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Active fault diagnosis (AFD) designs inputs to excite the system to obtain more operation information for fault diagnosis. Since both AFD and control are required to design systems’ inputs, individually designing inputs for fault diagnosis will restrict the systems’ control performance. In order to achieve satisfactory performance for both AFD and control, this paper presents a novel set-based input design method for simultaneous AFD and control. The new method adopts a decomposition-composition idea to integrate AFD and control for better control performance during the AFD stage. Particularly, the proposed method first considers each single system mode separately and establishes an optimization problem, combining the AFD and control objectives, to design the optimal input. Then, all possible system modes are comprehensively considered to select the final input from all inputs designed under the single mode by an adaptive selection strategy. Meanwhile, the proposed method realizes AFD by maximizing the separation trend of all output sets online, without considering the strict set separation conditions. Hence, the method in this paper has a simple mathematical form and lower computational complexity. At the end, simulation results based on two different examples are presented to verify the effectiveness and portability of the proposed method. Note to Practitioners—Set-based AFD methods have two main features. The first one is that they only require the bounds of system uncertainties, such as modelling errors, process disturbances and measurement noise, and do not require their specific distributions and values. This requirement can be easily satisfied by varieties of engineering systems. The second one is that they design inputs to actively excite the system to obtain more system operation information for fault diagnosis. This feature enables AFD to achieve high fault diagnosis sensitivity and detect more faults, such as incipient faults, small faults, etc. Thus, when AFD methods are used in active fault-tolerant control (FTC) systems, the whole FTC performance can be improved. However, set-based AFD has conflicting requirements on input design with control, while there only exist few works in the literature on the integrated design of set-based AFD and control. This paper proposes a novel integrated design method for AFD and control, which can achieve satisfactory performance for both AFD and control with low computational complexity. Besides, although this paper only considers discrete linear time-invariant (LTI) systems, the proposed method can be extended to more complex systems such as linear parameter-varying (LPV) systems, linear time-varying systems, etc. Moreover, based on LPV modelling techniques, equilibrium linearization techniques, etc., the proposed method can be used for fault diagnosis and FTC of some nonlinear systems as well. Therefore, the proposed method has advantages in online fault diagnosis and FTC applications of engineering systems such as unmanned aerial vehicles, unmanned ships, driverless automobiles, space systems, robots, process industries, etc., which own values and potential to improve safety and reliability of varieties of engineering systems.

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the reinforcement learning (RL) agent’s behavior as the environment changes over its lifetime while minimizing the catastrophic forgetting of the learned information. To address this challenge, in this article, we propose DaCoRL, that is, dynamics-adaptive continual RL. DaCoRL learns a context-conditioned policy using progressive contextualization, which incrementally clusters a stream of stationary tasks in the dynamic environment into a series of contexts and opts for an expandable multihead neural network to approximate the policy. Specifically, we define a set of tasks with similar dynamics as an environmental context and formalize context inference as a procedure of online Bayesian infinite Gaussian mixture clustering on environment features, resorting to online Bayesian inference to infer the posterior distribution over contexts. Under the assumption of a Chinese restaurant process (CRP) prior, this technique can accurately classify the current task as a previously seen context or instantiate a new context as needed without relying on any external indicator to signal environmental changes in advance. Furthermore, we employ an expandable multihead neural network whose output layer is synchronously expanded with the newly instantiated context and a knowledge distillation regularization term for retaining the performance on learned tasks. As a general framework that can be coupled with various deep RL algorithms, DaCoRL features consistent superiority over existing methods in terms of stability, overall performance, and generalization ability, as verified by extensive experiments on several robot navigation and MuJoCo locomotion tasks.

Federated learning (FL) can train collaboratively with several mobile terminals (MTs), which faces critical challenges in communication, resource, and privacy. Existing privacy-preserving methods usually adopt instance-level differential privacy (DP), which provides a rigorous privacy guarantee but with several bottlenecks: performance degradation, transmission overhead, and resource constraints. Therefore, we propose Fed-LTP, an efficient and privacy-enhanced FL framework with Lottery Ticket Hypothesis (LTH) and zero-concentrated DP (zCDP). It generates a pruned global model on the server side and conducts sparse-to-sparse training from scratch with zCDP on the client side. On the server side, two pruning schemes are proposed: (i) the weight-based pruning (LTH) determines the pruned global model structure; (ii) the iterative pruning further shrinks the size of the pruned model. Meanwhile, the performance of Fed-LTP is boosted via model validation based on the Laplace mechanism. On the client side, we use sparse-to-sparse training to solve the resource-constraints issue and provide tighter privacy analysis to reduce the privacy budget. We evaluate the effectiveness of Fed-LTP on several real-world datasets in both independent and identically distributed (IID) and non-IID settings. The results confirm the superiority of Fed-LTP over state-of-the-art (SOTA) methods in communication, computation, and memory efficiencies while realizing a better utility-privacy trade-off.

Metalenses with achromatic performance offer a new opportunity for high-quality imaging with an ultra-compact configuration; however, they suffer from complex fabrication processes and low focusing efficiency. In this study, we propose an efficient design method for achromatic microlenses on a wavelength scale using materials with low dispersion, an adequately designed convex surface, and a thickness profile distribution. By taking into account the absolute chromatic aberration, relative focal length shift (FLS), and numerical aperture (NA), microlens with a certain focal length can be realized through our realized map of geometric features. Accordingly, the designed achromatic microlenses with low-dispersion fused silica were fabricated using a focused ion beam, and precise surface profiles were obtained. The fabricated microlenses exhibited a high average focusing efficiency of 65% at visible wavelengths of 410–680 nm and excellent achromatic capability via white light imaging. Moreover, the design exhibited the advantages of being polarization-insensitive and near-diffraction-limited. These results demonstrate the effectiveness of our proposed achromatic microlens design approach, which expands the prospects of miniaturized optics such as virtual and augmented reality, ultracompact microscopes, and biological endoscopy.

