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Biological fouling seriously jeopardizes the development of the marine industry. Although hydrogels, as a kind of state-of-the-art antifouling material, have received wide attention, their mechanical strength is still relatively weak, and the synergistic antifouling method is comparatively single, thus limiting the performance of hydrogels. Here, a zwitterionic sulfobetaine methacrylate (SBMA)-acrylamide (AM)/sodium alginate (SA) double-network (DN) antifouling hydrogel with superb antifouling ability and outstanding mechanical properties was prepared by grafting MXene/Ag (M/Ag) and the powerful biocide polyhexamethylene biguanide (PHMB). The prepared M/Ag has a great bactericidal effect (99.9%), and the DN-M/Ag-PHMB hydrogel demonstrates high tensile strength (5.8 MPa), remarkable antiprotein adhesion, bactericidal, and antialgal adhesion. This work proves that the DN-M/Ag-PHMB hydrogel has an exceptional antifouling ability in all three stages of biofouling formation and has a promising future as a new type of green marine antifouling material.

Oral squamous cell carcinoma (OSCC) is a tumor characterized by cellular redox imbalance, rendering it particularly sensitive to ferroptosis treatment. However, traditional ferroptosis inducers have a few drawbacks. In this study, ultrasmall AuMn nanoclusters (AMNCs) with a bovine serum albumin (BSA) ligand were synthesized and encapsulated in natural killer (NK) cell-derived exosomes to form an Exo-AMNCs composite for targeted ferroptosis therapy of OSCC. Unlike previously reported alloyed metal nanoclusters, not only do AMNCs react with intracellular H2O2to produce reactive oxygen species (ROS) and induce ferroptosis but also the BSA ligand improves biocompatibility and water solubility. These properties render AMNCs ideal for fluorescence imaging in vivo. When combined with NK cell exosomes, the Exo-AMNCs composite exhibited strong targeted imaging and therapeutic effects on OSCC. Further investigation into the mechanistic details demonstrated that Exo-AMNCs downregulate the overexpression of fat mass and obesity-associated (FTO) in OSCC and regulate the key ferroptosis-related protein glutathione peroxidase 4 (GPX4).

The coronavirus disease 2019 (COVID-19) outbreak caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) developed into a global health emergency. Systemic microthrombus caused by SARS-CoV-2 infection is a common complication in patients with COVID-19. Cardiac microthrombosis as a complication of SARS-CoV-2 infection is the primary cause of cardiac injury and death in patietns with severe COVID-19. In this study, we performed single-cell sequencing analysis of the right ventricular free wall tissue from healthy donors, patients who died during the hypercoagulable period of characteristic coagulation abnormality (CAC), and patients who died during the fibrinolytic period of CAC. We collected 61,187 cells enriched in 24 immune cell subsets and 13 cardiac-resident cell subsets. We found that in the course of SARS-CoV-2 infected heart microthrombus, MYO1EhighRASGEF1Bhighmonocyte-derived macrophages promoted hyperactivation of the immune system and initiated the extrinsic coagulation pathway by activating chemokines CCL3, CCL5. This series of events is the main cause of cardiac microthrombi following SARS-CoV-2 infection. In a SARS-CoV-2 infected heart microthrombus, excessive immune activation is accompanied by an increase in cellular iron content, which in turn promotes oxidative stress and intensifies intercellular competition. This induces cells to alter their metabolic environment, resulting in increased sugar uptake via the glycosaminoglycan synthesis pathway. In addition, high levels of reactive oxygen species generated by elevated iron levels promote increased endogenous malondialdehyde synthesis in a subpopulation of cardiac endothelial cells. This exacerbates endothelial cell dysfunction and exacerbates the coagulopathy process.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed.

Glioblastoma (GBM) represents the most aggressive primary brain tumor, and urgently requires effective treatments. Oncolytic adenovirus (OA) shows promise as a potential candidate for clinical antitumor therapy, including in the treatment of GBM. Nevertheless, the systemic delivery of OA continues to face challenges, leading to significantly compromised antitumor efficacy. In this study, we developed an innovative approach by encapsulating CXCL11-armed OA with tannic acid and Fe3+(TA-Fe3+) to realize the systemic delivery of OA. The nanocarrier’s ability to protect the OA from elimination by host immune response was evaluated in vitro and in vivo. We evaluated the antitumor effect and safety profile of OA@TA-Fe3+in a GBM-bearing mice model. OA@TA-Fe3+effectively safeguarded the virus from host immune clearance and extended its circulation in vivo. After targeting tumor sites, TA-Fe3+could dissolve and release Fe3+and OA. Fe3+-induced O2production from H2O2relieved the hypoxic state, and promoted OA replication, leading to a remarkable alteration of tumor immune microenvironment and enhancement in antitumor efficacy. Moreover, the systemic delivery of OA@TA-Fe3+was safe without inflammation or organ damage. Our findings demonstrated the promising potential of systemically delivering the engineered OA for effective oncolytic virotherapy against GBM.

