Filler Wetting in Miscible ESBR/SSBR Blends and Its Effect on Mechanical Properties

The selective wetting behavior of silica in emulsion styrene butadiene rubber (ESBR)/solution styrene butadiene rubber (SSBR) blends is characterized by the wetting concept, which is further developed for filled blends based on miscible rubbers. It is found that not only the chemical rubber–filler affinity but also the topology of the filler surface significantly influences the selective filler wetting in rubber blends. The nanopore structure of the silica surface has been recognized as the main reason for the difference in the wetting behavior of the branched ESBR molecules and linear SSBR molecules. However, the effect of nanopore structure becomes more significant in the presence of silane. It is discussed that the adsorption of silane on silica surface constricts the nanopore to some extent that hinders effectively the space filling of the nanopores by the branched ESBR molecules but not by the linear SSBR molecules. As a result, in silanized ESBR/SSBR blends the dominant wetting of silica surface by the tightly bonded layer of SSBR molecules causes a low‐energy dissipation in the rubber–filler interphase. That imparts the low rolling resistance to the blends similar to that of a silica‐filled SSBR compound, while the ESBR‐rich matrix warrants the good tensile behavior, i.e., good abrasion and wear resistance of the blends.

     

Multi-objective optimization of circular magnetic abrasive polishing of SUS304 and Cu materials

In this paper, a Multi-objective particle swarm optimization algorithm (MOPSOA) is applied to optimize surface roughness of workpiece after circular magnetic abrasive polishing. The most important parameters of polishing model, namely current, gap between pole and workpiece, spindle speed and polishing time, were considered in this approach. The objective functions of the MOPSOA depend on the quality of surface roughness of polishing materials with both simultaneous surfaces (Ra1, Ra2), which are determined by means of experimental approach with the aid of circular magnetic field. Finally, the effectiveness of the approach is compared between the optimal results with the experimental data. The results show that the new proposed polishing optimization method is more feasible.

     

A novel hybrid model of Bagging-based Naïve Bayes Trees for landslide susceptibility assessment

Bulletin of Engineering Geology and the Environment - Landslide susceptibility assessment was performed using the novel hybrid model Bagging-based Naïve Bayes Trees (BAGNBT) at Mu Cang Chai...     

Highly sensitive electrochemical sensor based on zirconium oxide-decorated gold nanoflakes nanocomposite 2,4-dichlorophenol detection

Journal of Applied Electrochemistry - In this study, a sensitive and selective electrochemical sensor based on a zirconia oxide-decorated gold nanoflake nanocomposite-modified glassy carbon...     

Hidden Relationships between N-Glycosylation and Disulfide Bonds in Individual Proteins

N-Glycosylation (NG) and disulfide bonds (DBs) are two prevalent co/post-translational modifications (PTMs) that are often conserved and coexist in membrane and secreted proteins involved in a large number of diseases. Both in the past and in recent times, the enzymes and chaperones regulating these PTMs have been constantly discovered to directly interact with each other or colocalize in the ER. However, beyond a few model proteins, how such cooperation affects N-glycan modification and disulfide bonding at selective sites in individual proteins is largely unknown. Here, we reviewed the literature to discover the current status in understanding the relationships between NG and DBs in individual proteins. Our results showed that more than 2700 human proteins carry both PTMs, and fewer than 2% of them have been investigated in the associations between NG and DBs. We summarized both these proteins with the reported relationships in the two PTMs and the tools used to discover the relationships. We hope that, by exposing this largely understudied field, more investigations can be encouraged to unveil the hidden relationships of NG and DBs in the majority of membranes and secreted proteins for pathophysiological understanding and biotherapeutic development.     

Energy‐efficient power control for uplink spectrum‐sharing heterogeneous networks

This article considers energy‐efficient power control schemes for interference management in uplink spectrum‐sharing heterogeneous networks that maximize the energy efficiency of users, protect the macro base station, and support users with QoS consideration. In the first scenario, we define the objective function as the weighted sum of the energy efficiencies and develop an efficient global optimization algorithm with global linear and local quadratic rate of convergence to solve the considered problem. To ensure fairness among individual user equipments (UEs) in terms of energy efficiency, we consider the max‐min problem, where the objective is defined as the weighted minimum of the energy efficiencies, and a fractional programming theory and the dual decomposition method are jointly used to solve the problem and investigate an iterative algorithm. As by‐products, we further discuss the global energy efficiency problem and consider near‐optimal schemes. Numerical examples are provided to demonstrate significant improvements of the proposed algorithms over existing interference management schemes.     

Using a Short Wavelength Infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels

Sprout damage (pre-harvest germination) in wheat results in highly deleterious effects on end-product quality. Alpha-amylase, the pre-dominant enzyme in the early stage of sprouting has the most damaging effect. This paper introduces a new method using a SWIR hyperspectral imaging system (1000–2500 nm) to predict the α-amylase activity of individual wheat kernels. Two classes of Canadian wheat, Canada Western Red Spring (CWRS) and Canada Western Amber Durum (CWAD), with samples of differing degrees of sprout damage were investigated. Individual kernels were first imaged with the hyperspectral imaging system and then the α-amylase activity of each kernel was determined analytically. Individual kernel α-amylase activity prediction was significant (R ² 0.54 and 0.73) for CWAD and CWRS, respectively using Partial Least Square regression on the hyperspectral data. A classification method is proposed to separate CWRS kernels with high α-amylase activity level from those with low α-amylase activity giving an accuracy of above 80%. This work shows that hyper/multi-spectral imaging techniques can be used for rapidly predicting the α-amylase activity of individual kernels, detecting sprouting at early stage.