Aplicación de energía solar, para desarrollar capacidades referentes a la sustentabilidad en los alumnos

La educación en México así como en la UANL, esta tratando de concientizar a los estudiantes sobre la importancia del entorno social y ecológico desde los ámbitos profesional y humano. Por tal motivo, establece un programa por competencias, donde cada una de las unidades de aprendizaje contribuye a desarrollar competencias que permiten a los estudiantes favorecer la capacidad de reflexionar, buscar y seleccionar información para solucionar problemas, tanto de ingeniería como del medio ambiente, utilizando métodos y técnicas de análisis encaminados a proyectos de investigación básica utilizando recursos renovables como energía sustentable por ejemplo la energía solar, eólica, biomasa, flujos de agua, mareas, geotérmica etc. En este trabajo se presentan los resultados de un grupo de estudiantes, al desarrollar capacidades de esempeño de la Unidad de Aprendizaje de Física III, que conjuntan la integración de los conocimientos adquiridos en el curso, mediante un mini proyecto para utilizar energía solar en un modelo a escala de el salón de clase, para posteriormente extrapolarlo a una situación real como proyecto final, haciendo un uso efectivo de medios y recursos para el ahorro de energía y de la no contaminación del medio ambiente

Co-deposited Ni-Cr-B Nanocomposite Coatings for Protection Against Corrosion-Erosion

Electrodeposition is a low-cost and low-temperature method for producing metal matrix composite coatings. The electrodeposition of Ni matrix/Ni-Cr-B particles is considered as the co-deposition of Ni-Cr-B particles in a Ni matrix, resulting in nanocomposite coatings that can offer good wear and corrosion resistance between other applications. For comparison, the electrodeposition of Ni films and their wear and corrosion evaluation were also carried out under the same conditions. Some coatings usually contain oxide or carbide particles in micrometer size and are electrodeposited in a nickel matrix; however, the use of the mechanical alloying process offers the possibility to reduce the particle size in the order of nanometers obtaining solid solutions, amorphous phases, or intermetallic compounds during the development of new alloys to be co-deposited, improving the engineering materials properties. This kind of nanocomposite can be used in industrial components with an irregular geometry exposed in aggressive environments such as the energy generation and oil industry

Synthesis of TiB2 -Ni3 B nanocomposite coating by DC magnetron sputtering for corrosion-erosion protection

The research and development of functional coatings of new metal-ceramic materials using a new route of processing that combines Mechanical Alloying and PVD Sputtering offer great possibilities for protecting components exposed to aggressive environments where the wear and corrosion at high temperature are the leading root cause of failures.Significant contribution on corrosion-erosion resistance of Ni3B-TiB2 nanocomposite coating of 1 μm of thickness, deposited by DC magnetron Sputtering on stainless steel 304 substrates was studied. Nickel phase (γ Ni) plus Ni3B-TiB2 phases were synthesized previously by Mechanical Alloying (MA). Solid cathode (76.2 mm of diameter and 3 mm of thickness) used to grow thin films was manufactured with the alloyed powders, applying a uniaxial load of 70 MPa at room temperature and sintered at 900 °C for two hours. Microstructure and mechanical properties of the coatings were characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), nanoindentation, and wear test with a ball-on-disc tribometer. Compact coating of Ni3B-TiB2with a microstructure of prismatic crystals after annealing treatment, showing a uniform coating with good adherence and low friction coefficient of 0.5, correlated with a low roughness of Ra ≈ 0.0439 ± 0.0069 μm. The average hardness of 537.4 HV(5265.0 MPa) and wear coefficient at room temperature of 2.552E-10 m2 N−1 correspond with medium-hard phases with an elastic-plastic behavior suitable for fatigue applications. Geothermal fluid modified was synthesized in the lab with NaCl/Na2SO4 to evaluate the corrosion resistance of the films in a standard three electrodes cell, characterizing a corrosion rate of 0.0008 and 0.001 mm* year−1 at 25 and 80 °C respectively during 86.4 ks(24 h) of exposition; showing a resistive coating without corrosion products and with good response to the geothermal environmen

Depression Episodes Detection: A Neural Netand Deep Neural Net Comparison.

