Design methodology of a mini-missile considering flight performance and guidance precision

The design of mini-missiles(MMs)presents several novel challenges.The stringent mission requirement to reach a target with a certain precision imposes a high guidance precision.The miniaturization of the size of MMs makes the design of the guidance,navigation,and control(GNC)have a larger-thanbefore impact on the main-body design(shape,motor,and layout design)and its design objective,i.e.,flight performance.Pursuing a trade-off between flight performance and guidance precision,all the relevant interactions have to be accounted for in the design of the main body and the GNC system.Herein,a multi-objective and multidisciplinary design optimization(MDO)is proposed.Disciplines pertinent to motor,aerodynamics,layout,trajectory,flight dynamics,control,and guidance are included in the proposed MDO framework.The optimization problem seeks to maximize the range and minimize the guidance error.The problem is solved by using the nondominated sorting genetic algorithm II.An optimum design that balances a longer range with a smaller guidance error is obtained.Finally,lessons learned about the design of the MM and insights into the trade-off between flight performance and guidance precision are given by comparing the optimum design to a design provided by the traditional approach.

The role of axon guidance molecules in the pathogenesis of epilepsy

Current treatments for epilepsy can only manage the symptoms of the condition but cannot alter the initial onset or halt the progression of the disease. Consequently, it is crucial to identify drugs that can target novel cellular and molecular mechanisms and mechanisms of action. Increasing evidence suggests that axon guidance molecules play a role in the structural and functional modifications of neural networks and that the dysregulation of these molecules is associated with epilepsy susceptibility. In this review, we discuss the essential role of axon guidance molecules in neuronal activity in patients with epilepsy as well as the impact of these molecules on synaptic plasticity and brain tissue remodeling. Furthermore, we examine the relationship between axon guidance molecules and neuroinflammation, as well as the structural changes in specific brain regions that contribute to the development of epilepsy. Ample evidence indicates that axon guidance molecules, including semaphorins and ephrins, play a fundamental role in guiding axon growth and the establishment of synaptic connections. Deviations in their expression or function can disrupt neuronal connections, ultimately leading to epileptic seizures. The remodeling of neural networks is a significant characteristic of epilepsy, with axon guidance molecules playing a role in the dynamic reorganization of neural circuits. This, in turn, affects synapse formation and elimination. Dysregulation of these molecules can upset the delicate balance between excitation and inhibition within a neural network, thereby increasing the risk of overexcitation and the development of epilepsy. Inflammatory signals can regulate the expression and function of axon guidance molecules, thus influencing axonal growth, axon orientation, and synaptic plasticity. The dysregulation of neuroinflammation can intensify neuronal dysfunction and contribute to the occurrence of epilepsy. This review delves into the mechanisms associated with the pathogenicity of axon guidance molecules in epilepsy, offering a valuable reference for the exploration of therapeutic targets and presenting a fresh perspective on treatment strategies for this condition.

Silk-based nerve guidance conduits with macroscopic holes modulate the vascularization of regenerating rat sciatic nerve

Peripheral nerve injuries induce a severe motor and sensory deficit. Since the availability of autologous nerve transplants for nerve repair is very limited, alternative treatment strategies are sought, including the use of tubular nerve guidance conduits(tNGCs). However, the use of tNGCs results in poor functional recovery and central necrosis of the regenerating tissue, which limits their application to short nerve lesion defects(typically shorter than 3 cm). Given the importance of vascularization in nerve regeneration, we hypothesized that enabling the growth of blood vessels from the surrounding tissue into the regenerating nerve within the tNGC would help eliminate necrotic processes and lead to improved regeneration. In this study, we reported the application of macroscopic holes into the tubular walls of silk-based tNGCs and compared the various features of these improved silk^(+) tNGCs with the tubes without holes(silk^(–) tNGCs) and autologous nerve transplants in an 8-mm sciatic nerve defect in rats. Using a combination of micro-computed tomography and histological analyses, we were able to prove that the use of silk^(+) tNGCs induced the growth of blood vessels from the adjacent tissue to the intraluminal neovascular formation. A significantly higher number of blood vessels in the silk^(+) group was found compared with autologous nerve transplants and silk^(–), accompanied by improved axon regeneration at the distal coaptation point compared with the silk^(–) tNGCs at 7 weeks postoperatively. In the 15-mm(critical size) sciatic nerve defect model, we again observed a distinct ingrowth of blood vessels through the tubular walls of silk^(+) tNGCs, but without improved functional recovery at 12 weeks postoperatively. Our data proves that macroporous tNGCs increase the vascular supply of regenerating nerves and facilitate improved axonal regeneration in a short-defect model but not in a critical-size defect model. This study suggests that further optimization of the macroscopic holes silk^(+) tNGC approach containing macroscopic holes might result in improved grafting technology suitable for future clinical use.

