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Heart failure (HF) is emerging as a major public health problem both in high- and low - income countries. The mortality and morbidity due to HF is substantially higher in low-middle income countries (LMICs). Accessibility, availability and affordability issues affect the guideline directed therapy implementation in HF care in those countries. This call to action urges all those concerned to initiate preventive strategies as early as possible, so that we can reduce HF-related morbidity and mortality. The most important step is to have better prevention and treatment strategies for diseases such as hypertension, ischemic heart disease (IHD), type-2 diabetes, and rheumatic heart disease (RHD) which predispose to the development of HF. Setting up dedicated HF-clinics manned by HF Nurses, can help in streamlining HF care. Subsidized in-patient care, financial assistance for device therapy, use of generic medicines (including polypill strategy) will be helpful, along with the use of digital technologies.

This study compared the most common absolute sprint threshold (> 25.2 km/h) with relative and individualized thresholds (> 70%, > 75%, > 80%, > 85% and > 90% of peak match speed). Twenty elite soccer players, competing in the first division of the Portuguese League, were monitored using GNSS equipment during thirty-four official matches. Peak match speed was retrieved as the individual maximal speed reached during the full season. Distances were registered when speed overcame the absolute and the relative thresholds. Mean±SD of peak speeds and distances covered were calculated, and Pearson correlation ( r ) and mean paired differences were performed to analyze relationships and differences between thresholds. The peak match speed was 32.9±1.4 km/h. Correlations between distances covered using the absolute and relative thresholds varied from very strong (> 70%: r = 0.84, p 75%: r = 0.89, p 80%: r = 0.88, p 85%: r = 0.79, p 90%: r = 0.59, p 75% (ES: 0.23 [95% CI: 0.16, 0.31]) and the > 90% (ES: -1.65 [95%CI: -1.85, -1.48]) relative thresholds presented the smallest and largest differences, respectively, with the absolute threshold. Differences were also found when considering the playing positions. While the distances covered by central midfielders were similar between the absolute and > 80% thresholds (-0.03 [-0.16, 0.10]), fullbacks covered largely more distance -1.88 [-2.42 -1.50]) in the absolute threshold than in the > 80% threshold. The distances covered by players varied based on the selected threshold, affecting the distances covered by different playing positions. Being the highest speed threshold within displacements thresholds, the absolute sprint threshold showed greater similarity to lower rather than higher relative thresholds.

Abstract Affective computing has experienced substantial advancements in recognizing emotions through image and facial expression analysis. However, the incorporation of physiological data remains constrained. Emotion recognition with physiological data shows promising results in controlled experiments but lacks generalization to real-world settings. To address this, we present G-REx, a dataset for real-world affective computing. We collected physiological data (photoplethysmography and electrodermal activity) using a wrist-worn device during long-duration movie sessions. Emotion annotations were retrospectively performed on segments with elevated physiological responses. The dataset includes over 31 movie sessions, totaling 380 h+ of data from 190+ subjects. The data were collected in a group setting, which can give further context to emotion recognition systems. Our setup aims to be easily replicable in any real-life scenario, facilitating the collection of large datasets for novel affective computing systems.

To assess peak speeds, usually assess players capacity to quickly accelerate and achieve/maintain a maximal speed in 30 m distance (Altmann et al., 2019). During these tests, players often start from stationary positions, reacting to a sound signal (Haff & Triplett, 2016). However, one may question these test procedures since peak speeds achieved during tests generally exceed peak speeds registered during matches (Buchheit et al., 2021; Djaoui et al., 2017). Additionally, during matches, soccer players usually perform leading sprints (those where players achieved the sprint threshold [> 25.2 km/h)] while entering the high-speed running category [19.8-25.2 km/h] in the previous 0.5 seconds than explosive sprints (those where players reached the sprint threshold without entering the high-speed running category in the previous 0.5 seconds) (di Salvo et al., 2009). This research aimed to characterize match peak speeds, during a 20-second time window (10 seconds immediately before and after the match peak speed), in soccer matches. Twenty elite soccer players were monitored with GNSS devices during six soccer matches from the Brazilian first division. For each player during each match, speeds were collected at 0.1-second intervals (10 Hz) from 10 seconds before and 10 seconds after the match peak speed. Speeds (mean ± SD) were calculated for speeds at each 0.1-second intervals and intra-individual speed differences were compared at every second of the 20 seconds window using paired mean differences. Effect sizes (ES) were established as trivial (4.0) with 90% confidence intervals. Match peak speeds ranged from 29.11 km/h to 31.64 km/h. Speeds registered 10 seconds before and 10 seconds after the match peak speed ranged from 5.11 km/h to 9.21 km/h and 6.90 km/h to 7.65 km/h respectively. Speed increased (acceleration) moderately (ES: 0.68 [0.64, 0.72]) 4 seconds before the match peak speed and decreased (deceleration) moderately (ES: -0.73 [-0.77, -0.69]) 3 seconds after the maximal effort. Match peak speeds were achieved from leading starts, which questions the current sprint test procedures. After the match's peak speed, players decelerate quicker than they accelerate but without reaching a full stop. Nevertheless, preparing players for intense decelerations should not be disregarded. Field tests and training sessions should provide a stimulus similar to what is observed during competition.

