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Mobility is human body movement in all its forms, including bed-to-chair transfer, walking, daily tasks, participating in work and social functions, exercising, and using public transport. The mobility of people living with COPD is affected negatively by the disease symptoms. However, limited data are available on the life-space mobility in people with COPD on long-term oxygen therapy (LTOT). This study aimed to explore the life-space mobility in subjects with COPD on LTOT and verify whether life-space mobility is associated with comorbidities and symptoms, activity in daily life, exercise capacity performance, and quality of life.This cross-sectional study enrolled 61 subjects with COPD on LTOT (73.0 ± 8.8 y, FEVMobility restriction was identified in 90% of participants. Life-space mobility was negatively associated with the number of comorbidities (rSubjects with COPD on LTOT had limited life-space mobility. Interventions to reduce dyspnea and improve exercise capacity should be prioritized to increase this population's domestic and community mobility.

Background: Global estimates point to high prevalence of neuropsychiatric disorders in individuals hospitalized for COVID-19. In Brazil, anxiety and depression rates resulting from SARS-CoV-2 infection range from 29.7% to 68%, respectively, being more prevalent in young women, with lower educational level, with comorbidities and psychological problems. previous. Objective: Identify possible causes, verify prevalence and identify risk factors for anxiety, depression and post-traumatic stress disorder (PTSD) in patients hospitalized for COVID-19. Methods: An integrative literature review was carried out involving retrospective and/or prospective cohort studies and population-based clinical trials published in the last three years. The main evidence on the relationship between neuropsychiatric disorders and intrinsic changes in neuroimmunomodulation parameters was also raised. Results: Twenty-one studies were included that addressed the presence of symptoms of PTSD, anxiety, depression, fatigue in sleep disorders in COVID19 survivors. Conclusion: With this literature review, it can be concluded that PTSD, anxiety, depression, fatigue and sleep disturbances are highly prevalent symptoms in COVID-19 survivors, being persistent for up to one-year post-infection.

The heat treatment effect on kaolin waste from mining was evaluated on the structural and thermal behavior of poly(ε-caprolactone) (PCL). The PCL/KW (kaolin waste) and PCL/HTKW (heat-treated kaolin waste) composites were processed in an internal mixer and subsequently characterized by X-ray diffraction (XRD) and differential scanning calorimetry (DSC). The kaolin waste showed kaolinite and quartz in its composition, while the heat treatment at 1200°C modified it to mullite, quartz and silica-rich amorphous phase. By XRD, there was an increase in the intensity of the peak 2θ = 23.9° of the PCL/KW composites compared to neat PCL. In contrast, PCL/HTKW composites tended to reduce the intensity of the peak 2θ = 23.9°, especially at 5% HTKW. The crystalline melting temperature and the degree of crystallinity of PCL/KW and PCL/HTKW composites were practically unchanged, compared to PCL. However, the crystallization process was more effective with the kaolin waste (KW) without heat treatment, indicating that the HTKW amorphous phase inhibited crystallization. The PCL/KW development promoted an increase in crystallization temperature, relative crystallinity, and crystallization rate, surpassing PCL and the PCL/HTKW system. In view of this, kaolin waste has the potential to accelerate the PCL crystallization process, contributing to add value to a material that would otherwise be discarded and minimizing environmental impacts.

The polysaccharide β-glucan, found in the cell wall of cereals such as wheat, oats, and barley, is believed to lower the concentration of bad cholesterol in humans, but the molecular-level mechanisms responsible for such an action are unknown. In this study, we use Langmuir monolayers of cholesterol and dipalmitoyl phosphatidyl choline (DPPC) as cell membrane models that are made to interact with β-glucan. Neat cholesterol and mixed cholesterol/DPPC monolayers were expanded upon incorporating β-glucan from the aqueous subphase. This incorporation was found to induce ordering in mixed monolayers and dehydration of the carbonyl group at higher cholesterol concentrations. These effects are attributed to hydrophobic interactions as identified with polarization-modulated infrared reflection–absorption spectroscopy. They correlate well with the hypothesis that cholesterol levels can be lowered by the formation of soluble fibers with β-glucan through hydrophobic interactions, blocking cholesterol absorption by the organism.

The heat treatment effect on kaolin waste from mining was evaluated on the structural and thermal behavior of poly(ε-caprolactone) (PCL). The PCL/KW (kaolin waste) and PCL/HTKW (heat-treated kaolin waste) composites were processed in an internal mixer and subsequently characterized by X-ray diffraction (XRD) and differential scanning calorimetry (DSC). The kaolin waste showed kaolinite and quartz in its composition, while the heat treatment at 1200°C modified it to mullite, quartz and silica-rich amorphous phase. By XRD, there was an increase in the intensity of the peak 2θ = 23.9° of the PCL/KW composites compared to neat PCL. In contrast, PCL/HTKW composites tended to reduce the intensity of the peak 29 = 23.9°, especially at 5% HTKW. The crystalline melting temperature and the degree of crystallinity of PCL/KW and PCL/HTKW composites were practically unchanged, compared to PCL. However, the crystallization process was more effective with the kaolin waste (KW) without heat treatment, indicating that the HTKW amorphous phase inhibited crystallization. The PCL/KW development promoted an increase in crystallization temperature, relative crystallinity, and crystallization rate, surpassing PCL and the PCL/HTKW system. In view of this, kaolin waste has the potential to accelerate the PCL crystallization process, contributing to add value to a material that would otherwise be discarded and minimizing environmental impacts. Resumen Se evaluó el efecto del tratamiento térmico en los desechos de caolín de la minería sobre el comportamiento estructural y térmico de la poli (ε-caprolactona) (PCL). Los compuestos PCL/DC (desechos de caolín) y PCL/DCT (desechos de caolín tratados térmicamente) se procesaron en un mezclador interno y luego se caracterizaron por difracción de rayos X (DRX) y calorimetría de barrido diferencial (DSC). El residuo de caolín tenía caolinita y cuarzo en su composición, mientras que el tratamiento térmico a 1200 °C lo cambio a mullita, cuarzo y fase amorfa rica en sílice. Por XRD, hubo un aumento en la intensidad del pico 29 = 23,9° de los compuestos PCL/DC, en comparación con el PCL puro. Por el contrario, los compuestos PCL/DCT tendieron a reducir la intensidad máxima 2θ = 23,9°, especialmente con el 5 % de DCT. La temperatura de fusión cristalina y el grado de cristalinidad de los compuestos PCL/DC y PCL/DCT se mantuvieron prácticamente sin cambios, en relación al PCL. Sin embargo, el proceso de cristalización fue más eficiente con residuo de caolín (DC) sin tratamiento térmico, lo que indica que la fase amorfa del DCT inhibió la cristalización. El desarrollo de los PCL/DC promovió un aumento en la temperatura de cristalización, cristalinidad relativa y velocidad de cristalización, superando a la PCL y al sistema de los PCL/ DCT. Como resultado, el residuo de caolín tiene el potencial de acelerar el proceso de cristalización de la PCL, contribuyendo a agregar valor a un material que de otra manera sería descartado y minimizando los impactos ambientales.

