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ABSTRACTThe automatic extraction of bottom sediment annotations in large-scale raster nautical charts has limitations, including an imprecise semantic information description and low efficiency. To overcome them, we propose a convolutional neural network (CNN)-based method for the automatic extraction of bottom sediment annotations in raster nautical charts, using image processing techniques to improve it. First, an adaptive chart partitioning model that considers element completeness is constructed. Second, a principle for the unique identification of elements based on spatial conflicts is designed. Finally, a model for accurately extracting semantic information for bottom sediment annotations is established. To evaluate the effectiveness of the proposed method, we implemented a model based on the PyTorch framework and used the PIL library to analyze the results. We also conducted comparative experiments on multiple CNN models to recommend the selection of such models in the proposed method by comparing their classification and recognition performance. The experimental results indicate that (1) the proposed model can achieve high-precision extraction of bottom sediment annotations in raster nautical charts. (2) Furthermore, the proposed model generally has high recognition accuracy and semantic completeness, with better recognition precision than traditional pattern recognition methods.

In the fields of wind power, and other industries, the Al2O3/TiO2coating is prepared and ground on the bearing ring, in order to obtain the insulation resisting the electrical erosion for the motors at high voltage. A novel wet chemical mechanical grinding (WCMG) method is proposed to finish this hard-brittle composite material, utilizing the structured diamond abrasives pad and the NaOH solution. The softened workpiece's surface under the chemical reaction was demonstrated by means of Raman spectrum, indentation and scratching tests. The analytical model of surface roughness and fracture cracks is established based on the synthesis of geometry and kinematics, contact mechanics, material removal and indentation fracture mechanics. The surface roughness of coating was reduced from initial Sa3.293 μm down to final Sa0.049 μm in the WCMG process with grain size 3 μm and pH 12.01, mostly attributed to the ductile removal of chemically softened coating surface.

• Oxide scales are mainly composed of α-Al 2 O 3 and no obvious internal oxidation occurs in both solid and semi-solid intervals. • During solid oxidation, different scale formation kinetics occurs on various phases, which is caused by outward Al3+ diffusion rate difference in various component systems. • Increased degree of chaos and number of vacancies in liquid phase contribute to the rise of oxygen diffusion rate and flux, causing the oxidation corrosion around liquid phase region. • Oxidation resistance in semi-solid state attributes to dense Al 2 O 3 scales formed by selective oxidation of Al. In this work, possible short-term thermal exposure oxidation in hot processing including semi-solid processing is focused. We aim to investigate the short-term oxidation behavior of a novel powder metallurgy Al 0.8 Co 0.5 Cr 1.5 CuFeNi high entropy alloy (HEA) in high-temperature solid (1000–1100 °C) and semi-solid (1125–1225 °C) intervals. From 1000 to 1225 °C, mass gain increases from 0.31 to 0.45 mg cm–2 after 60 min oxidation, presenting superior oxidation resistance in the semi-solid state compared with other semi-solid alloys. Equiaxed α-Al 2 O 3 grains are the predominant component of oxide scales and no obvious internal oxidation layer occurs in both solid and semi-solid intervals. Al 2 O 3 scale growth is predominantly determined by outward Al3+ diffusion. For solid oxidation, different scale formation kinetics occur on various phases and the BCC(B2) phase has faster scale formation kinetics. This is attributed to outward Al3+ diffusion rate differences in various component systems. During semi-solid oxidation, the increased degree of chaos and number of vacancies in the liquid phase provide a diffusion channel, leading to rapid Al 2 O 3 formation. Oxidation resistance in the semi-solid state is attributed to dense Al 2 O 3 scales formed through selective oxidation of Al. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurately predicting carbon emissions trading price can assist governments in devising policies that strike a balance between economic development and environmental protection. In recent years, the decomposition integration framework has been widely applied for carbon price prediction. However, due to the inadequacy of feature extraction and lack of emphasis on interval forecasting, constructing a stable and efficient forecasting model for carbon price remains a challenging task. Thus, this study proposes an enhanced decomposition integration model for carbon price prediction that conducts multiple feature fusion based on the potential determinants and establishes a multivariate intelligent optimized ensemble model. Firstly, decomposition and entropy algorithms are employed to decompose and reconstruct the carbon price into several simple subsequences to strike the balance between computing speed and the complexity of the model. Subsequently, considering how external factors may affect the sequence, structured data, and unstructured data are both introduced to construct the multiple forecasting model. An intelligent optimization ensemble model is then developed for predicting the reconstructed components based on a deep learning model with an attention mechanism. Lastly, to quantify uncertainty, interval prediction is incorporated for research and analysis. Empirical analysis is conducted on three carbon finance pilot markets in China and the values of Mean Absolute Percentage Error (MAPE) are 2.047, 1.251, and 0.994 respectively. Furthermore, each module of the proposed model significantly contributes to the results and the developed model outperforms the baseline model across various error metrics.

