Modeling temporal variations of Gracilaria Greville and Hypnea J.V. Lamouroux (Rhodophyta) assemblages on a midlittoral reef platform at Piedade Beach, Pernambuco State, Brazil

The diversity of algal banks composed of species out the genera Gracilaria Greville and Hypnea J.V. Lamouroux have been impacted by commercial exploitation and coastal eutrophication. The present study sought to construct dynamic models based on algal physiology to simulate seasonal variations in the biomasses of Gracilaria and Hypnea an intertidal reef at Piedade Beach in Jaboatão dos Guararapes, Pernambuco State, Brazil. Five 20 × 20 cm plots in a reef pool on a midlittoral reef platform were randomly sampled during April, June, August, October, and December/2009 and in January and March/2010. Water temperature, pH, irradiance, oxygen and salinity levels as well as the concentrations of ammonia, nitrate and phosphate were measured at the sampling site. Forcing functions were employed in the model to represent abiotic factors, and algal decay was simulated with a dispersal function. Algal growth was modeled using a logistic function and was found to be sensitive to temperature and salinity. Maximum absorption rates of ammonia and phosphate were higher in Hypnea than in Gracilaria, indicating that the former takes up nutrients more efficiently at higher concentrations. Gracilaria biomass peaked at approximately 120 g (dry weight m-2) in March/2010 and was significantly lower in August/2009; Hypnea biomasses, on the other hand, did not show any significant variations among the different months, indicating that resource competition may influence the productivity of these algae.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.

Thermodynamic evaluation and modeling of proton and water exchange associated with benzamidine and berenil binding to ß-trypsin

Serine-proteases are involved in vital processes in virtually all species. They are important targets for researchers studying the relationships between protein structure and activity, for the rational design of new pharmaceuticals. Trypsin was used as a model to assess a possible differential contribution of hydration water to the binding of two synthetic inhibitors. Thermodynamic parameters for the association of bovine ß-trypsin (homogeneous material, observed 23,294.4 ± 0.2 Da, theoretical 23,292.5 Da) with the inhibitors benzamidine and berenil at pH 8.0, 25ºC and with 25 mM CaCl2, were determined using isothermal titration calorimetry and the osmotic stress method. The association constant for berenil was about 12 times higher compared to the one for benzamidine (binding constants are K = 596,599 ± 25,057 and 49,513 ± 2,732 M-1, respectively; the number of binding sites is the same for both ligands, N = 0.99 ± 0.05). Apparently the driving force responsible for this large difference of affinity is not due to hydrophobic interactions because the variation in heat capacity (DCp), a characteristic signature of these interactions, was similar in both systems tested (-464.7 ± 23.9 and -477.1 ± 86.8 J K-1 mol-1 for berenil and benzamidine, respectively). The results also indicated that the enzyme has a net gain of about 21 water molecules regardless of the inhibitor tested. It was shown that the difference in affinity could be due to a larger number of interactions between berenil and the enzyme based on computational modeling. The data support the view that pharmaceuticals derived from benzamidine that enable hydrogen bond formation outside the catalytic binding pocket of ß-trypsin may result in more effective inhibitors.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

Experimental results and modeling of poultry carcass cooling by water immersion

Poultry carcasses have to be chilled to reduce the central breast temperatures from approximately 40 to 4 °C, which is crucial to ensure safe products. This work investigated the cooling of poultry carcasses by water immersion. Poultry carcasses were taken directly from an industrial processing plant and cooled in a pilot chiller, which was built to investigate the influence of the method and the water stirring intensity on the carcasses cooling. A simplified empiric mathematical model was used to represent the experimental results. These results indicated clearly that the understanding and quantification of heat transfer between the carcass and the cooling water is crucial to improve processes and equipment. The proposed mathematical model is a useful tool to represent the dynamics of carcasses cooling, and it can be used to compare different chiller operational conditions in industrial plants. Therefore, this study reports data and a simple mathematical tool to handle an industrial problem with little information available in the literature.