Because ozone (O3) is a significant air pollutant, advanced O3elimination technologies, particularly those under high-humidity conditions, have become an essential research focus. In this study, a nickel–iron layered double hydroxide (NiFe-LDH) was modified via intercalation with octanoate to develop an effective hydrophobic catalyst (NiFe-OAa-LDH) for O3decomposition. The NiFe-OAa-LDH catalyst sustained its O3decomposition rate of >98% for 48 h under conditions of 90% relative humidity, 840 L/(g·h) space velocity, and 100 ppm inlet O3concentration. Moreover, it maintained a decomposition rate of 90% even when tested at a higher airflow rate of 2500 L/(g·h). Based on the changes induced by the Ni–OIIto Ni–OIIIbonds in NiFe-OAa-LDH during O3treatment, catalytic O3decomposition was proposed to occur in two stages. The first stage involved the reaction between the hydroxyl groups and O3, leading to the breakage of the O–H bonds, formation of NiOOH, and structural changes in the catalyst. This transformation resulted in the formation of abundant and stable hydrogen vacancies. According to density functional theory calculations, O3can be effectively decomposed at the hydrogen vacancies with a low energy barrier during the second stage. This study provides new insights into O3decomposition.

The transformation of toxic arsine (AsH3) gas into valuable elemental arsenic (As0) from industrial exhaust gases is important for achieving sustainable development goals. Although advanced arsenic removal catalysts can improve the removal efficiency of AsH3, toxic arsenic oxides generated during this process have not received adequate attention. In light of this, a novel approach for obtaining stable As0products was proposed by performing controlled moderate oxidation. We designed a tailored Ni-based catalyst through an acid etching approach to alter interactions between Ni and NaY. As a result, the 1Ni/NaY-H catalyst yielded an unprecedented proportion of As0as the major product (65%), which is superior to those of other reported catalysts that only produced arsenic oxides. Density functional theory calculations clarified that Ni species changed the electronic structure of oxygen atoms, and the formed [NiIII–OH (μ-O)] active centers facilitated the adsorption of AsH2*, AsH*, and As* reaction intermediates for As–H bond cleavage, thereby decreasing the direct reactivity of oxygen with the arsenic intermediates. This work presents pioneering insights into inhibiting excessive oxidation during AsH3removal, demonstrating potential environmental applications for recovery of As0from toxic AsH3.

Nonlocal self-similarity is an important property of Synthetic Aperture Radar (SAR) images to characterize the repetitiveness of features embodied by SAR images within nonlocal areas and has been used for enhancement of SAR images. Existing SAR ship detectors often independently handle small sub-images cropped from a large marine SAR image and do not exploit the nonlocal self-similarity therein. In this paper, we propose a new ship detector from the perspective of nonlocal self-similarity in SAR images to improve the ship detection performance, basically including three stages: prescreening, intra-cue calculation, and inter-cue calculation. In the pre-screening stage, we design a new Histogram-based Density (HD) feature to rapidly select candidate sub-images potentially containing ship targets from a large SAR image. In the intra-cue calculation stage, target cues within a single candidate sub-image are extracted. In the inter-cue calculation stage, thanks to the nonlocal self-similarity among different candidate sub-images in terms of density features, we innovatively extract a weighted superpixel-HD map to obtain accumulated intra-cues across all the candidate sub-images. Finally, for each candidate sub-image, we fuse its inter-cue and intra-cue to obtain final detection results. Experimental results based on real SAR images show that our newly proposed method provides a better target-to-clutter contrast and ship detection performance than those of other state-of-the-art detection approaches.

In this paper, we focus on the integration of motion planning and control for high-performance automated vehicles. We propose a novel tube-based nonlinear model predictive control (TNMPC) scheme that combines the strengths of MPC and boundary layer sliding control (BLSC) to drive the controlled vehicle along a real-time and high-performance trajectory while simultaneously ensuring practical satisfaction of constraints. Specifically, our approach uses an extended kinematic vehicle model with slip angle parameters to provide explicit expressions for modeling error, thereby facilitating the design of BLSC gains. We then reveal an inequality relation between the slip angles and acceleration, which allows the BLSC gains to be represented as functions of the optimization variables. This innovative approach enables the inclusion of constraints on the BLSC gains into the TNMPC motion planning problem, striking a balance among performance, modeling error, and practical control capability. The feasibility of the kinematic model is also guaranteed without the need for the commonly adopted acceleration constraint. Moreover, tightening of the state and input constraints in TNMPC is achieved through coordinate transformation of the tubes, interval analysis, and robust optimization theory. Simulations in racing scenarios demonstrate the robustness and effectiveness of the proposed TNMPC scheme, illustrating its ease of adaptability across various road friction conditions.

Most of the existing studies regarding cross-modal content-based remote sensing image retrieval (CM-CBRSIR) focus on reducing/enlarging the Euclidean distances of cross-modal (CM) data with the same/different content in a common feature space. The advantages of using Euclidean distance lie in its straightforwardness. However, the Euclidean distances of CM data features are sensitive to the outlier data and may lead to non-robust retrieval performance, particularly in the case of noisy images with low quality. To address this issue, we propose a robust Hirschfeld-Gebelein–Rényi maximal correlation (HGRMC) augmented algorithm for CM-CBRSIR in this work, named by HGRMC augmented CM-CBRSIR (HAC). In HAC, not only the projected features of CM data in Euclidean distance space but also maximal correlation information of HGRMC are learned during the training phase of the retrieval model, where HGRMC is additionally used to capture the statistical dependency between CM data to enhance the retrieval performance with the strongly noisy input data. In the retrieval phase, we also develop a fusion scheme based on the Dempster-Shafer (DS) evidence theory to combine the superiorities of Euclidean distance and HGRMC correlation criteria. Extensive experimental results demonstrate that our proposed HAC algorithm provides better and more robust retrieval performance in comparison with existing state-of-the-art CM-CBRSIR methods.

