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Laser powder bed fusion (LPBF) is a cutting-edge metal additive manufacturing technology with promising applications in aerospace, biomedicine, and industrial automation. Its ability to fabricate intricate geometric shapes, achieve high surface precision, and deliver comprehensive results renders it highly advantageous. In LPBF process, several factors such as the interference of laser processing fluctuations, rapid cooling rate, and environmental gas, contribute to the initiation and expansion of defects including pores and cracks, limiting the application of LPBF components. Laser ultrasonic testing (LUT) is a promising non-destructive evaluation method that can accurately detected defects in LPBF manufactured components by non-contact generation and detection. In this paper, a multi-physical field LUT model for the pores and cracks in LPBF additively manufactured Ti6Al4V alloy is proposed. The interaction of ultrasound with defects produces defect reflection echo wave, diffracted wave, and transmission wave. The transmission and diffraction phenomena occur in the longitudinal sound waves at defects. Furthermore, the echo wave reflected at crack exhibited enhanced prominence and an irregular shape. Clear differences were observed in the effects of laser ultrasonic detection on different defect types, pore depths and sizes. Finally, the LUT technology achieved the detection of 90 μm pore defects in LPBF manufactured Ti6Al4V alloy.

This article proposes a fast method for orbit uncertainty propagation and estimation. The proposed method is based on an orbit deviation propagation approach, which consists of an analytical two-body deviation propagation solution and a deep neural network (DNN) to compensate for the errors between the two-body and the true solutions. First, five types of sample forms for training the DNN are investigated, and the optimal one is selected through learning feature and training performance analyses. Then, an uncertainty propagation solution for propagating the mean and covariance is formulated by combining the DNN-based deviation propagation approach with an unscented transformation process. Finally, a more efficient version of the unscented Kalman filter (UKF) for orbit estimation is developed based on the formulated uncertainty propagation solution. The advantage of the proposed DNN-based method is that it avoids the integration of the state transition matrix or dozens of sigma points. The performance of the proposed method is investigated on a low-Earth-orbit example. Numerical results show that the proposed DNN-based estimation method can be one order of magnitude faster than the UKF and is comparable to the UKF in estimation accuracy. In addition, it estimates more accurately than the extended Kalman filter (EKF) and is approximately 10% faster than the EKF.

ABSTRACTIntroductionSjögren’s syndrome is a heterogeneous autoimmune condition that impairs quality of life because of dryness, fatigue, pain, and systemic involvements. Current treatment largely depends on empirical evidence, with no effective therapy approved. Clinical trials on targeted drugs often fail to report efficacy due to common factors.Areas coveredThis review summarizes the pathogenesis and what caused the failure of new investigational drugs in clinical trials, highlighting solutions for more effective investigations, with greater consistency between research outcomes, clinical use, and patient needs.Expert opinionUnlinked pathobiology with symptoms resulted in misidentified targets and disappointing trials. Useful stratification tools are necessary for the heterogeneous SS patients. Composite endpoints or improvements in ESSDAI scores are needed, considering the high placebo response, and the unbalance between symptom burden and disease activity. Compared to classic biologics, targeted cell therapy will be a more promising field of investigation in the coming years.

In conventional immersion and invariance (I&I) adaptive control design, control parameter adaptation is typically linear with respect to the parameter-error-induced perturbation, resulting in quadratic-rate dissipation of energy associated with the off-the-manifold variable. Departing from such a convention, this article contributes a novel strategy - polynomial adaptation. As the name suggests, control parameter adaptation in this approach takes the form of a general polynomial in relation to the perturbation. Accordingly, this new design induces polynomial-rate energy dissipation, which is faster than the quadratic one in the conventional scheme, thereby enhancing closed-loop control performance. The theoretical underpinnings of the new approach are demonstrated through the design of an I&I adaptive tracking control law for a general nth-order, single-input-single-output, parametrically uncertain, nonlinear system in the controllable canonical form. Additionally, a numerical study of the proposed method on the second-order forced Duffing oscillator showcases its improved transient performance in comparison to a baseline controller developed with the standard I&I adaptive control technique.

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov’s gradient method. Nesterov’s GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov’s GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.

A synthetic aperture radar (SAR) has the characteristics of all-weather and all-time operation, which can achieve uninterrupted detection of targets on the sea surface. Currently, small-sized ship targets in SAR images are difficult to detect in complex backgrounds due to limited pixel information, unclear azimuth information, and weak signals after imaging. This makes it challenging to detect small-scale ship targets in SAR images. In this article, we proposed an enhanced Cascade R-CNN algorithm for detecting small-sized ship targets in complex backgrounds of SAR images. To enhance the multiscale expression ability of the network, we introduce Res2Net with richer multiscale information and establish a spatial enhancement module to increase the weight of the ship target in the aspect map. In addition, a bidirectional feature pyramid structure is constructed to fuse the feature maps output at numerous stages, making the semantic information contained in the feature maps more abundant. To improve the accuracy of the target boundary in dense areas, we introduce a generalized focal loss (GFL) function and improve the output layer prediction network. Experiments conducted on the SAR-Ship-Dataset show that our algorithm achieves precision, recall, ${F}1$ , and mAP of 92.6%, 92.4%, 92.8%, and 92.5%, respectively. The proposed method exhibits significant advantages over previous advanced methods in ship detection of varying scales in dense scenes. The code will be made available on GitHub.

