All-inclusive tourism in Dominican Republic. An analysis from the perspective of the tourist demand

All Inclusive System has become a predominant service in sun and beach destinations. In Dominican Republic it is set as the main tourist attraction. The purpose of this research is to analyze the ratings of “all inclusive” international tourists that reach the tourist resort of Puerto Plata. This research is based on the responses obtained from of a questionnaire completed by foreign visitors. The main results show that tourists are a medium-high economic profile highlighting those visitors who choose this destination online. The beaches and ease of entry are the most highly valued

Single Multiplex Polymerase Chain Reaction To Detect Diverse Loci Associated with Diarrheagenic Escherichia coli

We developed and tested a single multiplex polymerase chain reaction (PCR) that detects enterotoxigenic, enteropathogenic, enteroinvasive, and Shiga-toxin–producing Escherichia coli. This PCR is specific, sensitive, and rapid in detecting target isolates in stool and food. Because of its simplicity, economy, and efficiency, this protocol warrants further evaluation in large, prospective studies of polymicrobial substances

The histone deacetylase inhibitor valproic acid attenuates phospholipase Cγ2 and IgE-mediated mast cell activation.

Mast cell activation through the high-affinity IgE receptor (FcεRI) plays a central role in allergic reactions. FcεRI-mediated activation triggers multiple signaling pathways leading to degranulation and synthesis of different inflammatory mediators. IgE-mediated mast cell activation can be modulated by different molecules, including several drugs. Herein, we investigated the immunomodulatory activity of the histone deacetylase inhibitor valproic acid (VPA) on IgE-mediated mast cell activation. To this end, bone marrow-derived mast cells (BMMC) were sensitized with IgE and treated with VPA followed by FcεRI cross-linking. The results indicated that VPA reduced mast cell IgE-dependent degranulation and cytokine release. VPA also induced a significant reduction in the cell surface expression of FcεRI and CD117, but not other mast cell surface molecules. Interestingly, VPA treatment inhibited the phosphorylation of PLCγ2, a key signaling molecule involved in IgE-mediated degranulation and cytokine secretion. However, VPA did not affect the phosphorylation of other key components of the FcεRI signaling pathway, such as Syk, Akt, ERK1/2, or p38. Altogether, our data demonstrate that VPA affects PLCγ2 phosphorylation, which in turn decreases IgE-mediated mast cell activation. These results suggest that VPA might be a key modulator of allergic reactions and might be a promising therapeutic candidate

Statistically derived contributions of diverse human influences to twentieth-century temperature changes

The warming of the climate system is unequivocal as evidenced by an increase in global temperatures by 0.8 °C over the past century. However, the attribution of the observed warming to human activities remains less clear, particularly because of the apparent slow-down in warming since the late 1990s. Here we analyse radiative forcing and temperature time series with state-of-the-art statistical methods to address this question without climate model simulations. We show that long-term trends in total radiative forcing and temperatures have largely been determined by atmospheric greenhouse gas concentrations, and modulated by other radiative factors. We identify a pronounced increase in the growth rates of both temperatures and radiative forcing around 1960, which marks the onset of sustained global warming. Our analyses also reveal a contribution of human interventions to two periods when global warming slowed down. Our statistical analysis suggests that the reduction in the emissions of ozone-depleting substances under the Montreal Protocol, as well as a reduction in methane emissions, contributed to the lower rate of warming since the 1990s. Furthermore, we identify a contribution from the two world wars and the Great Depression to the documented cooling in the mid-twentieth century, through lower carbon dioxide emissions. We conclude that reductions in greenhouse gas emissions are effective in slowing the rate of warming in the short term.F.E. acknowledges financial support from the Consejo Nacional de Ciencia y Tecnologia (http://www.conacyt.gob.mx) under grant CONACYT-310026, as well as from PASPA DGAPA of the Universidad Nacional Autonoma de Mexico. (CONACYT-310026 - Consejo Nacional de Ciencia y Tecnologia; PASPA DGAPA of the Universidad Nacional Autonoma de Mexico

Using the second-order information for reconfigurability analysis and design in the fault tolerant framework

The control reconfigurability measure defines the capability of a control system to allow recovery of performance when faults occur; therefore, it has been intended to be a tool for designing and synthesizing approaches in the fault tolerant control context. Reconfigurability depends on the controllability gramian, also known as the second-order information (SOI) in a broad sense. This paper proposes the assignation, by feedback, of the deterministic SOI in order to set the control reconfigurability of a given linear system. The theory concerned with this assignation is reviewed, then constructive theorems are given for finding constant feedback gains that approximate a required control reconfigurability for ease implementation. Also an unification of the reconfigurability measures proposed in the fault tolerance literature is given. Once the SOI is assigned by feedback, it can be computed online by using an identification method, which uses process input/output data. Results from simulation of the three tanks hydraulic benchmark, show that this approach can provide information about the system performance for fault tolerant purposes, thus online control reconfigurability computation and fault accommodation are considered. The approach presented in the paper gives an alternative for supervision taking into account the reconfigurability assigned by design

Structural, mechanical and electronic properties of two-dimensional structure of III-arsenide (1 1 1) binary compounds: An ab-initio study