Asphalt mixtures are widely used as a surfacing material for pavements due to their several advantages. For this reason, robust numerical models still need to be developed to improve the understanding of their fracture behaviour. Recently, an incremental generalised Kelvin (GK) contact model that relates increments in contact displacements with increments in contact forces was proposed to assess the viscoelastic behaviour of asphalt mixtures within a discrete element method (DEM) framework. In this work, the contact model is extended to allow its application to asphalt mixture fracture studies. Two damage models—a brittle and a bilinear softening—coupled with the GK contact model are proposed to consider damage initiation and propagation. A parametric study is presented that assesses the impact of the GK-Damage parameters, showing a sensitivity to the loading velocity and the Maxwell elements, particularly its viscosity element, on the stress–strain response of a single contact. A reduced-size numerical mastic is initially used to speed up the calibration process of the GK-Damage contact parameters, with subsequent validation on a specimen with real experimental dimensions. It is shown that the proposed calibrated damage models can successfully reproduce the time-dependent behaviour, peak stress, and crack path observed in experimental results, highlighting the benefits of the adopted methodology. For the GK-Bilinear model, the fracture energy and maximum contact tensile stress are shown to adjust both the peak stress and softening response. Uniaxial tensile tests on asphalt mixtures indicate that the GK-Bilinear model provides a more realistic characterisation of fracture development. A higher susceptibility to damage at aggregate-to-mastic contacts compared to contacts within the mastic phase is identified.

The International Classification of Diseases, 10th edition (ICD-10), has been widely used for the classification of patient diagnostic information. This classification is usually performed by dedicated physicians with specific coding training, and it is a laborious task. Automatic classification is a challenging task for the domain of natural language processing. Therefore, automatic methods have been proposed to aid the classification process. This paper proposes a method where Cosine text similarity is combined with a pretrained language model, PLM-ICD, in order to increase the number of probably useful suggestions of ICD-10 codes, based on the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. The results show that a strategy of using multiple runs, and bucket category search, in the Cosine method, improves the results, providing more useful suggestions. Also, the use of a strategy composed by the Cosine method and PLM-ICD, which was called PLM-ICD-C, provides better results than just the PLM-ICD.

O Centro para Controlo e Prevenção de Doenças propõe o termo COVID longo ou condições pós-COVID para sintomas desenvolvidos durante ou após a COVID-19 e que persistem pelo menos dois meses, não sendo explicados por outros diagnósticos. Este estudo transversal tem como objetivo determinar a proporção de doentes com condições pós-COVID, descrevendo as suas características demográficas e clínicas e sintomas persistentes mais comuns. Foram incluídos 497 adultos com doença ligeira a moderada por COVID-19, avaliados por questionário estruturado após 12 a 16 semanas da infeção por SARS-CoV-2. A incidência cumulativa de condições pós-COVID foi de 47,5% (n=236). Os sintomas persistentes mais frequentes foram a astenia (n=151, 30,4%) e a dispneia (n=131, 26,4%). Após análise multivariada verificou-se que o sexo masculino está associado a menor incidência de condições pós-COVID (OR=0,36, p

Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task. In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive comparison. Furthermore, existing Deep Learning approaches and techniques for point cloud odometry tasks are explored, and the main technological solutions are compared and discussed. Open challenges are also laid out for the reader, hopefully offering guidance to future researchers in their quest to apply deep learning to complex 3D non-matrix data to tackle localization and robot navigation problems.

In this work, a fiber Bragg gratings (FBGs) based sensing insole, capable of simultaneously measure plantar force (PF) and shear force (SF) is proposed. The insole has four measuring points, strategically located for a full gait analysis. Each sensing point contains a sensor-cell which consists of a polylactic acid (PLA) structure, covered by an epoxy resin layer, and crossed by one optical fiber with two FBGs, FBG1 and FBG2, respectively. Due to the specific design of the system, the FBG1 is sensitive to both forces (with higher sensitivity to the PF), while the FBG2 is designed to detect only the SF. The instrumented insole was tested during static and gait exercises, and the results, obtained for the PF and SF monitoring, were according to those theoretically expected.

