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Autonomous Mobility-on-Demand (AMoD) systems are a rapidly evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests. The control of these systems is typically formulated as a large network optimization problem, and reinforcement learning (RL) has recently emerged as a promising approach to solve the open challenges in this space. However, current RL-based approaches exclusively focus on learning from online data, fundamentally ignoring the per-sample-cost of interactions within real-world transportation systems. To address these limitations, we propose to formalize the control of AMoD systems through the lens of offline reinforcement learning and learn effective control strategies via solely offline data, thus readily available to current mobility operators. We further investigate design decisions and provide experiments on real-world mobility systems showing how offline learning allows to recover AMoD control policies that (i) exhibit performance on par with online methods, (ii) drastically improve data efficiency, and (iii) completely eliminate the need for complex simulated environments. Crucially, this paper demonstrates that offline reinforcement learning is a promising paradigm for the application of RL-based solutions within economically-critical systems, such as mobility systems.

Huntington’s disease (HD) is a neurodegenerative disease and one of few trinucleotide repeat disorders. HD is the result of an abnormal expansion of a DNA segment known as CAG. This expansion occurs within the huntingtin gene (HTT), causing the encoding of a mutant huntingtin protein (mHTT). This appears to be toxic to neuronal cells and can lead to nerve cell damage. Many antibodies have been developed to target HTT, aiding HD research. However, there are problems in the lack of information on their validation status and key attributes. This review aims to cover the validation status and attribute information of all current antibodies raised against mHTT.

Predicting the supply and demand of transport systems is vital for efficient traffic management, control, optimization, and planning. For example, predicting where from/to and when people intend to travel by taxi can support fleet managers to distribute resources; better predicting traffic speeds/congestion allows for pro-active control measures or for users to better choose their paths. Making spatio-temporal predictions is known to be a hard task, but recently Graph Neural Networks (GNNs) have been widely applied on non-euclidean spatial data. However, most GNN models require a predefined graph, and so far, researchers rely on heuristics to generate this graph for the model to use. In this paper, we use Neural Relational Inference to learn the optimal graph for the model. Our approach has several advantages: 1) a Variational Auto Encoder structure allows for the graph to be dynamically determined by the data, potentially changing through time; 2) the encoder structure allows the use of external data in the generation of the graph; 3) it is possible to place Bayesian priors on the generated graphs to encode domain knowledge. We conduct experiments on two datasets, namely the NYC Yellow Taxi and the PEMS road traffic datasets. In both datasets, we outperform benchmarks and show performance comparable to state-of-the-art. Furthermore, we do an in-depth analysis of the learned graphs, providing insights on what kinds of connections GNNs use for spatio-temporal predictions in the transport domain.

BACKGROUND Huntington's disease (HD) is a rare neurodegenerative disorder with protean clinical manifestations. Its management is challenging, consisting mainly of off-label treatments. OBJECTIVES The International Parkinson and Movement Disorder Society commissioned a task force to review and evaluate the evidence of available therapies for HD gene expansion carriers. METHODS We followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Eligible randomized controlled trials were identified via an electronic search of the CENTRAL, MEDLINE, and EMBASE databases. All eligible trials that evaluated one or more of 33 predetermined clinical questions were included. Risk of bias was evaluated using the Cochrane Risk of Bias tool. A framework was adapted to allow for efficacy and safety conclusions to be drawn from the balance between the GRADE level of evidence and the importance of the benefit/harm of the intervention. RESULTS Twenty-two eligible studies involving 17 interventions were included, providing data to address 8 clinical questions. These data supported a likely effect of deutetrabenazine on motor impairment, chorea, and dystonia and of tetrabenazine on chorea. The data did not support a disease-modifying effect for premanifest and manifest HD. There was no eligible evidence to support the use of specific treatments for depression, psychosis, irritability, apathy, or suicidality. Similarly, no evidence was eligible to support the use of physiotherapy, occupational therapy, exercise, dietary, or surgical treatments. CONCLUSIONS Data for therapeutic interventions in HD are limited and support only the use of VMAT2 inhibitors for specific motor symptoms. �� 2021 International Parkinson and Movement Disorder Society.

Competitive sports involve physical and cognitive skills. In traditional sports, there is a greater dependence on the development and performance of both motor and cognitive skills, unlike electronic sports (eSports), which depend much more on neurocognitive skills for success. However, little is known about neurocognitive functions and effective strategies designed to develop and optimize neurocognitive performance in eSports athletes. One such strategy is transcranial direct current stimulation (tDCS), characterized as a weak electric current applied on the scalp to induce prolonged changes in cortical excitability. Therefore, our objective is to propose anodal (a)-tDCS as a performance-enhancing tool for neurocognitive functions in eSports. In this manuscript, we discussed the neurocognitive processes that underlie exceptionally skilled performances in eSports and how tDCS could be used for acute modulation of these processes in eSports. Based on the results from tDCS studies in healthy people, professional athletes, and video game players, it seems that tDCS is applied over the left dorsolateral prefrontal cortex (DLPFC) as a potential performance-enhancing tool for neurocognition in eSports. info:eu-repo/semantics/publishedVersion

Shared mobility services require accurate demand models for effective service planning. On the one hand, modeling the full probability distribution of demand is advantageous because the entire uncertainty structure preserves valuable information for decision-making. On the other hand, demand is often observed through the usage of the service itself, so that the observations are censored, as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have performed well under such conditions. Further, in the last two decades, several papers have proposed to implement these models flexibly through Neural Networks. However, the models in current works estimate the quantiles individually, thus incurring a computational overhead and ignoring valuable relationships between the quantiles. We address this gap by extending current Censored Quantile Regression models to learn multiple quantiles at once and apply these to synthetic baseline datasets and datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark. The results show that our extended models yield fewer quantile crossings and less computational overhead without compromising model performance., Comment: 13 pages, 9 figures, 5 tables

Mobility-on-demand (MoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of vehicles. Crucially, the efficiency of an MoD system highly depends on how well supply and demand distributions are aligned in spatio-temporal space (i.e., to satisfy user demand, cars have to be available in the correct place and at the desired time). To do so, we argue that predictive models should aim to explicitly disentangle between temporal} and spatial variability in the evolution of urban mobility demand. However, current approaches typically ignore this distinction by either treating both sources of variability jointly, or completely ignoring their presence in the first place. In this paper, we propose recurrent flow networks (RFN), where we explore the inclusion of (i) latent random variables in the hidden state of recurrent neural networks to model temporal variability, and (ii) normalizing flows to model the spatial distribution of mobility demand. We demonstrate how predictive models explicitly disentangling between spatial and temporal variability exhibit several desirable properties, and empirically show how this enables the generation of distributions matching potentially complex urban topologies., Comment: 16 pages, 6 figures

Variational inference methods have been shown to lead to significant improvements in the computational efficiency of approximate Bayesian inference in mixed multinomial logit models when compared to standard Markov-chain Monte Carlo (MCMC) methods without compromising accuracy. However, despite their demonstrated efficiency gains, existing methods still suffer from important limitations that prevent them to scale to very large datasets, while providing the flexibility to allow for rich prior distributions and to capture complex posterior distributions. In this paper, we propose an Amortized Variational Inference approach that leverages stochastic backpropagation, automatic differentiation and GPU-accelerated computation, for effectively scaling Bayesian inference in Mixed Multinomial Logit models to very large datasets. Moreover, we show how normalizing flows can be used to increase the flexibility of the variational posterior approximations. Through an extensive simulation study, we empirically show that the proposed approach is able to achieve computational speedups of multiple orders of magnitude over traditional MSLE and MCMC approaches for large datasets without compromising estimation accuracy., Comment: 12 pages, 3 figures

This study presents a semi-nonparametric Latent Class Choice Model (LCCM) with a flexible class membership component. The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification with the aim of comparing the two approaches on various measures including prediction accuracy and representation of heterogeneity in the choice process. Mixture models are parametric model-based clustering techniques that have been widely used in areas such as machine learning, data mining and patter recognition for clustering and classification problems. An Expectation-Maximization (EM) algorithm is derived for the estimation of the proposed model. Using two different case studies on travel mode choice behavior, the proposed model is compared to traditional discrete choice models on the basis of parameter estimates' signs, value of time, statistical goodness-of-fit measures, and cross-validation tests. Results show that mixture models improve the overall performance of latent class choice models by providing better out-of-sample prediction accuracy in addition to better representations of heterogeneity without weakening the behavioral and economic interpretability of the choice models.

With an increased focus on minimizing traffic externalities in metropolitan areas, a growing interest in environmentally friendly and shared mobility systems has emerged, such as electric car-sharing systems. However, increasing demand and larger area coverage often make it difficult to keep cars available where and when customers need them. This problem can be alleviated by predicting for how long cars stay vacant at given pick-up/drop-off locations. To maximize their usage, it can be more beneficial to relocate the cars at certain periods to more desired locations. In this paper, we tackle the problem of predicting time-to-pickup for shared cars in a probabilistic way as a function of time by applying time-to-event modelling through survival analysis. Both statistical and deep neural network approaches to survival regression were investigated. The Cox proportional hazards model (CPH) is compared to the deep neural network model DeepSurv. To predict survival times, a two-step approach was formulated, where in the upper level a classification is used to classify cars into two groups based on idle time duration, whereas in the lower level for each given group time-to-event modelling is applied. DeepSurv method demonstrated a stronger fit compared to CPH. The two-step approach resulted in over 15% improvement in performance, comparing to the one-step approach, where no classification is used., Proceedings from the Annual Transport Conference at Aalborg University, Vol. 27 No. 1 (2020): Proceedings from the Annual Transport Conference at Aalborg University

© 2017 Informa UK Limited, trading as Taylor & Francis Group, Parkinson's disease (PD) is a progressive neurodegenerative disease. The currently available treatment options only have a symptomatic effect. With disease progression almost all antiparkinsonian pharmacological classes are tried, but the gold standard of pharmacological management is still L-dopa. Various strategies can be used to raise the dopaminergic tone. Catechol-O-methyltransferase (COMT) inhibitors attain this goal by decreasing L-dopa peripheral metabolism. Areas covered: Opicapone (Ongentys®) is a new COMT inhibitor developed to fulfill the need for more potent, safer and longer acting COMT inhibitors. This review puts into context opicapone's indications, its chemical and preclinical data, the pharmacodynamics and pharmacokinetic characteristics, and the efficacy and safety results delivered by clinical trials. Expert opinion: Opicapone is an efficacious COMT inhibitor. Its proprieties make it adequate for a once-a-day oral dose regimen. It has proved to reduce the off-time and to increase the on-time without troublesome dyskinesias in PD patients with motor fluctuations. The reported adverse events suggest an overall safe and well-tolerated profile. The most common adverse events were dyskinesia, and there were no issues of concern for hepatotoxicity, severe diarrhoea or chromaturia. Further evidence is still needed to conclude how it compares with other drugs for the treatment of motor fluctuations.

Supplemental Material for The Basic Psychological Need Satisfaction and Frustration Scale in Exercise (BPNSFS-E): Validity, Reliability, and Gender Invariance in Portuguese Exercisers by Filipe Rodrigues, Joseph F.Hair Jr.Henrique P. Neiva, Diogo S. Teixeira, Luís Cid and Diogo Monteiro in Perceptual and Motor Skills

Objective: The aim of the present study was to validate the Basic Psychological Need Satisfaction and Frustration Scale in Exercise for fitness instructors. Methods: Data from 477 exercise professionals (319 males, 158 females) was collected.Results: CFA supported the adapted and validated six-factor model: [χ2(237) = 1096.796, χ2/df= 4.63; B-S p < .001, CFI = .930, TLI = .918, SRMR= .0366, RMSEA = .079 (CI90% = .069, .089)], assessing satisfaction and frustration of basic psychological needs in Portuguese exercise professionals. Moreover, the analysis revealed acceptable composite reliability, and construct validity of the adapted version. Results revealed nomological validity, as well as invariance between male and female. No differences were found across latent means, and magnitude effects were trivial between gender. Conclusion: These results support the use of the adapted scale in exercise professionals, showing measurement invariance between gender. This scale is able to measure how exercise professionals experience satisfaction and frustration of basic needs when prescribing exercise to individuals in fitness context. RESUMEN: Objetivo: El objetivo del presente estudio fue validar la Escala de Satisfacción y Frustración de Necesidades Psicológicas Básicas en el Ejercicio para instructores de ejercicio físico. Métodos: Se recopilaron datos de 477 profesionales del ejercicio (319 hombres, 158 mujeres). Resultados: CFA apoyó el modelo de seis factores adaptado y validado: [χ2 (237) = 1096.796, χ2 / df = 4.63; B-S p

© Annals of Translational Medicine. All rights reserved. This journal is a peer reviewed, open access journal. All content of the journal is published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)., Huntington’s disease (HD) is a devastating neurodegenerative condition caused by a triplet repeat expansion of the Huntingtin gene. Although rare it is amongst the most frequent autosomal dominant causes of dementia, frequently affecting individuals in the most productive decades of their lives. Clinically, it is characterized by a classic triad of fluctuating neuropsychiatric symptoms, and progressive movement and cognitive disorders, accompanied by other symptoms such as weight loss and sleep impairment. It is severely debilitating, has a huge impact on quality of life and is fatal, with a median survival after motor onset of around a quarter of a century.

Spatio-temporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatio-temporal data, typical approaches tend to focus solely on conditional expectations of the output variables being modeled. In this paper, we propose a multi-output multi-quantile deep learning approach for jointly modeling several conditional quantiles together with the conditional expectation as a way to provide a more complete "picture" of the predictive density in spatio-temporal problems. Using two large-scale datasets from the transportation domain, we empirically demonstrate that, by approaching the quantile regression problem from a multi-task learning perspective, it is possible to solve the embarrassing quantile crossings problem, while simultaneously significantly outperforming state-of-the-art quantile regression methods. Moreover, we show that jointly modeling the mean and several conditional quantiles not only provides a rich description about the predictive density that can capture heteroscedastic properties at a neglectable computational overhead, but also leads to improved predictions of the conditional expectation due to the extra information and a regularization effect induced by the added quantiles., Comment: 12 pages, 9 figures

Huntington's disease (HD) is a neurodegenerative condition characterised by motor dysfunction with involuntary movements and loss of voluntary control, cognitive deterioration and psychiatric problems. We report a 51-year-old man with early HD who experienced stereotyped episodes of repetitive, purposeless complex movements and unresponsiveness. His neurological examination was compatible with HD as were all investigations. We diagnosed psychogenic non-epileptic seizures. While seizures are common in juvenile-onset HD, they are no more prevalent in adult-onset HD than in the general population. However, neuropsychiatric symptoms are common in HD and can involve a number of different complaints. Patients may experience dissociative attacks soon after manifesting a HD diagnosis. Such episodes can be managed with patient and carer education, cognitive behavioural therapy and anxiolytic selective serotonin reuptake inhibitors.

BackgroundNeurofilament light protein (NfL), a component of the axonal cytoskeleton, has been shown to be increased in cerebrospinal fluid (CSF) and blood and to respond to successful treatment in several neurological diseases. We set out to investigate NfL as a potential biomarker for Huntington’s disease (HD).MethodsWe studied NfL in plasma from 298 participants, in plasma and CSF in 37 participants, and in the R6/2 HD mouse model.ResultsNfL concentration was increased in plasma at every stage of HD including premanifest mutation carriers, rose with progression and had a striking relationship with HTT CAG repeat length. In premanifest HD, baseline plasma NfL predicted subsequent motor onset even after adjustment for age and CAG repeat length. NfL predicted clinical, cognitive and neuroimaging progression, and CSF and plasma levels were strongly associated (Byrne et al, Lancet Neurology 2017). VBM analysis revealed that NfL level predicted atrophy throughout the white matter and in the occipital grey matter (Johnson et al, Neurology 2018). In the R6/2 mouse model, NfL was increased in plasma and CSF and associated with brain volume and clinical measures (Soylu Kucharz et al, Scientific Reports 2017).ConclusionsNfL is a promising clinical and translational biomarker for HD.

Introduction Aquaculture practices, like any other human activity, affect the environment in different ways. Some can be considered beneficial while others are in conflict with long-term sustainability of natural ecosystems. Recently, some attention has been dedicated to the effects of discharges of effluents from certain types of aquaculture. Aquaculture operations cause the release of metabolic waste products such as faeces, pseudofaeces, excreta and uneaten food which is considered one of the most important factors causing organic and inorganic loading in the vicinities of aquatic farms (Grigorakis & Rigos, 2011). The amount of uneaten feed relies mostly on the personnel experience and qualifications, feeding management (automatic or hand feeding), and the ingredients comprising the feed (Grigorakis & Rigos, 2011; Pillay, 2004). Indeed, effluents from shrimp aquaculture typically are enriched in suspended solids, nutrients, chlorophyll a and with an increased biochemical oxygen demand (Páez-Osuna, 2001). Suspended solids have been considered one of the most important waste products affecting the quality of the receiving waters and their environment (Pillay, 2004). Aquatic animals need a high concentration of protein in their feed since their energy production pathway requires the oxidation and catabolism of proteins. Estimates of nutrient retention and potential release by fish into the water are not readily available, and are changing rapidly as feed, feeding practices, and culture methods evolve. Also, both in aquaculture facilities and in natural aquatic environment, the occurrence several diseases have emerged as a serious economic and ecological issue, and are a significant constraint to the expansion of the industry. The control of endemic diseases imposes severe year-on-year costs on producers. A global estimate of disease losses ranges about 3 to 4 billion USD per year (Stabili et al., 2010). For example, white-spot syndrome of shrimp (WSS) has cost billions of dollars world-wide. Moreover, the elimination of disease outbreaks, such as ISA (Infectious Salmon Anemia) in Scotland in 1998/1999, caused unexpected expenditure for both the industry and government (Murraya & Peeler, 2005). Even though water is the major recipient of dissolved residues from aquaculture, a considerable portion of the solid material is retained inside the ponds, discharge canals or in the vicinity of the farms. Both the culture media and coastal habitats where the activity is practiced usually shows high rates of biological activity and organic matter decomposition. Recently, particular emphasis has been directed on development sustainable approaches to coastal aquaculture. In this sense, the promotion of ecological practices to improve the ecosystem health has been highly encouraged, including water recycling, effluent management and biological treatment by integrated culture (Marinho-Soriano et al., 2011). The use of filter-feeding organisms as nutrient (inorganic and organic) extractors has proven to be a valid alternative for nutrient bioremediation. Some of the most frequently tested organisms are mollusks, which filter organic particles and phytoplankton (Marinho-Soriano et al., 2011). Integrated Multi-trophic Aquaculture (IMTA) has been proposed to achieve environmental sustainability through biomitigation of aquaculture wastes which, as compared to other accompanying methods, has advantages that may include economic stability by product diversification and risk reduction, and social acceptability through best management practices (Troell et al., 2009; Barrington et al., 2009). IMTA is a practice in which the by-products (wastes) from one species are recycled to become inputs (fertilizers, food and energy) for another through the cultivation, in the right proportions, of fed aquaculture species (e.g. finfish/shrimp) with organic extractive species (e.g. suspension and deposit feeders), and inorganic extractive aquaculture species (e.g. seaweeds) (Troell et al., 2009; Barrington et al., 2009). The reduction of microbial pollution within aquaculture can be achieved by the use of living organisms. Literature available reveals the potential capability of some invertebrates to remediate heavy metals, microbial contaminants, hydrocarbons, nutrients and persistent organic pollutants (Khoi & Fotedar, 2012; Stabili et al., 2006). Filter-feeding marine macroinvertebrates filter large volumes of water for their food requirements and exert high efficiency in retaining small particles including bacteria. The Mediterranean polychaete Sabella spallanzanii showed ability to filter, accumulate and remove from waste, bacterial groups including human potential pathogens and vibrios (Stabili et al., 2010). Licciano et al. (2005) calculated the clearance rates and filtration efficiencies for S. spallanzanii and Vibrio alginolyticus, revealing the ability of sabellids to filter bacteria with high efficiency. Therefore, sabellids are considered suitable to use in aquaculture farms as biofilters, also considering their action in removing suspended solids in waste waters to which bacteria can be attached. The aim of the present study was to assess the bioremediation potential of the filter-feeding polychaete Sabella spallanzanii, by its ability to remove bacterial groups, co-cultured with the highly valued marine shrimp Palaemon sp. Additionally, polychaete and shrimp growth was measured over time. Material and Methods In 2012, specimens of S. spallanzanii were hand collected from Peniche and Olhão coasts (Portugal) and, after an acclimatization period, they were randomly inserted in a net containing 14 individuals. The nets were inserted in an experimental recirculating aquaculture system, assembling shrimp Palaemon sp., filter-feeding polychaete S. spallanzanii and the macroalgae Ulva sp. rearing tanks. S. spallanzanii rearing tank received the wastes from the shrimp rearing tank. Their survival, growing capability and bacterial removal by filter-feeding was evaluated. The bacteriological analyses were performed using the water samples collected in the shrimp tanks at two sampling times: one time before starting water recirculation (T0), and after 21 days of experiment (T3). Bacteriological analyses included the quantitative analyses of culturable heterotrophic bacteria (22ºC), total culturable bacteria at 37ºC, culturable halophilic vibrios at 22 and 35ºC, and E. coli. Results and Discussion The results obtained in growth capability of S. spallanzanii showed an increase of 9.34% in weight, corresponding to 7.7 mg day-1, during the experiment. This could be explain due to the higher suspended solids in the culture medium. Stabili et al. (2010) reported a mean increase of total polychaete biomass of 9.0 mg day-1. Relatively to bacteriological analysis, the density of heterotrophic bacteria at 37ºC decreased substantially (Fig. 1a). The abundance of heterotrophic bacteria at 22ºC increased from T0 to T3 (Fig. 1b). This increasing density pattern of heterotrophic bacteria at 22ºC is in accordance with Stabili et al. (2010). The author stated that could be related with the effect of temperature, assessed by Pomeroy & Wiebe (2001). However, in this study no large temperature changes occur. Thus, more intensive bacteriological analyses should be done before performing any assumptions. Regarding culturable presumptive vibrios, the abundance decreased from T0 to T3 at 22 and 35ºC (Fig. 2). Regarding to E.coli, it was possible to observe a density decrease of 90% at the end of the experiment (Fig. 3). According with the decree-law n.º 236/98 of August 1st (N.º 176 – 1998), the maximum value allowable for faecal coliforms is 2000 per 100 mL, and the maximum value recommended is 100 per 100 mL. The results for E. coli stated previously are in accordance with the decree-law requirements. The use of S. spallanzanii as bioremediator of microbial pollution revealed to be efficient in a more complex culture system. The integration of this species in the culture system plays an important role since it is able to filter, accumulate and remove several bacterial groups from the water, including human potential pathogens, and thus, reduces susceptibility to disease outbreaks.

