A cluster-based artificial neural network model called CLASO (Classification-Assemblage-Association) has been proposed to predict the maximum of the 24-h moving average of PM 10 concentration on the next day in the three largest metropolitan areas of Mexico. The model is a self-organised, real-time learning neural network, which builds its topology via a process of pattern classification by using an historical database. This process is based on a supervised clustering technique, assigning a class to each centroid of the hidden layer, employing the Euclidean distance as a hierarchical criterion. A set of ARIMA models was compared with CLASO model in the forecast performance of the 24-h average PM 10 concentration on the next day. In general, CLASO model produced more accurate predictions of the maximum of the 24-h moving average of PM 10 concentration than the ARIMA models, although the latter showed a minor tendency to underpredict the results. The CLASO model solely requires to be built a historical database of the air quality parameter, an initial radius of classification and the learning factor. CLASO has demonstrated acceptable predictions of 24-h average PM 10 concentration by using exclusively regressive PM 10 concentrations. The forecasting capabilities of the model were found to be satisfactory compared to the classical models, demonstrating its potential application to the other major pollutants used in the Mexican air quality index.

In this paper, an analytical stability model for quantum square-shaped extended source P-N-P-N tunneling field-effect transistor (P-N-P-N TFET) with silicon carbide substrate is developed that includes the effects of gate-source capacitance, gate-drain capacitance, effective gate resistance, time constant and transconductance. A nonquasistatic RF small-signal model has been used and stability that was extracted from analytical equations of its Y-parameters. The obtained analytical expression for stability is compared with device simulation results and excellent agreement is found up to 100 GHz. The stability performance of a quantum square-shaped extended source P-N-P-N TFET with silicon carbide substrate is also studied analytically considering pocket width and doping variation. The results strongly confirm that the proposed model is accurate and suitable for the quantum square-shaped extended source P-N-P-N TFET in the high-frequency regime.

Indoor air quality in academic areas has become of vital importance in high educational institutions worldwide. This is very important since students spend a substantial time in such common areas. The current objective of this investigation was to evaluate the air pollutant levels in two common areas (coffee shop and library) at the Juarez Autonomous University of Tabasco, Mexico. The study consisted in monitoring carbon monoxide (CO) and particulate material (PM10) concentration regarding the carbon dioxide (CO2), temperature (T) and relative humidity (RH). From the indoor air measurements, lineal regression models were also obtained to explain the CO2, CO and PM10 behaviour as a function of the temperature and relative humidity. The hourly average of PM10, CO and CO2 were computed to evaluate the air quality and indoor comfort level based on EPA, WHO and ASHRAE (the American Society of Heating, Refrigerating and AirConditioning Engineers) regulations. At the coffee shop, the CO concentration levels were found to be exceeded according to the air quality standards established by WHO. For both library and coffee shop, the mean hourly values of CO2 and temperature exceeded the maximum values recommended by ASHRAE as comfort levels. Concerning the relative humidity in the library, values of 60 % were recorded exceeding the maximum levels established by ASHRAE. Finally, the current results revealed that temperature and relative humidity played an important role for bacteria growth, indicating its presence for indoor ambient spaces, under similar ambient conditions.