The present work focuses on the evaluation of the dynamic behavior of a centenary steel arch bridge, located in Portugal, under light railway traffic loads. This works aims to assess the dynamic behavior of the bridge subjected to an alternative type of railway vehicle, more specifically, a typical underground vehicle that is currently in service in the Lisbon Metro. The dynamic response of the system has been evaluated using two distinct methodologies, namely a moving loads model and a vehicle–bridge interaction model. To achieve this goal, finite element (FE) models from both the bridge and the vehicle have been developed and a comprehensive study has been carried to evaluate the influence of distinct factors in the dynamic response of the bridge–train system, namely the methodology used to assess the dynamic response, the location of the response reference point in the deck, the train speed and the vehicle configuration (single or double vehicle). Moreover, both the traffic safety, passenger comfort and pedestrian comfort have also been evaluated using normative criteria based on acceleration responses. The results shown that the normative limits related to traffic safety and passenger comfort were never exceeded in any condition analyzed in the study. However, the pedestrian comfort was jeopardized when the train speed exceeded 20[Formula: see text]km/h.

Ketamine has shown rapid antidepressant effects in depressed patients. However, the antidepressant and antisuicidal effects of repeated ketamine infusions in patients with treatment-resistant bipolar depression (TRBD) are not known.TRBD patients received six intravenous infusions of 0.5 mg/kg ketamine over 40 min on a Monday–Wednesday–Friday schedule during a 12-day period followed by a 2-week follow-up period. Depressive symptoms were measured by the Montgomery-Asberg Depression Rating Scale (MADRS) at baseline and at each follow-up visit.Nineteen patients with TRBD were enrolled in the study, and 16 patients (84.2%) received all six ketamine infusions. After the first infusion, the rates of response and remission were 21.1% (95% CI: 0.9 to 21.2) and 15.8% (95% CI: 0 to 33.9), respectively, and after the sixth infusion, the rates of response and remission were 73.7% (95% CI: 51.9 to 95.5) and 63.2% (95% CI: 39.3 to 87.0), respectively. The average times for nineteen patients who responded and remitted were 9.1 and 12.5 days, respectively. There were large decreases in the scores on the MADRS and the Scale for Suicidal Ideation-part 1 within 4 h after the first infusion, and the decreases were maintained across subsequent infusions. There were no significant increases in dissociative and psychotomimetic symptoms as measured by the Clinician-Administered Dissociative States Scale (CADSS) and the Brief Psychiatric Rating Scale (BPRS)-4 items, respectively.These pilot findings suggest the feasibility of repeated ketamine infusions at subanaesthetic doses for patients with TRBD. Future controlled studies are needed to confirm and expand these findings.

Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models. (C) 2018 Elsevier B.V. All rights reserved.