<p>  This paper explored the application of three machine learning models focused on predictive motor maintenance. Logistic Regression, Sequential Minimal Optimization (SMO), and NaïveBayes models. A comparative analysis of these models illustrated that while each had an accuracy greater than 95% in this study, the Logistic Regression Model exhibited the most reliable operation.</p>

[ES] La presente tesis doctoral parte de la pregunta de investigación ¿es la Gamificación una metodología eficaz para el aprendizaje de historia de la educación española en la formación universitaria de futuros docentes? Cuatro ejes, explícitos en esta pregunta, son los que estructuran esta investigación educativa. El primero es la metodología de Gamificación, que fundamenta y conforma la investigación llevada a cabo. El segundo es la historia de la educación española, el contenido impartido a través de la experiencia gamificada. El tercero representa el contexto universitario en el que se desarrolla la investigación. Finalmente, el cuarto muestra la población con la que se hace el estudio de campo, alumnado de los grados de Magisterio de Infantil y Primaria. Este último eje tiene especial relevancia, ya que el desarrollo de investigación acción con futuros docentes se integra en la cartografía de la buena docencia universitaria (Paricio et al., 2019) y en la investigación desde el vínculo y la transferibilidad del conocimiento pedagógico durante el proceso. Los últimos estudios sobre Gamificación abogan por extraer resultados que evidencien el efecto de la Gamificación en variables más allá de la motivación. Además, enfatizan la necesidad de abordar la investigación desde un enfoque cualitativo y cuantitativo. La presente tesis atiende a estas premisas, por ello, con la finalidad de dar respuesta a la pregunta de investigación y comprobar las diferentes hipótesis, se acota un conjunto de variables cualitativas y cuantitativas. Las más significativas son metodología de Gamificación, competencias específicas, competencias transversales, motivación, rendimiento académico, rol docente y juego como recurso didáctico. Además, se utiliza un método mixto de investigación que combina un análisis cualitativo, ratificado con diferentes análisis cuantitativos para aportar validez ecológica a la investigación y mayor rigor a las conclusiones. Entre las conclusiones más relevantes destacan la satisfacción de los sujetos participantes hacia la experiencia gamificada, la mejora de las competencias específicas y el desarrollo de diferentes competencias transversales. Se logra también un incremento en la motivación y un buen rendimiento académico. Respecto al rol docente, la valoración es positiva, al igual que el juego como recurso didáctico. Los diferentes análisis también aportan conclusiones destacables relacionadas con el carácter ecléctico de la metodología, el éxito en la asistencia, la capacidad de atención, el grado de participación, la evaluación, la atención a la diversidad de alumnado o la percepción del propio proceso de aprendizaje. Además, esta investigación acción considera los últimos estudios en Gamificación e investigación educativa y responde a las demandas y retos que estos plantean. De este modo, trata de lograr la proyección internacional a la que se aspira con esta tesis doctoral y colabora en la creación de conocimiento empírico en el campo de la Gamificación. / [CA] La present tesi doctoral part de la pregunta d'investigació ¿és la Gamificació una metodologia eficaç per a l'aprenentatge d'història de l'educació espanyola en la formació universitària de futurs docents? Quatre eixos, explícits en aquesta pregunta, són els que estructuren aquesta investigació educativa. El primer és la metodologia de Gamificació, que fonamenta i conforma la investigació duta a terme. El segon és la història de l'educació espanyola, el contingut impartit a través de l'experiència gamificada. El tercer representa el context universitari en el qual es desenvolupa la investigació. Finalment, el quart mostra la població amb la qual es fa l'estudi de camp, alumnat dels graus de Magisteri d'Infantil i Primària. Aquest últim eix té especial rellevància, ja que el desenvolupament d'investigació acció amb futurs docents s'integra en la cartografia de la bona docència universitària (Paricio et al., 2019) i en la investigació des del vincle i la transferibilitat del coneixement pedagògic durant el procés. Els últims estudis sobre Gamificació advoquen per extraure resultats que evidencien l'efecte de la Gamificació en variables més enllà de la motivació. A més, emfatitzen la necessitat d'abordar la investigació des d'un enfocament qualitatiu i quantitatiu. La present tesi atén aquestes premisses, per això, amb la finalitat de donar resposta a la pregunta d'investigació i comprovar les diferents hipòtesis, es delimita un conjunt de variables qualitatives i quantitatives. Les més significatives són metodologia de Gamificació, competències específiques, competències transversals, motivació, rendiment acadèmic, rol docent i joc com a recurs didàctic. A més, s'utilitza un mètode mixt d'investigació que combina una anàlisi qualitativa, ratificat amb diferents anàlisis quantitatives per a aportar validesa ecològica a la investigació i major rigor a les conclusions. Entre les conclusions més rellevants destaquen la satisfacció dels subjectes participants cap a l'experiència gamificada, la millora de les competències específiques i el desenvolupament de diferents competències