A pandemia COVID-19 obrigou a uma rápida adaptação dos serviços de saúde para que se pudesse manter a resposta e qualidade dos cuidados. De forma a garantir a acessibilidade dos utentes aos cuidados de saúde primários e minimizar as consequências do distanciamento social, oferecendo alternativas à consulta presencial, a nossa Unidade de Saúde Familiar (USF) criou a linha de apoio ao utente. Os profissionais da USF adquiriram de forma autónoma seis telemóveis e criaram seis linhas diferentes: duas linhas para apoio médico geral, duas linhas para assuntos relacionados com a COVID-19, uma linha de apoio de enfermagem e uma linha de apoio do secretariado clínico. Foi criada uma base de dados para registo de contactos médicos e de enfermagem. Esta base de dados permitiu ainda a referenciação direta a apoio de psicologia. Avaliou-se um período de seis meses (entre 16 de março e 16 de setembro de 2020). Foram registadas 7.535 chamadas: 5.281 nas linhas de apoio médico geral e 230 nas linhas médicas relacionadas com a COVID-19. Foram feitas 51 referenciações para apoio psicológico. As restrições que obrigaram a medidas extraordinárias serão levantadas e esta experiência pode fornecer bases para o futuro, nomeadamente como diversificar a acessibilidade de uma unidade de saúde familiar, salientando a necessidade de investimento nessa área. No contexto atual, a telemedicina tem-se revelado uma alternativa adequada para o atendimento e orientação dos utentes.

This paper addresses Visual Odometry (VO) estimation in challenging underwater scenarios. Robot visual-based navigation faces several additional difficulties in the underwater context, which severely hinder both its robustness and the possibility for persistent autonomy in underwater mobile robots using visual perception capabilities. In this work, some of the most renown VO and Visual Simultaneous Localization and Mapping (v-SLAM) frameworks are tested on underwater complex environments, assessing the extent to which they are able to perform accurately and reliably on robotic operational mission scenarios. The fundamental issue of precision, reliability and robustness to multiple different operational scenarios, coupled with the rise in predominance of Deep Learning architectures in several Computer Vision application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation. In this work, the performance and accuracy of Deep Learning methods on the underwater context is also benchmarked and compared to classical methods. Additionally, an extension of current work is proposed, in the form of a visual-inertial sensor fusion network aimed at correcting visual odometry estimate drift. Anchored on a inertial supervision learning scheme, our network managed to improve upon trajectory estimates, producing both metrically better estimates as well as more visually consistent trajectory shape mimicking.

The pinewood nematode (PWN), Bursaphelenchus xylophilus, one of the most serious forest pests worldwide, is considered the causal agent of the pine wilt disease (PWD). The main host species belong to the genus Pinus, and a variation in the susceptibility of several pine species to PWN infection is well-known. It is also recognized that there is variation in the virulence among B. xylophilus isolates. In the present study, we applied a quantitative mass spectrometry-based proteomics approach to perform a deep characterization of proteomic changes across two B. xylophilus isolates with different virulence from different hosts and geographical origins. A total of 1,456 proteins were quantified and compared in the two isolates secretomes, and a total of 2,741 proteins were quantified and compared in the nematode proteomes in pine tree extract and fungus stimuli conditions. From the proteomic analyses, a group of proteins was selected and identified as potential virulence biomarkers and shed light on putative most pathogenic proteins of this plant-parasitic nematode. Proteomic data are available via ProteomeXchange with identifier PXD029377.

O objetivo desta pesquisa consistiu em estudar a dinâmica espacial da precipitação no Estado de Alagoas, localizado no Nordeste do Brasil. Para responder ao objetivo da pesquisa foram empregadas técnicas geoestatísticas para construção de mapas de chuvas. Os resultados encontrados nesta pesquisa evidenciam uma grande variabilidade na distribuição das chuvas. O Leste Alagoano é o que possui maior precipitação acumulada, enquanto no Sertão as chuvas são escassas. Os resultados obtidos neste estudo podem auxiliar os órgãos responsáveis pelo gerenciamento dos recursos hídricos na tomada de decisão e na prevenção de desastres ambientais causados pelo excesso ou pela escassez das chuvas.

This paper addresses the development of a novel zero-shot learning method for remote marine litter hyperspectral imaging data classification. The work consisted of using an airborne acquired marine litter hyperspectral imaging dataset that contains data about different plastic targets and other materials and assessing the viability of detecting and classifying plastic materials without knowing their exact spectral response in an unsupervised manner. The classification of the marine litter samples was divided into known and unknown classes, i.e., classes that were hidden from the dataset during the training phase. The obtained results show a marine litter automated detection for all the classes, including (in the worst case of an unknown class) a precision rate over 56% and an overall accuracy of 98.71%.

Este artigo desenvolve em seu escopo o relato de experiências vivenciadas na prática docente com estudantes cegos. Visa retratar os métodos didáticos interiorizados nesse cotidiano. A compilação de informações foi obtida de forma empírica, por meio de observações decorrentes do convívio, das descrições obtidas diretamente com os estudantes, além da pesquisa bibliográfica. A experiência evidencia o desafio que se constitui trabalhar nos moldes inclusivos, visto que a formação docente atual não antecipa exemplos de realidade, exigindo do educador uma postura dinâmica e proativa, capaz de educar no processo de integração humana do âmbito escolar.

The pinewood nematode (PWN), Bursaphelenchus xylophilus, the pine wilt disease’s (PWD) causal agent, is a migratory endoparasitic nematode skilled to feed on pine tissues and on fungi that colonize the trees. In order to study B. xylophilus secretomes under the stimulus of pine species with different susceptibilities to disease, nematodes were exposed to aqueous pine extracts from Pinus pinaster (high-susceptible host) and P. pinea (low-susceptible host). Sequential windowed acquisition of all theoretical mass spectra (SWATH-MS) was used to determine relative changes in protein amounts between B. xylophilus secretions, and a total of 776 secreted proteins were quantified in both secretomes. From these, 22 proteins were found increased in the B. xylophilus secretome under the P. pinaster stimulus and 501 proteins increased under the P. pinea stimulus. Functional analyses of the 22 proteins found increased in the P. pinaster stimulus showed that proteins with peptidase, hydrolase, and antioxidant activities were the most represented. On the other hand, gene ontology (GO) enrichment analysis of the 501 proteins increased under the P. pinea stimulus revealed an enrichment of proteins with binding activity. The differences detected in the secretomes highlighted the diverse responses from the nematode to overcome host defenses with different susceptibilities and provide new clues on the mechanism behind the pathogenicity of this plant-parasitic nematode. Proteomic data are available via ProteomeXchange with identifier PXD024011.

Human-Computer Interaction (HCI) and games set a new domain in understanding people’s motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people’s health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson’s Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients’ quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.

Este artigo desenvolve em seu escopo o relato de experiências vivenciadas na prática docente com estudantes cegos. Visa retratar os métodos didáticos interiorizados nesse cotidiano. A compilação de informações foi obtida de forma empírica, por meio de observações decorrentes do convívio, das descrições obtidas diretamente com os estudantes, além da pesquisa bibliográfica. A experiência evidencia o desafio que se constitui trabalhar nos moldes inclusivos, visto que a formação docente atual não antecipa exemplos de realidade, exigindo do educador uma postura dinâmica e proativa, capaz de educar no processo de integração humana do âmbito escolar.

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain–Computer Interfaces (BCI) allows for unobtrusively monitoring one’s cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human–computer interaction variables.

Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between them with their application to a public diabetes and human resource datasets. The two mainly used categories that allow the learning process without requiring explicit programming are supervised and unsupervised learning. For that, we use Scikit-learn, the free software machine learning library for Python language. The best-performing algorithm was Random Forest for supervised learning, while in unsupervised clustering techniques, Balanced Iterative Reducing and Clustering Using Hierarchies and Spectral Clustering algorithms presented the best results. The experimental evaluation shows that the application of unsupervised clustering algorithms does not translate into better results than with supervised algorithms. However, the application of unsupervised clustering algorithms, as the preprocessing of the supervised techniques, can translate into a boost of performance.

Being the second most common neurodegenerative disease, Parkinson's disease (PD) can be symptomatically treated, although, unfortunately, it cannot be cured yet. Moreover, diagnosing and assessing PD patients is a complex process, requiring continuous monitoring. In this vein, the design, development, and validation of innovative assessment tools may be helpful in the management of patients with PD, in particular. Based on intelligent ICT interventions, the i-PROGNOSIS project intends to mitigate PD's specific symptoms, such as neurological movement disorders of gait, balance, coordination, and posture, already characterized in the early phase of the disease. From this perspective, an innovative iPrognosis motor assessment tool is presented here, taking into consideration the Unified Parkinson Disease Rating Scale (UPDRS) Part III motor skills testing items, for evaluating the motor skills status. The efficiency of the proposed Assessment Tests to reflect the motor skills status, similarly to the UPDRS Part III items, was validated via 27 participants (18 males; mean age = 62 years, SD = 10.36 years; range, 43–79 years) with early (n = 10) and moderate (n = 17) PD who performed the Assessment Tests. Features from the latter were then correlated with the corresponding clinically assessed UPDRS Part III items, and statistically significant negative correlations (range, −0.364 to −0.802) were identified between the median values of the Assessment Tests and the UPDRS Part III items. In this vein, the iPrognosis Assessment Tests were integrated within the personalized interventions of the i-PROGNOSIS project, providing alternative means of assessing their effect on the PD patient's motor skills enhancement. The promising results presented here elaborate on the concept of using ICT-based assessment means to achieve comparable outcomes with the clinical standards in motor skills assessment.

Steel slag is a byproduct generated as waste during the steelmaking process and can be considered a cost-effective and environmentally acceptable alternative to replace natural aggregates. Using steel slag aggregates (SSA) to produce asphalt mixtures promotes sustainability and circular economy principles by using an industrial byproduct as a raw material. Thus, this work mainly aims to design more sustainable asphalt mixtures with high amounts of SSA that fit the circular economy expectations. This work developed two asphalt mixtures with SSA for surface (AC 14 surf) and binder/base (AC 20 bin/base) courses. Initially, the excellent wearing and polishing resistance of SSA and their good affinity with bitumen demonstrated the potential of this byproduct to be used in asphalt mixtures. Then, when analyzing the influence of using two different SSA incorporation rates (50% and a percentage close to 100%) in both asphalt mixtures, it was concluded that the use of SSA should be limited to 75% to avoid excessive air void contents and durability problems. The importance of considering the different particle densities of SSA and natural aggregates was highlighted during the mix design by defining a relationship between an effective and equivalent binder content. Finally, the mechanical performance of AC 14 and AC 20 with 75% SSA incorporation was compared to identical conventional mixtures produced with natural granite aggregates. The results obtained showed that the asphalt mixtures with 75% SSA have some workability problems due to the rough and porous surface of SSA. However, they present an excellent water sensitivity and permanent deformation resistance, surpassing the performance of the conventional asphalt mixtures.

En este artículo se presentan los resultados de una investigación cualitativa realizada sobre el abandono escolar en cuatro planteles del bachillerato tecnológico agropecuario en México. La investigación se realizó con entrevistas cualitativas a directivos, docentes, tutores, alumnos y jóvenes que abandonaron la escuela. La razón principal ya no es, por la política de becas, económica, sino la reprobación. Ésta se origina por la insuficiencia académica de los estudiantes, el ausentismo en las aulas y la falta de interés en estudiar. Las razones del abandono se encuentran también asociadas a problemas familiares, falta de recursos económicos, desinterés de los padres en el proceso de formación de sus hijos, problemas de salud y embarazos tempranos. En las conclusiones se discute la política de flexibilización de los criterios de acreditación.

This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%.

This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications.

Objetivos: Determinar a prevalência do Síndrome de Fragilidade na população da região Norte de Portugal continental. Tipo de estudo: Estudo observacional, descritivo e transversal. Local: USF Nova Era, USF Veiga do Leça, USF Cristelo, USF São Miguel-O-Anjo. População: Utentes nascidos antes de 1952 à data da elaboração do estudo. Métodos: Foi selecionada uma amostra de conveniência de 408 participantes da população de utentes, nascidos antes de 1952, que se encontravam inscritos nas quatro unidades em estudo (N=7189) em Março de 2016. O número de participantes foi distribuído pelas quatro unidades, de acordo com o seu peso relativo na população (132 - USF Nova Era, 94 - USF Veiga do Leça, 86 - USF Cristelo e 96 - USF São Miguel-O-Anjo). Aplicou-se um questionário demográfico, o questionário PRISMA-7 e o Gait speed test (para uma distância de quatro metros). Para a análise estatística utilizou-se o programa SPSS® versão 20. Resultados: A média de idades foi de 74,3 ± 6,6, dos quais 54,7% eram mulheres. A maioria era casado (64,6%) e com o primeiro ciclo de escolaridade (46,6%). A Prevalência do Síndrome de Fragilidade foi de 34,5% e a prevalência de participantes Pré Frágeis foi de 18,3% para a amostra em estudo. A prevalência do Síndrome de Fragilidade aumenta com a idade, na população não casada e na população que tem menos de quatro anos de escolaridade. Conclusão: Os resultados obtidos da prevalência do Síndrome de Fragilidade são superiores aos referidos na literatura Europeia e Norte-Americana, mas semelhantes aos descritos nos países do Sul da Europa. Palavras chave: “Fragilidade”, “Geriatria”, “Idoso frágil” Tipologia do artigo: Investigação original

When temperatures drop to significantly low levels, road pavements are subjected to thermally-induced stresses, resulting in the appearance of thermal cracking, among other distresses. In these situations, polymers can be used as asphalt binder modifiers to improve certain asphalt binder properties, such as elastic recovery, cohesion, and ductility. Polymers also minimize some of the problems of asphalt mixtures, such as thermal and fatigue cracking and permanent deformation. This work’s objective was to study the behavior of asphalt mixtures at low temperatures, mainly when using modified binders. Thus, three binders were selected and tested: a standard 50/70 penetration grade bitumen and two polymer-modified binders (PMB), obtained by adding, respectively, 2.5% and 5.0% of styrene–butadiene–styrene (SBS) in the 50/70 pen grade bitumen. Then, the PMBs were incorporated into stone mastic asphalt mixtures (namely SMA 11), which were subjected to low-temperature mechanical tests based on the most recent European Standards. The asphalt binders and mixtures evaluated in this work were tested for thermal cracking resistance, creep, elastic recovery, cohesive strength, and ductility strength. Overall, it is concluded that the studied asphalt mixtures with PMB, with just 2.5% SBS, performed adequately at low temperatures down to −20 °C.

Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.

In this work, a fiber Bragg grating (FBG) based sensing system for wheelchair pressure ulcer prevention was developed. Six FBGs were strategically positioned in a wheelchair to monitor the more prominent bone areas, namely scapulas (right (SR) and left (SL)), ischiatic zone (right (IR) and left (IL)), and heels (right (HR) and left (HL)). The sensing architecture was tested by a female user during pressure relief exercises, to verify its effectiveness on pressure monitoring. The proposed system proves to be a compact and reliable solution for wheelchair pressure ulcer prevention, making it a suitable alternative to existing conventional electronic sensors, with the advantage of being immune to electromagnetic interferences and usable in humid environments. In addition to the pressure, the breathing rate was also monitored. By combining the proposed sensing architecture with a wheelchair user detection software, it is possible to create alerts for the user to know when a new position should be adopted, in order to relieve the pressure in a specific area, thus avoiding one of the biggest problems for such patients, pressure ulcers.

BACKGROUND Cutaneous leishmaniasis (CL) is a world-wide health problem which currently lacks effective, affordable and easy to use therapy. Recently, the meglumine antimoniate (MA) intralesional infiltration was included among the acceptable therapies for New World leishmaniasis. While this approach is attractive, there is currently little evidence to support its use in Americas. OBJECTIVES The aim of this study was to provide information about effectiveness and safety of a standardised MA intralesional infiltration technique for the treatment of CL. METHODS It is a single-arm phase II clinical trial conducted at a Brazilian referral centre. CL cases with parasitological confirmation presenting a maximum of three CL-compatible skin lesions were treated with weekly MA intralesional infiltration by using a validated technique, up to a maximum of eight infiltrations. RESULTS A total of 53 patients (62 lesions) were included. Overall, patients received a median of seven infiltrations (IQR25-75% 5-8) over a median treatment period of 43 days (IQR25-75% 28-52 days). The definitive cure rate at D180 was 87% (95% CI:77-96%). The majority of adverse events were local, with mild or moderate intensity. Bacterial secondary infection of the lesion site was observed in 13% of the treated patients, beside two intensity-three adverse events (hypersensitivity reactions).

The development of vision-based navigation systems for mobile robotics applications in outdoor scenarios is a very challenging problem due to frequent changes in contrast and illumination, image blur, pixel noise, lack of image texture, low image overlap and other effects that lead to ambiguity in the interpretation of motion from image data. To mitigate the problems arising from multiple possible interpretations of the data in outdoor stereo egomotion, we present a fully probabilistic method denoted as probabilistic stereo egomotion transform. Our method is capable of computing 6-degree of freedom motion parameters solely based on probabilistic correspondences without the need to track or commit key point matches between two consecutive frames. The use of probabilistic correspondence methods allows to maintain several match hypothesis for each point, which is an advantage when ambiguous matches occur (which is the rule in image feature correspondence problems), because no commitment is made before analysing all image information. Experimental validation is performed in simulated and real outdoor scenarios in the presence of image noise and image blur. Comparison with other current state-of-the-art visual motion estimation method is also provided. Our method is capable of significant reduction of estimation errors mainly in harsh conditions of noise and blur.

