A Simple Model to Quantify Radiolytic Production following Electron Emission from Heavy-Atom Nanoparticles Irradiated in Liquid Suspensions

We present a simple model for a component of the radiolytic production of any chemical species due to electron emission from irradiated nanoparticles (NPs) in a liquid environment, provided the expression for the G value for product formation is known and is reasonably well characterized by a linear dependence on beam energy. This model takes nanoparticle size, composition, density and a number of other readily available parameters (such as X-ray and electron attenuation data) as inputs and therefore allows for the ready determination of this contribution. Several approximations are used, thus this model provides an upper limit to the yield of chemical species due to electron emission, rather than a distinct value, and this upper limit is compared with experimental results. After the general model is developed we provide details of its application to the generation of HO(•) through irradiation of gold nanoparticles (AuNPs), a potentially important process in nanoparticle-based enhancement of radiotherapy. This model has been constructed with the intention of making it accessible to other researchers who wish to estimate chemical yields through this process, and is shown to be applicable to NPs of single elements and mixtures. The model can be applied without the need to develop additional skills (such as using a Monte Carlo toolkit), providing a fast and straightforward method of estimating chemical yields. A simple framework for determining the HO(•) yield for different NP sizes at constant NP concentration and initial photon energy is also presented.

Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification

In this paper, a novel and effective lip-based biometric identification approach with the Discrete Hidden Markov Model Kernel (DHMMK) is developed. Lips are described by shape features (both geometrical and sequential) on two different grid layouts: rectangular and polar. These features are then specifically modeled by a DHMMK, and learnt by a support vector machine classifier. Our experiments are carried out in a ten-fold cross validation fashion on three different datasets, GPDS-ULPGC Face Dataset, PIE Face Dataset and RaFD Face Dataset. Results show that our approach has achieved an average classification accuracy of 99.8%, 97.13%, and 98.10%, using only two training images per class, on these three datasets, respectively. Our comparative studies further show that the DHMMK achieved a 53% improvement against the baseline HMM approach. The comparative ROC curves also confirm the efficacy of the proposed lip contour based biometrics learned by DHMMK. We also show that the performance of linear and RBF SVM is comparable under the frame work of DHMMK.

A novel TRNSYS type for short-term borehole heat exchanger simulation: B2G model

Models of ground source heat pump (GSHP) systems are used as an aid for the correct design and optimization of the system. For this purpose, it is necessary to develop models which correctly reproduce the dynamic thermal behavior of each component in a short-term basis. Since the borehole heat exchanger (BHE) is one of the main components, special attention should be paid to ensuring a good accuracy on the prediction of the short-term response of the boreholes. The BHE models found in literature which are suitable for short-term simulations usually present high computational costs. In this work, a novel TRNSYS type implementing a borehole-to-ground (B2G) model, developed for modeling the short-term dynamic performance of a BHE with low computational cost, is presented. The model has been validated against experimental data from a GSHP system located at Universitat Politècnica de València, Spain. Validation results show the ability of the model to reproduce the short-term behavior of the borehole, both for a step-test and under normal operating conditions.

Numerical considerations for the implementation of a comprehensive composite damage model

A comprehensive continuum damage mechanics model [1] had been developed to capture the detailed
behaviour of a composite structure under a crushing load. This paper explores some of the difficulties
encountered in the implementation of this model and their mitigation. The use of reduced integration
element and a strain softening model both negatively affect the accuracy and stability of the
simulation. Damage localisation effects demanded an accurate measure of characteristic length. A
robust algorithm for determining the characteristic length was implemented. Testing showed that this
algorithm produced marked improvements over the use of the default characteristic length provided
by Abaqus. Zero-energy or hourglass modes, in reduced integration elements, led to reduced
resistance to bending. This was compounded by the strain softening model, which led to the formation
of elements with little resistance to deformation that could invert if left unchecked. It was shown,
through benchmark testing, that by deleting elements with excess distortions and controlling the mesh
using inbuilt distortion/hourglass controls, these issues can be alleviated. These techniques
contributed significantly to the viability and usability of the damage model.

