"Water users must be efficient producers". Women's access to and use of land in Chókwè irrigation scheme, Mozambique

The irrigation scheme Eduardo Mondlane, situated in Chókwè District - in the Southern part of the Gaza province and within the Limpopo River Basin - is the largest in the country, covering approximately 30,000 hectares of land. Built by the Portuguese colonial administration in the 1950s to exploit the agricultural potential of the area through cash-cropping, after Independence it became one of Frelimo’s flagship projects aiming at the “socialization of the countryside” and at agricultural economic development through the creation of a state farm and of several cooperatives. The failure of Frelimo’s economic reforms, several infrastructural constraints and local farmers resistance to collective forms of production led to scheme to a state of severe degradation aggravated by the floods of the year 2000. A project of technical rehabilitation initiated after the floods is currently accompanied by a strong “efficiency” discourse from the managing institution that strongly opposes the use of irrigated land for subsistence agriculture, historically a major livelihood strategy for smallfarmers, particularly for women. In fact, the area has been characterized, since the end of the XIX century, by a stable pattern of male migration towards South African mines, that has resulted in an a steady increase of women-headed households (both de jure and de facto). The relationship between land reform, agricultural development, poverty alleviation and gender equality in Southern Africa is long debated in academic literature. Within this debate, the role of agricultural activities in irrigation schemes is particularly interesting considering that, in a drought-prone area, having access to water for irrigation means increased possibilities of improving food and livelihood security, and income levels. In the case of Chókwè, local governments institutions are endorsing the development of commercial agriculture through initiatives such as partnerships with international cooperation agencies or joint-ventures with private investors. While these business models can sometimes lead to positive outcomes in terms of poverty alleviation, it is important to recognize that decentralization and neoliberal reforms occur in the context of financial and political crisis of the State that lacks the resources to efficiently manage infrastructures such as irrigation systems. This kind of institutional and economic reforms risk accelerating processes of social and economic marginalisation, including landlessness, in particular for poor rural women that mainly use irrigated land for subsistence production. The study combines an analysis of the historical and geographical context with the study of relevant literature and original fieldwork. Fieldwork was conducted between February and June 2007 (where I mainly collected secondary data, maps and statistics and conducted preliminary visit to Chókwè) and from October 2007 to March 2008. Fieldwork methodology was qualitative and used semi-structured interviews with central and local Government officials, technical experts of the irrigation scheme, civil society organisations, international NGOs, rural extensionists, and water users from the irrigation scheme, in particular those women smallfarmers members of local farmers’ associations. Thanks to the collaboration with the Union of Farmers’ Associations of Chókwè, she has been able to participate to members’ meeting, to education and training activities addressed to women farmers members of the Union and to organize a group discussion. In Chókwè irrigation scheme, women account for the 32% of water users of the familiar sector (comprising plot-holders with less than 5 hectares of land) and for just 5% of the private sector. If one considers farmers’ associations of the familiar sector (a legacy of Frelimo’s cooperatives), women are 84% of total members. However, the security given to them by the land title that they have acquired through occupation is severely endangered by the use that they make of land, that is considered as “non efficient” by the irrigation scheme authority. Due to a reduced access to marketing possibilities and to inputs, training, information and credit women, in actual fact, risk to see their right to access land and water revoked because they are not able to sustain the increasing cost of the water fee. The myth of the “efficient producer” does not take into consideration the characteristics of inequality and gender discrimination of the neo-liberal market. Expecting small-farmers, and in particular women, to be able to compete in the globalized agricultural market seems unrealistic, and can perpetuate unequal gendered access to resources such as land and water.

A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis

An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers. In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward. This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data. The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.

Application Platforms for the Internet of Things: Theory, Architecture, Protocols, Data Formats, and Privacy

The Internet of Things (IoT) is the next industrial revolution: we will interact naturally with real and virtual devices as a key part of our daily life. This technology shift is expected to be greater than the Web and Mobile combined. As extremely different technologies are needed to build connected devices, the Internet of Things field is a junction between electronics, telecommunications and software engineering. Internet of Things application development happens in silos, often using proprietary and closed communication protocols. There is the common belief that only if we can solve the interoperability problem we can have a real Internet of Things. After a deep analysis of the IoT protocols, we identified a set of primitives for IoT applications. We argue that each IoT protocol can be expressed in term of those primitives, thus solving the interoperability problem at the application protocol level. Moreover, the primitives are network and transport independent and make no assumption in that regard. This dissertation presents our implementation of an IoT platform: the Ponte project. Privacy issues follows the rise of the Internet of Things: it is clear that the IoT must ensure resilience to attacks, data authentication, access control and client privacy. We argue that it is not possible to solve the privacy issue without solving the interoperability problem: enforcing privacy rules implies the need to limit and filter the data delivery process. However, filtering data require knowledge of how the format and the semantics of the data: after an analysis of the possible data formats and representations for the IoT, we identify JSON-LD and the Semantic Web as the best solution for IoT applications. Then, this dissertation present our approach to increase the throughput of filtering semantic data by a factor of ten.