Space activities such as asteroid capture and on-orbit construction of ultra-large spacecraft will significantly increase the operating distance and torque requirements of the space manipulator. In this article, we propose a new cable-driven joint module for space robots based on the idea of a cable-stayed bridge structure, and deeply investigate its driving properties. First, we give the structural design for the cable drive joint module. Using a clever combination of drive cables, special hinges, and spreader construction, the designed joint module allows $\pm 180^{\circ }$ rotation and can achieve high torque and high stiffness properties at a low quality cost. Second, the kinematics model, statics, and stiffness model of the joint module are established. On this basis, the kinematic, Jacobi, torque, and stiffness characteristics of the joint are discussed in detail. Finally, we built an experimental system of the cable-driven joint module, and tested the repeatability, torque, and stiffness of the joint module. The results show the unique characteristics of the mechanism and verify the correctness of the theoretical model and characteristic analysis.

Existing satellite remote sensing change detection (CD) methods often crop large-scale bitemporal image pairs into small patch pairs and then use pixel-level CD methods for fair processing. However, due to the sparsity of change, existing pixel-level methods suffer from a waste of computational cost and memory resources on many unchanged areas, which reduces the processing efficiency on hardware platforms with extremely limited computation and memory resources. To address this issue, we propose a lightweight patch-level CD network (LPCDNet) to rapidly remove a lot of unchanged patch pairs in large-scale bitemporal optical image pairs, helping to accelerate the subsequent pixel-level CD process and reduce memory cost. In our LPCDNet, a sensitivity-guided network pruning method is proposed to remove unimportant channels and construct the lightweight backbone network based on the ResNet18 network. Then, the multilayer feature compression (MLFC) module with multiscale max-pooling structure is designed to compress and fuse the multilevel feature information of image patches. The output of MLFC module is fed into the fully connected decision network to generate the predicted binary label. Finally, a weighted cross-entropy loss is utilized in the training process to tackle the change/unchanged class imbalance problem. Experiments on two CD datasets demonstrate that our LPCDNet achieves more than 1000 frames/s on an edge computation platform, i.e., NVIDIA Jetson AGX Orin, which is more than three times that of the existing methods without noticeable performance loss. In addition, the computational cost of the pixel-level CD processing stage can be reduced by more than 60%.

The powerful learning ability of deep neural networks enables reinforcement learning (RL) agents to learn competent control policies directly from continuous environments. In theory, to achieve stable performance, neural networks assume identically and independently distributed (i.i.d.) inputs, which unfortunately does not hold in the general RL paradigm where the training data are temporally correlated and nonstationary. This issue may lead to the phenomenon of “catastrophic interference” and the collapse in performance. In this article, we present interference-aware deep Q-learning (IQ) to mitigate catastrophic interference in single-task deep RL. Specifically, we resort to online clustering to achieve on-the-fly context division, together with a multihead network and a knowledge distillation regularization term for preserving the policy of learned contexts. Built upon deep Q networks (DQNs), IQ consistently boosts the stability and performance when compared to existing methods, verified with extensive experiments on classic control and Atari tasks. The code is publicly available at https://github.com/ Sweety-dm/Interference-aware-Deep-Q-learning.

The grasping of transparent objects is challenging but of significance to robots. In this article, a visual–tactile fusion framework for transparent object grasping in complex backgrounds is proposed, which synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects. First, we propose a multiscene synthetic grasping dataset named SimTrans12 K together with a Gaussian-mask annotation method. Next, based on the TaTa gripper, we propose a grasping network named transparent object-grasping convolutional neural network for grasping position detection, which shows good performance in both synthetic and real scenes. Inspired by human grasping, a tactile calibration method and a visual–tactile fusion classification method are designed, which improve the grasping success rate by 36.7% compared with direct grasping and the classification accuracy by 39.1%. Furthermore, the tactile height sensing module and the tactile position exploration module are added to solve the problem of grasping transparent objects in irregular and visually undetectable scenes. The experimental results demonstrate the validity of the framework.

Goal-conditioned rearrangement of deformable objects (e.g. straightening a rope and folding a cloth) is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a prescribed goalconfiguration with only visual observations. These tasks are typically confronted with two main challenges: the high dimensionality of deformable configuration space and the underlying complexity, nonlinearity and uncertainty inherent in deformable dynamics. To address these challenges, we propose a novel representation strategy that can efficiently model the deformable object states with a set of keypoints and their interactions. We further propose local-graph neural network (GNN), a light local GNN learning to jointly model the deformable rearrangement dynamics and infer the optimal manipulation actions (e.g. pick and place) by constructing and updating two dynamic graphs. Both simulated and real experiments have been conducted to demonstrate that the proposed dynamic graph representation shows superior expressiveness in modeling deformable rearrangement dynamics. Our method reaches much higher success rates on a variety of deformable rearrangement tasks (96.3% on average) than state-of-the-art method in simulation experiments. Besides, our method is much more lighter and has a 60% shorter inference time than state-of-the-art methods. We also demonstrate that our method performs well in the multi-tasklearning scenario and can be transferred to real-world applications with an average success rate of 95% by solely fine tuning a keypoint detector. A supplementary video can be found at https://youtu.be/AhwTQo6fCM0.

The application requirements of the tendon-sheath mechanism in the field of precision machinery are becoming increasingly extensive. However, the contact friction between the tendon and sheath seriously affects the transmission accuracy. In the case of unavoidable friction, optimizing the tendon transmission path to reduce tension loss and elastic deformation has become an important research direction. In this article, the influence law of the tendon transmission path on the tension and displacement transmission is obtained using the two parameters related to the curvature of the transmission path: total bending angle and equivalent tendon length. Then, based on the optimal control theory and minimum principle, the different transmission path solutions of the minimum tension loss, the minimum tendon deformation, and the coupling of tension and displacement are obtained; the numerical optimization method verifies the correctness of the proposed theory. Finally, an optimal design of a tendon-constrained synchronous rotation mechanism for the manipulator is carried out, and the linkage performance is greatly improved by optimizing the transmission path.

