Prediction of Drought with Artificial Neural Networks and Genetic Algorithms Using Precipitation by Remote Perception

The aim of the study is to determine the performance of the regional agricultural drought prediction by the model of ANN(Artificial Neural Networks)type NARX,using the SPI(Standardized Precipitation Index),SPEI(Precipitation Index Standardized Evapotranspiration),VCI(Vegetation Condition Index)and GCI(Global Climate Indexes).There have been determined 10 homogeneous regions through RAF(regional frequency analysis)and L-moments,defining the most arid region and the index representing their respective time scale(SPEI 6 months)which responds to the growth and development of vegetation in the basin correlation Pearson equal to 0.58.Monthly rainfall and temperatures correspond to PISCO data prepared by SENAMHI-Peru,with space resolution of 0.05 degrees.For prediction,they have determined two groups,the first to build the model with 80% of the registration and validation of the model and the hypothesis with the remaining 20%.The results have been satisfactory prediction accepting the null hypothesis.

Estimation of Deep Soil Profiles in Lima Peru

Deep shear-wave velocity profiles at eight places in Lima Peru were estimated based on the inversion of dispersion curves. The dispersion curves were calculated from small and large microtremor arrays using two methods： the F-k proposed by Capon （1969） and the CCA proposed by Cho et al. （2004）. For the purpose of large array measurement we introduced a new type of sensor. Important results are the relative shallow depths to the basement rock in the area classified as alluvial gravel that covers most of the area of Lima city; and the relative large depth to the bedrock in places identified as VSV and CMA. It is recommended that this study be complemented with PS loggings in order to verify the estimated profiles.

A Prototype of a Semantic Platform with a Speech Recognition System for Visual Impaired People

In the world, 10% of the world population suffer with some type of disability, however the fast technological development can originate some barriers that these people have to face if they want to access to technology. This is particularly true in the case of visually impaired users, as they require special assistance when they use any computer system and also depend on the audio for navigation tasks. Therefore, this paper is focused on making a prototype of a semantic platform with web accessibility for blind people. We propose a method to interaction with user through voice commands, allowing the direct communication with the platform. The proposed platform will be implemented using Semantic Web tools, because we intend to facilitate the search and retrieval of information in a more efficient way and offer a personalized learning. Also, Google APIs (STT (Speech to Text) and TTS (Text to Speech)) and Raspberry Pi board will be integrated in a speech recognition module.

Production of Glucosamine Hydrochloride from Crustacean Shell

The use of chitin as raw material to obtain glucosamine hydrochloride at laboratory level was investigated. Chitin was extracted from shrimp shells by deproteinization, demineralization and depigmentation. Afterwards, glucosamine hydrochloride was produced in four main stages： （1） acid hydrolysis of chitin with 12 M hydrochloric acid using the reflux technique; （2） filtration of the solution to discard solid impurities; （3） recrystallization of the product using 95% ethyl alcohol as solvent, and （4） filtration, washing and drying of final product at 50 ℃. The FTIR spectrum of the product was compared to a commercial glucosamine hydrochloride of 99.86% purity, and a coincidence between 96.90% and 99.66% was obtained. The influence of temperature, solid/liquid ratio （g/mL）, and agitation （with-without） on acid hydrolysis was studied. The best correlation corresponds to the hydrolysis product obtained at solid/liquid ratio of 1：20, temperature of 85 ℃, and with agitation. The yields of glucosamine hydrochloride with respect to chitin were 42, 58, 36 and 48% for solid/liquid ratios of 1：10, 1：20, 1：30, and 1：40 respectively, at high hydrolysis reaction temperature and with agitation. These results showed that in the range examined, glucosamine hydrochloride with high quality is produced with solid/liquid ratio of 1：20.

Review of Statistical Water Temperature Models for a Peruvian Andean River

The rapid increase in Water Temperature Rivers (WTR) observed globally in recent decades and projections for the coming decades under climate change scenarios make water temperature prediction essential to assess changes in aquatic biota. Statistical models for stream temperature prediction have been widely used because they are computationally simple, involve few parameters, and because of their relatively good accuracy. However, these models have not been evaluated in Peruvian Andean rivers. This work evaluates the main water temperature statistical models from the literature and fits them with data recorded in the Ichu River experimental watershed, Huancavelica-Peru. Three well-known models were reviewed: the Stefan & Preud’homme linear regression model and the Mohseni & Stefan 3- and 4-parameter logistic regression models. Ichu river water temperatures were simulated using the SWAT (Soil and Water Assessment Tool) hydrometeorological model, which defaults to the Stefan & Preud’homme model. Modifications and adjustment of coefficients of the evaluated models were configured in the SWAT code using the “Latin Hypercube Sampling” technique. The evaluated models showed poor performance in predicting the water temperature in the Ichu River with NSE (Nash-Sutcliffe Efficiency) values ranging from -2.6 to 0.49, while the modified models showed NSE values of 0.72 in all three cases. Findings suggest that the statistical models shown in the literature should be validated for Andean rivers.

Dye-Sensitized Solar Cells Based on TiO₂Nanoparticles Modified by Wet Milling

TiO2 nanoparticles were produced from a commercial anatase powder through a wet milling process. The effect of grinding intensity, which is directly dependent on the operating parameters, was analyzed and the performance of polyethylene glycol (PEG400) as a dispersing agent in the milling system was also tested. The results showed that the processes using polyethylene glycol achieved a greater fragmentation of particles. This could be observed in the histograms made from SEM images taken from samples of powders from the processes, whose populations reached an average size of approximately 90 nm. The TiO2 powders obtained by milling were then used in the manufacture of dye-sensitized solar cells. It was verified that the powders produced using the dispersing agent achieved the greatest efficiencies, the highest being 0.94%. The current produced by the cells proved to be very low compared to the voltages obtained which gave acceptable values up to 0.81 V.

Non-polynomial Zig-Zag and ESL shear deformation theory to study advanced composites

The mechanical behavior of advanced composites can be modeled mathematically through unknown variables and Shear Strain Thickness Functions(SSTFs). Such SSTFs can be of polynomial or non-polynomial nature and some parameters of non-polynomial SSTFs can be optimized to get optimal results. In this paper, these parameters are called ‘‘r" and ‘‘s" and they are the argument of the trigonometric SSTFs introduced within the Carrera Unified Formulation(CUF). The Equivalent Single Layer(ESL) governing equations are obtained by employing the Principle of Virtual Displacement(PVD) and are solved using Navier method solution. Furthermore, trigonometric expansion with Murakami theory was implemented in order to reproduce the Zig-Zag effects which are important for multilayer structures. Several combinations of optimization parameters are evaluated and selected by different criteria of average error. Results of the present unified trigonometrical theory with CUF bases confirm that it is possible to improve the stress and displacement results through the thickness distribution of models with reduced unknown variables. Since the idea is to find a theory with reduced numbers of unknowns, the present method appears to be an appropriate technique to select a simple model. However these optimization parameters depend on the plate geometry and the order of expansion or unknown variables. So, the topic deserves further research.