Validation of a 3D damage model for predicting the response of composite structures under crushing loads

A 3D intralaminar continuum damage mechanics based material model, combining damage mode interaction and material nonlinearity, was developed to predict the damage response of composite structures undergoing crush loading. This model captures the structural response without the need for calibration of experimentally determined material parameters. When used in the design of energy absorbing composite structures, it can reduce the dependence on physical testing. This paper validates this model against experimental data obtained from the literature and in-house testing. Results show that the model can predict the force response of the crushed composite structures with good accuracy. The simulated energy absorption in each test case was within 12% of the experimental value. Post-crush deformation and the damage morphologies, such as ply splitting, splaying and breakage, were also accurately reproduced. This study establishes the capability of this damage model for predicting the responses of composite structures under crushing loads.

Model-based systems engineering: a system for traffic & environment

The contemporary world is crowded of large, interdisciplinary, complex systems made of other systems, personnel, hardware, software, information, processes, and facilities. The Systems Engineering (SE) field proposes an integrated holistic approach to tackle these socio-technical systems that is crucial to take proper account of their multifaceted nature and numerous interrelationships, providing the means to enable their successful realization. Model-Based Systems Engineering (MBSE) is an emerging paradigm in the SE field and can be described as the formalized application of modelling principles, methods, languages, and tools to the entire lifecycle of those systems, enhancing communications and knowledge capture, shared understanding, improved design precision and integrity, better development traceability, and reduced development risks. This thesis is devoted to the application of the novel MBSE paradigm to the Urban Traffic & Environment domain. The proposed system, the GUILTE (Guiding Urban Intelligent Traffic & Environment), deals with a present-day real challenging problem “at the agenda” of world leaders, national governors, local authorities, research agencies, academia, and general public. The main purposes of the system are to provide an integrated development framework for the municipalities, and to support the (short-time and real-time) operations of the urban traffic through Intelligent Transportation Systems, highlighting two fundamental aspects: the evaluation of the related environmental impacts (in particular, the air pollution and the noise), and the dissemination of information to the citizens, endorsing their involvement and participation. These objectives are related with the high-level complex challenge of developing sustainable urban transportation networks. The development process of the GUILTE system is supported by a new methodology, the LITHE (Agile Systems Modelling Engineering), which aims to lightening the complexity and burdensome of the existing methodologies by emphasizing agile principles such as continuous communication, feedback, stakeholders involvement, short iterations and rapid response. These principles are accomplished through a universal and intuitive SE process, the SIMILAR process model (which was redefined at the light of the modern international standards), a lean MBSE method, and a coherent System Model developed through the benchmark graphical modeling languages SysML and OPDs/OPL. The main contributions of the work are, in their essence, models and can be settled as: a revised process model for the SE field, an agile methodology for MBSE development environments, a graphical tool to support the proposed methodology, and a System Model for the GUILTE system. The comprehensive literature reviews provided for the main scientific field of this research (SE/MBSE) and for the application domain (Traffic & Environment) can also be seen as a relevant contribution.

Model of access to natural language sources in electronic government

For the actual existence of e-government it is necessary and crucial to provide public information and documentation, making its access simple to citizens. A portion, not necessarily small, of these documents is in an unstructured form and in natural language, and consequently outside of which the current search systems are generally able to cope and effectively handle. Thus, in thesis, it is possible to improve access to these contents using systems that process natural language and create structured information, particularly if supported in semantics. In order to put this thesis to test, this work was developed in three major phases: (1) design of a conceptual model integrating the creation of structured information and making it available to various actors, in line with the vision of e-government 2.0; (2) definition and development of a prototype instantiating the key modules of this conceptual model, including ontology based information extraction supported by examples of relevant information, knowledge management and access based on natural language; (3) assessment of the usability and acceptability of querying information as made possible by the prototype - and in consequence of the conceptual model - by users in a realistic scenario, that included comparison with existing forms of access. In addition to this evaluation, at another level more related to technology assessment and not to the model, evaluations were made on the performance of the subsystem responsible for information extraction. The evaluation results show that the proposed model was perceived as more effective and useful than the alternatives. Associated with the performance of the prototype to extract information from documents, comparable to the state of the art, results demonstrate the feasibility and advantages, with current technology, of using natural language processing and integration of semantic information to improve access to unstructured contents in natural language. The conceptual model and the prototype demonstrator intend to contribute to the future existence of more sophisticated search systems that are also more suitable for e-government. To have transparency in governance, active citizenship, greater agility in the interaction with the public administration, among others, it is necessary that citizens and businesses have quick and easy access to official information, even if it was originally created in natural language.

