Spatial analysis of invasive alien plant distribution patterns and processes using Bayesian network-based data mining techniques

Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumptions, implementation and interpretation of outputs. This study explores the usefulness of Bayesian networks (BNs) due their ability to model stochastic, nonlinear inter-causal relationships and uncertainty. Data-driven BNs were used to explore patterns and processes influencing the spatial distribution of 16 priority invasive alien plants in Swaziland. Various BN structure learning algorithms were applied within the Weka software to build models from a set of 170 variables incorporating climatic, anthropogenic, topo-edaphic and landscape factors. While all the BN models produced accurate predictions of alien plant invasion, the globally scored networks, particularly the hill climbing algorithms, performed relatively well. However, when considering the probabilistic outputs, the constraint-based Inferred Causation algorithm which attempts to generate a causal BN structure, performed relatively better. The learned BNs reveal that the main pathways of alien plants into new areas are ruderal areas such as road verges and riverbanks whilst humans and human activity are key driving factors and the main dispersal mechanism. However, the distribution of most of the species is constrained by climate particularly tolerance to very low temperatures and precipitation seasonality. Biotic interactions and/or associations among the species are also prevalent. The findings suggest that most of the species will proliferate by extending their range resulting in the whole country being at risk of further invasion. The ability of BNs to express uncertain, rather complex conditional and probabilistic dependencies and to combine multisource data makes them an attractive technique for species distribution modeling, especially as joint invasive species distribution models (JiSDM). Suggestions for further research are provided including the need for rigorous invasive species monitoring, data stewardship and testing more BN learning algorithms.Environmental SciencesD. Phil. (Environmental Science

Novel Approaches to Pervasive and Remote Sensing in Cardiovascular Disease Assessment

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, responsible for 45% of all deaths. Nevertheless, their mortality is decreasing in the last decade due to better prevention, diagnosis, and treatment resources. An important medical instrument for the latter processes is the Electrocardiogram (ECG). The ECG is a versatile technique used worldwide for its ease of use, low cost, and accessibility, having evolved from devices that filled up a room, to small patches or wrist- worn devices. Such evolution allowed for more pervasive and near-continuous recordings. The analysis of an ECG allows for studying the functioning of other physiological systems of the body. One such is the Autonomic Nervous System (ANS), responsible for controlling key bodily functions. The ANS can be studied by analyzing the characteristic inter-beat variations, known as Heart Rate Variability (HRV). Leveraging this relation, a pilot study was developed, where HRV was used to quantify the contribution of the ANS in modulating cardioprotection offered by an experimental medical procedure called Remote Ischemic Conditioning (RIC), offering a more objective perspective. To record an ECG, electrodes are responsible for converting the ion-propagated action potential to electrons, needed to record it. They are produced from different materials, including metal, carbon-based, or polymers. Also, they can be divided into wet (if an elec- trolyte gel is used) or dry (if no added electrolyte is used). Electrodes can be positioned either inside the body (in-the-person), attached to the skin (on-the-body), or embedded in daily life objects (off-the-person), with the latter allowing for more pervasive recordings. To this effect, a novel mobile acquisition device for recording ECG rhythm strips was developed, where polymer-based embedded electrodes are used to record ECG signals similar to a medical-grade device. One drawback of off-the-person solutions is the increased noise, mainly caused by the intermittent contact with the recording surfaces. A new signal quality metric was developed based on delayed phase mapping, a technique that maps time series to a two-dimensional space, which is then used to classify a segment into good or noisy. Two different approaches were developed, one using a popular image descriptor, the Hu image moments; and the other using a Convolutional Neural Network, both with promising results for their usage as signal quality index classifiers.As doenças cardiovasculares (DCVs) são a principal causa de morte no mundo, res- ponsáveis por 45% de todas estas. No entanto, a sua mortalidade tem vindo a diminuir na última década, devido a melhores recursos na prevenção, diagnóstico e tratamento. Um instrumento médico importante para estes recursos é o Eletrocardiograma (ECG). O ECG é uma técnica versátil utilizada em todo o mundo pela sua facilidade de uso, baixo custo e acessibilidade, tendo evoluído de dispositivos que ocupavam uma sala inteira para pequenos adesivos ou dispositivos de pulso. Tal evolução permitiu aquisições mais pervasivas e quase contínuas. A análise de um ECG permite estudar o funcionamento de outros sistemas fisiológi- cos do corpo. Um deles é o Sistema Nervoso Autônomo (SNA), responsável por controlar as principais funções corporais. O SNA pode ser estudado analisando as variações inter- batidas, conhecidas como Variabilidade da Frequência Cardíaca (VFC). Aproveitando essa relação, foi desenvolvido um estudo piloto, onde a VFC foi utilizada para quantificar a contribuição do SNA na modulação da cardioproteção oferecida por um procedimento mé- dico experimental, denominado Condicionamento Isquêmico Remoto (CIR), oferecendo uma perspectiva mais objetiva. Na aquisição de um ECG, os elétrodos são os responsáveis por converter o potencial de ação propagado por iões em eletrões, necessários para a sua recolha. Estes podem ser produzidos a partir de diferentes materiais, incluindo metal, à base de carbono ou polímeros. Além disso, os elétrodos podem ser classificados em húmidos (se for usado um gel eletrolítico) ou secos (se não for usado um eletrólito adicional). Os elétrodos podem ser posicionados dentro do corpo (dentro-da-pessoa), colocados em contacto com a pele (na-pessoa) ou embutidos em objetos da vida quotidiana (fora-da-pessoa), sendo que este último permite gravações mais pervasivas . Para este efeito, foi desenvolvido um novo dispositivo de aquisição móvel para gravar sinal de ECG, onde elétrodos embutidos à base de polímeros são usados para recolher sinais de ECG semelhantes a um dispositivo de grau médico. Uma desvantagem das soluções onde os elétrodos estão embutidos é o aumento do ruído, causado principalmente pelo contato intermitente com as superfícies de aquisição. Uma nova métrica de qualidade de sinal foi desenvolvida com base no mapeamento de fase atrasada, uma técnica que mapeia séries temporais para um espaço bidimensional, que é então usado para classificar um segmento em bom ou ruidoso. Duas abordagens diferentes foram desenvolvidas, uma usando um popular descritor de imagem, e outra utilizando uma Rede Neural Convolucional, com resultados promissores para o seu uso como classificadores de qualidade de sinal

