Efficient Scheduling Algorithms for Wireless Resource Allocation and Virtualization in Wireless Networks

The continuing growth in demand for better mobile broadband experiences has motivated rapid development of radio-access technologies to support high data rates and improve quality of service (QoS) and quality of experience (QoE) for mobile users. However, the modern radio-access technologies pose new challenges to mobile network operators (MNO) and wireless device designers such as reducing the total cost of ownership while supporting high data throughput per user, and extending battery life-per-charge of the mobile devices. In this thesis, a variety of optimization techniques aimed at providing innovative solutions for such challenges are explored. The thesis is divided into two parts. In the first part, the challenge of extending battery life-per-charge is addressed. Optimal and suboptimal power-efficient schedulers that minimize the total transmit power and meet the QoS requirements of the users are presented. The second outlines the benefits and challenges of deploying wireless resource virtualization (WRV) concept as a promising solution for satisfying the growing demand for mobile data and reducing capital and operational costs. First, a WRV framework is proposed for single cell zone that is able to centralize and share the spectrum resources between multiple MNOs. Consequently, several WRV frameworks are proposed, which virtualize the spectrum resource of the entire network for cloud radio access network (C-RAN)- one of the front runners for the next generation network architecture. The main contributions of this thesis are in designing optimal and suboptimal solutions for the aforementioned challenges. In most cases, the optimal solutions suffer from high complexity, and therefore low-complexity suboptimal solutions are provided for practical systems. The optimal solutions are used as benchmarks for evaluating the suboptimal solutions. The results prove that the proposed solutions effectively contribute in addressing the challenges caused by the demand for high data rates and power transmission in mobile networks

Kinetics of surfactin production by Bacillus subtilis in a 5 L stirred-tank bioreactor

A kinetic model of bacterial growth and metabolite production can adequately explain the trends and interaction of important parameters in a fermentation process. Production of surfactin by two bacterial strains, namely, Bacillus subtilis MSH1 and Bacillus subtilis ATCC 21322, in a 5 L bioreactor was investigated using Cooper’s media with 4% (v/v) glucose. The present kinetic study was carried out in order to determine the correlation between microbial cell growth, surfactin production and glucose consumption. Batch fermentation was performed by cultivation of each selected strain in a bioreactor at 30°C for 55 h. The experimental results showed production of surfactin in the culture medium after 5 and 10 h of incubation for B. subtilis ATCC 21332 and B. subtilis MSH1, respectively, at which the bacterial cells were at an early stage of the log phase. The maximum concentration of surfactin (Pmax) achieved by B. subtilis MSH1 and B. subtilis ATCC 21332 was 226.17 and 447.26 mg/L, respectively. The kinetic study of bacterial cell growth of both strains indicated that B. subtilis MSH1 had a specific growth rate (μmax) of 0.224 h-1 and attained a maximum biomass concentration (Xmax) as high as 2.90 g/L after 28 h of fermentation, while B. subtilis ATCC 21332, with μmax of 0.087 h-1, attained an Xmax of 2.62 g/L after 45 h of incubation. B. subtilis MSH1 showed higher growth kinetics, thus exhibited higher values of μmax and Xmax compared with B. subtilis ATCC 21332 under identical fermentation conditions. The Pmax achieved by B. subtilis ATCC 21332 was 447.26 mg/L, two times higher than that achieved by B. subtilis MSH1 (226.17 mg/L). The results obtained provide kinetics information including values of Pmax, μmax and Xmax for better understanding of interactions of bacterial cell growth and glucose consumption towards surfactin production by a commercial strain of B. subtilis ATCC 21332 and a local isolate of B. subtilis MSH1

Associação entre depressão e diabetes mellitus e relação com o autocuidado: uma revisão sistemática

A diabetes mellitus é uma doença caracterizada por um estado de hiperglicemia persistente causada por defeitos na secreção e/ou ação da insulina. Segundo os últimos dados coletados pela Federação Internacional de Diabetes (IDF) em 2019, cerca de 463 milhões de pessoas no mundo vivem com a doença. Por outro lado, a depressão atinge mais de 264 milhões de pessoas. Nesse contexto, é frequente a associação de doenças crônicas como a diabetes com a depressão. O paciente com diabetes tem o seu cotidiano drasticamente alterado devido às necessidades de autocuidado e mudanças de estilo de vida que a doença exige, além das possíveis complicações crônicas que interferem ainda mais na qualidade de vida desse grupo levando a repercussões biopsicossociais. Avaliar através de uma revisão sistemática da literatura a prevalência de sintomas depressivos em pacientes com diabetes mellitus e a relação com o autocuidado. Realizou-se uma revisão sistemática da literatura através das plataformas SCIELO, MEDLINE e GOOGLE SCHOLAR utilizando os descritores em ciências da saúde (DeCS): Depressão e Diabetes Mellitus. Foram incluídos artigos em língua portuguesa e inglesa, com data de publicação a partir do ano de 2000, que relacionassem depressão e diabetes mellitus tipo 1 e 2(DM1 e DM2). Foram excluídos os estudos em menores de 18 anos. Ao todo foram incluídos no estudo 13 artigos. A prevalência de sintomas depressivos nos estudos transversais que tratavam apenas de DM1 variou de 34,9% a 53,6%, nos que tratavam apenas de DM2 de 9,2% a 30 %. Em um estudo em pacientes com diabetes tipo 1 e 2 e pé ulcerado a prevalência de depressão foi de 64%, outro estudo que relacionava a polineuropatia distal diabética (PNDD) e depressão apontou que pacientes diabéticos com PNDD apresentavam maior sintomatologia depressiva do que aqueles sem PNDD. Não é possível estabelecer uma relação de causa e efeito entre depressão e diabetes, apenas que ambas podem ocasionar interferências mútuas. Foi apontado em alguns estudos que a depressão esteve associada a um pior controle glicêmico, baixa adesão ao tratamento e autocuidado inferior. Em contrapartida, em um único estudo transversal que relacionava depressão e DM2 em idosos a presença de sintomas depressivos não apresentou interferência sobre o autocuidado. A associação entre diabetes e depressão, principalmente nos pacientes com complicações crônicas, é concreta. Pacientes nessa condição podem apresentar prejuízos na qualidade de vida e no controle da diabetes. Nesse sentido, é necessária especial atenção à saúde para os portadores de DM com sintomas depressivos no intuito de fornecer avaliação psiquiátrica adequada. Além disso, é necessário implantar políticas públicas visando a prevenção da depressão nos seus 3 níveis nos grupos mais vulneráveis para diminuir o impacto dessas doenças e evitar as complicações mútuas

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security