Nanoindentation mapping of mechanical properties of cement paste and natural rocks

This paper reports a study to assess nanoindentation mapping of mechanical properties of cement paste and natural rocks. Initial work seems to show that mechanical property mapping by nanoindentation is feasible and can be related to microscopic information. Further work is however required on the effect of indent size and spacing. Such a testing technique can be very useful for materials with different phases to study the intrinsic properties of each component, and also the interaction and properties of the interfacial regions of different phases. The values of Young's modulus and hardness of the individual mineral phases were also determined by statistically analysing a large number of experimental data.     

Finite element modelling of skeletal muscles coupled with fatigue

In this study, Hill's muscle theory coupled with fatigue was proposed to describe mechanical behaviours of skeletal muscles. The force developed by a fatigued muscle was described by a muscle fatigue formula which was a time function of the activation α_a and the stretch λ. The modified Hill's muscle theory was hence incorporated into a three-dimensional (3D) finite element model using the PAK finite element code. In this paper, the theoretical derivation of the 3D muscle model was firstly described. After presenting the method of establishing the finite element programme, a case example of studying the mechanical response of a frog gastrocnemius muscle was used to illustrate the applicability of the proposed methodology. The effects of the muscle fatigue on the deformation as well as the stress and strain distribution of the frog muscle subject to a cyclic activation function have been determined. An experiment capturing the real-time shape change of a frog muscle was also conducted to assess the applicability of the proposed method. A comparison between the deformed shapes of the predictive model and the frog muscles was also made. It was shown that the method is capable of providing a reasonable model for describing the mechanical behaviour of skeletal muscles.     

Bead-based polymerase chain reaction on a microchip

We present a bead-based approach to microfluidic polymerase chain reaction (PCR), enabling fluorescent detection and sample conditioning in a single microchamber. Bead-based PCR, while not extensively investigated in microchip format, has been used in a variety of bioanalytical applications in recent years. We leverage the ability of bead-based PCR to accumulate fluorescent labels following DNA amplification to explore a novel DNA detection scheme on a microchip. The microchip uses an integrated microheater and temperature sensor for rapid control of thermal cycling temperatures, while the sample is held in a microchamber fabricated from (poly)dimethylsiloxane and coated with Parylene. The effects of key bead-based PCR parameters, including annealing temperature and concentration of microbeads in the reaction mixture, are studied to achieve optimized device sensitivity and detection time. The device is capable of detecting a synthetically prepared section of the Bordetella pertussis genome in as few as 10 temperature cycles with times as short as 15?min. We then demonstrate the use of the procedure in an integrated device; capturing, amplifying, detecting, and purifying template DNA in a single microfluidic chamber. These results show that this method is an effective method of DNA detection which is easily integrated in a microfluidic device to perform additional steps such as sample pre-conditioning.     

Support vector regression methodology for prediction of input displacement of adaptive compliant robotic gripper

The prerequisite for new versatile grippers is the capability to locate and perceive protests in their surroundings. It is realized that automated controllers are profoundly nonlinear frameworks, and a faultless numerical model is hard to get, in this way making it troublesome to control utilizing tried and true procedure. Here, a design of an adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict optimal inputs displacement of the gripper according to experimental tests and shapes of grasping objects. Instead of minimizing the observed training error, SVR _poly and SVR _rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach compared to other soft computing methodology.     

Robust Kalman filtering for nonlinear multivariable stochastic systems in the presence of non‐Gaussian noise

The presence of outliers can considerably degrade the performance of linear recursive algorithms based on the assumptions that measurements have a Gaussian distribution. Namely, in measurements there are rare, inconsistent observations with the largest part of population of observations (outliers). Therefore, synthesis of robust algorithms is of primary interest. The Masreliez–Martin filter is used as a natural frame for realization of the state estimation algorithm of linear systems. Improvement of performances and practical values of the Masreliez‐Martin filter as well as the tendency to expand its application to nonlinear systems represent motives to design the modified extended Masreliez–Martin filter. The behaviour of the new approach to nonlinear filtering, in the case when measurements have non‐Gaussian distributions, is illustrated by intensive simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of ionic liquids for the removal of heavy metals from wastewater and activated sludge

This paper presents the results of adsorption studies on the removal of heavy metals (Cr, Cu, Cd, Ni, Pb and Zn) from standard solutions, real wastewater samples and activated sewage sludge using a new technique of liquid-liquid extraction using quaternary ammonium and phosphonium ionic liquids (ILs). Batch sorption experiments were conducted using the ILs [PR4][TS], [PR4][MTBA], [A336][TS] and [A336][MTBA]. Removal of these heavy metals from standard solutions were not effective, however removal of heavy metals from the industrial effluents/wastewater treatment plants were satisfactory, indicating that the removal depends mainly on the composition of the wastewater and cannot be predicted with standard solutions. Removal of heavy metals from activated sludge proved to be more successful than conventional methods such as incineration, acid extraction, thermal treatment, etc. For the heavy metals Cu, Ni and Zn, ≥90% removal was achieved.     

Electrospun Poly(styrene) Fibers as a Protection for the First- and the Second-Generation Grubbs’ Catalyst

The study presents a novel method for protection of the first- and the second-generation Grubbs’ catalyst, by incorporation in poly(styrene) fibers through electrospinning technique. Both catalysts are sensitive to the presence of the amine hardeners in the epoxy-based self-healing composites and require protection from deactivation to retain their ability to promote polymerization reaction of the healing agent. Comparison of healing efficiencies of both catalysts suggested that poly(styrene) fibers offer better protection and dispersion for the first-generation Grubbs’ catalyst, although all the samples exhibited high-healing efficiency. Difference in stereoselectivity between two catalysts was also indicated.     

Variable neighbourhood simulated annealing algorithm for capacitated vehicle routing problems

This article presents the variable neighbourhood simulated annealing (VNSA) algorithm, a variant of the variable neighbourhood search (VNS) combined with simulated annealing (SA), for efficiently solving capacitated vehicle routing problems (CVRPs). In the new algorithm, the deterministic ‘Move or not’ criterion of the original VNS algorithm regarding the incumbent replacement is replaced by an SA probability, and the neighbourhood shifting of the original VNS (from near to far by k← k+1) is replaced by a neighbourhood shaking procedure following a specified rule. The geographical neighbourhood structure is introduced in constructing the neighbourhood structures for the CVRP of the string model. The proposed algorithm is tested against 39 well-known benchmark CVRP instances of different scales (small/middle, large, very large). The results show that the VNSA algorithm outperforms most existing algorithms in terms of computational effectiveness and efficiency, showing good performance in solving large and very large CVRPs.     

Semantic Mediation for Standard-Based B2B Interoperability

The authors discuss a semantic-mediation architecture to advance traditional approaches for standards-based business-to-business (B2B) interoperability. The architecture is supported by the Athena Knowledge Representation and Semantics Mediation tool suite. Initial experimentations with the architecture and the toolset offer discussions of key architectural and functional aspects and suggest directions for future tools enhancements.