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In an ever-growing technological world, there is a lot of value in being able to quickly analyze data and make decisions in order to maintain a successful business. Imaginary Cloud is a growing Software Development and UX/UI company, and with such growth, there is an increased amount of data to be stored and processed. Therefore, it is of great importance that each department can easily access their respective data, to increase the speed of their decision-making process. In order to better analyze its performance, the company intends on developing a dash-board system for internal use that allows the visualization of relevant data dynamically, in-cluding key performance indicators. The purpose of this project will be to work alongside Imaginary Cloud to develop a tool that extracts information from multiple sources as efficiently as possible and create a central-ized database to hold the extracted data, expected to be ever expanding. The database must be optimized for the retrieval of data in real data, so that it can be queried by the dashboard system (not to be developed in this project) at the user’s request. In the end, the existence of this tool should have a major impact on how easy each de-partment can access and interpret data, allowing for more informed and faster decision mak-ing, and increasing the overall company’s efficiency. Num mundo em constante desenvolvimento tecnológico, é muito importante ser capaz de analisar e tomar decisões rapidamente para que se consiga manter um negócio bem-sucedido. A Imaginary Cloud é uma empresa de desenvolvimento de Software e Design de UX/UI em crescimento, e com o seu aumento de escala, aumenta também a quantidade de dados a serem armazenados e processados. Como tal, é de alta importância que cada departamento consiga facilmente aceder aos seus respetivos dados, para que se aumente a velocidade do processo de tomada de decisões. Para melhorar a análise do seu desempenho, a empresa pretende desenvolver um sis-tema de dashboards para uso interno que permita visualizar dados relevantes de forma dinâ-mica, incluindo indicadores chave de desempenho. O objetivo deste projeto é colaborar com a Imaginary Cloud para desenvolver uma fer-ramenta que extraia informação de múltiplas fontes e criar uma base de dados centralizada que conterá os dados extraídos, que se espera que aumentem continuamente. A base de dados deve estar otimizada para a requisição de dados em tempo real, para que possa ser consultada pelo sistema de dashboards (que não será desenvolvido neste projeto) a pedido do utilizador. No final, a existência desta ferramenta deve ter um grande impacto na facilidade com que cada departamento consegue aceder e interpretar os seus dados, permitindo uma tomada de decisões mais rápida e informada, melhorando a eficiência geral da empresa.

Alzheimer’s disease is one of the most common neurodegenerative diseases in the western population. The incidence of this disease increases with age. Rising life expectancy and the resulting increase in the ratio of elderly in the population are likely to exacerbate socioeconomic problems. Alzheimer’s disease is a multifactorial disease. In addition to amyloidogenic processing leading to plaques, and tau pathology, but also other molecular causes such as oxidative stress or inflammation play a crucial role. We summarize the molecular mechanisms leading to Alzheimer’s disease and which potential interventions are known to interfere with these mechanisms, focusing on nutritional approaches and physical activity but also the beneficial effects of cognition-oriented treatments with a focus on language and communication. Interestingly, recent findings also suggest a causal link between oral conditions, such as periodontitis or edentulism, and Alzheimer’s disease, raising the question of whether dental intervention in Alzheimer’s patients can be beneficial as well. Unfortunately, all previous single-domain interventions have been shown to have limited benefit to patients. However, the latest studies indicate that combining these efforts into multidomain approaches may have increased preventive or therapeutic potential. Therefore, as another emphasis in this review, we provide an overview of current literature dealing with studies combining the above-mentioned approaches and discuss potential advantages compared to monotherapies. Considering current literature and intervention options, we also propose a multidomain interdisciplinary approach for the treatment of Alzheimer’s disease patients that synergistically links the individual approaches. In conclusion, this review highlights the need to combine different approaches in an interdisciplinary manner, to address the future challenges of Alzheimer’s disease.