Objective: Centrifugal pendulum vibration absorbers (CPVAs) are efficient devices for reducing torsional vibrations in rotating machinery. This study focuses on a circular-path unifilar CPVA and investigates its nonlinear behavior. Methods: A multibody dynamics (MBD) model of a CPVA-rotor system with two rotors was developed. The system's nonlinear behavior was analyzed using the shooting method to ensure model accuracy. Time integration results were used to validate the shooting method's outcomes. Frequency response analysis was conducted across a range of rotational speeds, including critical speeds, to evaluate the CPVA's vibration suppression performance. Results: The combination of MBD and shooting methods proved effective in analyzing the CPVA-rotor system with high accuracy. The study investigated the effects of varying the CPVA's radius of gyration while keeping mass constant. It was observed that small CPVA inertia or large excitation amplitudes could lead to saddle-node bifurcation, resulting in jump phenomena and large amplitude vibrations. Ranges of radius length and excitation amplitudes that prevent bifurcation were identified. Experimental results corroborated the numerical findings, including the observed frequency responses and bifurcation points. Conclusions: The combination of MBD and shooting methods offers an innovative approach for accurate CPVA system analysis. The study demonstrates the significant effect of CPVA inertia on vibration suppression performance. Experimental validation confirms the numerical analysis results, ensuring their reliability.

Although Deep neural networks (DNN) can achieve higher performance in automatic modulation recognition, they are known to vulnerable to adversarial perturbations, which are strategically added to inputs can fool the DNN model. In this letter, we propose a novel sparse attack scheme based on adversarial generative networks, which enables more covert attacks while preserving communication quality. This new scheme incorporates adversarial training into the discriminator, which not only improves attack performance but also enhances the stability of the training process for adversarial generative networks. Experimental results demonstrate that the proposed scheme outperforms other sparse attack approaches in terms of generation time and transferability.

Quantum key distribution (QKD) can provide long-term security for numerous users. Currently, quantum networks are still in the early stages of small-scale deployment, most of which can only support a single QKD protocol (QKDP). However, with the advancement of various QKDPs, a single-protocol quantum network is no longer sufficient to meet the demands of multiple users, prompting the emergence of multi-protocol quantum networks. Nevertheless, the transition from a single-protocol to a multi-protocol quantum network still faces many challenging issues, such as key negotiation interruptions due to device initialization and channel calibration during protocol updating. To address the quantum key negotiation interruption problem, we propose a seamless key negotiation oriented multi-protocol updating algorithm in this work, which can fulfill the protocol updating requests of different users in quantum metropolitan networks. Furthermore, to better improve network performance while meeting diverse user demands, we propose four heuristic algorithms for optimal QKDP recommendation, focusing on their applications for multi-protocol updating in different types of typical networks. We perform the simulations with different QKDP recommendation algorithms to analyze the impact of the cache time of the key cache area on the key negotiation interruption probability and the time resource utilization. Simulation results demonstrate that the proposed algorithm can reduce the key negotiation interruption probability by 77.7% while increasing the time resource utilization by 15.3% compared to no key cache area.

Vehicle roadway departure accidents are a major traffic safety concern as they oftentimes result in severe injuries and fatalities. To address such an issue, this article originates a novel driver-centric and neuro-adaptive-control-based lane-keeping assistance system (LKAS). The proposed control strategy synergizes a noncertainty-equivalent adaptive control design scheme, an adaptive radial-basis-function-based neural network (RBFNN) that captures the human driver's lane-keeping steering behavior, and a Gudermannian-function-based smooth parameter projection operator. The benefit and uniqueness of the proposed solution are threefold. First and foremost, the noncertainty-equivalent adaptive control design, which leverages the immersion-and-invariance-like methodology, ensures the asymptotical convergence of the parameter-estimation-error-induced perturbation despite the reference signal's persistency of excitation condition. Second, the LKAS is devised to be driver-centric, i.e., an adaptive RBFNN-based human driver steering model is embedded inside the LKAS's algorithm such that a human driver is assisted in a personalized and adaptive manner. Third, the Gudermannian-function-based smooth parameter projection operator ascertains the prescribed boundedness of the control parameters while maintaining the control action's smoothness. A pilot human-subject study using a high-fidelity moving-base driving simulator is conducted to validate the proposed LKAS. Further, its performance is compared with a baseline certainty-equivalent neuro-adaptive controller.

Active co-optimization of future speed profiles together with powertrain control is the optimal solution to further exploiting the energy benefit of electric vehicles (EVs) in real-world operation. However, with uncertainties in driving conditions and concerns about driving safety, speed planning results are cautious and with frequent speed variations, which deteriorates the energy economy of EVs in turn. To comprehensively optimize the energy economy and driving safety of EVs in a stochastic driving environment, this article develops a chance constraint model predictive control (CC-MPC) for co-optimizing the speed planning and powertrain control, which forms an advanced energy management method. To handle the instantaneous disturbance, a coordinated hierarchical method (CHM) is engineered for solving the CC-MPC. As suggested by simulation, the driving safety (measured by success rate) can be increased to 81% with the CC-MPC, which realizes a 62% improvement compared with situations without CC-MPC. Moreover, the proposed CC-MPC significantly mitigates the conflict between driving safety and the energy economy, and the worst deterioration of the energy economy is only 9.3%. Sacrificing merely 2.1% sub-optimality, CHM removes 86% computation loads, and the median of CPU time is merely 0.58s at each computation step (control interval 1s), which makes the CC-MPC promising for online implementation.