The casting process, the type and content of microalloying elements have a significant effect on the microstructure and mechanical properties of Al–12Si-4.5Cu–2Ni alloy. The results showed that with the increase of Zr and V, the grain size and secondary dendrite arm spacing (SDAS) of the alloy decreased. However, excessive Zr and V led to abnormal growth of the intermetallic phase, resulting in the separation phenomenon of the matrix. Compared with gravity casting, squeeze casting not only inhibited the segregation of alloying elements but also refined the size of the intermetallic phase. The mechanical properties of the alloy showed a trend of increasing first and then decreasing. When the content of Zr and V was 0.2, the Al–12Si-4.5Cu–2Ni alloy obtained the best mechanical properties. The ultimate tensile strength, yield strength and elongation were 261.4 MPa, 150.2 MPa and 1.93 %, respectively. The (AlSi)3(ZrV), stacking faults and dislocations in the alloy significantly improved the mechanical properties. The disappearance of pores and the refinement of microstructure elevated the elongation of the alloy, while the dotted intermetallic phase accelerated the initiation of microcracks, leading to a decrease in elongation.

Rapid formation of natural gas clathrate hydrates has been a significant hazard in off-shore natural gas drilling and gas transport. During operation, polymer inhibitors can serve as low-dosage kinetic inhibitors to prevent the formation of gas hydrates. A comprehensive consideration of the multiple factors involved in the actions of kinetic hydrate inhibitors (KHIs) can elucidate the inhibiting behavior of polymers. Here, amphiphilic poly(N-isopropylmethacrylamide) (PNIPMAm) (or poly(N-isopropylacrylamide)) are found to achieve weak to strong inhibition by the variable superimposed effect of the polymer’s amide group and hydrophobic effect of the polymer’s alkyl groups on the aqueous phase. The interfacial and bulk water mobility and hydrogen bonding energy in the polymer solutions are compared by low-temperature nuclear magnetic resonance (NMR) relaxometry. The mobility of the water is found to be sensitive to the hydrophobic α-methyl side group in PNIPMAm and its molecular weight when a guest molecule tetrahydrofuran is present. The water hydrogen bonding energies measured from NMR are also associated with the nonfreezable bound water amount revealed by differential scanning calorimetry. The α-methyl side group leads to a significant reduction of the water mobility, an increase in the hydrogen bonding energies of the interfacial water, and a decrease of the hydrate growth rate constants by 2 to 3 orders of magnitude. The retarded reaction kinetics of heterogeneous hydrate growth can be related to the hydrophobic effect of the polymer’s α-methyl side group and bound water properties (mobility and amount) in the interface.