transversals. S'aconsegueix també un increment en la motivació i un bon rendiment acadèmic. Respecte al rol docent, la valoració és positiva, igual que el joc com a recurs didàctic. Les diferents anàlisis també aporten conclusions destacables relacionades amb el caràcter eclèctic de la metodologia, l'èxit en l'assistència, la capacitat d'atenció, el grau de participació, l'avaluació, l'atenció a la diversitat d'alumnat o la percepció del propi procés d'aprenentatge. A més, aquesta investigació acció considera els últims estudis en Gamificació i investigació educativa i respon a les demandes i reptes que aquests plantegen. D'aquesta manera, tracta d'aconseguir la projecció internacional a la qual s'aspira amb aquesta tesi doctoral i col·labora en la creació de coneixement empíric en el camp de la Gamificació. / [EN] The forthcoming doctoral thesis stems from the research question, is gamification an efficient method for learning the history of Spanish education in university training for future teachers? Four themes, which are explicit in the question itself, will give structure to this educational research. The first is the methodology of Gamification, which constitutes the grounds on which this investigation is based. The second is the history of Spanish education, the content delivered via a gamified experience. The third represents the university context in which the research takes place. Lastly, the fourth shows the demographic of those involved in the field study, alumni from Primary and Nursery School Teaching university degrees. This last theme is particularly relevant, as developing action research with future teachers is an integral part of mapping out quality university teaching (Paricio et al., 2019) and of binding research and transferability of pedagogic knowledge during the process. The most recent studies on Gamification advocate for extracting results that underline the effects of Gamification in variables beyond motivation. Furthermore, they emphasize the need to approach the research from a qualitative and quantitative point of view. Thereupon, the present doctoral thesis handles these propositions with the aim to answer the research question and prove different hypotheses, narrowing down a group of qualitative and quantitative variables. The most significant are Gamification methodology, specific competences, transversal competences, motivation, academic performance, the role of the lecturer and games as a teaching resource. In addition, a mixed research method is used which combines qualitative analysis, reinforced by different quantitative analyses in order to provide ecological validity to the study and more rigour to the conclusions. Among the most relevant conclusions, the following stand out: the satisfaction of the participating subjects towards the gamified experience, progress in specific competences, and the development of different transversal competences. Regarding the lecturer's role, the assessment is positive, as well as games as a teaching resource. The different analyses also provide noteworthy conclusions related to the eclectic nature of the methodology, attendance success, attention capacity, degree of participation, assessment, attention to student diversity or the perception of the process of learning itself. In addition, this action research examines the latest Gamification and educational research and answers the demands and challenges that they posit. In so doing, it attempts to achieve the international outreach to which this doctoral thesis aspires, and it assists in the creation of empiric knowledge in the field of Gamification. / Edo Agustín, E. (2021). La metodología de Gamificación para el aprendizaje de historia de la educación española: investigación acción en la formación universitaria de docentes [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/178971

Text in English / In Swaziland, family nurse practitioners (FNPs) are professional nurses who have undergone preparation as general nurse, midwife and FNP. These nurses play an important role in the delivery of primary health care (PHC). Family nurse practice is an evolving concept introduced in Swaziland in 1979. It is a means of exploring nursing roles and primary health care services for deployment in under-served areas and to enable nurses to serve as the primary providers of health care services in clinics, health centres and in the outpatient departments of hospitals. Changing responsibilities within the health care setting require different skills and more knowledge. The expansion and extension of the nurses' role, including the techniques of diagnosing and treating, was a priority of the Ministry of Health and Social Welfare (MOH&SW) in Swaziland's five-year development plan for 1978-1983. It was regarded as a necessary component for raising the quality and effectiveness of PHC services. Some of the major and urgent challenges that confront FNPs today are the advent of the human immune virus/acquired immuno-deficiency syndrome (HIV/AIDS) scourge and the re-emergence of the tuberculosis epidemic. Both these health problems require proficient diagnosis and case management skills as well as new approaches. If FNPs are to remain relevant and to continue to provide quality services in spite of prevailing challenges, they have to engage in continuing education (CE). The main aim of this study was to investigate the perceptions of the FNP role, CE needs and issues relevant to the current practice of FNPs in Swaziland. A further aim was to