Objective: To determine the impact of preschool teacher food-related attitudes and behaviors on child food behaviors. Design: A twelve-week intervention and observational study with teachers completing questionnaires before and after the intervention. Setting: Head Start classrooms throughout Virginia. Participants: 177 preschool Head Start teachers and 1534 children. Intervention(s): Food Friends, a twelve-week social marketing campaign, was conducted by Head Start teachers during the Spring 2007, introducing children to novel foods with food puppets, nutrition-related activities and novel food tasting opportunities. Hypotheses related to the impact of preschool teachers' food-related attitudes and behaviors on children's food behaviors were tested, and changes in teacher and child food behaviors were measured. Main Outcome Measures: Teacher food-related attitudes and behaviors were measured/quantified. Child food behaviors were measured and compared to teacher attitudes and behaviors. Analysis: Descriptive, correlational and t-test statistics were conducted. Results: Teachers' and children's acceptance of novel foods improved after the Food Friends program, however, no direct correlations were found between teacher food-related attitudes and behaviors and child food behaviors. Conclusions and Implications: Preschool teacher attitudes and behaviors may not significantly impact child food-related behaviors. More research is needed to determine effective ways of encouraging positive child food behaviors. / Master of Science

Brushlesss dc (BLDC) motors and their drives are penetrating the market of home appliances, HVAC industry, and automotive applications in recent years because of their high efficiency, silent operation, compact form, reliability, and low maintenance. Traditionally, BLDC motors are commutated in six-step pattern with commutation controlled by position sensors. To reduce cost and complexity of the drive system, sensorless drive is preferred. The existing sensorless control scheme with the conventional back EMF sensing based on motor neutral voltage for BLDC has certain drawbacks, which limit its applications. In this thesis, a novel back EMF sensing scheme, direct back EMF detection, for sensorless BLDC drives is presented. For this scheme, the motor neutral voltage is not needed to measure the back EMFs. The true back EMF of the floating motor winding can be detected during off time of PWM because the terminal voltage of the motor is directly proportional to the phase back EMF during this interval. Also, the back EMF voltage is referenced to ground without any common mode noise. Therefore, this back EMF sensing method is immune to switching noise and common mode voltage. As a result, there are no attenuation and filtering necessary for the back EMFs sensing. This unique back EMF sensing method has superior performance to existing methods which rely on neutral voltage information, providing much wider motor speed range at low cost. Based on the fundamental concept of the direct Back EMF detection, improved circuitry for low speed /low voltage and high voltage applications are also proposed in the thesis, which will further expand the applications of the sensorless BLDC motor drives. Starting the motor is critical and sometime difficult for a BLDC sensorless system. A practical start-up tuning procedure for the sensorless system with the help of a dc tachometer is described in the thesis. This procedure has the maximum acceleration performance during the start-up and can be used for all different type applications. An advanced mixed-signal microcontroller is developed so that the EMF sensing scheme is embedded in this low cost 8-bit microcontroller. This device is truly SOC (system-on-chip) product, with high-throughput Micro core, precision-analog circuit, in-system programmable memory and motor control peripherals integrated on a single die. A microcontroller-based sensorless BLDC drive system has been developed as well, which is suitable for various applications, including hard disk drive, fans, pumps, blowers, and home appliances, etc. / Master of Science

In recent years government, academia and industry have been pursuing fuel cell technology as an alternative to current power generating technologies. The automotive industry has targeted fuel cell technology as a potential alternative to internal combustion engines. The goal of this research is to understand and quantify the impact and effects of low temperature operation has on the performance and efficiency of vehicle fuel cell systems through modeling. More specifically, this work addresses issues of the initial thermal transient known to the automotive community as "cold-start" effects. Cold-start effects play a significant role in power limitations in a fuel cell vehicle, and may require hybridization (batteries) to supplement available power. A fuel cell system model developed as part of this work allows users to define the basic thermal fluid relationships in a fuel cell system. The model can be used as a stand-alone version or as part of a complex fuel cell vehicle model. Fuel cells are being considered for transportation primarily because they have the ability to increase vehicle energy efficiency and significantly reduce or eliminate tailpipe emissions. A proton exchange membrane fuel cell is an electrochemical device for which the operational characteristics depend heavily upon temperature. Thus, it is important to know how the thermal design of the system affects the performance of a fuel cell, which governs the efficiency and performance of the system. This work revealed that the impact on efficiency of a cold-start yielded a 5 % increase in fuel use over a regulated drive cycle for the converted sport utility vehicle. The performance of the fuel cell vehicle also suffered due to operation at low temperatures. Operation of the fuel cell at 20 C yielded only 50% of the available power to the vehicle system. / Master of Science