Rational structure design benefits the development of efficient nanoplatforms for tumor theranostic application. In this work, a multifunctional polydopamine (PDA)-coated manganese sulfide (MnS) nanocluster was prepared. The polyhydroxy structure of PDA enhanced the water interaction with pH-responsive MnS nanoclusters via hydrogen bonds. At pH 5.5 conditions, the spin–lattice relaxation rate of MnS nanoclusters dramatically increased from 5.76 to 19.33 mM–1·s–1after the PDA coating, which can be beneficial for efficient tumor magnetic resonance imaging. In addition, PDA endowed MnS nanoclusters with excellent biocompatibility and good photothermal conversion efficiency, which can be used for efficient tumor photothermal therapy (PTT). Furthermore, MnS nanoclusters possess the ability to release H2S in the acidic tumor microenvironment, effectively inhibiting mitochondrial respiration and adenosine triphosphate production. As a result, the expression of heat shock protein was obviously reduced, which can reduce the resistance of tumor cells to photothermal stimulation and enhance the efficacy of PTT. The released Mn2+also displayed efficient peroxidase and glutathione oxidase-like activity, effectively inducing tumor cell ferroptosis and apoptosis at the same time. Therefore, this nanoplatform could be a potential nanotheranostic for magnetic resonance contrast enhancement and synergistic ferroptosis–PTT of tumors.

Sharp-bending waveguide is a key element for ultra-compact and densely integrated photonic devices, which is promising to enlarge the capability of modern information processing in a single chip. Topological photonics manifest the nature of robust propagation against sharp bending and such robustness has been experimentally demonstrated in topological integrated interfaces. It is important to quantify the bending loss of topological interface but has remained exclusive. In this work, we report on the characterization of sharp-bending robustness in the integrated topological interface of valley photonic crystals (VPCs) by experimentally quantifying the ultralow bending loss. The VPCs are designed on a standard silicon-on-insulator platform with the inversion-symmetry broken in honeycomb lattice, and four types of topological interfaces can be constructed by two topologically-distinct VPCs. As one of the representative cases, zigzag-AA interface is applied to demonstrate the robust propagation along sharp bending. In experiment, we fabricate a series of VPC interfaces with different turn number and the same transmission distance, which perform the ultralow bending loss less than 0.02 dB per 120-deg turning. Furthermore, we experimentally characterize the propagation loss in the integrated interfaces. Our approach not only shows the ability of VPC topological interfaces to suppress backscattering stemming from sharp bending, but also paves the way for topological nanophotonic dense integration.

Accurate 3-D object detection is vital in autonomous driving. Traditional LiDAR models struggle with sparse point clouds. We propose a novel approach integrating LiDAR and camera data to maximize sensor strengths while overcoming individual limitations for enhanced 3-D object detection. Our research introduces the channelwise and spatially guided multimodal feature fusion network (CSMNET) for 3-D object detection. First, our method enhances LiDAR data by projecting it onto a 2-D plane, enabling the extraction of class-specific features from a probability map. Second, we design class-based farthest point sampling (C-FPS), which boosts the selection of foreground points by utilizing point weights based on geometric or probability features while ensuring diversity among the selected points. Third, we developed a parallel attention (PAT)-based multimodal fusion mechanism achieving higher resolution compared to raw LiDAR points. This fusion mechanism integrates two attention mechanisms: channel attention for LiDAR data and spatial attention for camera data. These mechanisms enhance the utilization of semantic features in a region of interest (ROI) to obtain more representative point features, leading to a more effective fusion of information from both LiDAR and camera sources. Specifically, CSMNET achieves an average precision (AP) in bird’s eye view (BEV) detection of 90.16% (easy), 85.18% (moderate), and 80.51% (hard), with a mean AP (mAP) of 85.12%. In 3-D detection, CSMNET attains 82.05% (easy), 72.64% (moderate), and 67.10% (hard) with an mAP of 73.75%. For 2-D detection, the scores are 95.47% (easy), 93.25% (moderate), and 86.68% (hard), yielding an mAP of 91.72% for the KITTI dataset.

