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Inconel 718 is recognized as the difficult-to-cut material. TiAlN coated tool is frequently applied in machining of such material, due to its excellent hot hardness and oxidation resistance. However, research on the wear rate model during the cutting of Inconel 718 with TiAlN coated tools is limited. Therefore, this paper proposes a novel dual-stage wear rate model to predict wear of TiAlN coated tools when cutting Inconel 718 using finite element (FE) simulation. The dual-stage model integrates impacts from adhesive wear, diffusion wear and abrasive wear, and includes two wear rate models for the initial wear stage and steady wear stage, respectively. Subsequently, an in-depth analysis of tool wear mechanisms is conducted through Energy Dispersive Spectrometer (EDS) and Scanning Electron Microscope (SEM) techniques. The analysis results reveal that the adhesive wear dominates in the initial wear stage, while the steady wear stage witnesses the concurrent involvement of abrasive wear, diffusion wear and adhesive wear. Moreover, extensive cutting experiments validate that the model can predict wear of the TiAlN coated tools with an error margin of less than 8.5 %.

This work introduces an online learning-based model predictive control (MPC) approach for the modeling and control of switched nonlinear systems with scheduled mode transitions. Initially, recurrent neural network (RNN) models are constructed offline, utilizing sufficient historical operational data to capture the nominal system dynamics for each mode. Subsequently, we employ real-time process data to develop online learning RNN models, aiming to approximate the dynamics of switched nonlinear systems in the presence of of bounded disturbances. In cases where the initial RNN model is unavailable for a specific switching mode due to very limited historical data, we use real-time data from closed-loop operations under a proportional–integral (PI) controller to build online learning RNN models. To evaluate the predictive performance of online learning RNNs, a theoretical analysis on their generalization error bound is developed using statistical machine learning theory. Additionally, considering the presence or absence of initial RNN models, two MPC schemes are developed. These schemes employ RNNs as prediction models to stabilize switched nonlinear systems, ensuring closed-loop stability by accounting for the generalization error bound derived for online learning RNNs. Finally, the effectiveness of the proposed MPC schemes is demonstrated through a nonlinear process example with two switching modes.

Graphical Abstract:

Tracking individuals within a group is one of the major tasks of group target observation. Tracking radar must feature high frame rate and high range-angular resolution to achieve the stable multitarget tracking performance. However, two major problems arise from this scenario. First, the narrow beam of the tracking radar does not allow the complete observation of group target, causing the fluctuation of target number as the radar-target geometry changes; second, false alarms may be target-dependent and distributed around the targets, which is contrary to the traditional spatially uniform clutter model. This article proposes a Gaussian mixture probability hypothesis density (PHD) filter for multitarget tracking using a collaborative radar system. The system consists of one scanning radar and one tracking radar. The former outputs the group's collective states (centroid, extension, etc.), which are used as the priors for the tracking radar. The tracking radar is responsible for the multitarget tracking. The density of target birth and death are set according to the priors. The update equation of the PHD in target-dependent false alarms is derived and simplified to meet the practical application requirements. Finally, the effectiveness of the proposed filter is verified by the simulation and experimental results.

Group targets such as birds and unmanned aerial vehicles (UAVs) are the research hotspot in the radar field recently. Group target reconstruction that estimates the positions of individual targets becomes one of the core research requirements. However, group targets are usually closely spaced with a similar velocity and it is hard to distinguish multiple targets, resulting in an angular glint. Meanwhile, they usually have weak echoes and fly at a low altitude, which leads to lots of missed detections and false alarms. These characters increase the difficulty of group target reconstruction. Compared with a single radar, a multiradar system with complementary multiview observation can improve the accuracy and completeness of reconstruction. However, a large number of missed detections and false alarms make it difficult to correctly associate multisensor measurements. Moreover, large sensor bias further aggravates the difficulty. This article proposed a topology-feature-aided joint association and registration algorithm to simultaneously acquire association and bias estimation results. The topology feature is applied in the association step to improve association correctness. A nonlinear least median of squares estimator is proposed in the registration step to improve the accuracy and robustness of bias estimation. Multiframe information is used to further improve the association and registration performance. Simulation and experiment results show better bias estimation and association performance of the proposed method compared with existing algorithms and better reconstruction performance compared with a single radar.

This article studies a distributed Nash equilibrium (NE) seeking problem for multiple agents of aggregative games. A novel distributed algorithm is presented to assure both fast convergence and nonovershoot performance by the combination of an accelerated method and a gradient-based NE seeking algorithm. To be specific, an accelerated method (i.e., the heavy-ball method or Nesterov’s accelerated method) is introduced in the designed algorithm for the fast convergence of agents’ strategies to the NE. Moreover, a suitable switching mechanism is proposed to improve the transient performance by switching the distributed algorithm based on the accelerated method to the gradient-based algorithm. As a result, the presented distributed algorithm is modeled by a hybrid dynamical system (HDS). The semi-globally practical convergence is established by analyzing the stability of a parameterized HDS. An example of distributed energy resources is taken to verify the presented algorithm.

Resorption and atrophy of the alveolar bone, as two consequences of osteoporosis that remarkably complicate the orthodontic and prosthodontic treatments, contribute to the differentiated biological features and force-induced response of jaw bone marrow-derived mesenchymal stem cells (JBMSCs) in elderly patients. We isolated and cultured JBMSCs from adolescent and adult patients and then simulated the loading of orthodontic tension stress by constructing an in vitro three-dimensional (3D) stress loading model. The decline in osteogenic differentiation of aged JBMSCs was reversed by tensile stress stimulation. It is interesting to note that tension stimulation had a stronger effect on the osteogenic differentiation of elderly JBMSCs compared to the young ones, indicating a possible mechanism of aging rescue. High-throughput sequencing of microRNA (miRNAs) was subsequently performed before and after tension stimulation in all JBMSCs, followed by the comprehensive comparison of mechanically responsive miRNAs in the 3D strain microenvironment. The results suggested a significant reduction in the expression of miR-210-3p and miR-214-3p triggered by the 3D strain microenvironment in old-JBMSCs. Bioinformatic analysis indicated that both miRNAs participate in the regulation of critical pathways of aging and cellular senescence. Taken together, this study demonstrated that the 3D strain microenvironment efficiently rescued the cellular senescence of old-JBMSCs via modulating specific miRNAs, which provides a novel strategy for coordinating periodontal bone loss and regeneration of the elderly.

