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Este debate ocorreu na data de 30 de outubro do ano de 2021 como um lançamento em chamada pública para o dossiê temático de mesmo título. Ele está hospedado na plataforma YouTube no canal Luz Laboratório UDESC, pertencente ao Laboratório de iluminação da Universidade do Estado de Santa Catarina. O debate integrou a programação da décima edição do Evento A Luz em Cena. Teve como debatedores os integrantes da comissão editorial do Quarto Dossiê entre eles Flora Holderbaum, Thais Oliveira, Morgana Martins, Ernani Maleta e José Claudio Castanheira. A abertura e encerramento ficou sobre a coordenação de Marcelo Araújo como um representante do evento.

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments., The work received financial support from FCT through the PhD fellowships PD/BD/128183/2016 and SFRH/BD/124912/2016, the project “Neurofield” (PTDC/MAT-APL/31393/2017) and the research centre CMAT within the project UID/MAT/00013/2013.

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Na sociedade contemporânea, há um número crescente de textos multimodais e de recursos tecnológicos que constroem representações sociais e estabelecem relações interpessoais, que, por sua vez, influenciam a maneira como entendemos nossas experiências cotidianas nos mais diversos contextos. Neste artigo, investigamos representações de masculinidades presentes em capas de revistas produzidas para o público LGBT+. Com base nos estudos de Kress e van Leeuwen (2006) em sua gramática do design visual, bem como em conceitos propostos pela Análise Crítica do Discurso (ACD) de Fairclough (1989), além de estudos de gênero social (LOPES, 2006; CONNELL, 2005), foram analisadas seis capas da revista MaisJR, com circulação nacional, tendo sido escolhida por ter a maior tiragem entre revistas do gênero no Brasil. Nessas capas, buscamos encontrar aspectos que nos ajudassem a entender se tais representações contribuem para a manutenção de uma visão hegemônica de masculinidade, ou se, ao contrário, ajudam a desconstruir padrões conhecidos na sociedade e propagados em outros meios. Após a seleção das capas, fizemos a análise multimodal e discutimos os resultados textuais encontrados com base em conceitos da ACD. Resultados gerais indicam que a revista trabalha com uma política editorial que favorece a propagação de valores hegemônicos no que diz respeito a masculinidades, valorizando o homem forte, musculoso, que possui pelos no corpo e que transmite a ideia de poder por meio do vestuário e/ou de como se comporta no meio social.

The skin is the most extensive and outermost organ in the body and can be greatly exploited both from the point of view of alternative routes of systemic drug delivery and treatment of dermatological diseases. Because of its main function as a barrier against harmful external agents, it also becomes a barrier to drug administration, but there are strategies to reduce this limitation of this promising route of administration. The development of polymer-based film-forming formulations is extensively studied for this purpose, since the formation of a film on the skin increases the contact time of the drug, for this being characterized as a controlled release reservoir system. There are a multitude of possible polymers to compose these formulations and their choice must be made according to the purpose of each application. This work, therefore, aims to study the state of the art of film forming systems for topical application of pharmaceutical formulations.

Na sociedade contemporânea, são inúmeras as demandas criadas cotidianamente decorrentes das relações interseccionais que compõem as experiências identitárias de indivíduos e de grupos sociais diversos, com base em marcadores sociais da diferença como gênero, sexualidade, raça, classe, etnia, afiliação religiosa e deficiência, que frequentemente se tornam catalizadoras de preconceitos e de discriminação. Diante desse contexto, investigamos, neste artigo, a construção do imaginário sociodiscursivo sobre o Catimbó-Jurema por meio da análise de quadros de cenas selecionadas do filme O Auto da Compadecida (2000), com suporte teórico-metodológico e crítico da gramática sistêmico-funcional (HALLIDAY; MATTHIESSEN, 2014), mais especificamente do sistema de transitividade da metafunção experiencial, da gramática do design visual (KRESS; VAN LEEUWEN, 2021), com foco nas metafunções representacional e composicional, e da análise crítica do discurso (FAIRCLOUGH, 1995, 2015; RESENDE; RAMALHO, 2006), com base no modelo tridimensional de Fairclough, em relação transdisciplinar com estudos em antropologia, ciências da religião, sociologia e filosofia. Resultados gerais das análises críticas e sistêmico-funcionais do discurso multimodal sobre o Catimbó-Jurema no filme O Auto da Compadecida apontam que a construção desse imaginário social ocorre por meio da mobilização de recursos verbais e imagéticos que buscam associar religiões afro-indígenas ao demoníaco na intersecção de discursos e práticas sociais que se (re)articulam de formas complexas, históricas e situadas. Por fim, compreendemos que as análises empreendidas nos possibilitaram uma visão crítica não apenas quanto à utilização dos recursos multimodais neste filme, mas também no que diz respeito às inter-relações entre os processos de construção de sentido por meio da linguagem e as inteligibilidades que também são parte do imaginário sociocultural.

