Determination of the elastic component for polymeric melt under steady shear flow

Steady state shear flow in rectilinear coordinates can be generated by a direct shear rheometer. The rheological behavior of polymeric melts under shear flow can be characterized by the inception of steady shear flow and stress relaxation after cessation of steady shear flow. Experiments were performed for bisphenol A polycarbonate with various mol wts. The elastic components, such as the primary normal stress differences and the elastic recoverable shear strain of the polymeric melts, can be determined from the shear moduli. © 1993 John Wiley & Sons, Inc.     

Sliding-mode and proportional-derivative-type motion control with radial basis function neural network based estimators for wheeled vehicles

An obstacle avoidance problem of rear-steered wheeled vehicles in consideration of the presence of uncertainties is addressed. Modelling errors and additional uncertainties are taken into consideration. Controller designs for driving and steering motors are designed. A proportional-derivative-type driving motor controller and a sliding-mode steering controller combined with radial basis function neural network (RBFNN) based estimators are proposed. The convergence properties of the RBFNN-based estimators are proven by the Stone–Weierstrass theorem. The stability of the proposed control law is proven using Lyapunov stability analysis. The obstacle avoidance strategy utilising the sliding surface adjustment to an existing navigation method is presented. It is concluded that the driving velocity and steering-angle performances of the proposed control system are satisfactory.     

Synthesis and Characterization of Segmented Block Copolymers Based on Hydroxyl‐Terminated Liquid Natural Rubber and α,ω‐Diisocyanato Telechelics

Summary: Segmented block copolymers, consisting of non‐polar soft segments from hydroxyl‐terminated liquid natural rubber (HTNR) and polar hard segments from α,ω‐diisocyanato telechelics obtained by “criss‐cross”‐cycloaddition, have been synthesized. The block copolymer formation took place under relatively mild reaction conditions at 80 °C in dichloroethane in the presence of dibutyltin dilaurate as a catalyst. The resulting block copolymers were characterized by spectroscopic techniques (¹H NMR, FTIR, UV‐vis spectroscopy) as well as GPC for molar mass determination. The block copolymers were compression molded in a hot stage press, and the resulting samples were characterized by DSC and stress‐strain measurement. The solubility and phase morphology of the materials have also been studied.

Segmented block copolymer from HTNR and α,ω‐diisocyanato telechelics     

Catalytic Combustion of Model Carbon Particles (CPs) over Pt/β-SiC Catalyst

Platinum catalyst supported on a medium surface area -SiC was successfully used for the catalytic combustion of model carbon particles and compared to a catalyst supported on a low surface area -SiC. The -SiC-based catalyst showed no deactivation as a function of cycling tests while a strong deactivation was observed on the -SiC-based catalyst. This deactivation was attributed to the progressive encapsulation of the platinum particles by a layer of silica which built up during the combustion cycle. These results render possible the use of Pt/-SiC catalyst as a diesel carbon particle catalytic filter with continuous regeneration.     

Micro cam system driven by electrostatic comb-drive actuators based on SOI-MEMS technology

Microsystem Technologies - This paper reports a design and fabrication process of a micro cam system (MCS) with a flat-faced translating follower. The cam rim with cover diameter of 2.4 mm...     

Short packet communication in underlay cognitive network assisted by an intelligent reflecting surface

We propose short packet communication in an underlay cognitive radio network assisted by an intelligent reflecting surface (IRS) composed of multiple reconfigurable reflectors. This scheme, called the IRS protocol, operates in only one time slot (TS) using the IRS. The IRS adjusts its phases to give zero received cumulative phase at the secondary destination, thereby enhancing the end-to-end signal-to-noise ratio. The transmitting power of the secondary source is optimized to simultaneously satisfy the multi-interference constraints, hardware limitations, and performance improvement. Simulation and analysis results of the average block error rates (BLERs) show that the performance can be enhanced by installing more reconfigurable reflectors, increasing the blocklength, lowering the number of required primary receivers, or sending fewer information bits. Moreover, the proposed IRS protocol always outperforms underlay relaying protocols using two TSs for data transmission, and achieves the best average BLER at identical transmission distances between the secondary source and secondary destination. The theoretical analyses are confirmed by Monte Carlo simulations.     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.     

Visualization of diversity in large multivariate data sets

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.     

Recognition and analysis of cell nuclear phases for high-content screening based on morphological features

Automated analysis of molecular images has increasingly become an important research in computational life science. In this paper some new and efficient algorithms for recognizing and analyzing cell phases of high-content screening are presented. The conceptual frameworks are based on the morphological features of cell nuclei. The useful preprocessing includes: smooth following and linearization; extraction of morphological structural points; shape recognition based morphological structure; issue of touching cells for cell separation and reconstruction. Furthermore, the novel detecting and analyzing strategies of feed-forward and feed-back of cell phases are proposed based on gray feature, cell shape, geometrical features and difference information of corresponding neighbor frames. Experiment results tested the efficiency of the new method.