Algorithms and methodologies for decision support in energy efficiency on buildings

Dissertação apresentada na faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

How reporting and controlling systems can be effectively designed to support the decision-making process: The case of a Software SME

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.

Knowledge and practices related to dengue and its vector: a community-based study from Southeast Brazil

Abstract INTRODUCTION: This study investigated the knowledge of users of primary healthcare services living in Ribeirão Preto, Brazil, about dengue and its vector. METHODS: A cross-sectional survey of 605 people was conducted following a major dengue outbreak in 2013. RESULTS: Participants with higher levels of education were more likely to identify correctly the vector of the disease. CONCLUSIONS: The results emphasize the relevance of health education programs, the continuous promotion of educational campaigns in the media, the role of the television as a source of information, and the importance of motivating the population to control the vector.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Studies in search of a suitable experimental insect model for xenodiagnosis of hosts with Chagas' disease: 2 - Attempts to upgrade the reliability and the efficacy of xenodiagnosis in chronic Chagas' disease

In order to upgrade the reliability of xenodiagnosis, attention has been directed towards population dynamics of the parasite, with particular interest for the following factors: 1. Parasite density which by itself is not a research objective, but by giving an accurate portrayal of parasite development and multiplication, has been incorporated in screening of bugs for xenodiagnosis. 2. On the assumption that food availability might increase parasite density, bugs from xenodiagnosis have been refed at biweekly intervals on chicken blood. 3. Infectivity rates and positives harbouring large parasite yields were based on gut infections, in which the parasite population comprised of all developmental forms was more abundant and easier to detect than in fecal infections, thus minimizing the probability of recording false negatives. 4. Since parasite density, low in the first 15 days of infection, increases rapidly in the following 30 days, the interval of 45 days has been adopted for routine examination of bugs from xenodiagnosis. By following the enumerated measures, all aiming to reduce false negative cases, we are getting closer to a reliable xenodiagnostic procedure. Upgrading the efficacy of xenodiagnosis is also dependent on the xenodiagnostic agent. Of 9 investigated vector species, Panstrongylus megistus deserves top priority as a xenodiagnostic agent. Its extraordinary capability to support fast development and vigorous multiplication of the few parasites, ingested from the host with chronic Chagas' disease, has been revealed by the strikingly close infectivity rates of 91.2% vs. 96.4% among bugs engorged from the same host in the chronic and acute phase of the disease respectively (Table V), the latter comporting an estimated number of 12.3 x 10[raised to the power of 3] parasites in the circulation at the time of xenodiagnosis, as reported previously by the authors (1982).

A drug administration decision support system

Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori adaptation). This process can be computerized in order to make the prescription procedure in a fast, objective, inexpensive, non-invasive and accurate way. This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing. The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS. The addition of the RANdom SAmple Consensus (RANSAC) technique enhances the prediction accuracy by selecting inliers for SVM modeling. Experiments are performed for the drug imatinib case study which shows more than 40% improvement in the prediction accuracy compared with previous works. An important extension to the patient features' data is also proposed in this paper.

Oligodendroglia metabolically support axons and contribute to neurodegeneration.

Oligodendroglia support axon survival and function through mechanisms independent of myelination, and their dysfunction leads to axon degeneration in several diseases. The cause of this degeneration has not been determined, but lack of energy metabolites such as glucose or lactate has been proposed. Lactate is transported exclusively by monocarboxylate transporters, and changes to these transporters alter lactate production and use. Here we show that the most abundant lactate transporter in the central nervous system, monocarboxylate transporter 1 (MCT1, also known as SLC16A1), is highly enriched within oligodendroglia and that disruption of this transporter produces axon damage and neuron loss in animal and cell culture models. In addition, this same transporter is reduced in patients with, and in mouse models of, amyotrophic lateral sclerosis, suggesting a role for oligodendroglial MCT1 in pathogenesis. The role of oligodendroglia in axon function and neuron survival has been elusive; this study defines a new fundamental mechanism by which oligodendroglia support neurons and axons.

Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI.

The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification. For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained. Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%. In conclusion, our results suggest SVM classification based on quantitative meta-analyses of multicenter data as a valid method for individual AD diagnosis. Furthermore, combining imaging information from MRI and FDG-PET might substantially improve the accuracy of AD diagnosis.

