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Abstract In van der Waals heterostructures (vdWHs), the manipulation of interlayer stacking/coupling allows for the construction of customizable quantum systems exhibiting exotic physics. An illustrative example is the diverse range of states of matter achieved through varying the proximity coupling between two-dimensional (2D) quantum spin liquid (QSL) and superconductors within the TaS2 family. This study presents a demonstration of the intertwined physics of spontaneous rotational symmetry breaking, hidden magnetism, and Ising superconductivity (SC) in the three-fold rotationally symmetric, non-magnetic natural vdWHs 6R-TaS2. A distinctive phase emerges in 6R-TaS2 below a characteristic temperature (T *) of approximately 30 K, which is characterized by a remarkable set of features, including a giant extrinsic anomalous Hall effect (AHE), Kondo screening, magnetic field-tunable thermal hysteresis, and nematic magneto-resistance. At lower temperatures, a coexistence of nematicity and Kondo screening with Ising superconductivity is observed, providing compelling evidence of hidden magnetism within a superconductor. This research not only sheds light on unexpected emergent physics resulting from the coupling of itinerant electrons and localized/correlated electrons in natural vdWHs but also emphasizes the potential for tailoring exotic quantum states through the manipulation of interlayer interactions.

Abstract The interaction between lattice vibrations and electrons plays a key role in various aspects of condensed matter physics — including electron hydrodynamics, strange metal behavior, and high-temperature superconductivity. In this study, we present systematic investigations using Raman scattering and angle-resolved photoemission spectroscopy (ARPES) to examine the phononic and electronic subsystems of the topological superconductor candidate 2M-WS2. Raman scattering exhibits an anomalous nonmonotonic temperature dependence of phonon linewidths, indicative of strong phonon–electron scattering over phonon–phonon scattering. The ARPES results demonstrate pronounced dispersion anomalies (kinks) at multiple binding energies within both bulk and topological surface states, indicating a robust and mode-selective coupling between the electronic states and various phonon modes. These experimental findings align with previous calculations of the Eliashberg function, providing a deeper understanding of the highest superconducting transition temperature observed in 2M-WS2 (8.8 K) among all transition metal dichalcogenides as induced by electron–phonon coupling. Furthermore, our results may offer valuable insights into other properties of 2M-WS2 and guide the search for high-temperature topological superconductors.

Abstract Diode effects are of great interest for both fundamental physics and modern technologies. Electrical diode effects (nonreciprocal transport) have been observed in Weyl systems. Optical diode effects arising from the Weyl fermions have been theoretically considered but not probed experimentally. Here, we report the observation of a nonlinear optical diode effect (NODE) in the magnetic Weyl semimetal CeAlSi, where the magnetization introduces a pronounced directionality in the nonlinear optical second-harmonic generation (SHG). We demonstrate a six-fold change of the measured SHG intensity between opposite propagation directions over a bandwidth exceeding 250 meV. Supported by density-functional theory, we establish the linearly dispersive bands emerging from Weyl nodes as the origin of this broadband effect. We further demonstrate current-induced magnetization switching and thus electrical control of the NODE. Our results advance ongoing research to identify novel nonlinear optical/transport phenomena in magnetic topological materials and further opens new pathways for the unidirectional manipulation of light.

Abstract In-plane anisotropic van der Waals materials have emerged as a natural platform for anisotropic polaritons. Extreme anisotropic polaritons with in-situ broadband tunability are of great significance for on-chip photonics, yet their application remains challenging. In this work, we experimentally characterize through Fourier transform infrared spectroscopy measurements a van der Waals plasmonic material, 2M-WS2, capable of supporting intrinsic room-temperature in-plane anisotropic plasmons in the far and mid-infrared regimes. In contrast to the recently revealed natural hyperbolic plasmons in other anisotropic materials, 2M-WS2 supports canalized plasmons with flat isofrequency contours in the frequency range of ~ 3000-5000 cm−1. Furthermore, the anisotropic plasmons and the corresponding isofrequency contours can be reversibly tuned via in-situ ion-intercalation. The tunable anisotropic and canalization plasmons may open up further application perspectives in the field of uniaxial plasmonics, such as serving as active components in directional sensing, radiation manipulation, and polarization-dependent optical modulators.

