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Solid-state sodium metal batteries have attracted great interest because of their improved safety and abundant Na resources. However, the interfacial resistances and instabilities induced by parasitic reactions, together with Na dendrite issues, result in reduced rate capability and poor cycling stability. Here, we address these challenges by intrinsically inhibiting parasitic interfacial redox reactions through enhanced P-O covalency in Na 3 Zr 2 Si 2 PO 12 (NZSP) with Na 2 SiF 6 incorporation, wherein the high electronegativity of F strengthens P-O covalency. Additionally, SnF 2 coating provides a sodiophilic surface and stabilizes the NZSP interface, which is essential for effective electrochemical cycling. This integrated approach significantly reduces interfacial impedance to 2.0 Ω cm 2 , enabling stable Na plating/stripping for 3600 hours at 0.5 mA cm -2 /0.25 mAh cm -2 . The full cell with Na 3 V 2 (PO 4 ) 3 positive electrode demonstrates stable cycling with high-rate capability (87.5% capacity retention after 2500 cycles at 1 C and 96.1% capacity retention after 1200 cycles at 5 C). This study sheds light on the development of high-performance quasi-solid-state sodium batteries. Reactivity between Na 3 Zr 2 Si 2 PO 12 solid electrolyte and sodium metal limits battery performance. Here, the authors strengthen the P-O covalency by Na 2 SiF 6 doping and introduce a SnF 2 coating to reduce parasitic reactions and create a sodiophilic interface, enabling 1200 cycles at 5 C of a solid-state battery with Na 3 V 2 (PO 4 ) 3 .

Based on a 'shortcut-to-adiabaticity' (STA) scheme, we theoretically design and experimentally realize a set of high-fidelity single-qubit quantum gates in a superconducting Xmon qubit system. Through a precise microwave control, the qubit is driven to follow a fast 'adiabatic' trajectory with the assistance of a counter-diabatic field and the correction of derivative removal by adiabatic gates. The experimental measurements of quantum process tomography and interleaved randomized benchmarking show that the process fidelities of our STA quantum gates are higher than 94.9% and the gate fidelities are higher than 99.8%, very close to the state-of-art gate fidelity of 99.9%. An alternate of high-fidelity quantum gates is successfully achieved under the STA protocol.

In a 'shortcut to adiabaticity' (STA) protocol, the counter-diabatic Hamiltonian, which suppresses the non-adiabatic transition of a reference 'adiabatic' trajectory, induces a quantum uncertainty of the work cost in the framework of quantum thermodynamics. Following a theory derived recently (Funo et al 2017 Phys. Rev. Lett. 118 100602), we perform an experimental measurement of the STA work statistics in a high-quality superconducting Xmon qubit. Through the frozen-Hamiltonian and frozen-population techniques, we experimentally realize the two-point measurement of the work distribution for given initial eigenstates. Our experimental statistics verify (i) the conservation of the average STA work and (ii) the equality between the STA excess of work fluctuations and the quantum geometric tensor.

Nonadiabatic holonomic quantum computation has received increasing attention due to its robustness against control errors and high-speed realization. The original protocol of nonadiabatic holonomic one-qubit gates has been experimentally demonstrated with a superconducting transmon qutrit. However, it requires two noncommuting gates to complete an arbitrary one-qubit gate, doubling the exposure time of the gate to error sources and thus leaving the gate vulnerable to environment-induced decoherence. Single-shot protocol has been subsequently proposed to realize an arbitrary one-qubit nonadiabatic holonomic gate. In this paper, a single-shot protocol of nonadiabatic holonomic gates is experimentally demonstrated by using a superconducting Xmon qutrit, with all the single-qubit Clifford gates carried out by a single-shot implementation. Characterized by quantum process tomography and randomized benchmarking, the single-shot gates reach a fidelity exceeding 99%.

We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach. Our main contribution, index slicing, is a method that efficiently parallelizes the contraction by breaking it down into much smaller and identically structured subtasks, which can then be executed in parallel without dependencies. We benchmark our algorithm on a class of random quantum circuits, achieving greater than 10 5 times acceleration over the original estimate of the simulation cost. We then demonstrate applications of the simulation framework for aiding the development of quantum algorithms and quantum error correction. As tensor networks are widely used in computational science, our simulation framework may find further applications.

