The thesis deals with the risk management in Motortec s.r.o., a company that operates in the field of sales and maintenance service of passenger and commercial vehicles. The thesis is divided into the theoretical part, in which all the essential terms are defined that relate to the thesis itself, and an analytical part that employs the process of risk management. The process includes identification, analysis and subsequent remedies to risks identified. The identification of risks has been carried out using Porter's five forces analysis, SLEPT analysis and McKinsey 7S Framework. SWOT analysis has been carried out subsequently. The risks defined have been assessed using a scoring method and grouped into categories based on their severity. The last part includes measures to reduce the major risks that threaten the examined company.

The objective of this master´s thesis is an appreciation of financial stability of the company Sladovny Soufflet ČR, a.s. in terms of elementary methods of financial analysis. In connection with incorporation of outputs of financial analysis the proposals for improvements in problematic areas of company´s financial stability are formulated.

The Master's thesis deals with evulation of the company's financial health. On the basis of choice computational methods of financial analysis there is testing the company's financial situation and there are introducing possible proposals of measures leading to the improvement of company's financial business.

This master´s thesis is devoted to evaluating the financial situation of the company Sklárny Moravia, a.s. using selected indicators of financial analysis. The first part deals with the problems of the theory of financial analysis, its objectives, methods and formulas, which are the basis for processing the second part of the thesis. The practical part includes a financial analysis of the selected company and its evaluation. In the last section provides suggestions to improve the situation of the company based on financial analysis.

The thesis deals with risk management of business entity, which provides services in field of education officials. Risk management proces consists of several parts, such as identification, analysis and risks reduction. All of these parts, including basic concepts, are described in the theoretical part. In the analytical part there are methods, which are using for identification potential risks. It include SLEPT analysis, Porter analysis and 7S model, then comes SWOT analysis. Based on these analyzes are proposed measures and recommendations that will help reduce risks.

This master’s thesis is concerned with the issue of determining the value of the company based on theoretical assumptions mentioned in the introductory section of this thesis. In the practical part of the thesis is the company Trial servis, s. r. o. analyzed and evaluated.

No description available.

<p dir="ltr">Topological Data Analysis (TDA) with its roots embedded in the field of algebraic topology has successfully found its applications in computational biology, drug discovery, machine learning and in many diverse areas of science. One of its cornerstones, persistent homology, captures topological features latent in the data. Recent progress in TDA allows us to integrate these finer topological features into traditional machine learning and deep learning pipelines. However, the utilization of topological methods within a conventional deep learning framework remains relatively uncharted. This thesis presents four scenarios where computational topology tools are employed to advance machine learning.</p><p dir="ltr">The first one involves integrating persistent homology to explore high-dimensional cytometry data. The second one incorporates Extended persistence in a supervised graph classification framework and demonstrates leveraging TDA in cases where data naturally aligns with higher-order elements by extending graph neural networks to higher-order networks, applied specifically in non-manifold mesh classification. The third and fourth scenarios delve into enhancing graph neural networks through multiparameter persistence.</p>

