Trapping of two-dimensional electrons in substrate potentials

We report preliminary measurements of high (ripplon-limited) electron mobility on helium films supported by a hydrogen-coated glass substrate. From the smooth dependence of the mobility as a function of helium film thickness we deduce that no polaron transition occurs on films thicker than 500 Å at T>1 K. On poorer quality substrates we observe a rapid drop in the mobility at a substrate-dependent critical film thickness. We interpret these drops as due to localization in variations in the substrate potential.     

Very short integrated-optic Fabry-Perot cavity-coupler switch forDC and high bandwidth operation

A novel integrated-optic coupling configuration if proposed. The device operation is based on the confinement of the coupling zone to the length of a very short Fabry-Perot cavity. The cavity is electrooptically coupled with a parallel outcoupling guide. By employing accurate lithographic techniques for the fabrication of milled integrated mirrors, with total lengths of the order of 5-10 μm, devices 100 to 200 μm long can be considered. These can perform full coupling operation at bandwidths in the range of many tens of gigahertz with typically a 12-15-dB extinction ratio. The same configuration can also be used for low-frequency switching, where an extinction ratio of >20 dB is expected. Calculated results are based on the parameters of Ti:LiNbO₃ waveguides supporting 1.53-μm wavelength TM mode     

The computability of relaxed data structures: queues and stacks as examples

Most concurrent data structures being designed today are versions of known sequential data structures. However, in various cases it makes sense to relax the semantics of traditional concurrent data structures in order to get simpler and possibly more efficient and scalable implementations. For example, when solving the classical producer-consumer problem by implementing a concurrent queue, it might be enough to allow the dequeue operation (by a consumer) to return and remove one of the two oldest values in the queue, and not necessarily the oldest one. We define infinitely many possible relaxations of several traditional data structures and objects: queues, stacks, multisets and registers, and examine their relative computational power.     

Chitosan/OA nanoparticle as delivery system for celecoxib: Parameters affecting the particle size,encapsulation, and release

Self‐assembled nanoparticles prepared from amphiphilic chitosan/oleic acid (Ch/OA) have shown antibacterial activity and potential application as a carrier for hydrophobic anticancer drugs. In this study, a low molecular weight chitosan was modified with oleic acid obtaining a degree of substitution (DS) of 12%. The critical aggregation concentration (CAC) of the Ch/OA polymer obtained (0.025 mg mL^?1) is lower in comparison with some systems of chitosan‐fatty acids. The self‐assembled Ch/OA nanoparticle size was optimized by changing polymer concentration, solvent, method, and time of homogenization to obtain particles with sizes around 300 nm and positive zeta potential. The drug loading about 7 μg mL^?1 and encapsulation efficiency of 75.8 ± 3.6% for Celecoxib was affected by the drug concentration. In vitro release behavior performed in (PBS, pH 7.4) and MES buffer (pH 6) indicated a pH‐dependent drug release behavior. The self‐assembled systems show stability during 4 weeks after the encapsulation of the hydrophobic drug. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 44472.     

Simultaneous intraparticle forced convection,diffusion and reaction in a porous catalyst—III Depolymerization of paraldehyde

The effect of intraparticle forced convection on the effectiveness of a porous catalyst was studied experimentally. Depolymerization of paraldehyde, catalysed by nickel-sulfate supported on cylindrical pellet of porous silica-gel, was measured in a basket-type reactor. The effective reaction rate increased with increasing the intensity of convective fluxes within the porous pellets. The dependence of this enhancement on the Peclét number and Thiele modulus of the system shows good qualitative agreement with predictions based on idealized models.     

ConfDTree： A Statistical Method for Improving Decision Trees

Decision trees have three main disadvantages： reduced performance when the training set is small; rigid decision criteria; and the fact that a single ＂uncharacteristic＂ attribute might ＂derail＂ the classification process. In this paper we present ConfDTree （Confidence-Based Decision Tree） -- a post-processing method that enables decision trees to better classify outlier instances. This method, which can be applied to any decision tree algorithm, uses easy-to-implement statistical methods （confidence intervals and two-proportion tests） in order to identify hard-to-classify instances and to propose alternative routes. The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5%-9% in the AUC performance is reported.     

Location-Oblivious Distributed Unit Disk Graph Coloring

We present the first location-oblivious distributed unit disk graph coloring algorithm having a provable performance ratio of three (i.e. the number of colors used by the algorithm is at most three times the chromatic number of the graph). This is an improvement over the standard sequential coloring algorithm that has a worst case lower bound on its performance ratio of 4−3/k (for any k>2, where k is the chromatic number of the unit disk graph achieving the lower bound) (Tsai et al., in Inf. Process. Lett. 84(4):195–199, 2002). We present a slightly better worst case lower bound on the performance ratio of the sequential coloring algorithm for unit disk graphs with chromatic number 4. Using simulation, we compare our algorithm with other existing unit disk graph coloring algorithms.     

Detecting unknown computer worm activity via support vector machines and active learning

To detect the presence of unknown worms, we propose a technique based on computer measurements extracted from the operating system. We designed a series of experiments to test the new technique by employing several computer configurations and background application activities. In the course of the experiments, 323 computer features were monitored. Four feature-ranking measures were used to reduce the number of features required for classification. We applied support vector machines to the resulting feature subsets. In addition, we used active learning as a selective sampling method to increase the performance of the classifier and improve its robustness in the presence of misleading instances in the data. Our results indicate a mean detection accuracy in excess of 90?%, and an accuracy above 94?% for specific unknown worms using just 20 features, while maintaining a low false-positive rate when the active learning approach is applied.