Transdermal drug delivery (TDD) is limited by high resistance of the outer layer of the skin, namely stratum corneum which blocks any molecule that is larger than 500 Da. Research on TDD has become very active in recent years and various technologies have been developed to overcome the resistance of the stratum corneum. In particular, researchers have started to consider the possibility of combining the TDD technologies in order to achieve further increment for drug permeability. Microneedles (MNs) and sonophoresis are both promising technologies that can perform notable enhancement in drug permeation via different mechanisms and therefore give a good potential for combining with each other. We discuss the possible ways to achieve this combination as well as how this combination would increase the permeability. Some of the undeveloped (weaker) research areas of MNs and sonophoresis are also discussed in order to understand the true potential of combining the two technologies when they are developed further in the future. We propose several hypothetical combinations based on the possible mechanisms of MNs and sonophoresis.

The failure of many System of Systems (SoS) enterprises can be attributed to the inappropriate application of traditional Systems Engineering (SE) processes within the SoS domain, because of the mistaken belief that a SoS can be regarded as a single large, or complex, system. SoS Engineering (SoSE) is a sub-discipline of SE; Risk Management and Modelling and Simulation (M&S) are key areas within SoSE, both of which also lie within the traditional SE domain. Risk Management of SoS requires a different approach to that currently taken for individual systems; if risk is managed for each component system then it cannot be assumed that the aggregated affect will be to mitigate risk at the SoS level. A literature review was undertaken examining three themes: (1) SoS Engineering (SoSE), (2) M&S and (3) Risk. Theme 1 of the literature provided insight into the activities comprising SoSE and its difference from traditional SE with risk management identified as a key activity. The second theme discussed the application of M&S to SoS, providing an output, which supported the identification of appropriate techniques and concluding that, the inherent complexity of a SoS required the use of M&S in order to support SoSE activities. Current risk management approaches were reviewed in theme 3 as well as the management of SoS risk. Although some specific examples of the management of SoS risk were found, no mature, general approach was identified, indicating a gap in current knowledge. However, it was noted most of these examples were underpinned by M&S approaches. It was therefore concluded a general approach SoS risk management utilising M&S methods would be of benefit. In order to fill the gap identified in current knowledge, this research proposed a new model based approach to Risk Management where risk identification was supported by a framework, which combined SoS system of interest dimensions with holistic risk types, where the resulting risks and contributing factors are captured in a causal network. Analysis of the causal network using a model technique selection tool, developed as part of this research, allowed the causal network to be simplified through the replacement of groups of elements within the network by appropriate supporting models. The Bayesian Belief Network (BBN) was identified as a suitable method to represent SoS risk. Supporting models run in Monte Carlo Simulations allowed data to be generated from which the risk BBNs could learn, thereby providing a more quantitative approach to SoS risk management. A method was developed which provided context to the BBN risk output through comparison with worst and best-case risk probabilities. The model based approach to Risk Management was applied to two very different case studies: Close Air Support mission planning and the Wheat Supply Chain, UK National Food Security risks, demonstrating its effectiveness and adaptability. The research established that the SoS SoI is essential for effective SoS risk identification and analysis of risk transfer, effective SoS modelling requires a range of techniques where suitability is determined by the problem context, the responsibility for SoS Risk Management is related to the overall SoS classification and the model based approach to SoS risk management was effective for both application case studies.

Hybrid renewable energy system was introduced to improve the individual renewable energy power system’s productivity and operation-ability. This circumstance has led towards an extensive technological study and analysis on the hybrid renewable energy system. The extensive technological study is conducted using many different approaches, but in this research the linear programming, artificial intelligence and smart grid approaches are studied. This thesis proposed a complete hardware system development, implementation and construction of real-time DC Hybrid Renewable Energy System for solar-wind-battery energy source integrated with grid network support. The proposed real-time DC HRES hardware system adopts the hybrid renewable energy system concept which is composed of solar photovoltaic, wind energy system, battery energy storage system and grid network support. The real-time DC HRES hardware system research work is divided into three stages. Stage 1 involves modelling and simulation of the proposed system using MATLAB Simulink/Stateflow software. During this stage, system’s methodological design and development is emphasised. The obtained results are considered as fundamental finding to design, develop, integrate, implement and construct the real-time DC HRES hardware system. Stage II is designing and developing the electronic circuits for the real-time DC HRES hardware system using PROTEUS software. Real time simulation is performed on the electronic circuits to study and analyse the circuit’s behaviour. This stage also involves embedded software application development for the microcontroller PIC16F877A. Thus, continuous dynamic decision-making algorithm is developed and incorporated into microcontroller PIC16F877A. Next, electronic circuits and continuous dynamic decision-making algorithm are integrated with the microcontroller PIC16F877A as a real-time DC HRES hardware system to perform real time simulation. The real-time DC HRES hardware system simulation results are studied, analysed and compared with the results obtained in Stage 1. Any indifference between the obtained results in Stage 1 and Stage 2 are analysed and necessary changes are made. Stage 3 involves integrating, implementation and construction of real-time DC HRES. The continuous dynamic decision-making algorithm is also incorporated into the real microcontroller PCI16F877A development board. Real-time DC HRES’s experimental results have successfully demonstrated the system’s ability to perform supervision, coordination, management and control of all the available energy sources with lease dependency on the grid network. The obtained results demonstrated the energy management and optimisation of the available energy sources as primary power source deliver.