Chronic atrophic gastritis (CAG) is characterized by the loss of gastric glandular cells, which are replaced by the intestinal-type epithelium and fibrous tissue. Ginsenoside Rg1 (Rg1) is the prevalent ginsenoside in ginseng, with a variety of biological activities, and is usually added to functional foods. As a novel form of programmed cell death (PCD), pyroptosis has received substantial attention in recent years. Despite the numerous beneficial effects, the curative impact of Rg1 on CAG and whether its putative mechanism is partially via inhibiting pyroptosis still remain unknown. To address this gap, we conducted a study to explore the mechanisms underlying the potential anti-CAG effect of Rg1. We constructed a CAG rat model using a multifactor comprehensive method. A cellular model was developed by using 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) combined with Nigericin as a stimulus applied to GES-1 cells. After Rg1 intervention, the levels of inflammatory indicators in the gastric tissue/cell supernatant were reduced. Rg1 relieved oxidative stress via reducing the myeloperoxidase (MPO) and malonaldehyde (MDA) levels in the gastric tissue and increasing the level of superoxide dismutase (SOD). Additionally, Rg1 improved MNNG+Nigericin-induced pyroptosis in the morphology and plasma membrane of the cells. Further research supported novel evidence for Rg1 in the regulation of the NF-κB/NLRP3/GSDMD pathway and the resulting pyroptosis underlying its therapeutic effect. Moreover, by overexpression and knockout of GSDMD in GES-1 cells, our findings suggested that GSDMD might serve as the key target in the effect of Rg1 on suppressing pyroptosis. All of these offer a potential theoretical foundation for applying Rg1 in ameliorating CAG.

Ketopantoate hydroxymethyltransferase (KPHMT) plays a pivotal role in d-pantothenic acid biosynthesis. Most KPHMTs are homodecamers with low thermal stability, posing challenges for protein engineering and limiting output enhancement. Previously, a high-enzyme activity KPHMT mutant (K25A/E189S) from Corynebacterium glutamicumwas screened as mother strain (M0). Building upon this strain, our study focused on interface engineering modifications, employing a multifaceted approach including integrating folding-free energy calculation, B-factor analysis, and conserved site analysis. Preliminary screening led to the selection of five mutants in the interface─E106S, E98T, E98N, S247I, and S247D─showing improved thermal stability, culminating in the double-site mutant M8 (M0-E98N/S247D). M8 exhibited a T1/2value of 288.79 min at 50 °C, showing a 3.29-fold increase compared to M0. Meanwhile, the Tmvalue of M8 was elevated from 53.2 to 59.6 °C. Investigations of structural and molecular dynamics simulations revealed alterations in surface electrostatic charge distribution and the formation of increased hydrogen bonds between subunits, contributing to enhanced thermal stability. This investigation corroborates the efficacy of interface engineering modifications in bolstering KPHMT stability while showing its potential for positively impacting industrial d-pantothenic acid synthesis.

In-based halide perovskites have attracted a lot of attention because of their unique broadband emission properties. Herein, a series of In-based hybrid perovskites of (H2MP)2InCl7·H2O (1), (H2EP)2InCl7·H2O (2), (H2MP)2InBr7·H2O (3), and (H2EP)2InBr7·H2O (4) were synthesized under the control of halogen ions and organic cations. 1, 2, and 4exhibit obvious photoluminescence properties with peaks at 392, 442, and 652 nm, respectively. The effects of the different components on the crystal structure and photoluminescence properties are discussed by calculating the structural distortion of the [InX6]3–octahedron. The photoluminescence properties of 1and 4were significantly improved after Sb3+doping with PLQY values of 57.12 and 41.53%. Finally, a white LED was successfully fabricated with the two doped compounds coated onto the 365 nm blue LED chip.

This paper introduces the signed graph into fractional-order coupled neural networks (FCNNs) and the bipartite synchronization is investigated for leaderless FCNNs. Instead of formulating leader's state or isolated node's state as the synchronization reference target, the bipartite synchronization of leaderless FCNNs is discussed by developing a direct error approach. First, an important fractional-order inequality is rigorously proved by contradiction. By virtue of fractional-order inequality, gauge transformation and several analytical tools, the criteria of bipartite leaderless synchronization are obtained for FCNNs with heterogeneous and homogeneous coupling weights. Specially, for the two types coupling weights, the adaptive pinning schemes are adopted which just rely on partial network information based on the spanning tree and connected dominating set, respectively. Eventually, the theoretical analysis is verified by two numerical simulations. [ABSTRACT FROM AUTHOR]

The industrial anthraquinone method for H2O2production has the serious flaws, such as high pollution and energy consumption. Piezocatalytic H2O2evolution has been proven as a promising strategy, but its progress is hindered by unsatisfied energy conversion efficiency. Hence, we report the efficient piezocatalytic H2O2generation in pure water over oxygen vacancies mediated ultrathin Bi4O5Br2nanosheets (~5 nm). Oxygen vacancies and thin nanostructure not only enhance the piezoelectric properties of Bi4O5Br2, but also advance the separation and transfer of piezoinduced charges. Moreover, density functional theory (DFT) calculations also prove that the introduction of oxygen vacancies enhances the O2adsorption and activation ability with largely decreased Gibbs free energy of the reaction pathway. Profiting from these advantages, ultrathin Bi4O5Br2nanosheets optimized by oxygen vacancies exhibit a prominent H2O2evolution rate of 620 µmol g−1h−1in pure water and 2700 µmol g−1h−1in sacrificial system, dominated by a two-step single electron reaction, which exceeds most of reported piezocatalysts. This work demonstrates that oxygen vacancies and ultrathin structure can synergistically enhance the piezocatalytic performance, which presents perspectives into exploring the strategies of defects and nanostructure fabrication for promoting piezocatalytic activity.