Mirror detection aims to discover mirror regions in images to avoid misidentifying reflected objects. Existing methods mainly mine clues from spatial domain. We observe that the frequencies inside and outside the mirror region are distinctive. Besides, the low-frequency representing the feature semantics can help to locate the mirror region, and the high-frequency representing the details can refine it. Motivated by this, we introduce frequency guidance and propose the dual domain perception progressive refinement network (DPRNet) to mine dual-domain information. Specifically, we first decouple the images into high-frequency and low-frequency components by Laplace pyramid and vision Transformer, respectively, and design the frequency interaction alignment (FIA) module to integrate frequency features to initially localize the mirror region. To handle scale variations, we propose the multi-order feature perception (MOFP) module to adaptively aggregate adjacent features with progressive and gating mechanisms. We further propose the separation-based difference fusion (SDF) module to establish associations between entities and imagings and discover the correct boundary to mine the complete mirror region. Extensive experiments show that DPRNet outperforms the state-of-the-art method by an average of 3% with only about one-fifth of the parameters and FLOPs on four datasets. Our DPRNet also achieves promising performance on remote sensing and camouflage scenarios, validating its generalization. The code is available at https://github.com/winter-flow/DPRNet.

• Bu@PGMA m /GO microcapsules have the compact multi-shell structure, including the inner layer of PGMA m polymers, the outer layer of GO sheets, giving them excellent closure and stability properties. • The PU-FPDMS/MCs/Ag marine anti-fouling coating on the surface of imitation crab shells is constructed by assembling the Bu@PGMA m /GO microcapsules and AgNPs step by step on the PU-FPDMS matrix. • The PU-FPDMS/MCs/Ag bionic anti-fouling coatings achieve long-term and stable anti-fouling effect under the combination of robust PU-FPDMS matrix with low surface energy, steady-state sustained release of butenolide encapsulated by the compact multilayer shell, bionic surface formed by the microcapsules and AgNPs, and the release of Ag+. Polyurethane-fluorinated polysiloxane (PU-FPDMS) with high-strength, high-bonding and low surface energy is synthesized as the matrix, and the PU-FPDMS/MCs/Ag marine anti-fouling coating on the surface of imitation crab shells is constructed by assembling butenolide@1,1-stilbene-modified hydrolyzed polyglycidyl methacrylate/graphene oxide microcapsules (Bu@PGMA m /GO MCs) with compact multi-shell structure and Ag nanoparticles (AgNPs) step by step on the PU-FPDMS matrix. The PU-FPDMS/MCs/Ag bionic anti-fouling coatings achieve long-term and stable anti-fouling effect under the combination of robust low-surface-energy PU-FPDMS matrix, steady-state sustained release of butenolide encapsulated by the compact multi-shell, bionic surface formed by the microcapsules and AgNPs, and the release of Ag+. The shear strength, tensile strength, and elongation at break of the PU-FPDMS/MCs/Ag are 3.53 MPa, 6.7 MPa, and 192.83 %, respectively. Its static contact angle and sliding angle are 161.8° and 3.6°, respectively. The antibacterial rate of PU-FPDMS/MCs/Ag against Escherichia coli, Staphylococcus aureus , and Candida albicans can reach 100 %. Compared with glass blank, PU, PU-FPDMS, PU-FPDMS/Ag, and PU-FPDMS/MCs, both the adhesion number and coverage percentage of chlorella adhere to PU-FPDMS/MCs/Ag are the minimum values, which are 600 cell mm–2 and 1.53 %, respectively. After 6 months of marine field test, the primer blank, PU, PU-FPDMS all show different degrees of attachment by shellfish, spirorbis, algae and other biofouling, while the PU-FPDMS/MCs/Ag coating is still not covered with biofouling, while the PU-FPDMS/MCs/Ag coatings still exhibit little attachment of marine fouling. The PU-FPDMS/MCs/Ag bionic anti-fouling coatings are expected to be widely used in the fields of anti-fouling, anti-icing, anti-fogging, drag reduction, self-cleaning, and antibacterial. [Display omitted] [ABSTRACT FROM AUTHOR]

