Probabilistic methods for drug discovery

This thesis introduces a flexible visual data exploration framework which combines advanced projection algorithms from the machine learning domain with visual representation techniques developed in the information visualisation domain to help a user to explore and understand effectively large multi-dimensional datasets. The advantage of such a framework to other techniques currently available to the domain experts is that the user is directly involved in the data mining process and advanced machine learning algorithms are employed for better projection. A hierarchical visualisation model guided by a domain expert allows them to obtain an informed segmentation of the input space. Two other components of this thesis exploit properties of these principled probabilistic projection algorithms to develop a guided mixture of local experts algorithm which provides robust prediction and a model to estimate feature saliency simultaneously with the training of a projection algorithm.Local models are useful since a single global model cannot capture the full variability of a heterogeneous data space such as the chemical space. Probabilistic hierarchical visualisation techniques provide an effective soft segmentation of an input space by a visualisation hierarchy whose leaf nodes represent different regions of the input space. We use this soft segmentation to develop a guided mixture of local experts (GME) algorithm which is appropriate for the heterogeneous datasets found in chemoinformatics problems. Moreover, in this approach the domain experts are more involved in the model development process which is suitable for an intuition and domain knowledge driven task such as drug discovery. We also derive a generative topographic mapping (GTM) based data visualisation approach which estimates feature saliency simultaneously with the training of a visualisation model.

Structuring of Ranked Models

Prognostic procedures can be based on ranked linear models. Ranked regression type models are designed on the basis of feature vectors combined with set of relations defined on selected pairs of these vectors. Feature vectors are composed of numerical results of measurements on particular objects or events. Ranked relations defined on selected pairs of feature vectors represent additional knowledge and can reflect experts' opinion about considered objects. Ranked models have the form of linear transformations of feature vectors on a line which preserve a given set of relations in the best manner possible. Ranked models can be designed through the minimization of a special type of convex and piecewise linear (CPL) criterion functions. Some sets of ranked relations cannot be well represented by one ranked model. Decomposition of global model into a family of local ranked models could improve representation. A procedures of ranked models decomposition is described in this paper.

Modeling Pulsatility in the Human Cardiovascular System

AMS Subj. Classification: 92C30

Estimate of the (R)USLE rainfall erosivity factor from monthly precipitation data in mainland Spain

The need for continuous recording rain gauges makes it difficult to determine the rainfall erosivity factor (R-factor) of the (R)USLE model in areas without good temporal data coverage. In mainland Spain, the Nature Conservation Institute (ICONA) determined the R-factor at few selected pluviographs, so simple estimates of the R-factor are definitely of great interest. The objectives of this study were: (1) to identify a readily available estimate of the R-factor for mainland Spain; (2) to discuss the applicability of a single (global) estimate based on analysis of regional results; (3) to evaluate the effect of record length on estimate precision and accuracy; and (4) to validate an available regression model developed by ICONA. Four estimators based on monthly precipitation were computed at 74 rainfall stations throughout mainland Spain. The regression analysis conducted at a global level clearly showed that modified Fournier index (MFI) ranked first among all assessed indexes. Applicability of this preliminary global model across mainland Spain was evaluated by analyzing regression results obtained at a regional level. It was found that three contiguous regions of eastern Spain (Catalonia, Valencian Community and Murcia) could have a different rainfall erosivity pattern, so a new regression analysis was conducted by dividing mainland Spain into two areas: Eastern Spain and plateau-lowland area. A comparative analysis concluded that the bi-areal regression model based on MFI for a 10-year record length provided a simple, precise and accurate estimate of the R-factor in mainland Spain. Finally, validation of the regression model proposed by ICONA showed that R-ICONA index overpredicted the R-factor by approximately 19%.