Mathematical modeling of dehydration of 'Fuji' and 'Gala' apples slices using infrared

The objective of this work was to determine and model the infrared dehydration curves of apple slices - Fuji and Gala varieties. The slices were dehydrated until constant mass, in a prototype dryer with infrared heating source. The applied temperatures ranged from 50 to 100 °C. Due to the physical characteristics of the product, the dehydration curve was divided in two periods, constant and falling, separated by the critical moisture content. A linear model was used to describe the constant dehydration period. Empirical models traditionally used to model the drying behavior of agricultural products were fitted to the experimental data of the falling dehydration period. Critical moisture contents of 2.811 and 3.103 kgw kgs-1 were observed for the Fuji and Gala varieties, respectively. Based on the results, it was concluded that the constant dehydration rates presented a direct relationship with the temperature; thus, it was possible to fit a model that describes the moisture content variation in function of time and temperature. Among the tested models, which describe the falling dehydration period, the model proposed by Midilli presented the best fit for all studied conditions.

Mathematical modeling of microwave dried celery leaves and determination of the effective moisture diffusivities and activation energy

Celery (Apium graveolens L. var. secalinum Alef) leaves with 50±0.07 g weight and 91.75±0.15% humidity (~11.21 db) were dried using 8 different microwave power densities ranging between 1.8-20 W g-1, until the humidity fell down to 8.95±0.23% (~0.1 db). Microwave drying processes were completed between 5.5 and 77 min depending on the microwave power densities. In this study, measured values were compared with predicted values obtained from twenty thin layer drying theoretical, semi-empirical and empirical equations with a new thin layer drying equation. Within applied microwave power density; models whose coefficient and correlation (R²) values are highest were chosen as the best models. Weibull distribution model gave the most suitable predictions at all power density. At increasing microwave power densities, the effective moisture diffusivity values ranged from 1.595 10-10 to 6.377 10-12 m2 s-1. The activation energy was calculated using an exponential expression based on Arrhenius equation. The linear relationship between the drying rate constant and effective moisture diffusivity gave the best fit.

Modeling and kinetic study of bio-ethanol production from soy protein concentrate by-product

Abstract The aim of this work was to evaluate a non-agitated process of bioethanol production from soybean molasses and the kinetic parameters of fermentation using a strain of Saccharomyces cerevisiae (ATCC® 2345). Kinetic experiment was conducted in medium with 30% (w v-1) of soluble solids without supplementation or pH adjustment. The maximum ethanol concentration was in 44 hours, the ethanol productivity was 0.946 g L-1 h-1, the yield over total initial sugars (Y1) was 47.87%, over consumed sugars (Y2) was 88.08% and specific cells production rate was 0.006 h-1. The mathematical polynomial was adjusted to the experimental data and provided very similar parameters of yield and productivity. Based in this study, for one ton of soybean molasses can be produced 103 kg of anhydrous bioethanol.

Dynamic modeling and simulation of the airflow cleaning device

The objective of the work is to study the flow behavior and to support the design of air cleaner by dynamic simulation.In a paper printing industry, it is necessary to monitor the quality of paper when the paper is being produced. During the production, the quality of the paper can be monitored by camera. Therefore, it is necessary to keep the camera lens clean as wood particles may fall from the paper and lie on the camera lens. In this work, the behavior of the air flow and effect of the airflow on the particles at different inlet angles are simulated. Geometries of a different inlet angles of single-channel and double-channel case were constructed using ANSYS CFD Software. All the simulations were performed in ANSYS Fluent. The simulation results of single-channel and double-channel case revealed significant differences in the behavior of the flow and the particle velocity. The main conclusion from this work are in following. 1) For the single channel case the best angle was 0 degree because in that case, the air flow can keep 60% of the particles away from the lens which would otherwise stay on lens. 2) For the double channel case, the best solution was found when the angle of the first inlet was 0 degree and the angle of second inlet was 45 degree . In that case, the airflow can keep 91% of particles away from the lens which would otherwise stay on lens.

Enabling and Measuring Complexity in Evolving Designs using Generative Representations for Artificial Architecture

As the complexity of evolutionary design problems grow, so too must the quality of solutions scale to that complexity. In this research, we develop a genetic programming system with individuals encoded as tree-based generative representations to address scalability. This system is capable of multi-objective evaluation using a ranked sum scoring strategy. We examine Hornby's features and measures of modularity, reuse and hierarchy in evolutionary design problems. Experiments are carried out, using the system to generate three-dimensional forms, and analyses of feature characteristics such as modularity, reuse and hierarchy were performed. This work expands on that of Hornby's, by examining a new and more difficult problem domain. The results from these experiments show that individuals encoded with those three features performed best overall. It is also seen, that the measures of complexity conform to the results of Hornby. Moving forward with only this best performing encoding, the system was applied to the generation of three-dimensional external building architecture. One objective considered was passive solar performance, in which the system was challenged with generating forms that optimize exposure to the Sun. The results from these and other experiments satisfied the requirements. The system was shown to scale well to the architectural problems studied.

