Progress in Mechanical Modeling of Implantable Flexible Neural Probes

Implanted neural probes can detect weak discharges of neurons in the brain by piercing soft brain tissue,thus as important tools for brain science research,as well as diagnosis and treatment of brain diseases.However,the rigid neural probes,such as Utah arrays,Michigan probes,and metal microfilament electrodes,are mechanically unmatched with brain tissue and are prone to rejection and glial scarring after implantation,which leads to a significant degradation in the signal quality with the implantation time.In recent years,flexible neural electrodes are rapidly developed with less damage to biological tissues,excellent biocompatibility,and mechanical compliance to alleviate scarring.Among them,the mechanical modeling is important for the optimization of the structure and the implantation process.In this review,the theoretical calculation of the flexible neural probes is firstly summarized with the processes of buckling,insertion,and relative interaction with soft brain tissue for flexible probes from outside to inside.Then,the corresponding mechanical simulation methods are organized considering multiple impact factors to realize minimally invasive implantation.Finally,the technical difficulties and future trends of mechanical modeling are discussed for the next-generation flexible neural probes,which is critical to realize low-invasiveness and long-term coexistence in vivo.

A drug-loaded flexible substrate improves the performance of conformal cortical electrodes

Cortical electrodes are a powerful tool for the stimulation and/or recording of electrical activity in the nervous system.However,the inevitable wound caused by surgical implantation of electrodes presents bacterial infection and inflammatory reaction risks associated with foreign body exposure.Moreover,inflammation of the wound area can dramatically worsen in response to bacterial infection.These consequences can not only lead to the failure of cortical electrode implantation but also threaten the lives of patients.Herein,we prepared a hydrogel made of bacterial cellulose(BC),a flexible substrate for cortical electrodes,and further loaded antibiotic tetracycline(TC)and the anti-inflammatory drug dexamethasone(DEX)onto it.The encapsulated drugs can be released from the BC hydrogel and effectively inhibit the growth of Gram-negative and Gram-positive bacteria.Next,therapeutic cortical electrodes were developed by integrating the drug-loaded BC hydrogel and nine-channel serpentine arrays;these were used to record electrocorticography(ECoG)signals in a rat model.Due to the controlled release of TC and DEX from the BC hydrogel substrate,therapeutic cortical electrodes can alleviate or prevent symptoms associated with the bacterial infection and inflammation of brain tissue.This approach facilitates the development of drug delivery electrodes for resolving complications caused by implantable electrodes.

Prediction of collapse process and tipping points for mutualistic and competitive networks with k-core method

Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called resilience.However,once the external disturbances exceed the tipping points of the system resilience,the consequences would be catastrophic,and eventually lead the ecosystem to complete collapse.We capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method,and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest k-core.Then we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the ecosystem.Our work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions,and provides theoretical insights into further avoiding the occurrence of ecosystem collapse.

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

Protective performance of shear stiffening gel-modified foam against ballistic impact:Experimental and numerical study

As one of the most widely used personal protective equipment(PPE),body armors play an important role in protecting the human body from the high-velocity impact of bullets or projectiles.The body torso and critical organs of the wear may suffer severe behind-armor blunt trauma(BABT)even though the impactor is stopped by the body armor.A type of novel composite material through incorporating shear stiffening gel(STG)into ethylene-vinyl acetate(EVA)foam is developed and used as buffer layers to reduce BABT.In this paper,the protective performance of body armors composed of fabric bulletproof layers and a buffer layer made of foam material is investigated both experimentally and numerically.The effectiveness of STG-modified EVA in damage relief is verified by ballistic tests.In parallel with the experimental study,numerical simulations are conducted by LS-DYNA®to investigate the dynamic response of each component and capture the key mechanical parameters,which are hardly obtained from field tests.To fully describe the material behavior under the transient impact,the selected constitutive models take the failure and strain rate effect into consideration.A good agreement between the experimental observations and numerical results is achieved to prove the validity of the modelling method.The tests and simulations show that the impact-induced deformation on the human body is significantly reduced by using STG-modified EVA as the buffering material.The improvement of protective performance is attributed to better dynamic properties and more outstanding energy absorption capability of the composite foam.