With the soaring development of deep learning (DL) mechanisms in recent years, convolution neural network (CNN)-based methods have been extensively investigated to achieve high accuracy of ship detection in synthetic aperture radar (SAR) images. However, existing CNN-based SAR ship detection methods still suffer from challenges in complex inshore scenarios due to the strong interference therein. To tackle this issue, a novel semi-soft label-guided network based on self-distillation (SD) for SAR ship detection (S2LSDNet) is proposed in this article. First, different from the existing CNN-based detectors to extract features from the image domain only under the guidance of a one-hot label, an efficient SD training strategy is devised to extract semi-soft label information to boost the inshore ship detection accuracy. Second, an angle-related and balanced intersection-over-union (ArBIoU) loss is developed to enhance the inshore ship positioning performance by using the adaptive weights of center point bias and the aspect ratio difference. Experiments on the open SAR ship detection datasets demonstrate the effectiveness and superiority of the proposed method compared with the existing state-of-the-art approaches, especially in inshore scenes.

Convolutional neural networks (CNNs) have been widely applied in the context of ship detection in synthetic aperture radar (SAR) images, but the detection performance is still not ideal in scenarios with clutter interference. Mining frequency-domain information to suppress the sea clutter in SAR ship detection has attracted wide attention. However, existing frequency-domain ship detection methods do not process frequency-domain information adaptively, which results in the degradation of ship detection performance. To overcome this problem, this article proposes a novel deep learning network called YOLO-FA. YOLO-FA contains the proposed frequency attention module (FAM), which can process frequency-domain information of SAR images adaptively. The proposed method can suppress the sea clutter in the SAR images with the help of frequency-domain information. We evaluate the proposed method YOLO-FA on two datasets, i.e., the high-resolution SAR images’ dataset (HRSID) and SAR ship detection dataset (SSDD). Compared with the baseline method YOLOv5 and the existing commonly used methods, YOLO-FA achieves state-of-the-art detection performance on both the datasets.

Change detection (CD) in optical remote sensing images has significantly benefited from the development of deep convolutional neural networks (CNNs) due to their strong capability of local modeling in bitemporal images. In addition, the recent rise of transformer modules has led to the improvement of global feature extraction of bitemporal remote sensing images. Note that the existing simple cascade of deep CNNs and transformer modules shows limited CD performance on small changed areas due to deficiencies of multiscale information therein. To address the aforementioned issue, we propose a new CNN–transformer network (ConvTransNet) with multiscale framework to better exploit global–local information in optical remote sensing images. In our ConvTransNet, we propose the parallel-branch ConvTrans block as the basic component to generate global–local features, i.e., adaptively integrates the global features summarized by a transformer-based branch and the local features extracted by a convolution-based branch, providing better identifiability between changed areas and unchanged areas. By fusing multiple global–local features with different scales, our ConvTransNet improves the robustness of the CD performance on changed areas with different sizes, especially small changed areas. Experiments on two public CD datasets of optical remote sensing images, i.e., LEVIR-CD and CDD, demonstrate that our ConvTransNet achieves enhanced CD performance than the other commonly used methods.

In this paper, we propose an active fault-tolerant control (AFTC) method combining model predictive control (MPC) and reinforcement learning (RL) for the quadcopter with actuator faults. We take a data-based discriminant model as the fault detection and diagnosis (FDD) module indicating a system fault mode based on the state error. With the information of the fault mode and the state error, the RL controller generates auxiliary control signals to correct the system. To configure the MPC controller quickly, we propose an auxiliary signal-based method for estimation of fault parameters and prove its convergence. The AFTC framework reduces requirements for accurate modeling, and avoids the instability of the RL controller under a continuous operation. To validate the effectiveness of the proposed framework, two trajectory tracking simulations with single and multiple faults are carried out. The simulation results show satisfactory performance and verify that the proposed framework is real-time applicable.

This paper considers a distributed state estimation problem of a multisensor linear time-invariant system affected by bounded disturbances and measurement noises. We propose a zonotopic state estimation scheme called collector, which generates a directed rooted tree from the underlying graph by designating a node to collect information. Based on this idea, multi-hop decomposition is implemented, and properties of multi-hop decomposition under this framework are discussed. Furthermore, this paper presents a solution for treating one node as a collector. In the end, a case study is used to illustrate the effectiveness of the proposed estimator.

Flood monitoring is of crucial importance for protecting lives and properties. Change detection (CD) methods on multisource remote sensing images have been widely used for flood extent monitoring. In this article, we propose a spatiotemporal fusion CD (STFCD) algorithm, exploiting the spatial dependence and temporal interaction of multisource heterogeneous (MSH) satellite image time series (SITS), to realize improved flood CD performance in comparison with existing methods. The proposed STFCD algorithm mainly contains two steps, i.e., spatial clustering and temporal fusion (TF). In the spatial clustering step, we propose a sparse Markov random field (MRF)-based strategy to exploit contextually spatial features in each image of MSH-SITS, which provides a larger local receptive field than the commonly used MRF. In the TF step, the historical information of flood detection results is employed as constraints to effectively reduce the effects of terrain shadows in synthetic aperture radar (SAR) images and cloud shadows and topography shadows in optical images on flood CD results of existing methods in accordance with the temporal dependence among MSH-SITS. Experiments on real MSH-SITS (containing Gaofen-1, Gaofen-3, Gaofen-6, Sentinel-1, and Sentinel-2 satellite images) covering Chinese Amur and Huma Rivers show that the overall flood CD accuracy of our proposed STFCD algorithm is higher than the other commonly used algorithms for CD of flood extents and demonstrate the robustness of our proposed STFCD algorithm.

This article considers the problem of change detection (CD) with multisensor optical remote sensing (RS) images. Copulas are adopted to characterize the dependence structure between the image pair. For this problem, a conditional copula-based CD technique has been proposed in the literature; however, in this technique, it is difficult to select the best copula function in an analytical framework. Resulting copula misspecification may lead to performance degradation. To deal with this problem, we model the CD problem as a binary hypothesis testing problem and propose a new superpixel-level copula-based statistical method (SCOPS) for CD, where an explicit strategy for copula selection is provided for the proposed method. The effectiveness of the copula selection strategy is verified on CD tasks with simulated multisensor optical RS images. Experiments on real RS datasets demonstrate the superiority of SCOPS over state-of-the-art methods.