Depression is a frequent mental disorder. It is estimated thatit affects more than 300 million people in the world. In this investiga-tion, a motor activity database was used, from which the readings of 55patients (32 control patients and 23 patients with the condition) wereselected, during one week in one minute intervals, obtaining a total of385 observations (participants) and 1440 characteristics (time intervals)from which the most representative one minute intervals were extractedapplying genetic algorithms that reduced the number of data to process,with this strategy it is guaranteed that the most representative genes(characteristics) in the chromosome population is included in a singlemachine learning model of which applied deep neural nets and neuralnets with the aim of creating a comparative between the models gener-ated and determining which model offers better performance to detectingepisodes of depression. The deep neural networks obtained the best per-formance with 0.8086 which is equivalent to 80.86 % of precision, thisdeep neural network was trained with 270 of the participants which isequivalent to 70 % of the observations and was tested with 30 % Remain-ing data which is equal to 115 participants of which 53 were diagnosedas healthy and 40 with depression correctly. Based on these results, itcan be concluded that the implementation of these models in smart de-vices or in some assisted diagnostic tool, it is possible to perform theautomated detection of episodes of depression reliably.La depresión es un trastorno mental frecuente. Se estima que afecta a más de 300 millones de personas en el mundo. En esta investigación se utilizó una base de datos de actividad motora, de la cual se seleccionaron las lecturas de 55 pacientes (32 pacientes control y 23 pacientes con la condición), durante una semana en intervalos de un minuto, obteniendo un total de 385 observaciones (participantes) y 1440 características (intervalos de tiempo) de los cuales se extrajeron los intervalos de un minuto más representativos aplicando algoritmos genéticos que redujeron el número de datos a procesar, con esta estrategia se garantiza que los genes (características) más representativos de la población cromosómica se incluyan en un aprendizaje de una sola máquina modelo del cual se aplicó redes neuronales profundas y redes neuronales con el objetivo de crear una comparativa entre los modelos generados y determinar qué modelo ofrece mejor desempeño para detectar episodios de depresión. Las redes neuronales profundas obtuvieron el mejor desempeño con 0.8086 lo que equivale al 80.86% de precisión, esta red neuronal profunda fue entrenada con 270 de los participantes que es equivalente al 70% de las observaciones y se probó con el 30% de los datos restantes que es igual a 115 participantes de los cuales 53 fueron diagnosticados como sanos y 40 con depresión correctamente. En base a estos resultados, se puede concluir que la implementación de estos modelos en dispositivos inteligentes o en alguna herramienta de diagnóstico asistido, es posible realizar la detección automatizada de episodios de depresión de manera confiable

La publicidad de los actos jurisdiccionales electorales: entre la ponderación, la argumentación y la prudencia

Los tribunales electorales tienen un objetivo de pacificación social; pues se trata de salvaguardar la legitimidad de una determinada forma de organización socio-política. En este sentido, su responsabilidad es cumplir con el principio de transparencia y publicidad como forma de rendición de cuentas y vinculo necesario con quienes recurren a sus servicios. Por ello, y dado el interés político que revisten sus actividades, es deseable establecer parámetros mínimos respecto a la forma de comunicación de los juzgadores electorales con la ciudadanía, lo cual involucra, en su análisis. la búsqueda de equilibrio entre: la ética judicial, transparencia, derecho a la información, rendición de cuentas y libertad de expresión. Este equilibrio se logra bajo los presupuestos del discurso, la ponderación y la argumentación; estructuras previas que posibilitan justificar la excepcionalidad de la secrecía judicial como regla general en el proceso de comunicación previa a la existencia de la cosa juzgada. Ante esto, la trilogía presupuestal propuesta utilitaria sirve como herramienta para determinar los límites del juzgador electoral en la comunicación de sus proyectos de sentencia, previo a su discusión colegiada

