An enriched FEM technique for modeling hydraulically driven cohesive fracture propagation in impermeable media with frictional natural faults: Numerical and experimental investigations

In this paper, an enriched finite element technique is presented to simulate the mechanism of interaction between the hydraulic fracturing and frictional natural fault in impermeable media. The technique allows modeling the discontinuities independent of the finite element mesh by introducing additional DOFs. The coupled equilibrium and flow continuity equations are solved using a staggered Newton solution strategy, and an algorithm is proposed on the basis of fixed‐point iteration concept to impose the flow condition at the hydro‐fracture mouth. The cohesive crack model is employed to introduce the nonlinear fracturing process occurring ahead of the hydro‐fracture tip. Frictional contact is modeled along the natural fault using the penalty method within the framework of plasticity theory of friction. Moreover, an experimental investigation is carried out to perform the hydraulic fracturing experimental test in fractured media under plane strain condition. The results of several numerical and experimental simulations are presented to verify the accuracy and robustness of the proposed computational algorithm as well as to investigate the mechanisms of interaction between the hydraulically driven fracture and frictional natural fault. Copyright © 2015 John Wiley & Sons, Ltd.     

Two-stage Robust Network Design with Exponential Scenarios

We study two-stage robust variants of combinatorial optimization problems on undirected graphs, like Steiner tree, Steiner forest, and uncapacitated facility location. Robust optimization problems, previously studied by Dhamdhere et al. (Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005), Golovin et al. (Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006), and Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007), are two-stage planning problems in which the requirements are revealed after some decisions are taken in Stage 1. One has to then complete the solution, at a higher cost, to meet the given requirements. In the robust k-Steiner tree problem, for example, one buys some edges in Stage 1. Then k terminals are revealed in Stage 2 and one has to buy more edges, at a higher cost, to complete the Stage 1 solution to build a Steiner tree on these terminals. The objective is to minimize the total cost under the worst-case scenario. In this paper, we focus on the case of exponentially many scenarios given implicitly. A scenario consists of any subset of k terminals (for k-Steiner tree), or any subset of k terminal-pairs (for k-Steiner forest), or any subset of k clients (for facility location). Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007) give an LP-based general framework for approximation algorithms for a class of two stage robust problems. Their framework cannot be used for network design problems like k-Steiner tree (see later elaboration). Their framework can be used for the robust facility location problem, but gives only a logarithmic approximation. We present the first constant-factor approximation algorithms for the robust k-Steiner tree (with exponential number of scenarios) and robust uncapacitated facility location problems. Our algorithms are combinatorial and are based on guessing the optimum cost and clustering to aggregate nearby vertices. For the robust k-Steiner forest problem on trees and with uniform multiplicative increase factor for Stage 2 (also known as inflation), we present a constant approximation. We show APX-hardness of the robust min-cut problem (even with singleton-set scenarios), resolving an open question of (Dhamdhere et al. in Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005) and (Golovin et al. in Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006).     

A local linear radial basis function neural network for financial time-series forecasting

In this paper a Local Linear Radial Basis Function Neural Network (LLRBFN) is presented. The difference between the proposed neural network and the conventional Radial Basis Function Neural Network (RBFN) is connection weights between the hidden layer and the output layer which are replaced by a local linear model in the LLRBFN. A modified Particle Swarm Optimization (PSO) with hunter particles is introduced for training the LLRBFN. The proposed methods have been applied for prediction of financial time-series and the result shows the feasibility and effectiveness.     

On the use of the RMR system for estimation of rock mass deformation modulus

Estimation of rock mass deformation modulus is the subject of many studies in rock engineering research work. Although numerous predictive models have been developed for the estimation of the deformation modulus, they cannot be generalized for other sites because of inadequate accuracy. Furthermore, it is very valuable that the predictive models involve some accessible input parameters. The rock mass rating (RMR) is a well-known geomechanical parameter, which is usually determined to describe the quality of rock mass in rock engineering projects. In this study, five parameter ratings of the RMR classification system are used to predict the deformation modulus of rock mass in the abutment of the Gotvand earth dam. Statistical analysis and an artificial neural network are employed to present two new predictive models. Finally, probabilistic analysis is used to predict the rock mass deformation modulus, which overcomes the low accuracy caused by the inherent uncertainty in prediction. The results indicated that the parameter ratings used in the RMR classification system can predict the rock mass deformation modulus with a satisfactory correlation. However, the parameters don’t have the same influence on the rock mass deformability with the joint condition and the groundwater as the major and minor influencing parameters, respectively.     

A fuzzy irregular cellular automata-based method for the vertex colouring problem

ABSTRACT

Vertex colouring is among the most important problems in graph theory which has been widely applied across different real-world problems. In vertex colouring problem (VCP), the goal is to assign a distinct colour to each vertex of the graph in such a way that no two adjacent vertices have the same colour. This paper presents a fuzzy irregular cellular automaton (FICA) for finding a near-optimal solution for the VCP. FICA is an extension fuzzy cellular automaton (FCA) in which the cells of the automaton can be arranged in an irregular structure. The aim of the proposed method is to reap the benefits of both FCA and irregular cellular automata while minimising their drawbacks. To evaluate the proposed method, various computer simulations have been conducted on a variety of graphs. The results suggest that the proposed method is able to achieve better results in terms of the minimum number of required colours and the execution time of the algorithm, compared to other peer algorithms.     