Advances in the automotive industry are changing the relationship between cars and drivers. Advanced driver assistant systems, such as navigation systems, advanced cruise control, collision avoidance systems, and other safety systems, are now common and assist the driver in controlling the car. Smart key fobs have made getting in and starting the car almost effortless: the fob can be left in the pocket and the doors will unlock when a driver/user approaches the car and simply touches the door handle. This is a level of personalization and convenience that is almost standard today. The research presented here brings a new perspective on personalization and driver assistance systems. An online survey was conducted, which aimed to gather public opinion on the usefulness of endowing future (semi-)autonomous cars with social and cognitive behavior, such as the ability to learn drivers' routines and preferences in order to make decisions and perform actions in preparation for the next trip and to manage comfort within the cockpit without being commanded to do so. After filtering, the study included 657 respondents from 93 nations. The results demonstrate a favorable opinion towards such human-centered cognitive vehicles and could be helpful for designers in the automotive industry and other related stakeholders in the development of future cognitive vehicles.

Shunt surgery (SS) remains the most effective treatment for idiopathic Normal pressure hydrocephalus (iNPH), but the selection of the patients with the greatest potential benefit remains elusive.Identify gait features predictive of best response to SS in iNPH.Eight patients with iNPH were assessed at baseline, after Cerebrospinal fluid tap-test (CSF-TT) and SS, with clinical scales (Clinical/Patient Global Clinical Impression, EuroQol-5D, Clinical Dementia Rating Scale(CDR), MoCA test, Hoehn-Yahr Scale) and gait analysis with inertial sensors.The 8 included iNPH patients had a mean age of 73 years(59-81), moderate cognitive (CDR-1.5 (0.5-2); MoCA-9.5 (3-21)) and motor impairment (Hoehn-Yahr-2.75(2-3)). After SS, patients had a significant improvement in cognition (MoCA, p = 0.001) and quality of life. At baseline, patients with lower improvement (no change/ minimally improved) (n = 2), in comparison to patient with higher improvement (much/very much improved) (n = 6), already had higher cognitive impairment (MoCa-3(3-3) vs. 11(7-21)). Patients with lower improvement had a lower % of change in gait performance at LP (mean 10.2 %) and were absent of additional benefit after SS(mean -0.8 %). In contrast, gait performance in patients with higher improvement consistently got better from baseline to LP (mean 23.1 %) and from baseline to SS (mean 82.9 %). A significant negative correlation was observed between CDR score and several gait variables: speed (rThe magnitude of gait improvement after CSF-TT, quantified by gait analysis, can be used as an integral variable in the multimodal clinical approach to the prediction of improvement after SS.