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

Post-earthquake rescue missions are full of challenges due to the unstable structure of ruins and successive aftershocks.Most of the current rescue robots lack the ability to interact with environments,leading to low rescue efficiency.The multimodal electronic skin(e-skin)proposed not only reproduces the pressure,temperature,and humidity sensing capabilities of natural skin but also develops sensing functions beyond it—perceiving object proximity and NO2 gas.Its multilayer stacked structure based on Ecoflex and organohydrogel endows the e-skin with mechanical properties similar to natural skin.Rescue robots integrated with multimodal e-skin and artificial intelligence(AI)algorithms show strong environmental perception capabilities and can accurately distinguish objects and identify human limbs through grasping,laying the foundation for automated post-earthquake rescue.Besides,the combination of e-skin and NO2 wireless alarm circuits allows robots to sense toxic gases in the environment in real time,thereby adopting appropriate measures to protect trapped people from the toxic environment.Multimodal e-skin powered by AI algorithms and hardware circuits exhibits powerful environmental perception and information processing capabilities,which,as an interface for interaction with the physical world,dramatically expands intelligent robots’application scenarios.

VR Technology Applied in Engineering Practice Training Courses: A Case of Northwestern Polytechnical University’s “VR Assembly Composite Practice Training Course”

Under the background of“emerging engineering education,”the engineering education with national defense characteristics,has put forward higher requirements for the engineering practice training of college students.Based on this,focusing on three characteristics of virtual reality(VR)technology,this paper puts forward the construction ideas of VR technology applied in engineering practice training courses.Then,by taking Northwestern Polytechnical University’s“VR Assembly Composite Practice Training Course”for example,this paper designs the teaching process of the practice training course and analyzes the teaching effect through questionnaires.It finds that VR technology can avoid teaching risks,improve teaching quality,and have a high promotion value in the existing engineering practice training courses.Finally,three suggestions on the VR technology applied in engineering practice teaching are put forward.

3D Progressive Damage Based Macro-Mechanical FE Simulation of Machining Unidirectional FRP Composite

Finite element(FE) simulation is a powerful tool for investigating the mechanism of machining fiber?reinforced polymer composite(FRP). However in existing FE machining simulation works,the two?dimensional(2 D) progressive damage models only describe material behavior in plane stress,while the three?dimensional(3 D) damage models always assume an instantaneous sti ness reduction pattern. So the chip formation mechanism of FRP under machin?ing is not fully analyzed in general stress state. A 3 D macro?mechanical based FE simulation model was developed for the machining of unidirectional glass fiber reinforced plastic. An energy based 3 D progressive damage model was proposed for damage evolution and continuous sti ness degradation. The damage model was implemented for the Hashin?type criterion and Maximum stress criterion. The influences of the failure criterion and fracture energy dissipa?tion on the simulation results were studied. The simulated chip shapes,cutting forces and sub?surface damages were verified by those obtained in the reference experiment. The simulation results also show consistency with previous 2 D FE models in the reference. The proposed research provides a model for simulating FRP material behavior and the machining process in 3 D stress state.

Pre-compensation of Friction for CNC Machine Tools through Constructing a Nonlinear Model Predictive Scheme

Nonlinear friction is a dominant factor afecting the control accuracy of CNC machine tools.This paper proposes a friction pre-compensation method for CNC machine tools through constructing a nonlinear model predictive scheme.The nonlinear friction-induced tracking error is frstly modeled and then utilized to establish the nonlinear model predictive scheme,which is subsequently used to optimize the compensation signal by treating the friction-induced tracking error as the optimization objective.During the optimization procedure,the derivative of compensation signal is constrained to avoid vibration of machine tools.In contrast to other existing approaches,the proposed method only needs the parameters of Stribeck friction model and an additional tuning parameter,while fnely identifying the parameters related to the pre-sliding phenomenon is not required.As a result,it greatly facilitates the practical applicability.Both air cutting and real cutting experiments conducted on an in-house developed open-architecture CNC machine tool prove that the proposed method can reduce the tracking errors by more than 56%,and reduce the contour errors by more than 50%.

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.

Influence of bolt dynamic installation on topography characteristics and mechanical behaviors of CFRP interference-fit bolted joints

As the controlled research of Dynamic Installation(DI)and Static Installation(SI),a new interference installation method was developed based on electromagnetic loading to enhance the mechanical properties of composite structures.Four different interference-fit sizes were considered,ranging from a net fit to 2.0%.The experiments were conducted to evaluate the installation resistance and the mechanical behavior of the joint under external loads.Meanwhile,an FFA model to model the stress distribution and damage behavior of the bolt-hole contact interface was established.The load-displacement curve and damage modes of experiments were used to verify the FEA results.The results show that the installation resistance during DI process was remarkably lower than that of SI process corresponding to all interference-fit sizes,and the stress amplitudes induced by interference were larger and widely distributed.The damage of the hole wall was positively correlated with interference fit size,but DI can significantly reduce the damage compared to SI.In performance tests,DI enhanced the static bearing capacity and extended longer fatigue life of the joints than SI.DI methods can be an effective way to achieve highly reliable interference joints in composite structures.