Abstract Metallic 2M or 1T′-phase transition metal dichalcogenides (TMDs) attract increasing interests owing to their fascinating physicochemical properties, such as superconductivity, optical nonlinearity, and enhanced electrochemical activity. However, these TMDs are metastable and tend to transform to the thermodynamically stable 2H phase. In this study, through systematic investigation and theoretical simulation of phase change of 2M WS2, we demonstrate that ultrathin 2M WS2 has significantly higher intrinsic thermal stabilities than the bulk counterparts. The 2M-to-2H phase transition temperature increases from 120 °C to 210 °C in the air as thickness of WS2 is reduced from bulk to bilayer. Monolayered 1T′ WS2 can withstand temperatures up to 350 °C in the air before being oxidized, and up to 450 °C in argon atmosphere before transforming to 1H phase. The higher stability of thinner 2M WS2 is attributed to stiffened intralayer bonds, enhanced thermal conductivity and higher average barrier per layer during the layer(s)-by-layer(s) phase transition process. The observed high intrinsic phase stability can expand the practical applications of ultrathin 2M TMDs.

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object’s surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 mV, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services.

2D ferroelectric materials have attracted much interest due to their potential for developing flexible self‐powered nanogenerators. Niobium oxide diiodide (NbOI2) has in‐plane anisotropy of electrical properties and large lateral piezoelectric coefficient, which makes it possess high‐performance and unique behavior in flexible sensing. In this work, multidirectional piezoelectric nanogenerator (PENG) devices using NbOI2 flake are fabricated and excellent energy harvesting and sensing capabilities are found. Specifically, the NbOI2‐based PENG can exhibit a long‐time stable voltage output of 215 mV at a large strain of 1.1%. More importantly, the periodic output signal pattern of the twelve‐electrode NbOI2‐based PENG in six directions is investigated, and this anisotropy provides the possibility of achieving simultaneous signal harvesting in multiple directions. This work broadens the scope of applications of 2D materials in nano‐energy and provides new ideas and insights for further exploration of nano‐energy and smart wearable nano‐electronic devices.

Metal chalcogenides have shown promising performances for renewable hydrogen evolution and such activities are sensitive to the material electronic structures. Here, authors modulate the electronic properties of molybdenum sulfide in 1T'''-MoS2 for hydrogen evolution electrocatalysis.

The intrinsically superconducting Dirac semimetal 2M-WS_{2} is a promising candidate for realizing proximity-induced topological superconductivity in its protected surface states. A precise characterization of the bulk superconducting state is essential to understand the nature of surface superconductivity in the system. Here, we report a detailed experimental study of the temperature-dependent London penetration depth, λ(T), the upper critical field, H_{c2}(T), and the effects of nonmagnetic disorder on these quantities, as well as on the superconducting transition temperature T_{c} in single crystals of 2M-WS_{2}. We observe a power-law variation of λ(T)∝T^{3} at temperatures below 0.35T_{c}. Nonmagnetic pointlike disorder induced by 2.5 MeV electron irradiation at various doses results in a significant suppression of T_{c}. These observations are markedly different from expectations for a fully gapped isotropic s-wave superconductor. Together with the substantial increase of slope, dH_{c2}/dT|_{T=T_{c}}, with increasing disorder, our results suggest a strongly anisotropic s^{++} multiband superconducting state. These results have direct consequences for the expected proximity-induced superconductivity of the topological surface states.

Abstract Two‐dimensional (2D) ferroelectrics have attracted considerable attention due to their potential in the development of devices of miniaturization and multifunction. Although several van der Waals (vdW)‐layered materials show ferroelectricity, the experimental demonstrations of ferroelectric behavior in monolayers are very limited. Here we report the observation of room‐temperature out‐of‐plane switchable electric polarization in supported MoS2 monolayers exfoliated from 3R‐stacked bulk crystals under ambient conditions. Using in situ piezoelectric force microscopy and Kelvin probe force microscopy in a glovebox, we reveal that trapped water/ice molecules are responsible for this switchable electric polarization and this conclusion is strongly supported by theoretical simulations. It is worth noting that the water/ice trapping in the monolayers exfoliated from 2H‐stacked MoS2 crystals is not as much as that in 3R monolayers and, consequently, the out‐of‐plane electric polarization is missing there. Our findings indicate that monolayers with a trapped single layer of polar molecules might be emerging alternatives to 2D ferroelectrics. Furthermore, the stacking sequences may bring new properties and applications to 2D vdW materials not only when we stack them up but also when we thin them down.

