Territories of scarcity and creativity: a critical view of informal settlements and emerging tactics under conditions of scarcity in Nairobi, Kenya and Quito, Ecuador

This thesis explores the processes through which scarcity is constructed in informal settlements and how conditions emerging within its limits gives way to particular socio-spatial phenomena and influence the emergence of self-organisation and creative strategies from a non-expert perspective. At the same time, this thesis deconstructs these emerging tactics (reactive and transformative) in a diagrammatic way to generate a critical study of their potential for socio-spatial change that goes beyond the everyday survival. Most people associate scarcity with “not having enough” of something, most usually of a material nature. In contrast, this paper is based on the premise that scarcity is a constructed condition, therefore exploring it beyond its immediate manifestation and illustrating its discursive, distributive and socio-material components. In this line, the research uses Assemblage Theory as both an approach and a tool for analysis. This approach allows the research to depart from everyday narratives of the residents, and gradually evolve into a multi-scalar, non-linear reading of scarcity, by following leads into different realms and unpacking a series of routine events to uncover their connections to wider processes and particular elements affecting the settlement and the city as a whole. For this purpose, the research is based on a qualitative, flexible and multi-sited methodology, using different case studies as testing grounds. Collected data stems from a 11-months ethnographic fieldwork in informal settlements in Ecuador and Kenya, analysing the socio-spatial practices and strategies deployed by the different actors producing the built environment and arising from everyday and latent experiences of scarcity. The thesis examines the multi-scalar nature of these strategies, including self-building and management tactics, the mobilisation of grassroots organisations, the innovative ways of collaborating deployed by different coalitions and the reformulation of urban development policies. As outcomes of the research, the thesis will show illustrative diagrams that allow a better understanding of, firstly, the construction of scarcity in the built environment beyond its immediate manifestation and secondly, the way that emerging tactics a) improve existing conditions of scarcity, b) reinforce the status quo or c) contribute to the worsening of the original condition. Therefore, this thesis aims to offer lessons with both practical and theoretical considerations, by firstly, giving an insight into the complexity and transcalar nature of the construction of scarcity in informal settlements; secondly, by illustrating how acute conditions related to scarcity gives birth to a plethora of particular phenomena shaping the territory, social relationships and processes; and thirdly, by identifying specific characteristics within the informal that might allow for new readings of the city and possibilities for socio-spatial change under conditions of scarcity.

A Single-Frame Super-Resolution Innovative Approach

Super-resolution refers to the process of obtaining a high resolution image from one or more low resolution images. In this work, we present a novel method for the super-resolution problem for the limited case, where only one image of low resolution is given as an input. The proposed method is based on statistical learning for inferring the high frequencies regions which helps to distinguish a high resolution image from a low resolution one. These inferences are obtained from the correlation between regions of low and high resolution that come exclusively from the image to be super-resolved, in term of small neighborhoods. The Markov random fields are used as a model to capture the local statistics of high and low resolution data when they are analyzed at different scales and resolutions. Experimental results show the viability of the method.

Elements of a methodology to assess the alignment of core-values in collaborative networks

Collaborative networks are typically formed by heterogeneous and autonomous entities, and thus it is natural that each member has its own set of core-values. Since these values somehow drive the behaviour of the involved entities, the ability to quickly identify partners with compatible or common core-values represents an important element for the success of collaborative networks. However, tools to assess or measure the level of alignment of core-values are lacking. Since the concept of 'alignment' in this context is still ill-defined and shows a multifaceted nature, three perspectives are discussed. The first one uses a causal maps approach in order to capture, structure, and represent the influence relationships among core-values. This representation provides the basis to measure the alignment in terms of the structural similarity and influence among value systems. The second perspective considers the compatibility and incompatibility among core-values in order to define the alignment level. Under this perspective we propose a fuzzy inference system to estimate the alignment level, since this approach allows dealing with variables that are vaguely defined, and whose inter-relationships are difficult to define. Another advantage provided by this method is the possibility to incorporate expert human judgment in the definition of the alignment level. The last perspective uses a belief Bayesian network method, and was selected in order to assess the alignment level based on members' past behaviour. An example of application is presented where the details of each method are discussed.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

O fim de um ciclo? A coconstrução da articulação curricular entre o 1.º e o 2.º Ciclos do Ensino Básico

Dissertação Apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação - Especialidade Supervisão em Educação

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Zonal prices analysis supported by a data mining based methodology

A methodology based on data mining techniques to support the analysis of zonal prices in real transmission networks is proposed in this paper. The mentioned methodology uses clustering algorithms to group the buses in typical classes that include a set of buses with similar LMP values. Two different clustering algorithms have been used to determine the LMP clusters: the two-step and K-means algorithms. In order to evaluate the quality of the partition as well as the best performance algorithm adequacy measurements indices are used. The paper includes a case study using a Locational Marginal Prices (LMP) data base from the California ISO (CAISO) in order to identify zonal prices.