• The cobalt-free Mn-LLOs cathode materials with a small amount of Li 2 MnO 3 crystal domain in the bulk structure and gradient doping of Al and Ti elements have been developed. • The batteries with Mn-LLOs cathode deliver the energy density over 600 Wh kg−1, excellent cycle retention of 99.7 % after 800 cycles with low voltage decay of 0.03 mV cycle−1. • The Mn-LLOs cathode materials with bulk structure design and interface regulation can improve the long-time cycling stability of low-cost Mn-based energy storage batteries. The cobalt-free Mn-based Li-rich layered oxide material has the advantages of low cost, high energy density, and good performance at low temperatures, and is the promising choice for energy storage batteries. However, the long-cycling stability of batteries needs to be improved. Herein, the Mn-based Li-rich cathode materials with small amounts of Li 2 MnO 3 crystal domains and gradient doping of Al and Ti elements from the surface to the bulk have been developed to improve the structure and interface stability. Then the batteries with a high energy density of 600 Wh kg−1, excellent capacity retention of 99.7 % with low voltage decay of 0.03 mV cycle−1 after 800 cycles, and good rates performances can be achieved. Therefore, the structure and cycling stability of low voltage Mn-based Li-rich cathode materials can be significantly improved by the bulk structure design and interface regulation, and this work has paved the way for developing low-cost and high-energy Mn-based energy storage batteries with long lifetime. [Display omitted] [ABSTRACT FROM AUTHOR]

In this article, the robust state estimation for discrete-time systems subject to non-Gaussian process and measurement noises (PMNs) is considered. Employing the ability of mixture correntropy in tackling non-Gaussian noise (NGN), we construct the cost function in accordance with the maximum mixture correntropy criterion (MMCC) based on the nonlinear regression model. The fixed-point iteration method is then employed to solve the cost function, and the measurement matrix is updated using the latest estimation. To enhance the estimation performance of the iterated estimation algorithm, the kernel parameters are adjusted adaptively in real time using the Mahalanobis distance (MD). The algorithm for the linear system is also provided. The performance of the proposed algorithms is verified through both simulations and experiments.

Slippery liquid-infused porous surfaces (SLIPSs) have great potential to replace traditional antifouling coatings due to their efficient, green, and broad-spectrum antifouling performance. However, the lubricant dissipation problem of SLIPS severely restricts its further development and application, and the robust SLIPS continues to be extremely challenging. Here, a composite phase-change lubricant layer consisting of paraffin, silicone oil, and MXene is designed to readily construct a stable and NIR-responsive self-healing phase-change solid slippery surface (PCSSS). Collective results showed that PCSSS could rapidly achieve phase-change transformation and complete self-healing under NIR irradiation and keep stable after high-speed water flushing, centrifugation, and ultrasonic treatment. The antifouling performance of PCSSS evaluated by protein, bacteria, and algae antiadhesion tests demonstrated the adhesion inhibition rate was as high as 99.99%. Moreover, the EIS and potentiodynamic polarization experiments indicated that PCSSS had stable and exceptional corrosion resistance (|Z|0.01Hz= 3.87 × 108Ω·cm2) and could effectively inhibit microbiologically influenced corrosion. The 90 day actual marine test reveals that PCSSS has remarkable antifouling performance. Therefore, PCSSS presents a novel, facile, and effective strategy to construct a slippery surface with the prospect of facilitating its application in marine antifouling and corrosion protection.

Considering the complexities of the modern maritime operational environment and aiming for effective safe navigation and communication maintenance, research into the collaborative trajectory tracking problem of unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) clusters during patrol and target tracking missions holds paramount significance. This paper proposes a multi-agent deep reinforcement learning (MADRL) approach, specifically the action-constrained multi-agent deep deterministic policy gradient (MADDPG), to efficiently solve the collaborative maritime-aerial distributed information fusion-based trajectory tracking problem. The proposed approach incorporates a constraint model based on the characteristics of maritime-aerial distributed information fusion mode and two designed reward functions—one global for target tracking and one local for cross-domain collaborative unmanned clusters. Simulation experiments under three different mission scenarios have been conducted, and results demonstrate that the proposed approach possesses excellent applicability to trajectory tracking tasks in collaborative maritime-aerial settings, exhibiting strong convergence and robustness in mobile target tracking. In a complex three-dimensional simulation environment, the improved algorithm demonstrated an 11.04% reduction in training time for convergence and an 8.03% increase in reward values compared to the original algorithm. This indicates that the introduction of attention mechanisms and the design of reward functions enable the algorithm to learn optimal strategies more quickly and effectively.

Immune checkpoint inhibitor (ICI) therapy is effectively employed in treating various malignancies. However, the response rate is constrained to 5–30%, which is attributed to differences in immune responses across different tumors. Overcoming all obstacles of multistep immune activation with monotherapy is difficult. Here, maleimide-modified resiquimod (R848) prodrug nanoparticles (MAL-NPs) are reported and combined with radiotherapy (RT) and anti-PD1 to enhance ICI therapy. MAL-NPs can promote antigen endocytosis by dendritic cells and are radio-reduced to produce R848. When combined with RT, MAL-NPs can augment the concentration of nanoparticles at tumor sites and be selectively radio-reduced within the tumor, thereby triggering a potent antitumor immune response. The systemic immune response and long-term memory efficacy induced by MAL-NPs + RT + anti-PD1 significantly inhibit the abscopal tumor growth and prevent tumor recurrence. This strategy can achieve systemic therapy through selective training of the tumor immune microenvironment, offering a new approach to overcome the obstacles of ICI therapy.

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Automatic combustion control (ACC) system is the main control system of the current municipal solid waste (MSW) incineration power plant. In this work, the artificial neural network (ANN) model was first used for prediction and guiding of operation of the incineration system. Results showed that BP-ANN model was an effective tool for operation parameter prediction, especially the crucial parameters of steam mass flow and furnace temperature. The Log-Sigmoid function was more suitable to be used as the transfer function in hidden layer. Comparison of the input parameter showed that furnace temperature, grate speed, primary air, and secondary air are the most important and relevant parameters for prediction of steam mass flow in MSW incineration system. The genetic algorithm (GA) could be used to optimize the initial weight and threshold values of the BP-ANN network, which largely improved the prediction accuracy. Impact weight analysis results show that furnace temperature and secondary have the largest influence on the steam flow. With regard to furnace temperature, secondary air, primary air, and oxygen concentration in exhaust gas play the leading roles. The largest error for steam mass flow prediction of the real-time operation was lower than 8%.

Cloud cover poses a significant challenge in remote sensing image processing, affecting the extraction and analysis of terrestrial features. Despite advancements in multitemporal cloud removal methods, single-image declouding remains crucial for emergency response and disaster management, where rapid acquisition of cloud-free imagery is essential. Traditional approaches often rely on synthetic aperture radar (SAR) or cloud masks as guidance for cloud removal, introducing additional complexities and dependencies on extensive data. To address these limitations, we propose a density-aware cloud removal using a global-local fusion Transformer (DCR-GLFT), which leverages density information as guidance and does not rely on extensive data. Specifically, our method employs density labels to guide the cloud removal process through two primary stages: cloud density estimation and density-guided cloud removal. A cloud density classifier is proposed in the first stage, trained with roughly estimated ground truth, to generate density labels for guiding subsequent removal processes. The second stage integrates cloud density information with cloud-ground image features using a Transformer-based network, enabling precise and nuanced cloud removal while preserving underlying surface details through the integration of both global and local features. The proposed method achieved the state-of-the-art results (peak signal-to-noise ratio (PSNR) of 28.93 and structural similarity index measure (SSIM) of 0.84) on the renowned cloud-removal dataset SEN12MS-CR, even without utilizing SAR data for guidance. This accomplishment highlights its significant advancement in the single-image cloud removal task. Our code will be made available at https://github.com/ruiquan1214/DCR-GLFT.git.

Vegetation constitutes a significant portion of land cover. Due to the high spatial heterogeneity of site conditions and the similarities in spectral reflectance and shapes among different vegetation types, land cover mapping accuracy is often low in mixed regions with multiple vegetation types. In addition to traditional factors such as spectral characteristics, topography, and commonly used features, we present a novel land cover mapping framework that incorporates evapotranspiration, which exhibits significant variations among vegetation types. The proposed land cover mapping framework consists of the following steps: 1) estimating year-round actual evapotranspiration using the remote sensing SEABL model; 2) training a classifier to classify land cover based on integrated factors, including spectral bands, spectral indices, topography, night light data, and evapotranspiration; 3) generating land use and land cover mapping using evapotranspiration (ETLULC); and 4) comparing and evaluating the accuracy and variability of results against different input schemes and existing products. In a typical mixed region with multiple vegetation types, the ETLULC framework demonstrates impressive performance, achieving an overall accuracy of 93%. The classification accuracy for all land cover types exceeds 90%. Compared to traditional methodologies that do not incorporate evapotranspiration as an input, ETLULC significantly improves the recognition accuracy for cropland, forest, and grassland by 5.4%–15.3%, 0%–15.7%, and 3.0%–20.4%, respectively. Moreover, ETLULC exhibits strong agreement with existing products applied in the Ordos Basin, particularly for cropland (54.7%–82.3%), forest (32.2%–71.7%), and grassland (56.4%–94.3%). The performance of ETLULC underscores the effectiveness of this innovative land cover mapping framework. This study introduces a novel approach by leveraging the spatial heterogeneity of vegetation characterized by evapotranspiration to enhance the accuracy of land cover mapping. This method holds significant practical value and has broad applicability in identifying effective feature combinations for vegetation recognition in extensively distributed mixed vegetation regions.