Gels formed by combining gelatin and agar are often used to develop food products such as pudding and gummy bears. Sensory evaluation experiments, bionic chewing experiments, correlation analysis, and regression analysis were performed on four gel samples with varying gelatin contents to explore a method for texture detection of such gels. Results showed a remarkable increase in the scores of hardness, springiness, and chewiness in the sensory evaluation experiment with increasing gelatin content. The fracture force, peak chewing force, and accumulated chewing energy in the bionic chewing experiment also showed a remarkable increase. Correlation analysis of the bionic chewing and sensory evaluation results showed that sensory hardness was highly correlated with maximum chewing force, with a correlation coefficient of 0.996, and chewiness was highly correlated with accumulated chewing energy, with a correlation coefficient of 0.993. The regression models of the four sensory evaluation parameters based on the critical parameters of bionic chewing were all significantly less than 0.001 with a fit of 0.8. The proposed bionic chewing robot and texture detection method can be applied to texture detection of gelatin foods and are consistent with the sensory evaluation results.

Electric power transmission by tunnel transmission technology resolves limitations of overhead transmission lines restricted by complex terrain, geological conditions, and environmental protection. A tunnel transmission method involves a sophisticated line-laying procedure within a smaller measurement space. However, it remains a challenging task to monitor their real-time operational status. It is anticipated that monitoring by utilizing magnetic measurement can be a promising solution. In this article, we propose a method of optimizing magnetic sensor arrays using a differential evolution (DE) algorithm. The current reconstruction technique based on noncontact magnetic measurements for different laying topologies is presented. A novel condition number-based approach is proposed for the current measurement error assessment. Then, the DE algorithm is utilized for optimization of condition number index for optimal sensor array arrangement. Random noise is introduced in magnetic field to emulate real-world scenarios. The root-mean-square error (RMSE) of reconstructed current in the presence of magnetic noise is reduced from 8.867% to 1.267% for vertical line configuration by the proposed method. In comparison to other optimization methods, the proposed technique yields accurate and reliable results.

Impaired transcription factor EB (TFEB) function and deficient autophagy activity have been shown to aggravate intervertebral disc (IVD) degeneration (IDD), yet the underlying mechanisms remain less clear. Protein posttranslational modifications (PTMs) are critical for determining TFEB trafficking and transcriptional activity. Here, we demonstrate that TFEB activity is controlled by protein methylation in degenerated nucleus pulposus cells (NPCs), even though TFEB itself is incapable of undergoing methylation. Specifically, protein phosphatase 1 catalytic subunit alpha (PPP1CA), newly identified to dephosphorylate TFEB, contains a K141 mono-methylated site. In degenerated NPCs, increased K141-methylation of PPP1CA disrupts its interaction with TEFB and subsequently blocks TEFB dephosphorylation and nuclear translocation, which eventually leads to autophagy deficiency and NPC senescence. In addition, we found that the PPP1CA-mediated targeting of TFEB is facilitated by the protein phosphatase 1 regulatory subunit 9B (PPP1R9B), which binds with PPP1CA and is also manipulated by K141 methylation. Further proteomic analysis revealed that the protein lysine methyltransferase suppressor of variegation 3–9 homologue 2 (SUV39H2) is responsible for the K141 mono-methylation of PPP1CA. Targeting SUV39H2 effectively mitigates NPC senescence and IDD progression, providing a potential therapeutic strategy for IDD intervention.

ABSTRACTOptical coherence tomography (OCT) is a non-invasive imaging method that can be used to study the surface features and subsurface structures of delicate cultural heritage objects. However, the field of view of OCT severely limits the system’s scanning area. Previously, we have presented a hybrid scanning platform combined with an imaging stitching algorithm to achieve macroscopic OCT (macro-OCT) imaging. This paper further demonstrates the potential applications of the OCT data by rendering 3D volumetric data into standard virtual reality (VR), augmented reality (AR), and 3D printing formats. The 3D model can be 3D printed or interactively displayed through various platforms such as VR and AR headsets, smartphones, and web pages. The high-resolution 3D models obtained from the macro-OCT system can potentially improve the experience of accessing artworks online and assist individuals with visual impairments to study art through tactile feedback.

Current treatments of carbon fiber-reinforced polyetheretherketone (CFRPEEK) as orthopedic implants remain unsatisfactory due to the bioinert surface. The multifunctionalization of CFRPEEK, which endows it with regulating the immune inflammatory response, promoting angiogenesis, and accelerating osseointegration, is critical to the intricate bone healing process. Herein, a multifunctional zinc ion sustained-release biocoating, consisting of a carboxylated graphene oxide, zinc ion, and chitosan layer, covalently grafts on the surface of amino CFRPEEK (CP/GC@Zn/CS) to coordinate with the osseointegration process. The release behavior of zinc ions theoretically conforms to the different demands in the three stages of osseointegration, including the burst release of zinc ions in the early stage (7.27 μM, immunomodulation), continuous release in the middle stage (11.02 μM, angiogenesis), and slow release in the late stage (13.82 μM, osseointegration). In vitroassessments indicate that the multifunctional zinc ion sustained-release biocoating can remarkably regulate the immune inflammatory response, decrease the oxidative stress level, and promote angiogenesis and osteogenic differentiation. The rabbit tibial bone defect model further confirms that, compared to the unmodified group, the bone trabecular thickness of the CP/GC@Zn/CS group increases 1.32-fold, and the maximum push-out force improves 2.05-fold. In this study, a multifunctional zinc ion sustained-release biocoating constructed on the surface of CFRPEEK that conforms to the requirements of different osseointegration stages can be an attractive strategy for the clinical application of inert implants.

