National branding and Country-of-origin strategy (COO) has not only shown to be convenient for research, but also of considerable importance and interest for companies to enhance competitive advantage. With constantly increasing product range on the markets and continually new technology in the society, the buying behaviour and customer preferences have changed. The customer evaluates not only the product regarding its function or physical capacity, but also regarding the product's intangible value, such as its COO. The purpose with this study was to provide a deeper understanding about how a multinational company's (henceforth MNC) use of COO in their brand communication, can be described. To realize and accomplish the purpose, two research questions were formulated which this study had the intention to answer. The first research question was meant to describe what channels in the communication process a multinational company can use to communicate their brand. The second research question was meant to describe how a multinational company can use COO in the message component of the communication process. The fundamental purpose of the thesis in question was descriptive. The study was performed by observing a multinational case company. The observation of case company was performed partly with an interview and partly with a content analysis. The interview was held with a representative of the case company. The content analysis of case company was performed by reviewing websites in two different countries. As a result, we found that the case company, which represented a multinational company (MNC), among other channels, used paper catalogues and websites as a channel in the communication process. The study also showed that different COO strategies can be used complementary in the message component in the company´s communication process, with the aim to attain competitive advantage among other companies on the market.

A critical event in the origin of life is thought to have been the emergence of a molecule capable of self-replication and evolution. According to the RNA World hypothesis, this could have been an RNA polymerase ribozyme capable of generating copies of itself from simple nucleotide precursors. In vitro evolution experiments have provided modern examples of such ribozymes, such as the R18 RNA polymerase ribozyme, exhibiting basic levels of this crucial catalytic activity; R18’s activity, however, falls far short of that required of an RNA replicase, leaving unanswered the question of whether RNA can catalyse its self-replication. This thesis describes the development and use of a novel in vitro selection system, Compartmentalised Bead-Tagging (CBT), to isolate variants of the R18 ribozyme with improved sequence generality and extension capabilities. CBT evolution and engineering of polymerase ribozymes, together with RNA template evolution, allowed the synthesis of RNA molecules over 100 nucleotides long, as well as the RNA-catalysed transcription of a catalytic hammerhead ribozyme. This demonstrates the catalytic capabilities of ribozyme polymerases. The R18 ribozyme was also exploited as an analogue of a primordial replicase, to determine replicase behaviour in different reaction environments. Substantial ribozyme polymerisation occurred at −7˚C in the liquid eutectic phase of water-ice; increased ribozyme stability at these low temperatures allowed longer extension products to be generated than at ambient temperatures. The concentration effect of eutectic phase formation could also yield RNA synthesis from dilute solutions of substrates, and provide quasicellular compartmentalisation of ribozymes. These beneficial physicochemical features of ice make it a potential protocellular medium for the emergence of primordial replicases. Ice also could serve as a medium for CBT, allowing the isolation of a polymerase ribozyme adapted to the low temperatures in the ice phase, demonstrating the primordial potential and modern feasibility of ribozyme evolution in ice.

(Constance) Stanzie Tooth’s thesis exhibition titled, Hand to Ground, explores ideas of origin; both the mythology of a personal origin as well as the origins of painting and representation. Interrogating the history of landscape painting, the exhibition skews notions of identity by reconsidering representations of the landscape. Inspired by Margaret Atwood’s novel Surfacing, and paired with reminiscences of her childhood in rural Ontario, Tooth’s paintings unfold oblique narratives of a communion with land. These questions of personal ecology are filtered through an intense material expression, reinforcing the idea of coming into being. The exhibition was on view at the Karsh Masson Gallery in Ottawa from August 1st to September 10th, 2015.

The purpose of this Master's Thesis is to investigate the attitudes of high net worth individuals toward country of origin information within the luxury goods market with respect to the quality of the products. The primary method used for the research is qualitative research based on in-depth interviews. Based on these in-depth interviews, the thesis demonstrates the synergy between the literature on country of origin and luxury goods and the consumers' quality perception on luxury based on country of origin.

The effects of consumer involvement on product choice have been studied extensively. However, to the knowledge of the researcher of this work, no study has examined whether consumers become more involved with a product choice when it is associated with a country towards which they harbour feelings of animosity. Hence, this work examines whether feelings of animosity increase consumers' level of purchase involvement. This is a cross-cultural investigation conducted in the context of the Holocaust. 340 Israeli and British Jews took part in this experimental research. Structural equation modeling was employed to examine this relationship in a model adapted from Klein et al.(1998). A positive and statistically significant relationship was observed between consumer animosity and purchase involvement. This work suggests that subcultural differences are possible moderators of consumer animosity. Thus, marketing practitioners should segment markets by looking into subcultural differences among consumers in their target market.