Modeling short-range ballistic missile defense and Israel's Iron Dome system

This paper develops a model of short-range ballistic missile defense and uses it to study the performance of Israel’s Iron Dome system. The deterministic base model allows for inaccurate missiles, unsuccessful interceptions, and civil defense. Model enhancements consider the trade-offs in attacking the interception system, the difficulties faced by militants in assembling large salvos, and the effects of imperfect missile classification by the defender. A stochastic model is also developed. Analysis shows that system performance can be highly sensitive to the missile salvo size, and that systems with higher interception rates are more “fragile” when overloaded. The model is calibrated using publically available data about Iron Dome’s use during Operation Pillar of Defense in November 2012. If the systems performed as claimed, they saved Israel an estimated 1778 casualties and $80 million in property damage, and thereby made preemptive strikes on Gaza about 8 times less valuable to Israel. Gaza militants could have inflicted far more damage by grouping their rockets into large salvos, but this may have been difficult given Israel’s suppression efforts. Counter-battery fire by the militants is unlikely to be worthwhile unless they can obtain much more accurate missiles.

Intra- and inter-hemispheric interactions in somatosensory processing of pain : dynamical causal modeling analysis of fMRI data

La douleur est une expérience perceptive comportant de nombreuses dimensions. Ces dimensions de douleur sont inter-reliées et recrutent des réseaux neuronaux qui traitent les informations correspondantes. L’élucidation de l'architecture fonctionnelle qui supporte les différents aspects perceptifs de l'expérience est donc une étape fondamentale pour notre compréhension du rôle fonctionnel des différentes régions de la matrice cérébrale de la douleur dans les circuits corticaux qui sous tendent l'expérience subjective de la douleur. Parmi les diverses régions du cerveau impliquées dans le traitement de l'information nociceptive, le cortex somatosensoriel primaire et secondaire (S1 et S2) sont les principales régions généralement associées au traitement de l'aspect sensori-discriminatif de la douleur. Toutefois, l'organisation fonctionnelle dans ces régions somato-sensorielles n’est pas complètement claire et relativement peu d'études ont examiné directement l'intégration de l'information entre les régions somatiques sensorielles. Ainsi, plusieurs questions demeurent concernant la relation hiérarchique entre S1 et S2, ainsi que le rôle fonctionnel des connexions inter-hémisphériques des régions somatiques sensorielles homologues. De même, le traitement en série ou en parallèle au sein du système somatosensoriel constitue un autre élément de questionnement qui nécessite un examen plus approfondi. Le but de la présente étude était de tester un certain nombre d'hypothèses sur la causalité dans les interactions fonctionnelle entre S1 et S2, alors que les sujets recevaient des chocs électriques douloureux. Nous avons mis en place une méthode de modélisation de la connectivité, qui utilise une description de causalité de la dynamique du système, afin d'étudier les interactions entre les sites d'activation définie par un ensemble de données provenant d'une étude d'imagerie fonctionnelle. Notre paradigme est constitué de 3 session expérimentales en utilisant des chocs électriques à trois différents niveaux d’intensité, soit modérément douloureux (niveau 3), soit légèrement douloureux (niveau 2), soit complètement non douloureux (niveau 1). Par conséquent, notre paradigme nous a permis d'étudier comment l'intensité du stimulus est codé dans notre réseau d'intérêt, et comment la connectivité des différentes régions est modulée dans les conditions de stimulation différentes. Nos résultats sont en faveur du mode sériel de traitement de l’information somatosensorielle nociceptive avec un apport prédominant de la voie thalamocorticale vers S1 controlatérale au site de stimulation. Nos résultats impliquent que l'information se propage de S1 controlatéral à travers notre réseau d'intérêt composé des cortex S1 bilatéraux et S2. Notre analyse indique que la connexion S1→S2 est renforcée par la douleur, ce qui suggère que S2 est plus élevé dans la hiérarchie du traitement de la douleur que S1, conformément aux conclusions précédentes neurophysiologiques et de magnétoencéphalographie. Enfin, notre analyse fournit des preuves de l'entrée de l'information somatosensorielle dans l'hémisphère controlatéral au côté de stimulation, avec des connexions inter-hémisphériques responsable du transfert de l'information à l'hémisphère ipsilatéral.