Le bilan de carbone des écosystèmes forestiers implique de nombreux processus, rendant difficile la prédiction de leurs réponses aux changements climatiques. A des échelles larges, les processus écologiques ne peuvent être modélisés que de manière simplifiée et doivent donc se focaliser sur les processus importants. Par ailleurs, le développement de forêts mélangées est de plus en plus encouragé. Or ce type de forêt présente des degrés de complexité supplémentaires. D'une part la structuration du couvert en 3D est susceptible d'influencer les flux de carbone, et d'autre part les espèces coexistantes peuvent répondre de manière différentes aux changements climatiques. La forêt de Font-Blanche constitue un cas d'étude original car elle est spatialement hétérogène. De plus, les modèles climatiques prédisent une réduction importante des précipitations au cours du XXIe siècle en région méditerranéenne. Mais l'échelle du siècle peu être exigeante en temps de calcul lorsqu'on veut à prendre en compte la structure de la canopée. Dans cette these j'ai donc modifié le domaine d'utilisation d'un modèle d'écosystème méchaniste, de l'année au siècle, grâce à la technique méta-modélisation. Le méta-modéle a donné de bons résultats qui m'ont permis de réaliser une étude d'impact du changement climatique à l'échelle du siècle, sur la forest de Font-Blanche. Les résultats montrent que la représentation spatiale du couvert et l'effet de rétroiaction du bilan hydrique, jouent un rôle important et ne peuvent pas être simplifiés à long-terme à cause de la dynamique des espèces qui la composent qui représente la plus grande source de variations du bilan de carbone. / The carbon balance of forest ecosystems involves many complex processes. At larger scales, ecological processes can not be modelled in a simplified way, but these have not been clearly identified. Furthermore, the development of mixed forest is increasingly promoted and this type of stand has additional degrees of complexity. On the one hand, complex canopy structure is likely to influence carbon fluxes, and other coexisting species may respond differently to climate change. Font-Blanche forest is an original case study that has not been studied in modelling because of its heterogeneity. In add, climate models predict significant reductions in rainfall during the 21st century for the Mediterranean region; But the century time scale maybe very demanding in computation time if ones want to taking into account the canopy structure. Then in this thesis we are modified a 3D mechanistic forest ecosystem model (noTG) to extend its temporal scale from year to century, thanks to meta-modelling technique. The meta-modelling gives good results and we used the meta-modeled version of noTG (notgmeta) to predict carbon and water balance of Font-blanche forest between 2008-2100 according to differents climate change scenario. According to model simplification, we find that photosynthesis, soil respiration and plant respiration are stimulated until 2100 with a decrease of this stimulation at the end of the simulation. We find that spatial representation of canopy and feedback effect of the water balance plays an important role and can not be simplified in the long-term simulation since the dynamics of species represents the largest source of carbon balance variations.

Dentre as tendências de uso racional de recursos, principalmente energia, e da necessidade de assegurar a produtividade e qualidade na execução de atividades produtivas, destaca-se o conceito de edifício inteligente. Este ambiente materializa o conceito de integração dos sistemas prediais potencializando a otimização dos recursos e a eficiência do trabalho humano. Neste contexto, abordagens conceituais baseadas em sistemas a eventos discretos e técnicas derivadas de rede de Petri têm sido introduzidas como uma alternativa eficaz de modelagem e análise das soluções de integração dos sistemas prediais. Um resultado expressivo destas iniciativas são os métodos propostos para a modelagem e análise de estratégias de gerenciamento de sistemas de ar condicionado, utilizando uma abordagem híbrida, onde são considerados os aspectos de sistemas a eventos discretos e as variáveis de dinâmica contínua. Contudo, as abordagens e métodos existentes são limitados a soluções específicas de implementação, como os sistemas de ar condicionado com volume de ar contínuo. Assim, o presente trabalho introduz uma extensão destas abordagens para modelar e analisar soluções de automação predial que incluem sistemas de ar condicionado com volume de ar variável. A eficiência deste método na concepção e validação destas soluções é ilustrada através de um estudo de caso. / Among the trends of rational use of resources, especially energy, and the need to ensure productivity and quality in the implementation of productive activities, there is the concept of intelligent building. This environment materializes the concept of integrating building systems, powering the optimization of resources and the efficiency of human labor. In this context, conceptual approaches that are based on systems of discreet events and techniques, which are derived from the Petri nets, have been introduced as an effective alternative to modeling and analysis of solutions of building systems integration. A significant result of these initiatives are the proposed methods for modeling and the analysis of strategies for air conditioning systems management using a hybrid approach where the aspects of systems of discreet events and the variables of continuing dynamic are considered. However, the existing methods and approaches are limited to their specific implementation solutions, such as air conditioning systems with continuous volume of air. Thus, this work introduces an extension of these approaches to model and analyze the building automation solutions that include air conditioning systems with variable volume of air. The efficiency of this method in the design and validation of these solutions is illustrated through a case study.