To achieve optimal tool performance, it is essential to not only comprehend the wear patterns during the steady wear stage but also the final one where catastrophic wear patterns are usually involved. However, previous focus has been put individually into them, thereby the mechanism connections in between have not yet been completely demonstrated. This study aims to bridge this gap by developing an analytical model based on the slip-line theory and imaginary heat source formulations for worn tools with cutting edge geometry extracted from the steady wear stage. The model computes the thermomechanical loads on the tool surface, which are then incorporated into a mechanism-based wear rate model that has been carefully calibrated. Finally, a nodal-displacement algorithm is used to iteratively determine the further tool edge profiles until the final wear stage. The sequential edge profiles demonstrate that the most significant wear increments occur at the rear of the tool flank wear land, resulting in a gradually deepening and widening notched region. These predictions are consistent with experimental findings where a notch belt wear is observed to grow along the tool main cutting edge during the steady wear stage, which ultimately decreases the edge toughness and leads to catastrophic wear patterns.

Recent advances in deep learning (DL) have significantly improved the quality of precipitation nowcasting. Current approaches are either based on deterministic or generative models. Deterministic models perceive nowcasting as a spatiotemporal prediction task, relying on distance functions like $L2$ -norm loss for training. While improving meteorological evaluation metrics, they inevitably produce blurry predictions with no reference value. In contrast, generative models aim to capture realistic precipitation distributions and generate nowcasting products by sampling within these distributions. However, designing a generative model that produces realistic samples satisfying meteorological evaluation indexes in real-time remains challenging, given the triple dilemma of generative learning: achieving high sample quality, mode coverage, and fast sampling simultaneously. Recently, diffusion models exhibit impressive sample quality but suffer from time-consuming sampling, severely hindering their application in nowcasting. Moreover, samples generated by the U-Net denoiser of the current denoising diffusion model are prone to yield poor meteorological evaluation metrics such as CSI. To this end, we propose a spatiotemporal transformer-based conditional diffusion model with a rapid diffusion strategy. Concretely, we incorporate an adversarial mapping-based rapid diffusion strategy to overcome the time-consuming sampling process for standard diffusion models, enabling timely nowcasting. In addition, a meticulously designed spatiotemporal transformer-based denoiser is incorporated into diffusion models, remedying the defects in U-Net denoisers by estimating diffusion scores and improving nowcasting skill scores. Case studies of typical weather events such as thunderstorms, as well as quantitative indicators, demonstrate the effectiveness of the proposed method in generating sharper and more precise precipitation forecasts while maintaining satisfied meteorological evaluation metrics.

Predicting aerial animal migration is of great significance for biological research, ecological conservation, and agricultural production. The mechanism of animal migration is deeply coupled with spatiotemporal and meteorological factors. However, the existing large-scale prediction models using weather radar isolate the spatiotemporal characteristics and the meteorological factors. Additionally, their long-term prediction capabilities are limited, posing challenges in accurately forecasting long-term migration patterns to support applications, such as ecological warnings. This article introduces an aerial migration prediction neural network model combining multiple meteorological factors with weather radar data while expanding the horizon of the migration forecast to the scale of 7 days. Differentiated feature extraction methods are applied to different meteorological factors in the network. The transfer characteristics of the wind field in 2-D space are used to construct a dynamic migration model. The scalar meteorological data are encoded by entity embedding to perform feature fusion with the dynamic branch, collectively forming the forecast model that outputs future migration intensity. We validate the effectiveness of our model China weather radar network real data and reanalysis data, accurately forecasting migratory biomass within China for a horizon of up to 7 days. Moreover, our model is compared with two existing prediction models, demonstrating a maximum improvement of 14.00% in the coefficient of determination ( $R^{2}$ ) in long-term forecast, and the visualized results highlight the predictive effectiveness for the spring and autumn seasons. In future applications, more meteorological factors should be considered and radar data from more stations should be collected to enhance the dataset.

Due to the independence of the specific differential phase $K_{\text {DP}}$ from attenuation and miscalibration, it is often applied in attenuation estimation (i.e., estimation of the specific attenuation A from $K_{\text {DP}}$ ). However, the measured differential phase $\Psi _{\text {DP}}$ includes the backscattering differential phase $\delta _{\text {hv}}$ and noise, decreasing the accuracy of $K_{\text {DP}}$ estimation. Additionally, considering that the coefficient $\gamma $ of the $A-K_{\text {DP}}$ empirical relation is sensitive to temperature and drop size distribution, there is also concern over an a priori fixed value for $\gamma $ . Especially at higher frequency bands such as X-band, large $K_{\text {DP}}$ can amplify the biases caused by inaccurate $\gamma $ . Adaptive and high-resolution (AHR) and ZPHI are methods for estimating phase and attenuation, respectively. Their opposite inputs and outputs create a strong coupling between the two methods, allowing both methods to simultaneously reduce the impact of contaminated $\Psi _{\text {DP}}$ on estimation. Meanwhile, their high resolution (HR) makes it possible to conditioning $\gamma $ adaptively at a range resolution scale according to different rainfall situations. In this work, a method that combines the individual and common characteristics of AHR and ZPHI is proposed to improve the accuracy of phase and attenuation estimation by adaptively conditioning $\gamma $ at HR, as well as reducing the biases of $\delta _{\text {hv}}$ and noise. The improved effects of this method are assessed with a typical storm event observed by a polarimetric X-band weather radar in The Netherlands, and its performances are further evaluated with simulations comprehensively. The results show that this method can improve the accuracy of both phase and attenuation estimation significantly, especially at large $K_{\text {DP}}$ .