The use of non-aqueous absorbents has great potential in reducing energy consumption during CO2capture. However, current obstacles include the formation of precipitation or gelatinous substances after CO2absorption, which significantly hinder their further application. Moreover, it is crucial to address concerns about insufficient CO2capture performance and excessive regeneration energy. Therefore, non-aqueous homogeneous absorbents that do not produce precipitates, gels, or solids have gained considerable attention in recent years. In this study, a novel non-aqueous homogeneous absorbent based on polyamine/complex solvent was successfully developed by introducing ethylene glycol into the precipitation-producing polyamine/organic alcohol ether system. This modification resulted in improved CO2capture performance and reduced regeneration energy. The AEEA/EG/2EE non-aqueous homogeneous absorbent showed a CO2absorption capacity of 2.67 mol/kg and achieved an impressive regeneration efficiency of up to 81.65 % at the optimal desorption temperature (393 K) in only 40 min, surpassing the conventional MEA/H2O absorbent by a substantial margin (31.65 %). Thermodynamic analysis revealed that this absorbent exhibited a substantial reduction in both sensible and latent heat during CO2desorption, resulting in a remarkable decrease of 42.8 % in total regeneration energy compared to the conventional MEA/H2O absorbent. The composite solvent EG/2EE was found not to be involved in the reaction process according to the 13C NMR analysis. The main absorption process involved the reaction between organic amine AEEA and CO2to form carbamate, while the desorption process entailed reverse decomposition of carbamate for CO2release. Therefore, utilizing ethylene glycol's advantageous properties for regulating polyamine/organic solvent systems presents a promising.

To explore the therapeutic capacity of the Liangxue Xiaoban (LXXB) decoction and its disassembled prescriptions in the modulation of T cell subsets and recurrence-related indexes of psoriasis using a psoriasis-like mouse model.

This article focuses on the design of online optimal input sequence for robust active fault diagnosis of discrete-time linear parameter-varying systems using set-theoretic methods. Instead of the traditional set-separation constraint conditions leading to the design of offline input sequence, the proposed approach focuses on online (re)shaping of the input sequence based on the real-time information of the output to discriminate system modes at each time instant such that the diagnosability of system has potential to be further improved. The criterion on the design of optimal input is characterized based on a nonconvex fractional programming problem at each time instant, which is shown to be efficiently solved within a convex optimization framework. In addition to this main contribution, by exploiting Lagrange duality, the optimal input is explicitly obtained by solving a characteristic equation. At the end, a physical circuit model is provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Qinzhi Zhudan formula (QZZD) exhibits a prominent therapeutic effect in the treatment of vascular dementia (VaD). This study combined a network pharmacology approach and experimental validation to identify the underlying biological mechanism of QZZD against VaD.

In this paper, we propose a novel fault tolerant multi-sensor switching control framework for linear parameter-varying systems considering multiplicative and additive sensor faults simultaneously. We show that the closed-loop stability is preserved under sensor fault situation if established guaranteed fault detection conditions based on invariant sets are fulfilled. At each time instant, the switching control strategy selects the sensor group that provides the optimal closed-loop performance, which is characterized by a quadratic performance objective function. Furthermore, faults on each channel of all group of sensors can be also pre-guaranteed to be isolated under the fulfillment of established fault isolation conditions, which ensures a more precise determination of the location of fault occurrence. At the end, a practical vehicle model is provided to illustrate the effectiveness of the proposed method.

Soft robots have unique advantages over traditional rigid robots and have broad application prospects in many fields. To expand their bioinspired applications, we propose a novel Soft Pneumatic Actuator (SPA) associated with spiral configuration inspired by the structure and unwinding motion of the seahorse tail. Different from bending motion of common soft actuators, the spiral SPA can generate unwinding motion as input air pressure increases. First, to explore the effect of different initial spiral types on unwinding performance, three typical spiral SPAs are designed and simulated while keeping the outside arc of actuator body constant. Second, a static model of the spiral SPA is established by combining the hyperelastic material model, geometric relationships, and virtual work principle. To improve model accuracy, two geometric correction parameters are employed and their physical significance is analyzed by finite element simulations. Third, a prototype of the logarithmic spiral SPA (Log_spiral SPA) is fabricated and a Fiber Bragg Grating (FBG) sensor array is designed to detect and reconstruct unwinding shapes of the prototype. Finally, the unwinding performance, static model and output force capability of the prototype are tested and verified. Furthermore, we discuss prospects for this novel spiral SPA and test its practical applications in inchworm-like motion, assisting finger rehabilitation and object capture.

Byline: Xueqian. Wang, Hongsheng. Lin, L. Liyuan, Ying. Zhang, Jie. Liu, Zhiyan. Liu, Hong. Shi Objective: The purpose of this study was to evaluate the Kang`ai injection combined with chemotherapy [...]

Carbonyl sulfide (COS) is a major organic pollutant causing atmospheric aerosols, however, current removal technologies fail to meet the efficiency and by-product selectivity requirements. Here we address this issue by utilizing dielectric barrier discharge (DBD) plasma catalysis which combines the high reaction activity of a plasma and the high reaction selectivity of a catalyst. In this study, we investigated the conversion of carbonyl sulfide (COS) using a combination of dielectric barrier discharge (DBD) and a Cu/γ-Al2O3catalyst, comparing it with the conversion achieved through thermal catalysis and DBD alone. The removal efficiency of COS reached 98.9% at 4.5 kJ/L with no gaseous sulfur byproduct (H2S and SO2) generated at the first 4 hours when COS was treated by DBD combined with Cu/γ-Al2O3. The CuO on the Cu/γ-Al2O3catalyst is converted to Cu2S and CuS upon reacting with COS. According to the density functional theory calculations, the reaction energy barrier can be controlled by the electric field. The cycle reaction with the thus reduced energy barrier between Cu2S, CuS, and COS enhanced selective plasma-catalytic conversion of COS to sulfur. The outcomes contribute to the development of advanced plasma-electrified catalytic technologies for environmental remediation.

