Low temperature heat capacity of LaRu2 by a.c. technique

The low temperature heat capacity data of a small bulk sample of LaRu₂ show that the structural transformation occurring at T_m ～ 29.5K is rather spread out. The electronic heat capacity coefficient, γ, and Debye temperature, θ_D, are found to be respectively $\text{8.5}\text{mJ}\text{g}$. atom K² and 164 K. The multiple anomalies below T_m and T_c (4.45 K) are discussed.     

Homotopy type of neighborhood complexes of Kneser graphs, $$\varvec{KG_{2,k}}$$

Schrijver (Nieuw Archief voor Wiskunde, 26(3) (1978) 454–461) identified a family of vertex critical subgraphs of the Kneser graphs called the stable Kneser graphs \(SG_{n,k}\). Björner and de Longueville (Combinatorica 23(1) (2003) 23–34) proved that the neighborhood complex of the stable Kneser graph \(SG_{n,k}\) is homotopy equivalent to a k-sphere. In this article, we prove that the homotopy type of the neighborhood complex of the Kneser graph \(KG_{2,k}\) is a wedge of \((k+4)(k+1)+1\) spheres of dimension k. We construct a maximal subgraph \(S_{2,k}\) of \(KG_{2,k}\), whose neighborhood complex is homotopy equivalent to the neighborhood complex of \(SG_{2,k}\). Further, we prove that the neighborhood complex of \(S_{2,k}\) deformation retracts onto the neighborhood complex of \(SG_{2,k}\).     

On Dual Configurational Forces

The dual conservation laws of elasticity are systematically re-examined by using both Noether's variational approach and Coleman–Noll–Gurtin's thermodynamics approach. These dual conservation laws can be interpreted as the dual configurational force, and therefore they provide the dual energy–momentum tensor. Some previously unknown and yet interesting results in elasticity theory have been discovered. As an example, we note the following duality condition between the configuration force (energy–momentum tensor) and the dual configuration force (dual energy–momentum tensor) ,

NeuroGenetic approach for combinatorial optimization: an exploratory analysis

Given the NP-Hard nature of many optimization problems, it is often impractical to obtain optimal solutions to large-scale problems in reasonable computing time. For this reason, heuristic and metaheuristic search approaches are used to obtain good solutions fast. However, these techniques often struggle to develop a good balance between local and global search. In this paper we propose a hybrid metaheuristic approach which we call the NeuroGenetic approach to search for good solutions for these large scale optimization problems by at least partially overcoming this challenge. The proposed NeuroGenetic approach combines the Augmented Neural Network (AugNN) and the Genetic Algorithm (GA) search approaches by interleaving the two. We chose these two approaches to hybridize, as they offer complementary advantages and disadvantages; GAs are very good at searching globally, while AugNNs are more proficient at searching locally. The proposed hybrid strategy capitalizes on the strong points of each approach while avoiding their shortcomings. In the paper we discuss the issues associated with the feasibility of hybridizing these two approaches and propose an interleaving algorithm. We also provide empirical evidence demonstrating the effectiveness of the proposed approach.     

A novel approach for protein identification from complex cell proteome using modified peptide mass fingerprinting algorithm

A unique peptide based search algorithm for identification of protein mixture using PMF is proposed. The proposed search algorithm utilizes binary search and heapsort programs to generate frequency chart depicting the unique peptides corresponding to all proteins in a proteome. The use of binary search program significantly reduces the time for frequency chart preparation to ～2 s for a proteome comprising ～23 000 proteins. The algorithm was applied to a three‐protein mixture identification, host cell protein (HCP) analysis, and a simulation‐generated data set. It was found that the algorithm could identify at least one unique peptide of a protein even in the presence of fourfold higher concentration of another protein. In addition, two HCPs that are known to be difficult to remove were missed by MS/MS approach and were exclusively identified using the presented algorithm. Thus, the proposed algorithm when used along with standard proteomic approaches present avenues for enhanced protein identification efficiency, particularly for applications such as HCP analysis in biopharmaceutical research, where identification of low‐abundance proteins are generally not achieved due to dynamic range limitations between the target product and HCPs.     

On a conjecture of Lynch

Abstract

We comment on a conjecture of Lynch on annihilators of local cohomology.

Communicated by Lawrence Ein     

Controlled interparticle spacing for surface-modified gold nanoparticle aggregates

Aggregation of gold nanoparticles of increasing size has been studied as a consequence of adsorption of 2-aminothiophenol (ATP) on gold nanoparticle surfaces. The capping property of ATP in the acidic pH range has been accounted from UV-vis absorption spectroscopy and surface-enhanced Raman scattering (SERS) studies. The effect of nanoparticle size (8-55 nm) on the nature of aggregation as well as the variation in the optical response due to variable degree of interparticle coupling effects among the gold particles have been critically examined. Various techniques such as transmission electron microscopy, X-ray diffraction, zeta-potential, and average particle size measurement were undertaken to characterize the nanoparticle aggregates. The aggregate size, interparticle distances, and absorption band wavelengths were found to be highly dependent on the pH of the medium and the concentration of the capping agent, ATP. The acquired SERS spectra of ATP relate the interparticle spacing. It has been observed that the SERS signal intensities are different for different sized gold nanoparticles.     

Gram level synthesis of lead-free solder in the nanometer length scale obtained from tin and silver compounds using silicone oil

A straightforward route to gram level synthesis of a pure phase of the Sn-Ag nanoalloy in an eutectic composition (Sn/Ag 96.5:3.5) in silicone oil is reported. The composition, morphology, and microstructure of the alloy were fully characterized. In a mixture of ethylene glycol and silicone oil, direct reduction of Sn(II) acetate and Ag(I) nitrate gave the Sn-Ag nanoalloy. The nanoalloy disintegrates by sonication and reforms by heating, leading to smaller particles with a melting point as low as 128 degrees C.