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Extremely large antenna arrays (ELAAs) are being proposed for sixth-generation (6G) systems as an evolution of massive multiple-input, multiple-output (mMIMO). Besides having a large number of antennas, ELAAs can be physically large, which makes communication in the radiative near-field very likely. This means the plane wave approximation does not hold and channel models that accurately capture the propagation effects in the near-field are required. Recent works have highlighted the beam-focusing effect available in the near-field region, where antenna arrays can generate beams not only limited in width but also limited in depth. This work leverages a precise channel model for ELAAs intending to explore the inherent physical layer security (PLS) features in the radiative near-field. For that purpose, it is presented a study on the power ratio between a legitimate and a malicious user, considering the effects of the field region, relative distance, and beamforming direction. By taking advantage of that analysis, it is shown that both the jamming rejection and the secrecy rate can be substantially improved when communicating in the near field, demonstrating that beam-focusing constitutes an interesting PLS technique for upcoming 6G communications.

This work aims to describe the pedagogical proposal for training professionals in Early Childhood Education in the municipal public network of Palmeira dos Índios, Alagoas, Brazil, identifying challenges and possibilities. Based on the legal bases and on studies on child development, a pedagogical notebook was systematized, which presents the entire proposal for continuing education for the year 2022, and the observation of the didactic-pedagogical practice of professionals was developed, through visits schools and constant meetings. Data analysis was limited to observations carried out in schools and listening to coordinators and teachers of this level of education during two meetings. The results reveal that the production of significant experiences for the development and learning of children, pointing to the understanding of continuous training that allows facing challenges and opening possibilities for transformation. The importance of continuing investigations on training proposals is also highlighted so that the relationship between what is planned and what is done, in terms of pedagogical action, becomes something real and effective.

We examined mediating effects of the pillars of well-being (i.e., positive emotion, engagement, relationships, meaning, and accomplishment) on the relationship between PTSD symptoms and college life adjustment for student veterans. We recruited 205 student veterans. Mediation analysis was conducted to test whether the pillars of well-being mediate the relationship between PTSD and college life adjustment. The results showed that positive emotion and accomplishment had mediating effects on the relationship between PTSD symptoms and college adjustment.

The wide spreading of electric mobility is imposing the application of new technologies of hardware and software for supporting new operation paradigms, not only in the perspective of improving smart grids, but also each time more in the perspective of boosting the preponderance of smart homes. Concretely, nowadays it is clear that the emergent technologiesfor smart homes, targeting power quality and a more efficient power management, are also altering the conventional electrical power grids toward smart grids. In this panorama, by relating the strategic pillars of electric mobility and smart homes, this paper has as main objective the characterization of the role of the electric vehicle (EV) in smart homes, presenting a broad assessment based on the state of the art, as well as creating a link with upcoming advanced operations. From the smart home point of view, since it should be equipped to deal with the requirements of the EV, the assessment of both on-board and off-board EV battery charging systems are considered along the paper. Furthermore, the presence of technologies such as energy storage systems, renewable energy sources and dc electrical appliances in smart homes towards sustainability is also reflected in this paper, but framed from the point of view of an off-board EV battery charging system, since it is more convenient for enabling the interface of these mentioned technologies. Looking the paper as a whole, a relevant contribution is the discussion of future operations for the EV in smart homes, also envisioning the opportunity for ac, dc, or hybrid power grids. Illustrative and strategic results are shown, covering all these features with specific details, not only based on numerical simulations, but also based on experimental results, which were obtained with a proof-of-concept laboratory prototype.

Esta pesquisa tem por objetivo apresentar reflexos sociais que foram acarretados pelo fechamento das escolas por conta da pandemia da COVID-19. Para responder a tal cenário, se dispôs de pesquisa bibliográfica e documental a partir de jornais, legislação e documentos oficiais, sites e artigos científicos. E como resultado percebe-se reflexos potenciais nos alunos, professores e pais, tendo em vista que problemáticas derivadas desse fechamento como: a interrupção do aprendizado; alimentação escolar; adaptação dos professores a nova realidade tecnológica; pais sem preparação para as atividades em ensino remoto e em casa; desafio na melhoria e manutenção do ensino remoto; lacunas de assistência às crianças; aumento na taxa de evasão escolar; isolamento social das crianças; e, os desafios para validar e medir o aprendizado, tendem a afetar de forma direta e indiretamente esses indivíduos, principalmente aqueles que vivem em situação de vulnerabilidade e injustiça social. Isto posto, respostas a tais reflexos sociais devem advir das relações que entremeiam o Estado-escola-aluno-família de modo que o apoio mútuo poderá dar o suporte necessário a dirimir as consequências acarretadas pelo fechamento das escolas.

