Search

Oilseed rape is one of the most important oil crops worldwide. Stem rot disease of rapeseed is caused by Sclerotinia sclerotiorum, posing a threat to oil crop yield. NTL is a small class of membrane-tethered NAC transcription factors, which are stored on the membrane in dormant form, released upon stimuli, and then transported to the nucleus. Here, we identified BnaNTL1 from oilseed rape, and its relocation from the ER to the nucleus is induced by S. sclerotiorum.Plants overexpressing BnaNTL1-ΔTM(a truncated form without the transmembrane domain) are resistant to S. sclerotioruminfection and are accumulated with more JA and ROS. Genes related to the JA pathway and ROS signal were significantly induced by BnaNTL1. Furthermore, the dual-luciferase and EMSA results showed that BnaNTL1-ΔTM directly binds to the promoter regions of AOC3, LOX2, OPCL1, and PDF1.2, and it activates their expression. In summary, we identified that BnaNTL1 positively regulates plant resistance to S. sclerotioruminfection by modulating JA synthesis and ROS production.

The multiscale microstructure of a new type of high-strength martensitic stainless steel (HSMSS) used in aerobearings under different solid-solution temperatures is quantitatively characterized. Their relationships with strength and toughness are systematically studied based on experimental characterizations and theoretical calculations. The results indicate that the increasing solid-solution temperature leads to a decrease in the yield strength and an increase in the impact toughness. The optimal solid-solution temperature is accordingly determined to be 1100 °C. Via the multiscale characterizations by SEM, XRD, EBSD, and TEM, it shows that the sizes of high-angle grain have little variation with the solid-solution temperature, with an average size of approximately 1.8 μm. In addition, the increase in the solid-solution temperature leads to the dissolution of M23C6carbides at 1050 °C and of M6C carbides at 1100 °C. The dislocation density remains consistent across levels after tempering in the solid-solution temperature range of 1000–1150 °C. Nanoprecipitated Mo-enriched phase and spinodal decomposition also occur after post-tempering, resulting in a significant increase in strength. The size of Mo-enriched phase increases as the solid-solution temperature. By quantitively comparing the various strengthening mechanisms for yield strength, it was found that dislocation strengthening is the most key mechanism, accounting for more than 40% of the total increase. Yet, the impact toughness of HSMSS is jointly determined by the undissolved carbides and Mo-enriched phase. This study is, for the first time, to clarify its multi-scale microstructure key factor for mechanical properties in HSMSS and thus lays a foundation for fabricating excellent temperature-resistant aerobearing steel.

Recently, public and private providers within two-tier healthcare markets have engaged in a fierce competition for patients, which is significantly influenced by patients' multifaceted sensitivities to delays and payments, particularly when patients' potential balking is considered. However, existing literature studying the two-tier healthcare market often presupposes patients as homogeneous or one-dimensionally heterogeneous, and neglects their balking behavior. In stark contrast, this paper delves into the combined effects of patients’ delay- and payment-sensitive heterogeneities, alongside their joining or balking decisions, on the service competition dynamics within the two-tier healthcare market. Leveraging a game-theoretic framework, we initially derive subgame equilibria, both inclusive and exclusive of patient balking, uncovering that patient balking can lead to a subgame perfect Nash equilibrium in the presence of two-dimensional patient heterogeneity. Intriguingly, our findings further suggest that the non-profit public provider may, counterintuitively, curtail its service capacity despite budgetary adequacy, contradicting the conventional assumption that such providers would fully allocate funds towards patient care. Ultimately, from a patient welfare standpoint, we find heightened payment heterogeneity discrimination proves detrimental to patients, whereas increased differentiation in delay heterogeneity benefits them. This revelation underscores a pressing social issue inherent in the two-tier healthcare market model: It has the potential to exacerbate healthcare disparities rooted in economic inequalities within the population.

