Systems. The performances of the proposed approaches are evaluated on lymphoma datasets. The performance is evaluated based on the performance measures such as classification accuracy, classification time and convergence behavior. From the experimental results, it is observed that the proposed Fast Adaptive Neuro Fuzzy Inference Systems (FANFIS) approach provides significant performance with very high accuracy, less classification time and less convergence time compared to other methods.