Tuning of Dielectric Parameters of (NiFe<Subscript>2</Subscript>O<Subscript>4</Subscript>)<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript>/CuTl-1223 Nano-superconductor Composites by Temperature and Frequency

Nickel ferrite (NiFe₂O₄) nanoparticles and Cu_0.5Tl_0.5Ba₂Ca₂Cu₃O_10?δ (CuTl-1223) superconductor were prepared separately and then mixed in an appropriate ratios at the final stage to obtain (NiFe₂O₄) _x /CuTl-1223 (x = 0, 0.5, and 1.0 wt%) nano-superconductor composites. There was no significant change observed in crystal structure of the host CuTl-1223 superconducting matrix after the addition of NiFe₂O₄ nanoparticles. The value of zero-resistivity critical temperature { T _{c(R = 0)} (K)} was decreased with increasing content of these nanoparticles in these composites. Maximum values of dielectric loss tangent (tanδ) at lowest possible frequency of 40 Hz were increased with the increase of operating temperature, while its values were decreased and become almost zero at higher frequencies for all these samples at all operating temperatures. A peak in dielectric loss tangent was shifted towards lower frequency values with the addition of these nanoparticles in CuTl-1223 superconducting matrix. The dielectric loss tangent peak was also shifted towards lower frequency values in all these samples with increasing operating temperature, which shows the relaxator-like behavior in these samples. The dielectric parameters of these composites can be tuned by frequency, operating temperatures, and nature and content of these nanoparticles.     

Nanoelectromechanical Chip (NELMEC) Combination of Nanoelectronics and Microfluidics to Diagnose Epithelial and Mesenchymal Circulating Tumor Cells from Leukocytes

An integrated nano‐electromechanical chip (NELMEC) has been developed for the label‐free distinguishing of both epithelial and mesenchymal circulating tumor cells (ECTCs and MCTCs, respectively) from white blood cells (WBCs). This nanoelectronic microfluidic chip fabricated by silicon micromachining can trap large single cells (>12 µm) at the opening of the analysis microchannel arrays. The nature of the captured cells is detected using silicon nanograss (SiNG) electrodes patterned at the entrance of the channels. There is an observable difference between the membrane capacitance of the ECTCs and MCTCs and that of WBCs (measured using SiNG electrodes), which is the key indication for our diagnosis. The NELMEC chip not only solves the problem of the size overlap between CTCs and WBCs but also detects MCTCs without the need for any markers or tagging processes, which has been an important problem in previously reported CTC detection systems. The great conductivity of the gold‐coated SiNG nanocontacts as well as their safe penetration into the membrane of captured cells, facilitate a precise and direct signal extraction to distinguish the type of captured cell. The results achieved from epithelial (MCF‐7) and mesenchymal (MDA‐MB231) breast cancer cells circulated in unprocessed blood suggest the significant applications for these diagnostic abilities of NELMEC.     

Organometallic Bonding in an Ullmann‐Type On‐Surface Chemical Reaction Studied by High‐Resolution Atomic Force Microscopy

The on‐surface Ullmann‐type chemical reaction synthesizes polymers by linking carbons of adjacent molecules on solid surfaces. Although an organometallic compound is recently identified as the reaction intermediate, little is known about the detailed structure of the bonded organometallic species and its influence on the molecule and the reaction. Herein atomic force microscopy at low temperature is used to study the reaction with 3,9‐diiododinaphtho[2,3‐b:2′,3′‐d]thiophene (I‐DNT‐VW), which is polymerized on Ag(111) in vacuum. Thermally sublimated I‐DNT‐VW picks up a Ag surface atom, forming a C? Ag bond at one end after removing an iodine. The C? Ag bond is usually short‐lived, and a C? Ag? C organometallic bond immediately forms with an adjacent molecule. The existence of the bonded Ag atoms strongly affects the bending angle and adsorption height of the molecular unit. Density functional theory calculations reveal the bending mechanism, which reveals that charge from the terminus of the molecule is transferred via the Ag atom into the organometallic bond and strengths the local adsorption to the substrate. Such deformations vanish when the Ag atoms are removed by annealing and C? C bonds are established.     

Discriminative functional analysis of human movements

This paper investigates the use of statistical dimensionality reduction (DR) techniques for discriminative low dimensional embedding to enable affective movement recognition. Human movements are defined by a collection of sequential observations (time-series features) representing body joint angle or joint Cartesian trajectories. In this work, these sequential observations are modelled as temporal functions using B-spline basis function expansion, and dimensionality reduction techniques are adapted to enable application to the functional observations. The DR techniques adapted here are: Fischer discriminant analysis (FDA), supervised principal component analysis (PCA), and Isomap. These functional DR techniques along with functional PCA are applied on affective human movement datasets and their performance is evaluated using leave-one-out cross validation with a one-nearest neighbour classifier in the corresponding low-dimensional subspaces. The results show that functional supervised PCA outperforms the other DR techniques examined in terms of classification accuracy and time resource requirements.     