The core layer of disposable diapers typically contains a blend of superabsorbent polymer (SAP) and pulp, resulting in slow liquid absorption, layer separation, reverse osmosis, and potential skin issues owing to the addition of antibacterial agents to the surface layer. Therefore, a core layer with rapid liquid absorption, uniform retention, and antibacterial properties must be developed to improve wearer comfort. In this study, a three-dimensional network porous structure for the core layer of a disposable diaper was prepared by solution blow spinning (SBS). This structure comprised a superabsorbent fiber (SAF) and hydrolyzed polyacrylonitrile (HPAN) micro/nanofibers with a dual gradient in wetting/pore size. Progressively increasing the SAF content in each layer to incrementally increase wettability and controlling fiber diameter, a gradient pore structure with sizes of approximately 30 μm–16 μm–7 μm was formed. This design exhibited rapid infiltration capability, reducing the third liquid infiltration time by 13 s compared to those of commercial core layers while reducing reverse osmosis by 1.4 g, and the liquid absorption and retention rates are 47.7 times and 46.1 times, respectively, which is 1.6 times higher than those of commercial diapers. In addition, incorporating a natural antibacterial agent, ε-poly-l-lysine hydrochloride (ε-PLH), into the core layer resulted in an antibacterial rate exceeding 99.99% without direct contact with the skin; water transport capacity tests confirmed faster liquid infiltration speed, uniform absorption, and no fault formation.

Our understanding of the temporal dynamics of the Earth's surface has been significantly advanced by deep vision models, which often require a massive amount of labeled multi-temporal images for training. However, collecting, preprocessing, and annotating multi-temporal remote sensing images at scale is non-trivial since it is expensive and knowledge-intensive. In this paper, we present scalable multi-temporal change data generators based on generative models, which are cheap and automatic, alleviating these data problems. Our main idea is to simulate a stochastic change process over time. We describe the stochastic change process as a probabilistic graphical model, namely the generative probabilistic change model (GPCM), which factorizes the complex simulation problem into two more tractable sub-problems, i.e., condition-level change event simulation and image-level semantic change synthesis. To solve these two problems, we present Changen2, a GPCM implemented with a resolution-scalable diffusion transformer which can generate time series of remote sensing images and corresponding semantic and change labels from labeled and even unlabeled single-temporal images. Changen2 is a “generative change foundation model” that can be trained at scale via self-supervision, and is capable of producing change supervisory signals from unlabeled single-temporal images. Unlike existing “foundation models”, our generative change foundation model synthesizes change data to train task-specific foundation models for change detection. The resulting model possesses inherent zero-shot change detection capabilities and excellent transferability. Comprehensive experiments suggest Changen2 has superior spatiotemporal scalability in data generation, e.g., Changen2 model trained on 256$^{2}$2 pixel single-temporal images can yield time series of any length and resolutions of 1,024$^{2}$2 pixels. Changen2 pre-trained models exhibit superior zero-shot performance (narrowing the performance gap to 3% on LEVIR-CD and approximately 10% on both S2Looking and SECOND, compared to fully supervised counterpart) and transferability across multiple types of change tasks, including ordinary and off-nadir building change, land-use/land-cover change, and disaster assessment.

Rapid technological advancements in remote-controlled magnetic robots that operate inside the human body have strongly stimulated the development of magnetic actuation systems owing to their ability to produce demanding magnetic fields and forces. However, the efficient generation of the combined field and force in large workspaces still needs to be improved. To resolve this critical challenge, we first investigated the required number of mobile magnetic sources for a magnetic actuation system to regulate field and force in large workspaces. Subsequently, we demonstrated that a three-electromagnetic-coil configuration can effectively produce the desired field and force. This is achieved through the implementation of a field-priority force control method utilizing three robotic arms. Notably, this method considers Yoshikawa manipulability for each robotic arm, which matters for manipulating magnetic robots in large workspaces. Specifically, we evaluated the force error by sampling 50 different field-force combinations from the workspace. Finally, a reconfigurable electromagnetic actuation system (REMA) was developed by mounting three mobile electromagnetic coils mounted on three independent robotic arms. The effectiveness of the proposed method and platform was demonstrated by actuating a capsule robot in both 2-D and 3-D application scenarios.