Disparity energy model with keypoint disparity validation

A biological disparity energy model can estimate local depth information by using a population of V1 complex cells. Instead of applying an analytical model which explicitly involves cell parameters like spatial frequency, orientation, binocular phase and position difference, we developed a model which only involves the cells’ responses, such that disparity can be extracted from a population code, using only a set of previously trained cells with random-dot stereograms of uniform disparity. Despite good results in smooth regions, the model needs complementary processing, notably at depth transitions. We therefore introduce a new model to extract disparity at keypoints such as edge junctions, line endings and points with large curvature. Responses of end-stopped cells serve to detect keypoints, and those of simple cells are used to detect orientations of their underlying line and edge structures. Annotated keypoints are then used in the leftright matching process, with a hierarchical, multi-scale tree structure and a saliency map to segregate disparity. By combining both models we can (re)define depth transitions and regions where the disparity energy model is less accurate.

Generate disaggregated soil allocation data using a Minimum Cross Entropy Model

Montado ecosystem in the Alentejo Region, south of Portugal, has enormous agro-ecological and economics heterogeneities. A definition of homogeneous sub-units among this heterogeneous ecosystem was made, but for them is disposal only partial statistical information about soil allocation agro-forestry activities. The paper proposal is to recover the unknown soil allocation at each homogeneous sub-unit, disaggregating a complete data set for the Montado ecosystem area using incomplete information at sub-units level. The methodological framework is based on a Generalized Maximum Entropy approach, which is developed in thee steps concerning the specification of a r order Markov process, the estimates of aggregate transition probabilities and the disaggregation data to recover the unknown soil allocation at each homogeneous sub-units. The results quality is evaluated using the predicted absolute deviation (PAD) and the "Disagegation Information Gain" (DIG) and shows very acceptable estimation errors.

Seismo-acoustic ray model benchmarking against experimental tank data

Acoustic predictions of the recently developed TRACEO ray model, which accounts for bottom shear properties, are benchmarked against tank experimental data from the EPEE-1 and EPEE-2 (Elastic Parabolic Equation Experiment) experiments. Both experiments are representative of signal propagation in a Pekeris-like shallow-water waveguide over a non-flat isotropic elastic bottom, where significant interaction of the signal with the bottom can be expected. The benchmarks show, in particular, that the ray model can be as accurate as a parabolic approximation model benchmarked in similar conditions. The results of benchmarking are important, on one side, as a preliminary experimental validation of the model and, on the other side, demonstrates the reliability of the ray approach for seismo-acoustic applications. (C) 2012 Acoustical Society of America. [http://dx.doi.org/10.1121/1.4734236]

A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom

A 30-day ahead forecast method has been developed for grass pollen at north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21st May to 8th August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961-1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of one to four; and the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of one to four, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002 respectively when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

A Service Model For Delivering Care Closer to Home.

Upton Surgery (Worcestershire) has developed a flexible and responsive service model that facilitates multi-agency support for adult patients with complex care needs experiencing an acute health crisis. The purpose of this service is to provide appropriate interventions that avoid unnecessary hospital admissions or, alternatively, provide support to facilitate early discharge from secondary care. Key aspects of this service are the collaborative and proactive identification of patients at risk, rapid creation and deployment of a reactive multi-agency team and follow-up of patients with an appropriate long-term care plan. A small team of dedicated staff (the Complex Care Team) are pivotal to coordinating and delivering this service. Key skills are sophisticated leadership and project management skills, and these have been used sensitively to challenge some traditional roles and boundaries in the interests of providing effective, holistic care for the patient. This is a practical example of early implementation of the principles underlying the Department of Health’s (DH) recent Best Practice Guidance, ‘Delivering Care Closer to Home’ (DH, July 2008) and may provide useful learning points for other general practice surgeries considering implementing similar models. This integrated case management approach has had enthusiastic endorsement from patients and carers. In addition to the enhanced quality of care and experience for the patient, this approach has delivered value for money. Secondary care costs have been reduced by preventing admissions and also by reducing excess bed-days. The savings achieved have justified the ongoing commitment to the service and the staff employed in the Complex Care Team. The success of this service model has been endorsed recently by the ‘Customer Care’ award by ‘Management in Practice’. The Surgery was also awarded the ‘Practice of the Year’ award for this and a number of other customer-focussed projects.

Production of laminar extrudates containing particles of a model drug processed by supercritical fluids

Tese de doutoramento, Farmácia (Tecnologia Farmacêutica), Universidade de Lisboa, Faculdade de Farmácia, 2015

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

Simulation model for driving dynamics, energy use and power supply

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia