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Abstract Aerial base stations (AeBS), as crucial components of air-ground integrated networks, can serve as the edge nodes to provide flexible services to ground users. Optimizing the deployment of multiple AeBSs to maximize system energy efficiency is currently a prominent and actively researched topic in the AeBS-assisted edge-cloud computing network. In this paper, we deploy AeBSs using multi-agent deep reinforcement learning (MADRL). We describe the multi-AeBS deployment challenge as a decentralized partially observable Markov decision process (Dec-POMDP), taking into consideration the constrained observation range of AeBSs. The hypergraph convolution mix deep deterministic policy gradient (HCMIX-DDPG) algorithm is designed to maximize the system energy efficiency. The proposed algorithm uses the value decomposition framework to solve the lazy agent problem, and hypergraph convolutional (HGCN) network is introduced to strengthen the cooperative relationship between agents. Simulation results show that the suggested HCMIX-DDPG algorithm outperforms alternative baseline algorithms in the multi-AeBS deployment scenario.

Abstract Aerial base stations (AeBSs), as crucial components of air-ground integrated networks, are widely employed in cloud computing, disaster relief, and various applications. How to quickly and efficiently deploy multi-AeBSs for higher capacity gain has become a key research issue. In this paper, we address the 3D deployment optimization problem of multi-AeBSs with the objective of maximizing system capacity. To overcome communication overhead and privacy challenges in multi-agent deep reinforcement learning (MADRL), we propose a federated deep deterministic policy gradient (Fed-DDPG) algorithm for the multi-AeBS deployment decision. Specifically, a high-altitude platform (HAP)-assisted multi-AeBS deployment architecture is designed, in which low-altitude AeBS act as the local nodes to train its own deployment decision model, while the HAP acts as the global node to aggregate the weights of local models. In this architecture, AeBSs do not exchange raw data, addressing data privacy concerns and reducing communication overhead. Simulation results show that the proposed algorithm outperforms fully distributed MADRL algorithms and closely approximates the performance of multi-agent deep deterministic policy gradient (MADDPG), which requires global information during training, but with less training time.

Compute-in-memory (CIM) accelerator has become a popular solution to achieve high energy efficiency for deep learning applications in edge devices. Recent works have demonstrated CIM macros using nonvolatile memories [spin transfer torque (STT)-MRAM and resistive random access memory (RRAM)] to take advantages of their nonvolatility and high density. However, effective computation dynamic range is far lower than their static random access memory (SRAM)-CIM counterparts due to low device ON/ OFF ratio. In this work, we combine a nonvolatile memory based on a voltage-controlled magnetic tunneling junction (VC-MTJ) device, called voltage-controlled MRAM or VC-MRAM, and accurate switched-capacitor-based CIM using a novel in situ magnetic-to-digital converter (MDC). The VC-MTJ device has demonstrated $10\times $ lower write energy and switching time compared to STT-MRAM device and has comparable density, read energy, and read latency. The in situ MDCs embedded inside each VC-MRAM row convert magnetically stored weight information to CMOS logic levels and enable switched-capacitor-based multiply–accumulate (MAC) operation with accuracy comparable to the state-of-the-art SRAM-CIM. This article describes the schematic and layout level design of a VC-MRAM CIM macro in 28 nm. This is the first nonvolatile CIM design to enable analog MAC computation with 256 parallel rows turned ON simultaneously without degradation in dynamic range (< 1 LSB). Detailed circuit simulations including experimentally validated VC-MTJ compact models show $1.5\times $ higher energy efficiency and $2\times $ higher density compared to the state-of-the-art SRAM-based CIM.

Gastrointestinal (GI) tumor is a serious disease with high mortality rates and morbidity rates worldwide. Chemotherapy is a key treatment for GI, however, systematic side effects and inevitable drug resistance complicate the situation. In the process of therapy, P-glycoprotein (P-gp) could remove chemotherapy drugs from cells, thus causing multi-drug resistance. Chemodynamic therapy (CDT) utilizing Fenton chemistry has been used for cancer therapy, along with various combination therapies. The reactive oxygen species produced by CDT could inhibit P-gp’s efflux pump function, which reduce chemoagents excretion and reverse drug resistance. In the present study, we developed novel nanocrystals (Cu2O@Pt NCs) to overcome drug resistance by reducing mitochondria-derived ATP through chemo/CDT in GI cancer. Furthermore, in vivo results in tumor-bearing mice demonstrated that treatment with Cu2O@Pt NCs with CDT and chemotherapy could achieve the most effective antitumor therapeutic effect with the least amounts of adverse effects. As a result, Cu2O@Pt NCs could provide a promising strategy for chemo/CDT-synergistic therapy.

A quantum anomalous Hall (QAH) insulator breaks reciprocity by combining magnetic polarization and spin-orbit coupling to generate a unidirectional transmission of signals in the absence of an external magnetic field. Such behavior makes QAH materials a good platform for the innovation of circulator technologies. However, it remains elusive as to how the wavelength of the chiral edge plasmon relates to its frequency and how the plasmon wave packet is excited in the time domain in a QAH insulator. Here, we investigate the edge magnetoplasmon (EMP) resonances in Cr-(Bi,Sb)_{2}Te_{3} by frequency and time domain measurements. From disk shaped samples with various dimensions, we obtain the dispersion relation of EMPs and extract the drift velocity of the chiral edge state. From the time-resolved transport measurements, we identify the velocity of the plasmon wave packet and observe a transition from the edge to bulk transport at an elevated temperature. We show that the frequency and time domain measurements are well modeled by loss from the microwave induced dissipative channels in the bulk area. Our results demonstrate that the EMP decay rate can be significantly reduced by applying a low microwave power and fabricating devices of larger diameter ≥100µm. In a R=125µm sample, a nonreciprocity of 20 dB has been realized at 1.3 GHz, shining light on using QAH insulators to develop on-chip nonreciprocal devices.

Electrical manipulation of antiferromagnetic order is crucial for future memory devices, but existing switching schemes require a large current density. Here, the authors achieve record low current density switching in FeRh by taking advantage of its antiferromagnetic to ferromagnetic phase transition.

Insects that can perform flapping-wing flight, climb on a wall, and switch smoothly between the 2 locomotion regimes provide us with excellent biomimetic models. However, very few biomimetic robots can perform complex locomotion tasks that combine the 2 abilities of climbing and flying. Here, we describe an aerial–wall amphibious robot that is self-contained for flying and climbing, and that can seamlessly move between the air and wall. It adopts a flapping/rotor hybrid power layout, which realizes not only efficient and controllable flight in the air but also attachment to, and climbing on, the vertical wall through a synergistic combination of the aerodynamic negative pressure adsorption of the rotor power and a climbing mechanism with bionic adhesion performance. On the basis of the attachment mechanism of insect foot pads, the prepared biomimetic adhesive materials of the robot can be applied to various types of wall surfaces to achieve stable climbing. The longitudinal axis layout design of the rotor dynamics and control strategy realize a unique cross-domain movement during the flying–climbing transition, which has important implications in understanding the takeoff and landing of insects. Moreover, it enables the robot to cross the air–wall boundary in 0.4 s (landing), and cross the wall–air boundary in 0.7 s (taking off). The aerial–wall amphibious robot expands the working space of traditional flying and climbing robots, which can pave the way for future robots that can perform autonomous visual monitoring, human search and rescue, and tracking tasks in complex air–wall environments.

It remains challenging to integrate topological insulators (TI) with magnetic tunnel junctions (MTJ) for spintronics applications. Here, the authors achieve a large tunneling magnetoresistance ratio and a low switching current density in a TI-MTJ device at room temperature, very promising for TI-driven magnetic memory.

As a novel kind of focus tunable lens, dielectric elastomer actuated liquid lens has a compact structure, fast response, low cost, and outstanding tunability, which makes it attract extensive attention. Here, we present a computational model of a dielectric elastomer actuated lens with dual chambers that consist of three elastic membranes and two disconnected chambers filled with conductive and transparent liquid. The intermediate layer actuated by applied voltage deforms, changing the curvature of passive membranes and obtaining a new focal length. The simulation results calculated by the shooting method with two guessed values agree well with the experimental results. Based on this model, the effects of a set of parameters, including chamber radii, shear modulus, permittivity, prestretch ratios, and injected liquid volumes, on the tuning performance of the lens are analyzed. It is found that, by regulating the liquid volume in each chamber, both the initial focal length and the tuning range can be adjusted easily. Under the condition with specific liquid volumes, the lens possesses both positive and negative focal length during voltage actuation, indicating promoted tuning performance, which is acclaimed for optimal design.

Topological insulator (TI) based heterostructure is a prospective candidate for ultrahigh spin-to-charge conversion efficiency due to its unique surface states. We investigate the spin-to-charge conversion in (Bi,Sb)2Te3 (BST)/CoFeB, BST/Ru/CoFeB, and BST/Ti/CoFeB by spin pumping measurement. We find that the inverse Edelstein effect length (λIEE) increases by 60% with a Ru insertion while remains constant with a Ti insertion. This can be potentially explained by the protection of BST surface states due to the high electronegativity of Ru. Such enhancement is independent of the insertion layer thickness once the thickness of Ru is larger than 0.5 nm, and this result suggests that λIEE is very sensitive to the TI interface. In addition, an effectively perpendicular magnetic anisotropy field and additional magnetic damping are observed in the BST/CoFeB sample, which comes from the interfacial spin–orbit coupling between the BST and the CoFeB. Our work provides a method to enhance λIEE and is useful for the understanding of charge-to-spin conversion in TI-based systems.

The incoming water area of the Central Line Project of the South-to-North Water Diversion comprises the upper reaches of the Han River in China. Bacterioplankton are important members of river ecosystems. However, the mechanisms (deterministic or stochastic processes) regulating planktonic bacterial community structure in the upper Han River are not well understood. Herein, we documented the spatiotemporal patterns, assembly processes, and co-occurrence relationships of the planktonic bacterial communities in the Han River and its tributaries. Seasonal variations in bacterioplankton communities were clearly observed and were more obvious than spatial differences. The planktonic bacterial community showed a significant distance (dendritic distance) decay pattern during the two sampling periods, and the distance decay pattern was stronger in spring than in the other season. Network analysis showed that bacterial community networks displayed non-random co-occurrence patterns, and significant differences in the topological properties of empirical and random networks (average clustering coefficient, average path length, and average degree) were found. Compared to those of the tributaries, the network of the mainstem had the lowest modularity; this may be caused by the significant difference in biodiversity between the mainstem and the tributaries. Redundancy analysis revealed that pH exhibited significant effects on the planktonic bacterial community in both seasons. However, variation partitioning analysis showed that spatial factors (stochastic processes) contributed more to explaining community assembly than environmental factors during the two seasons. In addition, the neutral community model can also explain a large part of the community variation observed during the autumn and spring (R2 = 0.817 and 0.808, respectively). This study shows that seasonal variation in the bacterial community is greatly influenced by seasonal environmental variation. However, stochastic processes dominantly affect the structure of the bacterioplankton community within seasons. In order to effectively manage and protect microbial diversity, managers consider not only local species diversity, but also the effect of regional species dispersal.

Dielectric loss from different interfacial layers in the superconducting circuit and from external environment may cause superconducting qubit decoherence. Compared to modeling the entire device at once with a numerical solver, quantitatively formulating the dielectric loss can both describe all loss mechanisms and make the optimization more transparent. In this paper, we first analyze the expression formula of dielectric loss, and obtain a design scheme that can reduce the dielectric loss of qubits. That is, we replace the straight junction wires with the tapered junction wires. Based on this scheme, we perform a simulation to optimize the design of junction wires. Finally, a real experiment is conducted to verify our design. The results show that both the T1 time and T2 time of qubits are significantly improved.