Opportunistic Data Gathering and Dissemination in Urban Scenarios

In the era of the Internet of Everything, a user with a handheld or wearable device equipped with sensing capability has become a producer as well as a consumer of information and services. The more powerful these devices get, the more likely it is that they will generate and share content locally, leading to the presence of distributed information sources and the diminishing role of centralized servers. As of current practice, we rely on infrastructure acting as an intermediary, providing access to the data. However, infrastructure-based connectivity might not always be available or the best alternative. Moreover, it is often the case where the data and the processes acting upon them are of local scopus. Answers to a query about a nearby object, an information source, a process, an experience, an ability, etc. could be answered locally without reliance on infrastructure-based platforms. The data might have temporal validity limited to or bounded to a geographical area and/or the social context where the user is immersed in. In this envisioned scenario users could interact locally without the need for a central authority, hence, the claim of an infrastructure-less, provider-less platform. The data is owned by the users and consulted locally as opposed to the current approach of making them available globally and stay on forever. From a technical viewpoint, this network resembles a Delay/Disruption Tolerant Network where consumers and producers might be spatially and temporally decoupled exchanging information with each other in an adhoc fashion. To this end, we propose some novel data gathering and dissemination strategies for use in urban-wide environments which do not rely on strict infrastructure mediation. While preserving the general aspects of our study and without loss of generality, we focus our attention toward practical applicative scenarios which help us capture the characteristics of opportunistic communication networks.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Progettazione e implementazione di una piattaforma per la localizzazione indoor basata su tecniche di radio fingerprinting e data-crowdsourcing

Questa tesi ha come scopo principale l'analisi delle diverse tecnologie di localizzazione in ambito indoor, analizzando in particolare l'utilizzo del Wifi RSS Fingerprinting. La tecnica del Wifi RSS Fingerprinting è una tecnica per la localizzazione all'interno di ambienti chiusi, che consiste nella definizione di un 'impronta'(fingerprint) in un punto preciso dell'ambiente(definito reference point), andando a inserire in un database i valori di potenza del segnale ricevuto(RSS) da ogni access point rilevato all'interno di quel determinato reference point. Per l'implementazione di questa tecnica è stato sviluppato un applicativo con un architettura client-server. Il client è stato sviluppato in ambiente Android, realizzando una applicazione per la gestione della fase di salvataggio di nuovi fingerprint e per la fase di localizzazione della posizione corrente, tramite l'utilizzo dei vari fingerprint precedentemente inseriti all'interno del DB. Il server, sviluppato in Node.js(framework Javascript), gestirà le diverse richieste ricevute dal client tramite delle chiamate AJAX, prelevando le informazioni richieste direttamente dal database. All'interno delle applicativo sono stati implementati diversi algoritmi per la localizzazione indoor, in modo da poter verificare l'applicabilità di questo sistema in un ambito reale. Questi algoritmi sono stati in seguito testati per valutare l'accuratezza e la precisione di ciascuno, andando ad individuare gli algoritmi migliori da utilizzare in base a scenari diversi.

Progettazione ed implementazione di una piattaforma per il monitoraggio della copertura internet mobile attraverso un approccio di data-crowdsourcing

L'obiettivo di questa Tesi di laurea è di creare un applicativo che informi gli utenti sulle reti circostanti, in particolare sulla qualità del segnale, sulle zone in cui la rete mobile è carente e sui punti d'accesso aperti. Per l'implementazione del servizio, è stato adottato un modello di business, il Crowdsourcing, per raccogliere informazioni sui sistemi di connessione, affinché qualsiasi utente dotato di Smartphone possa aggiungere elementi al dataset.

Modelli e Strumenti di Classifciazione Automatica per il Controllo dell'Accesso su Piattaforme Big Data

Dall'analisi dei big data si possono trarre degli enormi benefici in svariati ambiti applicativi. Uno dei fattori principali che contribuisce alla ricchezza dei big data, consiste nell'uso non previsto a priori di dati immagazzinati in precedenza, anche in congiunzione con altri dataset eterogenei: questo permette di trovare correlazioni significative e inaspettate tra i dati. Proprio per questo, il Valore, che il dato potenzialmente porta con sè, stimola le organizzazioni a raccogliere e immagazzinare sempre più dati e a ricercare approcci innovativi e originali per effettuare analisi su di essi. L’uso fortemente innovativo che viene fatto dei big data in questo senso e i requisiti tecnologici richiesti per gestirli hanno aperto importanti problematiche in materia di sicurezza e privacy, tali da rendere inadeguati o difficilmente gestibili, gli strumenti di sicurezza utilizzati finora nei sistemi tradizionali. Con questo lavoro di tesi si intende analizzare molteplici aspetti della sicurezza in ambito big data e offrire un possibile approccio alla sicurezza dei dati. In primo luogo, la tesi si occupa di comprendere quali sono le principali minacce introdotte dai big data in ambito di privacy, valutando la fattibilità delle contromisure presenti all’attuale stato dell’arte. Tra queste anche il controllo dell’accesso ha riscontrato notevoli sfide causate dalle necessità richieste dai big data: questo elaborato analizza pregi e difetti del controllo dell’accesso basato su attributi (ABAC), un modello attualmente oggetto di discussione nel dibattito inerente sicurezza e privacy nei big data. Per rendere attuabile ABAC in un contesto big data, risulta necessario l’ausilio di un supporto per assegnare gli attributi di visibilità alle informazioni da proteggere. L’obiettivo di questa tesi consiste nel valutare fattibilità, caratteristiche significative e limiti del machine learning come possibile approccio di utilizzo.