Syftet med denna studie är att undersöka hur bageriföretag använder sig av Sveriges image och vilka egenskaper kopplas till deras varumärke. Vidare vill denna studie skapa förståelse på hur ett lands image kan användas som en varumärkesstrategi. Studiens resultat visar att svenska bageriföretag använder sig av olika svenska egenskaper som den svenska flaggan, svensk text och svenska symboler som älgar för att kommunicera deras svenska ursprung i utländska marknader. De intervjuade företag visar att det mest direkta sättet att påverka kommunikationen av deras svenska ursprung är genom förpackningar av deras produkter. Det svenska ursprunget av företagets varumärke och produkter kan ge konkurrensfördelar och hjälpa deras produkter att sticka ut från mängden i marknader som har en positiv respons för svenska livsmedelsprodukter.

abstract: Cancer researchers have traditionally used a handful of markers to understand the origin of tumors and to predict therapeutic response. Additionally, performing machine learning activities on disparate data sources of varying quality is fraught with inherent bias. The Caris Life Sciences Molecular Database (CMD) is an immense resource for discovery as it contains over 215,000 molecular profiles of tumors with consistently gathered clinical grade molecular data along with immense amounts of clinical outcomes data. This resource was leveraged to generate two artificial intelligence algorithms aiding in diagnosis and one for therapy selection. The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case. The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%. Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02). / Dissertation/Thesis / Doctoral Dissertation Molecular and Cellular Biology 2020

Current works of bike demand prediction mainly focus on cluster level and perform poorly on predicting demands of a single station. In the first task, we introduce a contextual based bike demand prediction model, which predicts bike demands for per station by combining spatio-temporal network and environment contexts synergistically. Furthermore, since people's movement information is an important factor, which influences the bike demands of each station. To have a better understanding of people's movements, we need to analyze the relationship between different places. In the second task, we propose an origin-destination model to learn place representations by using large scale movement data. Then based on the people's movement information, we incorporate the place embedding into our bike demand prediction model, which is built by using multi-source large scale datasets: New York Citi bike data, New York taxi trip records, and New York POI data. Finally, as deep learning methods have been successfully applied to many fields such as image recognition and natural language processing, it inspires us to incorporate the complex deep learning method into the bike demand prediction problem. So in this task, we propose a deep spatial-temporal (DST) model, which contains three major components: spatial dependencies, temporal dependencies, and external influence. Experiments on the NYC Citi Bike system show the effectiveness and efficiency of our model when compared with the state-of-the-art methods.

Commodity trading firms work to remain competitive in the evolving agricultural industry. They work to become more efficient by increasing economies of size and scale, vertically and horizontally integrating, and diversifying geographically, or any combination of these avenues. Geographically diverse firms have access to multiple origins between which, spatial arbitrage opportunities can occur. When spatial arbitrage opportunities occur, firms take advantage of them to generate profit. Origin switching options are one way to take advantage of these opportunities. Origin switching option allow the seller of grain to fill a contract with any listed origin at the cost of the premium negotiated. This thesis helps to determine the value of these origin type switching options by developing a Monte Carlo simulation model with real option analysis. Soybean and corn markets are analyzed in the U.S. Gulf, Pacific Northwest, Brazil, Argentine, and origins with China and Japan as the respective destinations.

The aim of this diploma thesis was to evaluate the possibility of determining the origin of the wine on the basis of elemental composition. For this purpose an analysis of major and minor elements was accomplished in selected varieties of wines. In total, 48 parameters for six wine subregions in Bohemia and Moravia was determined and used. The sum of 35 parameters represented the concentration of selected elements and other 13 parameters were created from the measured values. The concentration of elements was determined by inductively coupled plasma mass spectrometry (ICP-MS). For the purpose of classification of samples into groups multivariate statistical methods were used. The measured data set was firstly separated using analysis of variance (ANOVA) and discriminant analysis (DA). It has been proved that the selected variables influence the distribution of wines into categories according to their geographic location. Canonical discriminant analysis, principal component analysis (PCA) and cluster analysis has been used for graphical projection. Simultaneously the model based on our analysis was created to enable classify an unknown sample of wine according to the origin.