A Large Scale Network Analysis of Hashtags in Twitter
Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. Having the “universality” property, majority of the complex networks display common structures as a result of the underlying self-organizing principles that resemble. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks etc. Focusing on tag networks (namely, hashtags in twitter), we constructed a tag network having hashtags as nodes, where the co-occurrences of these tags in a single entry define the links connecting them. We observed that the universal properties of the networks like small-world property, clustering and scale-free degree distribution are also observed in the network of social network tags. The strong consistency of universal network properties indicate that the self-organizing phenomenon is purely imitated by this type of network. We also presented the visualization of this network provided by the software Gephi.
Application of Nonparametric Geographically Weighted Regression to Evaluate the Unemployment Rate in East Java
East Java Province has a first rank as a province that has the most counties and cities in Indonesia and has the largest population. In 2015, the population reached 38.847.561 million, this figure showed a very high population growth. High population growth is feared to lead to increase the levels of unemployment. In this study, the researchers mapped and modeled the unemployment rate with 6 variables that were supposed to influence. Modeling was done by nonparametric geographically weighted regression methods with truncated spline approach. This method was chosen because spline method is a flexible method, these models tend to look for its own estimation. In this modeling, there were point knots, the point that showed the changes of data. The selection of the optimum point knots was done by selecting the most minimun value of Generalized Cross Validation (GCV). Based on the research, 6 variables were declared to affect the level of unemployment in eastern Java. They were the percentage of population that is educated above high school, the rate of economic growth, the population density, the investment ratio of total labor force, the regional minimum wage and the ratio of the number of big industry and medium scale industry from the work force. The nonparametric geographically weighted regression models with truncated spline approach had a coefficient of determination 98.95% and the value of MSE equal to 0.0047.
Ensemble Methods in Machine Learning: An Algorithmic Approach to Derive Distinctive Behaviors of Criminal Activity Applied to the Poaching Domain
Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.
Some Classes of Lorentzian Alpha-Sasakian Manifolds with Respect to Quarter-Symmetric Metric Connection
The object of the present paper is to study a quarter-symmetric metric connection in a Lorentzian α-Sasakian manifold. We study some curvature properties of Lorentzian α-Sasakian manifold with respect to quarter-symmetric metric connection. We investigate quasi-projectively at, Φ-symmetric, Φ-projectively at Lorentzian α-Sasakian manifolds with respect to quarter-symmetric metric connection. We also discuss Lorentzian α-Sasakian manifold admitting quartersymmetric metric connection satisfying P.S = 0, where P denote the projective curvature tensor with respect to quarter-symmetric metric connection.
[Keynote Talk]: Mathematical and Numerical Modelling of the Cardiovascular System: Macroscale, Mesoscale and Microscale Applications
The cardiovascular system is centered on the heart and is characterized by a very complex structure with different physical scales in space (e.g. micrometers for erythrocytes and centimeters for organs) and time (e.g. milliseconds for human brain activity and several years for development of some pathologies). The development and numerical implementation of mathematical models of the cardiovascular system is a tremendously challenging topic at the theoretical and computational levels, inducing consequently a growing interest over the past decade. The accurate computational investigations in both healthy and pathological cases of processes related to the functioning of the human cardiovascular system can be of great potential in tackling several problems of clinical relevance and in improving the diagnosis of specific diseases. In this talk, we focus on the specific task of simulating three particular phenomena related to the cardiovascular system on the macroscopic, mesoscopic and microscopic scales, respectively. Namely, we develop numerical methodologies tailored for the simulation of (i) the haemodynamics (i.e., fluid mechanics of blood) in the aorta and sinus of Valsalva interacting with highly deformable thin leaflets, (ii) the hyperelastic anisotropic behaviour of cardiomyocytes and the influence of calcium concentrations on the contraction of single cells, and (iii) the dynamics of red blood cells in microvasculature. For each problem, we present an appropriate fully Eulerian finite element methodology. We report several numerical examples to address in detail the relevance of the mathematical models in terms of physiological meaning and to illustrate the accuracy and efficiency of the numerical methods.
Using Nonlinear Response History Analysis to Study Near-Fault Effect on High-Rise and Low-Rise Buildings
The near-fault effect on buildings and infrastructures is a significant issue of human life and property in Taiwan because there are numerous active faults inside this island. It is well-known that special characteristics with large displacement and high velocity can be observed close to a near-fault. However, it is difficult to reproduce such a near-fault earthquake record by using the existing test facilities of National Center for Research on Earthquake Engineering (NCREE). The nonlinear response history analysis is used to understand the structural behavior of reinforced concrete buildings under near-fault earthquake effect. This paper examines the influence of near-fault ground motions and far-field ground motions recorded in Taiwan on 3D-frame structure of high-rise and low-rise buildings through dynamic analysis of nonlinear structural simulation models. The near-fault ground motions were recorded during the Chi-Chi earthquake. The seismic responses of low- and high-rise buildings is studied. The objective of this paper is to compare the dynamic behavior of reinforced concrete buildings and steel buildings structure subjected to both near-fault and far-field ground motions. The ground motions data recorded at sides near the Chelungpu fault in the Taiwan Chi-Chi earthquake 1999 were used to analyze the near-fault dynamic behavior of the sample buildings. On another hand, the far-field ground motions data recorded at the same sites were also used. Based on nonlinear analyses, the value of story displacement and inter-story drift can be analyzed to study the structural damage.
An Alternative Richards’ Growth Model Based on Hyperbolic Sine Function
Richrads growth equation being a generalized logistic growth equation was improved upon by introducing an allometric parameter using the hyperbolic sine function. The integral solution to this was called hyperbolic Richards growth model having transformed the solution from deterministic to a stochastic growth model. Its ability in model prediction was compared with the classical Richards growth model an approach which mimicked the natural variability of heights/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using the coefficient of determination (R2), Mean Absolute Error (MAE) and Mean Square Error (MSE) results. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the behavior of the error term for possible violations. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic Richards nonlinear growth models better than the classical Richards growth model.
A Study on the Performance of 2-PC-D Classification Model
There are many applications of principle component method for reducing the large set of variables in various fields. Fisher’s Discriminant function is also a popular tool for classification. In this research, the researcher focuses on studying the performance of Principle Component-Fisher’s Discriminant function in helping to classify rice kernels to their defined classes. The data were collected on the smells or odour of the rice kernel using odour-detection sensor, Cyranose. 32 variables were captured by this electronic nose (e-nose). The objective of this research is to measure how well a combination model, between principle component and linear discriminant, to be as a classification model. Principle component method was used to reduce all 32 variables to a smaller and manageable set of components. Then, the reduced components were used to develop the Fisher’s Discriminant function. In this research, there are 4 defined classes of rice kernel which are Aromatic, Brown, Ordinary and Others. Based on the output from principle component method, the 32 variables were reduced to only 2 components. Based on the output of classification table from the discriminant analysis, 40.76% from the total observations were correctly classified into their classes by the PC-Discriminant function. Indirectly, it gives an idea that the classification model developed has committed to more than 50% of misclassifying the observations. As a conclusion, the Fisher’s Discriminant function that was built on a 2-component from PCA (2-PC-D) is not satisfying to classify the rice kernels into its defined classes.
Dynamic Model of Heterogeneous Markets with Imperfect Information for the Optimization of Company's Long-Time Strategy
This paper is dedicated to the development of the model, which can be used to evaluate the effectiveness of long-term corporate strategies and identify the best strategies. The theoretical model of the relatively homogenous product market (such as iron and steel industry, mobile services or road transport) has been developed. In the model, the market consists of a large number of companies with different internal characteristics and objectives. The companies can perform mergers and acquisitions in order to increase their market share. The model allows the simulation of long-time dynamics of the market (for a period longer than 20 years). Therefore, a large number of simulations on random input data was conducted in the framework of the model. After that, the results of the model were compared with the dynamics of real markets, such as the US steel industry from the beginning of the XX century to the present day, and the market of mobile services in Germany for the period between 1990 and 2015.
Influence of Mass Flow Rate on Forced Convective Heat Transfer through a Nanofluid Filled Direct Absorption Solar Collector
The convective and radiative heat transfer performance and entropy generation on forced convection through a direct absorption solar collector (DASC) is investigated numerically. Four different fluids; Cu-water nanofluid, Al2O3-waternanofluid, TiO2-waternanofluid and pure water are used as the working fluid. Entropy production has been taken into account in addition to the collector efficiency and heat transfer enhancement. Penalty finite element method with Galerkin’s weighted residual technique is used to solve the governing non-linear partial differential equations. Numerical simulations are performed for the variation of mass flow rate (m). The outcomes are presented in the form of isotherms, average output temperature, the average Nusselt number, collector efficiency, average entropy generation and Bejan number. The results present that the rate of heat transfer and collector efficiency enhance significantly for raising the values of m upto a certain range.
Application Difference between Cox and Logistic Regression Models
The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.