Orientador: Francisco Maugeri Filho / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de Alimentos / Made available in DSpace on 2018-08-17T02:08:08Z (GMT). No. of bitstreams: 1 AlvaradoHuallanco_MonicaBeatriz_D.pdf: 1075413 bytes, checksum: f34b7681cc54bc046c7344d2601d8d16 (MD5) Previous issue date: 2010 / Resumo: Os frutooligossacarídeos são considerados prebióticos, uma vez que promovem seletivamente o crescimento de micro-organismos probióticos como Lactobacillus acidophillus e Bifidobacterium bifidus. Novas enzimas, na forma livre ou imobilizada, representam uma das possibilidades para síntese destes compostos. Neste trabalho procedeu-se ao estudo da modelagem cinética e simulação da síntese de frutooligossacarídeos a partir de sacarose em diferentes tipos de reatores, pela enzima frutosiltransferase produzida pela levedura do gênero Rhodotorula, isolada em trabalhos prévios. Os estudos foram realizados sob condições de pH 4,5, 50°C e 5 UTF/mL de concentração da enzima. Tanto a enzima livre quanto a imobilizada mostraram seguir a cinética de Michaelis-Menten com inibição pelo substrato para concentrações acima de 70% e 60% (p/v), respectivamente. Observou-se inibição competitiva da glicose para os substratos sacarose, kestose e nistose. Por outro lado, considerou-se significativa a atividade hidrolítica da nistose, sendo incluída no modelo. Após a análise de sensibilidade dos parâmetros cinéticos, estes foram ajustados por simulação, e determinou-se seus valores intrínsecos. O modelo mostrou-se válido com desvios menores que 4% para a enzima livre (57% de FOS) e de 5% para enzima imobilizada (46% de FOS), indicando que ele pode ser utilizado no desenvolvimento e controle de biorreatores. No caso da enzima imobilizada incluiu-se no balanço de massa o efeito da resistência à transferência de massa externa. Devido ao suporte ser um sólido compacto, com porosidade interna desprezível, desprezou-se a difusão intraparticular, considerando-se que a imobilização da enzima foi somente na superfície da partícula. A otimização do processo em reator de cesto em batelada, tanto quanto na do reator de cesto contínuo, foi realizada segundo delineamentos do tipo composto central rotacional (DCCR), nas condições de 50% de sacarose, 50?C e pH 4,5. As condições ótimas para o processo em batelada, foram de 14 Ui/mL para a enzima imobilizada e 45 rpm para a agitação, sendo o rendimento de FOS igual a 50,60% após 24 horas de síntese. Para o reator de cesto contínuo as variáveis otimizadas foram de 15 Ui/mL para a enzima imobilizada e 45 rpm para a agitação, com rendimentos de FOS de 32,1% e produtividade de 5,0 g?L.h, com tempo de residência de 32 h. Neste último caso, o componente principal de FOS foi o GF4 (25%). Os resultados mostraram que a atividade biocatalítica e o coeficiente de transferência de massa tiveram influência significativa no curso da reação e no rendimento de produção de FOS e que o melhor processo foi o de batelada em reator de cesto / Abstract: Fructooligosaccharides (FOS) are considered prebiotics, since selectively promote the growth of microorganisms as probiotic Lactobacillus and Bifidobacterium bifidus acidophillus. New enzymes, in its free or immobilized form, represent one of the possibilities for this development. In this work, the kinetic modeling and simulation of the synthesis of FOS from sucrose in different types of reactors were carried out. The enzyme utilized was the fructosyltransferase from Rhodotorula sp., a microorganism isolated in a previous study. The studies were performed under conditions of pH 4.5, 50°C and 5 UFT/mL concentration of enzyme. Both free and immobilized enzymes showed the Michaelis-Menten kinetics with substrate inhibition, at concentrations above 70% and 60% (w/v), respectively. Additionally, it was shown that there is competitive inhibition of glucose over sucrose, kestose and nystose uptake. Moreover, the hydrolytic activity on nystose was considered significant, therefore, it was included in the model. After the parameter sensitivity analysis the intrinsic kinetic parameters were determined and the model was validated against experimental data for sucrose concentrations of 50 and 70%. The model proved to be valid with deviations of less than 4% for the free enzyme (57% FOS) and 5% for immobilized enzyme (46% FOS), indicating that it can be used in the development and control of bioreactors. The enzyme immobilization was by adsoption on the surface of a niobium ore, which is a compact solid with negligible internal porosity. For the batch and continuous basket reactor processes, the optimization experiments were simulated in two central composite rotatable designs (DCCR), using 50% of sucrose concentration, 50?C and pH 4.5. The optimum conditions for the batch reactor were: 14 Ui/mL for the activity of immobilized enzyme and agitation speed of 45 rpm, with yield of 50.60% of FOS, after 24 hours of synthesis. For the continuous basket reactor, the optimum conditions were: an activity of immobilized enzyme of 15 Ui/mL and an agitation speed of 45 rpm, performing a FOS yield of approximately 32.1% and produtivity of 5.0 g?L.h, with a residence time of 32 h. In the latter case of the main FOS fraction was the GF4, with about 25% of the total, a result very different from those obtained with other types of reactors. The results showed that the immobilized enzyme activity and the coefficient of mass transfer had a significant influence on the course of the reaction and the yield of FOS and that the best process for FOS production is the batch basket reactor / Doutorado / Engenharia de Alimentos / Doutor em Engenharia de Alimentos

