Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP * is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP * , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.

Polymer films provide a versatile platform in which complex functional relief patterns can be thermally imprinted with a resolution down to few nanometers. However, a practical limitation of this method is the tendency for the imprinted patterns to relax (“slump”), leading to loss of pattern fidelity over time. While increasing temperature above glass transition temperature (Tg) accelerates the slumping kinetics of neat films, we find that the addition of polymer-grafted nanoparticles (PGNP) can greatly enhance the thermal stability of these patterns. Specifically, increasing the concentration of poly(methyl methacrylate) (PMMA) grafted titanium dioxide (TiO2) nanoparticles in the composite films slows down film relaxation dynamics, leading to enhanced pattern stability for the temperature range that we investigated. Interestingly, slumping relaxation time is found to obey an entropy–enthalpy compensation (EEC) relationship with varying PGNP concentration, similar to recently observed relaxation of strain-induced wrinkling in glassy polymer films having variable film thickness. The compensation temperature, Tcomp was found to be in the vicintity of the bulk Tg of PMMA. Our results suggest a common origin of EEC relaxation in patterned polymer thin films and nanocomposites.

While the phase separation of binary mixtures of chemically different polymer-grafted nanoparticles (PGNPs) is observed to superficially resemble conventional polymer blends, the presence of a “soft” polymer-grafted layer on the inorganic core of these nanoparticles qualitatively alters the phase separation kinetics of these “nanoblends” from the typical pattern of behavior seen in polymer blends and other simple fluids. We investigate this system using a direct immersion annealing method (DIA) that allows for a facile tuning of the PGNPs phase boundary, phase separation kinetics, and the ultimate scale of phase separation after a sufficient “aging” time. In particular, by switching the DIA solvent composition from a selective one (which increases the interaction parameter according to Timmerman’s rule) to an overall good solvent for both PGNP components, we can achieve rapid switchability between phase-separated and homogeneous states. Despite a relatively low and non-classical power-law coarsening exponent, the overall phase separation process is completed on a time scale on the order of a few minutes. Moreover, the roughness of the PGNP blend film saturates at a scale that is proportional to the in-plane phase separation pattern scale, as observed in previous blend and block copolymer film studies. The relatively low magnitude of the coarsening exponent n is attributed to a suppression of hydrodynamic interactions between the PGNPs. The DIA method provides a significant opportunity to control the phase separation morphology of PGNP blends by solution processing, and this method is expected to be quite useful in creating advanced materials.

Chaotic maps play an important role in improving evolutionary algorithms (EAs) for avoiding the local optima and speeding up the convergence. However, different chaotic maps in different phases have different effects on EAs. This paper focuses on exploring the effects of chaotic maps and giving comprehensive guidance for improving multiobjective evolutionary algorithms (MOEAs) by series of experiments. NSGA-II algorithm, a representative of MOEAs using the nondominated sorting and elitist strategy, is taken as the framework to study the effect of chaotic maps. Ten chaotic maps are applied in MOEAs in three phases, that is, initial population, crossover, and mutation operator. Multiobjective problems (MOPs) adopted are ZDT series problems to show the generality. Since the scale of some sequences generated by chaotic maps is changed to fit for MOPs, the correctness of scaling transformation of chaotic sequences is proved by measuring the largest Lyapunov exponent. The convergence metricγand diversity metric Δ are chosen to evaluate the performance of new algorithms with chaos. The results of experiments demonstrate that chaotic maps can improve the performance of MOEAs, especially in solving problems with convex and piecewise Pareto front. In addition, cat map has the best performance in solving problems with local optima.

Serum pepsinogen test has a strong correlation with OLGA/OLGIM gastritis stage and could provide important information in assessment of atrophy/intestinal metaplasia.