Modeling and identification of gene regulatory networks: A Granger causality approach

It is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from time-series genomic data, i.e., to explore the interactions among a large number of genes and gene products over time. Currently, one common approach is based on Granger causality, which models the time-series genomic data as a vector autoregressive (VAR) process and estimates the GRNs from the VAR coefficient matrix. The main challenge for identification of VAR models is the high dimensionality of genes and limited number of time points, which results in statistically inefficient solution and high computational complexity. Therefore, fast and efficient variable selection techniques are highly desirable. In this paper, an introductory review of identification methods and variable selection techniques for VAR models in learning the GRNs will be presented. Furthermore, a dynamic VAR (DVAR) model, which accounts for dynamic GRNs changing with time during the experimental cycle, and its identification methods are introduced. © 2010 IEEE.published_or_final_versionThe 9th International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the 9th ICMLC, 2010, v. 6, p. 3073-307

A novel three-class ROC method for eQTL analysis

The problem of identifying genetic factors underlying complex and quantitative traits such as height, weight and disease susceptibility in natural populations has become a major theme of research in recent years. Aiming at revealing the inter-dependency and causal relationship between the underlying genotypes and observed phenotypes, researchers from different areas have developed a variety of methods for expression quantitative trait loci (eQTL) mapping. Most of these methods rely on resampling-based algorithms that are computationally very expensive. To overcome the disadvantages of the current techniques, we propose a novel nonparametric method based on the volume under surface (VUS) within the framework of three-class receiver operating characteristic (ROC) analysis. With the fast algorithms developed, we can reduce the computation time of the genomewide analysis from several months down to several days. © 2010 IEEE.published_or_final_versionThe 2010 International Conference on Machine Learning and Cybernetics (ICMLC 2010), Qingdao, China, 11-14 July 2010. In Proceedings of the International Conference on Machine Learning and Cybernetics, 2010, v. 6, p. 3056-306

Evaluación de la gestión del conocimiento: una revisión sistemática de literatura

La evaluación de la gestión del conocimiento (GC) es un tema importante para aquellas organizaciones que quieran saber “qué está pasando” con sus estrategias de GC. No obstante, no existe un consenso sobre qué evaluar y cómo evaluarlo. Por esta razón, el propósito del artículo es presentar una revisión sistemática de literatura de 43 artículos publicados en la última década. La revisión comprende un análisis cienciométrico básico y un análisis de contenido relacionado con varios aspectos de los modelos como su estructura, la función y objetivo de la evaluación, lo métodos de investigación utilizados, los sectores económicos de aplicación, y la ubicación de los aspectos evaluados respecto de una taxonomía de escuelas de pensamiento de la GC y una clasificación de las capacidades organizacionales de GC. Como principal hallazgo se muestra la predominancia del enfoque de GC como codificación de conocimiento. Además, se presentan varias brechas susceptibles de investigación futura

Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit

Supply Chain Information Collaborative Simulation Model Integrating Multi-Agent and System Dynamics

Supply chain collaboration management is a systematic, integrated and agile advanced management mode, which helps to improve the competitiveness of enterprises and the entire supply chain. In order to realise the synergy of supply chain, the most important is to realise the dynamic synergy of information. Here we proposed a strategy to integrate system dynamics and multi-agent system modelling methods. Based on the strategy of supply chain information sharing and coordination, a two-level aggregation hybrid model was designed and established. Through the computer simulation analysis of the two modes before and after information collaboration, it is found that under the information collaboration mode, the change trend of order or inventory of suppliers and manufacturers always closely matches that of retailers. After the implementation of supply chain information coordination, ordering and inventory can be reasonably planned and matched, and problems such as over-stocking or short-term failure to meet order demands caused by poor information communication will no longer occur, which can greatly reduce the “bullwhip effect”

State Estimator Design of Generalized Liu Systems with Application to Secure Communication and Its Circuit Realization

The generalized Liu system is firstly introduced and the state observation problem of such a system is explored. A simple state estimator for the generalized Liu system is developed to guarantee the global exponential stability of the resulting error system. Applications of proposed state estimator strategy to chaotic secure communication, circuit implementation, and numerical simulations are provided to show the effectiveness and feasibility of the obtained results. Besides, the guaranteed exponential convergence rate of the proposed state estimator and that of the proposed chaotic secure communication can be precisely calculated