This study presents a data-driven simulation framework in order to understand customer behaviour and therefore improve customer retention. The overarching system design methodology used for this study is aligned with the design science paradigm. The Social Media Domain Analysis (SoMeDoA) approach is adopted and evaluated to build a model on the determinants of customer satisfaction in the mobile services industry. Furthermore, the most popular machine learning algorithms for analysing customer churn are applied to analyse customer retention based on the derived determinants. Finally, a data-driven approach for agent-based modelling is proposed to investigate the social effect of customer retention. The key contribution of this study is the customer agent decision trees (CADET) approach and a data-driven approach for Agent-Based Modelling (ABM). The CADET approach is applied to a dataset provided by a UK mobile services company. One of the major findings of using the CADET approach to investigate customer retention is that social influence, specifically word of mouth has an impact on customer retention. The second contribution of this study is the method used to uncover customer satisfaction determinants. The SoMeDoA framework was applied to uncover determinants of customer satisfaction in the mobile services industry. Customer service, coverage quality and price are found to be key determinants of customer satisfaction in the mobile services industry. The third contribution of this study is the approach used to build customer churn prediction models. The most popular machine learning techniques are used to build customer churn prediction models based on identified customer satisfaction determinants. Overall, for the identified determinants, decision trees have the highest accuracy scores for building customer churn prediction models.