BackgroundThe coronavirus disease 2019 (COVID-19) is a widespread, highly contagious inflammatory process that causes respiratory, physical and psychological dysfunction. COVID-19 mainly affects the respiratory system and evolves in the acute phase from mild cases with common symptoms, such as fever, cough, and fatigue, to the moderate-to-severe form, causing massive alveolar damage resulting in dyspnea and hypoxemia that can rapidly progress to pneumonia, and acute respiratory distress syndrome. The acute form usually causes severe pulmonary sequelae such as pulmonary fibrosis or progression to organ failure, leading to worsening metabolic dysfunction and/or death.PurposeTo verify the effects of an outpatient and home pulmonary rehabilitation program (PRP) on clinical symptoms, pulmonary function, physical activity level, functional status, autonomic activity, peripheral muscle strength, static and functional balance, functional mobility, anxiety and depression, post-traumatic stress, health-related quality of life, and survival of patients with sequelae from COVID-19.MethodsThis study will be a cohort, parallel, two-arm multicentric study, to be carried out in three clinical centers, with blind evaluation, with 06 weeks of training and follow-up. This study was designed according to the recommendations of the CONSORT statement. To be involved in this clinical study, according to the inclusion criteria, women and men aged between 16 and 75 years affected by COVID-19. The proposed PRP is based on the guidelines recommended by the Global Initiative for Chronic Obstructive Lung Disease and, consists of a combination of aerobic and muscle strengthening exercises, lasting six weeks, with a frequency of three times a week.DiscussionIn patients infected with COVID-19 with persistent symptoms and sequelae, PRP mainly seeks to improve dyspnea, relieve anxiety and depression, prevent, and reduce complications and/or dysfunctions, reduce morbidity and mortality, and improve health-related quality of life.Trial registrationThis study was registered at clinicaltrials.gov (ID: COVID-19 PULMONARY REHAB NCT04982042).

Background: Due to the clinical situations faced, patients with COVID-19 who survive the intensive care unit (ICU) are at greater risk of developing post-intensive care syndrome (PICS), characterized by typical physical, psychological and cognitive consequences in the post hospital discharge. Given this situation, it is important to assess these patients for the presence of musculoskeletal and psychosocial changes, so that they are referred to an outpatient and/or home rehabilitation program. Objective: to identify in the scientific literature the effects of hospitalization on the functional status and health-related quality of life of patients with complications from COVID-19. Methods: Searches were performed for scientific articles indexed in the MEDLINE database (accessed by Pubmed), between the years 2019 to 2022. Articles that met the inclusion and exclusion criteria determined to compose this study were included. Results: from a total of 524 articles found in the literature, only 15 met the inclusion criteria and were included in the study. The sample was represented by eight cross-sectional studies and seven cohort studies, and the main outcomes found to assess functional status were the six minute walking test the Post-COVID-19 Functional Status scale and for quality of life the EuroQol visual analogue scale and the Short form-36. Conclusion: With this literature review, it can be concluded that patients hospitalized for complications of COVID-19 showed a significant decline in functional status and health-related quality of life.However, updates are necessary to characterize the symptoms and persistent sequelae in the post-COVID-19.

Ice-free areas of Antarctica represent an important study region that helps us understand how human activity affects plant communities and soil properties. The goal of this study was to determine the changes in plant composition and soil properties around a whale bone skeleton (WB) near Ferraz Station, King George Island, Antarctica from 1972 to 2020 (48 years). The WB was assembled in 1972 by Jacques-Yves Cousteau and his team. It is located in a large moss field and visited by many tourists. We studied the plant composition and development based on historical and recent photographs and phytosociological studies from 1986 to 2020. The soil was sampled in February 2009 to determine general properties. The results showed that human activity surrounding the WB directly affected the plant community composition and soil properties. The Syntrichia cushions were positively affected by the calcium deposits from bone dissolution. The principal component analysis revealed that mineralization of the bones increased soil nutrient assembly. A strong phosphatization process was observed in the WB area, similar to that in ornithogenic soils. The WB on the marine terrace enhanced soil fertility and changed the plant community.