establish a structure or framework for a CE programme that would contribute to the strengthening of CE for FNPs and identify enabling factors and barriers in the practice and education ofFNPs. Both quantitative and qualitative research methods were used for data collection. A survey was conducted to collect data from 5 7 FNPs and 11 nurse managers and nurse educators. The transcript from the questionnaires was subjected to quantitative-based content analysis. A total of thirty nurse managers, nurse educators and MOH&SW nurse executives participated in the focus group interviews. The collected data was subjected to qualitativebased content analysis. The findings identified the role of the FNP as manager, clinical practitioner, educator and researcher. The analyses highlighted the CE needs of FNPs, and the question of updating and upgrading the skills of practising FNPs. The identified enabling factors and barriers, although perceived as issues that are peripheral and auxiliary to the curriculum, appeared to have a strong bearing on programme planning. The findings from this study have implications for a structured CE programme for FNPs at the University of Swaziland. / Health Studies / D. Litt et Phil. (Nursing Sciences)

Text in English / In Swaziland, family nurse practitioners (FNPs) are professional nurses who have undergone preparation as general nurse, midwife and FNP. These nurses play an important role in the delivery of primary health care (PHC). Family nurse practice is an evolving concept introduced in Swaziland in 1979. It is a means of exploring nursing roles and primary health care services for deployment in under-served areas and to enable nurses to serve as the primary providers of health care services in clinics, health centres and in the outpatient departments of hospitals. Changing responsibilities within the health care setting require different skills and more knowledge. The expansion and extension of the nurses' role, including the techniques of diagnosing and treating, was a priority of the Ministry of Health and Social Welfare (MOH&SW) in Swaziland's five-year development plan for 1978-1983. It was regarded as a necessary component for raising the quality and effectiveness of PHC services. Some of the major and urgent challenges that confront FNPs today are the advent of the human immune virus/acquired immuno-deficiency syndrome (HIV/AIDS) scourge and the re-emergence of the tuberculosis epidemic. Both these health problems require proficient diagnosis and case management skills as well as new approaches. If FNPs are to remain relevant and to continue to provide quality services in spite of prevailing challenges, they have to engage in continuing education (CE). The main aim of this study was to investigate the perceptions of the FNP role, CE needs and issues relevant to the current practice of FNPs in Swaziland. A further aim was to establish a structure or framework for a CE programme that would contribute to the strengthening of CE for FNPs and identify enabling factors and barriers in the practice and education ofFNPs. Both quantitative and qualitative research methods were used for data collection. A survey was conducted to collect data from 5 7 FNPs and 11 nurse managers and nurse educators. The transcript from the questionnaires was subjected to quantitative-based content analysis. A total of thirty nurse managers, nurse educators and MOH&SW nurse executives participated in the focus group interviews. The collected data was subjected to qualitativebased content analysis. The findings identified the role of the FNP as manager, clinical practitioner, educator and researcher. The analyses highlighted the CE needs of FNPs, and the question of updating and upgrading the skills of practising FNPs. The identified enabling factors and barriers, although perceived as issues that are peripheral and auxiliary to the curriculum, appeared to have a strong bearing on programme planning. The findings from this study have implications for a structured CE programme for FNPs at the University of Swaziland. / Health Studies / D. Litt et Phil. (Nursing Sciences)

Many metropolitan cities motivate people to exploit public bike-sharing programs as alternative transportation for many reasons. Due to its’ popularity, multiple types of research on optimizing public bike-sharing systems is conducted on city-level, neighborhood-level, station-level, or user-level to predict the public bike demand. Previously, the research on the public bike demand prediction primarily focused on discovering a relationship with weather as an external factor that possibly impacted the bike usage or analyzing the bike user trend in one aspect. This work hypothesizes two external factors that are likely to affect public bike demand: weather and air pollution. This study uses a public bike data set, daily temperature, precipitation data, and air condition data to discover the trend of bike usage using multiple machine learning techniques such as Decision Tree, Naïve Bayes, and Random Forest. After conducting the research, each algorithm’s output is evaluated with performance comparisons such as accuracy, precision, or sensitivity. As a result, Random Forest is an efficient classifier for the bike demand prediction by weather and precipitation, and Decision Tree performs best for the bike demand prediction by air pollutants. Also, the three class labelings in the daily bike demand has high specificity, and is easy to trace the trend of the public bike system.

This study was devoted to researching the effectiveness of e-learning, in terms of the reported experiences of the Language Services section employees in Parliament RSA. At present, the internet is seen as a successful and influential educational tool in both academic and corporate institutions. Academically, it has been adopted for e-learning methods of teaching and learning. It is perceived as a cost-effective method of providing lifelong education. It is important to know about the pros and cons of e-learning in adult education and compare them with the experiences of the people who are and have been engaged in adult education. To create an adult education skills development framework that can be used successfully in e-learning, it is important to get information about and from the people concerned regarding their learning experiences. To understand more about the influence of e-learning, this study administered a questionnaire to the employees of the Language Services Section of Parliament who participated in the pilot learning programmes offered by Parliament. The questionnaire was designed to gather structured responses from the participants in the inquiry. The results were studied and applied to draw recommendations for what can be improved in the programme to promote and endorse continuing learning experiences as is the aim of the in-house programme offered by Parliament. The study also has relevance in terms of a discussion of issues that arise in the literature on adult education, which were located as part of the study. / Educational Studies / M. Ed. (Adult Education)

<p>Sequential decision-making is a fundamental class of problem that motivates algorithm designs of online machine learning and reinforcement learning. Arguably, the resulting online algorithms have supported modern online service industries for their data-driven real-time automated decision making. The applications span across different industries, including dynamic pricing (Marketing), recommendation (Advertising), and dosage finding (Clinical Trial). In this dissertation, we contribute fundamental statistical design advances for sequential decision-making algorithms, leaping progress in theory and application of online learning and sequential decision making under uncertainty including online sparse learning, finite-armed bandits, and high-dimensional online decision making. Our work locates at the intersection of decision-making algorithm designs, online statistical machine learning, and operations research, contributing new algorithms, theory, and insights to diverse fields including optimization, statistics, and machine learning.</p> <p><br></p> <p>In part I, we contribute a theoretical framework of continuous risk monitoring for regularized online statistical learning. Such theoretical framework is desirable for modern online service industries on monitoring deployed model's performance of online machine learning task. In the first project (Chapter 1), we develop continuous risk monitoring for the online Lasso procedure and provide an always-valid algorithm for high-dimensional dynamic pricing problems. In the second project (Chapter 2), we develop continuous risk monitoring for online matrix regression and provide new algorithms for rank-constrained online matrix completion problems. Such theoretical advances are due to our elegant interplay between non-asymptotic martingale concentration theory and regularized online statistical machine learning.</p> <p><br></p> <p>In part II, we contribute a bootstrap-based methodology for finite-armed bandit problems, termed Residual Bootstrap exploration. Such a method opens a possibility to design model-agnostic bandit algorithms without problem-adaptive optimism-engineering and instance-specific prior-tuning. In the first project (Chapter 3), we develop residual bootstrap exploration for multi-armed bandit algorithms and shows its easy generalizability to bandit problems with complex or ambiguous reward structure. In the second project (Chapter 4), we develop a theoretical framework for residual bootstrap exploration in linear bandit with fixed action set. Such methodology advances are due to our development of non-asymptotic theory for the bootstrap procedure.</p> <p><br></p> <p>In part III, we contribute application-driven insights on the exploration-exploitation dilemma for high-dimensional online decision-making problems. Such insights help practitioners to implement effective high-dimensional statistics methods to solve online decisionmaking problems. In the first project (Chapter 5), we develop a bandit sampling scheme for online batch high-dimensional decision making, a practical scenario in interactive marketing, and sequential clinical trials. In the second project (Chapter 6), we develop a bandit sampling scheme for federated online high-dimensional decision-making to maintain data decentralization and perform collaborated decisions. These new insights are due to our new bandit sampling design to address application-driven exploration-exploitation trade-offs effectively. </p>