We aim to propose a Riemann-problem-solver-free nonstaggered central difference scheme (NCDS), which is well-balanced and positivity-preserving for two-dimensional shallow water flows over nonflat bottom topography with wet-dry fronts. The well-balanced property of the NCDS for the two-dimensional shallow water equations at wet-dry fronts is still unclear. The NCDS is “generously multidimensional” and consists of three steps: a forward step, a corrector step, and a backward step. Each step needs to guarantee the water depth to be nonnegative and retain the lake at rest. The key ingredient of the NCDS is the backward step. It is very nontrivial and technical to obtain both the well-balanced and positivity-preserving properties when the computational domain contains wet-dry fronts for the NCDS. The traditional NCDS fails to retain the lake at rest when the computational domain contains wet-dry fronts. We introduce a novel backward step, which is well-balanced and positivity-preserving, to recover numerical solutions on nonstaggered cells. Finally, we show several numerical results of two-dimensional shallow water equations with wet-dry fronts to verify these properties.

To detect redox potential evolution during the initial stage of an acute wound, a redox-sensitive SERS-active optical fiber was fabricated by integrating redox-sensitive SERS probes in a hole of an optical fiber. The redox-sensitive SERS-active optical fibers carried redox-sensitive SERS probes into the inside of a wound to sense its redox potential. The laser was transmitted to the redox-sensitive SERS probes in the body by optical fibers, and the SERS signals of the redox-sensitive SERS probes were transferred out of the body by optical fibers to indicate the redox potentials in the wound. The redox-sensitive SERS probes dynamically sensed the redox potential in vivo, and their SERS signals were collected constantly to indicate the redox potentials. The assessments in vivoand in vitroproved the responsiveness of redox-sensitive SERS-active optical fibers. The redox potential evolution during the initial stage of an acute wound with the treatments of different concentrations of glucose was detected to verify the feasibility of redox-sensitive SERS-active optical fibers to dynamically detect redox potentials in vivo. The redox-sensitive SERS-active optical fiber would be a versatile tool to explore the roles of redox potentials in living organisms.

In modern electromagnetic countermeasure environments, traditional radar signal sorting (RSS) methods face challenges from incompletely intercepted parameter-dense pulses of multi-function radars (MFRs). To cope with this situation, this letter proposes a sequence-to-sequence RSS method based on a multiple self-attention coupling mechanism Transformer network. The method utilizes positional encoding to obtain stable temporal information. A multiple self-attention coupling mechanism is then designed to calculate the attention matrix, thereby extracting sequence relationships for the non-ideal pulse stream. Finally, a decoder network is employed to extract high-dimensional features and translate the corresponding labels for each pulse. Simulation experiments demonstrate that compared with some existing methods, the proposed method can achieve better average sorting accuracy with little computational cost under the conditions of overlapping parameters, limited label, missing pulses, and various modulation types of intercepted MFR signals.

Deep learning has made significant progress in the field of radar space target recognition. However, deep neural networks require significant amounts of data to train network parameters, posing challenges in achieving noncooperative target recognition. Therefore, it is of practical significance to research a fast and accurate target recognition method with limited radar data. In this article, we propose a novel radar space target recognition network based on high-dimensional feature maps, called HRPnet, which fully utilizes high-resolution range profile (HRRP), radar cross section (RCS), and polarization (POL) data obtained from the radar. First, a sparse autoencoder (SAE) is employed to conduct deep feature extraction on the three types of data. Second, the Gramian angular field (GAF) transformation is employed to obtain 2-D representation of HRRP, RCS, and POL data, respectively. These 2-D maps are then integrated to construct high-dimensional feature maps. Third, a feature map convolutional neural network (FMCNN) is designed for high-dimensional feature map classification and target recognition. Experimental results indicate that the proposed HRPnet outperforms existing methods in terms of recognition accuracy and noise resistance, particularly in the case of a limited sample size.