This article proposes a novel robust state estimation method for discrete linear time-invariant systems by using the positive system theory and the set theory to overcome the disadvantages of interval observers and set-valued observers to finally achieve an expected balance between computational complexity and robust state estimation conservatism. The proposed method first transforms the system into an equivalent system with its system matrix in the canonical companion form. Then, based on this canonical companion form, we partition the equivalent system into two subsystems, where the first subsystem owns a nonnegative subsystem matrix and the second subsystem has a subsystem matrix including possible negative elements of the system matrix of the equivalent system. In this way, the design of interval observers is divided into two steps. The first step is to design an interval observer for the first subsystem based on the positive system theory. The second step is to design a zonotopic set-valued observer for the second subsystem based on the set theory. Consequently, a so-called set-theoretic interval observer (SIO) of the whole system can be synthesized by integrating the interval estimations and set-valued estimations of the two subsystems together. At the end, a numerical system is used to illustrate the effectiveness of the proposed SIO. [ABSTRACT FROM AUTHOR]

Thallium (Tl) is known to be extremely toxic. However, thallium pollution from smelting tail gas has long been neglected as an environmental issue. This study aims to develop a gaseous thallium control technology based on manganese-based metal–organic framework (MOF) catalysts, using a manganese centered paddle wheel structure as a support. Tl removal testing showed that these catalysts were effective at both adsorbing and oxidizing Tl(I), producing Tl(III), which is less harmful. Details of the catalytic oxidation mechanism were investigated, which demonstrated that Tl(I) loses electrons to Mn(III), Mn(IV), and O2, generating Tl2O3. Over 90% removal efficiency was maintained for 10 h by a MOF catalyst with 10% Mn loading, at 423 K with 10% O2in the feed gas, which was determined to be the optimal level of O2. SO2inhibited thallium removal in this system, while 10% CO2had no significant effect. Regeneration through acid washing was found to be effective, with the catalyst maintaining thallium removal efficiency for five regeneration cycles. Molecular simulation of Tl removal is investigated, based on which the possible reaction path is found.

Metal oxide catalysts with different cerium and manganese ratios were synthesized by a co-precipitation method to remove HCN and NO from exhaust gas. Ce1Mn2exhibited the highest HCN conversion and NO removal effect with a maximum N2selectivity of 72%. The existence of NO can promote the conversion of HCN at low temperatures, which may be due to the reaction between the two substances forming formyl and nitrogen. CO was not detected as a product over the entire reaction temperature range. However, high concentrations of CO2, NH3, and a small amount of NOxwere detected. Results indicate that HCN was first oxidized to isocyanate followed by isocyanate further reacting with H2O to generate NH3and CO2. Part of NO was removed by the selective catalytic reduction (SCR) and oxidation reaction. Catalyst characterization showed that Ce1Mn2exhibited a higher specific surface area, with manganese oxide mainly existing in an amorphous state, potentially forming a solid solution structure with cerium oxide. The introduction of a suitable amount of cerium was beneficial, increasing the concentration of chemisorbed oxygen on the catalyst surface. The catalyst also exhibited the properties of adsorbing H2O, which was helpful for the isocyanic acid hydrolysis reaction.

In recent years, the utilization of copper tailing and removal of sulfur dioxide (SO2) have become the focus of research. In this study, copper tailing was used as an iron ion donator to remove SO2from flue gas. To improve the desulfurization performance, the additive was selected from metal sulfates, such as ferrous sulfate, ferric sulfate, manganese sulfate, and scrap iron. A series of experiments were conducted in a bubble reactor to study the effects of various critical factors, including the inlet SO2concentration, solid/liquid ratio, dosage of additive, gas flow rate, O2concentration, and reaction temperature, on the flue gas desulfurization performance. Various characterizations, such as X-ray fluorescence spectroscopy, X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy with energy-dispersive spectroscopy, X-ray photoelectron spectroscopy, inductively coupled plasma spectroscopy, and titration, were used to study the properties of copper tailing and absorption liquid. Furthermore, the reaction mechanism of SO2removal was also studied. The results showed that copper tailing combined with MnSO4·H2O exhibited the highest SO2removal efficiency. The desulfurization efficiency reached approximately 99% and was mainly affected by the flue gas flow rate and the absorption temperature. Mn2+served as a catalyst for liquid-phase catalytic oxidation. Furthermore, the leaching reaction contributed to the consumption of some H+, whereas Fe2+(primarily leached from fayalite in the copper tailing) served as its activation state and variable valence state with Mn2+for the efficient removal of SO2. Moreover, the reduction of flue gas desulfurization is attributed to the accumulation of SO42–and H+in the slurry.

This paper proposes a mixed active/passive robust fault detection and isolation (FDI) method for discrete-time linear paramter varying (LPV) systems based on set-theoretic unknown input observers (SUIO) and invariant sets. The robustness against system uncertainties in FDI of LPV systems can be guaranteed by actively decoupling or passively bounding their effect on residual signal. Furthermore, the quadratic H∞stability condition of the LPV-form state-estimation-error dynamics is established based on a group of linear matrix inequalities (LMIs). Under the precondition of stability, a family of residual sets are constructed to establish set-separation guaranteed fault isolation (FI) conditions using invariant sets off-line. As long as the occurred faults satisfy the guaranteed FI conditions, they can be isolated from each other. At the end, a numerical example is used to illustrate the effectiveness of the proposed method.