A start-up faces many challenges during different phases of its journey to become a successful sustainable business. A successful scaling of the business is critical to the potential of the start-up and its ability to generate revenue and grow. Leadership has a very important impact on a start-up, navigating the business through different phases and their challenges. Even though it is recognized that leadership is impacted by many factors such as team members and internal and external factors around the organization and evolves with the time to handle those challenges, little is known about the impact of leadership on startups during the scaling phase, particularly when dealing with challenges such as human and financial capital shortage. This research is designed to explore leadership effect on startup’s scaling up.The study was conducted through interviewing successful founders and leaders how have navigated the new venture through scaling up phase, the thematic analysis shows that diverse influences of leadership qualities and traits on startup scaling aspects challenges human and financial capital. There is a dynamic interplay between leadership characteristics and organizational contexts in the successful scaling of startups. Leadership qualities such as educational background, and prior leadership experience are scrutinized for their influence on strategic decision-making and team management during critical growth phases. The study highlights how leadership styles evolve from transactional to transformational to meet the increasing complexity of scaling enterprises. Additionally, team composition and their attributes and organizational changes, including hiring practices and structural adjustments, are pivotal in accommodating the evolving demands of the business, reflecting a thorough integration of leader attributes and environmental factors in scaling success.The study of leadership characteristics, team members, and organizational context in startup scaling provides several promising avenues for future research such as study in leadership evolution within specific industries or comparative studies across industries, global and cultural variations affecting leadership, role of gender and diversity in leadership, the effectiveness of different leadership styles and many more.

Notre travail de recherche porte sur la création de valeurs au sein des start-up innovantes. La littérature en Sciences de Gestion propose deux grandes catégories de valeurs : la valeur actionnariale, qui revient aux actionnaires, économique, et la valeur dite partenariale, qui revient aux parties prenantes, intangible donc (Charreaux, 1998). Si la majorité des travaux de recherche sur le sujet concerne depuis des années la valeur actionnariale, nous nous sommes focalisés sur la deuxième catégorie qui constitue pour les parties prenantes en quelque sorte une rémunération d’échange selon Charreaux (1998). Cette thèse est née du constat d’un manque dans la littérature concernant la qualification précise des valeurs créées par l’innovation, avec différents enjeux. L’enjeu sociétal de création de valeurs d’éthique des affaires (Igalens et Joras, 2002) étant de donner un éclairage sur les comportements humains formant un ensemble de sentiments de valeurs afin d’enrichir les principes RSE énoncés en 2000 par l’OCDE. L’enjeu managérial étant la définition d’un nouveau modèle de management par les valeurs visant à accroître la performance globale (Germain et Trébucq, 2004). Enfin, l’enjeu purement lié à la recherche en Sciences de Gestion est de formaliser enfin une grille de système de valeurs propre au management, puisqu’à ce jour aucun système de valeurs n’a encore fait consensus auprès des chercheurs (Bréchon, 2003). Sous un positionnement épistémologique interprétativiste, notre thèse répondra donc à la problématique générale : par quel modèle de management stratégique une Start-Up innovante peut-elle induire une valorisation de ses parties prenantes ? Notre première partie développe une revue de littérature qui se compose de trois chapitres : caractérisation d’une Start-Up, management stratégique et innovation, quelle création de valeurs ? Cette première partie se concluant par une proposition de soixante valeurs vulgarisées et trois questions de recherche : quelles sont les valeurs potentiellement créées au sein des parties prenantes d’une Start-Up ? pour quelle typologie de parties prenantes une Start-Up innovante peut-elle créer des valeurs ? quel modèle de management stratégique peut permettre à une Start-Up innovante de créer des valeurs auprès de ses partenaires ? Notre deuxième partie est une étude de cas terrain qui se confronte à notre revue de littérature en trois chapitres de nouveau : démarche de recherche, étude de cas, et analyse des résultats. Nous répondrons ainsi à nos trois questions de recherche et à notre problématique, et ferons quatre propositions. Nous concluons en effet notre travail en proposant un système universel des valeurs du management, deux nouveaux concepts démontrés (« Ip Financeur » et « Valorizing »), et un modèle de management par les valeurs pour les Start-Up innovantes. Nous proposons enfin nos apports théoriques, managériaux, et méthodologiques tout en exposant conjointement les limites de notre travail. / Our research focuses on values creation in innovative Start-Up. The literature in Management Science offers two broad categories of values: shareholder value, which returns to shareholders, economic, and partnership value, which amounts to stakeholders, thus intangible (Charreaux 1998). While the majority of research program on the subject for years relates shareholder value, we focused on the second category, which is for stakeholders somehow like an exchange earning for Charreaux (1998). This thesis is born from the observation of a lack in the literature concerning the precise qualification of values created by innovation, with different stakes. The social challenge of creating ethical business values (Igalens and Joras, 2002) is to shed light human behavior forming a set of feelings values, in order to enrich the CSR principles set out in 2000 by the OECD. The managerial challenge is to define a new management model by the values to increase the overall performance (Germain and Trébucq, 2004). Finally, the challenge purely related to research in Management Science is for good to formalize a framework of a values system dedicated to the management, since to date no system of values has yet done a consensus among researchers (Bréchon, 2003). Under an interpretativist epistemological position, our thesis will therefore address the general request: by what type of strategic management model an innovative Start-Up can induce a valorization of its stakeholders? Our first part develops a literature review that consists of three sections: characterization of a Start-Up, strategic management and innovation, what type of values creation? This first part ending with a proposal of sixty values popularized and three research questions: what are the values potentially created within the stakeholders of a Start-Up? for what type of stakeholder an innovative Start-Up can create values? what strategic management model can allow an innovative Start-Up creating values for its partners? The second part is a field case study that confronts our literature review in three sections again: research approach, case study, and results analysis. We thus answer to our three research questions and to our general request, and will do four proposals. We conclude indeed our work by proposing an universal system of management values, two new concepts demonstrated ("Ip Financeur" and "Valorizing"), and a values management model for innovative Start-Up. Finally, we propose our theoretical, managerial, and methodological contributions while exposing the limits of our work.