Southern giant petrels (Macronectes giganteus) are found in the Antarctic. They build their nests with rock fragments, disturbing large areas during incubation and chick feeding periods; however, their impact on vegetation is unknown. Thus, we aimed to evaluate the effect of Petrel nests and associated breeding activities on the diversity and structure of cryptogam communities of Stinker Point, Elephant Island. We selected 13 nests in February and March 2012 and continue the monitoring in 2018. The area of direct influence of breeding activities was photographed to calculate plant community coverage. The results demonstrated that species richness, community coverage and composition, and beta diversity showed significant differences between active and inactive nests. The linear mixed-effect models revealed that the positive effect of nest area mainly caused variation in community coverage, but had a negative effect on beta diversity. Sphaerophorus globosus (lichen) grew around the inactive nests, sometimes forming a ring up to 1 m in diameter. This ring was then surrounded by the Chorisodontium acyphyllum moss colonized by S. globosus, and a final ring of Sanionia uncinata, colonized by the same lichen. Recently constructed nests are generally surrounded by Prasiola crispa and Sanionia uncinata carpets.

Submitted by Gilson Rodrigues (gilson.rodrigues@ufvjm.edu.br) on 2022-08-22T18:44:28Z No. of bitstreams: 1 adriano_l?lis_medeiros.pdf: 1535522 bytes, checksum: de411e3a3908b5eef512b917193f7745 (MD5) Approved for entry into archive by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2022-08-23T17:18:24Z (GMT) No. of bitstreams: 1 adriano_l?lis_medeiros.pdf: 1535522 bytes, checksum: de411e3a3908b5eef512b917193f7745 (MD5) Made available in DSpace on 2022-08-23T17:18:24Z (GMT). No. of bitstreams: 1 adriano_l?lis_medeiros.pdf: 1535522 bytes, checksum: de411e3a3908b5eef512b917193f7745 (MD5) Previous issue date: 2022 Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) O principal objetivo da pesquisa acad?mica ? apresentar propostas que, aliadas ? reflex?o te?rica, busquem beneficiar a sociedade atrav?s de a??es pr?ticas. Dentro dessa vis?o, o maior desafio que se apresenta aos estudiosos do Direito, mais especificamente falando, ? o de atribuir efic?cia concreta a instrumentos normativos abstratos. A Justi?a do Trabalho surgiu como resposta a essas demandas, passando historicamente por diversas redistribui??es de compet?ncia, implementadas no sentido de assegurar tanto a empregados quanto a empregadores acesso ? presta??o jurisdicional em todo o territ?rio nacional. A S?mula Vinculante 53, publicada no ano de 2015, pretendia pacificar o conflito de compet?ncia existente entre a Justi?a do Trabalho e a Justi?a Federal no que diz respeito ? execu??o de contribui??es previdenci?rias devidas sobre v?nculo trabalhista reconhecido em Ju?zo, nas denominadas senten?as declarat?rias. Tal dispositivo referendou a posi??o j? adotada desde o ano de 2005 pela S?mula n? 368 do Tribunal Superior do Trabalho, relegando definitivamente ? Justi?a Federal a prerrogativa de cobrar e recolher tais valores. Essa pr?tica vem sendo renovada reiteradamente desde ent?o, o que acaba adiando por v?rios anos a arrecada??o de um montante significativo de receitas tribut?rias necess?rias ? manuten??o da m?quina p?blica e ao financiamento das pol?ticas sociais visto que, conforme dados do Conselho Nacional de Justi?a, a execu??o na seara trabalhista se processa de maneira muito mais c?lere do que a execu??o fiscal realizada pela Justi?a Federal. Nesse contexto, o presente estudo procurou demonstrar que a restri??o da capacidade arrecadat?ria da Justi?a do Trabalho ocasionada pela jurisprud?ncia dos tribunais superiores, materializada na S?mula Vinculante n? 53, n?o atende ? efici?ncia administrativa e ? responsabilidade fiscal. Para tanto buscou, atrav?s da an?lise estat?stica dos relat?rios ?Justi?a em N?meros? do CNJ publicados entre 2003 e 2021, complementada por pesquisa estat?stica descritiva envolvendo processos distribu?dos no ano de 2012 na Vara do Trabalho de Te?filo Otoni, estimar o montante da perda que poderia estar sendo causada por essa postura. Os resultados demonstraram estatisticamente uma diferen?a real e possivelmente elevada entre a m?dia dos valores executados na JT e a m?dia dos valores cuja execu??o se transfere para a JF, indicando a necessidade de maiores aprofundamentos no sentido de se determinar mais precisamente o tamanho dessa diferen?a. Disserta??o (Mestrado) ? Programa de P?s-gradua??o em Administra??o P?blica, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2022. The main objective of academic research is to present proposals that, allied to theoretical reflection, seek to benefit society through practical actions. Within this vision, the greatest challenge for legal scholars, more specifically speaking, is to attribute concrete efficacy to abstract normative instruments. The Labor Courts arose as a response to these demands, historically going through several redistributions of competence, implemented in order to ensure both employees and employers access to jurisdictional provision throughout the national territory. The Binding Precedent 53, published in 2015, was intended to pacify the conflict of jurisdiction between the Labor Court and the Federal Court regarding the enforcement of social security contributions due on the labor relationship recognized in court, in the so-called declaratory judgments. Such device endorsed the position already adopted since 2005 by Precedent no. 368 of the Superior Labor Court, definitively relegating to the Federal Court the prerogative to collect such amounts. This practice has been repeatedly renewed since then, which ends up delaying for several years the collection of a significant amount of tax revenue needed to maintain the public machine and fund social policies since, according to data from the National Council of Justice, the execution in the labor court is processed much faster than the tax execution carried out by the Federal Court. In this context, the present study sought to demonstrate that the restriction on the collection capacity of the Labor Court, caused by the jurisprudence of the higher courts, materialized in the Binding Precedent No. 53, does not comply with the administrative efficiency and fiscal responsibility. To this end, through statistical analysis of the CNJ's "Justi?a em N?meros" reports published between 2003 and 2021, complemented by descriptive statistical research involving cases distributed in 2012 in the Te?filo Otoni Labor Court, we sought to estimate the amount of loss that could be caused by this stance. The results statistically showed a real and possibly high difference between the average of the amounts executed in the JT and the average of the amounts whose execution is transferred to the JF, indicating the need for further study in order to determine more precisely the size of this difference.