Sustainability of vector control strategies in the Gran Chaco Region: current challenges and possible approaches

Sustainability has become a focal point of the international agenda. At the heart of its range of distribution in the Gran Chaco Region, the elimination of Triatoma infestans has failed, even in areas subject to intensive professional vector control efforts. Chagas disease control programs traditionally have been composed of two divorced entities: a vector control program in charge of routine field operations (bug detection and insecticide spraying) and a disease control program in charge of screening blood donors, diagnosis, etiologic treatment and providing medical care to chronic patients. The challenge of sustainable suppression of bug infestation and Trypanosoma cruzi transmission can be met through integrated disease management, in which vector control is combined with active case detection and treatment to increase impact, cost-effectiveness and public acceptance in resource-limited settings. Multi-stakeholder involvement may add sustainability and resilience to the surveillance system. Chagas vector control and disease management must remain a regional effort within the frame of sustainable development rather than being viewed exclusively as a matter of health pertinent to the health sector. Sustained and continuous coordination between governments, agencies, control programs, academia and the affected communities is critical.

Cluster kernels for semisupervised classification of VHR urban images

In this paper, we present and apply a semisupervised support vector machine based on cluster kernels for the problem of very high resolution image classification. In the proposed setting, a base kernel working with labeled samples only is deformed by a likelihood kernel encoding similarities between unlabeled examples. The resulting kernel is used to train a standard support vector machine (SVM) classifier. Experiments carried out on very high resolution (VHR) multispectral and hyperspectral images using very few labeled examples show the relevancy of the method in the context of urban image classification. Its simplicity and the small number of parameters involved make it versatile and workable by unexperimented users.

Vector control intervention towards interruption of transmission of Chagas disease by Rhodnius prolixus, main vector in Guatemala

In Guatemala, the Ministry of Health (MoH) began a vector control project with Japanese cooperation in 2000 to reduce the risk of Chagas disease infection. Rhodnius prolixus is one of the principal vectors and is targeted for elimination. The control method consisted of extensive residual insecticide spraying campaigns, followed by community-based surveillance with selective respraying. Interventions in nine endemic departments identified 317 villages with R. prolixus of 4,417 villages surveyed. Two cycles of residual insecticide spraying covered over 98% of the houses in the identified villages. Fourteen villages reinfestated were all resprayed. Between 2000-2003 and 2008, the number of infested villages decreased from 317 to two and the house infestation rate reduced from 0.86% to 0.0036%. Seroprevalence rates in 2004-2005, when compared with an earlier study in 1998, showed a significant decline from 5.3% to 1.3% among schoolchildren in endemic areas. The total operational cost was US$ 921,815, where the cost ratio between preparatory, attack and surveillance phases was approximately 2:12:1. In 2008, Guatemala was certified for interruption of Chagas disease transmission by R. prolixus. What facilitated the process was existing knowledge in vector control and notable commitment by the MoH, as well as political, managerial and technical support by external stakeholders.

Intrusive versus domiciliated triatomines and the challenge of adapting vector control practices against Chagas disease

Chagas disease prevention remains mostly based on triatomine vector control to reduce or eliminate house infestation with these bugs. The level of adaptation of triatomines to human housing is a key part of vector competence and needs to be precisely evaluated to allow for the design of effective vector control strategies. In this review, we examine how the domiciliation/intrusion level of different triatomine species/populations has been defined and measured and discuss how these concepts may be improved for a better understanding of their ecology and evolution, as well as for the design of more effective control strategies against a large variety of triatomine species. We suggest that a major limitation of current criteria for classifying triatomines into sylvatic, intrusive, domiciliary and domestic species is that these are essentially qualitative and do not rely on quantitative variables measuring population sustainability and fitness in their different habitats. However, such assessments may be derived from further analysis and modelling of field data. Such approaches can shed new light on the domiciliation process of triatomines and may represent a key tool for decision-making and the design of vector control interventions.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

How early can we predict Alzheimer's disease using computational anatomy?

Computational anatomy with magnetic resonance imaging (MRI) is well established as a noninvasive biomarker of Alzheimer's disease (AD); however, there is less certainty about its dependency on the staging of AD. We use classical group analyses and automated machine learning classification of standard structural MRI scans to investigate AD diagnostic accuracy from the preclinical phase to clinical dementia. Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative were stratified into 4 groups according to the clinical status-(1) AD patients; (2) mild cognitive impairment (MCI) converters; (3) MCI nonconverters; and (4) healthy controls-and submitted to a support vector machine. The obtained classifier was significantly above the chance level (62%) for detecting AD already 4 years before conversion from MCI. Voxel-based univariate tests confirmed the plausibility of our findings detecting a distributed network of hippocampal-temporoparietal atrophy in AD patients. We also identified a subgroup of control subjects with brain structure and cognitive changes highly similar to those observed in AD. Our results indicate that computational anatomy can detect AD substantially earlier than suggested by current models. The demonstrated differential spatial pattern of atrophy between correctly and incorrectly classified AD patients challenges the assumption of a uniform pathophysiological process underlying clinically identified AD.