Large Time Asymptotic Behavior to Solutions of a Forced Burgers Equation
We study the large time asymptotics of solutions to the Cauchy problem for a forced Burgers equation (FBE) with the initial data, which is continuous and summable on R. For which, we first derive explicit solutions of FBE assuming a different class of initial data in terms of Hermite polynomials. Later, by violating this assumption we prove the existence of a solution to the considered Cauchy problem. Finally, we give an asymptotic approximate solution and establish that the error will be of order O(t^(-1/2)) with respect to L^p -norm, where 1≤p≤∞, for large time.
Transport of Analytes under Mixed Electroosmotic and Pressure Driven Flow of Power Law Fluid
In this study, we have analyzed the transport of analytes under a two-dimensional steady incompressible flow of powerlaw fluids through rectangular nanochannel. A mathematical model based on the Cauchy momentum-Nernst-Planck-Poisson equations is considered to study the combined effect of mixed electroosmotic (EO) and pressure driven (PD) flow. The coupled governing equations are solved numerically by finite volume method. We have studied extensively the effect of key parameters, e.g., flow behavior index, concentration of the electrolyte, surface potential, imposed pressure gradient and imposed electric field strength on the net average flow across the channel. In addition to studying the effect of mixed EOF and PD on the analyte distribution across the channel, we consider a nonlinear model based on general convective-diffusion electromigration equation. We have also presented the retention factor for various values of electrolyte concentration and flow behavior index.
A Machine Learning-Based Model to Screen Antituberculosis Compound Targeted against LprG Lipoprotein of Mycobacterium tuberculosis
Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.
Fourier Galerkin Approach to Wave Equation with Absorbing Boundary Conditions
Numerical computation of wave propagation in a large domain usually requires significant computational effort. Hence, the considered domain must be truncated to a smaller domain of interest. In addition, special boundary conditions, which absorb the outward travelling waves, need to be implemented in order to describe the system domains correctly. In this work, the linear one-dimensional wave equation is approximated by utilizing the Fourier Galerkin approach. Furthermore, the artificial boundaries are realized with absorbing boundary conditions. Within this work, a systematic workflow for setting up the wave problem, including the absorbing boundary conditions, is proposed. As a result, a convenient modal system description with an effective absorbing boundary formulation is established. Moreover, the truncated model shows high accuracy compared to the global domain.
Analysis of Risk Factors Affecting the Motor Insurance Pricing with Generalized Linear Models
Casualty insurance business, the optimal premium pricing and adequate cost for an insurance company are important in risk management. Normally, the insurance pure premium can be determined by multiplying the claim frequency with the claim cost. The aim of this research was to study in the application of generalized linear models to select the risk factor for model of claim frequency and claim cost for estimating a pure premium. In this study, the data set was the claim of comprehensive motor insurance, which was provided by one of the insurance company in Thailand. The results of this study found that the risk factors significantly related to pure premium at the 0.05 level consisted of no claim bonus (NCB) and used of the car (Car code).
Prediction of Music Track Popularity: A Machine Learning Approach
Hit song science is a field of investigation wherein machine learning techniques are applied to music tracks in order to extract such features from audio signals which can capture information that could explain the popularity of respective tracks. Record companies invest huge amounts of money into recruiting fresh talents and churning out new music each year. Gaining insight into the basis of why a song becomes popular will result in tremendous benefits for the music industry. This paper aims to extract basic musical and more advanced, acoustic features from songs while also taking into account external factors that play a role in making a particular song popular. We use a dataset derived from popular Spotify playlists divided by genre. We use ten genres (blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, rock), chosen on the basis of clear to ambiguous delineation in the typical sound of their genres. We feed these features into three different classifiers, namely, SVM with RBF kernel, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model at the end. Predicting song popularity is particularly important for the music industry as it would allow record companies to produce better content for the masses resulting in a more competitive market.
Jordan Curves in the Digital Plane with Respect to the Connectednesses given by Certain Adjacency Graphs
Digital images are approximations of real ones and, therefore, to be able to study them, we need the digital plane Z2 to be equipped with a convenient structure that behaves analogously to the Euclidean topology on the real plane. In particular, it is required that such a structure allows for a digital analogue of the Jordan curve theorem. We introduce certain adjacency graphs on the digital plane and prove digital Jordan curves for them thus showing that the graphs provide convenient structures on Z2 for the study and processing of digital images. Further convenient structures including the wellknown Khalimsky and Marcus-Wyse adjacency graphs may be obtained as quotients of the graphs introduced. Since digital Jordan curves represent borders of objects in digital images, the adjacency graphs discussed may be used as background structures on the digital plane for solving the problems of digital image processing that are closely related to borders like border detection, contour filling, pattern recognition, thinning, etc.
The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks
Network embedding, encoding all vertices in a network as a set of numerical vectors according to the local and global structure of the network, has drawn wild spread attention in network and computer science communities. This problem is of importance because not only most of the significant features of a network, such as clustering and distribution of nodes can be read from the embedding, but also latent vector representations of nodes provide theoretical supports for wide applications, such as visualization, node classification, link prediction, anomaly detection, and recommendation. As the latest progress of the research, several algorithms based on random walks have been devised. Although their high scores of the learning efficiency and accuracy have drawn much attention, the lack of the theoretical explanation and the transparency of the algorithm have been doubted. Here, we propose a novel approach based on open flow network model to reveal the underlying flow structure and its hidden metric of different random walk strategies on networks. We point out that the essence of the embedding based on random walks is the latent metric structure defined on the open flow network. This can not only deepen our understanding on the algorithms but also help in finding some new potential applications e.g. the calculations of node centrality ranking classification and link prediction.
Mathematical Study for Traffic Flow and Traffic Density in Kigali Road
This work investigates a mathematical study for traffic flow and traffic density in Kigali roads and the data was collected from national police of Rwanda in the year 2012. While working on this topic, some mathematical models were used in order to analyze and compare traffic variables. This work has been carried out in Kigali roads specifically from roundabout of KBC to Prince House as our study site. During the work of this project, we used some mathematical tools to analyze the data collected and to understand the relationship between traffic variables. We applied Poisson distribution method to analyze and to know the number of accidents occurred in this section of the road which is from KBC to Prince House. The results have shown that accidents occurred in 2012 were at very high rate due to the fact that this section has two sided ways which are very small and this leads to high congestion of vehicles hence accidents occur. Using the data of speeds and densities collected from this section of road we found that the increment of the density results in a decrement of the speed of the vehicle. At the point where the density is equal to the jam density, the speed becomes zero. The approach is promising in capturing sudden changes on flow patterns and is open to be utilized within a series of intelligent management strategies especially noncurrent congestion effect detection and control.
Controlled Chemotherapy Strategy Applied to HIV Model
Optimal control can be helpful to test and compare different vaccination strategies of a certain disease. The mathematical model of HIV we consider here is a set of ordinary differential equations (ODEs) describing the interactions of CD4+T cells of the immune system with the human immunodeficiency virus (HIV). As an early treatment setting, we investigate an optimal chemotherapy strategy where control represents the percentage of effect the chemotherapy has on the system. The aim is to obtain a new optimal chemotherapeutic strategy where an isoperimetric constraint on the chemotherapy supply plays a crucial role. We outline the steps in formulating an optimal control problem, derive optimality conditions and demonstrate numerical results of an optimal control for the model. Numerical results illustrate how such a constraint alters the optimal vaccination schedule and its effect on cell-virus interactions.
An Analytical Method for Solving General Riccati Equation
In this paper, the general Riccati equation is analytically solved by a new transformation. By the method developed, looking at the transformed equation, whether or not an explicit solution can be obtained is readily determined. Since the present method does not require a proper solution for the general solution, it is especially suitable for equations whose proper solutions cannot be seen at first glance. Since the transformed second order linear equation obtained by the present transformation has the simplest form it can have, it is immediately seen whether or not the original equation can be solved analytically. The present method is exemplified by several examples.
Crime against Women in India: A Geospatial Analysis
Globally, women are more vulnerable to various forms of crimes than males. The crimes that are directed specifically towards women are classified as crime against women. Crime against women in India is observed to increase year after year and according to the National Crime Records Bureau (NCRB) report, in 2014 there was an increase of 9.2% cases of crime against women compared to the previous year. The violence in a population depends on socio-demographic factors, unemployment, poverty, number of police officials etc. There are very few studies that explored to identify hotspots of various types of crime against women in India. Hotspots are geographical regions where the number of observed cases is more than the expected number for that region. It is important to identify the hotspots of crime against women in India in order to control and prevent violence against women in that region. The goal of this study is to identify the hotspots of crime against women in India using spatial data analysis techniques. For the present study, we used the district level data of various types of crime against women in India in the year 2011 published by NCRB and the 2011 Census population in each of these districts. The study used spatial scan statistic to identify the hotspots using SaTScan software.
Stackelberg Security Game for Optimizing Security of Federated IoT Platform Instances
This paper presents an approach to optimal cyber security decisions for protecting instances of a federated IoT platform in the cloud. The presented solution implements the Stackelberg Security Game (SSG) by following a recently proposed model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the presented SSG (including SHARP and SUQR) with a reinforced learning technique called Naïve Q-Learning algorithm. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution, called a randomization policy. In this way, we combine GT and ML algorithms for finding optimal cyber security policies. The components will be included and validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.
Jointly Learning Python Programming and Analytic Geometry
The paper presents an original Python-based application that outlines the advantages of combining some elementary notions of mathematics with the study of a programming language. The application support refers to some of the first lessons of analytic geometry, meaning conics and quadrics and their reduction to a standard form, as well as some related notions. The chosen programming language is Python, not only for its closer to an everyday language syntax – and therefore, enhanced readability – but also for its highly reusable code, which is of utmost importance for a mathematician that is accustomed to exploit already known and used problems to solve new ones. The purpose of this paper is, on one hand, to support the idea that one of the most appropriate means to initiate one into programming is throughout mathematics, and reciprocal, one of the most facile and handy ways to assimilate some basic knowledge in the study of mathematics is to apply them in a personal project. On the other hand, besides being a mean of learning both programming and analytic geometry, the application subject to this paper is itself a useful tool for it can be seen as an independent, original Python package for analytic geometry.
Conjugate Mixed Convection Heat Transfer and Entropy Generation of Cu-Water Nanofluid in an Enclosure with Thick Wavy Bottom Wall
Mixed convection of Cu-water nanofluid in an enclosure with thick wavy bottom wall has been investigated numerically. A co-ordinate transformation method is used to transform the computational domain into an orthogonal co-ordinate system. The governing equations in the computational domain are solved through a pressure correction based iterative algorithm. The fluid flow and heat transfer characteristics are analyzed for a wide range of Richardson number (0.1 ≤ Ri ≤ 10), nanoparticle volume concentration (0.0 ≤ φ ≤ 0.2), amplitude (0.0 ≤ α ≤ 0.2) of the wavy thick- bottom wall and the wave number (ω) at a fixed Reynolds number. Obtained results showed that heat transfer rate increases remarkably by adding the nanoparticles. Heat transfer rate is dependent on the wavy wall amplitude (α) and wave number (ω) and decreases with increasing Richardson number for fixed amplitude and wave number. The Bejan number and the entropy generation are determined to analyze the thermodynamic optimization of the mixed convection.
Monte Carlo Methods and Statistical Inference of Multitype Branching Processes
A parametric estimation of the MBP with Power Series offspring distribution family is considered in this paper. The MLE for the parameters is obtained in the case when the observable data are incomplete and consist only with the generation sizes of the family tree of MBP. The parameter estimation is calculated by using the Monte Carlo EM algorithm. The estimation for the posterior distribution and for the offspring distribution parameters are calculated by using the Bayesian approach and the Gibbs sampler. The article proposes various examples with bivariate branching processes together with computational results, simulation and an implementation using R.
Warning about the Risk of Blood Flow Stagnation after Transcatheter Aortic Valve Implantation
In this paper, the hemodynamics in the sinus of Valsalva after Transcatheter Aortic Valve Implantation is numerically examined. We use an adaptive finite element methodology based on the banded level set method combined with a Lagrange multiplier technique that enables to couple the dynamics of blood flow and the leaflets movement. A massively parallel implementation of a monolithic and fully implicit solver allows more accuracy and significant computational savings. Realistic geometries are built and we perform computations under physiologically correct pressure loads in both two-dimensional and three-dimensional cases. We focus on the hemodynamic properties in the surrounding of the aortic valve after Transcatheter Aortic Valve Implantation in the two-dimensional case. Computational investigations depict that blood flow may be subject to stagnation in the lower domain of the sinus of Valsalva after Transcatheter Aortic Valve Implantation.
Analysis of Multilayer Neural Network Modeling and Long Short-Term Memory
This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.
Simulations of the Large Hadrons Collisions Using Monte Carlo Tools
In many cases, theoretical treatments are available for models for which there is no perfect physical realization. In this situation, the only possible test for an approximate theoretical solution is to compare with data generated from a computer simulation. In this paper, Monte Carlo tools are used to study and compare the elementary particles models. All the experiments are implemented using 10000 events, and the simulated energy is 13 TeV. The mean and the curves of several variables are calculated for each model using MadAnalysis 5. Anomalies in the results can be seen in the muons masses of the minimal supersymmetric standard model and the two Higgs doublet model.