Значението на гъбите Trichoderma и Gliocladium в почвената биоценоза на оранжерийни краставици

The analysis was conducted on the general biogenicity and phylogenetic structure of fungi in the soil microbiome of greenhouse cucumbers grown as a monoculture and after the precursors of pepper and lettuce. The relationship between the following indicators has been proved: the total biological activity of the soil, the species composition of micromycetes, the development of Fusarium root rot, and yield. The effect of biological preparations based on strains of Trichoderma viride and Gliocladium virens antagonistic fungi on the development of Fusarium root rot, nematodes and yield of cucumbers was studied. The results of the experiment show that when cucumbers are grown after the precursor pepper and lettuce, the development of Fusarium root rot and nematodes does not exceed critical values. The introduction of Trichoderma and Gliocladium fungi into the soil in the form of a dry preparation with a titre 2.0 x 1010 conidia/g when transplanting plants to a permanent place at a consumption rate of 40 kg/ha increases the yield of greenhouse cucumbers by 20-23%. The experiment is part of the study of the "soil exhaustion" syndrome and the possibilities of overcoming it. The obtained results will serve as bioindicators that can be used for preliminary diagnostics of the sanitary condition of degraded soils, selection of agrotechnical, breeding and protective measures of plants.Извършен е анализ на общата биогенност и филогенетична структура на гъбите в почвения микробиом на оранжерийни краставици, отглеждани като монокултура и след предшествениците пипер и маруля. Доказана е връзката между следните показатели: обща биологична активност на почвата, видов състав на микромицетите, развитие на фузариозно кореново гниене и добив. Изследвано е влиянието на биологични препарати на базата на щамове антагонистични гъби Trichoderma viride и Gliocladium virens върху развитието на фузариозно кореново гниене, нематоди и добива при краставици. Резултатите от опита показват, че при отглеждане на краставици след предшественици пипер и маруля развитието на фузариозно кореново гниене и нематоди не надвишава критичните стойности. Внасянето на гъбите Trichoderma и Gliocladium в почвата под формата на сух препарат с титър 2.0 x 1010 c/g при разсаждане на растенията на постоянно място при разходна норма 40 kg/ha повишава добива при оранжерийни краставици с 20-23%. Експериментът е част от изследването на синдрома на "уморената почва" и възможностите за неговото преодоляване. Получените резултати ще послужат като биоиндикатори, които могат да се използват за предварителна диагностика на санитарното състояние на деградирали почви и избор на агротехнически, селекционни и растително-защитни мерки при отглеждане на растения

Dynamic data assigning assessment clustering of streaming data

Discovering interesting patterns or substructures in data streams is an important challenge in data mining. Clustering algorithm are very often applied to identify substructures, although they are designed to partition a data set. Another problem of clustering algorithms is that most of them are not designed for data streams. They assume that the data set to be analysed is already complete and will not be extended by new data. This paper discusses an extension of an algorithm that uses ideas from cluster analysis, but was designed to identify single clusters in large data sets without the necessity to partition the whole data set into clusters. The new extended version of this algorithm can applied to stream data and is able to identify new clusters in an incoming data stream. As a case study weather data are use

An Extended Version ofGustafson-Kessel Clustering Algorithm for Evolving Data Stream Clustering Evolving Intelligent Systems: Methodology and Applications

The chapter deals with a recursive clustering algorithm that enables a real time partitioning of data streams. Proposed algorithm incorporates the advantages of the Gustafson-Kessel clustering algorithm of identifying clusters with different shape and orientation while expanding its area of application to the challenging problem of real time data clustering. The algorithm is applicable to a wide range of practical evolving system type applications as diagnostics and prognostics, system identification, real time classification, and process quality monitoring and control

Gustafson-Kessel Algorithm for Evolving Data Stream Clustering

A simplified clustering algorithm that enables on-line partitioning of data streams is proposed. The algorithm applies adaptive-distance metric to identify clusters with different shape and orientation. It is applicable to a wide range of practical evolving system type applications as diagnostics and prognostics, system identification, real time classification, and process quality monitoring and control

online condition monitoring of bearings for improved reliability in packaging materials industry

The production processes in the packaging materials industry has to be very efficient and cost-effective. These processes usually take place under extreme conditions and high speeds that requires a high level of reliability and efficiency. Rollers including their supporting bearings and motors are the most common components of production machines in the packaging materials industry. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore it is very important to take care of bearings during maintenance and detect their faults in an early stage in order to assure safe and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. After normalization and the wavelet transform of vibration signals, the standard deviation as a measure of average energy level and the logarithmic energy entropy as a measure of the degree of order/disorder are extracted in a few sub-bands of interest as representative features. Then the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection is performed by a quadratic classifier and the fault diagnosis by another two quadratic classifiers. Accuracy of the new technique was tested on the ball bearing data recorded at the Case Western Reserve University Bearing Data Center. In total four classes of the vibrations signals were studied, i.e. normal, with the fault of inner race, outer race and balls operation. An overall accuracy of 100% was achieved. The new technique can be used to increase reliability and efficiency by preventing unexpected faulty operation of machinery bearings

International conference Kosta P. Manojlović and the Idea of Slavic and Balkan Cultural Unification (1918-1941)

This conference is organised within the project Serbian musical identities within local and global frameworks: traditions, changes, challenges (No. 177004) financed by the Serbian Ministry of Education, Science and Technological Development. It is supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia as well as the Department of Fine Arts and Music SASA

How to Account for the Uncertainty in The QoS Selection Task

The paper presents an approach to QoS selection of web services. It introduces a theoretical frame and respective applicable selection procedures, in which the solution accounts for the uncertainty of the existing metrics data and client preference. Methods of two general cases are revealed in detail. The first method considers a selection based on one service quality. The second method assesses the integrated QoS of interesting properties. The theoretical analysis is validated through experimental investigation of real data of services’ quality metrics. ACM Computing Classification System (1998): H.3.7, K.3.1.*The author acknowledges the financial support of the National Science Fund, Bulgarian Ministry of Education and Science, under project grant DN 02/11 and the Science Fund of the St. Kliment Ohridski University of Sofia under project grant No 80-10-196/24.04.2017

An Iterative Unsupervised Method for Gene Expression Differentiation

For several decades, intensive research for understanding gene activity and its role in organism’s lives is the research focus of scientists in different areas. A part of these investigations is the analysis of gene expression data for selecting differentially expressed genes. Methods that identify the interested genes have been proposed on statistical data analysis. The problem is that there is no good agreement among them, as different results are produced by distinct methods. By taking the advantage of the unsupervised data analysis, an iterative clustering procedure that finds differentially expressed genes shows promising results. In the present paper, a comparative study of the clustering methods applied for gene expression analysis is presented to explicate the choice of the clustering algorithm implemented in the method. An investigation of different distance measures is provided to reveal those that increase the efficiency of the method in finding the real data structure. Further, the method is improved by incorporating an additional aggregation measure based on the standard deviation of the expression levels. Its usage increases the gene distinction as a new amount of differentially expressed genes is found. The method is summarized in a detailed procedure. The significance of the method is proved by an analysis of two mice strain data sets. The differentially expressed genes defined by the proposed method are compared with those selected by the well-known statistical methods applied to the same data set