Kondo effect and its destruction in hetero-bilayer transition metal dichalcogenides

Moir\'e structures, along with line-graph-based $d$-electron systems, represent a setting to realize flat bands. One form of the associated strong correlation physics is the Kondo effect. Here, we address the Kondo-driven heavy fermion state and its destruction in AB-stacked hetero-bilayer transition metal dichalcogenide with tunable filling factor and perpendicular displacement field. In an extended range of the tunable displacement field, the relative filling of the more correlated orbital is enforced to be $\nu_d \approx 1$ by the interaction, which agrees with the experimental observation. We also argue that the qualitative behavior of the crossover associated with the Kondo picture in an extended correlation regime provides the understanding of the energy scales that have been observed in this system. Our results set the stage to address the amplified quantum fluctuations that the Kondo effect may produce in these structures and new regimes that the systems open up for Kondo-destruction quantum criticality.Comment: 22 pages, 11 figure

Improved mechanical and electrical properties in electrospun polyimide/multiwalled carbon nanotubes nanofibrous composites

Highly aligned polyimide (PI) and PI/multi-walled carbon nanotubes (PI/MWCNTs) nanofibrous composites by incorporating poly(ethylene oxide) as the dispersing medium were fabricated using electrospinning technique. The morphology, mechanical, and electrical properties of the electrospun nanofibrous composites were investigated. Scanning electron microscope showed that the functionalized MWCNTs (f-MWCNTs) were well dispersed and oriented along the nanofiber axis. Analysis of electrical properties indicated a remarkable improvement on the alternating current conductivity by introduction of the aligned f-MWCNTs. Besides, with addition of 3 vol.% f-MWCNTs, the obvious enhancement of tensile modulus and strength was achieved. Thus, the electrospun PI/MWCNTs nanofibrous composites have great potential applications in multifunctional engineering materials

Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio

Comparing standard distribution and its Tsallis form of transverse momenta in high energy collisions

In this paper, the experimental (simulated) transverse momentum spectra of negatively charged pions produced at mid-rapidity in central nucleus-nucleus collisions at the Heavy Ion Synchrotron (SIS), Relativistic Heavy Ion Collider (RHIC), and Large Hadron Collider (LHC) energies obtained by different collaborations are selected by us to investigate, where a few simulated data are taken from the results of FOPI Collaboration who uses the IQMD transport code based on Quantum Molecular Dynamics. A two-component standard distribution and the Tsallis form of standard distribution are used to fit these data in the framework of a multisource thermal model. The excitation functions of main parameters in the two distributions are analyzed. In particular, the effective temperatures extracted from the two-component standard distribution and the Tsallis form of standard distribution are obtained, and the relation between the two types of effective temperatures is studied.Comment: 22 pages, 8 figures. Advances in High Energy Physics, accepte

Joint Multi-view Unsupervised Feature Selection and Graph Learning

Despite the recent progress, the existing multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure or similarity structure to guide the feature selection, neglecting the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by either global structure learning or local structure learning, lacking the capability of graph learning with both global and local structural awareness. In light of this, this paper presents a joint multi-view unsupervised feature selection and graph learning (JMVFG) approach. Particularly, we formulate the multi-view feature selection with orthogonal decomposition, where each target matrix is decomposed into a view-specific basis matrix and a view-consistent cluster indicator. Cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency, upon which an alternating optimization algorithm is developed with theoretically proved convergence. Extensive experiments demonstrate the superiority of our approach for both multi-view feature selection and graph learning tasks

Impact Analysis to Microstructure Primary Short Circuit Melted Mark under Different Heat Dissipation Condition

AbstractIn the identification of fire evidence, short circuit can be identified based on the metallurgical characteristics of the melted bead from the wire short-circuit. But because of the complexity in the real fire surroundings, short circuit melted bead is formed in many different ways. On the research, we analyze the microstructure characteristics of the short circuit melted bead in the condition of poor heat dissipation. By doing short circuit experiment in different cooling conditions, we can get the microstructure image of melted bead and compare them. Then analyze the difference and similarities and summary the variation law

Band Narrowing and Mott Localization in Iron Oxychalcogenides La2O2Fe2O(Se,S)2

Bad metal properties have motivated a description of the parent iron pnictides as correlated metals on the verge of Mott localization. What has been unclear is whether interactions can push these and related compounds to the Mott insulating side of the phase diagram. Here we consider the iron oxychalcogenides La2O2Fe2O(Se,S)2, which contain an Fe square lattice with an expanded unit cell. We show theoretically that they contain enhanced correlation effects through band narrowing compared to LaOFeAs, and we provide experimental evidence that they are Mott insulators with moderate charge gaps. We also discuss the magnetic properties in terms of a Heisenberg model with frustrating J1-J2-J2' exchange interactions on a "doubled" checkerboard lattice.Comment: 4 pages, 5 eps figures. Version to appear in Phys. Rev. Let

Improved PSO algorithm based on chaos theory and its application to design flood hydrograph

AbstractThe deficiencies of basic particle swarm optimization (bPSO) are its ubiquitous prematurity and its inability to seek the global optimal solution when optimizing complex high-dimensional functions. To overcome such deficiencies, the chaos-PSO (COSPSO) algorithm was established by introducing the chaos optimization mechanism and a global particle stagnation-disturbance strategy into bPSO. In the improved algorithm, chaotic movement was adopted for the particles' initial movement trajectories to replace the former stochastic movement, and the chaos factor was used to guide the particles' path. When the global particles were stagnant, the disturbance strategy was used to keep the particles in motion. Five benchmark optimizations were introduced to test COSPSO, and they proved that COSPSO can remarkably improve efficiency in optimizing complex functions. Finally, a case study of COSPSO in calculating design flood hydrographs demonstrated the applicability of the improved algorithm