Envenenamentos por serpentes do gênero Bothrops provocam reações sistêmicas e locais como coagulopatias, hemorragias, reação inflamatória, dor e mionecrose. Proteínas secretadas ricas em cisteínas (CRISPs) estão presentes nas peçonhas de serpentes e estão amplamente distribuídas entre mamíferos, répteis e anfíbios. Estão envolvidas em algumas reações biológicas, porém muitas funções ainda são desconhecidas. O presente trabalho objetivou o isolamento, a caracterização bioquímica/estrutural, enzimática e funcional, a avaliação do potencial inflamatório e avaliação da atividade sobre o sistema complemento de uma CRISP isolada da peçonha de Bothrops jararaca. A CRISP denominada BJ-CRP, foi isolada da peçonha de Bothrops jararaca através da combinação de três etapas cromatográficas: exclusão molecular em Sephacryl S-200, cromatografia de troca aniônica em coluna Source 15Q e cromatografia de fase reversa em coluna C18. O grau de homogeneidade foi determinado e confirmado por eletroforese SDS-PAGE, que mostrou uma banda única de 25,19 kDa, e por MALDI-TOF/TOF que apresentou a massa molecular de 24,6 kDa. A sequência N-terminal e a análise dos peptídeos trípticos por MALDI TOF/TOF demonstrou a presença de 100 resíduos de aminoácidos, os quais apresentaram até 96% de similaridade com sequências de outras CRISPs já descritas, porém de outros gêneros e espécies de serpentes, pois ainda não há CRISPs isoladas do gênero Bothrops. A BJ-CRP não possui atividade proteolítica sobre a azocaseína, o fibrinogênio e a fibrina. Também não apresentou atividade coagulante e hemorrágica, e não demonstrou atividade quando testada na concentração de 1?M em 13 diferentes canais para potássio dependentes de voltagem. Por outro lado, esta toxina foi capaz de induzir um processo inflamatório agudo (tempos de 1 e 4 horas), observado pelo recrutamento de neutrófilos e aumento da citocina pró-inflamatória IL-6 na cavidade peritoneal de camundongos. Ensaios realizados com a BJ-CRP e a peçonha de Bothrops jararaca mostraram modulação na atividade hemolítica promovida pela via clássica do sistema complemento. A BJ-CRP também promoveu ação direta sobre alguns componentes isolados do sistema complemento, como C3 e C4, conforme avaliado por SDS-PAGE e Western blot. O presente trabalho descreve a purificação da BJ-CRP, a primeira CRISP isolada da peçonha da serpente do gênero Bothrops. Os resultados obtidos são promissores e abrem perspectivas para o melhor entendimento desta classe de proteínas, e para a compreensão do mecanismo de ação desta classe de toxinas na resposta inflamatória induzida pelo envenenamento botrópico. / Envenomation by snakes from Bothrops genus is characterized by systemic and local effects such as coagulopathies, bleeding disorders, inflammation, pain and myonecrosis.The cysteine rich secretory proteins (CRISPs) are present in snake venoms and are widely distributed mammals, reptiles and amphibians. They are involved in certain biological activities, however many of their functions are still unknown. The aim of the present study was to isolate a CRISP from Bothrops jararaca and to biochemically/functionally characterize it by evaluating its involvement on inflammatory responses and on the complement system. The CRISP named BJ-CRP was isolated from Bothrops jararaca crude venom through the combination of three chromatographic steps: molecular exclusion on Sephacryl S-200 column, anion exchange chromatography on Source 15Q and reverse phase chromatography using C18 column. A high purity degree was obtained as confirmed by SDSPAGE, showing a single band of 25.19 kDa, and by MALDI-TOF/MS showing a molecular mass of 24.6 kDa. The N-terminal sequence and analysis of tryptic peptides by MALDI TOF/ MS resulted in the determination of 100 amino acid residues, which had up to 96% similarity to sequences from other snake venom CRISPs that were previously described, but from other genus and snake species. The BJ-CRP did not have proteolytic activity on azocasein, fibrinogen or fibrin. It did not show coagulant or hemorrhagic activity, and also did not show activity on 13 different voltage dependent potassium channels when tested at a concentration of 1?M. Moreover, this toxin was able to induce an acute inflammatory response (1 and 4 hours after injection), observed by the recruitment of neutrophils and increase of interleukin-6 into the peritoneal cavity of mice. BJ-CRP and B. jararaca crude venom were capable of modulating the hemolytic activity promoted by the classical pathway of the complement system, and BJ-CRP also showed direct action on some complement system components, such as C3 and C4 as evaluated by SDS-PAGE and Western blot. The present work describes the purification of BJ-CRP, the first CRISP isolated from a Bothrops snake venom. The results obtained showed to be promising and open up prospects in order to better understand the involvement of this class of toxins in the inflammatory response induced by Bothrops envenomation.

