A hydrophilic interaction chromatography has been investigated to separate 2-deoxycytidine chosen for nucleoside. A small molecule with specific features for human serum samples was 2-deoxycytidine tested. 2-deoxycytidine has been applied to self-made stationary hydrophilic phases (ZIC1 and ZIC5). The deoxycytidine (dCD) retention was investigated with varying concentrations of sodium acetate buffer, acetonitrile%, and pH. The results confirmed the hydrophilicity of 2-deoxycytidine. The exchanger retention mechanism was studied taking into account 2-deoxycytidine used for describing the interaction of hydrophilic and cation. For both ZIC1 and ZIC5 exchangers, we described and discussed the influence of chromatographic conditions (co

Viscosity (η) of solutions of 1-butanol, sec-butanol, isobutanol and tert-butanol were investigated in aqueous solution structures of ranged composition from 0.55 to 1 mol.dm-3 at 298.15 K. The data of (η/η˳) were evaluated based on reduced Jone - Dole equation; η/η˳ =BC+1. In the term of B value, the consequences based on solute-solvent interaction in aqueous solutions of alcohols were deliberated. The outcomes of this paper discloses that alcohols act as structure producers in the water. Additionally, it has shown that solute-solvent with interacting activity of identical magnitude is in water-alcohol system

<p>Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).