Observation of fractional topological numbers at photonic edges and corners

Topological phases of matter are featured with exotic edge states. However, the fractional topological numbers at edges, though predicted long ago by Jackiw and Rebbi, remain elusive in topological photonic systems. Here, we report on the observation of fractional topological numbers at the topological edges and corners in one- and two-dimensional photonic crystals. The fractional topological numbers are determined via the measurements of the photonic local density-of-states. In one-dimensional photonic crystals, we witness a rapid change of the fractional topological number at the edges rising from 0 to 1/2 when the photonic band gap experiences a topological transition, confirming the well-known prediction of Jackiw and Rebbi. In two-dimensional systems, we discover that the fractional topological number in the corner region varies from 0 to 1/2 and 1/4 in different photonic band gap phases. Our study paves the way toward topological manipulation of fractional quantum numbers in photonics.Comment: All comments are welcom

SCUBA2 High Redshift Bright Quasar Survey: Far-infrared Properties and Weak-line Features

We present a submillimetre continuum survey ('SCUBA2 High rEdshift bRight quasaR surveY', hereafter SHERRY) of 54 high redshift quasars at $5.6<z<6.9$ with quasar bolometric luminosities in a range of (0.2$-$$5)\times10^{14}\,L_{\odot}$, using the Submillimetre Common-User Bolometer Array-2 (SCUBA2) on the James Clerk Maxwell Telescope. About 30% (16/54) of the sources are detected with a typical 850$\mu$m rms sensitivity of 1.2 $\rm mJy\,beam^{-1}$ ($S\rm _{\nu,850\,\mu m} = 4$-5 mJy, at $>3.5\sigma$). The new SHERRY detections indicate far-infrared (FIR) luminosities of $\rm 3.5\times10^{12}$ to $\rm 1.4\times10^{13}$ $L_{\odot}$, implying extreme star formation rates of 90 to 1060 $M_{\odot}$ yr$^{-1}$ in the quasar host galaxies. Compared with $z =$ 2$-$5 samples, the FIR luminous quasars ($L_{\rm FIR} > 10^{13}\,L_{\odot}$) are more rare at $z \sim 6$. The optical/near-infrared (NIR) spectra of these objects show 11% (6/54) of the sources have weak Ly$\alpha$, emission line features, which may relate to different sub-phases of the central active galactic nuclei (AGNs). Our SCUBA2 survey confirms the trend reported in the literature that quasars with submillimeter detections tend to have weaker ultraviolet (UV) emission lines compared to quasars with nondetections. The connection between weak UV quasar line emission and bright dust continuum emission powered by massive star formation may suggest an early phase of AGN-galaxy evolution, in which the broad line region is starting to develop slowly or is shielded from the central ionization source, and has unusual properties such as weak line features or bright FIR emission.Comment: 28 pages, 10 figures, published in Ap

Short-term dietary choline supplementation alters the gut microbiota and liver metabolism of finishing pigs

Choline is an essential nutrient for pig development and plays a role in the animal's growth performance, carcass characteristics, and reproduction aspects in weaned pigs and sows. However, the effect of choline on finishing pigs and its potential regulatory mechanism remains unclear. Here, we feed finishing pigs with 1% of the hydrochloride salt of choline, such as choline chloride (CHC), under a basic diet condition for a short period of time (14 days). A 14-day supplementation of CHC significantly increased final weight and carcass weight while having no effect on carcass length, average backfat, or eye muscle area compared with control pigs. Mechanically, CHC resulted in a significant alteration of gut microbiota composition in finishing pigs and a remarkably increased relative abundance of bacteria contributing to growth performance and health, including Prevotella, Ruminococcaceae, and Eubacterium. In addition, untargeted metabolomics analysis identified 84 differently abundant metabolites in the liver between CHC pigs and control pigs, of which most metabolites were mainly enriched in signaling pathways related to the improvement of growth, development, and health. Notably, there was no significant difference in the ability of oxidative stress resistance between the two groups, although increased bacteria and metabolites keeping balance in reactive oxygen species showed in finishing pigs after CHC supplementation. Taken together, our results suggest that a short-term supplementation of CHC contributes to increased body weight gain and carcass weight of finishing pigs, which may be involved in the regulation of gut microbiota and alterations of liver metabolism, providing new insights into the potential of choline-mediated gut microbiota/metabolites in improving growth performance, carcass characteristics, and health

Attention-Shared Multi-Agent Actor&ndash;Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor&ndash;critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms

Video clips for human tactile explorations

Two video clips demonstrate the finger motions of a volunteer during “blind” tactile explorations of a chess piece and a sample surface.</p

Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme

Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids

An Improved Bald Eagle Search Algorithm for Global Path Planning of Unmanned Vessel in Complicated Waterways

The path planning of unmanned ships in complex waters using heuristics usually suffers from problems such as being prone to fall into the local optimum, slow convergence, and instability in global path planning. Given this, this paper proposes a Self-Adaptive Hybrid Bald Eagle Search (SAHBES) Algorithm by incorporating adaptive factors into the traditional BES in order to enhance the early global searching ability of the BES algorithm. Moreover, Pigeon-Inspired Optimization (PIO) is introduced to overcome the disadvantage of traditional BES algorithms: that it is easy for them to fall into local optimization. This study improves the fitness function by adding a distance between the ships’ path corners. The obstacle is based on the calculation of the path length. The curve optimization module is applied to smooth the obtained path to generate more rational path planning results, which means the path is the shortest and avoids collision successfully. A simulation test of the SAHBES algorithm on the path planning under different obstacle scenarios is conducted by using the MATLAB platform. The results show that SAHBES can generate the shortest safe, smooth path in different complex water environments, considering the limitations of fundamental ship maneuvering operations compared to other algorithms, thus verifying the feasibility and efficiency of the proposed SAHBES algorithm