Percutaneous access of the epicardial space for mapping ventricular and supraventricular arrhythmias in patients with and without prior cardiac surgery

There is a paucity of data on the success rates of achieving percutaneous epicardial access in different groups of patients.

Differences in access and patient outcomes across antiretroviral treatment clinics in the Free State province: a prospective cohort study

Objective. To assess differences in access to antiretroviral treatment (ART) and patient outcomes across public sector treatment facilities in the Free State province, South Africa. Design. Prospective cohort study with retrospective database linkage. We analysed data on patients enrolled in the treatment programme across 36 facilities between May 2004 and December 2007, and assessed percentage initiating ART and percentage dead at 1 year after enrolment. Multivariable logistic regression was used to estimate associations of facility-level and patient-level characteristics with both mortality and treatment status. Results. Of 44 866 patients enrolled, 15 219 initiated treatment within 1 year; 8 778 died within 1 year, 7 286 before accessing ART. Outcomes at 1 year varied greatly across facilities and more variability was explained by facility-level factors than by patient-level factors. The odds of starting treatment within 1 year improved over calendar time. Patients enrolled in facilities with treatment initiation available on site had higher odds of starting treatment and lower odds of death at 1 year compared with those enrolled in facilities that did not offer treatment initiation. Patients were less likely to start treatment if they were male, severely immunosuppressed (CD4 count ≤50 cells/μl), or underweight (<50 kg). Men were also more likely to die in the first year after enrolment. Conclusions. Although increasing numbers of patients started ART between 2004 and 2007, many patients died before accessing ART. Patient outcomes could be improved by decentralisation of treatment services, fast-tracking the most immunodeficient patients and improving access, especially for men.

Examining the Role of Vocational Rehabilitation on Access to Care and Public Health Outcomes for People Living with HIV/AIDS

The purpose of this study is to examine the role of vocational rehabilitation services in contributing to the goals of the National HIV/AIDS strategy. Three key research questions are addressed: (a) What is the relationship among factors associated with the use of vocational rehabilitation services for people living with HIV/AIDS? (b) Are the factors associated with use of vocational rehabilitation also associated with access to health care, supplemental employment services and reduced risk of HIV transmission? And (c) What unique role does use of vocational rehabilitation services play in access to health care and HIV prevention? Survey research methods were used to collect data from a broad sample of volunteer respondents who represented diverse racial (37% Black, 37% White, 18% Latino, 7% other), gender (65% male, 34% female, 1% transgender) and sexual orientation (48% heterosexual, 44% gay, 8% bisexual) backgrounds. The fit of the final structural equation model was good (root mean square error of approximation = .055, Comparative Fit Index=.953, Tucker Lewis Index=.945). Standardized effects with bootstrap confidence intervals are reported. Overall, the findings support the hypothesis that vocational rehabilitation services can play an important role in health and prevention strategies outlined in the National HIV/AIDS strategy.

Agricultural Policymaking: Interest Groups and Access to Governmental Officials

Conservation agriculture that focuses on soil recovery is both economically and environmentally sustainable. This lies in contrast with many of the current agricultural practices, which push for high production, which, in turn lead to over-depletion of the soil. Agricultural interest groups play a role in crafting farming policies with governmental officials. Therefore, my study examined three interest group types agribusinesses, farmer organizations, and environmental NGOs that seek to influence agricultural policy, specifically focusing on the federal farm bill, due to its large impact throughout the nation. The research in which data wasgathered through subject interviews, a literature review, and databases found that access to governmental officials affects the amount of influence a group can have. Access is contingent upon: 1) the number of networks (social, professional, and political), 2) amount of money spent through campaign contributions and lobbying expenditures, and 3) extent of business enterprises and subsidiaries. The evidence shows that there is a correlation between these variables and the extent of access. My research concludes that agribusiness interest groups have the most access to government officials, and thus have the greatest influence on agricultural policies. Because agribusinesses support subsidies of commodity-crops this indirectly impacts conservation agriculture, as the two programs compete in a zero-sum game for funding in the farm bills.

Evaluating the Impact of the Number of Access Points in Mobile Robots Localization Using Artificial Neural Networks

Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile node in an indoor space.

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect.