Quasiclassical Approach to the Vortex State in Iron-based Superconductors

The quasiclassical approach was applied to the investigation of the vortex properties in the ironbased superconductors. The special attention was paid to manifestation of the nonlocal effects of the vortex core structure. The main results are as follows: (i) The effects of the pairing symmetries (s+ and s₊₊) on the cutoff parameter of field distribution, ξh, in stoichiometric (like LiFeAs) and nonstoichiometric (like doped BaFe₂As₂) iron pnictides have been investigated using Eilenberger quasiclassical equations. Magnetic field, temperature and impurity scattering dependences of ξh have been calculated. Two opposite behavior have been discovered. The ξh /ξc2 ratio is less in s+ symmetry when intraband impurity scattering (Γ₀) is much larger than one and much larger than interband impurity scattering (Γπ), i.e. in nonstoichiometric iron pnictides. Opposite, the value ξh /ξc2 is higher in s+ case and the field dependent curve is shifted upward from the "clean" case (Γ₀ = Γπ = 0) for stoichiometric iron pnictides (Γ₀ = Γπ ≪ 1). (ii) Eilenberger approach to the cutoff parameter, ξh, of the field distribution in the mixed state of high

Visual customer segmentation and behavior analysis : a SOM-based approach

Ett ämne som väckt intresse både inom industrin och forskningen är hantering av kundförhållanden (CRM, eng. Customer Relationship Management), dvs. en kundorienterad affärsstrategi där företagen från att ha varit produktorienterade väljer att bli mera kundcentrerade. Numera kan kundernas beteende och aktiviteter lätt registreras och sparas med hjälp av integrerade affärssystem (ERP, eng. Enterprise Resource Planning) och datalager (DW, eng. Data Warehousing). Kunder med olika preferenser och köpbeteende skapar sin egen ”signatur” i synnerhet via användningen av kundkort, vilket möjliggör mångsidig modellering av kundernas köpbeteende. För att få en översikt av kundernas köpbeteende och deras lönsamhet, används ofta kundsegmentering som en metod för att indela kunderna i grupper utgående från deras likheter. De mest använda metoderna för kundsegmentering är analytiska modeller konstruerade för en viss tidsperiod. Dessa modeller beaktar inte att kundernas beteende kan förändras med tiden. I föreliggande avhandling skapas en holistisk översikt av kundernas karaktär och köpbeteende som utöver de konventionella segmenteringsmodellerna även beaktar dynamiken i köpbeteendet. Dynamiken i en kundsegmenteringsmodell innefattar förändringar i segmentens struktur och innehåll, samt förändringen av individuella kunders tillhörighet i ett segment (s.k migrationsanalyser). Vardera förändringen modelleras, analyseras och exemplifieras med visuella datautvinningstekniker, främst med självorganiserande kartor (SOM, eng. Self-Organizing Maps) och självorganiserande tidskartor (SOTM), en vidareutveckling av SOM. Visualiseringen anteciperas underlätta tolkningen av identifierade mönster och göra processen med kunskapsöverföring mellan den som gör analysen och beslutsfattaren smidigare. Asiakkuudenhallinta (CRM) eli organisaation muuttaminen tuotepainotteisesta asiakaskeskeiseksi on herättänyt mielenkiintoa niin yliopisto- kuin yritysmaailmassakin. Asiakkaiden käyttäytymistä ja toimintaa pystytään nykyään helposti tallentamaan ja varastoimaan toiminnanohjausjärjestelmien ja tietovarastojen avulla; asiakkaat jättävät jatkuvasti piirteistään ja ostokäyttäytymisestään kertovia tietojälkiä, joita voidaan analysoida. On tavallista, että asiakkaat poikkeavat toisistaan eri tavoin, ja heidän mieltymyksensä kuten myös ostokäyttäytymisensä saattavat olla hyvinkin erilaisia. Asiakaskäyttäytymisen monimuotoisuuteen ja tuottavuuteen paneuduttaessa käytetäänkin laajalti asiakassegmentointia eli asiakkaiden jakamista ryhmiin samankaltaisuuden perusteella. Perinteiset asiakassegmentoinnin ratkaisut ovat usein yksittäisiä analyyttisia malleja, jotka on tehty tietyn aikajakson perusteella. Tämän vuoksi ne monesti jättävät huomioimatta sen, että asiakkaiden käyttäytyminen saattaa ajan kuluessa muuttua. Tässä väitöskirjassa pyritäänkin tarjoamaan holistinen kuva asiakkaiden ominaisuuksista ja ostokäyttäytymisestä tarkastelemalla kahta muutosvoimaa tiettyyn aikarajaukseen perustuvien perinteisten segmentointimallien lisäksi. Nämä kaksi asiakassegmentointimallin dynamiikkaa ovat muutokset segmenttien rakenteessa ja muutokset yksittäisten asiakkaiden kuulumisessa ryhmään. Ensimmäistä dynamiikkaa lähestytään ajallisen asiakassegmentoinnin avulla, jossa visualisoidaan ajan kuluessa tapahtuvat muutokset segmenttien rakenteissa ja profiileissa. Toista dynamiikkaa taas lähestytään käyttäen nk. segmenttisiirtymien analyysia, jossa visuaalisin keinoin tunnistetaan samantyyppisesti segmentistä toiseen vaihtavat asiakkaat. Visualisoinnin tehtävänä on tukea havaittujen kaavojen tulkitsemista sekä helpottaa tiedonsiirtoa analysoijan ja päättäjien välillä. Visuaalisia tiedonlouhintamenetelmiä, kuten itseorganisoivia karttoja ja niiden laajennuksia, käytetään osoittamaan näiden menetelmien hyödyllisyys sekä asiakkuudenhallinnassa yleisesti että erityisesti asiakassegmentoinnissa.

A simulation model of the optimal maintenance strategy: conceptual agent-based approach

In this Master’s thesis agent-based modeling has been used to analyze maintenance strategy related phenomena. The main research question that has been answered was: what does the agent-based model made for this study tell us about how different maintenance strategy decisions affect profitability of equipment owners and maintenance service providers? Thus, the main outcome of this study is an analysis of how profitability can be increased in industrial maintenance context. To answer that question, first, a literature review of maintenance strategy, agent-based modeling and maintenance modeling and optimization was conducted. This review provided the basis for making the agent-based model. Making the model followed a standard simulation modeling procedure. With the simulation results from the agent-based model the research question was answered. Specifically, the results of the modeling and this study are: (1) optimizing the point in which a machine is maintained increases profitability for the owner of the machine and also the maintainer with certain conditions; (2) time-based pricing of maintenance services leads to a zero-sum game between the parties; (3) value-based pricing of maintenance services leads to a win-win game between the parties, if the owners of the machines share a substantial amount of their value to the maintainers; and (4) error in machine condition measurement is a critical parameter to optimizing maintenance strategy, and there is real systemic value in having more accurate machine condition measurement systems.

Experimental validation of a 3-D vision-based measurement system applied to robot calibration

One of the problems that slows the development of off-line programming is the low static and dynamic positioning accuracy of robots. Robot calibration improves the positioning accuracy and can also be used as a diagnostic tool in robot production and maintenance. A large number of robot measurement systems are now available commercially. Yet, there is a dearth of systems that are portable, accurate and low cost. In this work a measurement system that can fill this gap in local calibration is presented. The measurement system consists of a single CCD camera mounted on the robot tool flange with a wide angle lens, and uses space resection models to measure the end-effector pose relative to a world coordinate system, considering radial distortions. Scale factors and image center are obtained with innovative techniques, making use of a multiview approach. The target plate consists of a grid of white dots impressed on a black photographic paper, and mounted on the sides of a 90-degree angle plate. Results show that the achieved average accuracy varies from 0.2mm to 0.4mm, at distances from the target from 600mm to 1000mm respectively, with different camera orientations.

Co-culture of apoptotic breast cancer cells with immature dendritic cells: a novel approach for DC-based vaccination in breast cancer

A dendritic cell (DC)-based vaccine strategy could reduce the risk of recurrence and improve the survival of breast cancer patients. However, while therapy-induced apoptosis of hepatocellular and colorectal carcinoma cells can enhance maturation and antigen presentation of DCs, whether this effect occurs in breast cancer is currently unknown. In the present study, we investigated the effect of doxorubicin (ADM)-induced apoptotic MCF-7 breast cancer cells on the activation of DCs. ADM-induced apoptotic MCF-7 cells could effectively induce immature DC (iDC) maturation. The mean fluorescence intensity (MFI) of DC maturity marker CD83 was 23.3 in the ADM-induced apoptotic MCF-7 cell group compared with 8.5 in the MCF-7 cell group. The MFI of DC co-stimulatory marker CD86 and HLA-DR were also increased after iDCs were treated with ADM-induced apoptotic MCF-7 cells. Furthermore, the proliferating autologous T-lymphocytes increased from 14.2 to 40.3% after incubated with DCs induced by apoptotic MCF-7 cells. The secretion of interferon-γ by these T-lymphocytes was also increased. In addition, cell-cell interaction between apoptotic MCF-7 cells and iDCs, but not soluble factors released by apoptotic MCF-7 cells, was crucial for the maturation of iDCs. These findings constitute a novel in vitro DC-based vaccine strategy for the treatment of breast cancer by ADM-induced apoptotic MCF-7 cells.

