Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

A matter of time: Phases of customer co-creation community participation and their impact on customer behavior

Despite the growing relevance of co-creating customer communities only little scientific evidence is available on their impact on transactional behavior of participants. Previous research has mostly used self-reported data or distinguished only between during and pre-community phases obtaining mixed results. However, the author proposes that co-creating community activity takes place in five distinguishable phases and changes in transactional behavior are limited to certain phases. Using 33 months of transactional data of a Dutch online auction provider a study was conducted covering all five phases of the community co-creation process from community planning over community set-up, co-development and co-testing to post-launch. The overall results indicate mixed effects of community participation on the different transactional variables during the co-creation process. Community participation had positive effects on auctions listing behavior at the community set-up, co-development and post-launch phases, whereby the number of auctions listed peaked during the community set-up phase. These results suggest that the impact on transactional behavior differs between co-creation phases and different psychological mechanism limited to certain phases might trigger the respective changes.

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles.

Dynamic behavior of sylvatic yellow fever in Brazil (1954-2008)

INTRODUCTION: Sylvatic yellow fever (SYF) is enzootic in Brazil, causing periodic outbreaks in humans living near forest borders or in rural areas. In this study, the cycling patterns of this arbovirosis were analyzed. METHODS: Spectral Fourier analysis was used to capture the periodicity patterns of SYF in time series. RESULTS: SYF outbreaks have not increased in frequency, only in the number of cases. There are two dominant cycles in SYF outbreaks, a seven year cycle for the central-western region and a 14 year cycle for the northern region. Most of the variance was concentrated in the central-western region and dominated the entire endemic region. CONCLUSIONS: The seven year cycle is predominant in the endemic region of the disease due the greater contribution of variance in the central-western region; however, it was possible identify a 14 cycle that governs SYF outbreaks in the northern region. No periodicities were identified for the remaining geographical regions.

Molecular characterization of Trypanosoma cruzi Mexican strains and their behavior in the mouse experimental model

INTRODUCTION: For a long time, the importance of Chagas disease in Mexico, where many regarded it as an exotic malady, was questioned. Considering the great genetic diversity among isolates of Trypanosoma cruzi, the importance of this biological characterization, and the paucity of information on the clinical and biological aspects of Chagas disease in Mexico, this study aimed to identify the molecular and biological characterization of Trypanosoma cruzi isolates from different endemic areas of this country, especially of the State of Jalisco. METHODS: Eight Mexican Trypanosoma cruzi strains were biologically and genetically characterized (PCR specific for Trypanosoma cruzi, multiplex-PCR, amplification of space no transcript of the genes of the mini-exon, amplification of polymorphic regions of the mini-exon, classification by amplification of intergenic regions of the spliced leader genes, RAPD - (random amplified polymorphic DNA). RESULTS: Two profiles of parasitaemia were observed, patent (peak parasitaemia of 4.6×10(6) to 10(7) parasites/mL) and subpatent. In addition, all isolates were able to infect 100% of the animals. The isolates mainly displayed tropism for striated (cardiac and skeletal) muscle. PCR amplification of the mini-exon gene classified the eight strains as TcI. The RAPD technique revealed intraspecies variation among isolates, distinguishing strains isolated from humans and triatomines and according to geographic origin. CONCLUSIONS: The Mexican T. cruzi strains are myotrophic and belong to group TcI.

The effect of ownership concentration on management behavior in Belgian banks: A case study

Double Degree. A Work Project, presented as part of the requirements for the Award of a Master’s Degree in Finance from NOVA – School of Business and Economics and a Masters Degree in Management from Louvain School of Management

Online consumer behavior: The tablet shopper – A new kind of customer?

Tablet computers are on the rise and are increasingly superseding stationary computers in terms of modern online shopping. This paper therefore aims on understanding how tablet and website characteristics might impact online consumer behavior. The collected data resulted from focus groups and in-depth interviews conducted with a technology affine audience. It has shown that tablets indeed may have shifted shopping behavior from utilitarian towards rather recreational attributes. Discussions also revealed high customer expectations towards visited shopping websites and prevailing deficits. Thus, harnessing the concept of experiential flow is crucial. Compelling experiences are required to increase the duration of stay and consequently the likelihood of increased purchases.