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Cuernavaca, Mexico

Waterhouse A.F.,University of Florida | Valle-Levinson A.,University of Florida | Morales Perez R.A.,IMTA
Continental Shelf Research | Year: 2012

Observations of current velocity, sea surface elevation and vertical profiles of density were obtained in a tropical inlet to determine the effect of a bathymetric depression (hollow) on the tidal flows. Surveys measuring velocity profiles were conducted over a diurnal tidal cycle with mixed spring tides during dry and wet seasons. Depth-averaged tidal velocities during ebb and flood tides behaved according to Bernoulli dynamics, as expected. The dynamic balance of depth-averaged quantities in the along-channel direction was governed by along-channel advection and pressure gradients with baroclinic pressure gradients only being important during the wet season. The vertical structure of the along-channel flow during flood tides exhibited a mid-depth maximum with lateral shear enhanced during the dry season as a result of decreased vertical stratification. During ebb tides, along-channel velocities in the vicinity of the hollow were vertically sheared with a weak return flow at depth due to choking of the flow on the seaward slope of the hollow. The potential energy anomaly, a measure of the amount of energy required to fully mix the water column, showed two peaks in stratification associated with ebb tide and a third peak occurring at the beginning of flood. After the first mid-ebb peak in stratification, ebb flows were constricted on the seaward slope of the hollow resulting in a bottom return flow. The sinking of surface waters and enhanced mixing on the seaward slope of the hollow reduced the potential energy anomaly after maximum ebb. The third peak in stratification during early flood occurred as a result of denser water entering the inlet at mid-depth. This dense water mixed with ambient deep waters increasing the stratification. Lateral shear in the along-channel flow across the hollow allowed trapping of less dense water in the surface layers further increasing stratification. © 2012 Elsevier Ltd.


Van Steenbergen-Weijenburg K.M.,Pro Persona Center for Education and Research Pro | Van Der Feltz-Cornelis C.M.,University of Tilburg | Van Benthem T.B.,Onze Lieve Vrouwe Gasthuis | Horn E.K.,Landelijk Centrum Voor Persoonlijkheidsproblematiek | And 8 more authors.
Tijdschrift voor Psychiatrie | Year: 2015

Background: Depression is highly prevalent in patients with chronic physical illnesses. A promising intervention for this group of patients is the collaborative care treatment as developed in the us. AIM: To demonstrate the prevalence of depression and the risk factors of depression in diabetes patients, to describe how the screening for depression can be carried out and to assess whether the collaborative care treatment in the Netherlands is effective. METHOD: A questionnaire was completed every three months in order to determine whether there was an improvement in patients' depression and physical symptoms.The outcomes were analysed by means of the multilevel logistic regression analyses. RESULTS: On the basis of the Patient Health Questionnaire, about 26% of the diabetes patients were found to have a depression.This questionnaire was validated for the measurement of depression in diabetes patients, the best results being found at a cut-off point of 12. In cases of fairly severe depression, collaborative care had no effect on depressive symptoms but did reduce severe physical complications. In cases of more severe depression, collaborative care only had an effect on depressive symptoms, but was not found to have any effect on physical complications. CONCLUSION: There is evidence that collaborative care can reduce depression and physical complications in chronically ill patients. However, more research is needed to find out whether collaborative care can become more effective if it is supplemented with digital methods and group therapy.


Tamari S.,IMTA | Garcia F.,ENGEES | Arciniega-Ambrocio J.I.,ITCh | Porter A.,MECOPAA
Houille Blanche | Year: 2014

Among the non-contact instruments to measure water velocity in open channels, two handheld radars are available on the market since ten years. Due to the lack of information about these instruments, one model was tested in the laboratory and in the field. The radar was able to estimate the velocity of a water surface within [p = 0.95] ± 0.3 m/s at medium velocities (from 0.3 to 3 m/s) and within ± 10 % of the measured value at large velocities (up to at least 6 m/s). Although this is not very accurate, the ease of using handheld radars still makes them attractive to quickly estimate discharge at gauging stations, safely determine water velocity during a flood and investigate how water flows under difficult access conditions. Nevertheless, the tested radar was tending to underestimate the water velocity, above all when it was looking downstream. More studies are necessary to know why. © 2014 Société Hydrotechnique de France .


Gonzalez-Sanchez A.,IMTA | Frausto-Solis J.,UPEMOR | Ojeda-Bustamante W.,IMTA
Scientific World Journal | Year: 2014

Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63). © 2014 Alberto Gonzalez-Sanchez et al.

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