Van der Waals encapsulation of two-dimensional materials in hexagonal boron nitride (hBN) stacks is a promising way to create ultrahigh-performance electronic devices1–4. However, contemporary approaches for achieving van der Waals encapsulation, which involve artificial layer stacking using mechanical transfer techniques, are difficult to control, prone to contamination and unscalable. Here we report the transfer-free direct growth of high-quality graphene nanoribbons (GNRs) in hBN stacks. The as-grown embedded GNRs exhibit highly desirable features being ultralong (up to 0.25 mm), ultranarrow (<5 nm) and homochiral with zigzag edges. Our atomistic simulations show that the mechanism underlying the embedded growth involves ultralow GNR friction when sliding between AA′-stacked hBN layers. Using the grown structures, we demonstrate the transfer-free fabrication of embedded GNR field-effect devices that exhibit excellent performance at room temperature with mobilities of up to 4,600 cm2V–1s–1and on–off ratios of up to 106. This paves the way for the bottom-up fabrication of high-performance electronic devices based on embedded layered materials.

Weather radar serves as a crucial tool for monitoring aeroecology by enabling the observation of migrating birds and insects. Although dual-polarization weather radar offers the possibility of classifying echoes, extracting migration echoes of birds and insects from historical single-polarization weather radar data remains challenging. The current deep-learning methods have been successfully extracting aerial migrations from single-polarization weather radar data. However, it still faces challenges in distinguishing between birds and insects at the pixel level, primarily due to the absence of distinct semantic features for each. To tackle this challenge, we propose a semi-supervised radar data processing framework, which generates a large number of single polarization training datasets from a small amount of dual polarization truth data and trains the image segmentation network of single polarization data to distinguish between bird and insect echoes. The framework comprises three components: an image classifier, an image generator, and an image segmentation model. Specifically, the image classifier and image generator leverage a small set of manually annotated dual-polarization radar data to generate the pixel-level single-polarization dataset for training the image segmentation model. The well-trained image segmentation model extracts migration echoes of birds and insects from radar images. Experimental results demonstrate that the proposed method achieves a mean intersection over union (IoU) of 97% for segmenting precipitation, bird, and insect targets. The proposed framework can utilize historical archived single-polarization weather radar data to provide large-scale, long-term, and repeatable monitoring data for birds and insects.

U-Net has achieved good performance with the small-scale datasets through skip connections to merge the features of the low-level layers and high-level layers and has been widely utilized in biomedical image segmentation as well as recent microstructure image segregation of the materials. Three representative visual attention mechanism modules, named as squeeze-and-excitation networks, convolutional block attention module, and extended calibration algorithm, were introduced into the traditional U-Net architecture to further improve the prediction accuracy. It is found that compared with the original U-Net architecture, the evaluation index of the improved U-Net architecture has been significantly improved for the microstructure segmentation of the steels with the ferrite/martensite composite microstructure and pearlite/ferrite composite microstructure and the complex martensite/austenite island/bainite microstructure, which demonstrates the advantages of the utilization of the visual attention mechanism in the microstructure segregation. The reasons for the accuracy improvement were discussed based on the feature maps analysis.

Interferometric synthetic aperture radar (InSAR) has been a valuable tool for mapping topography and subtle deformations. However, dealing with a wide imaging integral angle (IIA), especially for low-frequency band unmanned aerial vehicle (UAV) InSAR systems, introduces challenges. The conventional interferometric phase model depends on the difference in two slant ranges between the synthetic aperture centers and the target in two observations. Accuracy limitations emerge when variations are encountered in differences of slant range history across the entire wide IIA. This article explores the impact of IIA on interferometric measurements and proposes a modified interferometric phase model to address these limitations. For a wide IIA, the proposed model focuses on the integral of differences in slant range history throughout IIA by considering the nonlinear trajectory of the UAV platform. Additionally, measurement models for the IIA are deduced, in which an additional scale factor expanded by the Bessel function is introduced. Simulated and experimental datasets are utilized to demonstrate improvements in the accuracy of topography and deformation measurements. These results validate the effectiveness of the modified model in overcoming the challenges posed by wide IIAs in UAV InSAR systems.

Deep learning (DL)-based precipitation nowcasting algorithms have garnered significant attention in recent years. However, the presence of variable spatial scales in precipitation patterns poses challenges for methods that solely focus on capturing spatiotemporal correlations at a single scale. Moreover, current DL-based algorithms tend to model short-term (e.g., 10-min time span) rainfall locally neglecting long-term, global (e.g., 2-h time span) life-cycle evolution. Furthermore, widely used pixel-wise losses are prone to produce low effective-spatial-resolution predictions. To this end, we introduce a multiscale deformable transformer network to leverage echo contexts from image patches of varying spatial scales. Meanwhile, a multihead deformable self-attention mechanism is introduced for capturing precipitation spatiotemporal dynamics in a global manner. Moreover, to improve the spatial resolution of predictions, the Fourier space regularization and adversarial losses are proposed by narrowing the discrepancy of the Fourier spectra of predictions and references. Thanks to the introduced loss function, our model generates highly effective spatial-resolution predictions with abundant details. Extensive experiments on two real datasets show the substantial superiority of our method in terms of critical success index (CSI) compared to recent competitive approaches. At the same time, our predictions have more realistic precipitation details and significantly better fidelity. For example, on a vertically integrated liquid (VIL) product dataset, compared to baseline methods, our approach reduces the Fréchet inception distance (FID) value by a factor of $2\sim 4$ while improves the CSI score by 3%~5% approximately.

Radar echo signals may experience the significant 2-D space dependence when it comes to the nonlinear trajectory of the synthetic aperture radar (SAR). The backprojection (BP) imaging algorithm is generally effective for achieving satisfactory focused SAR images under this condition. However, the conventional BP algorithm usually selects a uniform reference imaging plane, regardless of the actual topography of the observation scene. In undulating topographies, it has been proved that the range migration of the actual target and its projection point on the reference imaging plane may not remain consistent, leading to residual uncompensated phase errors and resulting in imaging defocusing during nonlinear trajectories. To address this problem, this article proposes an improved imaging method that involves the optimal topographic imaging plane reconstruction based on the image quality evaluation. The coarse plane and subsequent partitioned subplanes are sequentially constructed to create a topographic imaging plane that closely resembles the digital elevation model (DEM). The BP imaging algorithm is then applied to the reconstructed topographic imaging plane to overcome the defocusing problem. Both simulated and actual experiment datasets validate the effectiveness of the proposed method. Moreover, the proposed method significantly alleviates registration difficulties.

