Investing for impact. An exploration of impact investments in rural Ghana

This thesis offers an exploration of impact and social investments for agricultural development projects in Sub-Saharan Africa. It does so through the case of SustAgric-Africa (SAA), a social enterprise that aims to lift smallholder farmers out of poverty through the promotion of sustainable farming and operates with capital provided by a variety of investors who are committed to pairing financial returns with social and environmental outcomes. The thesis sets off to answer the following research questions: What is the moral dimension that emerges in finance with the establishment of environmental and social criteria? What kind of arrangements do social and impact investments give origin to? Is it possible to talk about a ‘spirit of the gift’ in such arrangements? What happens when abstract and globalizing ideas around ‘impact’ hit the ground? Drawing from the STS and Actor-Network Theory, I look at the formation engendered by social and impact investments in terms of a socio-technical arrangement, and look at the movements of “objects” between the main actors in terms of circuits. In these processes ideas about ‘value’ and ‘values’ articulate in complex ways in the interplay of gift, debt and credit in the relationships among the three main categories of involved actors: investors, SAA, and the farmers. In the case of SAA, I contend that the ways abstract and globalising ideas about ‘impact’ hit the ground produce uncertain results and contribute to the reproduction of inequalities and unequal wealth distribution and accumulation, deepening ongoing processes of financialization. However, my ethnography also reveals how actors depicted as beneficiaries of impact and social policies and resources, far from being passive recipients of policies and resources, actually question and appropriate them, potentially unsettling the whole arrangement and the moral and ethical claims underpinning it.

Failure detection for transport processes on networks

Diffusion on networks is a convenient framework to describe transport systems of different nature (from biological transport systems to urban mobility). The mathematical models are based on master equations that describe the diffusion processes by means of the weighted Laplacian matrix that connects the nodes. The link weight represent the coupling strength between the nodes. In this thesis we cope with the problem of localizing a single-edge failure that occurs in the network. An edge failure is meant to be as a sudden decrease of its transport capacities. An incomplete observation of the dynamical state of the network is available. An optimal clustering procedure based on the correlation properties among the node states is proposed. The network dimensionality is then reduced introducing representative nodes for each cluster, whose dynamical state is observed. We check the efficiency of the failure localization for our clustering method in comparison with more traditional techniques, using different graph configurations.

The Barcelonnettes: an example of network-entrepreneurs in XIX century Mexico. An explanation based on a theory of bounded rational choice with social embeddedness

A theory of network-entrepreneurs or "spin-off system" is presented in this paper for the creation of firms based on the community’s social governance. It is argued that firm’s capacity for accumulation depends on the presence of employees belonging to the same social/ethnic group with expectations of "inheriting" the firm and becoming entrepreneurs once they have been selected for their merits and loyalty towards their patrons. Such accumulation is possible because of the credibility of the patrons’ promises of supporting newcomers due to high social cohesion and specific social norms prevailing in the community. This theory is exemplified through the case of the Barcelonnettes, a group of immigrants from the Alps in the South of France (Provence) who came to Mexico in the XIX Century.

Clustering algorithms for anti-money laundering using graph theory and social network analysis

HEMOLIA (a project under European community’s 7th framework programme) is a new generation Anti-Money Laundering (AML) intelligent multi-agent alert and investigation system which in addition to the traditional financial data makes extensive use of modern society’s huge telecom data source, thereby opening up a new dimension of capabilities to all Money Laundering fighters (FIUs, LEAs) and Financial Institutes (Banks, Insurance Companies, etc.). This Master-Thesis project is done at AIA, one of the partners for the HEMOLIA project in Barcelona. The objective of this thesis is to find the clusters in a network drawn by using the financial data. An extensive literature survey has been carried out and several standard algorithms related to networks have been studied and implemented. The clustering problem is a NP-hard problem and several algorithms like K-Means and Hierarchical clustering are being implemented for studying several problems relating to sociology, evolution, anthropology etc. However, these algorithms have certain drawbacks which make them very difficult to implement. The thesis suggests (a) a possible improvement to the K-Means algorithm, (b) a novel approach to the clustering problem using the Genetic Algorithms and (c) a new algorithm for finding the cluster of a node using the Genetic Algorithm.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Adaptive Pointing Theory (APT) Artificial Neural Network

The choice value and the testing process against the vigilance parameter, characteristic of ART Neural Network, are merged. Only, a single unique test is required to determine if a committed category node can represent the current input or not. Advantages of APT over ART are: 1-Avoid testing every committed category node before deciding to train a committed category node or a new node must be committed, 2-The vigilance parameter is fixed during training, and 3-The choice value parameter is eliminated.

Environmental Wireless Sensor Network Deployment in Food Industry: from Theory to Practice

The difficulty behind Wireless Sensor Network deployments in industrial environments not only resides in the number of nodes or the communication protocols but also in the real location of the sensor nodes and the parameters to be monitored. Sensor soiling, high humidity and unreachable locations, among others, make real deployments a very difficult task to plan. Even though it is possible to find myriad approaches for floor planners and deployment tools in the state of the art, most of these problems are very difficult to model and foresee before actually deploying the network in the final scenario. This work shows two real deployments in food factories and how their problems are found and overcome.

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

Spectral properties of the tandem Jackson network, seen as a quasi-birth-and-death process

Quasi-birth-and-death (QBD) processes with infinite “phase spaces” can exhibit unusual and interesting behavior. One of the simplest examples of such a process is the two-node tandem Jackson network, with the “phase” giving the state of the first queue and the “level” giving the state of the second queue. In this paper, we undertake an extensive analysis of the properties of this QBD. In particular, we investigate the spectral properties of Neuts’s R-matrix and show that the decay rate of the stationary distribution of the “level” process is not always equal to the convergence norm of R. In fact, we show that we can obtain any decay rate from a certain range by controlling only the transition structure at level zero, which is independent of R. We also consider the sequence of tandem queues that is constructed by restricting the waiting room of the first queue to some finite capacity, and then allowing this capacity to increase to infinity. We show that the decay rates for the finite truncations converge to a value, which is not necessarily the decay rate in the infinite waiting room case. Finally, we show that the probability that the process hits level n before level 0 given that it starts in level 1 decays at a rate which is not necessarily the same as the decay rate for the stationary distribution.

A beam-forming network for a circular switched-beam phased array antenna

This paper reports on the design and development of a dividing/phasing network for a compact switched-beam array antenna for Land-vehicle mobile satellite communications, The device is formed by a switched radial divider/combiner and 1-bit phase shifters and generates a sufficient number of beams for the proper satellite tracking.

A new diffusion and flow theory for activated carbon from low pressure to capillary condensation range

In this paper, we develop a theory for diffusion and flow of pure sub-critical adsorbates in microporous activated carbon over a wide range of pressure, ranging from very low to high pressure, where capillary condensation is occurring. This theory does not require any fitting parameter. The only information needed for the prediction is the complete pore size distribution of activated carbon. The various interesting behaviors of permeability versus loading are observed such as the maximum permeability at high loading (occurred at about 0.8-0.9 relative pressure). The theory is tested with diffusion and flow of benzene through a commercial activated carbon, and the agreement is found to be very good in the light that there is no fitting parameter in the model. (C) 2001 Elsevier Science B.V. All rights reserved.

Use of liquid phase adsorption for characterizing pore network connectivity in activated carbon

A simple percolation theory-based method for determination of the pore network connectivity using liquid phase adsorption isotherm data combined with a density functional theory (DFT)-based pore size distribution is presented in this article. The liquid phase adsorption experiments have been performed using eight different esters as adsorbates and microporous-mesoporous activated carbons Filtrasorb-400, Norit ROW 0.8 and Norit ROX 0.8 as adsorbents. The density functional theory (DFT)-based pore size distributions of the carbons were obtained using DFT analysis of argon adsorption data. The mean micropore network coordination numbers, Z, of the carbons were determined based on DR characteristic plots and fitted saturation capacities using percolation theory. Based on this method, the critical molecular sizes of the model compounds used in this study were also obtained. The incorporation of percolation concepts in the prediction of multicomponent adsorption equilibria is also investigated, and found to improve the performance of the ideal adsorbed solution theory (IAST) model for the large molecules utilized in this study. (C) 2002 Elsevier Science B.V. All rights reserved.