With the development of sensor technology, the rational use of multimodal data has become a research hotspot in the field of remote sensing. The multimodal fusion method can effectively improve the accuracy of remote sensing data classification by using the complementary information of different modalities. However, the existing multimodal fusion methods face many challenges, including difficulties in suppressing spectral noise, fully mining contextual information, and learning the strong adaptive fusion pattern. To address the above challenges, a Gaussian mixture variational dynamic fusion network (GM-VDFN) is proposed. First, a multimodal multiscale spatial graph is constructed, and the graph convolution is used to learn the multiscale features. In this process, a spatial topology constraint based on GM (STC-GM) is proposed, which suppresses spectral noise by constraining the topological consistency of the two modalities. Second, a multiscale dynamic graph aggregation module (MDGAM) is constructed, which can capture the shareable class identification information from multiscale features and mine personalized fusion patterns suitable for each sample. Finally, the evidence lower bound for the multimodal joint distribution is derived, and a multimodal variational autoencoder (M-VAE) is designed. Optimizing the evidence lower bound to model multimodal joint distributions, thereby learning the strong adaptive fusion pattern between modalities. Experimental results on four fusion datasets (Houston 2013, Trento, MUUFL, and Houston 2018) show that GM-VDFN achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

The filtered-x least mean ${\mathcal {([A-Z]+)}}_{([a-z]+)}$ -norm (FxLMP) algorithm, renowned for its widespread usage, exhibits superior performance over the filtered-x Least Mean Square (FxLMS) algorithm in impulsive noise-afflicted environments. Nevertheless, the FxLMP algorithm experiences performance degradation when confronted with nonlinearity in either the primary or secondary path. In response to this challenge, we propose a novel algorithm known as the minimum output variance filtered-s LMP (MOV-FsLMP), which effectively tackles nonlinear active noise control problems by imposing constraints on variance or power. Notably, this proposed algorithm exhibits improved convergence and stability even in impulsive noise environments, all while maintaining lower computational complexity. Extensive simulations validate the efficacy of the proposed MOV-FsLMP method, surpassing that of state-of-the-art alternatives.

The continuous development of network media technology has driven the unceasing change in the online social environment, from PC social to mobile social, which are currently experiencing a new change: Metaverse social. The iteration of the social environment gives impetus to the uninterrupted upgrading of social patterns, which leads to the ceaseless innovation of the product promotion model. In this article, motivated by the characteristics of the Metaverse social, we devise virtual activity-oriented product promotion tactics. We propose an activity-oriented promotion utility maximization problem and tackle it systematically. We demonstrate the complexity and inapproximability of the problem. A continuity approximate concave relaxation method is devised to optimize the set function with a supermodularity ratio. We develop an approximate projected subgradient procedure to obtain the solution with an approximate factor guarantee. Experiments on two types (traditional social network and Metaverse social network) of real-world datasets verify the feasibility and scalability of our algorithm and model, and the research results have guiding significance for the online promotion of products.

Graphical Abstract: We reported a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating effects.

Trajectory optimization is a complex problem, as it works to improve the operational efficiency of airspace users and air traffic management (ATM) systems in trajectory-based operations. However, the current trajectory optimization methods generally typically encounter difficulties with large mesh computation and low optimization efficiency, which in turn leads to the reduction of airspace operation efficiency. Therefore, this paper first establishes a trajectory management framework between ATM and airspace user and constructs a multi-phase trajectory optimization model. Secondly, an improved adaptive mesh refinement method for four-dimensional trajectory optimization is proposed, and the flight trajectory is optimized by transforming the time-continuous optimization problem into a discretized nonlinear programming problem using Legendre–Gauss–Radau collocation points. Then, the mean integral error is calculated using the Romberg method based on the solution error, and the mesh intervals and collocation points of each flight phase are adjusted adaptively based on the mean integral error. Finally, the performance of the proposed method is verified through trajectory optimization results in the pre-flight and in-flight stages. The results show that the four-dimensional trajectory is optimized accurately and quickly based on aircraft performance, flight plan, flight objectives, and airspace constraints allocated by ATM, and compared with the H method and the traditional mesh refinement method, the proposed method reduces the number of mesh intervals and collocation points and improves the solution speed of trajectory optimization and the operational efficiency of ATM systems.