The development of alloys with high antioxidation performance is limited by the ambiguous details of the oxidation mechanism. Here, based on the structures of internal oxides detected by high-resolution transmission electron microscopy, a hybrid method combining first-principles calculation, climb image nudged elastic band method and quasi-harmonic Debye model has been implemented to explain the oxidation mechanism with an emphasis on the origin of delamination and cracking. The results showed that the delamination of oxides corresponds to the acceleration of diffusion of Cr element caused by lamellar structures. The reduction in the cracking occurrence at high temperature mainly results from the smaller bulk modulus of oxides. Furthermore, the stronger chemical bonds promoted by lamellar structures also correspond to the higher cracking resistance.

Conducting extensive vehicle detection through the high-altitude perspective offered by unmanned aerial vehicles (UAVs) poses significant challenges. The high-altitude operation of UAVs to acquire a broader reconnaissance view results in low-resolution and densely packed vehicle targets in the captured imagery, creating substantial difficulties for vehicle detection. To address this, we propose a vehicle detection network specifically designed for UAVs, incorporating an end-to-end network that takes scale consistency constraints into consideration. The cornerstone of our method is the dynamic feature refinement module (DFRM), designed to overcome the feature attenuation and limitations in utilizing high-level prior information common in traditional approaches. Initially, we developed an adaptive target suggestion module based on the prior characteristics of the targets and scenes, and the scale consistency hypothesis of similar vehicles at different UAV flying altitudes. This module optimizes the number and scale of anchors by introducing prior information, facilitating preliminary localization of small-scale imaging targets. Subsequently, we constructed a multilayer feature purification structure based on a feature pyramid network (FPN) to refine bounding boxes at each level with height prior, integrating additional contextual information. This approach allows us to utilize more contextual information for vehicle detection while enhancing localization accuracy through detailed height prior. Our application and evaluation on multiple open-source datasets with height labels demonstrate that our method, with minimal parameter introduction, achieves excellent mean average precision (mAP) value. This underscores the effectiveness of our approach in UAV-based vehicle detection.

This article aims to study the fixed-time (FxT) synchronization of different dimensional Filippov systems. New FxT stability lemmas containing the classical inequality $\dot {V}\leq {-c}_{1}V^{a}-c_{2}V^{b}$ proposed by Polyakov are established. Different from the previous FxT stability lemmas in the literature, the proposed one shows the new conclusion that the settling times can be larger or smaller as long as $c_{1}$ and $c_{2}$ satisfy the certain relationships, which synchronously reveals that the relationships between the control gains can lead to different settling times. Besides, a generalized economical inequality condition $\dot {V} \leq -c_{1}V-c_{2}V^{\upsilon +{\mathrm{ sign}}(V-r)}$ is proposed and a novel FxT stability lemma is also established. The complete theoretical proof is given to reveal that $r=1$ leads to desired settling time, some previous related results are improved. Based on the new FxT stability lemmas and differential inclusion theory, algebraic inequality conditions are provided to guarantee the FxT synchronization, which reports the first result on the FxT synchronization of different dimensional Filippov systems. Finally, numerical examples are provided to verify the correctness of the main theoretical results.

The orphan nuclear receptor Nur77 has been validated as a potential drug target for treating breast cancer. Therefore, focusing on the SAR study of the lead 8b(KDSPR(Nur77)= 354 nM), we found the active compound jawhich exhibited improved Nur77-binding capability (KDSPR(Nur77)= 91 nM) and excellent antiproliferative activities against breast cancer cell lines. Interestingly, jaacted as a potent and selective Nur77 antagonist, displaying good potency against triple-negative breast cancer (TNBC) cell lines but did not inhibit human normal breast cancer cell line MCF-10A (SI > 20). Exceptionally, jaNur77-dependently caused mitochondria dysfunction and induced the caspase-dependent apoptosis by mediating the TP53 phosphorylation pathway. Moreover, jasignificantly suppressed MDA-MB-231 xenograft tumor growth but had no apparent side effects in mice and zebrafish. Overall, jademonstrated to be the first Nur77 modulator mediating the TP53 phosphorylation pathway that has the potential as a novel anticancer agent for TNBC.

Dynamic programming track before detect (DP-TBD) is widely applied in the radar detection for weak targets. The algorithm selects the most advantageous state in each stage to integrate the merit function and return the corresponding target status to the merit function that exceeds the detection threshold in the final stage. However, due to the recursive search strategy included in DP, the constant false alarm rate (CFAR) detection threshold is difficult to be quickly determined by numerical integration. Monte Carlo counting, extreme value theory, or its extended algorithm are commonly used to determine the detection threshold of DP. However, these methods require many times of the Monte Carlo simulation, which is time-consuming and difficult to apply to practical projects. In order to achieve fast and accurate acquisition of CFAR detection threshold, this article proposes an efficient approximation algorithm to determine the DP-TBD CFAR threshold. The algorithm approximates the computational structure of DP-TBD by structural analogy, and then uses the saddle-point approximation method and the numerical integration of the fitting formula to quickly determine the CFAR threshold. Numerical simulation experiments under various conditions and radar field experiments are carried out to verify the high accuracy of the approximate threshold determined by the proposed algorithm. In addition, the algorithm is further extended and can be applied in the noise background with varying power. The corresponding numerical simulation experiments prove the effectiveness of the extended algorithm.

In this article, we focus on the design of coherent detectors in compound-Gaussian clutter using a nonlinear preprocessing system followed by the matched filter (MF). First, to evaluate the effectiveness of the preprocessing system, the signal-to-clutter ratio (SCR) gain of preprocessing system is proposed as its performance metric. It is proven that the upper bound of SCR gain is greater than unity for compound-Gaussian clutter, indicating that an appropriate preprocessing system can improve detection performance. Then the suprathreshold stochastic resonance (SSR) system composed of a parallel array of quantizers is introduced, which is an effective and easy-to-implement suboptimal preprocessing system. In compound-Gaussian clutter, a method for determining parameters of quantizer noise, which is purposely injected in the SSR system, is proposed. Finally, the performance improvement of the SSR preprocessing system for weak target in compound-Gaussian clutter is illustrated using both simulated clutter and real sea clutter data. Experiment results show that the proposed method can be applied to detect weak moving targets in sea clutter.

