Determining Flow Friction Factor in Irrigation Pipes Using Data Mining and Artificial Intelligence Approaches

The implicit Colebrook–White equation has been widely used to estimate the friction factor for turbulent fluid in irrigation pipes. A fast, accurate, and robust resolution of the Colebrook–White equation is, in particular, necessary for scientific intensive computations. In this study, the performance of some artificial intelligence approaches, including gene expression programming (GEP), which is a variant of genetic programming (GP); adaptive neurofuzzy inference system (ANFIS); and artificial neural network (ANN) has been compared to the M5 model tree, which is a data mining technique and, to most available approximations, is based on root mean squared error (RMSE), mean absolute error (MAE) and correlation coefficient (R). Results show that Serghides and Buzzelli approximations with RMSE (0.00002), MAE (0.00001), and R (0.99999) values had the best performances. Among the data mining and artificial intelligence approaches, the GEP with RMSE (0.00032), MAE (0.00026), and R (0.99953) values performed better. However, all 20 explicit approximations except Wood, Churchill (full range of turbulence including laminar regime) and Rau and Kumar estimated the friction factor more accurately than the GEP.     

An intelligent hybrid optimistic/pessimistic concurrency control algorithm for centralized database systems using modified GSA-optimized ART neural model

Concurrency control is the activity of synchronizing operations issued by concurrent executing transactions on a shared database. The aim of this control is to provide an execution that has the same effect as a serial (non-interleaved) one. The optimistic concurrency control technique allows the transactions to execute without synchronization, relying on commit-time validation to ensure serializability. Effectiveness of the optimistic techniques depends on the conflict rate of transactions. Since different systems have various patterns of conflict and the patterns may also change over time, so applying the optimistic scheme to the entire system results in degradation of performance. In this paper, a novel algorithm is proposed that dynamically selects the optimistic or pessimistic approach based on the value of conflict rate. The proposed algorithm uses an adaptive resonance theory–based neural network in making decision for granting a lock or detection of the winner transaction. In addition, the parameters of this neural network are optimized by a modified gravitational search algorithm. On the other hand, in the real operational environments we know the writeset (WS) and readset (RS) only for a fraction of transactions set before execution. So, the proposed algorithm is designed based on optional knowledge about WS and RS of transactions. Experimental results show that the proposed hybrid concurrency control algorithm results in more than 35 % reduction in the number of aborts in high-transaction rates as compared to strict two-phase locking algorithm that is used in many commercial database systems. This improvement is 13 % as compared to pure-pessimistic approach and is more than 31 % as compared to pure-optimistic approach.     

Clinical proteomics: Promises,challenges and limitations of affinity arrays

After the establishment of DNA/RNA sequencing as a means of clinical diagnosis, the analysis of the proteome is next in line. As a matter of fact, proteome-based diagnostics is bound to be even more informative, since proteins are directly involved in the actual cellular processes that are responsible for disease. However, the structural variation and the biochemical differences between proteins, the much wider range in concentration and their spatial distribution as well as the fact that protein activity frequently relies on interaction increase the methodological complexity enormously, particularly if an accuracy and robustness is required that is sufficient for clinical utility. Here, we discuss the contribution that protein microarray formats could play towards proteome-based diagnostics.     

PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources

Power efficiency is one of the main challenges in large-scale distributed systems such as datacenters, Grids, and Clouds. One can study the scheduling of applications in such large-scale distributed systems by representing applications as a set of precedence-constrained tasks and modeling them by a Directed Acyclic Graph. In this paper we address the problem of scheduling a set of tasks with precedence constraints on a heterogeneous set of Computing Resources (CRs) with the dual objective of minimizing the overall makespan and reducing the aggregate power consumption of CRs. Most of the related works in this area use Dynamic Voltage and Frequency Scaling (DVFS) approach to achieve these objectives. However, DVFS requires special hardware support that may not be available on all processors in large-scale distributed systems. In contrast, we propose a novel two-phase solution called PASTA that does not require any special hardware support. In its first phase, it uses a novel algorithm to select a subset of available CRs for running an application that can balance between lower overall power consumption of CRs and shorter makespan of application task schedules. In its second phase, it uses a low-complexity power-aware algorithm that creates a schedule for running application tasks on the selected CRs. We show that the overall time complexity of PASTA is $O(p.v^{2})$ where $p$ is the number of CRs and $v$ is the number of tasks. By using simulative experiments on real-world task graphs, we show that the makespan of schedules produced by PASTA are approximately 20 % longer than the ones produced by the well-known HEFT algorithm. However, the schedules produced by PASTA consume nearly 60 % less energy than those produced by HEFT. Empirical experiments on a physical test-bed confirm the power efficiency of PASTA in comparison with HEFT too.     

Effect of prepolymerization on propylene polymerization

Polymerization of propylene was performed using MgCl₂. EtOH.TiCl₄.ID.TEA.ED catalyst system in hexane, where internal donor (ID) was an organic diester and external donor (ED) was a silane compound and also triethyl aluminum (TEA) as activator. A new method called isothermal/nonisothermal method (INM), a combination of isothermal and nonisothermal methods, was applied to produce the spherical polymer particles. The effects of the INM method and prepolymerization temperature on the final polymer morphology, M_w, and catalyst activity were also investigated. The morphology of the polymers was evaluated through scanning electron microscopy (SEM) images. GPC results were used for molecular weight (M_w) evaluation. It was found that the polymers had a better morphology when they were prepared using INM method. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

A novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noise

Three-dimensional shape recovery from one or multiple observations is a challenging problem of computer vision. In this paper, we present a new Focus Measure for the estimation of a depth map using image focus. This depth map can subsequently be used in techniques and algorithms leading to the recovery of a three-dimensional structure of the object, a requirement of a number of high level vision applications. The proposed Focus Measure has shown robustness in the presence of noise as compared to the earlier Focus Measures. This new Focus Measure is based on an optical transfer function implemented in the Fourier domain. The results of the proposed Focus Measure have shown drastic improvements in estimation of a depth map, with respect to the earlier Focus Measures, in the presence of various types of noise including Gaussian, Shot, and Speckle noises. The results of a range of Focus Measures are compared using root mean square error and correlation metric measures.     

Geometrical indications of adsorbed hydrogen atoms on graphite producing star and ellipsoidal like features in scanning tunneling microscopy images: Ab initio study

Recent scanning tunneling microscopy (STM) experiments display images with star and ellipsoidal like features resulting from unique geometrical arrangements of a few adsorbed hydrogen atoms on graphite. Based on first-principles STM simulations, we have found that the model with three hydrogen atoms, in which the hydrogen atoms are symmetrically placed on the graphene sheet in an equilateral triangle, encompassing a complete hexagon ring of carbon atoms, reproduces the experimentally observed starlike STM patterns. Additionally, we confirm that an ortho-hydrogen pair is the configuration corresponding to the ellipsoidal images. These calculations reveal that when the hydrogen pairs are in the same orientation, they are energetically more stable.     

Comparison of stochastic filtering methods for 3D tracking

In the recent years, the 3D visual research has gained momentum with publications appearing for all aspects of 3D including visual tracking. This paper presents a review of the literature published for 3D visual tracking over the past five years. The work particularly focuses on stochastic filtering techniques such as particle filter and Kalman filter. These two filters are extensively used for tracking due to their ability to consider uncertainties in the estimation. The improvement in computational power of computers and increasing interest in robust tracking algorithms lead to increase in the use of stochastic filters in visual tracking in general and 3D visual tracking in particular. Stochastic filters are used for numerous applications in the literature such as robot navigation, computer games and behavior analysis. Kalman filter is a linear estimator which approximates system's dynamics with Gaussian model while particle filter approximates system's dynamics using weighted samples. In this paper, we investigate the implementation of Kalman and particle filters in the published work and we provide comparison between these techniques qualitatively as well as quantitatively. The quantitative analysis is in terms of computational time and accuracy. The quantitative analysis has been implemented using four parameters of the tracked object which are object position, velocity, size of bounding ellipse and orientation angle.     

Nanosilicon carbide/hydroxyapatite nanocomposites: structural,mechanical and in vitro cellular properties

In this study, bioceramic nanocomposites were synthesized by sintering compacted bodies of hydroxyapatite (HA) mixed with 5 or 15 wt% nanosilicon carbide at 1100 or 1200°C in a reducing atmosphere. Pure hydroxyapatite was also prepared for comparison. Phase compositions, structural and physical properties of the composites were studied using appropriate techniques. Some in vitro biological properties of the composites were also investigated by using newrat calvaria osteoblastic cells. X-ray diffraction analysis indicated that tricalcium phosphate (TCP) comprising negligible α-TCP and considerable β-TCP were formed in composites during sintering meanwhile hydroxyapatite and silicon carbide (SiC) were also existed in the composition. Based on the results, that composite made of 5 wt% nanosilicon carbide exhibited higher bending strength, fracture toughness and bulk density than pure HA and composite with 15 wt% silicon carbide. The scanning electron microscopy coupled with energy dispersive X-ray analysis revealed that the addition of nanosilicon carbide suppressed the grain growth and yielded a feature of island-type clusters consisting of blistered calcium phosphate (HA and TCP) and SiC grains. Also, in this study, better proliferation rate and alkaline phosphatase activity were observed for the osteoblastic cells seeded on top of the composites compared to pure HA. Overall, the results indicated that the composite of 95 wt% hydroxyapatite and 5 wt% SiC exhibited better mechanical and biological properties than pure HA and further addition of SiC failed strength and toughness.