Formation water (FW) and fracturing fluid (FF) significantly affect the adsorption rules of CH4on a shale surface. To clarify the impact rules and micromechanisms of FW and FF on CH4adsorption, isothermal adsorption experiments of CH4on different shale minerals with different equilibrium water were conducted. Then, the corresponding adsorption models were constructed by Grand Canonical Monte Carlo (GCMC) simulation to perform adsorption simulation after matching experiments. Finally, molecular dynamic (MD) simulation was carried out to study the micromechanisms of FW and FF affecting CH4adsorption on different shale minerals. The results show that the adsorption capacity of organic matter to CH4is much stronger than that of other minerals. Compared to dry conditions, the adsorption capacity of organic matter, smectite, illite, and total shale with FW (Sw≈ 15%) decreases to 65, 45, 70, and 55%, respectively. The impact of FF on CH4adsorption capacity is more significant than FW. The adsorption capacity decreases to 45, 30, 50, and 45% for organic matter, smectite, illite, and total shale with FF (Sw≈ 15%), respectively. FW molecules inhibit CH4adsorption by occupying adsorption sites on mineral surfaces. However, the HPAM in FF completely covers the mineral surface to compress adsorption space and hinder CH4adsorption. Although water molecules in both FW and FF occupy part of the adsorption sites on organic matter, the left sites can still absorb large amounts of CH4. It provides theoretical guidance for the efficient development of shale gas.

We propose a novel terahertz (THz) metamaterial (MM)-based sensor for the sensitive detection of doped polymer thin films. The MM constructed by periodic quasi-ring resonators can generate the analog of electromagnetically induced transparency (EIT) at the THz region and work in a wide range of incident angles. The simulated results show that the frequency and magnitude of the EIT-like resonance can be, respectively, affected by the refractive index and dielectric loss of the analyte, which is further explained by a coupled harmonic oscillator model. The theoretical sensitivity of the proposed sensor is calculated as 185 GHz/RIU [refractive index unit (RIU)], showing a good sensing capability. In experiments, the dispersed red 1 (DR1)-doped polymethylmethacrylate (PMMA) thin films with a subwavelength thickness of 600 nm were covered on the MM surface. The measured results show that the EIT-like resonance experiences frequency redshift and magnitude attenuation as the doping concentration increases, indicating the addition of DR1 can lead to the increase in both refractive index and dielectric loss of the PMMA thin film. The proposed MM-based sensor can successfully identify the dielectric change of extremely thin polymer films caused by doping, offering a novel way to achieve high-sensitivity thin-film sensing.

Deep neural networks (NNs) are considered a powerful tool for balancing the performance and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate feature extraction, high parallelism, and excellent inference ability. Graph NNs (GNNs) have recently demonstrated outstanding capability in learning enhanced message passing rules and have shown success in overcoming the drawback of inaccurate Gaussian approximation of expectation propagation (EP)-based MIMO detectors. However, the application of the GNN-enhanced EP detector to MIMO turbo receivers is underexplored and non-trivial due to the requirement of extrinsic information for iterative processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo receivers, which realizes the turbo principle of generating extrinsic information from the MIMO detector through a specially designed training procedure. Additionally, an edge pruning strategy is designed to eliminate redundant connections in the original fully connected model of the GNN utilizing the correlation information inherently from the EP algorithm. Edge pruning reduces the computational cost dramatically and enables the network to focus more attention on the weights that are vital for performance. Simulation results and complexity analysis indicate that the proposed MIMO turbo receiver outperforms the EP turbo approaches by over 1 dB at the bit error rate of 10${}^{\boldsymbol{-}\mathbf{5}}$, exhibits performance equivalent to state-of-the-art receivers with 2.5 times shorter running time, and adapts to various scenarios.

With the blooming of Internet-of-Things (IoT), we are witnessing an explosion in the number of IoT terminals, triggering an unprecedented demand for ubiquitous wireless access globally. In this context, the emerging low-Earth-orbit satellites (LEO-SATs) have been regarded as a promising enabler to complement terrestrial wireless networks in providing ubiquitous connectivity and bridging the ever-growing digital divide in the expected next-generation wireless communications. Nevertheless, the harsh conditions posed by LEO-SATs have imposed significant challenges to the current multiple access (MA) schemes and led to an emerging paradigm shift in system design. In this article, we first provide a comprehensive overview of the state-of-the-art MA schemes and investigate their limitations in the context of LEO-SATs. To this end, we propose a novel next generation MA (NGMA), which amalgamates the grant-free non-orthogonal multiple access (GF-NOMA) mechanism and the orthogonal time frequency space (OTFS) waveform, for simplifying the connection procedure with reduced access latency and enhanced Doppler-robustness. Critical open challenging issues and future directions are finally presented for further technical development.