As an efficient filter of airborne Hg compounds, forests retain a significant portion of the Hg deposited to the land. Forested watersheds have been identified as major sources of low-concentration Hg compounds to surrounding streams and lakes. Mercury export from forests is highly variable in both space and time. It is difficult to use field surveys alone to capture the spatiotemporal variation inherent in this variable. Mathematical models are required for improved representation. The objective of this Thesis is to develop and test a monthly dynamic model that can be used to estimate seasonal Hg export from forested watersheds to low-ordered forest streams. The fully developed model consists of four model components: (i) a forest hydrology component, to simulate variation in soil temperature, soil moisture, and stream discharge for input to the other model components; (ii) a forest nutrient cycling and biomass growth component, to simulate forest growth and litter production; (iii) a forest litter decomposition component, to simulate seasonal production of dissolved organic carbon (DOC); and (iv) a monthly DOC and Hg export component to simulate the translocation of DOC and Hg from forested watersheds to low-ordered streams. The Hg-export component incorporates an Hg-to-DOC binding coefficient estimated from a one-time stream survey of Hg and DOC concentrations. Simulations of in-stream Hg concentrations show two main trends: (i) an annual trend, associated with the seasonal (monthly) dynamics of forest litter production, decomposition, and DOC production and export, and (ii) a multiple-year trend, associated with forest harvesting and re-growth patterns of regenerating forests. This study demonstrates that (i) wetland- and conifer-dominated watersheds release a greater amount of Hg to aquatic ecosystems than upland- and deciduous species-dominated watersheds, and (ii) forests nearing maturity, export more Hg than young forests.

The negative consequences of rising global energy use have led governments and businesses to pursue methods of reducing reliance on fossil fuels. Plug-In Hybrid Electric Vehicles (PHEVs) and wind power represent two practical methods for mitigating some of these negative consequences. PHEVs use large onboard batteries to displace gasoline with electricity obtained from the grid, while wind power generates clean, renewable power that has the potential to displace fossil-fuel power generation. The emissions reductions realized by these technologies will be highly dependent on the energy system into which they are integrated, and also how they are integrated. This research aims to assess to cost of reducing emissions through the integration of PHEVs and wind power in three Canadian jurisdictions, namely British Columbia, Ontario and Alberta. An Optimal Power Flow (OPF) model is used to assess the changes in generation dispatch resulting from the integration of wind power and PHEVs into the local electricity network. This network model captures the geographic distribution of load and generation in each jurisdiction, while simulating local transmission constraints. A linear optimization model is developed in the MATLAB environment and is solved using the ILOG CPLEX Optimization package. The model solves a 168-hour generation scheduling period for both summer and winter conditions. Simulation results provide the costs and emissions from power generation when various levels of PHEVs and/or wind power are added to the electricity system. The costs and emissions from PHEV purchase and gasoline displacement are then added to the OPF results and an overall GHG reduction cost is calculated. Results indicate that wind power is an expensive method of GHG abatement in British Columbia and Ontario. This is due to the limited environmental benefit of wind over the nuclear and hydro baseload mixtures. The large premium paid for displacing hydro or nuclear power with wind power does little to reduce emissions, and thus CO2e costs are high. PHEVs are a cheaper method of GHG abatement in British Columbia and Ontario, since the GHG reductions resulting from the substitution of gasoline for hydro or nuclear power are significant. In Alberta, wind power is the cheaper method of GHG abatement because wind power is closer in price to the coal and natural gas dominated Alberta mixture, while offering significant environmental benefits. PHEVs represent a more expensive method of GHG abatement in Alberta, since substituting gasoline for expensive, GHG-intense electricity in a vehicle does less to reduce overall emissions. Results also indicate that PHEV charging should take place during off-peak hours, to take advantage of surplus baseload generation. PHEV adoption helps wind power in Ontario and British Columbia, as overnight charging reduces the amount of cheap, clean baseload power displaced by wind during these hours. In Alberta, wind power helps PHEVs by cleaning up the generation mixture and providing more environmental benefit from the substitution of gasoline with electricity.