In this research work we propose a new approach to estimate the number of passengers in a public transportation and determinate the users’ route path based on a passive approach without user intervention. The method is based on the probe requests of users mobile device through the collected data in wireless access point. This data is manipulated to extract the information about the numbers of users with mobile devices and track their route path and time. This data can be manipulated to extract useful knowledge related with users’ habits at public transportation and extract user mobility patterns.

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO2 emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO2 emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO2 emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO2 emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO2 emissions, Brazil would be the best option.

Due to the demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the produced data. While these new technologies require high levels of availability, a reduction in the cost and environmental impact is also expected. The present paper proposes a power balancing algorithm (power load distribution algorithm-depth (PLDA-D)) to optimize the energy distribution of data center electrical infrastructures. The PLDA-D is based on the Bellman and Ford–Fulkerson flow algorithms that analyze energy-flow models (EFM). EFM computes the power efficiency, sustainability and cost metrics of data center infrastructures. To demonstrate the applicability of the proposed strategy, we present a case study that analyzes four power infrastructures. The results obtained show about a 3.8% reduction in sustainability impact and operational costs.

Researchers have found that a firm's external environment plays a profound role in influencing corporate entrepreneurship.

CONTEXT AND OBJECTIVE: Knowledge of the profile of allergen sensitization among children is important for planning preventive measures. The objective of this study was to assess the prevalence and profile of sensitization to inhaled allergens and food among children and adolescents in an outpatient population in the city of Palmas. DESIGN AND SETTING: Cross-sectional study at outpatient clinics in Palmas, Tocantins, Brazil. METHODS: Ninety-four patients aged 1-15 years who were attending two pediatric outpatient clinics were selected between September and November 2008. All of the subjects underwent clinical interviews and skin prick tests. RESULTS: A positive skin prick test was observed in 76.6% of the participants (72.3% for inhalants and 28.9% for food allergens). The most frequent allergens were Dermatophagoides pteronyssinus (34%), cat epithelium (28.7%), dog epithelium (21.3%), Dermatophagoides farinae (19.1%), Blomia tropicalis (18.1%), cow's milk (9.6%) and grasses (9.6%). A positive skin prick test correlated with a history of atopic disease (odds ratio, OR = 5.833; P = 0.002), a family history of atopic disease (OR = 8.400; P < 0.001), maternal asthma (OR = 8.077; P = 0.048), pet exposure (OR = 3.600; P = 0.012) and cesarean delivery (OR = 3.367; P = 0.019). CONCLUSION: Dermatophagoides pteronyssinus was the most frequent aeroallergen and cow’s milk was the most prevalent food allergen. There was a positive correlation between a positive skin prick test and several factors, such as a family history of atopic disease, maternal asthma, pet exposure and cesarean delivery.

Introduction: Capsule endoscopy (CE) is a minimally invasive exam suitable of panendoscopic evaluation of the gastrointestinal (GI) tract. Nevertheless, CE is time-consuming with suboptimal diagnostic yield in the upper GI tract. Convolutional neural networks (CNN) are human brain architecture-based models suitable for image analysis. However, there is no study about their role in capsule panendoscopy. Methods: Our group developed an artificial intelligence (AI) model for panendoscopic automatic detection of pleomorphic lesions (namely vascular lesions, protuberant lesions, hematic residues, ulcers, and erosions). 355,110 images (6,977 esophageal, 12,918 gastric, 258,443 small bowel, 76,772 colonic) from eight different CE and colon CE (CCE) devices were divided into a training and validation dataset in a patient split design. The model classification was compared to three CE experts’ classification. The model’s performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the precision-recall curve. Results: The binary esophagus CNN had a diagnostic accuracy for pleomorphic lesions of 83.6%. The binary gastric CNN identified pleomorphic lesions with a 96.6% accuracy. The undenary small bowel CNN distinguished pleomorphic lesions with different hemorrhagic potentials with 97.6% accuracy. The trinary colonic CNN (detection and differentiation of normal mucosa, pleomorphic lesions, and hematic residues) had 94.9% global accuracy. Discussion/Conclusion: We developed the first AI model for panendoscopic automatic detection of pleomorphic lesions in both CE and CCE from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term success. We present a set of hypotheses about the influence of feedback information and systems thinking facilitation on mental models and management performance. We explore, under controlled conditions, the role of mental models in terms of structure and behaviour. A test based on a simulation experiment with a system dynamics model was performed. Three out of the four hypotheses were confirmed. Causal diagramming positively influences mental model structure similarity, mental model structure similarity positively influences mental model behaviour similarity, and mental model behaviour similarity positively influences the quality of the decision.