Background: In today's digitalized and globalized business environment, entrepreneurs are constantly challenged to carefully plan its start-ups products, services or business model. Any failure in one of those components may result in a less competitive company, which could lead to failure as consequence. Business model is often seen as a central and important part of a start-up. Over time, entrepreneurs look for new ways of improving its current business model or new ways doing business always aiming economic growth. Business Model Innovation is a technique that supports companies, business managers and entrepreneurs to look for business opportunities (or business models) that would somehow be related to the company. Purpose: The main purpose of this master thesis is to expose the challenges that start-ups face in terms of business model and then to understand how the companies studied overcame those challenges by making use of business model innovation (BMI). We also look to understand what kind of impact business model innovation generated in the start-up, in terms of economic growth. Method: Primary data and secondary data were collected through qualitative semi-structured interviews involving multiple case study of five technology start-ups in Sweden. Once all data were collected and stored, we made use of open and axial coding techniques in order to perform data analysis to possibly generate a theory and the answer to the research questions. Conclusion: All start-ups are aware about the importance and positive benefits that business model innovation could bring. When it comes to challenges, there are two main problems, where the first one is related about the difficulty to abstract different business model and then incorporate into the company’s context while the second one is once they manage to overcome the first challenge, they still needs to find a way to make sure it would be profitable. Another finding is that older start-ups tends to be more aware about the positive and negative impacts that business model innovation could bring, and each company has its own method to validate a business model innovation. We analyzed each case, identified some patterns and develop a model that helps start-ups to validate potential business models to be incorporated in the company.

