This systematic review aimed to describe and characterize internal tooth bleaching techniques, conventional (walking-bleach) and combined (inside–outside), referring to their efficacy. The research was conducted on the main databases: PubMed, Cochrane Library, EMBASE, and Web of Science. Only randomized controlled trials and cohort studies were considered, on humans of 18 years old or older. A Population, Intervention, Comparison, Outcome (PICO) question was designed to evaluate the scientific evidence. The quality of each randomized controlled trial and cohort study was evaluated using the Cochrane Handbook for Systematic Reviews of Interventions and the Methodological Index for Non-Randomized Studies (ROBINS-I), respectively. The walking-bleach and the combined techniques were both effective at the end of the treatment, obtaining similar aesthetic results. Regardless of the technique used, internal tooth bleaching is an effective procedure, with good aesthetic results, in the treatment of non-vital teeth. The cervical barrier is a standard of care in internal bleaching techniques and should be used. Considering the similarity in the esthetic results obtained in both techniques, the concentrations used for both, and since the biocompatibility of the bleaching agent is more important than its efficiency or speed in obtaining results, the combined technique should be considered the method of choice rather than the walking-bleach technique.

The monitoring of power generation installations is key for modelling and predicting their future behaviour. Many renewable energy generation systems, such as photovoltaic panels and wind turbines, strongly depend on weather conditions. However, in situ measurements of relevant weather variables are not always taken into account when designing monitoring systems, and only power output is available. This paper aims to combine data from a Numerical Weather Prediction model with machine learning tools in order to accurately predict the power generation from a photovoltaic system. An Artificial Neural Network (ANN) model is used to predict power outputs from a real installation located in Puglia (southern Italy) using temperature and solar irradiation data taken from the Global Data Assimilation System (GDAS) sflux model outputs. Power outputs and weather monitoring data from the PV installation are used as a reference dataset. Three training and testing scenarios are designed. In the first one, weather data monitoring is used to both train the ANN model and predict power outputs. In the second one, training is done with monitoring data, but GDAS data is used to predict the results. In the last set, both training and result prediction are done by feeding GDAS weather data into the ANN model. The results show that the tested numerical weather model can be combined with machine learning tools to model the output of PV systems with less than 10% error, even when in situ weather measurements are not available.

Operating rooms are stringent controlled environments. All influential factors, in particular, airborne particles, must be within the limits established by regulations. Therefore, energy efficiency stays in the background, prioritizing safety and comfort in surgical areas. However, the potential of improvement in energy savings without compromising this safety is broad. This work presents a new procedure, based on calibrated simulations, that allows the identification of potential energy savings in an operating room, complying with current airborne particle standards. Dynamic energy and airborne particle models are developed and then simulated in TRNSYS and calibrated with GenOpt. The methodology is validated through experimental contrast with a real operating room of a hospital in Spain. A calibrated model with around 2% of error is achieved. The procedure determines the variation in particle concentration according to the flow rate of ventilation supplied and the occupancy of the operating room. In conclusion, energy savings up to 51% are possible, reducing ventilation by 50% while complying with airborne particles standards.

The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%.

Hospital surgical suites are high consumers of energy due to the strict indoor air quality (IAQ) conditions. However, by varying the ventilation strategies, the potential for energy savings is great, particularly during periods without activity. In addition, there is no international consensus on the ventilation and hygrothermal requirements for surgical areas. In this work, a dynamic energy model of a surgical suite of a Spanish hospital is developed. This energy model is calibrated and validated with experimental data collected during real operation. The model is used to simulate the yearly energy performance of the surgical suite under different ventilation scenarios. The common issue in the studied ventilation strategies is that the hygrothermal conditions ranges are extended during off-use hours. The maximum savings obtained are around 70% of the energy demand without compromising the safety and health of patients and medical staff, as the study complies with current heating, ventilation and air conditioning (HVAC) regulations.