Clasificación sin supervisión de imágenes de dispositivos móviles

Cada día el uso de imágenes de dispositivos móviles como evidencias en procesos judiciales es más habitual y común. Por ello, el análisis forense de imágenes de dispositivos móviles cobra especial importancia. En este trabajo se estudia la rama del análisis forense que se basa en la identificación de la fuente, concretamente en la agrupación o clustering de imágenes según la fuente de adquisición. Como diferencia con otras técnicas del estado del arte para la identificación de la fuente, en el clustering no se tiene un conocimiento a priori del número de imágenes ni dispositivos a identificar, ni se tienen datos de entrenamiento para una futura fase de clasificación. Es decir, se realiza un agrupamiento por clases con todas las imágenes de entrada. La propuesta se basa en la combinación de clustering jerárquico y plano y en el uso del patrón de ruido del sensor. Se han realizado un conjunto de experimentos que emulan situaciones similares a las que se pueden dar en la realidad para mostrar la robustez y fiabilidad de los resultados de la técnica. Los resultados obtenidos son satisfactorios en todos los experimentos realizados superando en tasa de acierto a otras propuestas descritas en el estado del arte

Decision-making tools for sustainable planning and conceptual framework for the energy–water–food nexus

Suplemento 6 con el título: Technologies and Materials for Renewable Energy, Environment and Sustainability[EN] The impact assessment of energy strategies, more specifically those that promote an integrated approach on resource management in other sectors such as water and food, requires knowledge related to the evaluation of the quality and knowledge that may be estimated by quantitative means. The present paper makes inquiries into those knowledge requirements in addition to review the means used to obtain it—including the required entries and the results they provide. In response to the recognized problems in knowledge, this paper introduces a basic reference structure underlying a system to evaluate the way that a progressive development of inexhaustible energies in a particular geographical region can affect the demand of water and food. Then, the proposed conceptual framework constitutes a novel approach for energy policy makers which only consider partial impacts of the energy management. By considering the nexus of energy, water and food, energy management policies may be redefined and differences with current policies must be investigated.SICabildo de Tenerif

Early pronation in patients with respiratory distress due to COVID-19 pneumonia

Introduction: In the course of COVID-19, the management of patients with respiratory failure has been a challenge worldwide, due to the large number of patients affected by the aggressiveness of the virus, the use of human resources and the availability of equipment. Objective: To show the usefulness of early pronation for improving oxygenation and prevent, in many cases, endotracheal intubation in patients with respiratory failure due to COVID-19. Methods: An analytical, observational, retrospective and cross-sectional study was carried out with patients hospitalized in the intensive care unit of the Cuban Hospital in Qatar, belonging to the Hamad Medical Corporation, in the period from March to May 2021. Variables focused on demonstrating the ventilatory response were used. The Statistical Package Social Science (SPSS), version 21.0, was used, according to percentage and chi-square, as well as Student's t-test for deductive statistical analysis of related samples. Results: The predominant age group was 3-50 years, in patients with two or more comorbidities. Oxygenation with high-flow nasal cannula and combined with non-invasive ventilation were the most widely used. Most of the variables in the related samples test were highly significant. Conclusions: Early pronation, as an adjunct to intensive management of patients with respiratory failure caused by COVID-19, provides better recovery for patients and an indisputable improvement of ventilation and oxygenation parameters

“Texting & Driving” Detection Using Deep Convolutional Neural Networks

The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate.The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate

Primary Sjögren's syndrome as independent risk factor for subclinical atherosclerosis.

To assess the prevalence of subclinical atherosclerosis in patients with primary Sjögren's syndrome (pSS) and its possible association with clinical and analytical parameters of the disease. In this cross-sectional study, 38 consecutive patients with pSS were compared with 38 age and sex healthy controls. Demographic variables and classic cardiovascular risk factors (CVRFs): Hypertension, dyslipidemia, diabetes mellitus, obesity, and smoking habit were assessed in both groups, and also disease-related features were collected in pSS group. The presence of subclinical atherosclerosis was assessed by carotid ultrasound, with carotid intima-media thickness (CIMT) measurement and determination of the presence of atheromatous plaques. Subclinical atherosclerosis presence was remarkably greater in patients with pSS than in healthy controls (OR = 4.17, 95%CI [1.27-16.54]), as well as CIMT values (0.79 ± 0.43mm vs. 0.66 ± 0.27mm; P = .02). No differences for classic CVRFs were found between both groups. An association of subclinical atherosclerosis with erythrocyte sedimentation rate (ESR) and rheumatoid factor (RF) was observed in patients with pSS. This cohort showed a greater prevalence of subclinical atherosclerosis in patients with pSS, indicating this disease as an independent risk factor for presence of early vascular damage.The authors declared that this study has received no financial support.S