This article focuses on the observer-based asynchronous event-triggered (AET) security control for two-time-scale cyber-physical systems (TTSCPSs), which have slow and fast transmission channels (TCs) subject to hybrid attacks. A novel hybrid-attack model comprising denial-of-service (DoS) and deception attacks is characterized, which independently occurs on slow and fast TCs in consideration of their dual-scale feature. Then, a novel dual-rate-sampled AET scheme is adopted to orchestrate individual transmitting order of slow and fast TCs, respectively, by which we, as the first attempt, construct the AET security control framework. An $\varepsilon$-dependent switched Lyapunov function is designed to accomplish the security control task for the specified TTSCPSs, such that the closed-loop system acquires a prescribed $H_{\infty }$ index in the simultaneous existence of external disturbances and hybrid attacks. Furthermore, within the established design framework, the desired switched observer gain is quantized by solving the proposed criteria in the form of linear matrix inequalities, while the possible numerical stiffness is also averted. Finally, an inverted pendulum simulation is set up in the networked environment to demonstrate the effectiveness of the developed control strategy.

Two-dimensional layered materials and low-loss, high-quality on-chip functional devices formed by optical waveguides have received widespread attention in the field of integrated photonics. In this work, we demonstrate an electro-optic modulator based on Mach-Zehnder interference (MZI) for the first time, which is integrated with MoS2. The ionic liquid is directly cover the surface of the MoS2-SiN waveguide device to form a capacitor structure Due to the outstanding electro-refractive and electro-absorption properties of MoS2, the phase modulation efficiency obtained by adjusting the voltage on the long and short arms is 445 pm/V, the maximum phase change is 1.14π, the half-wave voltage length product VπL is 0.11 V·cm, and the maximum effective refractive index offset is 2.93 × 10−3 RIU. Notably, we can achieve a wavelength shift of 1780 nm and an electro-optic response bandwidth exceeding 8.5 GHz at voltage saturation. The proposed electro-optic modulator is expected to be applied to low-loss, high-performance large-scale photonic integrated systems.

With the development of artificial intelligence and information technology, drones working in tandem with manned aerial vehicle have become the new normal. Current paper focuses on the following theme: how to assess the effectiveness of manned/unmanned aerial vehicle systems under different formations. However, the analysis of the effectiveness of manned/unmanned aircraft cooperation faces the problem of unclear mechanisms and difficulty in tracing the key influencing factors. Therefore, in this paper, a closed frequent pattern mining method is used to analyze and design a new data structure based on cross-linked table improvement. In this paper, the units and capabilities in the manned/unmanned aerial vehicle system are mined and analyzed in a time-series manner to obtain the effectiveness of the patterns of manned/unmanned aircraft utilization in tandem under different formations. Finally, a typical maritime application scenario is used as an example to effectively compare the effectiveness of different manned/unmanned aircraft cooperative modes and to provide guidance for the subsequent development of manned/unmanned aircraft cooperative applications.

Voltage normalization is widely used in phase-locked loops (PLLs) to keep their dynamic and stability decoupled from the grid-voltage amplitude. In this article, a complex-type voltage normalization method is proposed for three-phase PLLs. First, the performances of the PLL under the conventional voltage magnitude normalization method and the $d$-axis voltage normalization method are reviewed. When large frequency and/or phase jumps occur, the voltage magnitude normalization method has undesirable oscillations, and the $d$-axis voltage normalization method exists unexpected stable equilibrium points. To address these, a complex-type voltage normalization method is proposed. Then, the large-signal model of the PLL under the complex-type voltage normalization is established. And the phase-portrait and Lyapunov-based analysis are provided. It is shown that for any large frequency jump and/or phase jump, the PLL with the complex-type voltage normalization still converges to the unique stable point (0,0). Experimental tests are also provided, which show the superior performance of the proposed complex-type voltage normalization method.

