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The dropout rate of university engineering studies is correlated with the absence of retention of freshman in the basic subjects. The aim of this work is to determine the effect of cooperative learning and the characteristics of students on learning, persistence, and academic performance. We have performed a study of learning, persistence and academic performance in an engineering physics course, comparing two groups with different teaching, one traditional and the other through cooperative learning. In addition, we have analysed the influence of the characteristics of the students on the results and the perceptions of the students regarding the teaching. The students of the cooperative learning group had a normalised average gain in the Force Concept Inventory that was 72.7% higher, a significantly higher persistence, and a final grade similar to that of the group receiving traditional teaching. The previous education and the size of the community of origin of the students had a significant effect on the persistence of the students. The perceptions of the subject and lecturer were similar in both groups.

Wildfires are considered one of the main causes of forest destruction. In recent years, the number of forest fires and burned area in Mediterranean regions have increased. This problem particularly affects Galicia (north-west of Spain). Conventional modelling of the number of forest fires in small areas may have a high error. For this reason, four area-level Poisson mixed models with time effects are proposed. The first two models contain independent time effects, whereas the random effects of the other models are distributed according to an autoregressive process AR(1). A parametric bootstrap algorithm is given to measure the accuracy of the plug-in predictor of fire number under the temporal models. A significant prediction improvement is observed when using Poisson regression models with random time effects. Analysis of historical data finds significant meteorological and socioeconomic variables explaining the number of forest fires by area and reveals the presence of a temporal correlation structure captured by the area-level Poisson mixed model with AR(1) time effects. Wildfires are one of the main causes of forest destruction and ecological disasters. By using auxiliary variables and past data, this work introduces temporal regression models for explaining and predicting the number of fires in forest areas during a given time period. The new methodology is an interesting tool to implement preventive policies and to support the design of more effective measures against fires. [ABSTRACT FROM AUTHOR]

Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months. [Display omitted] • Negative binomial mixed models are suitable for predicting wildfires and its overdispersion. • Binomial logit mixed models capture the excess of zeros. • Random effects, varying with month and province, model temporal and spatial variability. • Forecasts under expected scenarios give valuable information for decision-making. [ABSTRACT FROM AUTHOR]

Context: Opioids decrease pain and improve functional capacity and quality of life; however, they are not always effective and are associated with harmful side effects. Few studies have shown that relaxation-based therapies, in comparison with usual care, can decrease pain., Objective: The objective of the study was to investigate whether a controlled relaxation treatment, Jacobson progressive muscular relaxation (PMR), was effective in relieving chronic low-back pain (CLBP) and reducing pain comorbidities. The research team hypothesized that PMR-controlled relaxation could be more effective in reducing CLBP than music., Design: The research team designed a randomized, controlled, crossover study., Setting: The study took place in the pain unit, a clinic, in the Department of Health at Alicante-General Hospital (Alicante, Spain)., Participants: Participants in this study were 58 adults with nononcological CLBP, secondary to lumbar canal stenosis, who had been treated with opioids without any changes in the 3 mo prior to the study., Intervention: Participants were randomly assigned to 1 of 2 groups, each of which received 2 treatments, but in a different order (ie, either AB or BA where A was the standardized PMR, the intervention, and B was relaxing music, the control. For both groups, the 2 treatment periods were 8 wk in length, with a 1-mo washout period between them., Outcome Measures: The primary outcome measures included (1) a visual analogue scale-pain and relief intensity; (2) the 12-item short form health survey-quality of life; (3) the hospital anxiety and depression scale-anxiety and depression; and (4) the medical outcomes study sleep scale-sleep disturbances. Secondary outcome measures included a self-efficacy scale and a measure of satisfaction with treatment and compliance., Results: Pain was mostly mild to moderate. Greater decreases in pain between baseline and postintervention were observed for the PMR vs the control treatment in the mild pain category, with a VAS difference of 1.8 cm and P = .018. Significant differences were also found in anxiety, depression, quality of life, and sleep between participants in the 3 pain categories. Self-rated adherence was high., Conclusions: Findings support the efficacy and acceptability of a self-guided PMR intervention for reducing CLBP with minimal time with a therapist.

Concentrations of potential pollutant elements Na, Cl, and S were investigated in needles of Pinus canariensis grown at 55 field plots in Tenerife. Microelement concentrations (including heavy metals) were measured at a subset of 18 plots. Na and Cl concentrations were high at low elevations (up to 8 mg g(-1) Cl and 5.5 mg g(-1) Na). Na/Cl ratio close to standard seawater indicated sea spray influence up to 1200 m a.s.l. Only at few plots, sulphur concentrations indicated possible pollutant impact. Cluster and correlation analyses identified a related group of V, As, Cr, Fe, Mo, Ni, Cu, Pb, and Al, possibly related to traffic exhaust aggregated with soil particles. Mainly north-eastern, lower elevated plots were exposed to those immissions, but metal concentrations were generally low compared to data from other studies. In conclusion, seawater and soil particles explained most of the element distribution pattern in pine needles in Tenerife, but strong indications for some effect of local sources of air pollutants were detected.