Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

Extraction and implementation of muscle synergies in neuro-mechanical control of upper limb movement

This work faces the redundancy problem, a central concern in robotics, in a particular force-producing task by using muscle synergies to simplify the control. We extracted muscle synergies from human electromyograph signals and interpreted the physical meaning of the identified muscle synergies. Based on the human analysis results, we hypothesized a novel control framework that can explain the mechanism of the human motor control. The framework was tested in controlling a pneumatic-driven robotic arm to perform a reaching task. This control method, which uses only two synergies as manipulated variables for driving antagonistic pneumatic artificial muscles to generate desired movements, would be useful to deal with the redundancy problem; thus, suggesting a simple but efficient control for human-like robots to work safely and compliantly with humans.     

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.