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This study aims to evaluate the development and sustainability of a research management system in a private Peruvian university. The Research Management System (RMS) of Universidad Privada del Norte (UPN) was monitored from 2012-2017. The study adopts a non-experimental longitudinal design, which firstly inspects the research management status of the university throughout the year 2011. Based on this, the executed cycle-steps adopted between 2012 and 2017 were documented from the perspective of Deming's cycle to finally analyze the research management status of the university during 2018 and, thus, assess the development and sustainability achieved during the process. The results indicate that the methodology adopted for the development of the research management system, that follows Deming's cycle of continuous improvement, promotes the development and sustainability of the university. This is supported by monitoring the defined management indicators through the study timeline, whose rates indicate significant changes.

This paper presents the creation of an new asynchronous bidirectional communications protocol, for industrial application in automated processes using Programmable Logic Controllers (PLC) from different manufacturers or from a single manufacturer, making it possible to communicate with each other or with a local server, in an efficient way (in data and processes) thus achieving the basis for the remote monitoring and control (via internet) of the entire automated industrial process. The usual master-slave network architectures were replaced by a point-to-point architecture in industrial protocols such as ModBus TCP / IP, thus reducing the number of packets, to send only information on status changes or control. User - Machine Interface (UMI) is coined to show the changes in the state of the industrial process. Since the control and monitoring of industrial processes (locally) uses a human-machine interface (HMI), the need to use a new interface is detected to perform efficient communication, this could allows remote monitoring and control.

Routine monitoring of atmospheric particulate matter with aerodynamic diameters less than or equal to 2.5 μm (PM2.5) is relevant because of their potential negative impacts on human health and the environment. These air pollutants can be accurately measured using ground, mass-based devices. However, the limited availability and spatial coverage of ground-monitoring networks restrict their use for the analysis of pollution at local and regional scales. The use of ground-based measurements combined with satellite and reanalysis models data is an excellent tool to analyze air pollution events on a regional scale. In this study, a Neural Network Model (NN) that relates Aerosol Optical Depth (AOD) and meteorological variables from the MERRA-2-reanalysis model was developed for the estimation and mapping of PM2.5 concentration distribution in Northeastern Mexico. The NN was calibrated using ground-based PM2.5 concentrations from the Monterrey Metropolitan Area (MMA), the main urban area in the region, for a period covering 2010 to 2014. AOD, temperature, and relative humidity, along PM2.5 components (dust, sea salt, black carbon, organic carbon, and sulfate) from MERRA-2 reanalysis model were found to be factors that significantly influence the regional distribution of PM2.5. Monthly-averaged PM2.5 concentrations were mapped, with maximum levels located over the MMA. The largest PM2.5 concentrations occurred in winter and spring, while moderate concentrations were observed in autumn and low concentration in summer. In conclusion, MERRA-2 reanalysis data can be used to study atmospheric pollution distribution at regional and local scales. The magnitude of PM2.5 pollution events of PM2.5 varies in space and time, showing large seasonality and heterogeneous distribution across the region. The methodology developed and results obtained here can be used to improve air quality management in places with few or no ground-based measurements.

El Material Particulado con diametro aerodinamico menor o igual a 2.5 μm (PM 2.5 ), en altas concentraciones, impacta negativamente a la salud y al medio ambiente, por lo cual se hace relevante su monitoreo rutinario. La forma mas precisa para evaluar estos contaminantes atmosfericos se hace a traves de mediciones masicas en superficie, sin embargo, la cobertura espacial de estas mediciones limita su uso en la determinacion y analisis de patrones de contaminacion a nivel local y regional. Por otro lado, el uso de datos de monitoreo en tierra, combinados con datos satelitales y datos de modelos de reanalisis constituye una excelente herramienta para analizar eventos de contaminacion atmosferica a escala regional. La parte innovadora de este estudio es el desarrollo de un Modelo de Red Neuronal (MRN) capaz de estimar concentraciones de PM 2.5 a traves de su relacion con datos satelitales y variables meteorologicas del modelo de re-analisis MERRA-2, para el mapeo de la distribucion de PM 2.5 utilizando como caso de estudio el noreste de Mexico. El MRN se desarrollo y calibro con datos de concentracion de PM 2.5 del area metropolitana de Monterrey (AMM, la principal zona urbana de la region), durante los anos 2010 a 2014. Se analizaron variables influyentes en la distribucion regional de PM 2.5 y se generaron mapas de concentracion mensual de PM 2.5 . Se utilizaron datos de concentraciones locales de PM 2.5 provistos por el Sistema Integral de Monitoreo Ambiental y datos modelados a partir del modelo de re-analisis MERRA-2, que incluyeron: Profundidad Optica de Aerosoles (AOD), campos meteorologicos y componentes de PM 2.5 . AOD, temperatura, humedad relativa y los componentes de PM 2.5 : polvo, sal marina, carbono negro, carbono organico y sulfato, del modelo de re-analisis son factores que influyeron significativamente en la distribucion regional de PM 2.5 . Los mayores niveles de PM 2.5 se localizaron en el AMM. Los eventos de alta concentracion se presentaron en invierno y primavera, los moderados en otono y los de baja concentracion en verano. En conclusion, la magnitud de los eventos de contaminacion de PM 2.5 varia en espacio y tiempo, mostrando gran estacionalidad y distribucion heterogenea a traves de la region. La metodologia desarrollada y los resultados obtenidos aqui pueden servir para identificar necesidades y ubicaciones de nuevos sitios de monitoreo, ademas, de facilitarse como una herramienta para hacer frente a la falta de informacion en los lugares donde no se tienen mediciones.

We introduce a new kind of aerial manipulator based on multirotors. This device exploits the rarely used yaw movement which characterizes these aircrafts in order to generate and transmit forces and displacements trough a serial-chain of rigid links and other drones, as if the whole system was a flying robot arm with UAVS as rotational joints. Also we provide comparisons with the existing technologies and a condensed review concerning them. Finally, this paper proposes many open problems in order to develop the final system. Due to the scientific and social impact of this new approach, we provide some application examples.

Here is applied The Immersion and Invariance method (I&I) in order to design a novel observer to solve the chaotic synchronization for the master-slave configuration, where the master belongs to a class of feedback linearized systems. This paper shows a robust observer which asymptotically calculates the underlying dynamics of the master system. We performed convincing numerical simulations.

A control strategy for the regulation of a PVTOL, based on output-feedback, is introduced. To this end, a couple of controllers or actuators that operate concurrently are proposed. One of them stabilizes the vertical variable in finite time, by means of a novel sliding mode controller. The other actuator uses the energy control approach to balance, simultaneously, the horizontal and angular variables. The needed velocity is computed using the well-known supertwisting algorithm, which works independently of the controllers. To verify the performance of the stabilizing strategy, a numerical experiment and a numerical comparison among two well-known control strategies, were carried out.

In this work, we introduce a smooth control law to stabilize the damped inverted pendulum cart, assuming that the pendulum is initialized in the upper-half plane, and the linear viscous force is known. To this end suitable nonlinear transformations to express the system as if it were a four order chain of integrator and perturbed by a nonlinear function that diminished in the origin. Then, a convenient control law consisted of saturation functions for the system in its new form is designed. The corresponding stability analysis were carried out with several Lyapunov functions. The effectiveness of the obtained control law was assessed with numerical simulations.