Bird flocks are typical group targets with various linear formations and high dynamics due to swarm intelligence. This leads to several problems in traditional multisubobject group target tracking such as shape model mismatch and false correlations. This article proposes a multisubobject approach to dynamic formation target tracking. The algebraic graph theory is introduced to analyze the structure of formation targets, then measurements are clustered and combined into subobjects. Based on the existing random matrix approach, two additional filtering branches, including collective filtering and internal structure filtering, are introduced to achieve the robust tracking performance of formation targets. The Kullback–Leibler divergence between the prediction and updated densities of the collective filtering is used to determine the change of formation shape. The correct association between the measurements and the subobjects is realized by the guidance of the internal structure filter. Finally, the effectiveness of the proposed method is verified by the simulation and experimental results.

Purpose: Haptoglobin (Hp) is a hemoglobin-binding protein that functions as an antioxidant in human plasma. It is reported that glycemic variability (GV) plays a key role in diabetes-related complications associated with impaired glucose metabolism and oxidative stress. Here we aim to investigate whether the effect of GV on diabetic macroangiopathy depends on Hp genotype in type 2 diabetes. Methods: A number of 860 Chinese patients with type 2 diabetes was genotyped and assigned to two Hp subgroups (Hp 2-2 and Hp 1 carriers). Glycemic variability (GV) was assessed by using a retrospective continuous glucose monitoring system for three consecutive days, and it was measured using the glucose coefficient of variation (%CV), which is calculated as the ratio of glucose standard deviation to glucose mean. Clinical features, history of cardiac surgery, and vascular imaging tests were utilized to diagnose macroangiopathy. We evaluated the interaction between Hp genotypes and %CV on diabetic macroangiopathy. Furthermore, serum concentration of 8-hydroxy-2’-deoxyguanosine (8-OHdG) was measured using an enzyme-linked immunosorbent assay as a biomarker of oxidative stress. Results: Serum 8-OHdG levels were positively correlated with %CV in Hp 1 carriers (r = 0.117; p= 0.021). Patients in the highest %CV tertile were associated with a higher prevalence of diabetic macroangiopathy than those in the lowest %CV tertile in Hp 1 carriers (OR = 2.461 [95% CI, 1.183–5.121], p= 0.016), but not in those with Hp 2-2 genotype (OR = 0.540 [95% CI, 0.245–1.191], p= 0.127). A significant interactive effect of Hp genotypes and %CV on diabetic macroangiopathy was found (pinteraction = 0.008). Conclusion: Hp genotype modifies the effect of GV on diabetic macroangiopathy among Chinese patients with type 2 diabetes.

Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons, called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments, where small targets generally exhibit extremely low contrast against neighboring backgrounds. In this article, we develop an attention-and-prediction-guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely: 1) an attention module; 2) an STMD-based neural network; and 3) a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against a complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture, allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.

High-speed bearing has been widely applied in industrial machinery, and the health condition significantly affects its normal operation. However, the lack of practical condition monitoring system inevitably increases operation cost and risk simultaneously. Besides, deep learning (DL) has been regarded as a promising bearing fault diagnosis method, but available sample scale varies among factories due to signal acquisition cost, and the model performance may be hard to meet the requirements. Aiming at these problems, a novel DL framework named efficient channel attention-Siamese deep residual network-support vector machine (ECA-SDResNet-SVM) is proposed for bearing health condition recognition. Specifically, convolutional blocks are constructed and stacked in Siamese neural network (SNN) to learn features from randomly paired samples, and the ECA module is introduced to highlight sensitive components during model training; then, the SVM model is utilized to identify bearing fault status. Further comparison experimental results show that the ECA-SDResNet-SVM outperforms other compared transfer learning (TL) and DL models regardless of training sample scales or ambient noise levels, and the Acc results achieve 0.99364 ± 0.00045 at 0.45 split ratio, 0.67086 ± 0.02840 at 0.025 split ratio, and 0.70182 ± 0.01990 with additional −6-dB Gaussian white noise (GWN) at 0.3 split ratio. Further self-conducted bearing monitoring case has also validated the prominent performance of the proposed model.

This article proposes fixed-time stability lemmas for the Filippov system via some new inequality approaches. The adopted method no longer needs to integrate the Lyapunov function $V$ on the two integral intervals, which is quite different from the existing ones. Some new estimations of the settling times are provided. Also, the steepness exponents of the Lyapunov function $V$ in the previous fixed-time stability lemmas are improved. In order to further study the nondifferentiable delayed neural networks modeled by the Filippov system, a class of discontinuous uncertain Cohen–Grossberg neural networks (CGNNs) with mixed delays is formulated and the distributed delays are nondifferentiable, which is more general. Due to the existence of nondifferentiable distributed delays, the existence of the periodic solutions is proved by Kakutani’s fixed-point theorem before considering the stability. By virtue of the obtained fixed-time stability lemmas and the constructed delay-product-type Lyapunov–Krasovskii functional, the fixed-time stabilization is obtained via a no-chattering controller. Clearly, the designed controller does not contain integral terms and delay terms for dealing with the time delays in the closed-loop system, which is more simplified and practical. Finally, two examples help examine the correctness of the main results.

Non-orthogonal multiple access (NOMA) is viewed as a key technique to improve the spectrum efficiency and solve the issue of massive connectivity. However, for power domain NOMA, the required overall transmit power should be increased rapidly with the increasing number of users in order to ensure that the signal-to-interference-plus-noise ratio reaches a predefined threshold. In addition, since the successive interference cancellation (SIC) is adopted, the error propagation would become more serious as the order of SIC increases. Aiming at minimizing the total transmit power and satisfying each user's service requirement, this paper proposes a novel framework with group-based SIC for the deep integration between power domain NOMA and multi-antenna technology. Based on the proposed framework, a joint optimization of power control and equalizer design is investigated to minimize transmit power consumption for uplink multi-antenna NOMA system with error propagations. Based on the relationship between the equalizer and the transmit power coefficients, the original problem is transformed to a transmit power optimization problem, which is further addressed by a parallel iteration algorithm. It is shown by simulations that, in terms of the total power consumption, the proposed scheme outperforms the conventional OMA and the existing cluster-based NOMA schemes.