Sistemas de Recomendação (SR) vêm se apresentando como poderosas ferramentas para portais web tais como sítios de comércio eletrônico. Para fazer suas recomendações, os SR se utilizam de fontes de dados variadas, as quais capturam as características dos usuários, dos itens e suas transações, bem como de modelos de predição. Dada a grande quantidade de dados envolvidos, é improvável que todas as recomendações possam ser bem representadas por um único modelo global de predição. Um outro importante aspecto a ser observado é o problema conhecido por cold-start, que apesar dos avanços na área de SR, é ainda uma questão relevante que merece uma maior atenção. O problema está relacionado com a falta de informação prévia sobre novos usuários ou novos itens do sistema. Esta tese apresenta uma abordagem híbrida de recomendação capaz de lidar com situações extremas de cold-start. A abordagem foi desenvolvida com base no algoritmo SCOAL (Simultaneous Co-Clustering and Learning). Na sua versão original, baseada em múltiplos modelos lineares de predição, o algoritmo SCOAL mostrou-se eficiente e versátil, podendo ser utilizado numa ampla gama de problemas de classificação e/ou regressão. Para melhorar o algoritmo SCOAL no sentido de deixá-lo mais versátil por meio do uso de modelos não lineares, esta tese apresenta uma variante do algoritmo SCOAL que utiliza modelos de predição baseados em Máquinas de Aprendizado Extremo. Além da capacidade de predição, um outro fator que deve ser levado em consideração no desenvolvimento de SR é a escalabilidade do sistema. Neste sentido, foi desenvolvida uma versão paralela do algoritmo SCOAL baseada em OpenMP, que minimiza o tempo envolvido no cálculo dos modelos de predição. Experimentos computacionais controlados, por meio de bases de dados amplamente usadas na prática, comprovam que todos os desenvolvimentos propostos tornam o SCOAL ainda mais atraente para aplicações práticas variadas. / Recommender Systems (RS) are powerful and popular tools for e-commerce. To build its recommendations, RS make use of multiple data sources, capture the characteristics of items, users and their transactions, and take advantage of prediction models. Given the large amount of data involved in the predictions made by RS, is unlikely that all predictions can be well represented by a single global model. Another important aspect to note is the problem known as cold-start that, despite that recent advances in the RS area, it is still a relevant issue that deserves further attention. The problem arises due to the lack of prior information about new users and new items. This thesis presents a hybrid recommendation approach that addresses the (pure) cold start problem, where no collaborative information (ratings) is available for new users. The approach is based on an existing algorithm, named SCOAL (Simultaneous Co-Clustering and Learning). In its original version, based on multiple linear prediction models, the SCOAL algorithm has shown to be efficient and versatile. In addition, it can be used in a wide range of problems of classification and / or regression. The SCOAL algorithm showed impressive results with the use of linear prediction models, but there is still room for improvements with nonlinear models. From this perspective, this thesis presents a variant of the SCOAL based on Extreme Learning Machines. Besides improving the accuracy, another important issue related to the development of RS is system scalability. In this sense, a parallel version of the SCOAL, based on OpenMP, was developed, aimed at minimizing the computational cost involved as prediction models are learned. Experiments using real-world datasets has shown that all proposed developments make SCOAL algorithm even more attractive for a variety of practical applications.

Sistemas de Recomendação (SR) vêm se apresentando como poderosas ferramentas para portais web tais como sítios de comércio eletrônico. Para fazer suas recomendações, os SR se utilizam de fontes de dados variadas, as quais capturam as características dos usuários, dos itens e suas transações, bem como de modelos de predição. Dada a grande quantidade de dados envolvidos, é improvável que todas as recomendações possam ser bem representadas por um único modelo global de predição. Um outro importante aspecto a ser observado é o problema conhecido por cold-start, que apesar dos avanços na área de SR, é ainda uma questão relevante que merece uma maior atenção. O problema está relacionado com a falta de informação prévia sobre novos usuários ou novos itens do sistema. Esta tese apresenta uma abordagem híbrida de recomendação capaz de lidar com situações extremas de cold-start. A abordagem foi desenvolvida com base no algoritmo SCOAL (Simultaneous Co-Clustering and Learning). Na sua versão original, baseada em múltiplos modelos lineares de predição, o algoritmo SCOAL mostrou-se eficiente e versátil, podendo ser utilizado numa ampla gama de problemas de classificação e/ou regressão. Para melhorar o algoritmo SCOAL no sentido de deixá-lo mais versátil por meio do uso de modelos não lineares, esta tese apresenta uma variante do algoritmo SCOAL que utiliza modelos de predição baseados em Máquinas de Aprendizado Extremo. Além da capacidade de predição, um outro fator que deve ser levado em consideração no desenvolvimento de SR é a escalabilidade do sistema. Neste sentido, foi desenvolvida uma versão paralela do algoritmo SCOAL baseada em OpenMP, que minimiza o tempo envolvido no cálculo dos modelos de predição. Experimentos computacionais controlados, por meio de bases de dados amplamente usadas na prática, comprovam que todos os desenvolvimentos propostos tornam o SCOAL ainda mais atraente para aplicações práticas variadas. / Recommender Systems (RS) are powerful and popular tools for e-commerce. To build its recommendations, RS make use of multiple data sources, capture the characteristics of items, users and their transactions, and take advantage of prediction models. Given the large amount of data involved in the predictions made by RS, is unlikely that all predictions can be well represented by a single global model. Another important aspect to note is the problem known as cold-start that, despite that recent advances in the RS area, it is still a relevant issue that deserves further attention. The problem arises due to the lack of prior information about new users and new items. This thesis presents a hybrid recommendation approach that addresses the (pure) cold start problem, where no collaborative information (ratings) is available for new users. The approach is based on an existing algorithm, named SCOAL (Simultaneous Co-Clustering and Learning). In its original version, based on multiple linear prediction models, the SCOAL algorithm has shown to be efficient and versatile. In addition, it can be used in a wide range of problems of classification and / or regression. The SCOAL algorithm showed impressive results with the use of linear prediction models, but there is still room for improvements with nonlinear models. From this perspective, this thesis presents a variant of the SCOAL based on Extreme Learning Machines. Besides improving the accuracy, another important issue related to the development of RS is system scalability. In this sense, a parallel version of the SCOAL, based on OpenMP, was developed, aimed at minimizing the computational cost involved as prediction models are learned. Experiments using real-world datasets has shown that all proposed developments make SCOAL algorithm even more attractive for a variety of practical applications.