Background Mammographic breast density is an important predictor of breast cancer, but its measurement has limitations related to subjectivity of visual evaluation or to difficult access for automatic volumetric measurement methods. Herein, we describe the design and clinical validation of Aguida, a software program for automated quantification of breast density from flat mammography images. Materials and methods The software program was developed in MatLab. After image segmentation separating the background from the breast image, the operator positions a cursor defining a region of interest on the pectoralis major muscle from the mediolateral oblique view. Then, in the craniocaudal view, the threshold for separation of the dense tissue is based on the optical density of the pectoral muscle, and the proportion of dense tissue is calculated by the program. Mammograms obtained from 2 different occasions in 291 women were used for clinical evaluation. Results The intraclass correlation coefficient (ICC) between breast density measurements by the software and by a radiologist was 0.96, with a bias of only 0.67 percentage points and a 95% limit of agreement of 13.5 percentage points; the ICC was 0.94 in the interobserver reliability assessment by two radiologists with different experience; and the ICC was 0.98 in the intraobserver reliability assessment. The distribution among the density classes was close to the values obtained with the volumetric software. Conclusions Measurement of breast density with the Aguida program from flat mammography images showed high agreement with the visual determination by radiologists, and high inter- and intra-observer reliability.

The main purpose of stemming is to reduce the inflected words into its root form or stem. Thus, words can be mapped to the same concept, improving the process of information retrieval, regarding its ability to index documents and to reduce data dimensionality. However, the efficiency of those algorithms varies according to different aspects. Also, studies in the field area reached contrasting conclusions. This work assesses the use of stemmers in the retrieval of legislative documents written in Portuguese. Four stemmers together with BM25 were evaluated in two legislative corpora from the Brazilian Chamber of Deputies. RSLP-S and Savoy stemmers showed the best improvements in the information retrieval pipeline.

Given the actual volume and speed of financial transactions, financial fraud detection systems are constantly evolving based on new computational intelligence algorithms. Therefore, transaction monitoring and analysis prevent monetary losses caused by fraudsters. Since the fraud detection process is a labor-intensive task for human auditors given the huge amount of daily transactions processed by financial services information systems. Credit card is the financial product most explored in the financial fraud detection literature, while mobile money service is becoming a popular option for payments, fraud detection for such financial product has not yet been deeply explored. Therefore, it is interesting to optimize the auditing process and test new quantitative techniques, such as deep learning, to support human auditors before double-checking a suspicious transaction. Thus, we propose an integration of adversarial autoencoders and machine learning methods to perform an objective classification among three transaction types: regular, local, and global anomaly. The integration consists of using the autoencoder's generated latent vectors as features for the supervised learning algorithms. The experiments considered different latent vector space forms concerning their dimensionality and the clusters generated by a prior Gaussian mixture. The results show that some classifiers may accept latent characteristics well, getting better or similar performance when using all the original characteristics.

Digital Libraries have been developed nowadays as a way to dispose digital information through the Internet. This is particularly very useful when the information comes from historical documents. This research takes place in the PROHIST Project [Mello et al., 2008] which aims the creation of a digital library with methods to preserve and broadcast images of historical documents. In general, the access to original documents has to be done carefully as, because of its age, the paper is more susceptible to the wear and tear over time. In order to make the documents more easily accessible, digitization comes as the most efficient solution. In a digital media, as digital images, the documents can be visualized and copied. This also helps the preservation of the documents as they are digitized in high resolution and in true color format. It is common to use JPEG file format (Sayood, 1996) to store these images ensuring a good space storage/quality ratio. However, even in this format, to access an archive of thousands of high quality true color images is not an easy task even with the extended use of broad band Internet. The storage space of the images can be reduced with its conversion to black-and-white images. In this bi-level format and stored using GIF file format, the size of the file can be five times lower than the original true color JPEG image. Binarization or thresholding (Parker, 1997) is the process that converts an image into black-and-white: a threshold value is defined and the colors above that value are converted into white, while the colors below it are converted into black. This is a very simple process in digital image processing when one has a document with black ink written on a white paper. Historical documents, however, have several types of noises. The degradation yellows the sheet of paper and creates some noise that is perceptible to the digitizing process. Even more, in some cases, the ink has faded. This is particularly important when the document is written on both sides of the paper. In some cases, the ink of one side interferes in the other creating an effect called “ink bleeding”. Because of these problems, it is very difficult to find the best threshold value that separates the colors that belong to the paper from the colors that belong to the ink. An example of such a document is presented in Figure 1-left. In this paper, we present a new thresholding algorithm for color quantization based on genetic algorithms and image fidelity metrics. These metrics are used to define the convergence point of the genetic algorithm. The quantized image is then binarized based on