A maioria das pessoas passa grande parte do seu tempo em ambientes fechados, tais como habitações, escolas, entre outros. Por essa razão, a qualidade do ar interior (QAI) é um fator determinante para o desempenho de atividades nesses espaços. Em salas de aulas, o dióxido de carbono (CO2) é um dos poluentes que mais afeta os utilizadores e gestores desses espaços, pois apesar de não ser considerado um gás poluente no exterior, em espaços de lecionação pode atingir níveis superiores aos valores máximos recomendáveis. Neste contexto, desenvolveu-se um estudo, no Instituto Politécnico de Bragança, com o objetivo de avaliar a dinâmica do CO2 em salas de aulas sem ventilação mecânica, em função de alguns parâmetros como a dimensão do espaço, ocupação e abertura de janelas e portas, entre outros. Para este estudo foram selecionadas 4 salas com volumes compreendidos entre 90 e 380 m3. A monitorização do CO2, bem como da temperatura e da humidade relativa, foi realizada ao longo de várias semanas entre Março e Maio de 2013. As salas foram monitoradas em dias diferentes, entre as 9 e as 18 horas, tendo sido realizados um total de 6 ensaios por sala. As condições meteorológicas exteriores foram também registadas durante esse período. Foram ainda criadas as bases de uma ferramenta de gestão da qualidade do ar interior em salas de aulas, através do desenvolvimento/validação de um modelo simples de simulação dos níveis de CO2. Os principais resultados permitem identificar uma relação direta entre o poluente e o número de ocupantes, principalmente quando não ocorre renovação de ar. Nessas condições, os níveis de CO2 atingem rapidamente os 1.800 mg/m3, mesmo em situações com taxas de ocupação inferiores a 30%. Os valores médios para o período de duração dos ensaios, situam-se entre os 3.600 e 5.400 mg/m3 para as salas de volume inferior a 120 m3 e nas salas de maior dimensão variam entre os 1.900 e 2.500 mg/m3. Verificou-se ainda que, na ausência de aberturas a entrada de ar novo situou-se em média abaixo de 1 renovação por hora, e com aberturas entre 1,8 e 3 renovações por hora. Quanto ao CO2 simulado, o modelo utilizado consegue acompanhar bem as variações ao longo do tempo em 90% dos ensaios e explica com boa correlação cerca de 70%. A abertura de portas e janelas, em simultâneo ou não, poderá ser uma solução para assegurar a redução dos níveis de CO2 abaixo dos níveis máximos recomendáveis, mas implicará áreas e tempos de abertura relativamente longos, que poderão ser desaconselhados nas épocas mais frias e ventosas. Os cenários permitiram concluir que por vezes é necessário estar 1 janela aberta durante todo o período de aula para garantir que os níveis de CO2 cumpram o estabelecido no quadro legal, mesmo com ocupações inferiores a 50%. Desenvolvimentos futuros permitirão efetuar simulações que incluam outras variáveis do processo, de modo a prever com maior exatidão soluções no âmbito da ventilação natural. Most people spend much of their time indoors, such as schools, at home, and other spaces. Therefore, indoor air quality is a key factor for the health and well-being of those who are exposed, to some extent, to indoor air prevailing in these places. In classrooms, carbon dioxide (CO2) is one of the pollutants that most affects the users and managers of these spaces. CO2 is not considered a pollutant when outdoors, but in classrooms can reach high levels above the maximum recommended values. In this perspective, a study was conducted, at Instituto Politécnico de Bragança, in order to evaluate CO2 dynamics in classrooms without mechanical ventilation, regarding parameters like the dimensions, its occupancy rates, the opening of windows and doors, among others. Four rooms were selected with volumes ranging from 90 to 380 m3. CO2, along with temperature and relative humidity, were monitored amid March and May 2013. The classrooms were evaluated in different days, between 9 a.m. and 6 p.m., for a total of six trials per room. Outdoor weather conditions were also monitored during that period. Also, the basis for a management tool for indoor air quality in classrooms was created, through the development and validation of a simple CO2 simulation model. The main results showed a direct relation between CO2 and the number of occupants, especially when no air exchange occur. In those conditions, levels of carbon dioxide rapidly reached 1,800 mg/m3, even with occupancy rates lower than 30%. The averaged CO2 levels registered in the various tests ranged between 3,600 and 5,400 mg/m3 for the classrooms below 120 m3 and between 1,900 and 2,500 mg/m3 for bigger classrooms. It was also verified that the entry of new air was on average below 1 exchange per hour, with the windows and doors all shut, and between 1.8 and 3 with openings. Concerning the CO2 simulations, the model was able to follow the temporal variations of CO2 levels of 90% of all trials, explaining with a good correlation of about 70%. The opening of windows and doors, simultaneously or not, could be a good practical measure to ensure the reduction of CO2 levels below the recommended values, but it will comprise greater opening areas with fairly longer periods, that may be discouraged with cold and windy weather. The scenarios showed that it is sometimes necessary to have an open window for the entire period to ensure CO2 levels meet the legal requirements, even with occupation rates below 50%. Further simulation developments, with inclusion of other relevant variables, will be implemented in order to predict better solutions regarding natural ventilation.

