A Heuristic Approach for the Design of UAV-Based Disaster Relief in Optical Metro Networks

We propose a novel algorithm to dimension the backup elements in an optical metro network, by considering the adoption of Unmanned Aerial Vehicles (UAVs) and wireless interfaces to realize backup wireless links. Our key idea is to efficiently find the set of node pairs that have to be connected by means of multi-hop UAV-based wireless links, which are selected based on the simulation of multiple disaster events. Results, obtained over a set of meaningful scenarios, demonstrate that our solution can greatly reduce the total installation costs compared to a naive approach, which is instead solely tailored to the restoration of the disrupted links in a given disaster scenario

MGI :: Intelligent management module for optimization From the thermal comfort of the user in smart home

The incentive to integrate energy efficiency in electrical equipment has increased during last years. A portion of this growth is a result of incentives and the implementation of standards that help in inspection and regulation of minimum specifications for efficiency in electric energy consumption by equipments. In addition, with development of technology, new means of intelligent management are proposed to help optimize energy consumption. This study presents the specification and development of an intelligent module, to be integrated in Smart Consumption Management Architecture (SmartCoM), which involves collection and analysis of meteorological data (temperature and humidity) via the communication interface characteristic of the architecture, from Internet of Things (IoT), for the intelligent management of an air conditioner in order to optimize energy consumption, without losing thermal comfort of resident users. The proposed methodology proved to be efficient in reducing energy consumption compared to the conventional model. Experiments were carried out to estimate the energy consumption of an air conditioner during 2017. The experiments consisted in comparing results of the intelligent management module (MGI) with the conventional model, to show the efficiency of the proposed module, which achieved a general reduction of 24.5% for the night period and 21.1% for the daytime, within the standards of thermal comfort established by national and internationalCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorO estímulo a integração da eficiência energética em equipamentos elétricos vem aumentando durantes os últimos anos. Uma parcela desse crescimento é resultado dos incentivos e das implementações de normas que ajudam na fiscalização e na regulamentação de especificações mínimas para eficiência no consumo de energia elétrica pelos equipamentos. Além disso, com o desenvolvimento da tecnologia, novas formas de gerenciamento inteligente são propostas para ajudar a otimizar o consumo energético. Este estudo apresenta a especificação e o desenvolvimento do módulo inteligente, a ser integrado na arquitetura Smart Consumption Management Architecture (SmartCoM), que envolve a coleta e análise de dados meteorológicos (temperatura e umidade) via interface de comunicação característico da arquitetura, a partir de soluções de Internet das Coisas (IoT), para o gerenciamento inteligente de um condicionador de ar com o objetivo de otimizar o consumo de energia, sem perder o conforto térmico dos usuários residentes. A metodologia proposta mostrou-se eficiente na redução do consumo de energia comparado ao modelo convencional. Foram realizados experimentos com o intuito de estimar o consumo de energia do condicionador de ar para o ano de 2017. Os experimentos consistiram em comparar os resultados do módulo de gerenciamento inteligente (MGI) com o modelo convencional, e assim, mostrar a eficiência do módulo proposto que conseguiu, de forma geral, uma redução média anual de 24,5% para o período noturno e 21,1 % para o diurno ficando dentro dos padrões de conforto térmico estabelecidas pelas normas nacionais e internacionais

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency

SmartCoM: Smart Consumption Management Architecture for Providing a User-Friendly Smart Home based on Metering and Computational Intelligence

Abstract With advances in information technology for health and wellness, Smart Home-based solution providers using Internet of Things (IoT) technologies, have increased in importance and become accepted as an alternative means of saving energy when based on Home Energy Management Systems (HEMS). This paper defines a modern architecture (SmartCoM), which is implemented to monitor and manage residential dwellings by using IoT technologies. This involves setting out the parameters that can make interoperability possible between measurement and management, and the layers of data communication, which are the features necessary for the hardware required for monitoring and measurement. In addition, an interface is defined by a middleware layer to integrate the management of external installations and the visualization of data by means of a cloud service. The SmartCoM end-to-end architecture is defined in detail from the standpoint of the consumer and optimization strategies are employed for both the end customer and the utility. The main advantages of using SmartCoM were confirmed by the numerical results obtained from the proposed architecture. This paper ends by showing the current position of SmartCoM as well as suggesting further stages for this line of research

Neurological Findings in Children without Congenital Microcephaly Exposed to Zika Virus in Utero: A Case Series Study

The Zika virus can induce a disruptive sequence in the fetal brain and is manifested mainly by microcephaly. Knowledge gaps still exist as to whether the virus can cause minor disorders that are perceived later on during the first years of life in children who are exposed but are asymptomatic at birth. In this case series, we describe the outcomes related to neurodevelopment through the neurological assessment of 26 non-microcephalic children who had intrauterine exposure to Zika virus. Children were submitted for neurological examinations and Bayley Scales-III (cognition, language, and motor performance). The majority (65.4%) obtained satisfactory performance in neurodevelopment. The most impaired domain was language, with 30.7% impairment. Severe neurological disorders occurred in five children (19.2%) and these were spastic hemiparesis, epilepsy associated with congenital macrocephaly (Zika and human immunodeficiency virus), two cases of autism (one exposed to Zika and Toxoplasma gondii) and progressive sensorineural hearing loss (GJB2 mutation). We concluded that non-microcephalic children with intrauterine exposure to Zika virus, in their majority, had achieved satisfactory performance in all neurodevelopmental domains. One third of the cases had some impairment, but the predominant group had mild alterations, with low occurrence of moderate to severe disorders, similar to other studies in Brazil

CLAs in Animal Source Foods: Healthy Benefits for Consumers

Conjugated linoleic acid (CLA) is a group of polyunsaturated fatty acids that exist as positional and stereo-isomers of octadecadienoate (18:2). Among these isomers, the most studied two isomers are cis 9, trans 11-CLA and trans 10, cis 12- CLA due to their biological effects. CLA can be naturally synthesized in the rumen of ruminant animals by bacteria Butyrivibrio fibrisolvens via the Δ-9- desaturase of trans 11 octadecanoic acid pathway. The major dietary sources of CLA are represented by meat and milk from ruminant animals. Although references to CLA can be traced back to the 1950s, current interest in the health benefits of CLA started in the late 1980s, after it was identified as the anticarcinogenic component present in fried ground beef. Since then, an extensive literature has documented the anticarcinogenic effects of CLA. In addition, there is some evidence that CLA is also anti-atherosclerotic, has beneficial effects on type 2 diabetes, and may play a key role in helping to regulate body fat. The fact that the richest natural sources of CLA, meat and dairy products, are consumed by people worldwide has very interesting implications for public health