Generation and analysis of a mouse model for conditional inactivation of CTLA-4

Imperial Users onl

The Effect of the Buffer Size in QoS for Multimedia and bursty Traffic: When an Upgrade Becomes a Downgrade

This work presents an analysis of the buffer features of an access router, especially the size, the impact on delay and the packet loss rate. In particular, we study how these features can affect the Quality of Service (QoS) of multimedia applications when generating traffic bursts in local networks. First, we show how in a typical SME (Small and Medium Enterprise) network in which several multimedia flows (VoIP, videoconferencing and video surveillance) share access, the upgrade of the bandwidth of the internal network may cause the appearance of a significant amount of packet loss caused by buffer overflow. Secondly, the study shows that the bursty nature of the traffic in some applications traffic (video surveillance) may impair their QoS and that of other services (VoIP and videoconferencing), especially when a certain number of bursts overlap. Various tests have been developed with the aim of characterizing the problems that may appear when network capacity is increased in these scenarios. In some cases, especially when applications generating bursty traffic are present, increasing the network speed may lead to a deterioration in the quality. It has been found that the cause of this quality degradation is buffer overflow, which depends on the bandwidth relationship between the access and the internal networks. Besides, it has been necessary to describe the packet loss distribution by means of a histogram since, although most of the communications present good QoS results, a few of them have worse outcomes. Finally, in order to complete the study we present the MOS results for VoIP calculated from the delay and packet loss rate

Proposta de intervenção: Diagnóstico, prevenção e controle da leishmaniose tegumentar americana no programa de saúde da familia (PSF) Tijuco em São João Del Rei, Minas Gerais

A Leishmaniose é considerada, atualmente, um problema de saúde pública. No Brasil, a Leishmaniose Tegumentar Americana (LTA) é uma doença com diversidade de agentes, de reservatórios e de vetores que apresenta diferentes padrões de transmissão e um conhecimento, ainda limitado, sobre alguns aspectos, o que a torna de difícil controle. Este trabalho objetiva elaborar uma proposta de intervenção visando o diagnóstico, prevenção e controle da LTA no Programa de Saúde da Família (PSF) Tijuco, São João del-Rei, Minas Gerais. Para isso são propostas estratégias para o diagnóstico de pacientes infestados, visando tratamento, reabilitação dos indivíduos, além de ações educativas para a prevenção e promoção da saúde de famílias e comunidades no PSF Tijuco em São João del-Rei, Minas Gerais. Para alcançar esses objetivos, será realizado o diagnóstico da situação social e de saúde da população atendida, além de revisão de literatura dos principais temas relacionados à LTA e elaboração de um plano de intervenção com o intuito de encontrar os casos infestados e melhorar as condições de saúde da população alvo por meio da educação em saúd

Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals

T This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCADTM). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate

Alternative Network Deployments: Taxonomy, Characterization, Technologies, and Architectures

This document presents a taxonomy of a set of "Alternative Network Deployments" that emerged in the last decade with the aim of bringing Internet connectivity to people or providing a local communication infrastructure to serve various complementary needs and objectives. They employ architectures and topologies different from those of mainstream networks and rely on alternative governance and business models. The document also surveys the technologies deployed in these networks, and their differing architectural characteristics, including a set of definitions and shared properties. The classification considers models such as Community Networks, Wireless Internet Service Providers (WISPs), networks owned by individuals but leased out to network operators who use them as a low-cost medium to reach the underserved population, networks that provide connectivity by sharing wireless resources of the users, and rural utility cooperatives