Rotina computacional de obtenção, tratamento e visualização de dados oceanográficos

Atualmente, sensores remotos e computadores de alto desempenho estão sendo utilizados como instrumentos principais na coleta e produção de dados oceanográficos. De posse destes dados, é possível realizar estudos que permitem simular e prever o comportamento do oceano por meio de modelos numéricos regionais. Dentre os fatores importantes no estudo da oceanografia, podem ser destacados àqueles referentes aos impactos ambientais, de contaminação antrópica, utilização de energias renováveis, operações portuárias e etc. Contudo, devido ao grande volume de dados gerados por instituições ambientais, na forma de resultados de modelos globais como o HYCOM (Hybrid Coordinate Ocean Model) e dos programas de Reanalysis da NOAA (National Oceanic and Atmospheric Administration), torna-se necessária a criação de rotinas computacionais para realizar o tratamento de condições iniciais e de contorno, de modo que possam ser aplicadas a modelos regionais como o TELEMAC3D (www.opentelemac.org). Problemas relacionados a baixa resolução, ausência de dados e a necessidade de interpolação para diferentes malhas ou sistemas de coordenadas verticais, tornam necessária a criação de um mecanismo computacional que realize este tratamento adequadamente. Com isto, foram desenvolvidas rotinas na linguagem de programação Python, empregando interpoladores de vizinho mais próximo, de modo que, a partir de dados brutos dos modelos HYCOM e do programa de Reanalysis da NOAA, foram preparadas condições iniciais e de contorno para a realização de uma simulação numérica teste. Estes resultados foram confrontados com outro resultado numérico onde, as condições foram construídas a partir de um método de interpolação mais sofisticado, escrita em outra linguagem, e que já vem sendo utilizada no laboratório. A análise dos resultados permitiu concluir que, a rotina desenvolvida no âmbito deste trabalho, funciona adequadamente para a geração de condições iniciais e de contorno do modelo TELEMAC3D. Entretanto, um interpolador mais sofisticado deve ser desenvolvido de forma a aumentar a qualidade nas interpolações, otimizar o custo computacional, e produzir condições que sejam mais realísticas para a utilização do modelo TELEMAC3D.

Landscape metrics as indicators of coastal morphology and its use in ecological niche modelling of seagrass species

Dependence of some species on landscape structure has been proved in numerous studies. So far, however, little progress has been made in the integration of landscape metrics in the prediction of species associated with coastal features. Specific landscape metrics were tested as predictors of coastal shape using three coastal features of the Iberian Peninsula (beaches, capes and gulfs) at different scales. We used the landscape metrics in combination with environmental variables to model the niche and find suitable habitats for a seagrass species (Cymodocea nodosa) throughout its entire range of distribution. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We provided the first global model of the factors that can be shaping the environmental niche and distribution of C. nodosa throughout its range. Sea surface temperature and salinity were the most relevant variables. We identified areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. We also present some preliminary results of testing historical biogeographical hypotheses derived from distribution predictions under Last Glacial Maximum conditions and genetic diversity data.

Regressão Logística Geograficamente Ponderada aplicada a modelos de Credit Scoring

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Programa de Pós-Graduação em Administração, 2016.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

Environmental Dynamics of Dissolved Black Carbon in Aquatic Ecosystems

Black carbon (BC), the incomplete combustion product from biomass and fossil fuel burning, is ubiquitously found in soils, sediments, ice, water and atmosphere. Because of its polyaromatic molecular characteristic, BC is believed to contribute significantly to the global carbon budget as a slow-cycling, refractory carbon pool. However, the mass balance between global BC generation and accumulation does not match, suggesting a removal mechanism of BC to the active carbon pool, most probable in a dissolved form. The presence of BC in waters as part of the dissolved organic matter (DOM) pool was recently confirmed via ultrahigh resolution mass spectrometry, and dissolved black carbon (DBC), a degradation product of charcoal, was found in marine and coastal environments. However, information on the loadings of DBC in freshwater environments and its global riverine flux from terrestrial systems to the oceans remained unclear. The main objectives of this study were to quantify DBC in diverse aquatic ecosystems and to determine its environmental dynamics. Surface water samples were collected from aquatic environments with a spatially significant global distribution, and DBC concentrations were determined by a chemical oxidation method coupled with HPLC detection. While it was clear that biomass burning was the main sources of BC, the translocation mechanism of BC to the dissolved phase was not well understood. Data from the regional studies and the developed global model revealed a strong positive correlation between DBC and dissolved organic carbon (DOC) dynamics, indicating a co-generation and co-translocation between soil OC and BC. In addition, a DOC-assistant DBC translocation mechanism was identified. Taking advantage of the DOC-DBC correlation model, a global riverine DBC flux to oceans on the order of 26.5 Mt C yr-1 (1 Mt = 1012 g) was determined, accounting for 10.6% of the global DOC flux. The results not only indicated that DOC was an important environmental intermediate for BC transfer and storage, but also provided an estimate of a major missing link in the global BC budget. The ever increasing DBC export caused by global warming will change the marine DOM quality and may have important consequences for carbon cycling in marine ecosystem.

Subfilter-scale Stress Modelling for Large Eddy Simulations

A subfilter-scale (SFS) stress model is developed for large-eddy simulations (LES) and is tested on various benchmark problems in both wall-resolved and wall-modelled LES. The basic ingredients of the proposed model are the model length-scale, and the model parameter. The model length-scale is defined as a fraction of the integral scale of the flow, decoupled from the grid. The portion of the resolved scales (LES resolution) appears as a user-defined model parameter, an advantage that the user decides the LES resolution. The model parameter is determined based on a measure of LES resolution, the SFS activity. The user decides a value for the SFS activity (based on the affordable computational budget and expected accuracy), and the model parameter is calculated dynamically. Depending on how the SFS activity is enforced, two SFS models are proposed. In one approach the user assigns the global (volume averaged) contribution of SFS to the transport (global model), while in the second model (local model), SFS activity is decided locally (locally averaged). The models are tested on isotropic turbulence, channel flow, backward-facing step and separating boundary layer. In wall-resolved LES, both global and local models perform quite accurately. Due to their near-wall behaviour, they result in accurate prediction of the flow on coarse grids. The backward-facing step also highlights the advantage of decoupling the model length-scale from the mesh. Despite the sharply refined grid near the step, the proposed SFS models yield a smooth, while physically consistent filter-width distribution, which minimizes errors when grid discontinuity is present. Finally the model application is extended to wall-modelled LES and is tested on channel flow and separating boundary layer. Given the coarse resolution used in wall-modelled LES, near the wall most of the eddies become SFS and SFS activity is required to be locally increased. The results are in very good agreement with the data for the channel. Errors in the prediction of separation and reattachment are observed in the separated flow, that are somewhat improved with some modifications to the wall-layer model.

Quinn’s Leadership Roles: A Confirmatory Factor Analysis Study in Portuguese Health Services

Purpose: To evaluate psychometric properties of Quinn’s leadership questionnaire (CFV questionnaire; 1988) to the Portuguese health services. Design: Cross-sectional study, using the Quinn’s leadership questionnaire, administered to registered nurses and physicians in Portuguese health care services (N = 687). Method: Self-administered survey applied to two samples. In the first (of convenience; N = 249 Portuguese health professionals) were performed exploratory factor and reliability analysis to the CFV questionnaire. In the second sample (stratified; N = 50 surgical units of 33 Portuguese hospitals) was performed confirmatory factor analysis using LISREL 8.80. Findings: The first sample supported an eight-factor solution accounting for 65.46% of the variance, in an interpretable factorial structure (loadings> .50), with Cronbach’s α upper than .79. This factorial structure, replicated with the second sample, showed reasonable fit for each of the 8 leadership roles, quadrants, and global model. The models evidenced, generally, nomological validity, with scores between good and acceptable (.235 < x2/df < 2.055 e .00 < RMSEA < .077). Conclusions: Quinn’s leadership questionnaire presented good reliability and validity for the eight leadership roles, showing to be suitable for use in hospital health care context. Key-Words: Leadership; Quinn’s CVF questionnaire; health services; Quinn’s competing values.

Modeling, Component Selection and Optimization of Servo-controlled Automatic Machinery

A servo-controlled automatic machine can perform tasks that involve synchronized actuation of a significant number of servo-axes, namely one degree-of-freedom (DoF) electromechanical actuators. Each servo-axis comprises a servo-motor, a mechanical transmission and an end-effector, and is responsible for generating the desired motion profile and providing the power required to achieve the overall task. The design of a such a machine must involve a detailed study from a mechatronic viewpoint, due to its electric and mechanical nature. The first objective of this thesis is the development of an overarching electromechanical model for a servo-axis. Every loss source is taken into account, be it mechanical or electrical. The mechanical transmission is modeled by means of a sequence of lumped-parameter blocks. The electric model of the motor and the inverter takes into account winding losses, iron losses and controller switching losses. No experimental characterizations are needed to implement the electric model, since the parameters are inferred from the data available in commercial catalogs. With the global model at disposal, a second objective of this work is to perform the optimization analysis, in particular, the selection of the motor-reducer unit. The optimal transmission ratios that minimize several objective functions are found. An optimization process is carried out and repeated for each candidate motor. Then, we present a novel method where the discrete set of available motor is extended to a continuous domain, by fitting manufacturer data. The problem becomes a two-dimensional nonlinear optimization subject to nonlinear constraints, and the solution gives the optimal choice for the motor-reducer system. The presented electromechanical model, along with the implementation of optimization algorithms, forms a complete and powerful simulation tool for servo-controlled automatic machines. The tool allows for determining a wide range of electric and mechanical parameters and the behavior of the system in different operating conditions.