The process of cell-sorting is essential for development and maintenance of tissues. With the Differential Adhesion Hypothesis, Steinberg proposed that cellsorting is determined by quantitative differences in cell-type-specific intercellular adhesion strengths. An implementation of the Differential Adhesion Hypothesis is the Differential Migration Model by Voss-Böhme and Deutsch. There, an effective adhesion parameter was derived analytically for systems with two cell types, which predicts the asymptotic sorting pattern. However, the existence and form of such a parameter for more than two cell types is unclear. Here, we generalize analytically the concept of an effective adhesion parameter to three and more cell types and demonstrate its existence numerically for three cell types based on in silico time-series data that is produced by a cellular-automaton implementation of the Differential Migration Model. Additionally, we classify the segregation behavior using statistical learning methods and show that the estimated effective adhesion parameter for three cell types matches our analytical prediction. Finally, we demonstrate that the effective adhesion parameter can resolve a recent dispute about the impact of interfacial adhesion, cortical tension and heterotypic repulsion on cell segregation. / Der Prozess der Zellsortierung ist für die Entwicklung und Erhaltung von Geweben unerlässlich. Mit der Differentiellen Adhäsionshypothese schlug Steinberg vor, dass die Zellsortierung durch quantitative Unterschiede in den zelltypspezifischen interzellulären Adhäsionsstärken bestimmt wird. Eine Umsetzung der Differentiellen Adhäsionshypothese ist das Differentielle Migrationsmodell von Voss-Böhme und Deutsch. In diesem wurde für Systeme mit zwei Zelltypen ein effektiver Adhäsionsparameter analytisch hergeleitet, der das asymptotische Sortiermuster vorhersagt. Die Existenz und Form eines solchen Parameters für mehr als zwei Zelltypen ist jedoch unklar. Hier verallgemeinern wir analytisch das Konzept eines effektiven Adhäsionsparameters für drei und mehr Zelltypen und zeigen numerisch seine Existenz für drei Zelltypen auf der Basis von in silico Zeitreihendaten, die von einem zellulären Automaten des Differentiellen Migrationsmodells erzeugt werden. Darüber hinaus klassifizieren wir das Segregationsverhalten mithilfe statistischer Lernverfahren und zeigen, dass der geschätzte effektive Adhäsionsparameter für drei Zelltypen mit unserer analytischen Vorhersage übereinstimmt. Schließlich zeigen wir, dass der effektive Adhäsionsparameter eine kürzlich aufgekommene Diskussion über den Einfluss von Grenzflächenadhäsion, Kortikalspannung und heterotypischer Abstoßung auf die Zellsegregation lösen kann.

Der technologische Fortschritt im Bereich multimedialer Sensorik und zugehörigen Methoden zur Datenaufzeichnung, Datenhaltung und -verarbeitung führt im Big Data-Umfeld zu immensen Datenbeständen in Mediatheken und Wissensmanagementsystemen. Zugrundliegende State of the Art-Verarbeitungsalgorithmen werden oftmals problemorientiert entwickelt. Aufgrund der enormen Datenmengen lassen sich nur bedingt zuverlässig Rückschlüsse auf Güte und Anwendbarkeit ziehen. So gestaltet sich auch die intellektuelle Erschließung von großen Korpora schwierig, da die Datenmenge für valide Aussagen nahezu vollumfänglich semi-intellektuell zu prüfen wäre, was spezifisches Fachwissen aus der zugrundeliegenden Datendomäne ebenso voraussetzt wie zugehöriges Verständnis für Datenhandling und Klassifikationsprozesse. Ferner gehen damit gesonderte Anforderungen an Hard- und Software einher, welche in der Regel suboptimal skalieren, da diese zumeist auf Multi-Kern-Rechnern entwickelt und ausgeführt werden, ohne dabei eine notwendige Verteilung vorzusehen. Folglich fehlen Mechanismen, um die Übertragbarkeit der Verfahren auf andere Anwendungsdomänen zu gewährleisten. Die vorliegende Arbeit nimmt sich diesen Herausforderungen an und fokussiert auf die Konzeptionierung und Entwicklung einer verteilten holistischen Infrastruktur, die die automatisierte Verarbeitung multimedialer Daten im Sinne der Merkmalsextraktion, Datenfusion und Metadatensuche innerhalb eines homogenen Systems ermöglicht. Der Fokus der vorliegenden Arbeit liegt in der Konzeptionierung und Entwicklung einer verteilten holistischen Infrastruktur, die die automatisierte Verarbeitung multimedialer Daten im Sinne der Merkmalsextraktion, Datenfusion und Metadatensuche innerhalb eines homogenen aber zugleich verteilten Systems ermöglicht. Dabei sind Ansätze aus den Domänen des Maschinellen Lernens, der Verteilten Systeme, des Datenmanagements und der Virtualisierung zielführend miteinander zu verknüpfen, um auf große Datenmengen angewendet, evaluiert und optimiert werden zu können. Diesbezüglich sind insbesondere aktuelle Technologien und Frameworks zur Detektion von Mustern zu analysieren und einer Leistungsbewertung zu unterziehen, so dass ein Kriterienkatalog ableitbar ist. Die so ermittelten Kriterien bilden die Grundlage für eine