Comprehensive Safety Analysis of Vulnerable Road User Involved Motor Vehicle Crashes

This dissertation explores, identifies, and evaluates a multitude of factors significantly affecting motor vehicle crashes involving pedestrians and bicyclists, commonly defined as vulnerable road users (VRUs). The methodologies are guided by the concept of safe behavior of different parties that are primary responsible for a crash, either a pedestrian, a bicyclist or a driver, pertaining to roadway design, traffic conditions, land use and built environment variables; and the findings are beneficial for recommending targeted and effective safety interventions. The topic is motivated by the fact that human factors contribute to over ninety percent of the crashes, especially the ones involving VRUs. Studying the effect of road users’ behavior, their responses to the dynamics of traveling environment, and compliance rate to traffic rules is instrumental to precisely measure and evaluate how each of the investigated variables changes the crash risk. To achieve this goal, an extensive database is established based on data collected from sources such as the linework from topologically integrated geographic encoding and referencing, Google maps, motor vehicle accident reports, Wisconsin Information System for Local Roads, and Smart Location Dataset from Environmental Protection Agency. The crosscutting datasets represent various aspects of motorist and non-motorists travel decisions and behaviors, as well as their safety status. With this comprehensive database, intrinsic relationships between pedestrian-vehicle crashes and a broad range of socioeconomic and demographic factors, land use and built environment, crime rate and traffic violations, road design, traffic control, and pedestrian-oriented design features are identified, analyzed, and evaluated. The comprehensive safety analysis begins with the structural equation model (SEM) that is employed to discover possible underlying factor structure connecting exogenous variables and crashes involving pedestrians. Informed by the SEM output, the analysis continues with the development of crash count models and responsible party choice models to respectively address factors relating to roles in a crash by pedestrians and drivers. As a result, factors contributing to crashes where a pedestrian is responsible, a driver is responsible, or both parties are responsible can be specified, categorized, and quantified. Moreover, targeted and appropriate safety countermeasures can be designed, recommended, and prioritized by engineers, planners, or enforcement agencies to jointly create a pedestrian-friendly environment. The second aspect of the analysis is to specify the crash party at-fault, which provides evidence about whether pedestrians, bicyclists or drivers are more likely to be involved in severe crashes and to identify the contributing factors that affect the fault of a specific road user group. An extensive investigation of the available information regarding the crash (i.e., issued citations, actions/circumstances that may have played a role in the crash occurrence, and crash scenario completed by the police officer) are considered. The goal is to recognize and measure the factors affecting a specific party at-fault. This provides information that is vital for proactive crisis management: to decrease and to prevent future crashes. As a part of the result, a guideline is proposed to assign the party at-fault through crash data fields and narratives. Statistical methods such as the extreme gradient boosting (XGboost) decision tree and the multinomial logit (MNL) model are used. Appealing conclusions have been found and suggestions are made for law enforcement, education, and roadway management to enhance the safety countermeasures. The third aspect is to evaluate the enhancements of crash report form for its effectiveness of reporting VRU involved motor vehicle crashes. One of the State of Wisconsin projects aiming to develop crash report forms was to redesign the old MV4000 crash report form into the new DT4000 crash report form. The modification was applied from January 1, 2017, statewide. The reason behind this switch is to resolve some matters with the old MV4000 crash report form, including insufficient reporting in roadway-related data fields, lack of data fields describing driver distraction, intersection type, no specification of the exact traffic barrier, insufficient information regarding safety equipment usage by motorists and non-motorists, unclear information about the crash location, and inadequate evidence concerning non-motorists actions, circumstances and condition prior to the crash. Hence, the new DT4000 crash form modified some existing data fields incorporated new crash elements and more detailed attributes. The modified and new data fields, their associated attribute values have been thoroughly studied and the effectiveness of improved data collection in terms of a better understanding of factors associated with and contributing to VRU crashes has been comprehensively evaluated. The evaluation has confirmed that the DT4000 crash form provided more specific, details, and useful about the crash circumstances