This paper investigates the massive connectivity of low Earth orbit (LEO) satellite-based Internet-of-Things (IoT) for seamless global coverage. We propose to integrate the grant-free non-orthogonal multiple access (GF-NOMA) paradigm with the emerging orthogonal time frequency space (OTFS) modulation to accommodate the massive IoT access, and mitigate the long round-trip latency and severe Doppler effect of terrestrial–satellite links (TSLs). On this basis, we put forward a two-stage successive active terminal identification (ATI) and channel estimation (CE) scheme as well as a low-complexity multi-user signal detection (SD) method. Specifically, at the first stage, the proposed training sequence aided OTFS (TS-OTFS) data frame structure facilitates the joint ATI and coarse CE, whereby both the traffic sparsity of terrestrial IoT terminals and the sparse channel impulse response are leveraged for enhanced performance. Moreover, based on the single Doppler shift property for each TSL and sparsity of delay-Doppler domain channel, we develop a parametric approach to further refine the CE performance. Finally, a least square based parallel time domain SD method is developed to detect the OTFS signals with relatively low complexity. Simulation results demonstrate the superiority of the proposed methods over the state-of-the-art solutions in terms of ATI, CE, and SD performance confronted with the long round-trip latency and severe Doppler effect.

This paper proposes a personalized ground-vehicle lane-keeping assist system (LKA). To begin with, a control-oriented model unifying nonlinear vehicle-road dynamics and a linear driver lane-keeping steering angle model is derived. Subsequently, a novel LKA control strategy, alloying an L1+α-reachable adaptive sliding-mode controller and a shifted-logistic-function-based smooth parameter projection scheme, is formulated. Through a non-quadratic Lyapunov redesign, the sliding manifold is asymptotically reached, throughout which the trajectory of the switching function's value is confined inside the L1+αsignal space. In addition, the suggested smooth projection operator can prevent the unbounded control parameter drift whilst maintaining the control command's smoothness. A simulation study employing the CarSim-Simulink joint platform and a cyber driver model is performed to evaluate the proposed personalized LKA adaptive controller and compare it with a linear-robust-control-based solution.

Electric vehicles (EVs) have gained attention in recent years due to their environmental friendliness and higher fuel efficiency. However, EV users may have concerns about their driving range. To alleviate such an anxiety, a pre-trip estimation of EV energy consumption can be helpful. There are two main approaches to predicting EV energy consumption: traditional model-based methods that use physical knowledge, and data-driven techniques that rely on machine learning methods. Although both types of methods show promise, little attention has been paid to experimentally compare their performance differences. To bridge this gap, this paper presents an experimental comparison study of three model-based and data-driven EV energy consumption estimation algorithms. Notably, real-world EV road-test datasets from urban driving are used for the comparative evaluation. Furthermore, this study offers a discussion of the pros and cons of each method, providing a guideline for algorithm improvement and selection.

Long-term configuration stability is essential for an interferometric detection constellation (IDC), which is closely related to initial uncertainty. Therefore, it is vital to evaluate the uncertainty and characterize the configuration stability. In this study, an analytical method was developed for the configuration uncertainty propagation of a geocentric triangular IDC. The angular momentum and the argument latitude were found to be significantly affected by the initial uncertainty and were selected as the core variables. By averaging the perturbation in one revolution, an analytical solution was proposed for propagating the core orbital elements in one revolution. Subsequently, the analytical solution of the orbit elements during the mission period is obtained by multiplying the solutions in iterative revolutions. The relationship between the selected orbital elements and the configuration stability parameters was established using an analytical solution. The effects of the initial uncertainty in different directions on the configuration and stable domains were studied. Simulations show that the developed method is highly efficient and accurate in predicting the configuration stability. The relative error with respect to the Monte Carlo simulations was less than 3% with a time consumption of 0.1%. The proposed method can potentially be useful for constellation design and stability analysis.

An increasing demand for investigating the solar system has envisioned many asteroid exploration missions. Optical navigation is the principal technology to determine the explorer’s orbit relative to an asteroid when approaching it in an exploration mission. The approaching orbit determines the observing conditions and consequently affects the optical navigation accuracy. Aiming at improving the optical navigation performance, this article proposes the approach orbit optimization method considering optical navigation observability. The defect of the optical navigation along the line-of-sight (LOS) direction when the explorer moves along the conventional approaching orbit is revealed. The quantitative index of the navigation performance is designed by deriving the Fisher information matrix (FIM). The orbit optimization problem is constructed by involving the elements of FIM and fuel consumption together into the optimization index, and setting up the necessary engineering constraints. Numerical simulations show that compared with the conventional fuel-optimal cases, the navigation along the LOS direction is greatly improved. The navigation accuracy is improved from 86 to 99% and an additional 1.62 kg of fuel is consumed. The proposed method can effectively improve the navigation performance and has promising applications in asteroid approach trajectory design in the future asteroid exploration missions.

In this paper, the effect of Er on the hot cracking behaviour of Al-5Cu alloy is investigated by the means of wedge moulds, differential hot analyzer, optical microscope, and scanning electron microscope. The results show that the percentage of intergranular low-melting-point eutectic structure is increased by approximately 8% on average per 0.3 wt-% Er additions. The increase of low-melting-point eutectic structure enhances the fluidity of the liquid film in the late solidification stage, resulting in the improvement of the hot cracking resistance of the alloy. Moreover, the Al-5Cu-0.6Er alloy has the best hot cracking resistance under wedge-shaped copper casting experiments.