Structural, mechanical and electronic properties of two-dimensional single-layer hexagonal structures in the (1 1 1) crystal plane of IIIAs-ZnS systems (III = B, Ga and In) are studied by first-principles calculations based on density functional theory (DFT). Elastic and phonon dispersion relation display that 2D h-IIIAs systems (III = B, Ga and In) are both mechanical and dynamically stable. Electronic structures analysis show that the semiconducting nature of the 3D-IIIAs compounds is retained by their 2D single layer counterpart. Furthermore, density of states reveals the influence of σ and π bonding in the most stable geometry (planar or buckled) for 2D h-IIIAs systems. Calculations of elastic constants show that the Young's modulus, bulk modulus and shear modulus decrease for 2D h-IIIAs binary compounds as we move down on the group of elements of the periodic table. In addition, as the bond length between the neighboring cation-anion atoms increases, the 2D h-IIIAs binary compounds display less stiffness and more plasticity. Our findings can be used to understand the contribution of the σ and π bonding in the most stable geometry (planar o buckled) for 2D h-IIIAs systems. Structural and electronic properties of h-IIIAs systems as a function of the number of layers have been also studied. It is shown that h-BAs keeps its planar geometry while both h-GAs and h-InAs retained their buckled ones obtained by their single layers. Bilayer h-IIIAs present the same bandgap nature of their counterpart in 3D. As the number of layers increase from 2 to 4, the bandgap width for layered h-IIIAs decreases until they become semimetal or metal. Interestingly, these results are different to those found for layered h-GaN. The results presented in this study for single and few-layer h-IIIAs structures could give some physical insights for further theoretical and experimental studies of 2D h-IIIV-like systems

AGRONOMIC EVALUATION AND CHEMICAL COMPOSITION OF AFRICAN STAR GRASS (Cynodon plectostachyus) IN THE SOUTHERN REGION OF THE STATE OF MEXICO

African Star Grass is one of the forage resources most commonly used by farmers in regions with warm-humid climates. This study was carried out to determine the nutritional and agronomic characteristics of African Star Grass (Cynodon plectostachyus) through the following variables: crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), organic matter digestibility (OMD), net forage accumulation (NFA), stem:leaf ratio, and live:dead matter ratio in the three pastures evaluated. The work took place from April 2007 to March 2008, with evaluations carried out on a monthly basis. The data were analyzed in a randomized block design in which the blocks were the pastures, and the treatments were the months of evaluation. There were no differences between the pastures evaluated for the NDF, ADF or OMD (P>0.05). Differences were found, however, in CP, while in the monthly evaluation, differences were found between the periods evaluated (P<0.05) for these variables. Differences were also found in the agronomic evaluation of pastures (P<0.05) among height of pasture, net forage accumulation (NFA), live matter, dead matter, leaf and stem, both among pastures and in the monthly evaluations. African Star Grass can therefore be considered a good choice for milk production systems in the southern region of the state of Mexico, due to its nutritional and agronomic characteristics

Fault detection in unmanned aerial vehicles via orientation signals and machine learning

[EN] This work proposes an actuator fault detection and isolation scheme for a quadrotor unmanned aerial vehicle (UAV) under a data-driven approach using machine learning techniques. In this approach, an implicit model of the system is built through the information provided by the onboard sensors of the UAV. First, using a tailored flying platform, vibrations corresponding to the orientation, angular position and linear acceleration were captured with the UAV flying in hover mode under nominal conditions. This data is processed by Principal Component Analysis (PCA) for feature extraction. Subsequently, faults in the actuators are induced through a cut in each of the UAV propellers which generate a reduction in the thrust of the rotors. These data are also projected into the PCA subspace and compared to the nominal data. Hotelling’s T 2 statistic is used to discern between nominal data and data when the vehicle exhibits an actuator fault. Finally, the developed algorithms were complemented with k-nearest neighbors (k-NN) and support vector machine (SVM) classification algorithms. The results show a correct classification rate of 89.6 % (k-NN) and 92.4 % (SVM) respectively for 423 validation datasets.[ES] Este trabajo propone un esquema de detección y localización de fallas en los actuadores de un vehículo aéreo no tripulado (VANT) del tipo cuadrirrotor. Para ello, se considera un enfoque basado en datos haciendo uso de técnicas de aprendizaje de máquina. En este enfoque se construye un modelo implícito del sistema a través de la información proporcionada por los sensores del VANT. Primero, a través de un plataforma de vuelo de tipo giroscópica, se captan las vibraciones correspondientes a la orientación, posición angular y aceleración lineal cuando el vehículo se encuentra en vuelo estacionario en condiciones nominales. Estos datos se procesan mediante Análisis en Componentes Principales (PCA) para la extracción de características. Posteriormente, se induce una falla a los actuadores a través de un recorte en cada una de las hélices del VANT que ocasionan una reducción del empuje generado por los rotores. Estos datos se proyectan también al subespacio de componentes principales y se comparan con los datos nominales. Para discernir entre los datos nominales y los datos cuando el vehículo presenta falla, se emplea el estadístico T2 de Hotelling. Finalmente, el desarrollo se complementa con los algoritmos de clasificación de k-vecinos más cercanos (k-NN) y de máquina de vectores de soporte (SVM). Los resultados muestran una tasa de clasificación correcta del 89.6 % (k-NN) y 92.4 %(SVM) respectivamente para 423 conjuntos de datos de validación.López-Estrada, FR.; Méndez-López, A.; Santos-Ruiz, I.; Valencia-Palomo, G.; Escobar-Gómez, E. (2021). 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