Neurofeedback training (NFT) is a technique often proposed to train brain activity SR with promising results. However, some criticism has been raised due to the lack of evaluation, reliability, and validation of its learning effects. The current work evaluates the hypothesis that SR learning may be improved by priming the subject before NFT with guided mindfulness meditation (MM). The proposed framework was tested in a two-way parallel-group randomized controlled intervention with a single session alpha NFT, in a simplistic serious game design. Sixty-two healthy naïve subjects, aged between 18 and 43 years, were divided into MM priming and no-priming groups. Although both the EG and CG successfully attained the up-regulation of alpha rhythms (F(1,59) = 20.67, p &lt, 0.001, ηp2 = 0.26), the EG showed a significantly enhanced ability (t(29) = 4.38, p &lt, 0.001) to control brain activity, compared to the CG (t(29) = 1.18, p &gt, 0.1). Furthermore, EG superior performance on NFT seems to be explained by the subject’s lack of awareness at pre-intervention, less vigour at post-intervention, increased task engagement, and a relaxed non-judgemental attitude towards the NFT tasks. This study is a preliminary validation of the proposed assisted priming framework, advancing some implicit and explicit metrics about its efficacy on NFT performance, and a promising tool for improving naïve “users” self-regulation ability.

Water loss is one of the factors that most affect a concessionaire’s financial sustainability. Early detection of any anomaly in water consumption is very valuable. This article aims to carry out a preliminary study to detect change points in consumption associated with water meter malfunction. The dataset is composed of water consumption measurements of two different companies (a hotel and a hospital) located in the north of Portugal, obtained during a complete year. Different methods were implemented in order to study its effectiveness in the detection of change points in the time series related to a sharp decrease in water consumption. Results suggest that the Seasonal Decomposition of Time Series by Loess method (STL) and the combination of several breakpoint detection methods is a suitable approach to be implemented in a software system, in order to help the company in anomaly detection and in the decision-making process of substituting the water meters.

As partículas poliméricas são estruturas complexas, que podem ser produzidas a partir de diversos tipos de polímeros, e as suas propriedades físico-químicas podem ser modificadas a fim de alterar o perfil de liberação de ativos. Neste estudo foram obtidas partículas poliméricas de poli(ácido lático) (PLA), pela técnica de emulsão/difusão, com a utilização de poloxamer (PF68) como tensoativo do sistema. Depois de preparadas as partículas, a suspensão resultante foi seca por dois métodos: Spray Dryer ou secagem por aspersão e Liofilização, e caracterizados por Espalhamento Dinâmico de Luz (DLS) Análise Termogravimétrica (TGA), Calorimetria Diferencial de Varredura (DSC). No TGA, observou-se dois eventos de degradação, os quais podem ser relacionados aos componentes da formulação (PLA e PF68). Quando as amostras foram analisadas por DLS, a amostra seca por spray dryer teve menor tamanho de partícula do que a amostra liofilizada. Na análise de TGA, ambas as amostras mostraram que a temperatura de degradação inicial foi menor do que as dos polímeros puros (PLA e PF68) e esse resultado está correlacionado com o tamanho da partícula. A amostra liofilizada apresentou a maior temperatura (Tonset), devido a um colapso das partículas oriundo da falta de crioprotetor. Por meio da técnica de calorimetria diferencial de varredura (DSC) tem-se que, a amostra obtida por spray dryer possui 2 populações de cristais, que são apresentados por um pico alargado, em temperaturas mais baixas, resultante de cristais menores ou mais imperfeitos e um pico de cristais mais perfeitos ou maiores, em temperaturas mais elevadas. Já a amostra liofilizada há apenas uma população de cristais. O grau de cristalinidade das partículas obtidas por liofilização e por spray dryer foi de 28% e 61%, respectivamente. Dessa forma, as partículas desenvolvidas por spray dryer apresentaram uma maior organização. A utilização de diferentes técnicas de secagem de partículas representa uma estratégia para produzir sistemas poliméricos com diferentes características.

Accepted Manuscript, The literature on gait analysis in Vascular Parkinsonism (VaP), addressing issues such as variability, foot clearance patterns, and the effect of levodopa, is scarce. This study investigates whether spatiotemporal, foot clearance and stride-to-stride variability analysis can discriminate VaP, and responsiveness to levodopa. Fifteen healthy subjects, 15 Idiopathic Parkinson's Disease (IPD) patients and 15 VaP patients, were assessed in two phases: before (Off-state), and one hour after (On-state) the acute administration of a suprathreshold (1.5 times the usual) levodopa dose. Participants were asked to walk a 30-meter continuous course at a self-selected walking speed while wearing foot-worn inertial sensors. For each gait variable, mean, coefficient of variation (CV), and standard deviations SD1 and SD2 obtained by Poincaré analysis were calculated. General linear models (GLMs) were used to identify group differences. Patients were subject to neuropsychological evaluation (MoCA test) and Brain MRI. VaP patients presented lower mean stride velocity, stride length, lift-off and strike angle, and height of maximum toe (later swing) (p, projects NORTE-01-0145-FEDER-000016 (NanoSTIMA) and NORTE-01-0145-FEDER-000026 (DeM), financed by the Regional Operational Program of the North (NORTE2020), under PORTUGAL2020 and FEDER.

The identification of stop locations in GPS trajectories is an essential preliminary step for obtaining trip information. We propose a neural network approach, based on the theoretical framework of dynamic neural fields (DNF), to identify automatically stop locations from GPS trajectories using their spatial and temporal characteristics. Experiments with real-world GPS trajectories were performed to show the feasibility of the proposed approach. The outcomes are compared with results obtained from more conventional clustering algorithms (K-means, hierarchical clustering, and HDBSCAN) which usually limit the use of the available temporal information to the definition of a threshold for the duration of stay. The experimental results show that the DNF approach not only robustly identifies places visited for a longer time but also stop locations that are visited for shorter periods but with higher frequency. Moreover, the self-stabilized activation patterns that the network dynamics develop and continuously update in response to GPS input encode simultaneously the spatial information and the time spent in each location. The impact of the obtained results on systems that automatically detect drivers’ daily routines from GPS trajectories is discussed.

Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This article presents a neurocomputational model based on the theoretical framework of dynamic neural fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimensions. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions., This work was supported in part by FCT (Portuguese Foundation for Science and Technology) through the Ph.D. Fellowship under Grant PD/BD/128183/2016; in part by the European Structural and Investment Funds in the FEDER Component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) and National Funds, through the FCT under Project PTDC/MAT-APL/31393/2017 (NEUROFIELD) and Project POCI-01-0247FEDER-039334; and in part by Research and Development Units Project Scope under Project UIDB/00319/2020 and Project UIDB/00013/2020.

Idiopathic Parkinson's disease (IPD) and vascular parkinsonism (VaP) present highly overlapping phenotypes, making it challenging to distinguish between these two parkinsonian syndromes. Recent evidence suggests that gait assessment and response to levodopa medication may assist in the objective evaluation of clinical differences. In this paper, we propose a new approach for gait pattern differentiation that uses convolutional neural networks (CNNs) based on gait time series with and without the influence of levodopa medication. Wearable sensors positioned on both feet were used to acquire gait data from 14 VaP patients, 15 IPD patients, and 34 healthy subjects. An individual's gait features are affected by physical characteristics, including age, height, weight, sex, and walking speed or stride length. Therefore, to reduce bias due to intersubject variations, a multiple regression normalization approach was used to obtain gait data. Recursive feature elimination using the linear support vector machine, lasso, and random forest were applied to infer the optimal feature subset that led to the best results. CNNs were implemented by means of various hyperparameters and feature subsets. The best CNN classifiers achieved accuracies of 79.33%±6.46, 82.33%±10.62, and 86.00%±7.12 without (off state), with (on state), and with the simultaneous consideration of the effect of levodopa medication (off/on state), respectively. The response to levodopa medication improved classification performance. Based on gait time series and response to medication, the proposed approach differentiates between IPD and VaP gait patterns and reveals a high accuracy rate, which might prove useful when distinguishing other diseases related to movement disorders., This research was partially financed by NORTE2020 and FEDER within the project NORTE-01–0145-FEDER- 000026 (DeM-Deus Ex Machina) and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia) within the Projects UIDB/00013/2020, UIDP/00013/2020 and UIDB/00319/2020.

Nowadays an increasing number of vehicles are being equipped with powerful cockpit systems capable of collecting drivers’ footprints over time. The collection of this valuable data opens effective opportunities for routine prediction. With the growing ability of vehicles to collect spatial and temporal information solving the routine prediction problem becomes crucial and feasible. It is then extremely important to advance and take advantage of the capabilities of these cockpit systems. A vehicle that is capable of predicting the next destination of the driver and when the driver intends to leave to that destination can prepare the journey in advance. Previous studies tackling the next location prediction problem have made use of Traditional Markov models, Neural Networks, Dynamic models, among others. In this work, a framework based on the hierarchical density-based clustering algorithm followed by a Long Short-Term Memory (LSTM) recurrent neural network is proposed for spatial-temporal prediction of drivers’ routines. Based on real-life driving scenarios of three different users, the proposed approach achieved a test set accuracy of 96.20%, 90.23%, and 86.40% when predicting the next destination and a R2 Score of 93.69, 79.21, and 28.81 when predicting the departure time, respectively. The results indicate that the proposed architecture can be implemented on the vehicle cockpit for the assistance of the management of future trips., Programme (COMPETE 2020) and national funds, through the ADI Project Bosch & UMinho “Easy Ride: Experience is everything” , ref POCI-01-0247 FEDER-039334, FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and UIDB/00013/2020.

In the era of industrial revolution 4.0, an automotive industry flourished in a way that was never before. As different features added in the driver assistance systems time-to-time, hence, nowadays the driving process is not so much tedious as it seems before. This ensures the operation of hassle-free driving, which leads towards autonomous vehicles. Overall, it opens up many opportunities for researchers and business communities, but at the same time, raises concerns and issues for discussion that need to be analyzed before put the final product (here, in the sense of fully automated vehicle) on the road. We conducted an online-survey (N = 3139) with participants from 146 nations was participated and assembled their valuable feedback on the automated driving and human presence in the autonomous vehicles considering all the levels of driving automation. This paper explores the results in terms of useful implications, which highlights and implied us to re-examine the present regulations and policies in automated driving and autonomous vehicles.

Programming by demonstration allows non-experts in robot programming to train the robots in an intuitive manner. However, this learning paradigm requires multiple demonstrations of the same task, which can be time-consuming and annoying for the human tutor. To overcome this limitation, we propose a fast learning system – based on neural dynamics – that permits collaborative robots to memorize sequential information from single task demonstrations by a human-tutor. Important, the learning system allows not only to memorize long sequences of sub-goals in a task but also the time interval between them. We implement this learning system in Sawyer (a collaborative robot from Rethink Robotics) and test it in a construction task, where the robot observes several human-tutors with different preferences on the sequential order to perform the task and different behavioral time scales. After learning, memory recall (of what and when to do a sub-task) allows the robot to instruct inexperienced human workers, in a particular human-centered task scenario., POFC - Programa Operacional Temático Factores de Competitividade(POCI-01-0247-FEDER-024541)

Fabry disease (FD) is a rare disease commonly complicated with white matter lesions (WMLs). WMLs, which have extensively been associated with gait impairment, justify further investigation of its implication in FD. This study aims to identify a set of gait characteristics to discriminate FD patients with/without WMLs and healthy controls. Seventy-six subjects walked through a predefined circuit using gait sensors that continuously acquired different stride features. Data were normalized using multiple regression normalization taking into account the subject physical properties, with the assessment of 32 kinematic gait variables. A filter method (Mann Whitney U test and Pearson correlation) followed by a wrapper method (recursive feature elimination (RFE) for Logistic Regression (LR) and Support Vector Machine (SVM) and information gain for Random Forest (RF)) were used for feature selection. Then, five different classifiers (LR, SVM Linear and RBF kernel, RF, and K-Nearest Neighbors (KNN)) based on different selected set features were evaluated. For FD patients with WMLs versus controls the highest accuracy of 72% was obtained using LR based on 3 gait variables: pushing, foot flat, and maximum toe clearance 2. For FD patients without WMLs versus controls, the best performance was observed using LR and SVM RBF kernel based on loading, foot flat, minimum toe clearance, stride length variability, loading variability, and lift-off angle variability with an accuracy of 83%. These findings are the first step to demonstrate the potential of machine learning techniques based on gait variables as a complementary tool to understand the role of WMLs in the gait impairment of FD., This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and UIDB/00013/2020.

The goal of this study was grouping patients with parkinsonism that share similar gait characteristics based on principal component analysis (PCA). Spatiotemporal gait data during self-selected walking were obtained from 15 patients with Vascular Parkinsonism, 15 patients with Idiopathic Parkinson's Disease and 15 Controls. PCA was used to reduce the dimensionality of 12 gait characteristics for the 45 subjects. Fuzzy C-mean cluster analysis was performed plotting the first two principal components, which accounted for 84.1% of the total variability. Results indicates that it is possible to quantitatively differentiate different gait types in patients with parkinsonism using PCA. Objective graphical classification of gait patterns could assist in clinical evaluation as well as aid treatment planning., POCI-01-0145-FEDER-006961, National Funds through the FCT as part of project UID/EEA/50014/2013

This paper presents a novel analytic method to uniquely solve inverse kinematics of 7 degrees-of-freedom manipulators while avoiding joint limits and singularities. Two auxiliary parameters are introduced to deal with the self-motion manifolds: the global configuration (GC), which specifies the branch of inverse kinematics solutions; and the arm angle (ψ) that parametrizes the elbow redundancy within the specified branch. The relations between the joint angles and the arm angle are derived, in order to map the joint limits and singularities to arm angle values. Then, intervals of feasible arm angles for the specified target pose and global configuration are determined, taking joint limits and singularities into account. A simple metric is proposed to compute the elbow position according to the feasible intervals. When the arm angle is determined, the joint angles can be uniquely calculated from the position-based inverse kinematics algorithm. The presented method does not exhibit the disadvantages inherent to the use of the Jacobian matrix and can be implemented in real-time control systems. This novel algorithm is the first position-based inverse kinematics algorithm to solve both global and local manifolds, using a redundancy resolution strategy to avoid singularities and joint limits., This work was partially supported by the NETT Project [FP7-PEOPLE-2011-ITN-289146]; and Foundation for Science and Technology, Portugal [grant number SFRH/BD/86499/2012]., info:eu-repo/semantics/publishedVersion

Diagnosis of Fabry disease (FD) remains a challenge mostly due to its rare occurrence and phenotipical variability, with considerable delay between onset and clinical diagnosis. It is then of extreme importance to explore biomarkers capable of assisting the earlier diagnosis of FD. There is growing evidence supporting the use of gait assessment in the diagnosis and management of several neurological diseases. In fact, gait abnormalities have previously been observed in FD, justifying further investigation. The aim of this study is to evaluate the effectiveness of different machine learning strategies when distinguishing patients with FD from healthy controls based on normalized gait features. Gait features of an individual are affected by physical characteristics including age, height, weight, and gender, as well as walking speed or stride length. Therefore, in order to reduce bias due to inter-subject variations a multiple regression (MR) normalization approach for gait data was performed. Four different machine learning strategies - Support Vector Machines (SVM), Random Forest (RF), Multiple Layer Perceptrons (MLPs), and Deep Belief Networks (DBNs) - were employed on raw and normalized gait data. Wearable sensors positioned on both feet were used to acquire the gait data from 36 patients with FD and 34 healthy subjects. Gait normalization using MR revealed significant differences in percentage of stance phase spent in foot flat and pushing ( $p ), with FD presenting lower percentages in foot flat and higher in pushing. No significant differences were observed before gait normalization. Support Vector Machine was the superior classifier achieving an FD classification accuracy of 78.21% after gait normalization, compared to 71.96% using raw gait data. Gait normalization improved the performance of all classifiers. To the best of our knowledge, this is the first study on gait classification that includes patients with FD, and our results support the use of gait assessment on the clinical assessment of FD.