一种基于血液检测的诊断和预测HBV相关疾病的弹性方法

Chronic hepatitis B virus(HBV)infection,which threatens global public health,is a major contributor to liver-related morbidity and mortality.Examinations for liver diseases related to chronic HBV infection-including laboratory tests,ultrasounds,computed tomography(CT),and liver biopsies-may take up medical resources,particularly since they overlap in most instances.Thus,there is an urgent need to establish an economical and effective diagnosis method in order to streamline the medical process for HBV-related disea ses.Using complex network models constructed based on clinical blood tests,we provide such a method by defining the novel measure of functional resilience to assess patients’liver conditions.By combining network models and dynamics,we discovered the pivotal items and their corresponding thresholds,which can guide further research on preventing disease deterioration in critical states of these diseases.The macro-averaged precision of our method,functional resilience,is84.74%,whereas the macro-averaged precision of physicians’experience without assistance from imaging or biopsy is 55.63%.From an economic perspective,our approach could save the equivalent of at least30 USD per visit for most Chinese patients and at least 400 USD per visit for most US patients,compared with general diagnostic methods.Globally,this will add to savings of at least 10.5 billion USD annually.Our method can comprehensively evaluate the condition of patients’livers and help avert the waste of medical resources during the diagnosis of liver disease by reducing excessive imaging exams.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Kinematics and Dynamics of Deployable Structures with Scissor-Like-Elements Based on Screw Theory

Because the deployable structures are complex multi-loop structures and methods of derivation which lead to simpler kinematic and dynamic equations of motion are the subject of research effort, the kinematics and dynamics of deployable structures with scissor-like-elements are presented based on screw theory and the principle of virtual work respectively. According to the geometric characteristic of the deployable structure examined, the basic structural unit is the common scissor-like-element（SLE）. First, a spatial deployable structure, comprised of three SLEs, is defined, and the constraint topology graph is obtained. The equations of motion are then derived based on screw theory and the geometric nature of scissor elements. Second, to develop the dynamics of the whole deployable structure, the local coordinates of the SLEs and the Jacobian matrices of the center of mass of the deployable structure are derived. Then, the equivalent forces are assembled and added in the equations of motion based on the principle of virtual work. Finally, dynamic behavior and unfolded process of the deployable structure are simulated. Its figures of velocity, acceleration and input torque are obtained based on the simulate results. Screw theory not only provides an efficient solution formulation and theory guidance for complex multi-closed loop deployable structures, but also extends the method to solve dynamics of deployable structures. As an efficient mathematical tool, the simper equations of motion are derived based on screw theory.

Mobility Analysis of the Deployable Structure of SLE Based on Screw Theory

Scissor-like element has a number of applications in deployable structures such as planar deployable structure (PDS) and ring deployable structure(RDS). However, the mobility analysis of the multi-loop deployable structures is made more difficulty by the traditional mobility formula, because the deployable structure is a very complex structure with multi-loop. Therefore, On the basis of screw theory, the calculation method of mobility of deployable structures of SLE is thoroughly discussed. In order to investigate the mobility, decomposing and composing structures(DCS) are developed, and the basic units are able to be obtained. On the basis of the deployable structures’ geometrical characteristics, there exists a closed-loop quadrilateral structure and some non-closed-loop quadrilateral structures in PDS. Also, a six legs parallel structure is present in RDS. The basic units’ mobility can be solved by both the methods of screw theory and topology constraint graphs. Then, composing the related basic units, the formula of planar deployable structures’ mobility can be built and solves the mobility of ring deployable structure. The analysis method solves the mobility analysis of the multi-loop deployable structures which is difficulty by the traditional method, and plays an important role in further research about the mobility of other complex deployable structures.

Flexible Bio-tensegrity Manipulator with Multi-degree of Freedom and Variable Structure