Over the past two decades, superconducting quantum circuits have become one of the essential platforms for realizing quantum computers. The Hamiltonian of a superconducting quantum circuit system is the key to describing the dynamic evolution of the system. For this reason, various methods for analyzing the Hamiltonian of a superconducting quantum circuit system have been proposed, among which the LOM (Lumped Oscillator Model) and the EPR (Energy Participation Ratio) methods are the most popular ones. To analyze and improve the design methods of superconducting quantum chips, this paper compares the similarities and differences of the LOM and the EPR quantification methods. We verify the applicability of these two theoretical approaches to the design of 2D transmon quantum chips. By comparing the theoretically simulated results and the experimentally measured data at extremely low temperature, the errors between the theoretical calculation and observed measurement values of the two methods were summarized. Results show that the LOM method has more parameter outputs in data diversity and the qubit frequency calculation in LOM is more accurate. The reason is that in LOM more coupling between different systems are taken into consideration. These analyses would have reference significance for the design of superconducting quantum chips.

The Weyl antiferromagnet Mn3Sn has recently attracted significant attention as it is not only a novel magnetic quantum material of fundamental interest, but it also opens opportunities to investigate a number of exotic spin-dependent transports for practical antiferromagnetic devices. Here, we report the large spin to charge conversion observed in YIG/Mn3Sn. Evidenced by both spin Seebeck and spin pumping measurements, the spin to charge conversion efficiency of Mn3Sn is found ∼2.5 times of that for the conventional heavy metal Ta. Our results suggest a promising potential for employing a topological non-trivial antiferromagnet to achieve more efficient spin to charge conversion than conventional metallic materials.

The Weihe River is the largest tributary of the typically sediment-laden Yellow River in China. Assuming that high sediment concentrations may increase the turbidity of water bodies, and therefore affect riverine ecosystem, in which the bacterioplankton are crucial components and serve as important indicators of environmental and ecological status through their structure and response to the environment change, it is essential to deep understanding the bacterioplankton structure and influencing factors in turbid and clear river ecosystems. Here, high-throughput sequencing was used to analyze the differences in bacterioplankton structure and major determinants in the typically turbid Weihe River and its clear tributaries from the northern foot of the Qinling Mountains from October to November 2017 and April to May 2018. The results demonstrated that bacterioplankton communities shared similar dominant subcommunities in the Weihe River mainstem and its tributaries, mainly consisting of Proteobacteria and Bacteroidetes phyla and Flavobacterium and Acinetobacter genera. The Faith phylogenetic diversity (PD) index was significantly higher in the turbid mainstem than in the tributaries and was related to turbidity (Turb). Variation partitioning analysis (VPA) showed that geographical distance exhibited notable influences on the community composition in the mainstem; however, the communities in the tributaries had a much stronger response to local environmental factors. Bacteria exhibited a significant distance-decay relationship in the mainstem, whereas geographical distance was not correlated with community dissimilarities in the tributaries. Chlorophyll a emerged as the major determinant of the bacterioplankton structure in the turbid Weihe River, indicating the importance of phytoplankton-bacterioplankton relationships in the turbid river system. These results provide important insights into future research on microbial ecology, environmental monitoring and classified management in diverse river systems.

In this study, we present the complete mitogenome and a phylogenetic analysis of Callionymus olidus, determined by long PCR and primer walking methods. The complete mitochondrial genome is a circular molecule of 16,491 bp in length and contains the same set of 37 mitochondrial genes (13 protein-coding genes, 2 ribosomal RNA (rRNA), 22 transfer RNA (tRNA)), and a control region as other bony fishes. The base composition of the entire mitogenome showed a slight excess of AT bias. The entire mitogenome data produced in this study provides the genomic resources available for future evolutionary studies.