Whey is a by-product of cheese manufacture, containing a mixture of proteins of commercial value, each having unique attributes for nutritional, biological and food ingredient applications. A tremendous amount of whey, normally treated as a waste product, is produced worldwide each year. This work describes the cation-exchange adsorption of the two major whey proteins, alpha-lactalbumin (ALA) and beta-lactoglobulin (BLG) with the purpose of optimising a process for isolating them from whey. Adsorption of pure BLG and ALA was studied onto SP Sepharose FF using 0.1M acetate buffer. Batch experiments were carried out at various pH values for ALA and BLG, and the relevant Langmuir isotherm parameters, dissociation constant, Kd, and maximum binding capacity, qm, were determined. The optimum pH for separation was chosen to be pH 3.7. At pH 3.7, both Kd and qm pertaining to ALA were found to have higher numerical values than those of BLG, implying different characteristics of adsorption of the two proteins on this adsorbent. The Kd for the former protein was almost four times larger than the latter, while qm was 1.3 times higher. Packed-bed column adsorption was performed using a 1-ml column at pH 3.7, flow rate 1 ml/min and initial concentration of 3 mg/ml for BLG and 1.5 mg/ml for ALA both in 0.1M sodium acetate buffer. The t1/2 for the resulting ALA breakthrough was 75% longer than its BLG counterpart. The above results suggest the possibility of the occurrence of competitive adsorption between the proteins when adsorbed simultaneously. In traditional batch uptake experiments, the kinetic rate constants of ALA and BLG in both the single- and two-component systems were determined using the simple kinetic model. The values so obtained implied that BLG was adsorbed faster than ALA. In the confocal laser scanning microscopy experiments, the different behaviour of ALA and BLG in the single-component system with regard to their penetration within the adsorbent beads suggested that the two proteins have different transport mechanisms governing their adsorption. The two-component system results showed that ALA was able to displace BLG in spite of the lower affinity of the former protein to the adsorbent. The packed-bed adsorption and elution of a mixture of ALA and BLG were then investigated under the above conditions but using a 5-ml column. BLG breakthrough occurred first, and its concentration in the outlet exceeded its feed value by 1.6 fold before declining to the feed value, followed by the breakthrough of ALA. ALA displaced and eluted all the BLG from the column in a pure form. Pure ALA could then be eluted with good recovery. The single- and two-component breakthrough curves for ALA and BLG were simulated by the simple kinetic model using the isotherm parameters, but the overshoot phenomenon could only be predicted after correcting these parameters. The evidence of the competitive nature of adsorption observed in binary mixtures was used to develop a facile separation procedure for the two proteins from aqueous solutions of whey concentrate powders. A novel consecutive two-stage separation process was developed to separate ALA and BLG from whey concentrate mixtures. Almost all the BLG in the feed was recovered, with 78% being recovered at 95% purity and a further 20% at 86% purity. In addition, 67% of ALA was recovered, 48% at 54% purity and 19% at 60% purity. The correction factors employed for the pure binary mixture were used to simulate the breakthrough curves of the two proteins in experiments conducted with whey concentrate in each of the two stages of the novel separation process, and there was agreement between the experimental and theoretical results.

Numerous studies have concluded that roughly half of all implemented software requirements are never or rarely used in practice, and that failure to realise expected benefits is a major cause of software project failure. This thesis presents an exploration of these concepts, claims, and causes. It evaluates the literature s proposed solutions to them, and then presents a unified framework that covers additional concerns not previously considered. The value of a requirement is assessed often during the requirements engineering (RE) process, e.g., in requirement prioritisation, release planning, and trade-off analysis. In order to support these activities, and hence to support the decisions that lead to the aforementioned waste, this thesis proposes a framework built on the modelling languages of Goal Oriented Requirements Engineering (GORE), and on the principles of Value Based Software Engineering (VBSE). The framework guides the elicitation of a requirement s value using philosophy and business theory, and aims to quantitatively model chains of instrumental value that are expected to be generated for a system s stakeholders by a proposed software capability. The framework enriches the description of the individual links comprising these chains with descriptions of probabilistic degrees of causation, non-linear dose-response and utility functions, and credibility and confidence. A software tool to support the framework s implementation is presented, employing novel features such as automated visualisation, and information retrieval and machine learning (recommendation system) techniques. These software capabilities provide more than just usability improvements to the framework. For example, they enable visual comprehension of the implications of what-if? questions, and enable re-use of previous models in order to suggest modifications to a project s requirements set, and reduce uncertainty in its value propositions. Two case studies in real-world industry contexts are presented, which explore the problem and the viability of the proposed framework for alleviating it. The thesis research questions are answered by various methods, including practitioner surveys, interviews, expert opinion, real-world examples and proofs of concept, as well as less-common methods such as natural language processing analysis of real requirements specifications (e.g., using TF-IDF to measure the proportion of software requirement traceability links that do not describe the requirement s value or problem-to-be-solved). The thesis found that in general, there is a disconnect between the state of best practice as proposed by the literature, and current industry practice in requirements engineering. The surveyed practitioners supported the notion that the aforementioned value realisation problems do exist in current practice, that they would be treatable by better requirements engineering practice, and that this thesis proposed framework would be useful and usable in projects whose complexity warrants the overhead of requirements modelling (e.g., for projects with many stakeholders, competing desires, or having high costs of deploying incorrect increments of software functionality).