Instrumental color and sensory acceptance of soy-based emulsions: a response surface approach

Response Surface Methodology (RSM) was applied to evaluate the chromatic features and sensory acceptance of emulsions that combine Soy Protein (SP) and red Guava Juice (GJ). The parameters analyzed were: instrumental color based on the coordinates a* (redness), b* (yellowness), L* (lightness), C* (chromaticity), h* (hue angle), visual color, acceptance, and appearance. The analyses of the results showed that GJ was responsible for the high measured values of red color, hue angle, chromaticity, acceptance, and visual color, whereas SP was the variable that increased the yellowness intensity of the assays. The redness (R²adj = 74.86%, p < 0.01) and hue angle (R²adj = 80.96%, p < 0.01) were related to the independent variables by linear models, while the sensory data (color and acceptance) could not be modeled due to a high variability. The models of yellowness, lightness, and chromaticity did not present lack of fit but presented adjusted determination coefficients bellow 70%. Notwithstanding, the linear correlations between sensory and instrumental data were not significant (p > 0.05) and low Pearson coefficients were obtained. The results showed that RSM is a useful tool to develop soy-based emulsions and model some chromatic features of guava-based emulsions through RSM.

Chemical pattern of brazilian apples: a chemometric approach based on the Fuji and Gala varieties

The chemical composition of apple juices may be used to discriminate between the varieties for consumption and those for raw material. Fuji and Gala have a chemical pattern that can be used for this classification. Multivariate methods correlate independent continuous chemical descriptors with the categorical apple variety. Three main descriptors of apple juice were selected: malic acid, total reducing sugar and total phenolic compounds. A chemometric approach, employing PCA and SIMCA, was used to classify apple juice samples. PCA was performed with 24 juices from Fuji and Gala, and SIMCA, with 15 juices. The exploratory and predictive models recognized 88% and 64%, respectively, as belonging to a mixed domain. The apple juice from commercial fruits shows a pattern related to cv. Fuji and Gala with boundaries from 0.18 to 0.389 g.100 mL-1 (malic acid), from 8.65 to 15.18 g.100 mL-1 (total reducing sugar) and from 100 to 400 mg.L-1 (total phenolic compounds), but such boundaries were slightly shorter in the remaining set of commercial apple juices, specifically from 0.16 to 0.36 g.100 mL-1, from 9.25 to 15.5 g.100 mL-1 and from 180 to 606 mg.L-1 for acidity, reducing sugar and phenolic compounds, respectively, representing the acid, sweet and bitter tastes.

Targeting oncogenic signaling proteins : new insights using a FRET-based chemical biology approach

Cancer affects more than 20 million people each year and this rate is increasing globally. The Ras/MAPK-pathway is one of the best-studied cancer signaling pathways. Ras proteins are mutated in almost 20% of all human cancers and despite numerous efforts, no effective therapy that specifically targets Ras is available to date. It is now well established that Ras proteins laterally segregate on the plasma membrane into transient nanoscale signaling complexes called nanoclusters. These Ras nanoclusters are essential for the high-fidelity signal transmission. Disruption of nanoclustering leads to reduction in Ras activity and signaling, therefore targeting nanoclusters opens up important new therapeutic possibilities in cancer. This work describes three different studies exploring the idea of membrane protein nanoclusters as novel anti-cancer drug targets. It is focused on the design and implementation of a simple, cell-based Förster Resonance Energy Transfer (FRET)-biosensor screening platform to identify compounds that affect Ras membrane organization and nanoclustering. Chemical libraries from different sources were tested and a number of potential hit molecules were validated on full-length oncogenic proteins using a combination of imaging, biochemical and transformation assays. In the first study, a small chemical library was screened using H-ras derived FRET-biosensors. Surprisingly from this screen, commonly used protein synthesis inhibitors (PSIs) were found to specifically increase H-ras nanoclustering and downstream signalling in a H-ras dependent manner. Using a representative PSI, increase in H-ras activity was shown to induce cancer stem cell (CSC)-enriched mammosphere formation and tumor growth of breast cancer cells. Moreover, PSIs do not increase K-ras nanoclustering, making this screening approach suitable for identifying Ras isoform-specific inhibitors. In the second study, a nanoncluster-directed screen using both H- and K-ras derived FRET biosensors identified CSC inhibitor salinomycin to specifically inhibit K-ras nanocluster organization and downstream signaling. A K-ras nanoclusteringassociated gene signature was established that predicts the drug sensitivity of cancer cells to CSC inhibitors. Interestingly, almost 8% of patient tumor samples in the The Cancer Genome Atlas (TCGA) database had the above gene signature and were associated with a significantly higher mortality. From this mechanistic insight, an additional microbial metabolite screen on H- and K-ras biosensors identified ophiobolin A and conglobatin A to specifically affect K-ras nanoclustering and to act as potential breast CSC inhibitors. In the third study, the Ras FRET-biosensor principle was used to investigate membrane anchorage and nanoclustering of myristoylated proteins such as heterotrimeric G-proteins, Yes- and Src-kinases. Furthermore, Yes-biosensor was validated to be a suitable platform for performing chemical and genetic screens to identify myristoylation inhibitors. The results of this thesis demonstrate the potential of the Ras-derived FRETbiosensor platform to differentiate and identify Ras-isoform specfic inhibitors. The results also highlight that most of the inhibitors identified predominantly perturb Ras subcellular distribution and membrane organization through some novel and yet unknown mechanisms. The results give new insights into the role of Ras nanoclusters as promising new molecular targets in cancer and in stem cells.

