Shape-Memory-Alloy supplemented Lead Rubber Bearing (SMA-LRB) for seismic isolation

Optimization of isolation system involves a trade-off between the isolation efficiency and isolator displacement to ensure optimal performance. Quite often, the latter aspect is overruled, even though, large isolator displacements are known to be linked with adverse consequences. A class of smart material referred as Shape Memory Alloy (SMA) has recently been proposed to supplement the conventional isolation bearings that is shown to facilitate such trade-off, owing to its Superelastic property. Recent usage of SMA in supplementing rubber bearing to isolate highway bridge is notable. Presently, the SMA is employed in conjunction with the Lead Rubber Bearing (LRB) (referred as SMA-LRB), isolating multi-storied building frame. It is demonstrated that SMA-LRB largely reduces the isolator displacement with very little sacrifice in the isolation efficiency while compared with the conventional LRB. The nonlinear random vibration analysis in conjunction with stochastic linearization of SMA behavior demonstrate that optimal combination of the SMA and LRB characteristics strengths are essential to ensure such performance. This includes minimizing the isolator displacement as well as maximizing the isolation efficiency, dealt in a framework of bi-objective optimization. Robustness of the optimal design is verified through parametric studies concerning the anticipated variations in system parameters and seismic loading.     

Breast cancer classification using deep belief networks

Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN). Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD). The classifier complex gives an accuracy of 99.68% indicating promising results over previously-published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train-test partitions.     

Role-Based Channel Hopping Algorithm for a Cognitive Radio Network in Asynchronous Environment

Wireless Personal Communications - Cognitive radio network (CRN) has been recognized by researchers to solve the spectrum shortage problem, where the unlicensed users opportunistically exploit the...     

Cap-layer and charge sheet effect in InP based pnp δ-doped heterojunction bipolar transistor

Microsystem Technologies - The effect of varying the cap layer and the δ doped charge sheet has been investigated for an InP based heterojunction bipolar transistor (HBT) using the SILVACO...     

Change in Orientation of Polyacrylic Acid and Chitosan Networks by Imprintment of Gold Nanoparticles

ABSTRACT

Herein, gold nanoparticles (Au Nps) imprinted polyacrylic acid/chitosan (PAA/CS) nanocomposite hydrogels are designed by in situ polymerization technique. The change in crystallite size and d-spacing is established from X-ray diffraction study. It is interesting to notice that the layered morphology of PAA/CS/Au nanocomposite hydrogel is achieved from field emission scanning electron microscopy due to entanglement of polymeric chains by imprintment of Au Nps. The orientations of polymeric chains reveal the enhancement in pH-responsive swelling percentage and prolong water holding capacity of PAA/CS/Au nanocomposites. Biodegradation of the as-synthesized material is studied from which the antimicrobial activity of Au Nps is predicted.     

Phase transition and electrical properties of gallium- and indium-doped Bi<Subscript>10</Subscript>Ti<Subscript>3</Subscript>W<Subscript>3</Subscript>O<Subscript>30</Subscript>

Polycrystalline samples of gallium- and indium-doped Bi₁₀Ti₃W₃O₃₀ (mixed-layer Aurivillius phase with the Ti⁴⁺ and W⁶⁺ distributed at random over the perovskite-like slabs) have been prepared by solid-state reactions, and their polymorphism and electrical properties have been studied. Doping with both In³⁺ and Ga³⁺ yields limited solid solutions and shifts the ferroelectric phase transition to lower temperatures. The heterovalent substitutions of In³⁺ and Ga³⁺ for Ti⁴⁺ and W⁶⁺ increase the oxygen vacancy concentration and, accordingly, the conductivity of the material relative to the undoped compound.     

An optimised FOPID controller for dynamic voltage stability and reactive power management in a stand-alone micro grid

This paper proposes the application of fractional order PID controller (FOPID) for reactive power compensation and stability analysis in a stand-alone micro grid. For enhancement of voltage stability and reactive compensation of the isolated system, a SVC based controller has been incorporated. This paper emphasizes the role of fractional PID based SVC controller for reactive power management and improved stability in the stand alone micro grid, as it provides a special advantage of having two more degree of freedom for accurate tuning in comparison with the conventional controller The system performance, particularly the variations in different parameters values are studied properly with different input parameters and loading conditions. Further improvement of stability margin and optimisation of the system parameters have been achieved by the controller, based on Imperialist competitive algorithm.     

A new fuzzy peer assessment methodology for cooperative learning of students

In this paper, a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed. Instead of numerals, words are used in the evaluation process, in order to provide greater flexibility. The proposed methodology is a synthesis of perceptual computing (Per-C) and a fuzzy ranking algorithm. Per-C is adopted because it allows uncertainties of words to be considered in the evaluation process. Meanwhile, the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student's contribution in a group, and subsequently rank the student accordingly. A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students.