Modeling wind speed with a long-term horizon and high-time interval with a hybrid fourier-neural network model

The limited availability of local climatological stations and the limitations to predict the wind speed (WS) accurately are significant barriers to the expansion of wind energy (WE) projects worldwide. A methodology to forecast accurately the WS at the local scale can be used to overcome these barriers. This study proposes a methodology to forecast the WS with high-resolution and long-term horizons, which combines a Fourier model and a nonlinear autoregressive network (NAR). Given the nonlinearities of the WS variations, a NAR model is used to forecast the WS based on the variability identified with the Fourier analysis. The NAR modelled successfully 1.7 years of windspeed with 3 hours of the time interval, what may be considered the longest forecasting horizon with high resolution at the moment

Computational Tool for Post-Earthquake Evaluation of Damage in Buildings

A method and a computational tool oriented to assist the damage and safety evaluation of buildings after strong earthquakes is described in this article. The input of the model is the subjective and incomplete information on the building state, obtained by inspectors which are possibly not expert professionals of the field of building safety. The damage levels of the structural components are usually described by linguistic qualifications which can be adequately processed by computational intelligence techniques based on neuro-fuzzy systems what facilitate the complex and urgent tasks of engineering decision-making on the building occupancy after a seismic disaster. The hybrid neuro-fuzzy system used is based on a special three-layer feedforward artificial neural network and fuzzy rule bases and is an effective tool during the emergency response phase providing decisions about safety, habitability, and reparability of the buildings. Examples of application of the computer program are given for two different building classes

The Future of Micro-grids in Ecuador

An analysis is made on the development of power lines worldwide and that offer the approaches of the impacts that are generated in the economic and environmental, which justify the application of smart grids in Ecuador, as an effective way to raise the efficiency of the electric power service and to achieve a more efficient use of the energy that is generated by showing the different technologies used in electricity generation where renewable energy sources are incorporated. A comparative analysis of how the installed generation power has been increasing until 2016 is shown

An Intelligent Approach Using Machine Learning Techniques to Predict Flow in People

The goal of this study is to estimate the state of consciousness known as Flow, which is associated with an optimal experience and can indicate a person’s efficiency in both personal and professional settings. To predict Flow, we employ artificial intelligence techniques using a set of variables not directly connected with its construct. We analyse a significant amount of data from psychological tests that measure various personality traits. Data mining techniques support conclusions drawn from the psychological study. We apply linear regression, regression tree, random forest, support vector machine, and artificial neural networks. The results show that the multilayer perceptron network is the best estimator, with an MSE of 0.007122 and an accuracy of 88.58%. Our approach offers a novel perspective on the relationship between personality and the state of consciousness known as Flow

Artificial intelligence assisted Mid-infrared laser spectroscopy in situ detection of petroleum in soils

A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the e ectiveness of rapidly di erentiating between di erent soil types and detecting the presence of petroleum traces in di erent soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum

An Ethical Overview on Cloning in Nigeria

This work titled "An Ethical Overview on Cloning in Nigeria" analyzed the debate on cloning for which many scholars believe that cloning is not just ethically and morally acceptable, but beneficial in that they allow otherwise infertile couples to have children and permit the study of genetic diseases and indeed genetic development. This work examined cloning from an ethical/moral perspective and held the view that there is everything inherently wrong with the idea of human cloning. As a scientific discovery, it violates the dignity, respect, and value of human life and concluded that cloning coupled with its related procedures, placed the human offspring at risk of genuine harm. Also, it is shown that Cloned offspring's run the risk of misplaced or distorted genealogy. The work relied on library materials, analysis, critical exposition, and evaluative methods to achieve its objectives

Issue 25.1 Endnotes

Issue 25.1 Endnote

A systematic mapping study of micro-grid architectures

Introduction:  Generation and energy consumption are a major issue in different countries around the world. Nowadays, projects under development seek the modernization of electric power generation and distribution systems. One of the main strategies is the design of context-adaptable micro-grid architectures. The micro-grid concept focuses on a controlled, monitored and highly autonomous use of electric power supported on information technologies, for the optimization of energy transfer, minimize risks and increase the system’s quality, efficiency and reliability. Goal: This article, therefore, aims to identify, classify and compare different micro-grid architectures, based on their applicability and research trends. Method: A systematic mapping study of micro-grid architectures is conducted to examine the experimental and theoretical contributions made by the scientific community. Results: This article categorizes and quantifies the different studies related to the subject, identifying and analyzing the strengths and opportunities for improvement in the applicability of micro-grid architectures. The trends observed highlight five strategies as the most relevant, whose different characteristics contribute to an automated and intelligent organization of the distribution, control and supervision of electricity according to supply versus demand

A systematic mapping study of micro-grid architectures

Introduction:  Generation and energy consumption are a major issue in different countries around the world. Nowadays, projects under development seek the modernization of electric power generation and distribution systems. One of the main strategies is the design of context-adaptable micro-grid architectures. The micro-grid concept focuses on a controlled, monitored and highly autonomous use of electric power supported on information technologies, for the optimization of energy transfer, minimize risks and increase the system’s quality, efficiency and reliability. Goal: This article, therefore, aims to identify, classify and compare different micro-grid architectures, based on their applicability and research trends. Method: A systematic mapping study of micro-grid architectures is conducted to examine the experimental and theoretical contributions made by the scientific community. Results: This article categorizes and quantifies the different studies related to the subject, identifying and analyzing the strengths and opportunities for improvement in the applicability of micro-grid architectures. The trends observed highlight five strategies as the most relevant, whose different characteristics contribute to an automated and intelligent organization of the distribution, control and supervision of electricity according to supply versus demand