The concept of R-duals of a sequence was first introduced with the motivation to obtain a general version of duality principle in Gabor analysis. Since then, various R-duals (types II, III, IV) and some relaxations of the R-dual setup have been introduced and studied by some mathematicians. All these “R-duals” provide a powerful tool in the analysis of duality relations in general frame theory. It is of independent interest in mathematics and far beyond the duality principle in Gabor analysis. Observe that the underlying sequences of a R-dual are a pair of orthonormal bases. In this paper we introduce the concept of weak R-duals based on a pair of Parseval frames. It is a new relaxation of the R-dual setup. We obtain a characterization of frames based on their weak R-duals, and prove that the weak R-dual of a frame (Riesz basis) is a frame sequence (frame). We also characterize (unitarily) equivalent frames in terms of weak R-duals. Finally, we present an explicit expression of the canonical duals of weak R-duals.

Osteoarthritis (OA) is the most common age-related musculoskeletal disease. However, there is still a lack of therapy that can modify OA progression due to the complex pathogenic mechanisms. The aim of the study was to explore the role and mechanism of XJB-5-131 inhibiting chondrocytes ferroptosis to alleviate OA progression.

To ensure the quality of semantic communication, an optimization scheme for multiple-input and multiple-output (MIMO) antenna design using in semantic-based mobile is proposed and verified. The scheme is based on a modified algorithm MOEA/D-BH, which integrates the black-hole (BH) algorithm into multi-objective EA based on decomposition (MOEA/D). By introducing controllable absorption distance and neighborhood learning mechanism, MOEA/D-BH can deal with high-dimension parameters well in a multi-objective optimization problem. Thus, in a limited design space, a satisfied antenna can be optimized by proposed scheme efficiently. This can be a feasible candidate scheme for MIMO antenna design. A single-band and dual-band MIMO antenna, which are applied for semantic-based mobile system, are optimized by proposed scheme sharing the same model. Both the data of simulation and measurement show good electrical and MIMO performances in the working frequency band. Thus not only the performance of the semantic communication definitely improved, but also it provides useful exploration for the development of intelligent semantic mobile systems.

The vulnerability of satellite navigation signals to suppressive interference is a major concern owing to the extremely low power. The polarization-sensitive array adds polarization diversity capability, providing outstanding anti-jamming properties. Null depth is a crucial indicator of interference mitigation capability. This article proposes a new weight vector calculation method for space-polarization-domain adaptive processing (SPAP) by focusing on interference null depth, which can improve the anti-interference performance of polarization-sensitive arrays. First, the realistic received signal model of the polarization-sensitive array is built. Second, the realistic radiation pattern of the crossed dipole antenna and the polarization characteristics of the signal are considered in the constraint matrix of the minimum variance distortionless response (MVDR) to obtain a new anti-jamming weight matrix. Finally, the orthogonal projection subspace method is introduced to effectively deepen the interference null, and its orthogonality with the interference subspace is improved by projecting the new anti-interference weight matrix into the noise subspace of the received signal, and the rationality and effectiveness of the method are verified by theoretical derivation. Through simulation experiments on a uniform linear array (ULA) and a uniform circular array (UCA), the results indicate that the proposed method can effectively increase the null depth in the space and polarization domains by 12–28 dB, which significantly improves the array’s anti-interference capability.

This letter presents an ultrawideband miniaturized ultrathin flexible metamaterial absorber (MMA) based on frequency selective surface (FSS). The proposed MMA consists of an inner planar helix and four groups of outer convoluted lightweight indium tin oxide resistive film etched on a transparent substrate polyethylene terephthalate substrate. The MMA demonstrates stable absorption across the frequency range of 1.98 to 25.88 GHz, achieving a remarkable maximum fractional bandwidth of 171.6%. The unit cell size of the MMA is 0.06${\lambda }_L$×0.06${\lambda }_L$×0.035${\lambda }_L$, where ${\lambda }_L$ represents the wavelength corresponding to the lowest absorption frequency. The structure can cover both the L- and S-bands without increasing its size and achieve stable absorption under a curved surface. Therefore, it is suitable for low-frequency applications such as satellite navigation systems and satellite communications.