Thesis (MScEng)-- Stellenbosch University, 2013. / ENGLISH ABSTRACT: The measurement of key process quality variables is important for the efficient and economical operation of many chemical and mineral processing systems, as these variables can be used in process monitoring and control systems to identify and maintain optimal process conditions. However, in many engineering processes the key quality variables cannot be measured directly with standard sensors. Inferential sensing is the real-time prediction of such variables from other, measurable process variables through some form of model. In vision-based inferential sensing, visual process data in the form of images or video frames are used as input variables to the inferential sensor. This is a suitable approach when the desired process quality variable is correlated with the visual appearance of the process. The inferential sensor model is then based on analysis of the image data. Texture feature extraction is an image analysis approach by which the texture or spatial organisation of pixels in an image can be described. Two texture feature extraction methods, namely the use of grey-level co-occurrence matrices (GLCMs) and wavelet analysis, have predominated in applications of texture analysis to engineering processes. While these two baseline methods are still widely considered to be the best available texture analysis methods, several newer and more advanced methods have since been developed, which have properties that should theoretically provide these methods with some advantages over the baseline methods. Specifically, three advanced texture analysis methods have received much attention in recent machine vision literature, but have not yet been applied extensively to process engineering applications: steerable pyramids, textons and local binary patterns (LBPs). The purpose of this study was to compare the use of advanced image texture analysis methods to baseline texture analysis methods for the prediction of key process quality variables in specific process engineering applications. Three case studies, in which texture is thought to play an important role, were considered: (i) the prediction of platinum grade classes from images of platinum flotation froths, (ii) the prediction of fines fraction classes from images of coal particles on a conveyor belt, and (iii) the prediction of mean particle size classes from images of hydrocyclone underflows. Each of the five texture feature sets were used as inputs to two different classifiers (K-nearest neighbours and discriminant analysis) to predict the output variable classes for each of the three case studies mentioned above. The quality of the features extracted with each method was assessed in a structured manner, based their classification performances after the optimisation of the hyperparameters associated with each method. In the platinum froth flotation case study, steerable pyramids and LBPs significantly outperformed the GLCM, wavelet and texton methods. In the case study of coal fines fractions, the GLCM method was significantly outperformed by all four other methods. Finally, in the hydrocyclone underflow case study, steerable pyramids and LBPs significantly outperformed GLCM and wavelet methods, while the result for textons was inconclusive. Considering all of these results together, the overall conclusion was drawn that two of the three advanced texture feature extraction methods, namely steerable pyramids and LBPs, can extract feature sets of superior quality, when compared to the baseline GLCM and wavelet methods in these three case studies. The application of steerable pyramids and LBPs to further image analysis data sets is therefore recommended as a viable alternative to the traditional GLCM and wavelet texture analysis methods. / AFRIKAANSE OPSOMMING: Die meting van sleutelproseskwaliteitsveranderlikes is belangrik vir die doeltreffende en ekono-miese werking van baie chemiese– en mineraalprosesseringsisteme, aangesien hierdie verander-likes gebruik kan word in prosesmonitering– en beheerstelsels om die optimale prosestoestande te identifiseer en te handhaaf. In baie ingenieursprosesse kan die sleutelproseskwaliteits-veranderlikes egter nie direk met standaard sensors gemeet word nie. Inferensiële waarneming is die intydse voorspelling van sulke veranderlikes vanaf ander, meetbare prosesveranderlikes deur van ‘n model gebruik te maak. In beeldgebaseerde inferensiële waarneming word visuele prosesdata, in die vorm van beelde of videogrepe, gebruik as insetveranderlikes vir die inferensiële sensor. Hierdie is ‘n gepaste benadering wanneer die verlangde proseskwaliteitsveranderlike met die visuele voorkoms van die proses gekorreleer is. Die inferensiële sensormodel word dan gebaseer op die analise van die beelddata. Tekstuurkenmerkekstraksie is ‘n beeldanalisebenadering waarmee die tekstuur of ruimtelike organisering van die beeldelemente beskryf kan word. Twee tekstuurkenmerkekstraksiemetodes, naamlik die gebruik van grysskaalmede-aanwesigheidsmatrikse (GSMMs) en golfie-analise, is sterk verteenwoordig in ingenieursprosestoepassings van tekstuuranalise. Alhoewel hierdie twee grondlynmetodes steeds algemeen as die beste beskikbare tekstuuranalisemetodes beskou word, is daar sedertdien verskeie nuwer en meer gevorderde metodes ontwikkel, wat beskik oor eienskappe wat teoreties voordele vir hierdie metodes teenoor die grondlynmetodes behoort te verskaf. Meer spesifiek is daar drie gevorderde tekstuuranalisemetodes wat baie aandag in onlangse masjienvisieliteratuur geniet het, maar wat nog nie baie op ingenieursprosesse toegepas is nie: stuurbare piramiedes, tekstons en lokale binêre patrone (LBPs). Die doel van hierdie studie was om die gebruik van gevorderde tekstuuranalisemetodes te vergelyk met grondlyntekstuuranaliesemetodes vir die voorspelling van sleutelproseskwaliteits-veranderlikes in spesifieke prosesingenieurstoepassings. Drie gevallestudies, waarin tekstuur ‘n belangrike rol behoort te speel, is ondersoek: (i) die voorspelling van platinumgraadklasse vanaf beelde van platinumflottasieskuime, (ii) die voorspelling van fynfraksieklasse vanaf beelde van steenkoolpartikels op ‘n vervoerband, en (iii) die voorspelling van gemiddelde partikelgrootteklasse vanaf beelde van hidrosikloon ondervloeie. Elk van die vyf tekstuurkenmerkstelle is as insette vir twee verskillende klassifiseerders (K-naaste bure en diskriminantanalise) gebruik om die klasse van die uitsetveranderlikes te voorspeel, vir elk van die drie gevallestudies hierbo genoem. Die kwaliteit van die kenmerke wat deur elke metode ge-ekstraheer is, is op ‘n gestruktureerde manier bepaal, gebaseer op hul klassifikasieprestasie na die optimering van die hiperparameters wat verbonde is aan elke metode. In die platinumskuimflottasiegevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM–, golfie– en tekstonmetodes presteer. In die steenkoolfynfraksiegevallestudie het die GSMM-metode betekenisvol slegter as al vier ander metodes presteer. Laastens, in die hidrosikloon ondervloeigevallestudie het stuurbare piramiedes en LBPs betekenisvol beter as die GSMM– en golfiemetodes presteer, terwyl die resultaat vir tekstons nie beslissend was nie. Deur al hierdie resultate gesamentlik te beskou, is die oorkoepelende gevolgtrekking gemaak dat twee van die drie gevorderde tekstuurkenmerkekstraksiemetodes, naamlik stuurbare piramiedes en LBPs, hoër kwaliteit kenmerkstelle kan ekstraheer in vergelyking met die GSMM– en golfiemetodes, vir hierdie drie gevallestudies. Die toepassing van stuurbare piramiedes en LBPs op verdere beeldanalise-datastelle word dus aanbeveel as ‘n lewensvatbare alternatief tot die tradisionele GSMM– en golfietekstuuranalisemetodes.

