A Computational Study of the Intermolecular Interactions in the Head-To-Tail Arrangement of the 5CB Liquid Crystal Monomers

In the present work, we have analyzed the molecular interactions existing in a 4-n-pentyl-4-cyano-biphenyl (5CB) monomer and its dimer, in the head-tail configuration. The optimized geometrical structures of the monomer and dimer were obtained using the theoretical level wB97-XD/6-311G++(d,p). The intermolecular interactions were analyzed through the non-covalent interaction index (NCI) and the localized molecular orbital energy decomposition analysis method (LMO-EDA). Our results suggest that the antiparallel alignment of the 5CB liquid crystal is caused by attractive contributions arising from the intermolecular interactions between the aromatic rings. Furthermore, these interactions were found to cause deformations in the geometries of the monomers forming the dimer

Estudio y análisis de medidor de voltaje controlado por dispositivos móviles

The world’s population is continually growing, and urbanisation is expected to add another 2.5 billion people to cities over the next three decades. Cities have been the epicenter of innovation and technological development. A smart city, is an urban area that uses different types of electronic sensor to collect data. With these data it is possible to manage assets and resources efficiently using the Internet of Things technology that belongs to the domain of Industry 4.0. The smart city concept applied in homes, integrates new information and communication technologies of Industry 4.0, such as ciberphysical systems connected to Internet of things networks through cloud computing applications to optimize the efficiency of home operations and services and connect to citizens. A smart home is one that provides its home owners comfort, security, energy efficiency (low operating costs) and convenience at all times, regardless of whether anyone is home. In this sense, the smart home is a term commonly used to define a residence that has appliances, lighting, heating, air conditioning, TVs, computers, entertainment audio & video systems, security, and camera systems that are capable of communicating with one another and can be controlled remotely by a time schedule, from any room in the home, as well as remotely from any location in the world by phone or internet. However, all the mentioned devices consume electrical energy when they are being used and also when they are not. In this research work, the deveolpment of technology for the smart sensing of electrical consumtion in smart homes in an internet of things environment is presented. This smart device is capable to analize the power consumption of electrical devices connected to electrical power by using mobile devices. The smart meter has a ciberphysical system with an embedded cloud computing application, which can be accesed by movile devices, which is capable to show the electrical consumption of electrical devices when when they are being used and also when they are not. This technological developmento contributes to detect the phantom consumption of electrical energy in order to promote energy saving. The results obtained shows that this technology contributes to the energy saving in smart homes which decreases the economic expense in for home owners and at the same time it allows to observe and analyze the electrical energy consumption of different electrical devices through the use of mobile devices that are connected through the Internet to an application embedded in a cyberphysical system.La población mundial crece continuamente, y se espera que la urbanización agregue otros 2.500 millones de personas a las ciudades durante las próximas tres décadas. Las ciudades han sido el epicentro de la innovación y el desarrollo tecnológico. Una ciudad inteligente, es un área urbana que utiliza diferentes tipos de sensores electrónicos para recopilar datos. Con estos datos es posible administrar activos y recursos de manera eficiente utilizando la tecnología de Internet de las Cosas que pertenece al dominio de la Industria 4.0. El concepto de ciudad inteligente aplicado en los hogares integra las nuevas tecnologías de información y comunicación de la Industria 4.0, como los sistemas ciberfísicos conectados a Internet de las redes de cosas a través de aplicaciones de computación en la nube para optimizar la eficiencia de las operaciones y servicios en el hogar y conectarse con los ciudadanos. Una casa inteligente es aquella que brinda a sus propietarios comodidad, seguridad, eficiencia energética (bajos costos de operación) y conveniencia en todo momento, independientemente de si hay alguien en casa. En este sentido, el hogar inteligente es un término comúnmente utilizado para definir una residencia que tiene electrodomésticos, iluminación, calefacción, aire acondicionado, televisores, computadoras, sistemas de entretenimiento de audio y video, sistemas de seguridad y cámaras que son capaces de comunicarse entre sí. y se puede controlar de forma remota por un horario, desde cualquier habitación de la casa, así como de forma remota desde cualquier lugar del mundo por teléfono o internet. Sin embargo, todos los dispositivos mencionados consumen energía eléctrica cuando se usan y también cuando no. En este trabajo de investigación, se presenta el desarrollo de la tecnología para la detección inteligente del consumo eléctrico en hogares inteligentes en un entorno de Internet de las cosas. Este dispositivo inteligente es capaz de analizar el consumo de energía de los dispositivos eléctricos conectados a la energía eléctrica mediante el uso de dispositivos móviles. El medidor inteligente tiene un sistema ciberfísico con una aplicación integrada de computación en la nube, a la que se puede acceder mediante dispositivos móviles, que es capaz de mostrar el consumo eléctrico de los dispositivos eléctricos cuando se usan y cuando no. Este desarrollo tecnológico contribuye a detectar el consumo fantasma de energía eléctrica para promover el ahorro de energía. Los resultados obtenidos muestran que esta tecnología contribuye al ahorro de energía en hogares inteligentes, lo que disminuye el gasto económico para los propietarios de viviendas y al mismo tiempo permite observar y analizar el consumo de energía eléctrica de diferentes dispositivos eléctricos mediante el uso de dispositivos móviles que están conectados a través de Internet a una aplicación integrada en un sistema ciberfísico