Este trabalho trata da voz enquanto relação entre um corpus de composições e performances para voz, uma revisão de literatura sobre algumas noções discursivas sobre voz e um diário vocal pessoal que desenvolvi, o qual articula esses campos analíticos e práticos. Problematizo as noções de voz e vocalidade através da análise das formações discursivas dentro dos estudos da voz e analiso a relação destas formações com as vozes e vocalidades propostas em algumas composições e performances, com ênfase em obras de Valéria Bonafé, Lílian Campesato e Inés Terra. Questiono como os silenciamentos históricos da voz, enquanto evidência de corporalidade, foram entronizados no modo como compreendemos a voz e criamos com ela. Além da análise documental/textual e das análises das obras, o método aqui escolhido articula a problematização e a conceituação da voz enquanto uma prática vocal. Isso é feito a partir de um processo duplo que chamei de diário vocal, que consiste da realização e gravação de conversas com as compositoras e performers e de minha própria voz gravada em meus estudos e leituras diários, bem como da subsequente escuta e transcrição destes registros. Essa prática de documentação possibilitou um processo de análise musical/sonora que não se deu previamente sobre as peças abordadas, em forma de prognóstico, mas demandou uma escuta ativa da voz gravada e uma recepção da modulação sonora da minha voz e da voz das artistas em foco, caso a caso, entre pensamento, som, fala, escuta, conversa, discussão e escrita. Assim, o processo se constituiu como uma geografia afetiva de vozes e vocalidades, no cruzamento dos âmbitos da performance/composição musicais, dos estudos da voz e do método de voz, através do diário vocal. Deste modo, considero o diário vocal um método de cartografia da voz, pois é a ele que é endereçado o desvio metodológico entre prática (práxis) e discurso (lexis), a fim de analisar a problemática vocal na criação atual ao mesmo tempo em que \'se faz\' o que se pesquisa: pensar os limites do logos vocal atual é escutar/\'performar\' o grão radical da voz e vice-versa. This work includes a discussion on voice as a relation between a corpus of vocal compositions and performances, a literature review on discursive notions about voice, and a personal vocal diary developed within this thesis to articulate these analytical and practical fields. I posit notions of voice and vocality by analysing discursive formations within studies of voice, and scrutinizing the relation of such discursive formations with the voices and vocalities proposed in selected compositions and performances, with emphasis on works by Valéria Bonafé, Lílian Campesato and Inés Terra. I question how historical silencings of voice, as evidence of corporeality, have been ingrained in the way we understand voice and create with it. Beyond the documentary/textual analysis and analyses of selected works, the method articulates the postulation and conceptualization of voice as vocal praxis. This is achieved by means of a dualprocess that I named vocal diary, which consists of the performance and recording of conversations with composers and performers, and of my own voice in my daily studies and readings, as well as of the subsequent listening and transcription of these records. Such documentation practice enabled a process of musical/sound analysis that was not previously given over the selected works in form of prognosis, demanding an active listening of the recorded voice, a reception of the sonorous modulation of my own voice and of the artists in focus, case by case, between thought, sound, speech, listening, conversation, discussion and writing. Thus, the process is constituted as a affective geography of voices and vocalities, located in the intersection between performance and musical composition, studies of voice, and the voice method consisting in the vocal diary. Ergo, I consider the vocal diary a new method of voice cartography-because it is towards it that the methodological deviation between practice (praxis) and discourse (lexis) is directed, in order to analyze the vocal matter in the actual creation and at the same time,\'do\' what we research: to think the limits of the current vocal logos is to listen/perform the radical grain of voice...and vice versa.

The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a “resting state” to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions., The work received financial support from FCT through the PhD fellowship PD/BD/128183/2016, the project ”Neurofield” (POCI-01-0145-FEDER-031393) and the research centre CMAT within the project UID/MAT/00013/2013.

Neural field models, formalized by integro-differential equations, describe the large-scale spatio-temporal dynamics of neuronal populations [1]. They have been used in the past as a framework for modeling a wide range of brain functions, including multi-item working memory [2]. Neural field equations support spatially localized regions of high activity (or bumps) that are initially triggered by brief sensory inputs and subsequently become self-sustained by recurrent interactions within the neural population. We apply a special class of oscillatory coupling functions and analyze how the shape and spatial extension of multi-bump solutions change as the spatial ranges of excitation and inhibition within the field are varied [3]. More precisely, we use numerical continuation to find and follow solutions of neural field equations as the parameter controlling the distance between consecutive zeros of the coupling function is varied [4]. Important for a working memory application (e.g. [5]), we investigate how changes in this parameter affect the shape of bump solutions and therefore the maximum number of bumps that may exist in a given finite interval., The work received financial support from FCT through the PhD fellowship PD/BD/128183/2016.