Efficient routing techniques in heterogeneous 3D Networks-on-Chip

Three-dimensional Networks-on-Chips (3D NoCs) have recently been proposed to address the on-chip communication demands of future highly dense 3D multi-core systems. Homogeneous 3D NoC topologies have many Through Silicon Vias (TSVs) which have a costly and complex manufacturing process. Also, 3D routers use more memory and are more power hungry than conventional 2D routers. Alternatively, heterogeneous 3D NoCs combine both the area and performance benefits of 2D and 3D static router architectures by using a limited number of TSVs. To improve the performance of heterogeneous 3D NoCs, we propose an adaptive router architecture which balances the traffic in such NoCs. Particularly, experimental results show that our proposed architecture significantly improves the performance up to 75% by replacing 2D static routers with adaptive 2D routers in heterogeneous 3D NoCs, while keeping the maximum clock frequency, power and energy consumption of the adaptive router nearly at the same level as the static router.     

Minimizing ordered weighted averaging of rational functions with applications to continuous location

This paper considers the problem of minimizing the ordered weighted average (or ordered median) function of finitely many rational functions over compact semi-algebraic sets. Ordered weighted averages of rational functions are, in general, neither rational functions nor the supremum of rational functions so current results available for the minimization of rational functions cannot be applied to handle these problems. We prove that the problem can be transformed into a new problem embedded in a higher dimensional space where it admits a convenient polynomial optimization representation. This reformulation allows a hierarchy of SDP relaxations that approximates, up to any degree of accuracy, the optimal value of those problems. We apply this general framework to a broad family of continuous location problems showing that some difficult problems (convex and non-convex) that up to date could only be solved on the plane and with Euclidean distance can be reasonably solved with different _?p-norms${?}_p\mathrm{-}\mathrm{norms}$ in finite dimensional spaces. We illustrate this methodology with some extensive computational results on constrained and unconstrained location problems.     

Multi-Fault Tolerance for Cartesian Data Distributions

Faults are expected to play an increasingly important role in how algorithms and applications are designed to run on future extreme-scale systems. Algorithm-based fault tolerance is a promising approach that involves modifications to the algorithm to recover from faults with lower overheads than replicated storage and a significant reduction in lost work compared to checkpoint-restart techniques. Fault-tolerant linear algebra algorithms employ additional processors that store parities along the dimensions of a matrix to tolerate multiple, simultaneous faults. Existing approaches assume regular data distributions (blocked or block-cyclic) with the failures of each data block being independent. To match the characteristics of failures on parallel computers, we extend these approaches to mapping parity blocks in several important ways. First, we handle parity computation for generalized Cartesian data distributions with each processor holding arbitrary subsets of blocks in a Cartesian-distributed array. Second, techniques to handle correlated failures, i.e., multiple processors that can be expected to fail together, are presented. Third, we handle the colocation of parity blocks with the data blocks and do not require them to be on additional processors. Several alternative approaches, based on graph matching, are presented that attempt to balance the memory overhead on processors while guaranteeing the same fault tolerance properties as existing approaches that assume independent failures on regular blocked data distributions. Evaluation of these algorithms demonstrates that the additional desirable properties are provided by the proposed approach with minimal overhead.     

Negotiation strategies considering market, time and behavior functions for resource allocation in computational grid

Providing an efficient resource allocation mechanism is a challenge to computational grid due to large-scale resource sharing and the fact that Grid Resource Owners (GROs) and Grid Resource Consumers (GRCs) may have different goals, policies, and preferences. In a real world market, various economic models exist for setting the price of grid resources, based on supply-and-demand and their value to the consumers. In this paper, we discuss the use of multiagent-based negotiation model for interaction between GROs and GRCs. For realizing this approach, we designed the Market- and Behavior-driven Negotiation Agents (MBDNAs). Negotiation strategies that adopt MBDNAs take into account the following factors: Competition, Opportunity, Deadline and Negotiator’s Trading Partner’s Previous Concession Behavior. In our experiments, we compare MBDNAs with MDAs (Market-Driven Agent), NDF (Negotiation Decision Function) and Kasbah in terms of the following metrics: total tasks complementation and budget spent. The results show that by taking the proposed negotiation model into account, MBDNAs outperform MDAs, NDF and Kasbah.     

An optimal algorithm for two robots path planning problem on the grid

This paper is a study on the problem of path planning for two robots on a grid. We consider the objective of minimizing the maximum path length which corresponds to minimizing the arrival time of the last robot at its goal position. We propose an optimal algorithm that solves the problem in linear time with respect to the size of the grid. We show that the algorithm is complete; meaning that it is sure to find an optimal solution or report if any does not exist.     

ANFIS-based prediction of the compressive strength of geopolymers with seeded fly ash and rice husk–bark ash

In the present work, compressive strength of geopolymers made from seeded fly ash and rice husk–bark ash has been predicted by adaptive network-based fuzzy inference systems (ANFIS). Different specimens, made from a mixture of fly ash and rice husk–bark ash in fine and coarse forms and a mixture of water glass and NaOH mixture as alkali activator, were subjected to compressive strength tests at 7 and 28 days of curing. The curing regimes were different: one set of the specimens were cured in water at room temperature until 7 and 28 days and the other sets were oven-cured for 36 h at the range of 40–90°C and then cured at room temperature until 7 and 28 days. A model based on ANFIS for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of ANFIS models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk–bark ash in the ashes mixture, the percentage of coarse rice husk–bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters in the ANFIS models, the compressive strength of each specimen was predicted. The training and testing results in ANFIS models showed a strong potential for predicting the compressive strength of the geopolymeric specimens.