In this article, a high-speed permanent magnet synchronous motor (HSPMSM) full-speed-range sensorless control strategy is proposed based on the prediction methodology. By leveraging the predictive idea, on the one hand, this strategy generates an error current that encompasses position information, from which the speed and position are directly demodulated during the start stage to adjust the given current vector in real time. It effectively reduces motor speed fluctuations at startup, enhances system anti-interference, and seamlessly transitions from the start algorithm to the double closed-loop sensorless control algorithm. On the other hand, it establishes the relationship between the position estimation error and the predicted current and the measured current, aiming to enhance the position estimation precision and optimize the HSPMSM double closed-loop sensorless control performance. Importantly, this strategy does not need the additional voltage sensors for the instantaneous power and can obtain the total position estimation error without supplementary controller design, which is highly practical. The control performance of the proposed strategy is verified on a 1.1 kW, 30000 r/min HSPMSM experimental platform, and compared with the traditional method. The experimental results prove the feasibility and effectiveness of the proposed strategy.

Complementary neural network circuits combining multifunctional high-performance p-type with n-type organic artificial synapses satisfy sophisticated applications such as image cognition and prosthesis control. However, implementing the dual-modal memory features that are both volatile and nonvolatile in a synaptic transistor is challenging. Herein, for the first time, we propose a single vertical n-type organic synaptic transistor (VNOST) with a novel polymeric organic mixed ionic-electronic conductor as the core channel material to achieve dual-modal synaptic learning/memory behaviors at different operating current densities via the formation of an electric double layer and the reversible ion doping. As a volatile synaptic device, the resulting VNOST demonstrated an unprecedented operating current density of MA cm–2. Meanwhile, it is capable of 150 analog states, symmetric conductance modulation, and good state retention (100 s) for a nonvolatile synapse. Importantly, the artificial neural networks (ANNs) for recognition accuracy of the handwritten digital data sets recognition rate up to 94% based on its nonvolatile feature. This study provides a promising platform for building organic neuromorphic network circuits in complex application scenarios where high-performing n-type organic synapse transistors with dual-mode memory characters are necessitated.

Magnetically actuated robots have recently shown great capabilities for remote applications in medical procedures. However, the efficient actuation of magnetic robots with dexterous field and gradient generation in large workspaces remains challenging. To overcome the critical challenges, we report a reconfigurable electromagnetic actuation system (REMA) for regulating magnetic fields (maximum: 17 mT) and gradients (maximum: 120 mT/m) in large workspaces. Reconfigurable coil configurations are achieved by employing three mobile electromagnetic coils mounted on three independent 6-DOF robotic arms. Furthermore, the field characteristics generated by a single coil and three coils were modeled via Finite-element method (FEM) and measurements from experiments, respectively. Since there are non-linearities between desired field generation and coil configuration, we propose a multi-objective optimization (MOO) method for generating the Pareto-optimized coil configuration to achieve field and force control in large workspaces. Finally, extensive experiments were conducted to demonstrate the capability and dexterity of our system for autonomous magnetic manipulation in large workspaces, thus showing its potential for clinical applications. Note to Practitioners—This paper aims to address the dexterous generation of magnetic fields and gradients in large workspaces, aiming to realize accurate, efficient, and automated control of different magnetic robots. This paper introduces a reconfigurable electromagnetic actuation system based on three independent robotic arms with three electromagnetic coils. Subsequently, we propose a multi-objective optimization (MOO) method to regulate the coil configuration for generating different fields and gradients. This approach facilitates the application of magnetically driven helical robots, catheters, and capsule robots in various medical scenarios. The results demonstrate that our proposed platform and optimization strategy can effectively implement magnetic manipulations across diverse application scenarios. Looking ahead, we anticipate integrating our work with medical imaging devices to furnish doctors with enhanced tools for medical applications.

To ensure aviation safety, monitoring aero-engine blade quality is essential. Traditional manual inspection using industrial endoscopes is costly and inefficient due to metallic reflections and blade similarity. This paper introduces an innovative end-to-end convolutional neural network (CNN) approach for detecting and tracking aero-engine blades, leveraging borescope vision to aid in blade quality monitoring. A variable kernel convolution module is developed to tackle the problem of detection inaccuracy caused by morphological changes in blades from various perspectives. Besides, an innovative three-dimensional attention mechanism is developed, which allows the network to effectively capture semantics across the blade's width, height, and channels. Finally, an optical flow-assisted blade tracking strategy is introduced to resolve identity recognition confusion due to high similarity between blades. The algorithm has been tested on the developed aero-engine blade inspection platform. The experimental results have validated the effectiveness of the proposed method.