This paper proposes a generalized minimum detectable fault(MDF) computation method based on the set-separation condition between the healthy and faulty residual sets for discrete-time linear parameter varying(LPV) systems with bounded inputs and uncertainties. First, we equivalently transform the multiple multiplicative actuator faults into the form of multiple additive actuator faults, which is beneficial to simplify the problem. Then, by considering the 1-norm of the fault vector, we define the generalized MDF in the case of multiple additive actuator faults, which can be computed via solving a simple linear programming(LP) problem. Moreover, an analysis of the effect of the input vector on the magnitude of the generalized MDF is made. Since the proposed generalized MDF computation method is robust by considering the bounds of inputs and uncertainties, robust fault detection(FD) can be guaranteed whenever the sum of the magnitudes of all occurred faults is larger than the magnitude of the generalized MDF. At the end of this paper, a numerical example is used to illustrate the effectiveness of the proposed method.

This paper designs an interval observer for discrete linear periodic time-varying descriptor (LPTVD) systems. First, the discrete LPTVD system is transformed into a linear time-invariant (LTI) descriptor system by using a stacked form of periodic systems. Then, in order to reduce complexity, we further transform the equivalent LTI descriptor system into a minimal-order form. Finally, by using the obtained minimal-order implementation of the discrete LPTVD system, an interval observer is designed to estimate the system state interval at each step. At the end, an example is used to illustrate the effectiveness of the proposed results.

With the goal of simultaneous removal of elemental mercury and arsine from simulated yellow phosphorus off-gas, a series of Cl–Ce modified activated carbon samples were prepared via an impregnation method and investigated. The Cl3–Ce10/AC exhibited the best performance at 150 °C, with an Hg0removal efficiency of 92.2%, and the reaction time of AsH3removal efficiency greater than 90% was 15.5 h. The presence of Hg0had little impact on AsH3removal, but conversely AsH3significantly reduced Hg0removal efficiency. The effects of individual flue gas components on the simultaneous removal of Hg0and AsH3were also studied. O2clearly promoted the simultaneous removal of Hg0and AsH3and CO had almost no influence, while H2S and water vapor inhibited the removal process. The physicochemical properties of the samples were characterized using BET, FESEM-EDS, XRD, and XPS. By combining experimental results with physical and chemical characterization, a mechanism for simultaneous removal of Hg0and AsH3was proposed. It was found that chemical adsorption and catalytic oxidation occurred on the surface of the samples, due to the presence of chlorine covalent bonds and cerium oxides, and that the presence of cerium makes it easier for mercury to combine with chlorine. The main products in the used samples were found to be HgCl2, HgO, and As2O3.

This paper proposes a novel robust fault detection (FD) approach and designs a set-theoretic unknown input observer (SUIO) for linear parameter-varying (LPV) systems with both state and output equations scheduled by inexact scheduling variables. First, for such LPV systems, we propose a novel robust FD method by combing the set theory with the unknown input observer, which considers the bounds of measurement errors of scheduling variables to generate FD-oriented sets. In general, as long as sensors with sufficiently high precision are equipped to measure the scheduling variables, the bounds of measurement errors of scheduling variables can be less conservative than those direct bounds of scheduling variables, which can reduce robust FD conservatism in this way. Second, we give the unknown input decoupling condition of SUIO for such LPV systems and propose an SUIO design method under this condition for robust state estimation (SE). Besides, stability conditions for the proposed methods are established via matrix inequalities. At the end of this paper, a case study is used to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Economic and environmentally friendly strategies are needed to promote the bifunctional catalytic removal of carbonyl sulfide (COS) by hydrolysis and hydrogen sulfide (H2S) by oxidation. N doping is considered to be an effective strategy, but the essential and intrinsic role of N dopants in catalysts is still not well understood. Herein, the conjugation of urea and biochar during Cu/biochar annealing produced pyridine N, which increased the combined COS/H2S capacity of the catalyst from 260.7 to 374.8 mg·g–1and enhanced the turnover frequency of H2S from 2.50 × 10–4to 5.35 × 10–4s–1. The nucleophilic nature of pyridine N enhances the moderate basic sites of the catalyst, enabling the attack of protons and strong H2O dissociation. Moreover, pyridine N also forms cavity sites that anchor CuO, improving Cu dispersion and generating more reactive oxygen species. By providing original insight into the pyridine N-induced bifunctional catalytic removal of COS/H2S in a slightly oxygenated and humid atmosphere, this study offers valuable guidance for further C═S and C–S bond-breaking in the degradation of sulfur-containing pollutants.

To study the effects of social stress (CS) and social stress combined with high-fat diet on fat mobilization as a candidate mechanism for the induction or aggravation of non-alcoholic fatty liver disease (NAFLD).

Human oocyte maturation is a precondition for fertilization and ensuing embryonic development. Previously, we identified TUBB8variants as a genetic determinant of human oocyte maturation arrest and showed that these variants cause variable and mixed phenotypes in oocyte maturation and early embryo development. We also estimated that rare inherited or de novo variants in the TUBB8gene accounted for 30% of individuals in a small cohort of patients affected by oocyte maturation arrest. In the present study, we recruited a further 87 patients from unrelated families diagnosed with oocyte maturation or early embryonic arrest and identified 30 patients carrying TUBB8variants. The corresponding phenotypes not only include oocyte maturation arrest, failure of fertilization, and early embryonic arrest, but also extend to the new phenotype of failure of embryo implantation. These observations provide the most detailed mutational and phenotypic spectrum of TUBB8, further extend the spectrum of variants and dysfunctional oocyte and embryo phenotypes caused by TUBB8variants, and confirm previous findings for a critical role of TUBB8 during oocyte maturation and early embryonic development. Thus, TUBB8mutation screening might not only be a genetic diagnostic marker for patients with oocyte maturation arrest, but might also have clinical implications for evaluating the competence of patients’ functional oocytes with first polar body (PB1).