The fixed-time synchronization and preassigned-time synchronization are investigated for a class of quaternion-valued neural networks with time-varying delays and discontinuous activation functions. Unlike previous efforts that employed separation analysis and the real-valued control design, based on the quaternion-valued signum function and several related properties, a direct analytical method is proposed here and the quaternion-valued controllers are designed in order to discuss the fixed-time synchronization for the relevant quaternion-valued neural networks. In addition, the preassigned-time synchronization is investigated based on a quaternion-valued control design, where the synchronization time is preassigned and the control gains are finite. Compared with existing results, the direct method without separation developed in this article is beneficial in terms of simplifying theoretical analysis, and the proposed quaternion-valued control schemes are simpler and more effective than the traditional design, which adds four real-valued controllers. Finally, two numerical examples are given in order to support the theoretical results.

S and P co-doping for NiSe2nanoparticles embedded in hollow carbon nanospheres improves the electrochemical performance as anodes for both KIBs and LIBs.

Age‐associated sarcopenia is characterized of progressed loss of skeletal muscle power, mass, and function, which affects human physical activity and life quality. Besides, accompanied with sarcopenia, aged population also faces a series of metabolic dysfunctions. Irisin, the cleaved form of fibronectin type III domain‐containing protein 5 (FNDC5), is a myokine induced by exercise and has been shown to exert multiple beneficial effects on health. The goal of the study is to investigate the alterations of Fndc5/irisin in skeletal muscles during ageing and whether irisin administration could ameliorate age‐associated sarcopenia and metabolic dysfunction. The mRNA and protein levels of FNDC5/irisin in skeletal muscle and serum from 2‐ and 24‐month‐old mice or human subjects were analysed using qRT‐PCR and western blot. FNDC5/irisin knockout mice were generated to investigate the consequences of FNDC5/irisin deletion on skeletal muscle mass, as well as morphological and molecular changes in muscle during ageing via histological and molecular analysis. To identify the therapeutic effects of chronic irisin treatment in mice during ageing, in vivointraperitoneal administration of 2 mg/kg recombinant irisin was performed three times per week in ageing mice (14‐month‐old) for 4 months or in aged mice (22‐month‐old) for 1 month to systematically investigate irisin's effects on age‐associated sarcopenia and metabolic performances, including grip strength, body weights, body composition, insulin sensitivity, energy expenditure, serum parameters and phenotypical and molecular changes in fat and liver. We showed that the expression levels of irisin, as well as its precursor Fndc5, were reduced at mRNA and protein expression levels in muscle during ageing. In addition, via phenotypic analysis of FNDC5/irisin knockout mice, we found that FNDC5/irisin deficiency in aged mice exhibited aggravated muscle atrophy including smaller grip strength (−3.23%, P< 0.05), muscle weights (quadriceps femoris [QU]: −20.05%; gastrocnemius [GAS]: −17.91%; tibialis anterior [TA]: −19.51%, all P< 0.05), fibre size (QU: P< 0.01) and worse molecular phenotypes compared with wild‐type mice. We then delivered recombinant irisin protein intraperitoneally into ageing or aged mice and found that it could improve sarcopenia with grip strength (+18.42%, P< 0.01 or +13.88%, P< 0.01), muscle weights (QU: +9.02%, P< 0.01 or +16.39%, P< 0.05), fibre size (QU: both P< 0.05) and molecular phenotypes and alleviated age‐associated fat tissues expansion, insulin resistance and hepatic steatosis (all P< 0.05), accompanied with altered gene signatures. Together, this study revealed the importance of irisin in the maintenance of muscle physiology and systematic energy homeostasis during ageing and suggested a potent therapeutic strategy against age‐associated metabolic diseases via irisin administration.

Designing repeat ground track (RGT) synthetic aperture radar (SAR) constellations for achieving rapid revisits over key areas is essential to employ spaceborne differential interferometric SAR (D-InSAR) technology in Earth observation missions, such as geological disaster monitoring and prediction. In this article, the features of average revisit time (ART) maps are first introduced and investigated, and then, an efficient and resource-friendly approach to calculate the ART of constellations is proposed. On this basis, a systematic method for designing an RGT constellation is provided, incorporating lookup-table-based optimization. Once the requirements of the expected RGT constellation, the incident angle of sensors on the constellation, and the orbital elements of the seed satellite in the constellation are given, the range of the optimal inclination and longitude of the ascending node (LAN) of the seed satellite can be found, and then, the entire constellation is determined. The proposed method enhances the efficiency of revisit time analysis and avoids the repeated modeling when the observation requirements change. Therefore, it is applicable not only prior to launch but also guides orbital maneuvering to adjust constellation configuration for an effective response to sudden disasters and so on. Finally, multiple RGT constellation design tasks are presented to demonstrate the proposed method.

Insect mass could be estimated by estimators calculated by the radar cross Section (RCS) measured by entomological radar, which is essential for the statistics of migratory biomass and classification of insects. In order to obtain the insect mass through radar, the RCS of various insects is measured in the microwave anechoic chamber by a specially designed system containing dual-polarization antennas, which point at insects in the measurement process, and the mapping between RCS and the insect mass is constructed and applied to entomological radar. However, insects might deviate from beam center in practice, and the echo intensity will decrease. The decrease cannot be compensated for radar without angle measurement capability. Through the study of insect scattering matrix (SM), it is found that co-polarization estimators, such as co-polarization ratio (CR) and co-polarization phase (CP), are echo intensity independent and correlated with insect mass. Therefore, a co-polarization estimator calculation method driven by model and data is elaborately designed, and an estimation method for insect mass is given on the basis of the co-polarization estimators. Analyses present that the method is suitable for insects below 200 mg, and the mean relative mass estimation error is lower than 30% in X-band and 20% in Ku-band. The effectiveness and robustness of this method is verified by measuring 39 individual insects with a Ku-band fully polarimetric radar (FPR) in field. The proposed methods provide a way to estimate insect mass for entomological radar without angle measurement capability.