The financial time series forecasting is considered a rather difficult problem, due to the many complex features frequently present in such time series (irregularities, volatility, trends and noise). Several approaches have been studied for the development of predictive models able to predict time series, based on its past and present data. In the attempt to solve the time series prediction problem, a wide number of linear statistical models were proposed. Among them, the popular linear statistical approach based on Auto Regressive Integrated Moving Average (ARIMA) models [1] is one of the most common choices. However, since the ARIMA models are linear and most real world applications involve nonlinear problems, this can introduce an accuracy limitation of the generated forecasting models. In the attempt to overcome linear statistical models limitations, other nonlinear statistical approaches have been developed, such as the bilinear models [2], the threshold autoregressive models [3], the exponential autoregressive models [4], the general state dependent models [5], amongst others. The drawbacks of those nonlinear statistical models are the high mathematical complexity associated with them (resulting in many situations in similar performances to the linear models) and the need, most of the time, of a problem dependent specialist to validate the predictions generated by the model, limiting the development of an automatic forecast system [6]. Alternately, Artificial Neural Networks (ANNs) based approaches have been applied for nonlinear modeling of time series in the last two decades [7-14]. However, in order to define a solution to a given problem, ANNs require the setting up of a series of system parameters, some of them are not always easy to determine. The ANN topology, the number of processing units, the algorithm for ANN training (and its corresponding variables) are just some of the parameters that require definition. In addition to those, in the particular case of time series forecasting, another crucial element necessary to determine is the relevant time lags to represent the series [15]. In this context, evolutionary approaches for the definition of neural network parameters have produced interesting results [16{20]. Some of these works have focused on the evolution of the network weights whereas others aimed at evolving the network architecture.

Several neural network models have been proposed in the literature to predict the future behavior of financial time series. However, an intrinsic limitation arises from this particular prediction task with modeling via neural networks, since the prediction, when sampled in daily frequency, have 1-step-ahead delay with respect to real time series observations. In order to overcome such drawback, we present a deep increasing–decreasing-linear neural network (wherein each layer is composed of a set of increasing–decreasing-linear processing units) to predict the behavior of financial time series. In addition, we present a learning process to train the proposed model using a descending gradient-based approach. In order to assess the model’s prediction performance, we use twelve financial time series from relevant stock markets around the world. The obtained results show that the proposed model have competitiveness, in terms of predictive performance, and have better effectiveness when compared to recent models presented in the literature of time series prediction.

A substantial number of expert and intelligent systems rely on deep learning methods to solve problems in areas such as economics, physics, and medicine. Improving the accuracy of the activation functions used by such methods can directly and positively impact the overall performance and quality of the mentioned systems at no cost whatsoever. In this sense, enhancing the design of such theoretical fundamental blocks is of great significance as it immediately impacts a broad range of current and future real-world deep learning based applications. Therefore, in this paper, we turn our attention to the interworking between the activation functions and the batch normalization, which is practically a mandatory technique to train deep networks currently. We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization. Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models. These Convolutional Neural Networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learning computer vision datasets. The results showed DReLU speeded up learning in all models and datasets. Besides, statistical significant performance assessments (p

In many regression and time series forecasting problems, the input data is not fully available at the beginning of the training phase. Conventional machine learning methods for batch data are not able to handle this problem. The sequential version of ELM, called Online Sequential Extreme Learning Machine (OS-ELM), addresses this problem through the least squares recursive solution for updating the network output weights. However, the implementation of OS-ELM and its extensions suffer from the problem of multicollinearity and its side effect on the variance of the weight estimates. This paper introduces a new method of sequential learning for handling the effects of multicollinearity. The proposed method, called Kalman Learning Machine (KLM), uses the Kalman filter to sequentially update the output weights of a Single Layer Feedforward Network (SLFN) based on OS-ELM. An extension of the proposed method, called Extended Kalman Learning Machine (EKLM), is presented in order to address the problem of nonlinear data. The proposed method was compared with some of the most recent and effective methods for handling the effects of multicollinearity in sequential learning problems. The experiments performed showed that the proposed method performs better than most state-of-the-art methods considering both the prediction error and training time.

The increasing demand for renewable energy has been promoting a rapid development of photovoltaic technologies. Given this, photoinduced thermal emission is being explored with the aim of improving solar cell performance by converting low-energy IR photons into visible light. Here, we report the light-induced blackbody emission from LnO2 (Ln = Pr and Tb) as a potential emitter for thermophotovoltaic applications. Lanthanide dioxides display broad IR absorption and a direct optical band gap of 1.49 (PrO2) and 1.51 eV (TbO2). These materials achieve a maximum temperature of ∼1500 K under a power density excitation of 160 W cm−2 and display a stable bright light emission. Thermal emission can be tuned from laser power density modulation.

Tropical forests provide essential environmental services to human well-being. In the world, Brazil has the largest continuous area of these forests. However, in the state of Maranhão, in the eastern Amazon, only 24% of the original forest cover remains. We integrated and analyzed active fires, burned area, land use and land cover, rainfall, and surface temperature datasets to understand forest fragmentation and forest fire dynamics from a remote sensing approach. We found that forest cover in the Maranhão Amazon region had a net reduction of 31,302 km2 between 1985 and 2017, with 63% of losses occurring in forest core areas. Forest edges extent was reduced by 38%, while the size of isolated forest patches increased by 239%. Forest fires impacted, on average, around 1031 ± 695 km2 year−1 of forest edges between 2003 and 2017, the equivalent of 60% of the total burned forest in this period. Our results demonstrated that forest fragmentation is an important factor controlling temporal and spatial variability of forest fires in the eastern Amazon region. Thus, both directly and indirectly, forest fragmentation can compromise biodiversity and carbon stocks in this Amazon region.