Severe speech and motor impairments caused by several neurological disorders can limit communication skills to simple yes/no replies. Variability among patients’ physical and social conditions justifies the need of providing multiple sources of signals to access to Augmentative and Alternative Communication (AAC) systems. Our study presents the development of a new user-computer interface that can be controlled by the detection of various sources of biosignals. Wireless sensors are placed on the body and users learn to enhance the control of detected signals by visual biofeedback, on a switch based control approach. Experimental results in four patients with just few residual movements showed that different sensors can be placed in different body locations and detect novel communication channels, according to each person’s physiological and social condition. Especially in progressive conditions, this system can be used by therapists to anticipate progression and assess new channels for communication.

Background: Postoperative delirium (POD) is a frequent post-surgical complication that is associated with increased mortality and poor patient outcomes. POD is a complex disorder with multiple risk factors such as pre-existing patient comorbidities and perioperative complications. The aim of this study was to evaluate the incidence of POD and to identify risk factors for the development of POD in a post-anesthesia care unit (PACU). Methods: We enrolled 97 adult patients admitted to a PACU over a five-day period (start date September 6, 2010). Patient demographics and intraoperative and postoperative data were collected. Patients were followed for the development of delirium using the Intensive Care Delirium Screening Checklist. Descriptive analyses of variables were used to summarize data, and the Mann–Whitney U test was used to compare continuous variables; the chi-square or Fisher's exact test was used for comparisons. Univariate analysis was performed using simple binary logistic regression with odds ratios (OR) and 95% confidence intervals (95% CI). The significance level for multiple comparisons was controlled by applying the Bonferroni correction for multiple comparisons and variables were deemed significant if p≤0.0025. Results: Six percent of patients developed POD. These patients were older and more likely to have higher American Society of Anesthesiologists (ASA) physical status (83 vs. 22% with ASA III/IV, p=0.004) as well as a higher frequency of congestive heart failure (50 vs. 3%, p=0.003) and a higher Revised Cardiac Risk Index (RCRI) score (33 vs. 6% with RCRI ≥2, p=0.039). The duration of anesthesia for patients with POD was also longer and they received a greater volume of crystalloids, colloids, and erythrocytes during surgery. Congestive heart disease was an independent risk factor for POD (OR 29.3, 95% CI 4.1–210.6; p

Se presentan los resultados de los estudios de adsorción de trihalometanos (THM) (cloroformo, bromodiclorometano, dibromoclorometano y bromoformo) sobre carbones activados (CA) obtenidos a partir de materiales carbonosos de la región de Cuyo de la República Argentina, (carozos de damascos, escobajo de uva y madera de eucaliptus). El programa de experimentación incluyó dos etapas básicas: la preparación de los carbones activados y los ensayos para obtener las isotermas de adsorción de cada trihalometano. Las isotermas correspondientes a cada sistema adsorbato-adsorbente fueron correlacionadas adecuadamente por la ecuación de Freundlich, y se determinaron los parámetros correspondientes. Se evaluó la influencia sobre la capacidad adsorbente de cada carbón activado el tipo de trihalometano a retener, la materia prima empleada para preparar el CA, la temperatura y la concentración inicial de los THM en los ensayos de adsorción. Los resultados mostraron que el tipo de materia prima empleada para la preparación de loscarbones activados influye notablemente sobre las capacidades de adsorción, capacidades éstas que no puedenser explicadas solamente por sus valores de superficies específicas. Por el contrario, la capacidad adsorbente delos carbones activados es sólo levemente influenciada por la temperatura a la que se desarrollan las pruebas deadsorción.

This paper shows the use and value of natural resources from a social, sustainable and legal perspective, under the scheme of Sustainable Forest Management Units (UMAFOR by its Spanish acronym). Two participatory workshops were carried out in March 2010, one in UMAFOR Sierra La Madera and another in UMAFOR Sierra Alta, belonging to the Sierra de Sonora. An adaptation of “Problems, Causes and Consequences” of Ramírez-García (2004) methodology was used to discuss issues such as conservation, productive activities, society, laws, research and support programs. The objective was to analyze challenges and opportunities in the Sierra de Sonora under this management scheme. The ratting of natural resource for inhabitant and producers was documented based in ecosystems type and its value for use. Economic value was the most important for productive and non productive use; however, there are opportunities to transit from traditional to sustainable activities. Local producers are interested in linking whit research centers and consultants to get support for diversification of productive activities. It is possible to have changes according to sustainable development; UMAFOR is an alternative to achieve those changes.