Development of numerical tools for the simulation, design and optimization of a Helicon Plasma Thruster

This work thesis focuses on the Helicon Plasma Thruster (HPT) as a candidate for generating thrust for small satellites and CubeSats. Two main topics are addressed: the development of a Global Model (GM) and a 3D self-consistent numerical tool. The GM is suitable for preliminary analysis of HPTs with noble gases such as argon, neon, krypton, and xenon, and alternative propellants such as air and iodine. A lumping methodology is developed to reduce the computational cost when modelling the excited species in the plasma chemistry. A 3D self-consistent numerical tool is also developed that can treat discharges with a generic 3D geometry and model the actual plasma-antenna coupling. The tool consists of two main modules, an EM module and a FLUID module, which run iteratively until a steady state solution is converged. A third module is available for solving the plume with a simplified semi-analytical approach, a PIC code, or directly by integration of the fluid equations. Results obtained from both the numerical tools are benchmarked against experimental measures of HPTs or Helicon reactors, obtaining very good qualitative agreement with the experimental trend for what concerns the GM, and an excellent agreement of the physical trends predicted against the measured data for the 3D numerical strategy.

A Distributed Support based on Kubeflow to Ease MLOps of Federated Learning Algorithms

The main objective of my thesis work is to exploit the Google native and open-source platform Kubeflow, specifically using Kubeflow pipelines, to execute a Federated Learning scalable ML process in a 5G-like and simplified test architecture hosting a Kubernetes cluster and apply the largely adopted FedAVG algorithm and FedProx its optimization empowered by the ML platform ‘s abilities to ease the development and production cycle of this specific FL process. FL algorithms are more are and more promising and adopted both in Cloud application development and 5G communication enhancement through data coming from the monitoring of the underlying telco infrastructure and execution of training and data aggregation at edge nodes to optimize the global model of the algorithm ( that could be used for example for resource provisioning to reach an agreed QoS for the underlying network slice) and after a study and a research over the available papers and scientific articles related to FL with the help of the CTTC that suggests me to study and use Kubeflow to bear the algorithm we found out that this approach for the whole FL cycle deployment was not documented and may be interesting to investigate more in depth. This study may lead to prove the efficiency of the Kubeflow platform itself for this need of development of new FL algorithms that will support new Applications and especially test the FedAVG algorithm performances in a simulated client to cloud communication using a MNIST dataset for FL as benchmark.

Development of iodine and air chemistry models for the simulation of plasma in Helicon Plasma Thrusters

The aim of this work is to analyse the chemistry models of low pressure Helicon discharges fed with iodine and air. In particular the focus of this research is to understand the plasma dynamics in order to predict propulsive performances of iodine and air-breathing Helicon Plasma Thrusters. The two systems have been simulated and analysed with the use of global models, i.e. a 0 dimensional tool to solve the set of governing equations by assuming that all quantities are volume averaged. Furthermore, some strategies have been implemented to improve the accuracy of this approach. A verification have been accomplished on the global models for both iodine and air, comparing results against simulations taken from literature. Moreover, the iodine global model has been validated against the experimental measurements of REGULUS, an helicon plasma thruster developed by the Italian company T4i, with a good agreement. From the analysis of iodine model, it has been found a significantly higher density for atomic positive ions with respect to molecular ions. Negative ions, instead, have shown to have negligible effect on the propulsive results. Also, the influence of reactions between heavy particles has been analysed with the global model. Results have demonstrated that, in the iodine case, chemistry is almost entirely affected by electronic collisions. For what concerns air-breathing results, it has been investigated the effects of the orbital height on propulsive performances. In particular, the global model has shown that at lower height, the values of thrust and specific impulse are lower due a change in atmosphere concentration. Finally, the iodine chemistry model has been introduced in the fluid code 3D-VIRTUS in order to preliminary assess the plasma properties of a Helicon discharge chamber for electric propulsion.