Monotonic and Cyclic Behavior of Two Calcareous Soils of Different Origins

The behavior of two calcareous soils—Goodwyn (GW) and Ledge Point (LP)—is studied through a series of monotonic and cyclic triaxial tests. These two soils are selected because they represent two extreme formation conditions in terms of their depositional environments, physical characteristics, and grain strength. The experimental investigation included isotropic compression tests to high stress levels, undrained monotonic shearing tests, and undrained cyclic shearing tests under one-way and two-way loading conditions. Tests were performed on samples with different initial conditions. The experimental results show that, although the overall qualitative stress-strain behavior of both GW and LP soils is similar to that of other silicious soils, significant quantitative differences are observed between the two soils and also between calcareous and silicious soils, especially in terms of volumetric reduction during compression, monotonic and cyclic shear strength, and the strain required to mobilize the strength. This paper explores the mechanical behavior of the two calcareous soils and highlights the similarities and differences between their behavior and also between calcareous and silicious soils.     

Purification and properties of two fibrinolytic enzymes from Fusarium oxysporum N.R.C.1

OBJECTIVE: To find differences in effect on sperm motility of agents that increase intracellular cAMP: manganese ion, methyl-isobutyl-xanthine (MIX), 2-deoxyadenosine, glucose, and Mn-MIX and Mn-glucose. DESIGN: Nine men with asthenozoospermia vs. fertile donors. METHODS: Sperm was washed in Hepes-buffered saline, motility tested by laser-Doppler technique. RESULTS: Best activation was obtained with Mn and 2-deoxyadenosine; generally poor response to MIX or glucose. CONCLUSIONS: Usually, poor endogenous stimulation of adenylyl cyclase, and probably not limited energy supply, is the cause of impaired motility.     

Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work.     

Diagnosing bot infections using Bayesian inference

Prior research in botnet detection has used the bot lifecycle to build detection systems. These systems, however, use rule-based decision engines which lack automated adaptability and learning, accuracy tunability, the ability to cope with gaps in training data, and the ability to incorporate local security policies. To counter these limitations, we propose to replace the rigid decision engines in contemporary bot detectors with a more formal Bayesian inference engine. Bottleneck, our prototype implementation, builds confidence in bot infections based on the causal bot lifecycle encoded in a Bayesian network. We evaluate Bottleneck by applying it as a post-processing decision engine on lifecycle events generated by two existing bot detectors (BotHunter and BotFlex) on two independently-collected datasets. Our experimental results show that Bottleneck consistently achieves comparable or better accuracy than the existing rule-based detectors when the test data is similar to the training data. For differing training and test data, Bottleneck, due to its automated learning and inference models, easily surpasses the accuracies of rule-based systems. Moreover, Bottleneck’s stochastic nature allows its accuracy to be tuned with respect to organizational needs. Extending Bottleneck’s Bayesian network into an influence diagram allows for local security policies to be defined within our framework. Lastly, we show that Bottleneck can also be extended to incorporate evidence trustscore for false alarm reduction.     

Two collaborative filtering recommender systems based on sparse dictionary coding

This paper proposes two types of recommender systems based on sparse dictionary coding. Firstly, a novel predictive recommender system that attempts to predict a user’s future rating of a specific item. Secondly, a top-n recommender system which finds a list of items predicted to be most relevant for a given user. The proposed methods are assessed using a variety of different metrics and are shown to be competitive with existing collaborative filtering recommender systems. Specifically, the sparse dictionary-based predictive recommender has advantages over existing methods in terms of a lower computational cost and not requiring parameter tuning. The sparse dictionary-based top-n recommender system has advantages over existing methods in terms of the accuracy of the predictions it makes and not requiring parameter tuning. An open-source software implemented and used for the evaluation in this paper is also provided for reproducibility.     

Impacts of gold nanoparticles on MHD mixed convection Poiseuille flow of nanofluid passing through a porous medium in the presence of thermal radiation,thermal diffusion and chemical reaction

Impacts of gold nanoparticles on MHD Poiseuille flow of nanofluid in a porous medium are studied. Mixed convection is induced due to external pressure gradient and buoyancy force. Additional effects of thermal radiation, chemical reaction and thermal diffusion are also considered. Gold nanoparticles of cylindrical shape are considered in kerosene oil taken as conventional base fluid. However, for comparison, four other types of nanoparticles (silver, copper, alumina and magnetite) are also considered. The problem is modeled in terms of partial differential equations with suitable boundary conditions and then computed by perturbation technique. Exact expressions for velocity and temperature are obtained. Graphical results are mapped in order to tackle the physics of the embedded parameters. This study mainly focuses on gold nanoparticles; however, for the sake of comparison, four other types of nanoparticles namely silver, copper, alumina and magnetite are analyzed for the heat transfer rate. The obtained results show that metals have higher rate of heat transfer than metal oxides. Gold nanoparticles have the highest rate of heat transfer followed by alumina and magnetite. Porosity and magnetic field have opposite effects on velocity.

     

N-Body Simulation Inspired by Metaheuristics Optimization

The N-body problem in classical physics, is the calculation of force of gravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists and mathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been the major hurdles in solving this problem for large and complex galaxies. Advent of high performance computational machines have mitigated the problem to much extent, but still for large number of bodies it consumes huge amount of resources and days for computation. Conventional algorithms have been able to reduce the computational complexity from to by splitting the space into a tree or mesh network, researchers are still looking for improvements. In this research work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. The results are compared with that of conventional algorithms. The outcomes show about 50% time saving with almost no loss in accuracy. The proposed approach being a metaheuristics optimization technique, attempts to find optimal solution to the problem, searching the entire space in a unique and efficient manner in a very limited amount of time.     

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embedding process. Then, N-gram based feature extraction technique is derived to generate feature vectors. Lastly, WELM model is applied for the detection and classification of fake news, in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm. The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions. The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.     

An integrated optimal control algorithm for discrete-time nonlinear stochastic system

Consider a discrete-time nonlinear system with random disturbances appearing in the real plant and the output channel where the randomly perturbed output is measurable. An iterative procedure based on the linear quadratic Gaussian optimal control model is developed for solving the optimal control of this stochastic system. The optimal state estimate provided by Kalman filtering theory and the optimal control law obtained from the linear quadratic regulator problem are then integrated into the dynamic integrated system optimisation and parameter estimation algorithm. The iterative solutions of the optimal control problem for the model obtained converge to the solution of the original optimal control problem of the discrete-time nonlinear system, despite model-reality differences, when the convergence is achieved. An illustrative example is solved using the method proposed. The results obtained show the effectiveness of the algorithm proposed.