The steps associated with the analytical question solving in terms of LISp-Miner system in ADAMEK medical data are the theme of this thesis. The operating sequence of using 4ft-Miner and SD4ft-Miner procedures in ADAMEK data together with the possibility of further use of formalized background knowledge and preparing routing for automatization of the downrighted steps are the objectiv of this thesis. The summary of the basic concepts and axioms of association rules and GUHA method is the content of the theoretical part of the thesis. Operativ part starts from CRISP-DM methodology. The operating sequence enabling searching for interesting association rules in different data, that is applied on STULONG medical data afterwards in order to get instigations for it's revision, is the produce of this thesis. Used data that come from EuroMISE are concern with cardiological patients.

Data mining techniques are used by companies to gain competitive advantages. In today's marketplace, they are also used by marketers mainly for personalization of advertising and for maintaining long-term relationship with customers. Progress in knowledge discovery in databases and availability of computational power comes not only with positive impact, but also with challenges. The practical part of the thesis aims to explore and describe data mining techniques applied to e-commerce dataset. Dataset consists of transaction and web analytics data. The goal of experimental application aims to make a selection of users who most probably react to a marketing communication and to identify the factors which influence them. Target segment of users is obtained through the use of data mining technique clustering. The classification model uses decision tree algorithm to predict whether users submit transaction with an accuracy of 75%. The results are useful for optimization of marketing and business strategy.

Software as a service (SaaS) är en affärsmodell som syftar till att användaren prenumererar på en mjukvara Mjukvaran levereras över internet vilket medför att användaren inte behöver tänka på mjukvaruuppdateringar och driftunderhåll av servrar. Churn innebär att användaren avslutar sin prenumeration hos ett företag och därmed slutar vara kund. Förvärv av nya kunder är en dyr process, som kan kosta upp till fem gånger mer än att sälja till en redan befintlig kund. Tidigare forskning inom churn har främst varit koncentrerad till telekombolag. Undersökningar har specialiserats på maskininlärningsmetoder för att studera churn. Tidigare studier beskriver att det finns begränsad forskning för churn inom SaaS-företag med B2B kunder. De studier som har undersökt churn har främst varit fallstudier där olika kundattribut har studerats utifrån generella- och beteendekundattribut. Studien har i samarbete med ett SaaS-företag undersökt flera kundattribut på ett lönehanteringssystem. Syftet har varit att undersöka vilka kundattribut som är intressanta att ta ut statistik på när churn studeras. Ovanstående ska medföra att det studerade företaget kan införskaffa insikter och arbeta mer med datadrivna beslut. För att förstå vilka kunder som väljer att avsluta sin prenumeration behövs data samlas in om kunderna. En kvantitativ fallstudie utfördes genom att undersöka flera kundattribut hos de kunder som har churnat. Undersökningen utfördes med modellen CRISP-DM för att genomföra dataanalysen på ett systematiskt tillvägagångsätt. Undersökningen studerade kundattribut utifrån variablerna generella- och beteendekundattribut. Dataanalysen genomfördes med hjälp av Python-kod och resultatet presenterades med grafer och tabeller. Studiens resultat visade att vissa värden på följande kundattribut var överrepresenterade vid churn: Kundtyper, Bolagsform, Antal Anställda, Licenser, Antal skickade specifikationer och inloggning. Tidigare forskning har undersökt olika kundattribut och funnit att de kan behöva anpassas för det studerade företaget. / Software as a service (SaaS) is a business model that aims the user to subscribe to a software. The software is delivered over the internet, which means that the user does not have to consider updates and operational maintenance of servers. Churn means that the user cancels his subscription with a company and thereby stops to be a customer. Acquiring new customers is an expensive process, which can cost up to five times more than selling to an existing customer. Previous research in churn has mainly been concentrated in the telecommunications industry. In the mentioned area, churn has long been a problem for companies. Research has concentrated on machine learning methods for studying churn. Previous research describes that there are limited studies in churn with SaaS as a business model. Studies about churn have mainly been case studies where different attributes have been studied based on general and behavioral customer attributes. This study has in collaboration with a SaaS company, examined several customer attributes on a salary management program. The purpose has been to investigate which customer attributes that are interesting to collect statistics when churn is studied. This should enable that the studied company can acquire insights and work more with data-driven decisions. To understand which customers that unsubscribe, data needs to be collected about the customers. A quantitative case study was performed by examining several customer attributes of the customers who have churned. The survey was carried out with the CRISP-DM model to accomplish the data analysis in a systematic approach. The survey studied customer attributes based on the variables general and behavioral customer attributes. The data analysis was performed using Python code and the results were presented with graphs and tables. The results of the study showed that certain values of the following customer attributes were overrepresented in churn: Customer types, Business type, Number of Employees, Licenses, Number of specifications sent and Login. Previous research has examined various customer attributes and found that they may need to be adapted for the studied company.