On ne peut pas reproduire l'expérience d'une épidémie à l'échelle d'un pays ou d'une région. Or, en l'absence d'expérience reproductible, la notion usuelle de « validation » de modèle qui consiste à tester si le modèle A* d'un système A permet de reproduire le comportement de A, n'a aucun sens statistique.Marvin Minsky donne la définition suivante de ce qu'est un modèle: Pour un observateur B, un objet A* est un modèle d'un objet A s'il permet à B de répondre à une question qu'il se pose sur A. Nous appelons triade de Minsky, l'ensemble des trois objets A, B et A*. Nous proposons d'utiliser la Théorie de la Modélisation et de la Simulation (TMS) pour modéliser et simuler la triade de Minsky. Cela nous permet de modéliser la triade vue comme un système dynamique composée des objets A, B et A*. Nous pouvons ainsi nous interroger sur l'utilisation d'un modèle A* par un utilisateur B et sur l'impact que cette utilisation a sur la trajectoire du système A.Nous appliquons ce cadre à une triade de Minsky empruntée à notre contexte d'étude. Il s'agit d'un cas d'école où une épidémie (l'objet A) est observée et contrôlée par un système de surveillance et de contrôle (l'observateur B) et où un modèle épidémiologique (A*) est utilisé pour évaluer les mesures de contrôle. / The spread of a disease at national or international scale isn't a reproducible experiment. If experiment is not reproducible, the usual concept of model validation has no statistical meaning because it would involve the comparison between model behaviour and system behaviour.We use Marvin Minsky's definition of model: « to an observer B, an object A* is a model of an object A to the extent that B can use A* to answer questions that interest him about A ». The tree objects A, B and A* are the triad of Minsky. We propose to use the Theory of Modelling and Simulation (TMS) to model and simulate the triad seen as a dynamic system composed of the objects A, B and A*. We hence can answer questions about the use of A* and the impact it has on A.We apply this framework to a triad of Minsky in epidemiological surveillance in animal health. An epidemics (object A) is observed and controlled by a surveillance system and a control system (observer B) and an epidemiological model (model A*) is used to evaluate the control measures.

<p>As the need for outdoor recreation grows, the profound impact of recreational activities upon wildlife is a major concern. For example, the presence of humans may increase risk-averse behavior by wildlife, restricting access to essential resources, and reducing foraging, thereby negatively impacting breeding. Ultimately, the impacts that recreationists have on wildlife include directly or indirectly altering population structure and community composition. Unfortunately, understanding the impacts of recreating humans upon wildlife is a complex challenge that is dependent upon wildlife species and human activity types. Our understanding of human-wildlife relationships can be improved by combining results from empirical studies with simulation models to extrapolate mechanisms to a broader range of circumstances and investigate their implications. Accordingly, we developed an ABM modeling framework, that enables both dynamic virtual human and wildlife agents to change their actions. These changes are based upon their state as a consequence of their interactions with their environment and other virtual agents. A unique aspect of the framework we developed is the explicit simulation of both wildlife and human agent behavior as emergent rather than imposed. We use this framework to model the disturbance of birds, in the Lawrence Creek Forest Unit (LCFU) of Fort Harrison State Park, IN, by human recreation. We parameterize the model with human recreation data collected through an intercept survey of recreationists at the park and bird data from published studies. We compare our modeling framework to a more traditional model type where human behavior is imposed while wildlife behavior is emergent. Our results indicate that the frequency of humans entering the park influences the rates of disturbance of birds more than model types. Examining simulation behavior within our new framework, the utility and off-trail options had the most influence across all scenarios. These comparisons illustrate that the use of a modeling framework that allows managers to explore factors altering wildlife disturbance rates. Despite the marginal influence of model type upon our results, our research elucidates the value of a model that allows emergent behavior for multiple agent types. The emergent human and wildlife responses of simulated interacting agents provides new insight when managing these relationships. <b></b></p>

This thesis presents a tool for conceptual design of a traditional configuration aircraft by using a parametric six degrees of freedom (6DOF) flight mechanics model implemented in the Modelica Language using Wolfram System Modeler. Being first only able to model and simulate the uncontrolled flight of an aircraft with fixed mass and centre of gravity (CG), and requiring detailed aerodynamic parameters as an input, the 6DOF model is improved by developing new features to reduce the number of required inputs while also increasing the data output of the simulations. First, the propulsion submodel is added with models for alternative propulsions to the existing model of turbofan engines. The energy and fuel consumption is also modelled for all propulsion types, and thus the aircraft model has no longer fixed mass properties, except for aircraft with electric propulsion. To further evaluate the fuel consumption for pre-defined flight missions with given flight speed, altitude and track angles, autopilots for a few different aircraft types are developed. Additionally, the 6DOF model is improved by establishing algebraic and statistical relationships between the aircraft geometric input parameters, aerodynamic coefficients and moments of inertia such that the values for the two last mentioned can be estimated inside the 6DOF model based on the minimum amount of design variables, geometric input parameters and aerodynamic properties of the 2D airfoils used in the wings. Ultimately, the improved 6DOF model is evaluated and analysed in terms of its performance in initial weight estimation on aircraft conceptual design stage as well as in predicting the aerodynamic properties.