In this article, an on-line calibration method for strap-down inertial navigation system (SINS)/Doppler velocity log (DVL) based integrated navigation system is proposed to jointly estimate the calibration parameters of SINS and DVL, where the DVL lever arm and gyroscope scale factor error are considered. The full-parameters, including the gyroscope scale factor error, gyroscope bias, accelerometer bias, DVL installation misalignment angle, and DVL lever arm, are modeled as calibration parameters. A new state–space model for on-line calibration of SINS/DVL integrated navigation system is established, which also introduces the measurements of the global navigation satellite system (GNSS) and celestial navigation system (CNS) to improve the observability of calibration parameters. To suppress the effects of nonlinear error introduced by large installation error between SINS and DVL, a full-parameters feedback correction scheme is proposed. In addition, a special trajectory for the proposed on-line calibration method is designed based on the observability analysis results and the actual motion conditions of the ship, which ensures those calibration parameters in the calibration model can be observed. Simulation results and semi-physical simulation results illustrate that the proposed on-line calibration method can achieve better calibration results than existing methods compared in this article, and the full-parameters can be jointly calibrated online, even in the case of large installation errors between SINS and DVL.

In this brief, we consider the robust state estimation for a linear system with colored heavy-tailed process and measurement noises. We employ the state augmentation and measurement differencing methods to whiten the colored noise and use the Student’s t distribution to model the heavy-tailed property, which makes a new state space model with the augmented state vector. The posterior estimation of the system state, inaccurate scale matrices, and auxiliary parameters are jointly inferred with the variational Bayes method by constructing the hierarchical Gaussian forms of the prediction and likelihood probability density functions and selecting the proper prior distributions of the scale matrices and auxiliary parameters. A typical target tracking simulation is given to confirm the performance of the proposed robust Kalman filter.

A multimode fiber (MMF) speckle pattern is proposed to demodulate a distributed Bragg reflector (DBR) fiber laser sensor. DBR light source emits a highly coherent laser, and the speckle pattern is caused by intermodal interference in the MMF. The speckle change induced by salinity and temperature is identified using the image correlation algorithm through the alteration of the speckle pattern. Zero mean normalized cross correlation (ZNCC) is used to process the speckle data, and the test range was extended by adjusting the reference speckle. In the temperature range of 25 °C–75 °C and 75 °C–100 °C, the speckle sensitivity is −0.0150/°C and −0.0148/°C, respectively. The salinity sensitivity is −0.0158/ ‰, compared with nearly no spectrum shift in the optical spectrum analyzer (OSA) under the same conditions. The proposed sensor can be applied in environmental monitoring and other fields.

Deep Metric Learning (DML) is very effective for many computer vision applications such as image retrieval or cross-modal matching. The common paradigm for DML is to seek metric spaces that can encode semantically similar objects close while locating the dissimilar ones far away from each other. To make features more discriminative, the mainstream methods usually design various specific loss functions to seek the help of hard negatives through complex hard mining strategies or hard synthesizing with additional networks. In spite of their fruitfulness, these approaches ignore the impact of low-level information in images on the performance, which may degrade the discerning ability of learned embedding. To alleviate these problems, we introduce a simple yet effective augmentation method to generate more hard negatives by swapping the low-frequency spectra of negative instances with anchors in the Fourier domain. Specifically, unlike previous methods, our proposed approach does not involve any complex design strategies but enriches hard negatives by manipulating the low-level variability of images only with simple Fourier transforms. In addition, our method is treated as a universal plug-in, which can be incorporated into different models for performance improvement. In the end, we conduct extensive experiments to evaluate our method on the widely-used datasets including CUB-200–2011, CARS-196, and Stanford Online Products. Our quantitative results demonstrate that the proposed plug-in outperforms previous approaches consistently and significantly across different datasets and evaluation metrics.

Ammonia/methane fuel blends have gathered interest as a promising solution for the development of a low-carbon energy system. In that pursuit, this work focused on the effects of turbulent intensity (u′ = 1.3–2.6 m/s), ammonia contents (XNH3= 0.0–1.0), and equivalence ratios (ϕ = 0.5–0.9) on the flame structure and NO emissions of bluff-body swirl-stabilized premixed flames of ammonia/methane/air as well as their links to hydroxyl planar laser-induced fluorescence (OH-PLIF) intensity. Three swirling flame shapes are distinguished, which are jointly determined by the ammonia content and equivalence ratio. The normalized OH-PLIF intensity (Iaverage/SL) of all XNH3, u′, and ϕ can be collapsed well onto one unified correlation as a function of the normalized turbulent intensity u′/SL. The flame surface density (FSD, marked as Σ) of confined swirling flames was rarely reported before, and the FSD in a V shape increases obviously when transiting to a M shape as the ammonia content reduces. The turbulent flame area ratio (AT/AL) derived from the FSD is increasing as u′/SLincreases; however, it decreases first and then increases as ϕ increases. Then, a new description of the link between NO, OH, and XNH3was provided, which improves the correlation performance more than the previous studies only considering the link between NO and OH.