A recent study has presented a model, called the increasing-decreasing-linear (IDL) model, which is able to efficiently predict the high-frequency stock market. Nevertheless, a drawback arises from the IDL's learning process, which consists of its costly methodology to circumvent the non-differentiability problem of increasing and decreasing operators. In this sense, trying to reduce the computational cost of the IDL design, we propose evolutionary learning machines, using the genetic algorithm, the particle swarm optimizer, the backtracking search algorithm, the firefly algorithm and the cuckoo search, to design the IDL model. Five relevant high-frequency time series from the Brazilian stock market are used to assess performance, and the achieved results have demonstrated better prediction performance with smaller computational cost when compared to those achieved by the IDL model designed by its classical learning process, as well as to those achieved by some relevant prediction models presented in the literature.

As analogias presentes em uma das obras do Plano Nacional dos Livros Didáticos (PNLD)/2015 para a componente curricular Química foram estudadas neste trabalho. A maioria das analogias identificadas foi classificada como simples e a exploração superficial dessa ferramenta foi evidenciada pela ausência de analogias do tipo estrutural-funcional e abstrata-abstrata. Os resultados indicam que as analogias presentes na obra avaliada não favorecem a aprendizagem do aluno devido, principalmente, a falta de orientações e discussão das limitações.

SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context., arXiv admin note: text overlap with arXiv:2004.03373

The purpose of this article is to study the geometry of gradient almost Yamabe solitons immersed into warped product manifolds $I\times_{f}M^{n}$ whose potential is given by the height function from the immersion. First, we present some geometric rigidity on compact solitons due to a curvature condition on the warped product manifold. In the sequel, we investigate conditions for the existence of totally geodesic, totally umbilical and minimal solitons. Furthermore, in the scope of constant angle immersions, a classification of rotational gradient almost Yamabe soliton immersed into $\mathbb{R}\times_{f}\mathbb{R}^{n}$ is also made., arXiv admin note: text overlap with arXiv:2010.02487

Data science concerns to look for ways for extracting knowledge from extraordinary volume of data in any domain. This paper introduces a new three-stage approach for time series forecasting based on quartile data that combines aggregation, prototype selection and forecasting in the Symbolic Data Analysis (SDA) framework. Initially, time series are summarized and classes of entities are obtained by representing new time series. A prototype selection process based on mutual information is applied in order to obtain a more representative class data set. Each class is described by a list of three continues values, called quartile symbolic data. Boxplots time series are displayed, as a special case. In this framework, symbolic data are obtained to take into account variability between the entities of the classes. Approaches for boxplots forecasting are constructed from multivariate statistical modeling. Synthetic and real symbolic time series are considered in the experiments to demonstrate the usefulness of the proposed approach.

This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV). SigNet is a state of the art Deep CNN model for feature representation in the HSV context and contains 2048 dimensions. Some of these dimensions may include redundant information in the dissimilarity representation space generated by the dichotomy transformation (DT) used by the writer-independent (WI) approach. The analysis is carried out on the GPDS-960 dataset. Experiments demonstrate that the proposed method is able to control overfitting during the search for the most discriminant representation.

In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy. Current out-of-distribution (OOD) detection approaches usually do not directly fix the SoftMax loss drawbacks, but rather build techniques to circumvent it. Unfortunately, those methods usually produce undesired side effects (e.g., classification accuracy drop, additional hyperparameters, slower inferences, and collecting extra data). In the opposite direction, we propose replacing SoftMax loss with a novel loss function that does not suffer from the mentioned weaknesses. The proposed IsoMax loss is isotropic (exclusively distance-based) and provides high entropy posterior probability distributions. Replacing the SoftMax loss by IsoMax loss requires no model or training changes. Additionally, the models trained with IsoMax loss produce as fast and energy-efficient inferences as those trained using SoftMax loss. Moreover, no classification accuracy drop is observed. The proposed method does not rely on outlier/background data, hyperparameter tuning, temperature calibration, feature extraction, metric learning, adversarial training, ensemble procedures, or generative models. Our experiments showed that IsoMax loss works as a seamless SoftMax loss drop-in replacement that significantly improves neural networks' OOD detection performance. Hence, it may be used as a baseline OOD detection approach to be combined with current or future OOD detection techniques to achieve even higher results., Accepted for publication in the IEEE Transactions on Neural Networks and Learning Systems: Special Issue on Deep Learning for Anomaly Detection

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation. Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context. In this work, we present a white-box analysis of this approach highlighting how it handles the challenges, the dynamic selection of references through fusion function, and its application for transfer learning. All the analyses are carried out at the instance level using the instance hardness (IH) measure. The experimental results show that, using the IH analysis, we were able to characterize “good” and “bad” quality skilled forgeries as well as the frontier region between positive and negative samples. This enables futures investigations on methods for improving discrimination between genuine signatures and skilled forgeries by considering these characterizations.

The detection of objects considering a 6DoF pose is a common requirement to build virtual and augmented reality applications. It is usually a complex task which requires real-time processing and high precision results for adequate user experience. Recently, different deep learning techniques have been proposed to detect objects in 6DoF in RGB images. However, they rely on high complexity networks, requiring a computational power that prevents them from working on mobile devices. In this paper, we propose an approach to reduce the complexity of 6DoF detection networks while maintaining accuracy. We used Knowledge Distillation to teach portables Convolutional Neural Networks (CNN) to learn from a real-time 6DoF detection CNN. The proposed method allows real-time applications using only RGB images while decreasing the hardware requirements. We used the LINEMOD dataset to evaluate the proposed method, and the experimental results show that the proposed method reduces the memory requirement by almost 99\% in comparison to the original architecture with the cost of reducing half the accuracy in one of the metrics. Code is available at https://github.com/heitorcfelix/singleshot6Dpose., This paper was accepted by 2020 International Joint Conference on Neural Networks (IJCNN)