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Dentre as tendências de uso racional de recursos, principalmente energia, e da necessidade de assegurar a produtividade e qualidade na execução de atividades produtivas, destaca-se o conceito de edifício inteligente. Este ambiente materializa o conceito de integração dos sistemas prediais potencializando a otimização dos recursos e a eficiência do trabalho humano. Neste contexto, abordagens conceituais baseadas em sistemas a eventos discretos e técnicas derivadas de rede de Petri têm sido introduzidas como uma alternativa eficaz de modelagem e análise das soluções de integração dos sistemas prediais. Um resultado expressivo destas iniciativas são os métodos propostos para a modelagem e análise de estratégias de gerenciamento de sistemas de ar condicionado, utilizando uma abordagem híbrida, onde são considerados os aspectos de sistemas a eventos discretos e as variáveis de dinâmica contínua. Contudo, as abordagens e métodos existentes são limitados a soluções específicas de implementação, como os sistemas de ar condicionado com volume de ar contínuo. Assim, o presente trabalho introduz uma extensão destas abordagens para modelar e analisar soluções de automação predial que incluem sistemas de ar condicionado com volume de ar variável. A eficiência deste método na concepção e validação destas soluções é ilustrada através de um estudo de caso. / Among the trends of rational use of resources, especially energy, and the need to ensure productivity and quality in the implementation of productive activities, there is the concept of intelligent building. This environment materializes the concept of integrating building systems, powering the optimization of resources and the efficiency of human labor. In this context, conceptual approaches that are based on systems of discreet events and techniques, which are derived from the Petri nets, have been introduced as an effective alternative to modeling and analysis of solutions of building systems integration. A significant result of these initiatives are the proposed methods for modeling and the analysis of strategies for air conditioning systems management using a hybrid approach where the aspects of systems of discreet events and the variables of continuing dynamic are considered. However, the existing methods and approaches are limited to their specific implementation solutions, such as air conditioning systems with continuous volume of air. Thus, this work introduces an extension of these approaches to model and analyze the building automation solutions that include air conditioning systems with variable volume of air. The efficiency of this method in the design and validation of these solutions is illustrated through a case study.

Cuba has vast natural resources for domestic renewable energy generation, but their energy mix is heavily dominated by fossil fuels. This contributes to a high dependence on expensive oil imports and has led to significant generation shortfalls, which in turn has resulted in extensive power outages and serious fuel crises. Additionally, large amounts of CO2 emissions are generated from power generation based on oil or gas. George Washington is a small industrial town in the Villa Clara province in Cuba that frequently experiences these problems. It holds a rum factory, a sugar mill, and a small residential area containing 710 households. The implementation of a microgrid utilizing the available solar, wind, and biomass potential could work to simultaneously reduce the town's dependence on energy imports, increase the renewable electricity share, and increase self-sufficiency of the electricity demand, enabling the industries and residential area to access energy services even when the national grid is not delivering power. By examining different potential microgrid configurations in HOMER Pro, an optimal system was decided based on cost parameters such as CAPEX and NPV, the self-sufficiency share of the electricity consumed, and the available potential to utilize domestic natural resources as well as the available workforce able to operate such a system. Because of Cuba's difficulties in accessing investment capital, a low CAPEX, high self-sufficiency index, and a high NPV was considered the best possible system. The scenario that best correlated with this outcome was the Middle Road scenario. By considering the area limiations of George Washington, one model run of the Middle Road scenario produced