Object detection on datasets with imbalanced distributions (i.e. long‐tail distributions) dataset is a significantly challenging task. Some re‐balancing solutions, such as re‐weighting and re‐sampling have two main disadvantages. First, re‐balancing strategies only utilise a coarse‐grained global threshold to suppress some of the most influential categories, while overlooking locally influential categories. Second, very few studies have specifically designed algorithms for object detection tasks under long‐tail distribution. To address these two issues, a dual‐balanced network for fine‐grained re‐balancing object detection is proposed. Our re‐balancing strategies are both in proposal and classification logic, corresponding to two sub‐networks, the Balance Region Proposal Network (BRPN) and the Balance Classification Network (BCN). The BRPN sub‐network equalises the number of proposals in the background and foreground by reducing the sampling probability of simple backgrounds, and the BCN sub‐network equalises the logic between head and tail categories by globally suppressing negative gradients and locally fixing the over‐suppressed negative gradients. In addition, the authors advise a balance binary cross entropy loss to jointly re‐balance the entire network. This design can be generalised to different two‐stage object detection frameworks. The experimental mAP result of 26.40% on this LVIS‐v0.5 dataset outperforms most SOTA methods. In this paper, a dual‐balanced network is proposed for long‐tail distribution object detection. The proposal in RPN and the logic in classification networks are balanced, which reduces the AP difference between the head and tail categories and improves detection performance overall.

We propose a novel integral‐imaging (II) based near‐eye display (NED) combined with high‐pixel‐density display screen, voxel‐based rendering method and nano‐imprint metalens array. The effect of depth of field in AR and VR of such II‐based NED is shown, and the 3D parallax effect on the real mode is demonstrated. We successfully miniaturize the II‐based NED which shows a great potential using in AR and VR.

Progress has been made in the application of nanomedicine in rheumatoid arthritis (RA) treatment. However, the whole process of monitoring and treatment of RA remains a formidable challenge due to the complexity of the chronic autoimmune disease. In this study, we develop a Janus nanoplatform (denoted as Janus-CPS) composed of CeO2–Pt nanozyme subunit on one side and periodic mesoporous organosilica (PMO) subunit on another side for simultaneous early diagnosis and synergistic therapy of RA. The Janus nanostructure, which enables more active sites to be exposed, enhances the reactive oxygen species scavenging capability of CeO2–Pt nanozyme subunit as compared to their core–shell counterpart. Furthermore, micheliolide (MCL), an extracted compound from natural plants with anti-osteoclastogenesis effects, is loaded into the mesopores of PMO subunit to synergize with the anti-inflammation effect of nanozymes for efficient RA treatment, which has been demonstrated by in vitrocellular experiments and in vivocollagen-induced arthritis (CIA) model. In addition, by taking advantage of the second near-infrared window (NIR-II) fluorescent imaging, indocyanine green (ICG)-loaded Janus-CPS exhibits desirable effectiveness in detecting RA lesions at a very early stage. It is anticipated that such a Janus nanoplatform may offer an alternative strategy of functional integration for versatile theranostics.

The electrical microenvironment plays an important role in bone repair. However, the underlying mechanism by which electrical stimulation (ES) promotes bone regeneration remains unclear, limiting the design of bone microenvironment–specific electroactive materials. Herein, by simple co-incubation in aqueous suspensions at physiological temperatures, biocompatible regenerated silk fibroin (RSF) is found to assemble into nanofibrils with a β-sheet structure on MXene nanosheets, which has been reported to inhibit the restacking and oxidation of MXene. An electroactive hydrogel based on RSF and bioencapsulated MXene is thus prepared to promote efficient bone regeneration. This MXene/RSF hydrogel also acts as a piezoresistive pressure transducer, which can potentially be utilized to monitor the electrophysiological microenvironment. RNA sequencing is performed to explore the underlying mechanisms, which can activate Ca2+/CALM signaling in favor of the direct osteogenesis process. ES is found to facilitate indirect osteogenesis by promoting the polarization of M2 macrophages, as well as stimulating the neogenesis and migration of endotheliocytes. Consistent improvements in bone regeneration and angiogenesis are observed with MXene/RSF hydrogels under ES in vivo. Collectively, the MXene/RSF hydrogel provides a distinctive and promising strategy for promoting direct osteogenesis, regulating immune microenvironment and neovascularization under ES, leading to re-establish electrical microenvironment for bone regeneration.