Object detection is the core issue in the interpretation of optical remote sensing images, and it is widely used in fields such as intelligence reconnaissance, target monitoring, and disaster rescue.Firstly, combined with the research progress of deep learning optical remote sensing image object detection algorithms, the two types of algorithms based on candidate regions and regression analysis were reviewed.Secondly, the improvement of object detection algorithms for four types of common task-specific scenes were summarized, including rotating objects, small objects, multi-scales, and dense objects.Then, combined with commonly used remote sensing image data sets, the performance of different algorithms was compared and analyzed.Finally, the issues worthy of attention in remote sensing image object detection in the future were prospected, and ideas for follow-up related research were provided.

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.

With the rapid development of space technology, the environment of the space domain has become more and more complex and changeable, which brought great difficulties in cognition of space domain activity. As space domain awareness (SDA) required, any relevant information and knowledge from various sources are needed as much as possible, while all of those can be sorted and integrated for effective cognition of space objects, including the cognition of their status. This paper proposes a knowledge integration framework (SSC-KA) designed for the cognition of space target and its status. In the framework (SSC-KA), open-source information and data acquired from multi-kinds of sensors are sent into parallel channels, and then processed by the algorithm this paper designed into sequence data as the channels’ output. Furthermore, the rules and four statuses defined in this paper can be judged for the anomaly detection of satellites. Based on the space domain knowledge acquisition framework of SSC-KA, this paper describes a complete abnormal state detection method for satellites step by step through multi-level feature engineering. Therefore, the method is used to analyze four different statuses of satellites in this paper, to verify the validity and feasibility of the application of the method in the cognition of spatial events, thus laying the foundation for the cognition of Space Domain Awareness.

We develop a facile one-step solid-state-chemistry strategy for synthesizing BiOI crystals with layered microstructure. The BiOI photocatalyst shows typical mesoporous feature with available surface area, considerable wide-spectrum light absorption and carriers lifetime properties, and remarkable solar to thermal effect for efficient solar-driven photocatalytic CO2 reduction to CH4. The engineered BiOI displays a superior CH4 formation performance with the space-time yield of 13.1 μmol g−1 h−1 and the selectivity of 82.3% as well as excellent photostability for CO2 photoreduction under solar light irradiation. Furthermore, the versatile BiOI can boost the photo-stimulated efficiency on clean H2 generation and pollutant degradation.

Abstract Inducing new phases of transition metal dichalcogenides by controlling the d‐electron‐count has attracted much interest due to their novel structures and physicochemical properties. 1T’’ ReSe2 is a promising candidate for sodium storage, but the low electronic conductivity and limited active sites hinder its electrochemical capacity. Herein, new‐phase 1T’ Re0.75V0.25Se2 crystals (P2/m) with zig‐zag chains are successfully synthesized. The 1T’’‐1T’ phase transition results from the electronic reorganization of 5d orbitals via electron extraction after V‐atom doping. The electrical conductivity of 1T’ Re0.75V0.25Se2 is 2.7 × 105 times higher than that of 1T’’ ReSe2. Moreover, density functional theory (DFT) calculations reveal that 1T’ Re0.75V0.25Se2 has a larger interlayer spacing, lower bonding energy, and migration energy barrier for Na+ ions than 1T’’ ReSe2. As a result, 1T’ Re0.75V0.25Se2 electrode shows an excellent rate capability of 203 mAh g−1 at 50 C with no capacity fading over 5000 cycles for sodium storage, which is superior to most reported sodium‐ion anode materials. This 1T’ Re0.75V0.25Se2 provides a new platform for various applications such as electronics, catalysis, and energy storage.

Object detection in remote sensing has developed rapidly and has been applied in many fields, but it is known to be vulnerable to adversarial attacks. Improving the robustness of models has become a key issue for reliable application deployment. This paper proposes a robust object detector for remote sensing images (RSIs) to mitigate the performance degradation caused by adversarial attacks. For remote sensing objects, multi-dimensional convolution is utilized to extract both specific features and consistency features from clean images and adversarial images dynamically and efficiently. This enhances the feature extraction ability and thus enriches the context information used for detection. Furthermore, regularization loss is proposed from the perspective of image distribution. This can separate consistent features from the mixed distributions for reconstruction to assure detection accuracy. Experimental results obtained using different datasets (HRSC, UCAS-AOD, and DIOR) demonstrate that the proposed method effectively improves the robustness of detectors against adversarial attacks.

