Técnicas de previsão em sistemas de informação e comunicação: aplicação às redes móveis celulares

The rapid proliferation of information and communication technologies around the world has increased the need for careful planning of infrastructures. This is true in the situation like mobile telecommunication networks, data centers, web servers, etc. By developing and implementing intelligent solutions to improve the measurement and planning, telecommunication service operators can anticipate problems and minimize cost. This dissertation focuses on forecasting mobile telecommunication capacity networks to maximize existing resources and avoid problems related to performance or capacity, such as bottlenecks and latencies. In the case of mobile telecommunication networks, the data forecast can be used to predict traffic growth, contributing to better network planning. Therefore, a poorly designed forecast may lead to mobile network operators not being prepared for possible network problems, such as reaching the limit of an RNC, which may become expensive in terms of OPEX. Thus, it is fundamental to study different quantitative methods to forecast the trend and the volume of traffic in the RNCs. This paper analyzes some basic forecast methods used in scenarios where the data do not present behavioral complexity and some models such as ARIMA and Holt Winters used in data with behavioral complexity (presence of trend and seasonality). It also evaluates the accuracy of the forecasts determined from the application of these models.A rápida proliferação de tecnologias de informação e comunicação em todo o mundo aumentou a necessidade de um planeamento cuidadoso das infraestruturas. Este facto também é verdade em redes de telecomunicações móveis, data centers, servidores web, etc. Ao desenvolver e implementar soluções inteligentes para melhorar o dimensionamento e planeamento, os operadores de serviços de telecomunicações podem antecipar problemas e minimizar custos. Esta dissertação foca-se na previsão de capacidade de redes de telecomunicações móveis de forma a maximizar recursos existentes e evitar problemas relacionados ao desempenho ou capacidade, como “bottlenecks” e latências. No caso das redes de telecomunicações móveis, a previsão de dados pode ser aplicada para prever o crescimento de tráfego, contribuindo para um melhor planeamento da rede. Portanto, uma previsão mal projetada pode levar os operadores de redes móveis a não estarem preparados para possíveis problemas de rede, como alcance do limite de capacidade de um RNC, isto pode se tornar muito caro em termos de OPEX. Assim, é fundamental estudar diferentes métodos quantitativos para prever a tendência e o volume de tráfego nos RNCs. Neste trabalho são analisados alguns métodos básicos de previsão utilizados em cenários em que os dados não apresentam complexidade comportamental e alguns modelos como ARIMA e Holt Winters utilizados em dados com complexidade comportamental (presença de tendência e sazonalidade). Também se avalia a exatidão das previsões que resultam da aplicação destes modelos.Mestrado em Engenharia Informátic

Time Use by Small Farm Families in Southwest Virginia: An Approach for the Inclusion of Household in Farming Systems Research and Extension

In this paper we first present an overview of Farming Systems Research and Extension (FSR/E), contrasting the importance placed on the household in the FSRIE conceptual framework with the lack of explicit attention given it in FSR/E methodology. An initial 47-farm survey suggested that female household members play key roles in intensive crop production and off-farm income generation. A time allocation study during May-October, 1982, investigated the roles of female and male household heads and working age children in a representative 10-farm subsample. Results indicated greater women\u27s contributions to livestock-related production activities than originally hypothesized and reduced time spent by families with female off-farm employment in household production activities. Based on these results, an agriculture-family resources para-professional technician team is suggested as an alternate extension model to be tested for U.S. and developing country limited resource farms

Clinical consequences of switching from olanzapine to risperidone and vice versa in outpatients with schizophrenia: 36-month results from the worldwide schizophrenia outpatients health outcomes (W-SOHO) study

BACKGROUND: With many atypical antipsychotics now available in the market, it has become a common clinical practice to switch between atypical agents as a means of achieving the best clinical outcomes. This study aimed to examine the impact of switching from olanzapine to risperidone and vice versa on clinical status and tolerability outcomes in outpatients with schizophrenia in a naturalistic setting. METHODS: W-SOHO was a 3-year observational study that involved over 17,000 outpatients with schizophrenia from 37 countries worldwide. The present post hoc study focused on the subgroup of patients who started taking olanzapine at baseline and subsequently made the first switch to risperidone (n=162) and vice versa (n=136). Clinical status was assessed at the visit when the first switch was made (i.e. before switching) and after switching. Logistic regression models examined the impact of medication switch on tolerability outcomes, and linear regression models assessed the association between medication switch and change in the Clinical Global Impression-Schizophrenia (CGI-SCH) overall score or change in weight. In addition, Kaplan-Meier survival curves and Cox-proportional hazards models were used to analyze the time to medication switch as well as time to relapse (symptom worsening as assessed by the CGI-SCH scale or hospitalization). RESULTS: 48% and 39% of patients switching to olanzapine and risperidone, respectively, remained on the medication without further switches (p=0.019). Patients switching to olanzapine were significantly less likely to experience relapse (hazard ratio: 3.43, 95% CI: 1.43, 8.26), extrapyramidal symptoms (odds ratio [OR]: 4.02, 95% CI: 1.49, 10.89) and amenorrhea/galactorrhea (OR: 8.99, 95% CI: 2.30, 35.13). No significant difference in weight change was, however, found between the two groups. While the CGI-SCH overall score improved in both groups after switching, there was a significantly greater change in those who switched to olanzapine (difference of 0.29 points, p=0.013). CONCLUSION: Our study showed that patients who switched from risperidone to olanzapine were likely to experience a more favorable treatment course than those who switched from olanzapine to risperidone. Given the nature of observational study design and small sample size, additional studies are warranted