Radiation damage in polymer films from grazing‐incidence X‐ray scattering measurements

Grazing‐incidence X‐ray scattering (GIXS) is widely used to analyze the crystallinity and nanoscale structure in thin polymer films. However, ionizing radiation will generate free radicals that initiate crosslinking and/or chain scission, and structural damage will impact the ordering kinetics, thermodynamics, and crystallinity in many polymers. We report a simple methodology to screen for beam damage that is based on lithographic principles: films are exposed to patterns of X‐ray radiation, and changes in polymer structure are revealed by immersing the film in a solvent that dissolves the shortest chains. The experiments are implemented with high throughput using the standard beam line instrumentation and a typical GIXS configuration. The extent of damage (at a fixed radiation dose) depends on a range of intrinsic material properties and experimental variables, including the polymer chemistry and molecular weight, exposure environment, film thickness, and angle of incidence. The solubility switch for common polymers is detected within 10–60 s at ambient temperature, and we verified that this first indication of damage corresponds with the onset of network formation in glassy polystyrene and a loss of crystallinity in polyalkylthiophenes. Therefore, grazing‐incidence X‐ray “patterning” offers an efficient approach to determine the appropriate data acquisition times for any GIXS experiment. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016 , 54, 1074–1086     

A digital implementation of 2D Hindmarsh–Rose neuron

Nonlinear Dynamics - Different architectures and techniques have developed in the neuromorphic field to mimic and investigate the activity of biological neural networks. This paper presents a set...     

Scheduling in network flow shops

We consider the general problem of static scheduling of a set of jobs in a network flow shop. In network flow shops, the scheduler not only has to sequence and schedule but also must concurrently determine the process routing of the jobs through the shop. In this paper, we establish the computational complexity of this new class of scheduling problem and propose a general purpose heuristic procedure. The performance of the heuristic is analyzed when makespan, cycle time and average flow time are the desired objectives.This research has been supported by the UCLA Academic Senate Grant #95.     

A Game-Theoretic Analysis for Coordinating Cooperative Advertising in a Supply Chain

In this paper, we investigate the coordination of cooperative advertising decisions in a supply chain with one manufacturer and one retailer. The manufacturer and the retailer invest in national and local advertising, respectively. The manufacturer also agrees to share part of the total local-advertising costs with the retailer. The model is analyzed using game theory. We also compare the results of our analysis with similar results from the literature developed for cooperative advertising.     

Anti-plane elastodynamic analysis of planes with multiple defects

The solution of a screw dislocation under time-harmonic condition is obtained in an infinite isotropic plane by means of the Fourier transform method. The stress components reveal the familiar Cauchy singularity at the location of dislocation. The solution is employed to derive integral equations for a plane weakened by cracks and cavities. Cavities are considered as closed curved cracks without singularity. Several examples are solved and the stress intensity factor of cracks and hoop stress on cavities are obtained.     

A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.