The accelerated sediment supply from agricultural soils to riverine and lacustrine environments leads to negative off-site consequences. In particular, the sediment connectivity from agricultural land to surface waters is strongly affected by landscape patchiness and the linear structures that separate field parcels (e.g. roads, tracks, hedges, and grass buffer strips). Understanding the interactions between these structures and sediment transfer is therefore crucial for minimising off-site erosion impacts. Although soil erosion models can be used to understand lateral sediment transport patterns, model-based connectivity assessments are hindered by the uncertainty in model structures and input data. Specifically, the representation of linear landscape features in numerical soil redistribution models is often compromised by the spatial resolution of the input data and the quality of the process descriptions. Here we adapted the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM) using high-resolution spatial data (2 m × 2 m) to analyse the sediment connectivity in a very patchy mesoscale catchment (73 km2) of the Swiss Plateau. We used a global sensitivity analysis to explore model structural assumptions about how linear landscape features (dis)connect the sediment cascade, which allowed us to investigate the uncertainty in the model structure. Furthermore, we compared model simulations of hillslope sediment yields from five subcatchments to tributary sediment loads, which were calculated with long-term water discharge and suspended sediment measurements. The sensitivity analysis revealed that the assumptions about how the road network (dis)connects the sediment transfer from field blocks to water courses had a much higher impact on modelled sediment yields than the uncertainty in model parameters. Moreover, model simulations showed a higher agreement with tributary sediment loads when the road network was assumed to directly connect sediments from hillslopes to water courses. Our results ultimately illustrate how a high-density road network combined with an effective drainage system increases sediment connectivity from hillslopes to surface waters in agricultural landscapes. This further highlights the importance of considering linear landscape features and model structural uncertainty in soil erosion and sediment connectivity research.

The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML tasks such as speech processing and computational vision, with a performance that scales with the amount of available data. The lack of large datasets inhibits the flourish of deep learning applications in wireless communications. This paper presents a methodology that combines a vehicle traffic simulator with a ray-tracing simulator, to generate channel realizations representing 5G scenarios with mobility of both transceivers and objects. The paper then describes a specific dataset for investigating beam-selection techniques on vehicle-to-infrastructure using millimeter waves. Experiments using deep learning in classification, regression and reinforcement learning problems illustrate the use of datasets generated with the proposed methodology.

Biochar (BCH) is a solid product, rich in carbon, obtained by heating biomass under controlled conditions of oxygenation, in a process known as pyrolysis. Its benefits are associated with improvements to the physical, chemical and biological properties of soil. Furthermore, BCH can affect the growth of phytopathogenic microorganisms. Despite research advances in this area, there is still a lack of information on the effect of BCH concentration on different soilborne microorganisms. This work evaluated the direct effect of sewage sludge biochar (SSB) on the in vitro growth of different soilborne fungi. Eight phytopathogens [Fusarium oxysporum, F. oxysporum f. sp. lycopersici (CEN 1456), Macrophomina phaseolina (429), Sclerotinia sclerotiorum, S. sclerotiorum (CEN 1147), Sclerotium rolfsii (CEN 216), Sclerotium cepivorum (CEN 1357), Rhizoctonia solani] were evaluated. Additionally, a biological control agent [Trichoderma afroharzianum (T-22)] was also studied. Microorganisms were subjected to growth in PDA (Potato-Dextrose-Agar) culture medium enriched with SSB. Biochars, produced at 300 and 500 ºC, were applied at different doses: 0.0 (control), 0.2, 0.4, 0.6, 0.8 and 1.0 (g of SSB/100 ml of PDA). Biochars showed inhibitory effects on the mycelial growth of the different fungi evaluated. It was observed that there is a certain specificity of biochar concentration that must be evaluated for the control of phytopathogens. In most cases, the 0.4 and 0.6% concentrations had a greater inhibitory effect on phytopathogens and did not affect the biological control agent. Therefore, SSB proved to be a promising product for the control of different soilborne phytopathogens.