Graphical Abstract:

Image registration forms the foundation of collaborative processing in multimodal remote sensing imagery (MRSI). However, high-resolution MRSIs frequently display complex distortions due to imaging characteristics and terrain variations, with both global and local distortions present. Effectively addressing these complex distortions necessitates the identification of uniformly and densely distributed corresponding points across the entire image. Existing methods primarily focus on global affine distortions and often extract only sparse and unevenly distributed corresponding points, which makes the effective handling of these coexisting distortions a significant challenge. To address this problem, we propose a progressive symmetric registration learning network (PSRNet) for MRSIs. In PSRNet, multimodal remote sensing image registration (MRSIR) is redefined as a symmetric dense regression task, differing from the traditional pipeline that concentrates on unidirectional sparse transformation parameter prediction. Specifically, PSRNet consists of three primary components: 1) a multiscale feature projector (MFP), which employs a dual-branch structure with nonshared weights to achieve modality-specific representation of different modal images across multiple scales, 2) a progressive cross-modal transformer (PCMT) to further mine modality-invariant features and progressively predict symmetric deformation fields, and 3) a symmetric consistency loss (SCL) function capable of elegantly achieving high-precision reversible alignment of image pairs, encompassing endpoint error loss, bidirectional alignment loss, and smoothness loss. Experimental results demonstrate that PSRNet achieves more comprehensive and advanced registration performance on our self-constructed large-scale high-resolution MRSIR dataset, which includes complex global-local geometric distortions and significant nonlinear radiometric differences (NRD).

Cyber-physical systems (CPSs) are multidimensional and complex systems that integrate computing, networking, and physical environment, including various sensors that can produce vast multivariate time series data. Recently, scholars have proposed many unsupervised anomaly detection methods to monitor and alarm potential faults of CPSs. However, current methods still have some limitations. First of all, it is challenging to capture spatial-temporal relationships explicitly. In the second place, the anomaly detection model based on the variational autoencoder (VAE) may not use the latent variable and often falls into a kind of collapse, which is encouraged by the Kullback–Leibler (KL) divergence term. In the end, more information about the anomalies is needed. This article proposes a novel anomaly detection method called variational graph attention networks with self-supervised learning (VGATSL). We use stacked spatial-temporal graph attention (STGAT) networks to capture the temporal and feature correlations. Next, a VAE is constructed based on bidirectional gated recurrent units (BiGRUs), and the von Mises-Fisher (vMF) distribution is used as its latent space. Indeed, this distribution places latent representation on the surface of a unit hypersphere to allow for uninformed priors, thus preventing the model collapse. Additionally, self-supervised learning is introduced to simulate abnormal behaviors and to help the model form more explicit normality boundaries. Extensive experiments on three public real-world datasets demonstrate that VGATSL outperforms the baseline methods. We also illustrate that VGATSL has superior interpretability and flexibility.

The microdrill serves as precision cutting tool employed in the drilling of printed circuit boards (PCBs). The edge defects, such as grinding marks and burrs resulting from the grinding process, significantly impairs both the drilling quality and service life. Hence, it is imperative to meticulously prepare the cutting edge to ensure optimal cutting performance. In this paper, the cutting edge of cemented carbide microdrill was prepared by shear thickening polishing (STP) method. To achieve efficient cutting edge preparation of microdrill, STP experiment was carried out to evaluate the polishing force and determine the suitable processing parameters. Furthermore, the electrolysis combined shear thickening polishing (E-STP) method was employed in microdrill edge preparation, and the influence of different electrolytic voltage on the edge preparation effect was studied. The experimental results indicate that cutting edge preparation efficiency of microdrill in the STP process can be successfully increased by increasing the polishing speed, the main cutting edge radius increases from the initial 2.77 ± 0.4 μm to the highest 3.9 ± 0.3 μm after 2 min processing (polishing speed v= 85 rpm). The E-STP method is proven as an effective way in removing microdrill edge defects with a smaller polishing speed (v= 55 rpm). But, drilling experiments show that the wear resistance and drilling accuracy of the E-STP prepared microdrill (raverage 3.6 μm) is significantly worse than that of the STP prepared microdrill (raverage 3.5 μm) due to the Co loss. Overall, our research provides a new idea for realizing efficient cutting edge preparation of microdrill.