Moment of Inertia for Axisymmetric Neutron Stars in the Standard-Model Extension

We develop a consistent approach to calculate the moment of inertia (MOI) for axisymmetric neutron stars (NSs) in the Lorentz-violating Standard-Model Extension (SME) framework. To our knowledge, this is the first relativistic MOI calculation for axisymmetric NSs in a Lorentz-violating gravity theory other than deformed, rotating NSs in the General Relativity. Under Lorentz violation, there is a specific direction in the spacetime and NSs get stretched or compressed along that direction. When a NS is spinning stationarily along this direction, a conserved angular momentum and the concept of MOI are well defined. In the SME framework, we calculate the partial differential equation governing the rotation and solve it numerically with the finite element method to get the MOI for axisymmetric NSs caused by Lorentz violation. Besides, we study an approximate case where the correction to the MOI is regarded solely from the deformation of the NS and compare it with its counterpart in the Newtonian gravity. Our formalism and the numerical method can be extended to other theories of gravity for static axisymmetric NSs.Comment: 9 pages, 3 figure

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning

Upgrading the quality of recycled aggregates from construction and demolitionwaste by using a novel brick separation and surface treatment method

Mixed recycled aggregates (MRA) from construction and demolition waste (CDW) with high-purity and environmental performance are required for highway construction application in base layer and precast concrete curbs. The main problematic constituents that reduce the quality level of the recycled aggregates applications are brick components, flaky particles, and attached mortar, which make up a large proportion of CDW in some countries. This paper studies the potential of brick separation technology based on shape characteristics in order to increase the recycled concrete aggregates (RCA) purity for MRA quality improvement. MRA after purification was also processed with surface treatment experiment by rotating in a cylinder to improve the shape characteristics and to remove the attached mortar. The purity, strength property, densities, water absorption ratio, shape index, and mortar removal ratio of MRA were studied before and after the use of the brick separation and surface treatment proposed in this study. Finally, the recycled aggregates upgradation solution was adopted in a stationary recycling plant designed for a length of 113 km highway construction. The properties of CDW mixed concrete for precast curbs manufacturing were conducted. The results indicate that problematic fractions (brick components, particle shape, and surface weakness) in the MRA were significantly reduced by using brick separation and surface treatment solution. Above all, it is very important that the proposed brick separation method was verified to be practically adopted in CDW recycling plant for highway base layer construction and concrete curbs manufacturing at a low cost