Sistemas hidropónicos abierto y cerrado en la producción de tomate (Lycopersicon esculentum, Mill)

Tomato (Lycopersicum esculentum Mill.) Is one of the most important vegetables in the world and its cultivation is increasing, mainly in greenhouses and hydroponics where you have an alternative production and marketing opportunity. Hydroponic systems are open, when excess nutrient solution drained is not reused and is discarded, and are closed, when excess nutrient solution is recovered and reused. The objective of the research was to know the differences in fruit production and quality in a closed hydroponic system compared to an open one, in the tomato crop of the El Cid variety, using pots with thin perlite substrate previously used. The research was carried out in 2015 in the Academic Unit of Agronomy of the Autonomous University of Zacatecas. Plant growth (stem length and diameter) was measured; Fruit production (number, size, weight and yield of fruits); And fruit quality (electrical conductivity, hydrogen potential, acidity, total soluble solids, and fruit maturity index and fruit weight loss) in two stages of production in three maturity indices. Growth variables were maintained within normal and adequate parameters. There was no significant difference in the quality variables evaluated in the three maturity indices of the fruits measured in two stages. There was significant difference only for the equatorial diameter of fruit, however, not for polar diameter, number and weight of fruits and yield, so the closed system is a production alternative potentially comparable to the open system. With the closed hydrological system, neither the yield nor the quality of the fruits produced is affected, but it does obtain a saving of 26.81% of water and 28.9% of fertilizers, which represents a rental rate of 22.2% greater than the open hydroponic system.El tomate o jitomate (Lycopersicum esculentum Mill.) es una de las hortalizas más importantes del mundo y su cultivo va en aumento, principalmente en invernaderos e hidroponía donde se tiene una alternativa de producción y oportunidad de comercialización. Los sistemas hidropónicos son abiertos, cuando el exceso de solución nutritiva drenada no es reusado y es desechado, y son cerrados, cuando la solución nutritiva excedente es recuperada y reusada. El objetivo de investigación fue conocer las diferencias en producción y calidad de frutos en un sistema hidropónico cerrado en comparación con uno abierto, en el cultivo de tomate de variedad El Cid, utilizando macetas con sustrato perlita fina previamente utilizado. La investigación se realizó en el año 2015 en la Unidad Académica de Agronomía de la Universidad Autónoma de Zacatecas. Se midió el crecimiento de la planta (longitud y diámetro de tallo); la producción de frutos (número, tamaño, peso y rendimiento de frutos); y la calidad de los frutos (conductividad eléctrica, potencial de hidrógeno, acidez, sólidos solubles totales, e índice de madurez en el zumo y pérdida de peso de fruto) en dos etapas de la producción en tres índices de madurez. Las variables de crecimiento se mantuvieron dentro de parámetros normales y adecuados. No hubo diferencia significativa en las variables de calidad evaluadas en los tres índices de madurez de los frutos medidos en dos etapas. Hubo diferencia significativa sólo para el diámetro ecuatorial de fruto, sin embargo, no para diámetro polar, número y peso de frutos y rendimiento, por lo cual el sistema cerrado es una alternativa de producción potencialmente comparable con el sistema abierto. Con el sistema hidropónico cerrado no se afecta el rendimiento ni la calidad de los frutos producidos, pero si se obtiene un ahorro de 26.81 % de agua y 28.9 % de fertilizantes, lo cual representa una tasa de rentabilidad 22.2 % mayor respecto al sistema hidropónico abierto

Optimización de redes neuronales artificiales para la reconstrucción del espectro de neutrones y sus dosis equivalentes

En el presente trabajo se utilizo la metodología de diseño robusto de redes neuronales artificiales para determinar una topología óptima de red capaz de resolver con eficiencia los problemas de espectrometría y dosimetría de neutrones. Para el diseño de la topología de red optimizada se entrenaron 36 distintas arquitecturas de red en base a un arreglo ortogonal con una configuración L9(34), L4(32). Para el entrenamiento de las redes neuronales, se utilizo un código de cómputo desarrollado en el entorno de programación de Matlab, el cual automatiza el procesamiento y análisis de la información, reduciendo considerablemente el tiempo empleado en esta actividad para el investigador. Para el entrenamiento de las redes de propagación hacia adelante se utilizo un compendio de espectro de neutrones publicado por la Agencia Internacional de Energía Atómica, donde del total se utilizaron el 80% para el entrenamiento y 20% para la prueba, entrenada con un algoritmo de propagación inversa siendo los datos de entrada las tasas de conteo correspondientes a las 7 esferas del sistemas espectrométrico de esferas Bonner, como datos de salida, la red neuronal obtiene el espectro de neutrones expresado en 60 grupos de energía y se calculan de forma simultánea 15 cantidades dosimétricas