BACKGROUND: We verify the prevalence of low zinc levels among critically ill patients infected by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the intensive care unit (ICU) who required invasive mechanical ventilation, as well as its association with severity of acute respiratory distress syndrome (ARDS). METHODS: This is an observational study composed of patients admitted to the ICU. Demographics, anthropometric data for calculating body mass index (BMI), and laboratory data were obtained at admission: blood count, ferritin, arterial blood gas, serum zinc levels, and C-reactive protein. Also, arterial oxygen tension (PaO2 ) divided by fractional inspired oxygen (FiO2 ) was calculated by the first arterial blood gas after intubation. A diagnosis of severe ARDS was determined if the PaO2 /FiO2 ratio was ≤100 mm Hg. Low zinc levels were established if zinc levels were

Background & aim Verify the prevalence of hypovitaminosis D and obesity in elderly patients infected by new coronavirus. The patients developed severe symptoms and were admitted in intensive care unit (ICU) to receive invasive ventilation due to diagnosis of acute respiratory distress syndrome (ARDS). Methods A cross-sectional descriptive study composed of elderly (age ≥ 60 years) admitted to the ICU. Were collected demographic (sex, age), anthropometric data, presence of comorbidities (hypertension, diabetes, heart disease, lung, neurological and oncological diseases), severity score in ICU (SAPS III), PaO2/FiO2 ratio, analysis of C-reactive protein (CRP) and serum dosage of 25-hydroxy vitamin D (25 OHD) in the first day of hospitalization to identify elderly with hypovitaminosis D (low values

Background. Chronic inflammation, as defined by persistent immune activation, is associated with adverse clinical outcomes. People who inject drugs (PWID) have evidence of persistent immune activation. Here, in a cohort of PWID with or without hepatitis C virus (HCV) infection, we sought to dissect out the contribution of chronic HCV infection (common in PWID) from the effects of injection drug use itself. Methods. Four groups of study volunteers were recruited: group 1 comprised active PWID; group 2, individuals who ceased injecting drugs 1–2 months before recruitment; group 3, individuals who ceased injecting drugs 3–4 months before recruitment; and group 4, healthy volunteers. Soluble and cell-associated markers of immune activation were quantified. Results. HCV-viremic PWID have elevated levels of immune activation when compared to healthy volunteers. Cessation of injection drug use results in a decline in immune activation in the absence of HCV viremia, while HCV-viremic individuals who previously were PWID continue to harbor elevated levels of immune activation, as defined by increased levels of soluble CD14 and tumor necrosis factor α and by the presence of CD38+HLA-DR+ CD4+ and CD8+ T cells. Conclusions. Immune activation, a well-defined surrogate of poor clinical outcome that is elevated in PWID, can regress to normal levels in former injection drug users who are HCV aviremic. Therefore, enhanced harm-reduction efforts should incorporate aggressive treatment of HCV infection. Clinical Trials Registration. {"type":"clinical-trial","attrs":{"text":"NCT01831284","term_id":"NCT01831284"}}NCT01831284.

Introduction The proper use of the position of the arm and wrist while typing may reduce muscle overload and prevent musculoskeletal disorders.Objective To evaluate the electromyographic activity of upper trapezius and wrist extensor muscles during two typewriting conditions.Materials and methods : Six healthy females (Xage = 42 years,SD= 10), (Xheight = 1.65m, SD = 0.05) and (Xweight = 71kg, SD = 16) participated in this study. The task was performed with a newly developed arm support and without the support. A perceived exertion scale was used with all subjects. An ANOVA with repeated measures was used to verify differences in perceived exertion and root mean square (RMS).Results There were no statistically significant differences for the RMS between the typewriting tasks. The condition without arm support presented a significantly greater mean velocity and amount of words typed (P= 0.02; P= 0.03) and there was a significant difference in perceived exertion during the condition without arm support (P= 0.03). Electromyographic activity did not present differences.Conclusion The muscle electrical activity was not altered regardless the arm support and occurred the improvement of the perceived exertion after 10 minutes of typing without support. Long-term studies are needed. Introdução O uso adequado da posição do braço e do punho durante a digitação pode reduzir a sobrecarga muscular e prevenir doenças musculoesqueléticas.Objetivo Avaliar a atividade eletromiográfica dos músculos trapézio superior e extensores do punho durante duas condições de digitação.Materiais e métodos Seis mulheres sadias (X idade = 42 anos, DP = 10), (Xaltura = 1,65m, DP = 0,05) e (Xmassa = 71 kg, DP = 16) participaram deste estudo. A tarefa foi realizada com e sem um apoio de braço. Uma escala de percepção de esforço foi aplicada para todos participantes. ANOVA de medidas repetidas foi utilizada para verificar diferenças na percepção de esforço e root mean square (RMS) para a atividade eletromiográfica.Resultados Não houve diferença estatisticamente significante para o RMS entre as tarefas de digitação. A condição sem apoio de braço apresentou maior média de velocidade e quantidade de palavras digitadas (P = 0,02; P = 0,03) e houve uma diferença significante na percepção de esforço durante a condição sem apoio de braço (P = 0,03). A atividade eletromiográfica não apresentou diferença.Conclusão A atividade elétrica muscular não se alterou independente do apoio e ocorreu a melhora do esforço percebido após 10 minutos de digitação sem apoio. Estudos em longo prazo são necessários.

Mestrado em Engenharia Mecânica Submitted by Alexandra Bastos (alexandrabastos@ua.pt) on 2014-02-06T15:01:13Z No. of bitstreams: 1 Tese_Pedro_Silva.pdf: 11219191 bytes, checksum: 8f5fc3421ccdc8db6ce538b36d006b5d (MD5) Made available in DSpace on 2014-02-06T15:01:13Z (GMT). No. of bitstreams: 1 Tese_Pedro_Silva.pdf: 11219191 bytes, checksum: 8f5fc3421ccdc8db6ce538b36d006b5d (MD5) Previous issue date: 2013

Mestrado em Engenharia Mecânica This work exploits a vision based pedestrian recognition technique to build a framework capable of performing detection in images of urban setting. This method takes advantage of the richness of information present in multiple channels of an image to assemble different detection techniques in a simple and generic infrastructure. The general idea behind this method is that pedestrians present specifc visual properties that can be used to differentiate them apart from a scene. So, to exploit such properties, features are extracted from the channels in an optimized manner through the use of integral images and a machine learning engine based on AdaBoost is trained through positive and negative examples the relationship of those features with the presence of pedestrians on visual data. This framework then integrates a multi-scale, sliding window approach to perform visual pedestrian detection. To evaluate the algorithm, tests were carried out in two distinct datasets and results confirm its validity. Este trabalho explora uma técnica de deteção visual de peões que visa estabelecer uma estrutura capaz de realizar deteção em imagens de ambiente urbano. O método tira partido da riqueza de informação presente em múltiplos canais da imagem para integrar diferentes técnicas de deteção numa estrutura simples e genérica. Esta técnica tem como base a idéia de que um peão apresenta propriedades visuais específicas que permitem a sua distinção do meio envolvente. Então, com vista a explorar essas propriedades, são recolhidos descritores dos múltiplos canais de uma forma otimizada com o uso da imagem integral, e, através de exemplos positivos e negativos, um algorítmo de aprendizagem baseado no AdaBoost é treinado para relacionar esses descritores com a presença de peões. Esta técnica integra uma estrutura de varrimento de imagem a múltiplas escalas para efectuar o reconhecimento visual de peões. O método é testado em dois conjuntos de dados diferentes e os resultados confirmam a sua validade.