This research will help to design a system of Oreochromis niloticus intensive fish farming in the community San Clemente de Térapa, Moctezuma, Sonora, México, using thermal water from a perennial spring in circular ponds during 12 weeks. Production of O. niloticus is a common practice in aquaculture to local consume, because this specie has an adequate growth rate and feed conversion, also resist critical environmental conditions like temperature, anoxia, and tolerance to water hardness and high organic matter content. This design was according to quality and quantity of water. Body growth and survive was measured, also the farming viability under the particular conditions of the prototype design. Two circular ponds of 4.5 m and 3 m in diameter were installed, where were introduced 800 and 400 masculinized “young fish”, whit an average weight of 0.84 and 0.54 g respectively. Biometrics measured and physicochemical parameters of water were assessment (temperature, dissolved oxygen and pH). At the end of the culture was obtained a survive rate of 98.5% and 98.25 % en each pond respectively, an excellent adaptation of the organisms and an acceptable body mass. This study shows is possible to get positive results under these conditions.

OBJETIVO: avaliar a frequência de testes anti-HIV realizados no pré-natal e de testes rápidos solicitados para estantes internadas para o parto. MÉTODO: trata-se de um estudo de corte transversal com 711 gestantes atendidas no momento do parto no período de janeiro a julho de 2010. Excluíram-se do estudo aquelas admitidas para controle clínico e as que não permitiram que seus dados fossem incluídos na pesquisa. Utilizou-se o teste do χ² ou o teste de Fisher para comparação de proporções na análise univariada. Foram incluídas no modelo de regressão logística todas as variáveis com valor pPURPOSE: to evaluate the frequency of HIV tests performed during prenatal care and rapid tests ordered for pregnant women admitted at the time of delivery. METHOD: this was a cross-sectional study with 711 pregnant women at delivery during the period from January to July 2010. Women admitted for clinical control and those that did not allow their clinical data to be included in the study were excluded. The χ² test or the Fisher's Exact test was used for comparison of the proportion in univariate analysis. All the variables with p

Multidrug- (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) present a challenge to disease control and elimination goals. Lisbon, Portugal, has a high TB incidencerate and unusual and successful XDR-TB strains that have been found in circulation foralmost two decades. For the last 20 years, a continued circulation of two phylogenetic clades, Lisboa3 and Q1, which are highly associated with MDR and XDR, have been observed. In recent years, these strains have been well characterized regarding the molecular basis of drug resistance and have been inclusively subjected to whole genome sequencing (WGS). Researchers have been studying the genomic diversity of strains circulating in Lisbon and its genomic determinants through cutting-edge next generation sequencing. An enormous amount of whole genome sequence data are now available for the most prevalent and clinically relevant strains circulating in Lisbon. It is the persistence, prevalence and rapid evolution towards drug resistance that has prompted researchers to investigate the properties of these strains at the genomic level and in the future at a global transcriptomic level. Seventy Mycobacterium tuberculosis (MTB) isolates, mostly recovered in Lisbon, were genotyped by 24-loci Mycobacterial Interspersed Repetitive Unit – Variable Number of Tandem Repeats (MIRU-VNTR) and the genomes sequenced using a next generation sequencing platform – Illumina HiSeq 2000. The genotyping data revealed three major clusters associated with MDR-TB (Lisboa3-A, Lisboa3-B and Q1), two of which are associated with XDR-TB (Lisboa3-B and Q1), whilst the genomic data contributed to elucidating the phylogenetic positioning of circulating MDR-TB strains, showing a high predominance of a single SNP cluster group 5. Furthermore, a genome-wide phylogeny analysis from these strains, together with 19 publicly available genomes of MTB clinical isolates, revealed two major clades responsible for MDR/XDR-TB in the region: Lisboa3 and Q1. The data presented by this study contributes to the expanding knowledge of MTB genomic diversity yielding insights on microevolution and identification of novel compensatory mutations. Additionally, the analysis of nonsynonymous/ synonymous ratios revealed heterogeneities across the chromosome, genotype and Clusters of Orthologous Groups, highlighting possible and different evolution strategies. Overall, the results that are presented support the notion of an increasing genomic diversity that may support both setting and host adaptation.

Tuberculosis is a disease relatively frequent in renal transplant patients, presenting a wide variety of clinical manifestations, often involving various organs and potentially fatal. Gastrointestinal tuberculosis, although rare in the general population, is about 50 times more frequent in renal transplant patients. Intestinal tuberculosis has a very difficult investigational approach, requiring a high clinical suspicion for its diagnosis. Therapeutic options may be a problem in the context of an immunosuppressed patient, requiring adjustment of maintenance therapy. The authors report two cases of isolated gastro-intestinal tuberculosis in renal transplant recipients that illustrates the difficulty of making this diagnosis and a brief review of the literature on its clinical presentation, diagnosis, and therapeutic approach.