a system with additional solar PV (2.9 MW) and wind capacity (9.2 MW) paired with the already existing 6 MW of bagasse-fired CHP capacity in the sugar mill and 688 kW of solar PV capacity. It had a low investment cost of $34 million USD, a high NPV at $112 million USD, and a self-sufficiency index at 91.33%. Another model run of the Middle Road scenario that didn't take avaliable area into consideration produced a microgrid with an additional 43.1 MW of wind capacity. This model run had an NPV of $292 million USD, an investment cost of $79 million USD, and a self-sufficiency index of 94%. By implementing more capacity than this in the 100% Self-sufficient scenario, the self-sufficiency index reached a maximum of 100%, but had a lower NPV at $282 million USD, and a much higher investment cost of $1.324 billion USD. These scenarios only used biomass, solar, or wind energy for microgrid electricity generation, and therefore only consumed fossil fuels when importing electricity from the grid. / Kuba har en stor mängd naturliga resurser för att generera förnybar energi, men deras energimix idag domineras av fossilt bränsle. Landet är beroende av att importera dyr olja, vilket bidrar till en otillräcklig inhemsk energiproduktion samt många timmars strömavbrott och svåra bränslebrister. Stora mängder CO2-utsläpp genereras även när olja eller gas används för kraftproduktion. George Washington är en liten industriell by som ligger i provinsen Villa Clara, i Kuba, och som ofta får erfara dessa problem. I byn finns det en romfabrik, en sockerkvarn och ett litet bostadsområde som består av 710 hem. Installationen av ett microgrid som utnyttjar lokal solenergi, vindenergi samt biomassa kan minska byns beroende av importerad energi, öka andelen förnybar energi samt öka självförsörjningen av elbehovet. Ett sådant microgrid skulle möjliggöra byns tillgång till viktiga energitjänster även när det nationella nätverket inte har möjlighet att leverera elektricitet. Genom att undersöka flera olika microgridkonfigurationer i mjukvaruverktyget HOMER Pro valdes ett optimalt system baserat på parametrarna CAPEX, NPV, självförsörjningsgraden av den konsumerade elektriciteten, potentialen att använda sig av de lokala naturresurserna samt tillgängligheten av arbetskraft för att kunna driva ett sådant nätverkssystem. På grund av de begränsade tillgångarna till investeringskapital i Kuba så blev ett lågt CAPEX, hög självförsörjning samt ett högt NPV viktiga parametrar för att utse det bästa möjliga systemet. Det scenario som genererade system som bäst stämde överens med dessa egenskaper är Middle Road-scenariot. För att undersöka potentialen hos systemet och samtidigt ta hänsyn till den begränsade landtillgången i George Washingtons närområde så kördes en av systemsimuleringarna av Middle Road-scenariot med en areabegränsning i HOMER Pro. Detta resulterade i ett system med ytterligare 2.9 MW kapacitet från solpaneler, 9.2 MW vindkraft tillsammans med de redan existerande 6 MW av bagasse-drivna turbiner i sockerkvarnen samt de 688 kW av solpaneler som är installerade på romfabrikens tak. Systemet har en investeringskostnad (CAPEX) på $34 miljoner USD, ett högt NPV på $112 miljoner USD och ett självförsörjningsindex på 91.33%. När systemsimuleringen av Middle Road inte tog hänsyn till tillgänglig landyta så blev resultatet att det bästa systemet hade ytterligare 43.1 MW av vindkraft. Detta system har ett NPV på $292 miljoner USD, en investeringskostnad på $79 miljoner USD och ett självförsörjningsindex på 94%. Genom att implementera en högre kapacitet i 100% Self-sufficient-scenariot så blev resultatet ett självförsörjandeindex på 100%, men samtidigt ett lägre NPV på $282 miljoner USD och en mycket högre investeringskostnad på $1.324 miljarder USD. I dessa scenarion så används biomassa, solenergi samt vindenergi för generering av elektricitet i microgridet och konsumtion av fossilt bränsle sker endast när elektricitet importeras från det nationella elnätverket.

Yes / Computational models are perceived as an attractive alternative to mathematical models (e.g., ordinary differential equations). These models incorporate a set of methods for specifying, modeling, testing, and simulating biological systems. In addition, they can be analyzed using algorithmic techniques (e.g., formal verification). This paper shows how formal verification is utilized in systems and synthetic biology through qualitative vs quantitative analysis. Here, we choose two well-known case studies: quorum sensing in P. aeruginosas and pulse generator. The paper reports verification analysis of two systems carried out using some model checking tools, integrated to the Infobiotics Workbench platform, where system models are based on stochastic P systems. / EPSRC