Detection of Coronavirus illness using Techniques of Deep Learning and CNN

A year has been passed with the global pandemic creating havocs in everyone’s life. The novel Coronavirus is still raging around the globe causing catastrophic consequences on the entire health and wealth of humankind. Tests are being conducted in an insane amount on the suspected individuals. Infections that are gained through respiratory course, for example, the lethal SARS-CoV-2, are determined to have the assistance of direct identification of viral parts in respiratory examples. The two most generally utilized techniques to do this are nucleic corrosive enhancement tests through polymerase chain response/reaction (PCR) or antigen-based tests. This can take a while to generate results as there is steady increase in number of cases and causing delay in laboratories. Early detection of the virus is life saviour, if the virus is left unnoticed it can be fatal for ones’ life. The current industrial era is ruled by fields of artificial intelligence and machine learning; hence this paper is an attempt to use one of these practices for novel corona virus prediction using chest radiogram images. Here dataset of Chest Roentgenogram images of patients infected with the corona virus and normal Chest Roentgenogram images are used to detect coronavirus infection. The study employs an efficient approach of application Convolutional Neural Network in predicting if the patient is affected and unaffected with the virus. The prepared model created a precision pace of 92.77% at the time of the performance preparation

A power consumption monitoring, displaying and evaluation system for home devices

[EN] This paper presents an implementation of a smart meter monitoring system based on Arduino platform and accessible from Android mobile devices. On the one hand, it has been developed a measure device, based on Arduino, which monitors the power consumption from each home device and sends this information to a server. On the other hand, users can see by means of their mobile devices the power consumption in real time. Also, users can know the detailed consumption from previous months. This way, the developed application is aim to satisfy two different consumer needs: improve their economy and their sustainability in the way electrical power is consumed.Miquel Murcia, A.; Belda Ortega, R.; De Fez Lava, I.; Arce Vila, P.; Fraile Gil, F.; Guerri Cebollada, JC.; Martínez Zaldívar, FJ.... (2013). A power consumption monitoring, displaying and evaluation system for home devices. WAVES. 5:5-13. http://hdl.handle.net/10251/52792S513

Impact of Temperament Types and Anger Intensity on Drivers\u27 EEG Power Spectrum and Sample Entropy: An On-road Evaluation Toward Road Rage Warning

"Road rage", also called driving anger, is becoming an increasingly common phenomenon affecting road safety in auto era as most of previous driving anger detection approaches based on physiological indicators are often unreliable due to the less consideration of drivers\u27 individual differences. This study aims to explore the impact of temperament types and anger intensity on drivers\u27 EEG characteristics. Thirty-two drivers with valid license were enrolled to perform on-road experiments on a particularly busy route on which a variety of provoking events like cutting in line of surrounding vehicle, jaywalking, occupying road of non-motor vehicle and traffic congestion frequently happened. Then, muti-factor analysis of variance (ANOVA) and post hoc analysis were utilized to study the impact of temperament types and anger intensity on drivers\u27 power spectrum and sample entropy of θ and β waves extracted from EEG signals. The study results firstly indicated that right frontal region of the brain has close relationship with driving anger. Secondly, there existed significant main effects of temperament types on power spectrum and sample entropy of β wave while significant main effects of anger intensity on power spectrum and sample entropy of θ and β wave were all observed. Thirdly, significant interactions between temperament types and anger intensity for power spectrum and sample entropy of β wave were both noted. Fourthly, with the increase of anger intensity, the power spectrum and sample entropy both decreased sufficiently for θ wave while increased remarkably for β wave. The study results can provide a theoretical support for designing a personalized and hierarchical warning system for road rage

Development and Coverage Evaluation of ZigBee-Based Wireless Network Applications

Network coverage is one of the basic issues for information collection and data processing in ZigBee-based wireless sensor networks. Each node may be randomly distributed in a monitoring area, reflecting the network event of tracking in ZigBee network applications. This paper presents the development and coverage evaluation of a ZigBee-based wireless network application. A stack structure node available for home service integration is proposed, and all data of sensing nodes with an adaptive weighted fusion (AWF) processing are passed to the gateway and through the gateway to reexecute packet processing and then reported to the monitoring center, which effectively optimize the wireless network to the scale of the data processing efficiency. The linear interpolation theory is used for background graphical user interface so as to evaluate the working status of each node and the whole network coverage case. A testbed has been created for validating the basic functions of the proposed ZigBee-based home network system. Network coverage capabilities were tested, and packet loss and energy saving of the proposed system in longtime wireless network monitoring tasks were also verified