Business models of international banks in the wake of the 2007-2009 global financial crisis

Artículo de revist

Software educativo para el trabajo con matrices

En el presente trabajo mostramos un software educativo desarrollado por un grupo de investigadores de diferentes universidades con el objetivo de apoyar el proceso de enseñanza aprendizaje de las operaciones con matrices en las carreras de ingeniería. También se muestran los primeros resultados obtenidos con su aplicación

Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications

Thyroid markers and body composition predict LDL-cholesterol change in lean healthy women on a ketogenic diet: experimental support for the lipid energy model

Introduction: There is a large heterogeneity in LDL-cholesterol change among individuals adopting ketogenic diets. Interestingly, lean metabolically healthy individuals seem to be particularly susceptible, with an inverse association between body mass index and LDL-cholesterol change. The lipid energy model proposes that, in lean healthy individuals, carbohydrate restriction upregulates systemic lipid trafficking to meet energy demands. To test if anthropometric and energy metabolism markers predict LDL-cholesterol change during carbohydrate restriction. Methods: Ten lean, healthy, premenopausal women who habitually consumed a ketogenic diet for ≥6 months were engaged in a three-phase crossover study consisting of continued nutritional ketosis, suppression of ketosis with carbohydrate reintroduction, and return to nutritional ketosis. Each phase lasted 21 days. The predictive performance of all available relevant variables was evaluated with the linear mixed-effects models. Results: All body composition metrics, free T3 and total T4, were significantly associated with LDL-cholesterol change. In an interaction model with BMI and free T3, both markers were significant independent and interacting predictors of LDL-cholesterol change. Neither saturated fat, HOMA-IR, leptin, adiponectin, TSH, nor rT3 was associated with LDL-cholesterol changes. Discussion: Among lean, healthy women undergoing carbohydrate restriction, body composition and energy metabolism markers are major drivers of LDL-cholesterol change, not saturated fat, consistent with the lipid energy model

Exogenous ketosis in patients with type 2 diabetes : safety and effect on glycaemic control

Introduction Ketogenic diets have shown to improve glycaemic control in patients with type 2 diabetes. This study investigated the safety, tolerability, and effects on glycaemic control in patients with type 2 diabetes of an exogenous ketone monoester (KE) capable of inducing fasting-like elevations in serum β-hydroxybutyrate (βHB) without the need for caloric or carbohydrate restriction. Methods Twenty one participants (14 men and 7 women, aged 45 ± 11 years) with insulin-independent type 2 diabetes, and unchanged hypoglycaemic medication for the previous 6 months, were recruited for this non-randomised interventional study. Participants wore intermittent scanning glucose monitors (IS-GM) for a total of 6 weeks and were given 25 ml of KE 3 times daily for 4 weeks. Serum electrolytes, acid-base status, and βHB concentrations were measured weekly and cardiovascular risk markers were measured before and after the intervention. The primary endpoints were safety and tolerability, with the secondary endpoint being glycaemic control. Results The 21 participants consumed a total of 1,588 drinks (39.7 litres) of KE over the course of the intervention. Adverse reactions were mild and infrequent, including mild nausea, headache, and gastric discomfort following fewer than 0.5% of the drinks. Serum electrolyte concentrations, acid-base status, and renal function remained normal throughout the study. Compared to baseline, exogenous ketosis induced a significant decrease in all glycaemic control markers, including fructosamine (335 ± 60 μmol/L to 290 ± 49 μmol/L, p < .01), HbA1c (61 ± 10 mmol/mol to 55 ± 9 mmol/mol [7.7 ± 0.9% to 7.2 ± 0.9%], p < .01), mean daily glucose (7.8 ± 1.4 mM to 7.4 ± 1.3 mM [140 ± 23 mg/dl to 133 ± 25 mg/dl], p < .01) and time in range (67 ± 11% to 69 ± 10%, p < .01). Conclusions Constant ketone monoester consumption over 1 month was safe, well tolerated, and improved glycaemic control in patients with type 2 diabetes

Forest maturity has a stronger influence on the prevalence of spider monkeys than howler monkeys in an anthropogenically impacted rainforest landscape

[EN] The transformation and depletion of primary forest over the past few decades have placed almost half of the world's primate species under the threat of extinction. Developing any successful conservation program for primates requires distribution and demography data, as well as an understanding of the relationships between these factors and their habitat. Between March and June 2010 and 2011 we collected data on the presence and demographic parameters of howler and spider monkeys by carrying out surveys, and validated our findings using local knowledge. We then examined the relationship between forest type and the presence of these primates at 54 sites in the northern area of the Selva Zoque Corridor, Mexico. We detected 86 spider monkey groups across 31 plots and censused 391 individuals (mean +/- SD = 5.9 +/- 3.0 individuals per sub-group, n = 67 sub-groups). We also detected 69 howler monkey groups across 30 plots and censused 117 individuals (mean +/- SD = 5.3 +/- 2.4 individuals per group, n = 22 groups). Howler monkey presence was not related to any specific vegetation type, while spider monkeys were present in areas with a higher percentage of tall forest (trees > 25 m high). Overall, spider monkeys were more prevalent than howler monkeys in our sampling sites and showed demographic characteristics similar to those in better protected areas, suggesting that the landscape features in the Uxpanapa Valley are suitable for their needs. Conversely, howler monkey presence was found to be more limited than in other regions, possibly due to the extended presence of spider monkeys.We thank the National Council of Science of Technology (CONACyT) and the Veracruz State Government for providing financial support to the project (grant no. 108990). ASG received support from Bournemouth University, the Centro de Investigaciones Tropicales and CONACyT (registration 195409) to conduct all the data analysis as part of her PhD. JCD received funding from the Isaac Newton Trust. We thank C. A. Munoz-Robles for providing the base map of the study site. We are very grateful to the communities of the Uxpanapa Valley for helping us throughout the development of this project. We are grateful to Dr Bicca-Marques and to two anonymous reviewers for their comments, which greatly helped in improving this manuscript. This study complied with the legal requirements of Mexico (SEMARNAT-DGVS/03660/11) and was approved by Universidad Veracruzana

Development of electricity consumption profiles of residential buildings based on smart meter data clustering

In the present research, a high-resolution, detailed electric load dataset was assessed, collected by smart meters from nearly a thousand households in Hungary, many of them single-family houses. The objective was to evaluate this database in detail to determine energy consumption profiles from time series of daily and annual electric load. After representativity check of dataset daily and annual energy consumption profiles were developed, applying three different clustering methods (k-means, fuzzy k-means, agglomerative hierarchical) and three different cluster validity indexes (elbow method, silhouette method, Dunn index) in MATLAB environment. The best clustering method for our examination proved to be the kmeans clustering technique. Analyses were carried out to identify different consumer groups, as well as to clarify the impact of specific parameters such as meter type in the housing unit (e.g. peak, offpeak meter), day of the week (e.g. weekend, weekday), seasonality, geographical location, settlement type and housing type (single-family house, flat, age class of the building). Furthermore, four electric user profile types were proposed, which can be used for building energy demand simulation, summer heat load and winter heating demand calculatio

Endothelial C-type natriuretic peptide maintains vascular homeostasis

PMCID: PMC4151218Wellcome Trust (084449/Z/07/Z and 078496/Z/05/Z