Background/aimTo evaluate the clinical outcomes of penetrating canaloplasty in traumatic angle recession glaucoma at 1 year.MethodsPatients with angle recession glaucoma underwent penetrating canaloplasty, a new Schlemm’s canal-based internal drainage procedure, which creates a direct canal for flow of aqueous humour from the anterior chamber to the ostia of Schlemm’s canal via a window created at the corneal scleral bed without use of antimetabolites. Postoperative intraocular pressure (IOP), number of glaucoma medications, and procedure-related complications were evaluated. Success was defined as an IOP ≤21 mm Hg without (complete) or with (qualified) use of glaucoma medication.ResultsForty eyes in 40 patients with angle recession glaucoma underwent successful circumferential catheterisation. The mean patient age was 42±13 years. In patients with penetrating canaloplasty that was deemed to be completely successful, the mean IOP decreased from a preoperative value of 37.8±12.3 mm Hg on 3.3±1.2 anti-glaucoma medications to 18.5±6.4 mm Hg on 1.2±1.4 medications, 14.9±4.6 mm Hg on 0.1±0.5 medications, 15.7±5.4 mm Hg on 0.1±0.4 medications and 14.8±3.6 mm Hg on 0.1±0.5 medications at 1, 3, 6 and 12 months postoperatively (p<0.05). Complete success was achieved in 35/40 eyes (87.5%) at 6 months and in 34/38 (89.5%) at 12 months. Hyphema (18/40, 45.0%) and transient IOP elevation (≥30 mm Hg, 9/40, 22.5%) were the most common postoperative complications.ConclusionPenetrating canaloplasty significantly reduces IOP and has a high success rate in angle recession glaucoma.Trial registration numberChiCTR1900020511.

Collision detection is one of the most challenging tasks for unmanned aerial vehicles (UAVs). This is especially true for small or micro-UAVs due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula giant movement detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect’s visual neuron, LGMD is considered to be an ideal basis for building UAV’s collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues, such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial–temporal synaptic interactions, which is inspired by recent findings in locusts’ synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then, radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the nonpreferred visual motions. This spatial–temporal competition between excitation and inhibition in our model is, therefore, tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably and has the potential to be implemented on embedded collision detection systems for small or micro-UAVs.

The problem of fixed-time (FXT) and preassigned-time (PAT) optimization is concerned in this article based on multiagent systems (MASs) and power-law algorithms. Under the framework of strong convexity of the cost functions, two types of piecewise algorithms are proposed, which ensure that the FXT optimization can be solved either by first achieving the FXT consensus or by first achieving local optimization. Correspondingly, the PAT optimization problem is also considered by designing several piecewise protocols, where the finished time of optimization can be arbitrary prescribed according to actual demands. Furthermore, these piecewise power-law algorithms on the weighted undirected graphs are generalized to the weighted digraphs. Finally, by providing two numerical examples, the presented algorithms are further verified.

Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro-robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying insects can effectively detect mates and track prey during rapid pursuits, even though the small targets equate to only a few pixels in their visual field. The high degree of sensitivity to small target movement is supported by a class of specialized neurons called small target motion detectors (STMDs). Existing STMD-based computational models normally comprise four sequentially arranged neural layers interconnected via feedforward loops to extract information on small target motion from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this article, we propose an STMD-based neural network with feedback connection (feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop and find that it shows a preference for high-velocity objects. Extensive experiments suggest that the feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppressing background false positive movements which display lower velocities. The proposed feedback model provides an effective solution in robotic visual systems for detecting fast-moving small targets that are always salient and potentially threatening.

This work develops a Lyapunov-based economic model predictive control (LEMPC) scheme that utilizes recurrent neural networks (RNNs) with online update to optimize the economic benefits of switched nonlinear systems subject to a prescribed switching schedule. We first develop an initial offline-learning RNN using historical operational data, and then update RNN models using real-time data to improve model prediction accuracy. The generalized error bound for RNNs updated online with non-independent and identically distributed (non-i.i.d.) data samples is first derived. Subsequently, by incorporating the online update of RNNs within LEMPC, probabilistic closed-loop stability and economic optimality are achieved simultaneously for switched nonlinear systems accounting for the RNN generalized error bound. A chemical process example with scheduled mode transitions is used to demonstrate that the closed-loop economic performance under LEMPC can be improved using online learning of RNNs.

Purpose: Heterozygous inactivating mutations in the glucokinase (GCK) gene result in the asymptomatic fasting hyperglycemia named as GCK-MODY or MODY2. The genetic testing can effectively avoid the misdiagnosis and inappropriate treatment for GCK-MODY. Methods: A total of 25 unrelated families with MODY were screened for mutations in coding region of GCKby using direct sequencing. Three different bioinformatics tools such as PolyPhen2, Mutation Taster and PROVEAN were performed to predict the function of mutant proteins. The glucose profile was recorded by continuous glucose monitoring system (CGMS) to evaluate the glycemic variability for the GCK-MODY patient. Results: Our study identified five GCKmutations in 24% of the families (6/25): two novel mutations (I126fs and G385A) and three already described mutations (G44S, H50fs and S383L). In silico analyses predicted that these mutations altered structural conformational changes. The values of mean amplitude of glycemic excursions (MAGE), an important index of blood glucose fluctuation in CGMS system, were 0.81 in the first 24 h and 1.61 in the second 24 h record in the patient with GCK-MODY (F3), suggesting little glucose fluctuation. Conclusion: The genetic testing is suggested to be important to differentiate GCK-MODY from other types of diabetes. CGMS might be used to screen GCK-MODY cases prior to genetic testing.