Anforderungsanalyse und die Konzeptionierung der notwendigen Infrastruktur. Diese Architektur bildet die Grundlage für Experimente im Big Data-Umfeld in kontextspezifischen Anwendungsfällen aus wissenschaftlichen Evaluationskampagnen, wie beispielsweise TRECVid. Hierzu wird die generische Applizierbarkeit in den beiden Aufgabenfeldern Instance Search und Activity in Extended Videos eruiert.:Abbildungsverzeichnis Tabellenverzeichnis 1 Motivation 2 Methoden und Strategien 3 Systemarchitektur 4 Instance Search 5 Activities in Extended Video 6 Zusammenfassung und Ausblick Anhang Literaturverzeichnis / Technological advances in the field of multimedia sensing and related methods for data acquisition, storage, and processing are leading to immense amounts of data in media libraries and knowledge management systems in the Big Data environment. The underlying modern processing algorithms are often developed in a problem-oriented manner. Due to the enormous amounts of data, reliable statements about quality and applicability can only be made to a limited extent. Thus, the intellectual exploitation of large corpora is also difficult, as the data volume would have to be analyzed for valid statements, which requires specific expertise from the underlying data domain as well as a corresponding understanding of data handling and classification processes. In addition, there are separate requirements for hardware and software, which usually scale in a suboptimal manner while being developed and executed on multicore computers without provision for the required distribution. Consequently, there is a lack of mechanisms to ensure the transferability of the methods to other application domains. The focus of this work is the design and development of a distributed holistic infrastructure that enables the automated processing of multimedia data in terms of feature extraction, data fusion, and metadata search within a homogeneous and simultaneously distributed system. In this context, approaches from the areas of machine learning, distributed systems, data management, and virtualization are combined in order to be applicable on to large data sets followed by evaluation and optimization procedures. In particular, current technologies and frameworks for pattern recognition are to be analyzed and subjected to a performance evaluation so that a catalog of criteria can be derived. The criteria identified in this way form the basis for a requirements analysis and the conceptual design of the infrastructure required. This architecture builds the base for experiments in the Big Data environment in context-specific use cases from scientific evaluation campaigns, such as TRECVid. For this purpose, the generic applicability in the two task areas Instance Search and Activity in Extended Videos is elicited.:Abbildungsverzeichnis Tabellenverzeichnis 1 Motivation 2 Methoden und Strategien 3 Systemarchitektur 4 Instance Search 5 Activities in Extended Video 6 Zusammenfassung und Ausblick Anhang Literaturverzeichnis

<p>From idea generation to conceptualization and development of products and technologies is a non-linear and iterative process. The work in this thesis follows a process that initiates with the review of existing technologies and products, examining their unique value proposition in the context of the specific applications for which they are designed. Next, the unmet needs of novel or emerging applications are identified that require new product or technologies. Once these user needs and product requirements are identified, the specific functions to be addressed by the product are specified. The subsequent process of design of products and technologies to meet these functions is enabled by engineering tools such as three-dimensional modelling, physics-based simulations, and manufacturing of a minimum viable prototype. In these steps, un-biased decisions have to be taken using weighted decision matrices to cater to the design requirements. Finally, the minimum viable prototype is tested to demonstrate the principal functionalities. The results obtained from the testing process identify the potential future improvements in the next generations of the prototype that would subsequently inform the final design of product. This thesis adopted this methodology to initiate the design two product-prototypes: i) an image-recognition-integrated service (IRIS) robotic hand for children and ii) cascaded multi-core vapor chamber (CMVC) for improving performance of next-generation computing systems. Minimum viable product-prototypes were manufactured to demonstrate the principal functionalities, followed by clear identification of future potential improvements. Tests of the prosthetic hand indicate that the image-recognition based feedback can successfully drive the actuators to perform the intended grasping motions. Experimental testing with the multi-core vapor chamber demonstrates successful performance of the prototype, which offers notable reduction in temperatures relative to the existing benchmark solid copper spreader. </p>