Deep metric learning has become a key component of cross-modal retrieval. By learning to pull the features of matched instances closer while pushing the features of mismatched instances farther away, one can learn highly robust multi-modal representations. Most existing cross-modal retrieval methods leverage vanilla triplet loss to train the network, which cannot adaptively penalize pairs with different hardness. Although various weighting strategies have been designed for unimodal matching tasks, few weighting strategies have been applied to cross-modal tasks due to the specificity of cross-modal tasks. While few weighting strategies are designed for cross-modal scenarios, they usually involve a lot of hyper-parameters, which require a lot of computational resources to fine-tune. In this paper, we introduce a new exponential loss, which can assign appropriate weights to individual positive and negative pairs according to their similarity so that it can adaptively penalize pairs with different hardness. Furthermore, the exponential loss has only two hyper-parameters, making it easier to find the optimal parameters to suit various data distributions in practice. Exponential loss can be universally applied to well-established cross-modal models and further boost their retrieval performance. We exhaustively ablate our method on Image-Text matching, Video-Text matching, as well as unimodal Image matching. Experimental results show that a standard model trained with exponential loss can achieve noticeable performance gains.

Although many antibiofouling materials have been developed based on either bacterial-killing or antiadhesion effects, the integration of both the effects in one material remains challenging for achieving highly enhanced synergistic antibiofouling. In this study, we have explored a nano-CeO2-loaded double-network hydrogel by introducing CeO2nanorods into a polyzwitterionic hydrogel via a simple one-pot method for achieving highly efficient antifouling. First, the CeO2nanorods dispersed in the hydrogel, as an outstanding nanozyme, have highly efficient bacterial-killing performance. Second, the superhydrophilic polyzwitterionic hydrogel provides a dense hydrated layer on the surface and subsequently excellent broad-spectrum antiadhesion behavior. Most importantly, the bacterial killing and antiadhesion of this hydrogel can work synergistically to largely improve the marine-antifouling performance. Moreover, the double-network structure of this hydrogel, including the covalently cross-linked polyzwitterion hard network and the physically cross-linked poly(vinyl alcohol) soft network, can provide greatly improved mechanical properties (2.44 MPa of tensile strength reaches and 21.87 MPa of compressive strength). As a result, among the existing marine-antifouling hydrogels, the CeO2-loaded polyzwitterionic double-network hydrogel can achieve outstanding antifouling performance, which can sustain for over 6 months in a real marine environment. This work provides a promising marine-antifouling hydrogel, which will also inspire antifouling research of a new strategy and materials.

This article looks into the topic of distributed formation control for a group of quadrotors that are vulnerable to denial-of-service (DoS) attacks. By preventing information interaction between adjacent quadrotors, the DoS attacks, which happen independently on individual communication channels, disrupt the formation mission. A decomposition-combination control framework is devised for the formation task. The corresponding high-order dynamic system is partitioned into slow and fast subsystems using the singular perturbation technique, which lessens the computing load and resolves the under-driven issue. A composition formation control scheme is then put forward, which includes a resilient slow-scale controller to tackle DoS attacks along with an optimal fast-scale controller to assure flying performance and stability. It should be noted that position and velocity information, which can be acquired and processed entirely by subcontroller in the proposed method, is the main content of communication between quadrotors, permitting more effective system execution. Finally, a slow-fast composition mechanism is provided, and simulation results of a system containing four quadrotors serve as validation for the suggested methods.

This article addresses the replay attack detection issue for a class of industrial process operation optimization (IPOO) cyber-physical systems (CPSs). In contrast to conventional CPSs with a single-loop structure, the IPOO CPSs employ a dual-layer network environment consisting of a wireless network for the setpoint optimization loop and a controller area network (CAN) bus for the device control loop. Consequently, the issue of attack detection is more complicated for the IPOO CPSs, and this motivates our current research. First, a unified model of the IPOO CPS is built, with PI controller controlling the physical plants and linear quadratic Gaussian (LQG) controller managing setpoint optimization. Then, a novel joint watermarking detection mechanism is established with the PI controller watermarking, LQG controller watermarking, and a watermarking compensator. The proposed watermarking compensator with an augmented Kalman filter is utilized to efficiently eliminate false alarms brought on by information interactions of the coupled cyber-layers, enabling the accurate detection and location of replay attacks. Furthermore, a linear relationship is established between watermarking parameters and system performance loss. Finally, simulations with a quadruple water tank system are conducted to verify the effectiveness of the proposed algorithm.