Las redes sociales: herramienta de mercadotecnia para el sector restaurantero

This investigation aimed to determine the way companies of the restaurant sector integrate social networks as a marketing strategy and how these impact positioning, brand image, and volume of sales in Ciudad Victoria, Tamaulipas (northeast of Mexico). We carried out a quantitative research by means of a questionnaire applied to 46 companies of this sector. The results we obtained showed that restaurants use social networks as an advertising tool, promotion, and as source of information about their customers. This has generated a positive impact within the positioning and brand image, even though the volume of sales has not been determined yet.El objetivo de la investigación consistió en determinar cómo las empresas del sector restaurantero integran las redes sociales en la estrategia de mercadotecnia y cómo estas impactan dentro de los aspectos de posicionamiento, imagen de marca y volumen de ventas en Ciudad Victoria, Tamaulipas (noreste de México). Con una investigación cuantitativa, en la que se aplicó un cuestionario a 46 empresas de este sector, cuyos resultados obtenidos evidenciaron que los restaurantes integran las redes sociales como herramienta de publicidad, promoción y como fuente de información sobre los clientes, las cuales han generado un impacto positivo dentro del posicionamiento e imagen de marca, pero en el volumen de ventas no se ha determinado

Functional connectivity analysis of cerebellum using spatially constrained spectral clustering

The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data

Magnetic Gel Composites for Hyperthermia Cancer Therapy

Hyperthermia therapy is a medical treatment based on the exposition of body tissue to slightly higher temperatures than physiological (i.e., between 41 and 46 °C) to damage and kill cancer cells or to make them more susceptible to the effects of radiation and anti-cancer drugs. Among several methods suitable for heating tumor areas, magnetic hyperthermia involves the introduction of magnetic micro/nanoparticles into the tumor tissue, followed by the application of an external magnetic field at fixed frequency and amplitude. A very interesting approach for magnetic hyperthermia is the use of biocompatible thermo-responsive magnetic gels made by the incorporation of the magnetic particles into cross-linked polymer gels. Mainly because of the hysteresis loss from the magnetic particles subjected to a magnetic field, the temperature of the system goes up and, once the temperature crosses the lower critical solution temperature, thermo-responsive gels undergo large volume changes and may deliver anti-cancer drug molecules that have been previously entrapped in their networks. This tutorial review describes the main properties and formulations of magnetic gel composites conceived for magnetic hyperthermia therapy

Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach, which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures

Resting state connectivity and cognitive performance in adults with cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy

Cognitive impairment is an inevitable feature of cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), affecting executive function, attention and processing speed from an early stage. Impairment is associated with structural markers such as lacunes, but associations with functional connectivity have not yet been reported. Twenty-two adults with genetically-confirmed CADASIL (11 male; aged 49.8 ± 11.2 years) underwent functional magnetic resonance imaging at rest. Intrinsic attentional/executive networks were identified using group independent components analysis. A linear regression model tested voxel-wise associations between cognitive measures and component spatial maps, and Pearson correlations were performed with mean intra-component connectivity z-scores. Two frontoparietal components were associated with cognitive performance. Voxel-wise analyses showed an association between one component cluster and processing speed (left middle temporal gyrus; peak −48, −18, −14; ZE = 5.65, pFWEcorr = 0.001). Mean connectivity in both components correlated with processing speed (r = 0.45, p = 0.043; r = 0.56, p = 0.008). Mean connectivity in one component correlated with faster Trailmaking B minus A time (r = −0.77, p < 0.001) and better executive performance (r = 0.56, p = 0.011). This preliminary study provides evidence for associations between cognitive performance and attentional network connectivity in CADASIL. Functional connectivity may be a useful biomarker of cognitive performance in this population

Enhanced Electric Vehicle Integration in the UK Low Voltage Networks with Distributed Phase Shifting Control

Electric vehicles (EV) have gained global attention due to increasing oil prices and rising concerns about transportation-related urban air pollution and climate change. While mass adoption of EVs has several economic and environmental benefits, large-scale deployment of EVs on the low-voltage (LV) urban distribution networks will also result in technical challenges. This paper proposes a simple and easy to implement single-phase EV charging coordination strategy with three-phase network supply, in which chargers connect EVs to the less loaded phase of their feeder at the beginning of the charging process. Hence, network unbalance is mitigated and, as a result, EV hosting capacity is increased. A new concept, called Maximum EV Hosting Capacity (HC max) of low voltage distribution networks, is introduced to objectively assess and quantify the enhancement that the proposed phase-shifting strategy could bring to distribution networks. The resulting performance improvement has been demonstrated over three real UK residential networks through a comprehensive Monte Carlo simulation study using Matlab and OpenDSS tools. With the same EV penetration level, the under-voltage probability was reduced in the first network from 100% to 54% and in the second network from 100% to 48%. Furthermore, percentage voltage unbalance factors in the networks were successfully restored to their original values before any EV connection.Peer reviewedFinal Accepted Versio

Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach.

Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments