The MeerKAT Galaxy Cluster Legacy Survey: I. Survey overview and highlights

Please abstract in the article.The South African Radio Astronomy Observatory (SARAO), the National Research Foundation (NRF), the National Radio Astronomy Observatory, US National Science Foundation, the South African Research Chairs Initiative of the DSI/NRF, the SARAO HCD programme, the South African Research Chairs Initiative of the Department of Science and Innovation.http://www.aanda.orghj2022Physic

Beijing Training Project for the Leading Talents in S T[Z151100000315020]

The challenge for autonomic network management is the provision of future network management systems that have the characteristics of self-management, self-configuration, self-protection and self-healing, in accordance with the high level objectives of the enterprise or human end-user. This paper proposes an abstract model for network configuration that is intended to help understand fundamental underlying issues in self-configuration. We describe the cascade problem in self-configuring networks: when individual network components that are securely configured are connected together (in an apparently secure manner), a configuration cascade can occur resulting in a mis-configured network. This has implications for the design of self-configuring systems and we discuss how a soft constraint-based framework can provide a solution

The effect of temperature on wear and friction of a high strength steel in fretting

This paper investigates the effect of temperature (between 24 °C and 450 °C) on the wear rate and friction coefficient of a high strength alloy steel (Super-CMV) in gross sliding fretting in air. It was found that whilst there was significant loss of material from the contact during fretting at room temperature, the overall loss of material from the contact had become negative even with a modest increase in temperature to 85 °C. At temperatures greater than 85 °C, negative wear was maintained, with the coefficient of friction dropping monotonically with increasing temperature up to 450 °C. It is proposed that the changes in wear rate and friction coefficient were due to changes in the way that the oxide particles sintered to form a protective debris bed, with sintering of the oxide debris particles at these low temperatures being promoted by the nano-scale at which the oxide debris is formed

Computationally Efficient Data and Application Driven Color Transforms for the Compression and Enhancement of Images and Video

An important step in color image or video coding and enhancement is the linear transformation of input (typically red-green-blue (RGB)) data into a color space more suitable for compression, subsequent analysis, or visualization. The choice of this transform becomes even more critical when operating in distributed and low-computational power environments, such as visual sensor networks or remote sensing. Data-driven transforms are rarely used due to increased complexity. Most schemes adopt fixed transforms to decorrelate the color channels which are then processed independently. Here we propose two frameworks to find appropriate data-driven transforms in different settings. The first, named approximate Karhunen–Loève Transform (aKLT), performs comparable to the KLT at a fraction of the computational complexity, thus favoring adoption on sensors and resource-constrained devices. Furthermore, we consider an application-aware setting in which an expert system (e.g., a classifier) analyzes imaging data at the receiver’s end. In a compression context, distortion may jeopardize the accuracy of the analysis. Since the KLT is not optimal in this setting, we investigate formulations that maximize post-compression expert system performance. Relaxing decorrelation and energy compactness constraints, a second transform can be obtained offline with supervised learning methods. Finally, we propose transforms that accommodate both constraints, and are found using regularized optimization