Machine learning is when a computer can learn from data and draw its own conclusions without being explicitly programmed to do so. To implement machine learning effectively and correctly, it is important to have a structured framework to follow. Today, there exist several different frameworks but no framework is suited for all purposes of machine learning. This thesis evaluates three chosen frameworks CRISP-DM, SEMMA and KDD for the purpose of imple- menting machine learning in signal processing. This study was conducted at Saab AB in Ja¨rf¨alla. The specific problem area of signal processing that was evaluated in the thesis was radar warn- ing systems. A hypothesis is that they could become more efficient with machine learning. To evaluate the chosen frameworks, it was studied what was demanded from a framework when implementing machine learning in the chosen problem area. The evaluation was done with a theoretical comparison where no implementations of the different frameworks were done. The frameworks were evaluated through an evaluation method created by the authors. The evaluation method was used for the purpose of finding a framework suitable for signal processing when developing the software for a radar warning system. The result is that CRISP-DM is the most well-suited of the three frame- works. This because it originates from a business perspective, is distinct in how to use it and is easy to implement in an agile process like Scrum. / Maskininlärning är när en dator kan lära sig från data och dra egna slutsatser utan att specifikt vara programmerad att göra det. För att lyckas med att implementera maskininlärning på ett effektivt sätt så krävs det att man följer ett tydligt ramverk. Idag finns det många ramverk men inget som är lämpat för alla typer av maskininlärning. Denna rapport utvärderar tre valda ramverk: CRISP- DM, SEMMA och KDD. Detta med syftet att implementera maskininlärn-ing i signalbehandling. Studien utfördes på Saab AB i Järfälla. Det specifika problemområde inom signalbehandling som utvärderades i rapporten var radarvarningssys- tem. En hypotes är att de kan bli mer effektiva med maskininlärning. För att utvärdera de valda ramverken så studerades vad som krävdes av ett ramverk för det valda problemområdet. Utvärderingen skedde genom en teoretisk jämförelse där ingen implementation av de olika ramverken genomfördes. Ramverken utvärderades genom en utvärderingsmetod skapad av förfat-tarna. Utvärderingsmetoden användes med syftet att finna ett ramverk som var lämpligt för signalbehandling vid utveckling av mjukvara för ett radarvarningssystem. Resultatet var att CRISP-DM var den mest lämpade metoden. Detta för att den utgår från ett affärsperspektiv, har tydliga riktlinjer hur den ska användas och att den enkelt kan implementeras i agila processer såsom Scrum.

Syftet med denna kandidatuppsats är att undersöka om och hur det går att kunna förutspå antalet gäster för en specifik skidanläggning i Sverige. Eftersom skidanläggningar är dyra att bedriva är det en viktig aspekt att kunna planera personal kostnadseffektivt. Genom att analysera skidortens stora datamängder angående historiska kunddata, tillsammans med historiska och reala väderdata, kan prediktiva analyser genomföras. Detta leder till att skidorten kan utforma bättre tillsättning av personal för att reducera liftköer i backarna, minska matsvinnet i restauranger och även minska eventuella förluster kopplade till överbemanning. Tack vare system som framkallar beslutsunderlag, så kallade beslutsstödsystem, kan företag agera konkurrenskraftigt på marknaderna. Den här studien försöker därför undersöka huruvida det går att framkalla en eventuell prognos för framtida gästantal. Genom att samla in olika typer av både kund- respektive väderdata, har tvättning av data genomförts för att sedan låta olika prediktiva modeller förutspå framtiden. Resultatet för studien påvisar betydelsen gällande bearbetningsprocessen av data, och avslutas med intressanta tankar gällande framtida forskning. Utifrån detta kan det konstateras att en eventuell prediktion är möjlig, men endast i mån av en ungefärlig gräns utifrån antalet gäster. Ett överskridande av gränsen riskerar prediktionsförmågan att försämras.