Most of the automatic fire alarm systems detect the fire presence through sensors like thermal, smoke, or flame. One of the new approaches to the problem is the use of images to perform the detection. The image approach is promising since it does not need specific sensors and can be easily embedded in different devices. However, besides the high performance, the computational cost of the used deep learning methods is a challenge to their deployment in portable devices. In this work, we propose a new deep learning architecture that requires fewer floating-point operations (flops) for fire recognition. Additionally, we propose a portable approach for fire recognition and the use of modern techniques such as inverted residual block, convolutions like depth-wise, and octave, to reduce the model's computational cost. The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops. One of our models presents 71\% fewer parameters than FireNet, while still presenting competitive accuracy and AUROC performance. The proposed methods are evaluated on FireNet and FiSmo datasets. The obtained results are promising for the implementation of the model in a mobile device, considering the reduced number of flops and parameters acquired., Comment: Accepted in the IEEE International Joint Conference on Neural Networks (IJCNN), 2020

Com a evolução das Tecnologias da Informação e Comunicação (TIC), a educação sofreu mutações no ambiente escolar. Porém, poucos professores utilizam tais tecnologias em sua didática. Visando contribuir e dinamizar o processo de ensino-aprendizagem, o projeto EDUCATECH foi implantado numa escola pública municipal. Este trabalho analisa resultados de um questionário com abordagem quantitativa aplicado no decorrer do projeto. Os resultados apontam grande impacto positivo, visto que o aprendizado e ensino melhoraram de forma considerável na escola referida.

Introduction The Ts65Dn mouse is the most widely used animal model of Down syndrome (DS). Differences in autonomic regulation of heart rate variability (HRV) in individuals with DS have been hypothesized. Pharmacological studies in animal models may help us understand mechanisms underlying observed changes in HRV in people with DS. Objective To investigate the use a new, noninvasive technique to assess cardiac autonomic modulation in Ts65Dn mice under the effect of adrenergic and cholinergic agonists. Method We recorded electrocardiograms (ECGs) from 12 Ts65Dn and 12 euploid control mice. A 30‐min baseline recording was followed by the injection of an adrenergic (isoproterenol [Iso]) or cholinergic (carbachol [CCh]) agonist. Heart rate and HRV were analyzed using a series of methods customized for mice. Results and Discussion The ECG apparatus described here allowed us to detect noninvasively long series of heartbeats in freely‐moving animals. During baseline conditions, the yield of detectable heartbeats was 3%–27% of the estimated total number of events, which increased to 35%–70% during the 15‐min period after either Iso or CCh injections. Ts65Dn mice displayed a robust enhanced Iso‐induced negative chronotropic rebound response compared with euploid control mice. We observed a significantly smaller CCh response in Ts65Dn versus control euploid mice in the 6‐ to 10‐min‐interval postcarbachol injection. Conclusion This work showed that the techniques described here are sufficient for this type of study. However, future studies involving the use of more selective pharmacological agents and/or genetic manipulations will be key to advance a mechanistic understanding of cardiac autonomic regulation in DS., We used a new, noninvasive technique to assess cardiac autonomic modulation in the Ts65Dn mouse model of Down syndrome under the effect of adrenergic and cholinergic agonists. This new ECG recording apparatus has the potential to be very useful to investigators studying heart rate and heart rate variability in rodent models of human disorders. However, future studies involving the use of more selective pharmacological agents and/or genetic manipulations of the mouse models of DS will be necessary to complement this work.

The lack of labeled data and the need to update the learning model are the two main challenges when performing Opinion Mining with data streams. Usual Opinion Mining approaches are not able to deal with these challenges, nor with the characteristics brought by this kind of data. Moreover, the occurrence of changes (drifts) in the concepts and/or opinions is another issue. Possible alternative solutions to these problems are: the use of Semi-supervised learning, such as Active Learning, which labels selected data rather than the entire data set; or Multiple Classifiers Systems, which combines different classifiers and are well suited to deal with drifts. In this study, we combined these two approaches, proposing the use of eight Active Learning sampling strategies as a generation method for Multiple Classifiers Systems. The Active Learning approach requires the choice of a strategy to select the instances, and each strategy results in the creation of a distinct classification model. Our method was evaluated in 14 Twitter stream data sets and the results showed that it can be better for Opinion Mining with data streams than popular ensemble generation methods present in the literature, such as Bagging and AdaBoost.

Supervised machine learning and opinion lexicon are the most frequent approaches for opinion mining, but they require considerable effort to prepare the training data and to build the opinion lexicon, respectively. In this paper, a novel unsupervised clustering approach is proposed for opinion mining. Three swarm algorithms based on Particle Swarm Optimization are evaluated using three corpora with different levels of complexity with respect to size, number of opinions, domains, languages, and class balancing. K-means and Agglomerative clustering algorithms, as well as, the Artificial Bee Colony and Cuckoo Search swarm-based algorithms were selected for comparison. The proposed swarm-based algorithms achieved better accuracy using the word bigram feature model as the pre-processing technique, the Global Silhouette as optimization function, and on datasets with two classes: positive and negative. Although the swarm-based algorithms obtained lower result for datasets with three classes, they are still competitive considering that neither labeled data, nor opinion lexicons are required for the opinion clustering approach.

Documents written in natural language constitute a major part of the artefacts produced during the software engineering life cycle. Studies indicate that more than 80% of enterprise data is stored in some sort of unstructured form, mainly as text. Therefore, the growth of user-generated content, especially from social media, provides a huge amount of data which allows discovering the experiences, opinions, and feelings of users. Text mining refers to the set of tools, techniques, and algorithms adopted to extract useful information from unstructured data. Considering that Portuguese ranks among the ten most spoken languages, and it is the second most common in Twitter, this study aims to map current primary studies that relate to the application of text mining for Portuguese. A systematic mapping method was applied and 6075 primary studies were retrieved up to the year 2014. A total of 203 studies were included, from which more than 60% analyse texts written in Brazilian variant. The majority of studies focus on the text classification task. Support vector machine and Naive Bayes appear as main the algorithms. Folha de Sao Paulo and Publico newspapers appear as main corpora, followed by the Portuguese Attorney General's Office corpus and Twitter.

