Discrete unified gas kinetic scheme for flows of binary gas mixture based on the McCormack model

The discrete unified gas kinetic scheme (DUGKS) was originally developed for single-species flows covering all the regimes, whereas the gas mixtures are more frequently encountered in engineering applications. Recently, the DUGKS has been extended to binary gas mixtures of Maxwell molecules on the basis of the Andries–Aoki–Perthame kinetic (AAP) model [P. Andries et al., “A consistent BGK-type model for gas mixtures,” J. Stat. Phys. 106, 993–1018 (2002)]. However, the AAP model cannot recover a correct Prandtl number. In this work, we extend the DUGKS to gas mixture flows based on the McCormack model [F. J. McCormack, “Construction of linearized kinetic models for gaseous mixtures and molecular gases,” Phys. Fluids 16, 2095–2105 (1973)], which can give all the transport coefficients correctly. The proposed method is validated by several standard tests, including the plane Couette flow, the Fourier flow, and the lid-driven cavity flow under different mass ratios and molar concentrations. Good agreement between results of the DUGKS and the other well-established numerical methods shows that the proposed DUGKS is effective and reliable for binary gas mixtures in all flow regimes. In addition, the DUGKS is about two orders of magnitude faster than the direct simulation Monte Carlo for low-speed flows in terms of the wall time and convergent iteration steps

Energy-Efficient URLLC Service Provision via a Near-Space Information Network

The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. The formulated problem is challenging in terms of accurate channel estimation, RIS phase alignment, theoretical analysis, and effective solution. We propose a joint resource allocation algorithm to handle these challenges. In this algorithm, we develop an accurate channel estimation approach by exploring message passing and optimize phase shifts of RIS reflecting elements to further increase the channel gain. Besides, we derive an analysis-friend expression of decoding error probability and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Extensive simulations have been conducted to verify the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve outstanding channel estimation performance and is more energy-efficient than diverse benchmark algorithms

Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures

The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness

THE CONSTRUCTION OF SMALL TOWN INFORMATION PORTAL USING OPEN SOURCE SOFTWARE

Abstract: Along with the development of small towns, traditional or common methods of urban informatization construction are not fit for small towns. Therefore it's essential to bring forward an appropriate way. By studying on the latest open source portal software uPortal, the paper discussed the application of personalized service, portal technology and information integration technology in informatization construction of small towns. Finally, the design and realization of the information portal and a portal website of small towns, which achieve the management and sharing of information in small towns, were presented

Magnetic phase transitions in the triangular-lattice spin-1 dimer compound K2Ni2(SeO3)3

In our study, we conduct magnetization and heat capacity measurements to investigate field-induced magnetic phase transitions within the newly synthesized compound K2Ni2(SeO3)3, a spin-1 dimer system arranged on a triangular lattice. The Ni-Ni dimers exhibit a ferromagnetic intra-dimer interaction, effectively behaving as an ensemble with a total spin of S=2. In contrast, antiferromagnetic interactions manifest between these dimers on the triangular lattice. The trigonal distortion of the NiO6 octahedra introduces easy-axis magnetic anisotropy, accounting for the distinct magnetic phase diagrams observed when applying c-axis directional and in-plnae magnetic fields. Notably, our investigation unveils a two-step phase transition with the magnetic field aligned with the c direction. We propose that the system at the first transition is from a paramagnetic state to an up-up-down state, characterized by the Z3 lattice-symmetry breaking. Subsequently, a Berezinskii-Kosterlitz-Thouless transition, involving the breaking of the c-axis spin-rotation symmetry, leads to the formation of the "Y state" at low temperatures. These findings yield valuable insights into the magnetic phase transitions inherent to geometrically frustrated magnetic systems featuring dimer structures.Comment: 10 pages, 11 figure

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202