Molecular imaging agents are considered to be promising tracers for tumor imaging and guided therapy. TMTP1 was screened through the FliTrx bacterial peptide display system in our laboratory previously and shown to specifically target to primary tumors and metastatic foci. In this study, small peptide TMTP1 was designed to conjugate to a near-infrared fluorescent agent ICG derivative ICG-OSu through PEG4, forming the novel probe TMTP1-PEG4-ICG. It was successfully synthesized and certified. CCK-8 assay showed that it was nontoxic to normal cells and cancerous cells. Dynamics study indicated that the probe was cleared through the liver–intestine and kidney–bladder pathway. Tumor targeting capability of this probe in vitro was evaluated on 4T1, SiHa, HeLa, S12, and HaCaT cells by flow cytometry. In vivo imaging of 4T1 and HeLa tumor-bearing mice further identified the tumor homing ability. As we had expected, the probe showed excellent affinity to cancer cells not only in vitro but also in vivo, whether in murine tumor or humanized tumor. In conclusion, TMTP1-PEG4-ICG demonstrated ideal imaging effects on tumor-bearing mice model, providing new opportunities for tumor diagnostic or guiding resection.

In this work, copper-based titanium sorbents (CuOx/TiO2) synthesized by sol–gel method were used to remove arsine (AsH3) at low temperatures. The Cu content, calcination temperature, pH, oxygen concentration, and reaction temperature were the five key factors that were determined and analyzed in this work. The optimum breakthrough adsorption capacity for arsine was promising and had the value of 534.3 mg/g for the Cu content of 20 wt %, calcination temperature of 400 °C, pH of 10, oxygen concentration of 2%, and reaction temperature of 120 °C. The chemical and structural features of the initial and arsine-exposed sorbents were analyzed using the Brunauer–Emmett–Teller method, X-ray diffraction analysis, temperature-programmed desorption of carbon dioxide, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and scanning electron microscopy. It was found that the contents of anatase and rutile TiO2in sorbents could be regulated by changing the pH of sorbents. Additionally, at pH of 10, the sorbent exhibited the highest specific surface area and a higher number of basic sites, whereas TiO2mainly existed in mixing of crystal material with anatase TiO2in dominating structure, which showed a significant promoting effect on the adsorption of arsine. Furthermore, CuO was the primary active component on the surface of sorbent, while As2O3and H2O were the main products of arsine oxidation. As the reaction temperature increased, the preference for the formation of As2O5was observed.

In this work, a new adaptive control scheme is presented for a class of bilateral teleoperation system with system uncertainties, jittering time delays, and state-constraint problem. A fixed-time control strategy with a novel proposed exponential-type Barrier Lyapunov Function (EBLF) is incorporated to achieve fixed-time convergence with state error constraints through the “adding a power integrator” technique and the full-order sliding mode method. During the sliding motion, the system behaves as a desirable full-order dynamics rather than a reduced-order dynamics. The singularity phenomena and chattering problem are avoided while fixed-time convergence without violation of full-state constraints is guaranteed. Simulation results further demonstrate the effectiveness of the proposed method.

Background:Spinal Muscular Atrophy (SMA) is an autosomal recessive motor neuron disease that results in loss of spinal motor neurons, muscular weakness and, in severe cases, respiratory failure and death. SMA is caused by a deletion or mutation of the SMN1gene and retention of the SMN2gene that leads to low SMN expression levels. The measurement of SMN mRNA levels in peripheral blood samples has been used in SMA clinical studies as a pharmacodynamic biomarker for response to therapies designed to increase SMN levels. We recently developed a postnatal porcine model of SMA by the viral delivery of a short-hairpin RNA (shRNA) targeting porcine SMN (pSMN). scAAV9-mediated knockdown of pSMN mRNA at postnatal day 5 results in denervation, weakness and motor neuron and ventral root axon loss that begins 3-4 weeks after viral delivery, and this phenotype can be ameliorated by subsequent viral delivery of human SMN (hSMN). Objective:To determine if the effect of modulating SMN levels using gene therapy can be measured in blood. Methods:We measured expression of pSMN mRNA and hSMN mRNA by quantitative droplet digital PCR (ddPCR). Results:We found that the endogenous expression of pSMN mRNA in blood increases in the first month of life. However, there were no significant differences in blood levels of pSMN mRNA after knock-down or of human SMN mRNA after gene therapy. Conclusions:Our results, obtained in a large animal model of SMA that is similar in size and anatomy to human infants, suggest that measurement of SMN mRNA levels in blood may not be informative in SMA clinical trials involving intrathecal delivery of SMN-modulating therapies.

With the aim of improving the arsine (AsH3) removal efficiency, 5A molecular sieves were modified by the transition metal oxides and used as catalysts. MnOxcatalysts supported on 5A molecular sieves (Mn-5A) were prepared by an impregnation method and subsequently tested toward the catalytic oxidation of AsH3under micro-oxygen conditions at 150 °C. The effects of the metal oxide types that supported, the impregnate concentration of manganese nitrate, the calcination temperature, the oxygen content, and the fixed bed reaction temperature on the AsH3removal efficiency were studied. The optimum catalyst was prepared by a solution impregnation method with a 0.5 mol·L–1Mn2+aqueous solution and subsequently calcined at 500 °C. This catalyst showed the highest activity toward the oxidation of AsH3among the samples prepared herein (100% conversion) over a good low-temperature operation window (120–160 °C) and under low oxygen concentration (0–2.0%) conditions. The majority presence of Mn4+active species is believed to be one predominant factor accounting for the excellent AsH3removal efficiency of this catalyst. The dominant active Mn4+species of Mn-5A is found to be a mixture of MnO2, Mn3O4, and Mn2O3. BET area analysis coupled with EDS measurement show that after oxidizing AsH3with O2, the resulting As2O5seems to block the surface of Mn-5A leading to a decrease of the activity. XPS data indicates that the oxidation state of MnOxspecies on Mn-5A was not affected after reaction. So the main deterioration mechanism of Mn-5A on AsH3removal under our reaction condition is mainly due to physical blocking effect. According to the characterizations results, the Mn-5A catalyst possessed a higher number of active sites than the rest of samples and formed active intermediates during reaction, both factors leading the highly efficient removal of AsH3under low-temperature micro-oxygen conditions.