Metamaterial absorbers over a broadband spectrum with high absorption, good angular tolerance, and easy configurations have essential importance for optical and optoelectronic devices. In this study, a hybrid metamaterial absorber comprising multilayered cermet thin films (multi-cermet) with tapered structure is designed and experimentally demonstrated. Combining optical interference of multi-cermet films and optical field localization of nano-cone structures, the average absorbance of both simulation and measurement are more than 98% in an ultrabroad bandwidth (300–3000 nm), and the proposed absorber shows a good angular tolerance as well. The composite process of two easy-operated and efficient methods, colloidal lithography, and magnetron sputtering, is employed for large-area fabrication. In addition, owing to flexible polyimide substrate, the proposed absorber also shows good bending and heating resistance, which reflects its potential in engineering application.

Contrastive learning (CL) with learnable examples performs outstandingly in data representation. However, when dealing with hard samples, instance-level alignment with excessive uniformity may descend into trivial clusters, especially when confronted with interclass similarity and intraclass diversity in hyperspectral images (HSIs). To solve this problem, we regard prototypical CL as tracing the potential probability density distribution. Then, a novel pretraining method, learnable sparse contrastive sampling (LSCoSa), is proposed for discriminative representation learning, containing sparse positive sampling and multiple positives learning. Specifically, on the basis of cooperative–adversarial CL, we first exert a Kullback–Leibler (KL) divergence regularizer on the average activation probability of the prototypes, suppressing fake density prototypes for sparse positive sampling. Furthermore, we propose multiple positives learning, in which the top- $k$ potential positives are retrieved and dynamically weighted for contrastive supervision, to avoid trivial clusters and cover satisfying semantic variations. Comprehensive experiments on three HSI benchmark datasets demonstrate that LSCoSa achieves significant advantages over other HSI classification (HSIC) methods. The code is available at https://github.com/sakurashine/LSCoSa.

The dense, complex and variable electromagnetic environment poses a serious challenge to radar signal sorting (RSS) in modern electronic reconnaissance systems. In order to improve RSS performance, this letter proposes a semi-supervised learning framework-based RSS method via a residual graph convolutional network (ResGCN-RSS) to effectively improve the generalization ability of the signal sorting models in small data scenarios. Firstly, the graph structure construction of intercepted radar signals is performed via K-nearest neighbor algorithm. Then, the three-layer ResGCN is designed to adaptively improve the features learning. Finally, RSS can be effectively and efficiently implemented through an end-to-end ResGCN with small labeled graph data of interleaved radar signals. The simulation experimental results show that our proposed method can achieve better average accuracy with little computational cost increasing when the labeled data is very small, compared to some existing methods.

In order to study the influence of various sintering variables more intuitively and clearly, electromagnetic field analysis and computational fluid dynamics are used to analyze the influence of gas flow, heating power and other process parameters on the temperature range of medium-frequency sintering in this paper. The results indicate that the uniformity of temperature field is determined by the gas flow rate and heating power. When the heating power is 50 kW, the stable temperature drops about 10 K for every 1 m3·h−1increase of hydrogen flow. The peak value of the maximum temperature difference on the tungsten rods gradually increases with the increase of the hydrogen flow rate, and it appears slightly later. The temperature distribution of tungsten rods in furnace is similar with different hydrogen flow rate. At the same time, the closer to the furnace wall, the higher the temperature of the tungsten rod. The temperature difference between tungsten rods mainly occurred within 1 h after the beginning of heating. Increasing the heating power can improve the stable temperature, but has little effect on the heating time. Increasing heating power can increase the peak value of the maximum temperature difference on the tungsten rods, but has little effect on the occurrence time of the peak value of the maximum temperature difference on the tungsten rods. Increasing the heating power will increase the temperature difference of a single tungsten rod, but the temperature difference of a single tungsten rod never exceeds 65 K. This study can be used to guide the process optimization design. Combined with numerical simulation calculation, the sintering parameters with low energy consumption and high temperature uniformity can be quickly and effectively found, thus improving the microstructure and properties of sintered tungsten products.