Optimal methodology for renewable energy dispatching in islanded operation

The management of energy resources for islanded operation is of crucial importance for the successful use of renewable energy sources. A Virtual Power Producer (VPP) can optimally operate the resources taking into account the maintenance, operation and load control considering all the involved cost. This paper presents the methodology approach to formulate and solve the problem of determining the optimal resource allocation applied to a real case study in Budapest Tech’s. The problem is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The problem has also been solved by Evolutionary Particle Swarm Optimization (EPSO). The obtained results are presented and compared.

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature.

A probabilistic methodology for distributed generation location in isolated electrical service area

Distributed generation unlike centralized electrical generation aims to generate electrical energy on small scale as near as possible to load centers, interchanging electric power with the network. This work presents a probabilistic methodology conceived to assist the electric system planning engineers in the selection of the distributed generation location, taking into account the hourly load changes or the daily load cycle. The hourly load centers, for each of the different hourly load scenarios, are calculated deterministically. These location points, properly weighted according to their load magnitude, are used to calculate the best fit probability distribution. This distribution is used to determine the maximum likelihood perimeter of the area where each source distributed generation point should preferably be located by the planning engineers. This takes into account, for example, the availability and the cost of the land lots, which are factors of special relevance in urban areas, as well as several obstacles important for the final selection of the candidates of the distributed generation points. The proposed methodology has been applied to a real case, assuming three different bivariate probability distributions: the Gaussian distribution, a bivariate version of Freund’s exponential distribution and the Weibull probability distribution. The methodology algorithm has been programmed in MATLAB. Results are presented and discussed for the application of the methodology to a realistic case and demonstrate the ability of the proposed methodology for efficiently handling the determination of the best location of the distributed generation and their corresponding distribution networks.

Empreendedorismo Social, o papel das IPSS: estudo de caso

O empreendedorismo social tem vindo, nas últimas décadas, a ser denominado como um novo paradigma determinante para o funcionamento da economia, em grande parte, porque a economia social tornou-se basilar na sociedade, por um lado, pelo crescimento exponencial da exclusão social, elevado desemprego e envelhecimento da população e, por outro, devido às dificuldades orçamentais dos governos. O empreendedorismo social, utilizado por Instituições Particulares de Solidariedade Social sem fins lucrativos, procura resolver problemas sociais de forma inovadora e sustentável, com a finalidade de dar resposta aos grandes desafios sociais da atualidade, através da ação social na prevenção e no apoio nas diversas situações de fragilidade, exclusão ou carência humana, promovendo a inclusão, a integração social e o desenvolvimento local. O objetivo fundamental do presente trabalho, pretende verificar até que ponto as IPSS podem ser definidas como empreendedores sociais, através da prestação de serviços, nas variadas áreas à população local, de forma a alcançar o valor social. Neste estudo enveredou-se pela metodologia qualitativa, utilizando o método do estudo de caso único, recorrendo ao questionário como instrumento de recolha de dados numa instituição particular de solidariedade social do concelho da Maia. Deste estudo foi possível concluir a IPSS tem uma proximidade às populações, através das diversas valências vocacionadas para a resolução de problemas sociais emergentes, promovendo a inclusão a integração social, e alcançar o valor social. Assim, consideramos a IPSS estudada como sendo parte integrante e promotora do empreendedorismo social.

The importance of qualitative methodology in management empirical research

Purpose: The aim of this paper is to highlight the importance of qualitative research within the scope of management scientific studies, referring to its philosophy, nature and instruments. It also confronts it with quantitative methodology, approaching its differences as well as its complementariness and synergies, with the purpose of explaining, from a more analytic point of view, the relevance of qualitative methodology in the course of an authentic and real research despite its complexity. Design/methodology/approach: Regardless of its broad application, one may attest the scarcity literature that focuses on qualitative research applied to the management scientific area, as opposed to the large amount that refers to quantitative research. Findings: The paper shows the influence that qualitative research has on management scientific research. Originality/value:. Qualitative research assumes an important role within qualitative research by allowing for the study and analysis of certain types of phenomena that occur inside organisations, and in respect of which quantitative studies cannot provide an answer.