Production Quality Analysis of Special Elevator Cars Based on Six Sigma Approach

Project scope is to utilize Six Sigma DMAIC approach and lean principles to improve production quality of the case company. Six Sigma tools and techniques are explored through a literature review and later used in the quality control phase. The focus is set on the Pareto analysis to demonstrate the most evident development areas in the production. Materials that are not delivered to the customer or materials that damaged during transportation comprise the biggest share of all feedbacks. The goal is set to reduce these feedbacks by 50 %. Production observation pointed out that not only material shortages but also over-production is a daily situation. As a result, an initial picking list where the purchased and own production components can be seen, is created, reduction of over- and underproduction and material marking improvement are seen the most competitive options so that the goal can be reached. The picking list development should still continue to make sure that the list can be used not only in the case study but also in the industrial scale. The reduction of material missing category can be evaluated reliably not sooner than in few years because it takes time to gather the needed statistical information.

GIS-based approach for optimization of onshore wind park infrastructure alignment in Finland

Wind power is a rapidly developing, low-emission form of energy production. In Fin-land, the official objective is to increase wind power capacity from the current 1 005 MW up to 3 500–4 000 MW by 2025. By the end of April 2015, the total capacity of all wind power project being planned in Finland had surpassed 11 000 MW. As the amount of projects in Finland is record high, an increasing amount of infrastructure is also being planned and constructed. Traditionally, these planning operations are conducted using manual and labor-intensive work methods that are prone to subjectivity. This study introduces a GIS-based methodology for determining optimal paths to sup-port the planning of onshore wind park infrastructure alignment in Nordanå-Lövböle wind park located on the island of Kemiönsaari in Southwest Finland. The presented methodology utilizes a least-cost path (LCP) algorithm for searching of optimal paths within a high resolution real-world terrain dataset derived from airborne lidar scannings. In addition, planning data is used to provide a realistic planning framework for the anal-ysis. In order to produce realistic results, the physiographic and planning datasets are standardized and weighted according to qualitative suitability assessments by utilizing methods and practices offered by multi-criteria evaluation (MCE). The results are pre-sented as scenarios to correspond various different planning objectives. Finally, the methodology is documented by using tools of Business Process Management (BPM). The results show that the presented methodology can be effectively used to search and identify extensive, 20 to 35 kilometers long networks of paths that correspond to certain optimization objectives in the study area. The utilization of high-resolution terrain data produces a more objective and more detailed path alignment plan. This study demon-strates that the presented methodology can be practically applied to support a wind power infrastructure alignment planning process. The six-phase structure of the method-ology allows straightforward incorporation of different optimization objectives. The methodology responds well to combining quantitative and qualitative data. Additional-ly, the careful documentation presents an example of how the methodology can be eval-uated and developed as a business process. This thesis also shows that more emphasis on the research of algorithm-based, more objective methods for the planning of infrastruc-ture alignment is desirable, as technological development has only recently started to realize the potential of these computational methods.

Chemoenzymatic synthesis of amaryllidaceae alkaloids and their C-1 analogues : symmetry based approach to total synthesis of thebaine

Described herein is the chemoenzymatic total synthesis of several Amaryllidaceae constituents and their unnatural C-I analogues. A new approach to pancratistatin and related compounds will be discussed along with the completed total synthesis of 7 -deoxypancratistatin and trans-dihydrolycoricidine. Evaluation of all new C-l analogues as cancer cell growth inhibitory agents is described. The enzymatic oxidation of dibromobenzenes by Escherichia coli 1M 109 (pDTG60 1) is presented along with conversion of their metabolites to (-)-conduritol E. Investigation into the steric and functional factors governing the enzymatic dihydroxylation of various benzoates by the same organism is also discussed. The synthetic utility of these metabolites is demonstrated through their conversion to pseudo-sugars, aminocyclitols, and complex bicyclic ring systems. The current work on the total synthesis of some morphine alkaloids is also presented. Highlighted will be the synthesis of several model systems related to the efficient total synthesis of thebaine.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.