The dilation-erosion-linear perceptron is a hybrid morphological neuron which has been recently proposed in the literature to solve some prediction problems. However, a drawback arises from such model for building mappings to solve tasks with complex input-output nonlinear relationships within effort estimation problems. In this sense, to overcome this limitation, we present a particular class of hybrid multilayer perceptrons, called the multilayer dilation-erosion-linear perceptron (MDELP), to deal with software development effort estimation problems. Each processing unit of the proposed model is composed of a mix between a hybrid morphological operator (given by a balanced combination between dilation and erosion operators) and a linear operator. According to Pessoa and Maragos’s ideas, we propose a descending gradient-based learning process to train the proposed model. Besides, we conduct an experimental analysis using relevant datasets of software development effort estimation and the achieved results are discussed and compared, according to MMRE and PRED25 measures, to those obtained by classical and state of the art models presented in the literature.

Identifying the language of a text is an important step for several natural language processing applications. State-of-the-art language identification (LID) systems perform very well when discriminating between unrelated languages on standard datasets. However, the LID task has a bottleneck when discriminating between similar languages or language varieties. Furthermore, LID has also proven to be very challenging when dealing with short texts such as the ones from Twitter. In this paper, we propose the use of smoothed n-gram language models to classify tweets in both Brazilian and European Portuguese variants. Word and character n-gram language models were combined and evaluated through five different classifiers. We have compared the smoothed n-gram language models together with the Term Frequency and Inverse Document Frequency weighting scheme. This paper also proposes an ensemble model, in which the class labels output were combined using majority voting and algebraic combiners. The best configuration reached accuracy of 92.71% using an ensemble model, which combines Lidstone (0.1) character 6-gram, Good–Turing word unigram, and Witten–Bell word bigram models, together with the Log-Likelihood Ratio estimation method.

Financial time series represent the stock prices over time and exhibit behavior similar to a data stream. Many works report on the use of data mining techniques to predict the future direction of stock prices and to discover patterns in the time series data to provide decision support for trading operations. Traditional optimization methods do not take into account the possibility that the function to be optimized, namely, the final financial balance for operations considering some stock, may have multiple peaks, i.e., be represented by multimodal functions. However, multimodality is a known feature of real-world financial time series optimization problems. To deal with this issue, this article proposes the PAA-MS-IDPSO-V approach (Piecewise Aggregate Approximation - Multi-Swarm of Improved Self-adaptive Particle Swarm Optimization with Validation). The proposed method aims to find patterns in financial time series to support investment decisions. The approach uses multi-swarms to obtain a better particle initialization for the final optimization phase since it aims to tackle multimodal problems. Furthermore, it uses a validation set with early stopping to avoid overfitting. The patterns discovered by the method are used together with investment rules to support decisions and thus help investors to maximize the profit in their operations in the stock market. The experiments reported in this paper compare the results obtained by the proposed model with the Buy-and-Hold, PAA-IDPSO approaches and another approach found in the literature. We report on experiments conducted with S&P100 index stocks and using the Friedman Non-Parametric Test with the Nemenyi post-hoc Test both with 95% confidence level. The results show that the proposed model outperformed the competing methods and was able to considerably reduce the variance for all stocks.

The Pd-catalyzed selective hydrogenation of C C double bond in (3,3-diphenylallyl)diisopropylamine or 3,3-diphenylallyl alcohol was evaluated using different catalytic systems [Pd/C, Pd(OAc)2/ionic liquid, isolated Pd(0) nanoparticles]. For the (3,3-diphenylallyl)diisopropylamine, hydrogenolysis is preferred over hydrogenation, and only moderate selectivities were obtained for the desired product. However, complete conversion and 100% selectivity were obtained for the hydrogenation of 3,3-diphenylallyl alcohol using isolated Pd(0) nanoparticles under mild condition. This successful strategy enabled the effective synthesis of diisopromine and feniprane drugs and opens new possibilities for the preparation of other biologically active compounds.

In this paper we present a study about the time series generator phenomenon from air pollutant concentration forecasting problems. It provides several evidences suggesting that this kind of generator phenomenon has nonlinear characteristics with long-term dependencies. Based on that, we present a nonlinear morphological model able to forecast these time series. Also, a descending gradient-based method, using ideas from the back-propagation algorithm, is presented to design the proposed model. Furthermore, an empirical analysis is conducted with the proposed model using a set of six relevant air pollutant concentration forecasting problems and employing three statistical measures to assess forecasting performance.

The dilation–erosion perceptron (DEP) is a hybrid morphological processing unit, composed of a balanced combination between dilation and erosion morphological operators, recently presented in the literature to solve some problems. However, a drawback arises from such model for building complex decision surfaces for non-linearly separable data. In this sense, to overcome this drawback, we present a particular class of morphological neural networks with multilayer structure, called the dilation–erosion neural network (DENN), to deal with binary classification problems. Each processing unit of the DENN is composed by a DEP processing unit. Also, a descending gradient-based learning process is presented to train the DENN, according to ideas from Pessoa and Maragos. Furthermore, we conduct an experimental analysis with the DENN using a relevant set of binary classification problems, and the obtained results indicate similar or superior classification performance to those achieved by classical and state of the art models presented in the literature.