Modeling and Simulation of Temperature and Relative Humidity Inside a Growth Chamber

Modeling and simulation of internal variables such as temperature and relative humidity are relevant for designing future climate control systems. In this paper, a mathematical model is proposed to predict the internal variables temperature and relative humidity (RH) of a growth chamber (GCH). Both variables are incorporated in a set of first-order differential equations, considering an energy-mass balance. The results of the model are compared and assessed in terms of the coefficients of determination (R2) and the root mean squared error (RMSE). The R2 and RMSE computed were R2 = 0.96, R2 = 0.94, RMSE = 0.98 C, and RMSE = 1.08 C, respectively, for the temperature during two consecutive weeks; and R2 = 0.83, R2 = 0.81, RMSE = 5.45%RH, and RMSE = 5.48%RH, respectively, for the relative humidity during the same period. Thanks to the passive systems used to control internal conditions, the growth chamber gives average differences between inside and outside of +0.34 C for temperature, and +15.7%RH for humidity without any climate control system. Operating, the GCH proposed in this paper produces 3.5 kg of wet hydroponic green forage (HGF) for each kilogram of seed (corn or barley) harvested on average

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN

A neutron spectrum unfolding code based on generalized regression artificial neural networks

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation

Circulating levels of specific members of chromosome 19 microRNA cluster are associated with preeclampsia development

Purpose: To perform serum microRNA expression profiling to identify members of chromosome 19 miRNA cluster involved in preeclampsia development. Methods: Serum chromosome 19 miRNA cluster microRNA expression profiling was evaluated at 12, 16, and 20 gestational weeks and at the time of preeclampsia diagnosis, in women who developed preeclampsia (WWD-PE; n = 16) and controls (n = 18) using TaqMan low density array plates. Results: A total of 51 chromosome 19 microRNA cluster members were evaluated. The circulating hsa-miRs 512-3p, 518f3p, 520c-3p, and 520d-3p, were differentially expressed between groups (P < 0.05). Compared with controls, serum levels of hsa-miR-518f-3p at 20 GW were useful for identifying WWD-Mild-PE (P = 0.035) and WWD-Severe-PE(P = 0.007). Conclusions: Serum hsa-miRs 512-3p, 518f-3p, 520c-3p, and 520d-3p, are differentially expressed between WWD-PE and controls and their role in the development of preeclampsia should be investigated further

Alfalfa (Medicago sativa L.) biomass yield at different pasture ages and cutting frequencies

Las frecuencias de defoliación y la edad de la pradera son variables estratégicas en el manejo del cultivo de la alfalfa para incrementar la biomasa producida. El objetivo del presente trabajo fue determinar el efecto de tres frecuencias de corte en el ciclo primavera-verano sobre la producción de materia seca, tasa de crecimiento y componentes del rendimiento de praderas de alfalfa de uno, dos y tres años de establecimiento. Se utilizó un diseño en bloques al azar con arreglo factorial 3 x 3 (frecuencias de corte y edad de la pradera). La mayor producción promedio de materia seca (7,528 Kg MS ha-1) y tasa de crecimiento (257 Kg MS ha-1día) se registró en praderas de un año de establecimiento (P<0.01). De otra forma, la frecuencia de corte a cuatro semanas (6,844 Kg MS ha-1) superó en 29 y 16 %, respectivamente a las frecuencias de tres y cinco semanas en la producción de materia seca. La producción de hoja y tallo en la pradera de un año de establecida superó en 45 % a la de tres años y la altura en 32 %; mientras que en la frecuencia de corte cada cuatro semanas los valores de hoja y tallo fueron 21 y 49 % superiores a tres semanas de corte y la altura en 33 %. Las variables evaluadas y su interacción determinan los componentes de rendimiento estimados en praderas de alfalfa variedad Oaxaca Criolla.Cutting frequency and pasture age are strategic variables in defining alfalfa crop management aimed at increasing biomass yield. An analysis was done to identify the effects of three cutting frequencies (three, four and five weeks) in the spring-summer cycle on dry matter production, growth rate and performance variables in alfalfa (Medicago sativa L. Oaxaca criolla) in three pasture ages (one, two and three years). A random block design with a 3x3 factorial arrangement (cutting frequency and pasture age) was used. Highest (P<0.01) average dry matter yield (7,528 kg DM ha-1) and growth rate (257 kg DM ha-1 d-1) were recorded at the one-year pasture age. Average dry matter yield was highest at the four-week cutting frequency (6,844 kg DM ha-1), which was 29% higher than at three weeks and 16% higher than at five weeks. In the one-year pasture, leaf and stem production was 45% higher than in the three-year pasture and forage height was 32% higher. At the four-week cutting frequency leaf production was 21% higher than at the three-week frequency, while stem production was 49% higher and forage height was 33% higher. The evaluated variables and their interactions determined estimated alfalfa component yield