Differential diagnosis between Idiopathic Parkin-son's disease (IPD) and Vascular Parkinsonism (VaP) is a difficult task, especially early in the disease. There is growing evidence to support the use of gait assessment in diagnosis and management of movement disorder diseases. The aim of this study is to evaluate the effectiveness of some machine learning strategies in distinguishing IPD and VaP gait. Wearable sensors positioned on both feet were used to acquire the gait data from 15 IPD, 15 VaP, and 15 healthy subjects. A comparative classification analysis was performed by applying two supervised machine learning algorithms: Multiple Layer Perceptrons (MLPs) and Deep Belief Networks (DBNs). The decisional space was composed of the gait variables, with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top-ranked in an error incremental analysis. In the classification task of characterizing parkinsonian gait by distinguishing between patients (IPD+VaP) and healthy control, from the all strides classification of the gait performed by the person, high accuracy (93% with or without MoCA) was obtained for both algorithms. In the classification task of the two groups of patients (VaP vs. IPD), DBN classifier achieved higher performance (73% with MoCA). To the best of our knowledge, this is the first study on gait classification that includes a VaP group. DBN classifiers are not frequently applied in literature to similar studies, but the results here obtained demonstrate that the use of DBN classifiers based on gait analysis is promising to be a good support to the neurologist in distinguishing VaP and IPD.

Fires are an important land use tool, a growing global hazard, and a factor involved in landscape processes with feedbacks on land degradation, especially in the Southwestern Mediterranean Europe, where the importance of fires in shaping ecological processes has long been recognized. As fires become recurrent, their intensity and dimension have increased and they have taken on catastrophic proportions, losing their role as catalysts of ecosystem renewal. Fires, and especially large forest fires (LFF), are the main drivers of land degradation in forest areas in Mediterranean sub-humid regions and are likely to increase as a result of climate and other global changes. The study area consists of the whole of mainland Portugal, which for the purpose of civil protection, is divided into 18 districts. The data used, including physical and demographic characteristics, change in land use and land cover, and economic structures are provided mainly by the Institute for the Conservation of Nature and Forests in statistical format and in shapefile format. Throughout the years, LFF in Portugal have become increasingly important. Although LFF in mainland Portugal represent a small fraction of the total occurrences (0.8 %), in accordance with the trends of the Mediterranean Basin, they are responsible for a large percentage of burned areas (>70 %). With the use of the GIS software, we were able to use the available information to generate a map of forest fire recurrences and, with the purpose of detecting homogeneous groups between the districts of mainland Portugal, a cluster analysis was applied. Using a multiple regression analysis, we present the LFF and their recurrence in mainland Portugal at the district level, demonstrating their regional incidence and the similarity between districts.

"Available online 3 March 2016", We study the conditions for the formation of multiple regions of high activity or “bumps” in a one-dimensional, homogeneous neural field with localized inputs. Stable multi-bump solutions of the integro-differential equation have been proposed as a model of a neural population representation of remembered external stimuli. We apply a class of oscillatory coupling functions and first derive criteria to the input width and distance, which relate to the synaptic couplings that guarantee the existence and stability of one and two regions of high activity. These input-induced patterns are attracted by the corresponding stable one-bump and two-bump solutions when the input is removed. We then extend our analytical and numerical investigation to NN-bump solutions showing that the constraints on the input shape derived for the two-bump case can be exploited to generate a memory of N>2N>2 localized inputs. We discuss the pattern formation process when either the conditions on the input shape are violated or when the spatial ranges of the excitatory and inhibitory connections are changed. An important aspect for applications is that the theoretical findings allow us to determine for a given coupling function the maximum number of localized inputs that can be stored in a given finite interval., The work received financial support from FCT through a PhD grant (SFRH/BD/41179/2007) and from the EU-FP7 ITN project NETT: Neural Engineering Transformative Technologies (nr. 289146).