Det finns många olika Data Mining-processer som kan tillämpas i ett Data Mining-projekt. Fördelen med att använda en Data Mining-process är att projektet blir strukturerat, processen kan hjälpa till att minska risker som annars kan uppstå och kan medföra att projektmålet förändras. Data Mining-processen som studien har undersökt är generell och studien försöker därmed precisera olika faser av processen, för att anpassas till ett prognostiseringsprojekt.Studien utvärderar den preciserade prognostiseringsprocessen genom att följa och dokumentera ett prognostiseringsprojekt på en tillverkningsindustri. Studien analyserar teoretiskt vilka implikationer tillverkningsindustrin kan möta och även om studiens framtagna process är tillämpningsbar i detta fall. Studien visar att det på en teoretisk nivå går att genomföra studiens preciserade Data Mining-process och visar även vilka risker som kan uppkomma om ett prognostiseringsprojekt inte följer en Data Mining-process. / Program: Systemarkitekturutbildningen

Para llevar a cabo en forma sistemática el proceso de descubrimiento de conocimiento en bases de datos, conocido como minería de datos, es necesaria la implementación de una metodología. Actualmente las metodologías para minería de datos se encuentran en etapas tempranas de madurez, aunque algunas como CRISP-DM ya están siendo utilizadas exitosamente por los equipos de trabajo para la gestión de sus proyectos. En este trabajo se establece un análisis comparativo entre las metodologías de minería de datos más difundidas en la actualidad. Para lograr dicha tarea, y como aporte de esta tesis, se ha propuesto un marco comparativo que explicita las características que se deberían tener en cuenta al momento de efectuar esta confrontación.

CRISP is a data acquisition and image reconstruction technique that offers theoretical increases in signal-to-noise ratio (SNR) and dynamic range over traditional methods in magnetic resonance imaging (MRI). The incoming broadband MRI signal is de-multiplexed into multiple narrow frequency bands using analog filters. Signal from each narrowband channel is then individually captured and digitized. The original signal is recovered by recombining all the channels via weighted addition, where the weights correspond to the frequency responses of each narrowband filter. With ideal bandpasses and bandwidth dependent noise after filtering, SNR increase is proportional to sqrt(N), where N is the number of bandpasses. In addition to SNR improvement, free induction decay (FID) echoes in CRISP experience a slower decay rate. In situations where resolution is limited by digitization noise, CRISP is able to capture data further out into the higher frequency regions of k-space, which leads to a relative increase in resolution. The conversion from one broadband MR signal into multiple narrowband channels is realized using a comb or bank of active analog bandpass filters. A custom CRISP RF receiver chain is implemented to downconvert and demodulate the raw MR signal prior to narrowband filtering, and to digitize the signals from each filter channel simultaneously. Results are presented demonstrating that the CRISP receiver chain can acquire 2D MR images (without narrowband filters) with SNR similar to SNR of images obtained with a clinical system. Acquiring 2D CRISP images (with narrowband filters) was not possible due to the lack of phase lock between rows in k-space. RMS noise of narrowband, broadband and unfiltered 1D echoes are compared.

Fuzzy Analytical Hierarchy Process (fuzzy AHP) is a helpful MCDM approach for the selection of public financing of cooperative R&D projects developed by firms in collaboration with government. A technical committee for Industrial Technology Development Program (ITDP) in Taiwan regularly evaluates and decides proper public financing of cooperative R&D projects. In this study, we first discuss important criteria for R&D projects selection. We apply fuzzy AHP to integrating decisions of members in the technical committee. Especially we utilize crisp judgment matrix instead of interval judgment matrix to integrate subject judgments of these members. Our results indicate that scientific & technology merit criterion (0.389) is most important considered in overall technical committees. Besides that, the project execution (0.260) is more important criteria than potential benefits (0.204) and project risk (0.147) in ITDP selection. Moreover, we utilize the simulation to analyze relative important of criteria under risky environment. Our results also indicate that the relative important of criteria will reverse when technical committee faces different risk level. Generally speaking, the paper reveals below results: (1) the fuzzy AHP is an appropriate method in multi-criteria R&D projects selection; (2) the crisp judgment matrix is suitable to integrate subject judgments of technical committee; (3) the relative important of criteria will reverse under different risky environment.