GaFeO3-type oxides are promising multiferroic materials due to the coexistence of spontaneous magnetization and ferroelectric polarization properties at room temperature. As these ferroic properties feature a large anisotropy, single crystals are required. However, the magnetization of GaFeO3-type single crystals remains low at room temperature. In this study, we largely enhanced the magnetization at room temperature of GaFeO3-type single crystals by increasing the Fe content and co-doping Sc3+ and Al3+. Single crystals of AlxSc0.1−xGa0.6Fe1.3O3 (x = 0.01–0.04) were prepared using the optical floating-zone method. The single crystals were rod-shaped, with a diameter and length of ∼6 mm and 7 cm, respectively. X-ray diffraction measurements confirmed the ferroelectric polarization of the crystals. In addition, they exhibited room-temperature ferrimagnetism, with Curie temperature in the range of 326–338 K; the crystals exhibit magnetic anisotropy along the a-axis. The magnetization of the single crystal at 300 K and 0.3 kOe was 13 emu g−1, which is over ten times larger than those of previously reported single crystals with GaFeO3-type crystal structure.

This paper proposes an improved sampling-based approach for spacecraft proximity operation path planning under Clohessy-Wiltshire-Hill dynamics. The proposed approach is based on a modified version of the FMT* (Fast Marching Tree) algorithm with safety strategy which is divided into three parts: (1) incorporating relative ellipse to simplify the sampling state space and avoid collision with target; (2) combining internal/external ellipse-based collision detection algorithms for hovering obstacle and non-coplanar ellipse obstacle; (3) using rotating hyperplane method to handle the coplanar ellipse obstacle and uncertainty obstacle. By referring the safety strategy to simplify the state space before FMT* algorithm, the approach can reduce the complexity of path planning, especially the resampling and collision avoidance detection cyclic process in FMT*, thereby improve the planning efficiency. Two simulated scenarios, a coplanar path planning with/without coplanar ellipse obstacle, are developed to illustrate the approach. As a result, the proposed approach appears to be potential for spacecraft proximity real-time path planning as well as other complex space mission path planning generalized to different dynamics and environments, such as On-Orbit Service, and enabling a real-time computation of low-cost trajectory.

Background: Whether combination administration of angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), and beta-blockers (BBs) has a "reversal" effect on cardiac structure and function for first-diagnosed idiopathic dilated cardiomyopathy (FSIDCM) patients with unclear etiologies and inducements is unknown. Materials and Methods: We studied the effect of the protocol on FSIDCM patients. The effect was investigated in 26 FSIDCM patients. The criteria of "complete reversal" included left ventricular end-diastolic diameter (LVEDD) ≤50 mm for females or ≤55 mm for males and left ventricular ejection fraction (LVEF) ≥45%; the criteria of "partial reversal" was the decreased rate of LVEDD (ΔLVEDD) >10% or the increase rate of LVEF (ΔLVEF) >10%; the criteria of "no reversal" included LVEDD >50 mm for females or >55 mm for males and ΔLVEDD

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model’s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model.

Rb0.21(H2O)yWS2, rubidium hydrate dithiotungstate, is a new quasi two-dimensional sulfide. Its crystal structure consists of ordered WS2 layers, separated by disordered Rb+ ions and water molecules. All atomic sites are located on mirror planes. The WS2 layers are composed of edge-sharing [WS6] octahedra and extend parallel to (001). The presence of structural water was revealed by thermogravimetry, but the position and exact amount could not be determined in the present study. The temperature dependence of the electrical resistance indicates that Rb0.21(H2O)yWS2 is semiconducting between 80–300 K.

In this paper, we explore a scalable deep reinforcement learning (DRL) method for environments with multi-agents. Due to the explosive increase of the input dimensionality with the number of agents, most existing DRL methods are only able to cope with single-agent settings, or for only a small number of agents. To address this problem, we adopt a centralized training with decentralized execution framework, in which an observation embedding is used to reduce the curse of dimensionality. Perturbations are injected into the parameter space of the actor network for each agent to encourage exploration. The experiments demonstrate the effectiveness of the proposed approach on both cooperative and competitive environments.