Ideal bone implant materials need to provide multiple functions such as biocompatibility, non-cytotoxicity, and bone tissue regeneration guidance. To tackle this challenge, according to our previous work, carbon fiber (40 mm)-reinforced polyetheretherketone (CFPEEK) composites were developed by using 3D needle-punched CFPEEK preform molding technology. Because of the excellent mechanical properties, the CFPEEK needled felt matrix composites have a broad application prospect in orthopedic internal fixation and implant materials. In order to expand the application range of composite materials, it is very necessary to improve the surface bioactivity of composite materials. The surface modification of CFPEEK with 2D titanium carbide (MXene) nanosheets (sulfonated CFPEEK (SCFPEEK)-polydopamine (PDA)-Ti3C2Tx) for enhanced photothermal antibacterial activity and osteogenicity was explored in this study. Here, the new composites we constructed are composed of Ti3C2Txnanosheets, PDA, and biologically inert SCFPEEK, which gave the bio-inert composites bimodal therapeutic features: photothermal antibacterial activity and in vivoosseointegration. To our knowledge, this is the first time that a CFPEEK implant with a bioactive surface modified by Ti3C2Txnanosheets was demonstrated. Due to the synergistic photothermal therapy (PTT) treatment of Ti3C2Tx/PDA, SCFPEEK-PDA-Ti3C2Tx(SCP-PDA-Ti) absorbed heat and the temperature increased to 40.8–59.6 °C─the high temperature led to bacterial apoptosis. The SCP-PDA-Ti materials could effectively kill bacteria after 10 min of near-infrared (NIR) irradiation at 808 nm. SCP-PDA-Ti (2.5) and SCP-PDA-Ti (3.0) achieved a 100% bacteriostasis rate. More importantly, the multifunctional implant SCP-PDA-Ti shows good cytocompatibility and an excellent ability to promote bone formation in terms of cytotoxicity, diffusion, alkaline phosphatase activity, alizarin red activity, real-time polymerase chain reaction analysis, and in vivobone defect osteogenesis experiments. This provides a more extendable development idea for the application of carbon fiber-reinforced composites as orthopedic implants.

Deep neural networks are vulnerable to adversarial examples. However, existing attacks exhibit relatively low efficacy in generating transferable adversarial examples. Improved transferability to address this issue is proposed via a rotation‐invariant attack method that maximizes the loss function w.r.t the random rotated image instead of the original input at each iteration, thus mitigating the high correlation between the adversarial examples and the source models and making the adversarial examples more transferable. Extensive experiments show that the proposed method can significantly improve the transferability of the adversarial examples with almost no extra computational cost and can be integrated into various methods. In addition, when this method is easily applied through a plug‐in, the average attack success rate against six robustly trained models increases by 5.4% over the state‐of‐the‐art baseline method, demonstrating its effectiveness and efficiency. The codes used are publicly available at https://github.com/YeXinD/Rotation‐Invariant‐Attack.

In this letter, a systematic synthesis of a new class of smooth parameter projection operators is presented. To elaborate such an approach, the adaptive control problem for a nth-order single-input linearly parametrizable nonlinear system in the controllable canonical structure is considered. The stability of the closed-loop adaptive system, with the augmentation of such a class of smooth projection operators, is analyzed by a Lyapunov-like analysis. With this systematic construction, two novel smooth projection operators are devised. A simulation study is performed to validate the proposed strategy and compare its performance against a non-smooth, parameter-projection solution.

Model predictive control (MPC), a control technique that can systematically and explicitly cope with system constraints, has become increasingly prevalent in the field of automotive engineering. Respecting the advanced driver-assistance systems (ADAS) design, the MPC has found its applications in various kinds of ground vehicular lane-keeping assistance systems (LKAS). In this paper, an MPC-based LKAS is synthesized by leveraging the linear programming (LP) methodology. Compared to the conventional MPC-based LKAS that is based on quadratic programming (QP), the LP alternative is less computationally demanding, which is desired for commercial vehicular electronic control units. Besides, the LP scheme enables the designer to formulate the MPC's performance criterion in the sense of L1/L∞norms, which differs from the QP's L2-norm-based measure. The proposed LP-based MPC-LKAS is examined in CarSim-Simulink joint simulations and its performance is compared with a QP-based solution.

This paper innovates an adaptive control law that unifies the Immersion and Invariance (I&I) principle and a novel arctangent-function-based smooth projection operator. Such an adaptive controller is exploited to accomplish the command tracking task of a parametric uncertain linear motor system. The uniqueness and advantage of the proposed method lie twofold. First and foremost, the novel nonaffine I&I scheme ensures the asymptotical convergence of the parameter estimation error despite the reference command's persistency of excitation conditions. Second, the arctangent-function-based smooth projection operator is a one-piece infinitely differentiable (C∞) function that requires no boundary layer construction nor piecewise-defined functions compared to existing smooth projection techniques. Simulation results are presented to examine the proposed adaptive controller. Furthermore, its performance (especially the transient one) is compared against a certainty-equivalence-based method.

Electric vehicles (EVs) are considered one of the most promising ways to reduce greenhouse gas (GHG) emissions and address fossil fuel shortage. However, due to EV's limited driving range and battery's fluctuating capacity at different temperatures, EV drivers may question EVs’ ability to reach their destinations. In light of this, an accurate EV energy consumption estimation/prediction is vital to relieve drivers' concerns. This paper proposes an EV energy consumption estimation framework that explicitly considers the vehicle longitudinal wheel slip ratio. Besides, a machine-learning-based dynamic efficiency map is devised to capture the energy transfer ratio between electric motor and battery. Furthermore, a mixed second-order L1/H2estimator is used to calculate the derivatives of velocity data. The method is evaluated based on on-road EV test data, and the result testifies its enhanced performance over a baseline method.