In this thesis, I use the new generation of radio interferometer along with X-ray observations to investigate the dynamics and energetics of radio-loud active galaxies which are key to understanding AGN feedback and the evolution of galaxies as a whole. I present new JVLA observations of powerful radio source and use innovative techniques to undertake a detailed analysis of JVLA observations of powerful radio galaxies. I compare two of the most widely used models of spectral ageing, the Kardashev-Pacholczyk and Jaffe-Perola models and also results of the more complex, but potentially more realistic, Tribble model. I find that the Tribble model provides both a good fit to observations as well as providing a physically realistic description of the source. I present the first high-resolution spectral maps of the sources and find that the best-fitting injection indices across all models take higher values than has previously been assumed. I present characteristic hot spot advance speeds and compare them to those derived from dynamical ages, confirming that the previously known discrepancy in speed remains present in older radio sources even when ages are determined at high spectral and spatial resolutions. I show that some previously common assumptions made in determining spectral ages with narrow-band radio telescopes may not always hold. I present results from a study of the powerful radio galaxy 3C223 at low frequencies with LOFAR to determine its spectrum on spatially small scales and tightly constrain the injection index, which I find to be consistent with the high values found at GHz frequencies. Applying this new knowledge of the low energy electron population, I perform synchrotron / inverse-Compton model fitting and find that the total energy content of the radio galaxy lobes increases by a factor greater than 2 compared to previous studies. Using this result to provide revised estimates of the internal pressure, I find the northern lobe to be in pressure balance with the external medium and the southern lobe to be overpressured. I go on to present the first large sample investigation of the properties of jets in Fanaroff and Riley type I radio galaxies (FR-I) at X-ray energies based on data from the Chandra archive. I explore relations between the properties of the jets and the properties of host galaxies in which they reside. I find previously unknown correlations to exist, relating photon index, volume emissivity, jet volume and luminosity, and find that the previously held assumption of a relationship between luminosities at radio and X-ray wavelengths is linear in nature when bona fide FR-I radio galaxies are considered. In addition, I attempt to constrain properties which may play a key role in determination of the diffuse emission process. I test a simple model in which large-scale magnetic field variations are primarily responsible for determining jet properties; however, we find that this model is inconsistent with our best estimates of the relative magnetic field strengths in my sample.