Conventional manipulators with rigid structures and sti ness actuators have poor flexibility,limited obstacle avoidance capability,and constrained workspace.Some developed flexible or soft manipulators in recent years have the characteristics of infinite degrees of freedom,high flexibility,environmental adaptability,and extended manipulation capability.However,these existing manipulators still cannot achieve the shrinking motion and independent control of specified segments like the animals,which hinders their applications.In this paper,a flexible bio-tensegrity manipulator,inspired by the longitudinal and transversal muscles of octopus tentacles,was proposed to mimic the shrinking behavior and achieve the variable motion patterns of each segment.Such proposed manipulator uses the elastic spring as the backbone,which is driven by four cables and has one variable structure mechanism in each segment to achieve the independent control of each segment.The variable structure mechanism innovatively contains seven lock-release states to independently control the bending and shrinking motion of each segment.After the kinematic modeling and analysis,one prototype of such bionic flexible manipulator was built and the open-loop control method was proposed.Some proof-of-concept experiments,including the shrinking motion,bending motion,and variable structure motion,were carried out by controlling the length of four cables and changing the lock-release states of the variable structure mechanism,which validate the feasibility and validity of our proposed prototype.Meanwhile,the experimental results show the flexible manipulator can accomplish the bending and shrinking motion with the relative error less than 6.8%through the simple independent control of each segment using the variable structure mechanism.This proposed manipulator has the features of controllable degree-of-freedom in each segment,which extend their environmental adaptability,and manipulation capability.

A New Flexible Multibody Dynamics Analysis Methodology of Deployable Structures with Scissor-Like Elements

There are vast constraint equations in conventional dynamics analysis of deployable structures,which lead to differential-algebraic equations(DAEs)solved hard.To reduce the difficulty of solving and the amount of equations,a new flexible multibody dynamics analysis methodology of deployable structures with scissor-like elements(SLEs)is presented.Firstly,a precise model of a flexible bar of SLE is established by the higher order shear deformable beam element based on the absolute nodal coordinate formulation(ANCF),and the master/slave freedom method is used to obtain the dynamics equations of SLEs without constraint equations.Secondly,according to features of deployable structures,the specification matrix method(SMM)is proposed to eliminate the constraint equations among SLEs in the frame of ANCF.With this method,the inner and the boundary nodal coordinates of element characteristic matrices can be separated simply and efficiently,especially on condition that there are vast nodal coordinates.So the element characteristic matrices can be added end to end circularly.Thus,the dynamic model of deployable structure reduces dimension and can be assembled without any constraint equation.Next,a new iteration procedure for the generalized-a algorithm is presented to solve the ordinary differential equations(ODEs)of deployable structure.Finally,the proposed methodology is used to analyze the flexible multi-body dynamics of a planar linear array deployable structure based on three scissor-like elements.The simulation results show that flexibility has a significant influence on the deployment motion of the deployable structure.The proposed methodology indeed reduce the difficulty of solving and the amount of equations by eliminating redundant degrees of freedom and the constraint equations in scissor-like elements and among scissor-like elements.

Active Damping of Milling Vibration Using Operational Amplifier Circuit

The problem of chatter vibration is associated with adverse consequences that often lead to tool impairment and poor surface finished in a workpiece, and thus, controlling or suppressing chatter vibrations is of great significance to improve machining quality. In this paper, a workpiece and an actuator dynamics are considered in modeling and controller design. A proportional-integral controller(PI) is presented to control and actively damp the chatter vibration of a workpiece in the milling process. The controller is chosen on the basis of its highly stable output and a smaller amount of steady-state error. The controller is realized using analog operational amplifier circuit. The work has contributed to planning a novel approach that addresses the problem of chatter vibration in spite of technical hitches in modeling and controller design. The method can also lead to considerable reduction in vibrations and can be beneficial in industries in term of cost reduction and energy saving. The application of this method is verified using active damping device actuator(ADD) in the milling of steel.

Quasi-Static and Dynamic Behaviors of Helical Gear System with Manufacturing Errors

Time?varying mesh stiffness(TVMS) and gear errors include short?term and long?term components are the two main internal dynamic excitations for gear transmission. The coupling relationship between the two factors is usually neglected in the traditional quasi-static and dynamic behaviors analysis of gear system. This paper investigates the influence of short?term and long?term components of manufacturing errors on quasi?static and dynamic behaviors of helical gear system considering the coupling relationship between TVMS and gear errors. The TVMS, loaded static transmission error(LSTE) and loaded composite mesh error(LCMS) are determined using an improved loaded tooth contact analysis(LTCA) model. Considering the structure of shaft, as well as the direction of power flow and bearing location, a precise generalized finite element dynamic model of helical gear system is developed, and the dynamic responses of the system are obtained by numerical integration method. The results suggest that lighter loading conditions result in smaller mesh stiffness and stronger vibration, and the corresponding resonance speeds of the system become lower. Long?term components of manufacturing errors lead to the appearance of sideband frequency components in frequency spectrum of dynamic responses. The sideband frequency components are predominant under light loading conditions. With the increase of output torque, the mesh frequency and its harmonics components tend to be enhanced relative to sideband frequency components. This study can provide effective reference for low noise design of gear transmission.