For delivery fleets with medium-duty electric trucks, battery sizing and energy efficient routing play an important role in cost reduction. In scenarios where periodic trends such as weekly or monthly delivery demands exist, the optimization problem needs to consider the requests for each day in the time horizon simultaneously, which is a challenging task to address. The existing heuristic and metaheuristic approaches designed for a single instance of vehicle routing problem (VRP) may not guarantee an optimal solution over the longer period of interest. In this paper, a two-stage grouping genetic algorithm (GGA) that builds on the existing GA approach for delivery cost minimization is proposed. Independent VRPs are solved using a GGA with battery degradation in consideration. The resulting population is reformulated in a way such that a second GGA can utilize the information to converge the solution to a single battery combination and the respective daily optimal routes that minimize the operational cost over a fixed time horizon. Compared to the approaches using single-day data, the two-stage GGA was able to obtain solutions that give up to 7.31% lower daily cost of operation.

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Cells are continuously exposed to various mechanical forces and respond to these mechanical stimuli by mechanosensing and subsequent mechanotransduction. The integration of mechanical signals coordinates gene expression via epigenetic regulation of chromatin and transcription to change the cell state and orchestrate cellular mechanobiology.

Digital signature is a key technique in information security, especially for identity authentications. Compared to classical correspondence, quantum digital signatures (QDSs) provide a considerably higher level of security. At present, its performance is limited by key generation protocols, which are fundamentally limited in terms of channel capacity. Based on the idea of twin-field quantum key distribution, this Letter presents a twin-field QDS protocol and details a corresponding security analysis. In its distribution stage, a specific key generation protocol, the sending-or-not-sending twin-field protocol, has been adopted. Besides, we present a systematic model to evaluate the performance of a QDS protocol and compare the performance of our protocol to other typical QDS protocols. Numerical simulation results show that the new protocol exhibits outstanding security and practicality compared to other existing protocols. Therefore, our protocol paves the way toward real-world applications of QDSs.

In this study, the problem of finite-time stability and boundedness for parabolic singular distributed parameter systems in the sense of L2norm is investigated. First, two new results on L2norm-based finite-time stability and finite-time boundedness for above-mentioned systems, inspired by the light of partial differential equations theory and Lyapunov functional method, are presented. Then, some sufficient conditions of L2norm-based finite-time stability and boundedness are established by virtue of differential inequalities and linear matrix inequalities. Furthermore, the distributed state feedback controllers are constructed to guarantee the L2norm-based finite-time stable and bounded of the closed-loop singular distributed parameter systems. Finally, numerical simulations on a specific numerical example and the building temperature control system equipped with air conditioning are given to demonstrate the validity of the proposed methods.

In this paper, the D-type iterative learning control (ILC) protocol based on the local neighbor information is designed to achieve tracking synchronization for linearly coupled reaction-diffusion neural networks in presence of time delay and iteration-varying switching topology under a repetitive environment. Firstly, based on non-collocated sensors and actuators network, the proposed D-type ILC update law can realize tracking synchronization by utilizing output tracking errors. Then, by virtue of the contraction mapping principle, the sufficient convergence conditions of tracking synchronization errors are presented under the fixed commutation topology. Subsequently, the synchronization conclusions are extended to the iteration-varying commutation topology scenario. Finally, two numerical examples are provided to verify the efficacy of the obtained results. [ABSTRACT FROM AUTHOR]

Ultra-thin perovskite solar cells (UTPSCs) have garnered significant attention for their high specific power and potential application in space missions. However, the efficiency of ultrathin solar cells has been constrained by challenges in handling and fabricating them on the fragile ultra-thin substrates, leading to notable performance disparities compared to their rigid counterparts. Here, we present a novel method for fabricating efficient and stable ultrathin flexible PSCs. By employing a polydimethylsiloxane (PDMS) buffer layer and controlling the imidization temperature of colorless polyimide (CPI), we prepared an ultra-thin CPI substrate with a thickness of 1-3 μm, which can be easily peeled off. This technique provides a heat-resistant ultra-thin substrate with a smooth surface for UTPSC fabrication. Additionally, the ultra-thin substrate exhibits superior thermal conductivity and more uniform temperature distribution compared to conventional flexible substrates. Ultimately, we achieve a 1.5 μm UTPSC with a power conversion efficiency of 22.13% and an outstanding specific power density of 50W/g, surpassing most existing solar cells. Remarkably, the UTPSCs demonstrate exceptional mechanical robustness, maintaining performance even under rigorous bending and twisting. Moreover, CPI exhibited better thermal expansion matching with perovskite and demonstrated enhanced stability under low-temperature conditions and thermal cycling, showing potential for space applications. Our approach of preparing ultrathin CPI substrates offers an effective pathway for fabricating lightweight and highly flexible electronics.

A novel cathode material VPS-1 for sodium-ion batteries was prepared by hydrothermal method, which was feasible for sodium-ion half-cells, full-cells and fast-charging tests.