Most ferroptosis nanomedicines based on organic or inorganic carriers have difficulties in further clinical translation due to their serious side effects and complicated preparation. Self-assembled nanomedicines can reduce the biological toxicity caused by additional chemical modifications and excipients, offering better biocompatibility and safety. Ferroptosis therapy is an iron-associated programmed cell death dependent on lipid peroxidation with efficient tumor selectivity and biosafety. Therefore, the application of self-assembled nanomedicines with good biosafety in the ferroptosis treatment of tumors has attracted extensive attention. In this review, recent advances in the field of ferroptosis-based self-assembled nanomaterials for cancer therapy are presented, with emphasis on how these nanomaterial components interact and their distinct mechanisms for inducing ferroptosis in tumor cells, including iron metabolism, amino acid metabolism and CoQ/FSP1, as well as their respective advantages and challenges. This review would therefore help the spectrum of advanced and novice researchers interested in this area to quickly zoom in on the essential information and glean some thought-provoking ideas to advance this subfield in cancer nanomedicine.

Integral imaging (II) display, one of the most critical true-3D display technologies, has received increasing research recently. Significantly, an achromatic metalens array has realized a broadband metalens-array-based II (meta-II). However, the past micro-scale metalens arrays were incompatible with commercial micro-displays; furthermore, the elemental image array (EIA) rendering is always slow. The two hinders in device and algorithm prevent meta-II from being used for practical video-rate near-eye displays (NEDs). This research demonstrates a meta-II NED combining a commercial micro-display and a metalens array. The large-area nanoimprint technology fabricates the metalens array, and a novel real-time rendering algorithm is proposed to generate the EIA. The hardware and software efforts solve the bottlenecks of video-rate meta-II displays. We also build a see-through prototype based on our meta-II NED, demonstrating the feasibility of augmented reality. Our work explores the potential of video-rate meta-II displays, which we expect can be valuable for future virtual and augmented reality.

Site-selective superassembly was used to synthesize a nanorobot with bionic rod-shaped head/hollow tail structure, exhibiting strong photothermal effect, high drug loading capacity, precise and controlled drug release, and excellent motility.Injectable regenerated silk fibroin (RSF) nanofibril hydrogels were developed via simple sonication, offering significant production, structural, and performance advantages.Nanorobot/thrombin (Thr)/RSF nanofibril hydrogels reduced intraoperative bleeding of hepatocellular carcinoma (HCC) spinal metastasis, provided starvation embolization, and prevented post-surgery recurrence.Nanorobot/Thr/RSF nanofibril hydrogels inhibited blood supply to residual tumors and reduced neovascularization, inhibiting post-surgery HCC spinal metastasis recurrence.

O2mixed with CO2gas has been successfully applied in converter smelting; however, up to now, there have been few studies regarding the jet characteristics of the mixed injection of the swirl oxygen lance nozzle and the influence of different ambient temperature conditions on jet characteristics compared with common nozzles. In this study, Fluent software was used to simulate the jet characteristics of a four-hole common nozzle and a four-hole swirl nozzle under four different ambient temperature conditions to inject 95% O2+ 5% CO2and analyze the influence of different ambient temperatures on nozzle-jet characteristics. The results show that with an increase in the ambient temperature, the jet-axis velocity and nozzle centerline speed increase. Under the same distance condition, the distance between the maximum radial velocity point of the jet and the centerline of the nozzle becomes larger, with a velocity greater than that of the swirl nozzle. However, the influence of the ambient temperature on the offset of the jet centerline is small. With an increase in the ambient temperature and distance, the jet-axis temperature increases and the temperature of the nozzle centerline decreases. The research results can provide a theoretical reference for the optimal design of a CO2+ O2swirl oxygen lance nozzle.