As a critical task in underground coal mining, personnel identification and positioning in fully mechanized mining faces are essential for safety. Yet, complex environmental factors—such as narrow tunnels, heavy dust, and uneven lighting—pose significant challenges to accurate detection. In this paper, we propose a personnel detection network, MSS-YOLO, for fully mechanized mining faces based on YOLOv8. By designing a Multi-Scale Edge Enhancement (MSEE) module and fusing it with the C2f module, the performance of the network for personnel feature extraction under high-dust or long-distance conditions is effectively enhanced. Meanwhile, by designing a Spatial Pyramid Shared Conv (SPSC) module, the redundancy of the model is reduced, which effectively compensates for the problem of the max pooling being prone to losing the characteristics of the personnel at long distances. Finally, the lightweight Shared Convolutional Detection Head (SCDH) ensures real-time detection under limited computational resources. The experimental results show that compared to Faster-RCNN, SSD, YOLOv5s6, YOLOv7-tiny, YOLOv8n, and YOLOv11n, MSS-YOLO achieves AP50 improvements of 4.464%, 10.484%, 3.751%, 4.433%, 3.655%, and 2.188%, respectively, while reducing the inference time by 50.4 ms, 11.9 ms, 3.7 ms, 2.0 ms, 1.2 ms, and 2.3 ms. In addition, MSS-YOLO is combined with the SGBM binocular stereo vision matching algorithm to provide a personnel 3D spatial position solution by using disparity results. The personnel location results show that in the measurement range of 10 m, the position errors in the x-, y-, and z-directions are within 0.170 m, 0.160 m, and 0.200 m, respectively, which proves that MSS-YOLO is able to accurately detect underground personnel in real time and can meet the underground personnel detection and localization requirements. The current limitations lie in the reliance on a calibrated binocular camera and the performance degradation beyond 15 m. Future work will focus on multi-sensor fusion and adaptive distance scaling to enhance practical deployment.

The significance of topological phases has been widely recognized in the community of condensed matter physics. The well controllable quantum systems provide an artificial platform to probe and engineer various topological phases. The adiabatic trajectory of a quantum state describes the change of the bulk Bloch eigenstates with the momentum, and this adiabatic simulation method is however practically limited due to quantum dissipation. Here we apply the “shortcut to adiabaticity” (STA) protocol to realize fast adiabatic evolutions in the system of a superconducting phase qubit. The resulting fast adiabatic trajectories illustrate the change of the bulk Bloch eigenstates in the Su-Schrieffer-Heeger (SSH) model. A sharp transition is experimentally determined for the topological invariant of a winding number. Our experiment helps identify the topological Chern number of a two-dimensional toy model, suggesting the applicability of the fast adiabatic simulation method for topological systems.

WRKY proteins are a class of transcription factors involved in plant growth and development, biotic and abiotic stress, and other biological processes. In order to systematically study the structure and function of WRKY proteins in cabbage, we used the whole genome sequence of cabbage in the Brassica database (BRAD) and bioinformatics methods. A total of 150 WRKY genes were identified in cabbage, named BolWRKY1-BolWRKY150. These genes were unevenly distributed on 9 chromosomes. In this study, the WRKY proteins were classified into three classes: I, II and III. Cis-acting elements analysis showed that the BolWRKY gene was involved in multiple hormone and stress responses. The protein interaction network analysis suggested that BolWRKY19/100/105 genes might play similar functions in the same stress response. Based on transcriptome data analysis, we found that different BolWRKY genes showed tissue-specific expression. Quantitative real-time PCR analysis showed that several BolWRKY genes were strongly induced by drought stress and ABA (abscisic acid) treatment, indicating members of BolWRKY gene family were closely related to the stress responses such as drought. Our study lays a foundation for further understanding the regulatory mechanism and function of the WRKY gene in response to abiotic stress.

Clear underwater images are necessary in many underwater applications, while absorption, scattering, and different water conditions will lead to blurring and different color deviations. In order to overcome the limitations of the available color correction and deblurring algorithms, this paper proposed a fusion-based image enhancement method for various water areas. We proposed two novel image processing methods, namely, an adaptive channel deblurring method and a color correction method, by limiting the histogram mapping interval. Subsequently, using these two methods, we took two images from a single underwater image as inputs of the fusion framework. Finally, we obtained a satisfactory underwater image. To validate the effectiveness of the experiment, we tested our method using public datasets. The results showed that the proposed method can adaptively correct color casts and significantly enhance the details and quality of attenuated underwater images.

Carrying out full lifecycle management of SF6 gas recovery, purification, and recycling, developing new digital control technologies, and eliminating SF6 gas emissions from the source are of great significance for achieving the “dual carbon” goal. Given the low level of digital control of SF6 gas at present, especially in the situation, the SF6 gas recovery rate, the effective volume of the gas chamber, and gas weight cannot be accurately measured at an on-site inspection surface, this paper carries out research on SF6 gas recovery rate measurement at on-site inspection surface and key technologies of gas measurement. An intelligent online monitoring device such as SF6 gas recovery rate and effective chamber volume of high voltage electrical equipment was developed, which can quickly identify SF6 gas density, recovery rate, effective chamber volume, weight, and other parameters in high voltage electrical equipment, and evaluate the detection methods such as gas recovery rate and effective chamber volume. The results show that when the recovery rate was about 96.5%, the maximum positive deviation between the recovery rate measured by the detection device and that measured by the weighing method was 0.32% and the maximum negative deviation was -0.24%, both of which are less than 0.50%. When the container to be tested was 1000 L, the maximum indication error was -1.51%, which can meet the requirements of digital control in the SF6 gas recycling and reuse process of high-voltage electrical equipment.