There is an inherent need to track and catalog space debris (objects) in geosynchronous earth orbits (GEO) based on space-based surveillance networks and a large amount of observation data. However, for objects in GEO, angle-only measurements containing noises have been regarded as difficult for short-arc orbit determination (OD) when pursuing high accuracy. In this paper, from a data-driven perspective, we propose a novel method for space-based OD based on distribution regression (DR), which is called the weighting distribution-regression OD (WDR-OD) method. The OD is treated as a regression process, which is learned from abundant observation data and the corresponding orbits of known objects. First, we propose the structure of space-based OD samples, wherein the feature variables with a weighting matrix are introduced to enhance prediction accuracy. Second, a two-stage sampled learning theory is employed to learn the mapping from measurements to objects' orbit through kernel mean embedding. The proposed method is experimentally compared with the improved Laplace method and shows greater robustness in measurements with white Gaussian noise (WGN) and colored noise. The positional RMSE reaches 0.8793 km with WGN and 1.6972 km with colored noise, which are significantly smaller than the corresponding Laplace method's 5.0804 km and 14.8132 km. Furthermore, we propose a RIP-based ROMP algorithm to provide the theoretical bound of sparsity and then to pursue a sparse solution. Although the positional RMSE increases to 1.6554 km in the sparse method, it shrinks the 90% to 93% nonzero elements of the coefficients matrix to zero, which is helpful in reducing the computing load, and it meaningfully extends the application domain of the WDR-OD method.

Decentralized partially observable Markov decision processes (Dec-POMDPs) are general multi-agent models for planning under uncertainty, but are intractable to solve. Doubly exponential growth of the search space as the horizon increases makes a brute-force search impossible. Heuristic methods can guide the search towards the right direction quickly and have been successful in different domains. In this paper, we propose a new Q-value function representation—Monte Carlo Q-value function Q MC , which is proved to be an upper bound of the optimal Q-value function Q * . We introduce two Monte Carlo tree search enhancements—heavy playout for a simulation policy and adaptive samples—to speed up computation of Q MC . Then, we present a clustering and expansion with Monte-Carlo algorithm (CEMC)—an offline planning algorithm using Q MC as Q-value function, which is based on the generalized multi-agent A* with incremental clustering and expansion (GMAA*-ICE or ICE). CEMC calculates Q-value functions as required, without computing and storing all Q-value functions. An extended policy pruning strategy is used in CEMC. Finally, we present empirical results demonstrating that CEMC outperforms the best heuristic algorithm with a compact Q-value presentation in term of runtime for the same horizon, and has less memory usage for larger problems.

OBJECTIVE: The aim of this study was to evaluate whether frequent premature ventricular contractions originating from the right ventricular outflow tract remodel the cardiac structure and function in patients with a “seemingly normal heart” and whether radiofrequency ablation can reverse this remodeling. METHODS: Sixty-eight patients with idiopathic frequent premature ventricular contractions originating from the right ventricular outflow tract and normal heart structure and function were enrolled in this study. The patients were divided into three groups according to the therapeutic method: radiofrequency ablation group (24 cases), anti-arrhythmia drug group (26 cases), and control group (18 cases without any treatment). Clinical Registration number: ChiCTR-ONRC-12002834 RESULTS: The basic patient characteristics were comparable between the three groups, except for the premature ventricular contraction rate, which was significantly lower in the control group. After six months of follow up, the premature ventricular contraction rate was significantly reduced in the radiofrequency ablation group, which was accompanied by a significant decrease in the following cardiac cavity inner diameters, as determined by echocardiography: right atrium (33.33±3.78 vs. 30.05±2.60 mm, p = 0.001), right ventricle (23.24±2.40 vs. 21.05±2.16 mm, p = 0.020), and left ventricle (44.76±4.33 vs. 41.71±3.44 mm, p = 0.025). These results were similar in the anti-arrhythmia drug group, although this group exhibited a smaller extent of change (right atrium: 33.94±3.25 vs. 31.27±3.11 mm, p = 0.024; right ventricle: 22.97±3.09 vs. 21.64±2.33 mm, p = 0.049; left ventricle: 45.92±6.38 vs. 43.84±5.67 mm, p = 0.039), but not in the control group (p>0.05). There was a tendency toward improvement in the cardiac functions in both the radiofrequency ablation and anti-arrhythmia drug groups. However, these differences were not statistically significant (p>0.05). CONCLUSIONS: These results indicate that radiofrequency ablation can potentially reverse the cardiac remodeling caused by frequent premature ventricular contractions even in structurally normal hearts and that frequent premature ventricular contractions should be abated even in structurally normal hearts.