Peripheral nerve injuries (PNI) can lead to mitochondrial dysfunction and energy depletion within the affected microenvironment. The objective is to investigate the potential of transplanting mitochondria to reshape the neural regeneration microenvironment. High‐purity functional mitochondria with an intact structure are extracted from human umbilical cord‐derived mesenchymal stem cells (hUCMSCs) using the Dounce homogenization combined with ultracentrifugation. Results show that when hUCMSC‐derived mitochondria (hUCMSC‐Mitos) are cocultured with Schwann cells (SCs), they promote the proliferation, migration, and respiratory capacity of SCs. Acellular nerve allografts (ANAs) have shown promise in nerve regeneration, however, their therapeutic effect is not satisfactory enough. The incorporation of hUCMSC‐Mitos within ANAs has the potential to remodel the regenerative microenvironment. This approach demonstrates satisfactory outcomes in terms of tissue regeneration and functional recovery. Particularly, the use of metabolomics and bioenergetic profiling is used for the first time to analyze the energy metabolism microenvironment after PNI. This remodeling occurs through the enhancement of the tricarboxylic acid cycle and the regulation of associated metabolites, resulting in increased energy synthesis. Overall, the hUCMSC‐Mito‐loaded ANAs exhibit high functionality to promote nerve regeneration, providing a novel regenerative strategy based on improving energy metabolism for neural repair. Mitochondria from mesenchymal stem cells promote proliferation, migration, and respiratory metabolism of Schwann cells. Loading mitochondria onto decellularized nerve grafts promotes nerve regeneration and functional recovery. Mitochondrial transplantation therapy provides a novel regenerative strategy based on energy metabolism for nerve repair.

Mesoporous bioactive glasses (MBGs), which belong to the category of modern porous nanomaterials, have garnered significant attention due to their impressive biological activities, appealing physicochemical properties, and desirable morphological features. They hold immense potential for utilization in diverse fields, including adsorption, separation, catalysis, bioengineering, and medicine. Despite possessing interior porous structures, excellent morphological characteristics, and superior biocompatibility, primitive MBGs face challenges related to weak encapsulation efficiency, drug loading, and mechanical strength when applied in biomedical fields. It is important to note that the advantageous attributes of MBGs can be effectively preserved by incorporating supramolecular assemblies, miscellaneous metal species, and their conjugates into the material surfaces or intrinsic mesoporous networks. The innovative advancements in these modified colloidal inorganic nanocarriers inspire researchers to explore novel applications, such as stimuli-responsive drug delivery, with exceptional in-vivo performances. In view of the above, we outline the fabrication process of calcium-silicon-phosphorus based MBGs, followed by discussions on their significant progress in various engineered strategies involving surface functionalization, nanostructures, and network modification. Furthermore, we emphasize the recent advancements in the textural and physicochemical properties of MBGs, along with their theranostic potentials in multiple cancerous and non-cancerous diseases. Lastly, we recapitulate compelling viewpoints, with specific considerations given from bench to bedside.

•Two sensitive directions for orbital uncertainty propagation are found.•A reduced-order configuration uncertainty propagation method is proposed.•The performance of the proposed method is verified on the example of the LISA project.

The Mg17Al12–BaO composite is synthesized via mechanical milling and the effect of BaO on the hydrogen sorption properties of Mg17Al12is studied. Experimentally, we prepare the Mg17Al12–Ba, Mg17Al12–BaO, Mg17Al12–BaF2, and Mg17Al12–BaCl2mixtures and find that the Mg17Al12–BaO composite shows a superior hydrogen storage performance. For instance, the hydrogenation (dehydrogenation) enthalpy of the Mg17Al12decreases from 62.4 (91.2) to 58.6 (71.7) kJ mol–1after adding BaO. When 1.0 wt % of H2is absorbed/desorbed, the hydrogen absorption/desorption temperature of the Mg17Al12–BaO is 181/271 °C, which is 73/37 °C lower than that of the Mg17Al12. Furthermore, the catalytic mechanism of BaO on the hydrogenation of Mg17Al12(110) surface is investigated by density functional theory (DFT). Calculations indicate that compared with the Mg17Al12(110) surface, the adsorption energy and dissociation barrier of hydrogen on the Mg17Al12–BaO (110) surface are both improved significantly. Our experimental and theoretical results are helpful for understanding the effect of metal oxide on hydrogen storage properties of Mg17Al12.

Accurate detection of driver steering interventions is crucial for assuring safe control transitions from the autonomous driving system (ADS) to the human driver as well as for seamless human-automation shared driving. In particular, the estimated human driver steering torque is an important decisive signal for initiating such taking-over transitions. During the past decade, numerous research endeavors were made to study the driver torque estimation problems. Yet, the majority of the existing solutions are based upon the traditional power-assisted steering (PAS) systems, which rely on passive hydraulic, electric, or electrohydraulic power assistance. On the other hand, the maturation of advanced driver-assistance systems (ADAS) and the advancement in autonomous driving have promoted the broad adaption of the next-generation steer-by-wire (SBW) technology. To tackle the new problem of human driver torque estimation in the SBW system setup, this paper proposes a novel model-based estimator. To begin with, a state-space model for the uncertain steering system dynamics is derived. The corresponding observability is subsequently verified. Next, in light of the generalized H2filtering theory, a multi-objective robust observer is synthesized. Finally, the accuracy and robustness of the proposed driver torque estimation algorithm are validated in simulations.