This paper describes the development and evaluation of a novel effort control system for cycling, which contributes to promote the users’ mobility and physical health. This system provides automatic control of the motor assistance level of an electric bicycle in order to ensure that the cyclist’s effort remains inside the desired target zone, regardless of changes in other variables which normally affect the effort, such as the slope of the road. The system presented in this paper controls the pedaling resistance perceived by the cyclist through the use of a sensor device placed inside of the bicycle crankset, which provides the required torque signal. The data processing, effort control algorithm and user interface are implemented in a smartphone application, whereas a microcontroller on the bicycle is responsible for the data acquisition, wireless data exchange with the smartphone, and real-time control of the motor assistance level. Experimental results validate the effectiveness of the implemented effort control system.

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Trabalho final de mestrado integrado em Medicina área científica de Cirurgia, apresentado à Faculdade de Medicina da Universidade de Coimbra A infecção do local cirúrgico (ILC) é definida como uma infecção que ocorre num período de tempo até 30 dias após a cirurgia (ou 1 ano no caso de implantação de material protésico), envolvendo a incisão superficial ou os tecidos profundos no local cirúrgico. Apesar do progresso notável verificado durante as últimas décadas no respeitante à profilaxia, a ILC, cuja prevalência é estimada no universo de doentes cirúrgicos em aproximadamente 5%, continua a representar, em virtude da morbi-mortalidade significativas e acréscimo dos custos assistenciais que lhe estão associados, um verdadeiro desafio para o cirurgião e, num sentido mais amplo, para o próprio sistema de saúde. O presente artigo de revisão pretende, por um lado, apresentar uma visão actual e abrangente sobre a profilaxia da ILC e, por outro, analisar pormenorizadamente as medidas preventivas que representam, presentemente, objecto de particular interesse para a comunidade cirúrgica By definition, a surgical site infection (SSI) is an infection that develops within 30 days after an operation, or within one year if an implant is placed and the infection appears to be related to the surgery, affecting tissue in the surgical site. Despite the outstanding progress in development of preventive strategies, SSI is still prevalent in an estimated 5% of surgical patients. The morbidity and mortality rates along with high economic costs associated to SSI continue to pose a true challenge to the surgeon and, in a wider sense, to the healthcare system. This review aims to present an updated and global view on the prophylaxis of SSI and to analyze in detail the preventive measures which are presently areas of interest for the surgical community.

Submitted by Diogo Monteiro (diogomonteiro@esdrm.ipsantarem.pt) on 2019-04-04T15:08:46Z No. of bitstreams: 1 BPNESF_Instructors_Rodrigues_et_al.(2019).pdf: 4048982 bytes, checksum: aa81b68091a8e521c954555a800b113b (MD5) Approved for entry into archive by Helena Vicente (helenavicente@esdrm.ipsantarem.pt) on 2019-04-15T12:40:34Z (GMT) No. of bitstreams: 1 BPNESF_Instructors_Rodrigues_et_al.(2019).pdf: 4048982 bytes, checksum: aa81b68091a8e521c954555a800b113b (MD5) Made available in DSpace on 2019-04-15T12:40:34Z (GMT). No. of bitstreams: 1 BPNESF_Instructors_Rodrigues_et_al.(2019).pdf: 4048982 bytes, checksum: aa81b68091a8e521c954555a800b113b (MD5) Previous issue date: 2018-12-30 info:eu-repo/semantics/publishedVersion