In recent years, the performance of personal computers has evolved with the production of ever faster processors, a fact that enables the adoption of speech processing in computer-assisted education. There are several speech technologies that are effective in education, among which text-to-speech (TTS) and automatic speech recognition (ASR) are the most prominent. TTS systems [45] are software modules that convert natural language text into synthesized speech. ASR [18] can be seen as the TTS inverse process, in which the digitized speech signal, captured for example via a microphone, is converted into text.

This paper presents a novel Long Baseline (LBL) position and velocity navigation filter for underwater vehicles based directly on the sensor measurements. The solution departs from previous approaches as the range measurements are explicitly embedded in the filter design, therefore avoiding inversion algorithms. Moreover, the nonlinear system dynamics are considered to their full extent and no linearizations are carried out whatsoever. The filter error dynamics are globally asymptotically stable (GAS) and it is shown, under simulation environment, that the filter achieves similar performance to the Extended Kalman Filter (EKF) and outperforms linear position and velocity filters based on algebraic estimates of the position obtained from the range measurements.

ResumoObjetivoAnalisar a associação e a concordância dos critérios de referência (CR) da fitnessgram para aptidão a cardiorrespiratória, índice de massa corporal (IMC) e força em jovens.MétodosParticiparam do estudo 781 jovens, 386 do sexo feminino, entre 10 a 18 anos de Londrina (PR). Foram aplicados testes de aptidão cardiorrespiratória e força muscular e foi calculado o IMC. A associação entre os testes foi analisada por meio da regressão de Poisson para se obter a razão de prevalência (RP) e os respectivos intervalos de confiança de 95%, enquanto que a concordância dos critérios foi feita com o índice Kappa.ResultadosForam encontradas associações significativas entre a aptidão cardiorrespiratória e IMC (RP=1,49, 1,27‐1,75), força muscular e IMC (RP=1,55, 1,17‐2,08), aptidão cardiorrespiratória e força muscular (RP=1,81, 1,47‐2,24). A concordância entre o atendimento dos CR variou de fraca a razoável, de 48,8% (k=0,05; p=0,10) para aptidão cardiorrespiratória e IMC, 52,9% (k=0,09; p=0,001) para a força muscular e IMC e 38,4% (k=0,22; p

Natural radioactivity in the marine environment has been present since the Earth's formation, while artificial radionuclides were introduced into the oceans in 1944. More recent direct sources exist that feed the oceans, such as low-level liquid discharges from reprocessing plants, large-scale releases due to disasters (e.g. Fukushima hit by the tsunami in 2011), and smaller-scale radiological events. Exploration of submarine environments should consider the existence of radioactivity in terms of its short- and long-term impact on marine and coastal ecosystems, also in correlation to natural hazards, such as seismic activity over submarine faults or activity of hydrothermal vent fields near the seabed. Significantly undersampled in oceans, radioactivity poses real risks to marine ecosystems and human population, urging for detailed, data-driven modeling. RAMONES is a new H2020-EU FET Proactive Project [2] aiming to offer new and efficient solutions for in in situ, continuous, long-term monitoring of radioactivity in harsh subsea environments. A new generation of submarine radiation sensing instruments, assisted by state-of-the-art (SoA) robotics and artificial intelligence (AI) will be developed towards understanding radiation related risks near and far from coastal areas, while providing data towards shaping new policies and guidelines for environmental sustainability, economic growth and human health, offering a framework for defining future environmental intelligence guidelines and practices. The main ambition is to lay a radical new path to close the existing marine radioactivity under-sampling gap and foster new interdisciplinary research in threatened natural deep-sea ecosystems. RAMONES will invest a significant effort to provide tools for long-term, rapid deployments, propose new robotics and AI-driven supported methodologies, and offer scaled-up solutions to researchers, policy makers and communities. RAMONES will combine SoA equipment from various disciplines and advanced modeling in fine synergy, and design new and effective approaches for the marine environment to provide efficient response to natural and man-made hazards, shaping future policies for the global population.