A Photoclick Hydrogel for Enhanced Single‐Cell Immunoblotting

Advances in single‐cell immunoblotting assays, which facilitate the exploration of cell‐to‐cell variation that affects biological systems from cancer development to stem cell biology, have attracted much attention. A tetrazole‐functionalized photoclick hydrogel is reported for single‐cell proteomic analysis. The gel serves as a molecular sieving matrix for sodium dodecyl sulfate–polyacrylamide gel electrophoresis and a protein immobilization scaffold for in‐gel immunoblotting. Upon a very short time (60 s) of long‐wavelength ultraviolet irradiation, it can effectively capture the electrophoretically separated proteins in the gel for the subsequent in situ antibody incubation. As a proof of concept, its performance is demonstrated in profiling cell‐to‐cell variations of P‐glycoprotein expression in GES‐1/MGC803 cell lines treated with different drugs. Combined with single‐cell immunoblotting method, employing this photoactive gel enables the monitoring simultaneously in ≈2000 individual cells of subtle protein expression level changes that may be concealed using conventional techniques. The proposed gel has the advantages of excellent electrophoretic separation ability, high protein photoimmobilization efficiency, low autofluorescence, and it can be used as a promising photoactive polyacrylamide gel for in‐gel/in situ capillary and microfluidic immunoblotting assays, especially for developing novel single cell immunoblotting methods.     

Mangrove Oyster (Crassostrea belcheri) as a Biomonitor Species for Bioavailability of Polycyclic Aromatic Hydrocarbons (PAHs) from Sediment of the West Coast of Peninsular Malaysia

ABSTRACT

Rapid industrialization and urbanization in the west coast of Peninsular Malaysia has caused increasing pollution particularly of petroleum and petroleum by-products. Surface sediment and mangrove oyster (Crassostrea belcheri) were collected from five mangrove ecosystems in the west coast of Peninsular Malaysia and investigated for bioavailability of polycyclic aromatic hydrocarbons (PAHs). Sampling locations were selected from both remote areas with few or no previous records of petroleum pollution such as Pulau Merambong and polluted areas that are under international attention such as Klang mangrove ecosystem. PAH fractions were obtained through soxhlet extraction and two-step column chromatography and the fractions were injected to gas chromatography-mass spectrometry (GC-MS) for analysis. The concentrations of PAHs ranged from 151 to 4973 ng g^?1 dw in the sediments, while from 309 to 2225 ng g^?1 dw in the oysters. When tested for diagnostic ratios, a predominance of pyrogenic source PAHs was detected in the sediments, whereas PAHs in the oysters had mixed petrogenic and pyrogenic sources. A significant correlation (p < 0.05) was found between high molecular weight (HMW) PAHs in the sediments and oysters and biota accumulation factors (BAFs) of PAHs were approaching or exceeding unity indicating the ability of mangrove oyster in bioaccumulation of PAHs. Overall, this study indicates that mangrove oyster (C. belcheri) can be used as a biomonitor species for PAHs in an aquatic environment.     

P75 and S100 gene expression induced by cell-imprinted substrate and beta-carotene to nerve tissue engineering

Here we aimed to differentiate adipose derived stem cells (ADSCs) to Schwann cells (SCs), as one of the major and instrumental cell sources in nerve regeneration, by synergistic application of imprinting method and β-carotene. Accordingly, the topography of Schwann cells was imprinted on poly dimethyl siloxane (PDMS) substrates via mold casting and human ADSCs seeded on substrates; moreover, β-carotene was added to induce hADSCs differentiation. Physiochemical evaluations of PDMS by FTIR spectra presented its silicon-methyl bond (Si CH₃) at 1260 cm⁻¹. Morphology analysis by crystal violet, picrosirus red staining, and SEM images illustrated that MSCs seeded on imprinted substrates have formed SC-like morphology. Furthermore, according to q-PCR and ICC evaluations, SCs specific markers; S100 and P75 in addition of 5 μl β-carotene (BC) were upregulated (p-value<0.001). Also, the expression was detected on the imprinted surfaces without β-carotene to a lesser degree. Our study revealed that Schwann cell imprinted substrates can mimic the morphology and topography of SCs and induce differentiation signals in mesenchymal stem cells (MSCs). In addition, the potency of β-carotene as an organic substance in boosting and stimulating the neural differentiation was demonstrated. Relevantly, the reports have confirmed the synergistic pivotal roles of β-carotene and patterned surfaces in directing MSCs into SC-like cells differentiation without applying expensive and less safe chemical growth factors.     

sEMG-based impedance control for lower-limb rehabilitation robot

Recently, various rehabilitation robots have been developed for therapeutic exercises. Additionally, several control methods have been proposed to control the rehabilitation robots based on user’s motion intention. One of the common control methods used is torque-based impedance control. This paper presents an electromyogram-based robust impedance control for a lower-limb rehabilitation robot using a voltage-based strategy. The proposed control strategy uses surface electromyogram (sEMG) signals in place of force sensors to estimate the exerted force. In addition, the control is based on the voltage control strategy, which differs from the common torque control strategies. For example, unlike the torque-based impedance control, the controller is not dependent on the dynamical models of the patient and the robot. This is particularly important as the dynamic of the patient is both difficult to model precisely and changes during the rehabilitation period. These simplifications results in a significant reduction in calculation time. To illustrate the effectiveness of the control approach, a 1-DOF lower-limb rehabilitation robot is designed. Experimental sEMG-force data are collected and used to train an artificial neural network. Simulation results show that compared with a torque-based control approach, the voltage-based is simpler, less computational and more efficient while it considers the presence of actuators. Finally, we design an adaptive fuzzy system to estimate and compensate the uncertainty in performing the impedance rule. The adaptive fuzzy system has an advantage that does not need new feedback to estimate the uncertainty. The control approach is further verified by stability analysis. Simulation results show the efficiency of the control approach in performing some therapeutic exercises.