A matrix metalloproteinase-responsive hydrogel system controls angiogenic peptide release for repair of cerebral ischemia/reperfusion injury

Vascular endothelial growth factor and its mimic peptide KLTWQELYQLKYKGI(QK)are widely used as the most potent angiogenic factors for the treatment of multiple ischemic diseases.However,conventional topical drug delivery often results in a burst release of the drug,leading to transient retention(inefficacy)and undesirable diffusion(toxicity)in vivo.Therefore,a drug delivery system that responds to changes in the microenvironment of tissue regeneration and controls vascular endothelial growth factor release is crucial to improve the treatment of ischemic stroke.Matrix metalloproteinase-2(MMP-2)is gradually upregulated after cerebral ischemia.Herein,vascular endothelial growth factor mimic peptide QK was self-assembled with MMP-2-cleaved peptide PLGLAG(TIMP)and customizable peptide amphiphilic(PA)molecules to construct nanofiber hydrogel PA-TIMP-QK.PA-TIMP-QK was found to control the delivery of QK by MMP-2 upregulation after cerebral ischemia/reperfusion and had a similar biological activity with vascular endothelial growth factor in vitro.The results indicated that PA-TIMP-QK promoted neuronal survival,restored local blood circulation,reduced blood-brain barrier permeability,and restored motor function.These findings suggest that the self-assembling nanofiber hydrogel PA-TIMP-QK may provide an intelligent drug delivery system that responds to the microenvironment and promotes regeneration and repair after cerebral ischemia/reperfusion injury.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

法舒地尔缓解β-淀粉样蛋白1-42诱导的SH-SY5Y细胞凋亡

背景:法舒地尔对阿尔茨海默病小鼠脑内的线粒体动力学有调节作用,并且可以抑制神经炎症,但能否调节线粒体自噬和NLRP3炎症小体进而减轻β-淀粉样蛋白毒性尚不清楚。目的:探究法舒地尔对β-淀粉样蛋白1-42诱导的人源神经母细胞瘤细胞株SH-SY5Y凋亡和线粒体自噬以及NLRP3炎症小体的调节作用。方法:将SH-SY5Y细胞接种于孔板内,细胞贴壁后分3组干预:对照组不加入任何药物,模型组加入20μmol/L β-淀粉样蛋白1-42,法舒地尔组同时加入20μmol/L β-淀粉样蛋白1-42与15 mg/L法舒地尔,干预24 h后,采用MTT法检测细胞活性,TUNEL染色检测细胞凋亡,qRT-PCR和Western blot检测凋亡相关蛋白表达,免疫荧光染色和Western blot检测线粒体自噬相关蛋白表达,免疫荧光染色和Western blot检测NLRP3炎症小体相关蛋白表达。结果与结论:(1)与对照组比较,模型组细胞活性降低、凋亡率升高(P<0.05);与模型组比较,法舒地尔组细胞活性升高、凋亡率降低(P<0.05);(2)qRT-PCR和Western blot检测结果显示,与对照组比较,模型组细胞Bax mRNA与蛋白表达升高(P<0.05),Bcl-2 mRNA与蛋白表达降低(P<0.05);与模型组比较,法舒地尔组细胞Bax mRNA与蛋白表达降低(P<0.05),Bcl-2 mRNA与蛋白表达升高(P<0.05);(3)免疫荧光染色和Western blot检测结果显示,与对照组相比,模型组细胞PINK1、帕金森病蛋白和LC3蛋白表达降低(P<0.05),p62蛋白表达升高(P<0.05);与模型组相比,法舒地尔组细胞PINK1、帕金森病蛋白和LC3蛋白表达升高(P<0.05),p62蛋白表达降低(P<0.05);(4)免疫荧光染色和Western blot检测结果显示,与对照组相比,模型组细胞NLRP3、ASC、Caspase-1和白细胞介素1β蛋白表达升高(P<0.05);与模型组相比,法舒地尔组细胞NLRP3、ASC、Caspase-1和白细胞介素1β蛋白表达降低(P<0.05);(5)结果表明,法舒地尔可以减轻β-淀粉样蛋白1-42诱导的SH-SY5Y细胞凋亡,其机制可能与激活线粒体自噬且抑制NLRP3炎症小体激活有关。

A Blockchain-Based Access Control Scheme for Reputation Value Attributes of the Internet of Things

The Internet of Things(IoT)access controlmechanism may encounter security issues such as single point of failure and data tampering.To address these issues,a blockchain-based IoT reputation value attribute access control scheme is proposed.Firstly,writing the reputation value as an attribute into the access control policy,and then deploying the access control policy in the smart contract of the blockchain system can enable the system to provide more fine-grained access control;Secondly,storing a large amount of resources fromthe Internet of Things in Inter Planetary File System(IPFS)to improve system throughput;Finally,map resource access operations to qualification tokens to improve the performance of the access control system.Complete simulation experiments based on the Hyperledger Fabric platform.Fromthe simulation experimental results,it can be seen that the access control system can achieve more fine-grained and dynamic access control while maintaining high throughput and low time delay,providing sufficient reliability and security for access control of IoT devices.

Communication Resource-Efficient Vehicle Platooning Control With Various Spacing Policies

Platooning represents one of the key features that connected automated vehicles may possess as it allows multiple automated vehicles to be maneuvered cooperatively with small headways on roads. However, a critical challenge in accomplishing automated vehicle platoons is to deal with the effects of intermittent and sporadic vehicle-to-vehicle data transmissions caused by limited wireless communication resources. This paper addresses the co-design problem of dynamic event-triggered communication scheduling and cooperative adaptive cruise control for a convoy of automated vehicles with diverse spacing policies. The central aim is to achieve automated vehicle platooning under various gap references with desired platoon stability and spacing performance requirements, while simultaneously improving communication efficiency. Toward this aim, a dynamic event-triggered scheduling mechanism is developed such that the intervehicle data transmissions are scheduled dynamically and efficiently over time. Then, a tractable co-design criterion on the existence of both the admissible event-driven cooperative adaptive cruise control law and the desired scheduling mechanism is derived. Finally, comparative simulation results are presented to substantiate the effectiveness and merits of the obtained results.

A 16-bit 18-MSPS flash-assisted SAR ADC with hybrid synchronous and asynchronous control logic

This paper presents a 16-bit,18-MSPS(million samples per second)flash-assisted successive-approximation-register(SAR)analog-to-digital converter(ADC)utilizing hybrid synchronous and asynchronous(HYSAS)timing control logic based on an on-chip delay-locked loop(DLL).The HYSAS scheme can provide a longer settling time for the capacitive digital-to-analog converter(CDAC)than the synchronous and asynchronous SAR ADC.Therefore,the issue of incomplete settling or ringing in the DAC voltage for cases of either on-chip or off-chip reference voltage can be solved to a large extent.In addition,the fore-ground calibration of the CDAC’s mismatch is performed with a finite-impulse-response bandpass filter(FIR-BPF)based least-mean-square(LMS)algorithm in an off-chip FPGA(field programmable gate array).Fabricated in 40-nm CMOS process,the proto-type ADC achieves 94.02-dB spurious-free dynamic range(SFDR),and 75.98-dB signal-to-noise-and-distortion ratio(SNDR)for a 2.88-MHz input under 18-MSPS sampling rate.

Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.

自旋-轨道耦合作用下极化激元凝聚中的调制不稳定性

利用线性稳定性分析方法,对存在自旋-轨道耦合(SOS)作用的二维极化激元玻色-爱因斯坦凝聚(BEC)系统中的调制不稳定性(MI)进行了研究.分析了组分内部,组分之间以及SOC相互作用对系统调制不稳定性的影响.结果显示,当系统内部不存在SOC作用,组分之间的相互作用为0,组分内部存在排斥作用时,不会出现调制不稳定性,组分内部存在吸引作用时,会出现调制不稳定性,并且调制不稳定性区间长度随吸引作用的增强而增加;组分之间相互作用不为0时,组分之间的相互作用以平方形式出现,其正负不会对调制不稳定性产生实质性影响.存在SOC相互作用时,SOC相互作用会引起增益谱曲线的不规则振荡,破坏原来的调制不稳定性区间.

双偶氮苯-二苯并[b,i]噻蒽-[2,3-b]苯-5,7,12,14-四酮衍生物分子的二阶非线性光学性质

使用密度泛函理论(DFT)M06-2X方法、采用6-311+g(d,p)基组,分别对26个双偶氮-二苯并[b,i]噻蒽-[2,3-b]苯-5,7,12,14-四酮衍生物分子进行结构优化与频率计算;使用含时密度泛函理论(TD-DFT)TD-M06-2X方法计算了a1~d6分子的前线分子轨道与电子吸收光谱,采用有效场FF方法研究了二阶非线性光学性质(NLO).研究结果表明,26个噻蒽四酮类衍生物分子的能隙在1.33—2.02 eV范围,归属于有机半导体;最低能量吸收峰波长在601.8~609.5nm范围;在增大分子的二阶非线性光学系数β_(μ)(或β_(0))值方面,含相同偶氮苯基团或含不同偶氮苯基团分别引入到二苯并[b,i]噻蒽-[2,3-b]苯-5,7,12,14-四酮分子两侧的2,10位优于2,9位,在2,10位分别端接含推、拉基团的偶氮苯优于含相同给电子基团的偶氮苯.在偶氮苯苯环对位分别端接强吸电子基(-NO_(2))与强供电子基(如-N(CH_(3))_(2)、-N(Ph)_(3)、-N-苯基咔唑等)可增强体系的二阶非线性光学性能,获得性能良好的非线性光学材料.

Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems

This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems.The nonlinear system is modeled using linear parameter-varying(LPV)systems.A model-based probabilistic safe controller is first designed to guarantee probabilisticλ-contractivity(i.e.,stability and invariance)of the LPV system with respect to a given polyhedral safe set.To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model,its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable.It is shown that the variance of the closedloop system,as well as the probability of safety satisfaction,depends on the decision variable and the noise covariance.A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level.This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set,and thus minimizes the risk of safety violation.Unlike the certainty-equivalent approach that results in a risk-neutral control design,the minimum-variance method leads to a risk-averse control design.It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation.Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.

Quantization and Event-Triggered Policy Design for Encrypted Networked Control

This paper proposes a novel event-driven encrypted control framework for linear networked control systems(NCSs),which relies on two modified uniform quantization policies,the Paillier cryptosystem,and an event-triggered strategy.Due to the fact that only integers can work in the Pailler cryptosystem,both the real-valued control gain and system state need to be first quantized before encryption.This is dramatically different from the existing quantized control methods,where only the quantization of a single value,e.g.,the control input or the system state,is considered.To handle this issue,static and dynamic quantization policies are presented,which achieve the desired integer conversions and guarantee asymptotic convergence of the quantized system state to the equilibrium.Then,the quantized system state is encrypted and sent to the controller when the triggering condition,specified by a state-based event-triggered strategy,is satisfied.By doing so,not only the security and confidentiality of data transmitted over the communication network are protected,but also the ciphertext expansion phenomenon can be relieved.Additionally,by tactfully designing the quantization sensitivities and triggering error,the proposed event-driven encrypted control framework ensures the asymptotic stability of the overall closedloop system.Finally,a simulation example of the secure motion control for an inverted pendulum cart system is presented to evaluate the effectiveness of the theoretical results.

Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties

This paper investigates the prescribed-time control(PTC) problem for a class of strict-feedback systems subject to non-vanishing uncertainties. The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC synthesis nontrivial. In this work, a control method that does not involve infinite time-varying gain is proposed, leading to a practical and global prescribed time tracking control solution for the strict-feedback systems, in spite of both the mismatched and nonvanishing uncertainties. Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point, in our method a softening unit is exclusively included to ensure the continuity of the control action. Furthermore, in contrast to most existing prescribed-time control works where the control scheme is only valid on a finite time interval, in this work, the proposed control scheme is valid on the entire time interval. In addition, the prior information on the upper or lower bound of gi is not in need,enlarging the applicability of the proposed method. Both the theoretical analysis and numerical simulation confirm the effectiveness of the proposed control algorithm.

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle(UAV)in the presence of parameter uncertainties,unmodeled uncertainties and wind disturbances.First,a fixed-time disturbance observer(FXDO)based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model.Then,a fixed-time integral sliding mode control(FXISMC)is combined with the FXDO to achieve strong robustness and chattering reduction.Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability.Finally,numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances.In addition,the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Seismic control of multi-degrees-of-freedom structures by vertical mass isolation method using MR dampers

Vertical mass isolation(VMI)is one of the novel methods for the seismic control of structures.In this method,the entire structure is assumed to consist of two mass and stiffness subsystems,and an isolated layer is located among them.In this study,the magnetorheological damper in three modes:passive-off,passive-on,and semi-active mode with variable voltage between zero and 9 volts was used as an isolated layer between two subsystems.Multi-degrees-of-freedom structures with 5,10,and 15 floors in two dimensions were examined under 11 pairs of near field earthquakes.On each level,the displacement of MR dampers was taken into account.The responses of maximum displacement,maximum inter-story drift,and maximum base shear in controlled and uncontrolled buildings were compared to assess the suggested approach for seismic control of the structures.According to the results,the semi-active control method can reduce the response by more than 12%compared to the uncontrolled mode in terms of maximum displacement of the mass subsystem of the structures.This method can reduce more than 16%and 20%of the responses compared to the uncontrolled mode in terms of maximum inter-story drift and base shear of the structure,respectively.

Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control

In this paper, a model predictive control(MPC)framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system.Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically,and is supported by simulation examples.