This master`s thesis is focused on performance evaluation of SRP Ltd. company while Model START was applied. The first part of the thesis contains theory of the evaluation of business performance using of Model START based on the EFQM Excellence Model. The next part deals with the evaluation of current situation of the company and the evaluation of questionnaire. Suggestions for performance improvement are provided at the end of the thesis.

Ce travail de thèse a pour objectif la mise en place d’un processus d’intelligence économique au sein d’une start-up proposant une technologie émergente. Dans ce cas d’étude, nous avons mis en évidence une double émergence : l’environnement nouveau et l’entreprise en construction.Dans un premier temps, nous mobilisons un cadre analytique original pour le processus d’intelligence économique : les TIS – Technological Innovation Systems. Cette grille de lecture propose une analyse dynamique du système d’innovation de l’entreprise à travers la structure et les interactions auxquelles les acteurs du système prennent part. Dans un deuxième temps, nous abordons l’intérêt de considérer les éléments intrinsèques de la start-up pour la mise en place d’un processus d’intelligence économique. Notre compréhension des éléments spécifiques de la start-up, comme sa structure adhocratique, a permis dans un troisième temps, l’implémentation d’outils cohérents avec la prégnance de la dimension humaine et les ressources que l’entreprise peut mobiliser. Nous avons organisé la création de connaissances à partir du cycle de l’information, proposé une première évaluation du processus d’intelligence économique en place et déduit les prolongements envisagés. Dans un quatrième temps, nous nous sommes focalisés sur l’utilisation du brevet pour la compréhension de notre domaine technologique.Réalisée dans une démarche de recherche-action (menée dans le cadre d’une convention CIFRE), cette thèse expose l’expérimentation de notre méthode d’intelligence économique au sein de Poietis, start-up française de bioimpression. / This thesis aims to implement a competitive intelligence process within a start-up that develops an emerging technology. A double emergence has been identified: the environment of the company and the company itself.First, we call upon an original analytical framework for competitive intelligence: Technological Innovation Systems (TIS). This framework allows for a dynamic analysis of the innovation system of the company through the structure and the interactions between the agents within the system. Second, we address the benefit of taking into the account the intrinsic characteristics of the company for the implementation of a competitive intelligence process. Our understanding of specific elements of the start-up, its adhocratic structure for instance, has allowed in a third step to implement tools in line with the importance of the human dimension and the resources that the company can mobilize.We organized the creation of knowledge from the information cycle, suggest a first evaluation of the competitive intelligence process and deduced the considered extensions.Finally, we focused on the use of patent for the understanding of a technological domain.Carried out in an action research approach (conducted as part of a CIFRE contract), this thesis shows the test of our method of technology intelligence within Poietis, a French bioprinting start-up.