Recorded recurrent deep reinforcement learning guidance laws for intercepting endoatmospheric maneuvering missiles

This work proposes a recorded recurrent twin delayed deep deterministic(RRTD3)policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise.The attack-defense engagement scenario is modeled as a partially observable Markov decision process(POMDP).Given the benefits of recurrent neural networks(RNNs)in processing sequence information,an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs.The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency.During training,the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent.The training curves show that the proposed RRTD3 successfully enhances data efficiency,training speed,and training stability.The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.

Command filtered integrated estimation guidance and control for strapdown missiles with circular field of view

In this paper,an integrated estimation guidance and control(IEGC)system is designed based on the command filtered backstepping approach for circular field-of-view(FOV)strapdown missiles.The threedimensional integrated estimation guidance and control nonlinear model with limited actuator deflection angle is established considering the seeker's FOV constraint.The boundary time-varying integral barrier Lyapunov function(IBLF)is employed in backstepping design to constrain the body line-of-sight(BLOS)in IEGC system to fit a circular FOV.Then,the nonlinear adaptive controller is designed to estimate the changing aerodynamic parameters.The generalized extended state observer(GESO)is designed to estimate the acceleration of the maneuvering targets and the unmatched time-varying disturbances for improving tracking accuracy.Furthermore,the command filters are used to solve the"differential expansion"problem during the backstepping design.The Lyapunov theory is used to prove the stability of the overall closed-loop IEGC system.Finally,the simulation results validate the integrated system's effectiveness,achieving high accuracy strikes against maneuvering targets.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Computational intelligence interception guidance law using online off-policy integral reinforcement learning

Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

Six-Dimensional Guidance: The Strategies of Thinking Quality Cultivation in Senior High School English Discourse Learning

Taking the discourse learning of the new senior high school English textbook published by the People’s Education Press as an example,combined with the“six-dimensional guidance”deep reading strategy,and through the six-skill training strategies of“memory skill training,understanding skill training,application skill training,analytical skill training,evaluation skill training,creative skill training,”this paper aims to cultivate students’thinking profundity,logic,flexibility,sensitivity,criticality,and originality.It also promotes the real implementation of senior high school English deep reading that points to the cultivation of thinking quality in classroom teaching,and realizes the transformation from“conventional reading”to“deep reading”that reflects the core literacy of the discipline.

Exploration and Research on Employment Guidance System in Universities from the Perspective of Artificial Intelligence

In the context of the era of continuous development of artificial intelligence, the labor value of university students is impacted by technological substitution. Simultaneously, university students are also required to constantly update their skills. All of the above will be the challenge of university students’ employment prospects. However, artificial intelligence will also bring new opportunities, which will stimulate the innovation ability of university students and bring new directions for employment. In order to better cope with the possible impact of artificial intelligence, universities should incorporate employment guidance services into the “three-wide education” system. To achieve this, universities need to take the following measures: developing the dynamic monitoring system of university employment based on big data, constructing the employment guidance curriculum system of university students throughout the whole process, updating the mode of diversified employment guidance service as well as establishing a team of employment guidance teachers keeping pace with the times. These measures aim to better adapt to the job market demands in the context of artificial intelligence, guide students to actively respond to the possible impact of artificial intelligence technology, cultivate their core competencies and qualities that are less likely to be replaced by artificial intelligence, and promote the high-quality employment of university students.

Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

IoV and Blockchain-Enabled Driving Guidance Strategy in Complex Traffic Environment

Diversified traffic participants and complex traffic environment(e.g.,roadblocks or road damage exist)challenge the decision-making accuracy of a single connected and autonomous vehicle(CAV)due to its limited sensing and computing capabilities.Using Internet of Vehicles(IoV)to share driving rules between CAVs can break limitations of a single CAV,but at the same time may cause privacy and safety issues.To tackle this problem,this paper proposes to combine IoV and blockchain technologies to form an efficient and accurate autonomous guidance strategy.Specifically,we first use reinforcement learning for driving decision learning,and give the corresponding driving rule extraction method.Then,an architecture combining IoV and blockchain is designed to ensure secure driving rule sharing.Finally,the shared rules will form an effective autonomous driving guidance strategy through driving rules selection and action selection.Extensive simulation proves that the proposed strategy performs well in complex traffic environment,mainly in terms of accuracy,safety,and robustness.

Cooperative trajectory optimization of UAVs in approaching stage using feedback guidance methods

This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles.Based on the optimal trajectory trends and feedback guidance methods,a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage.Based on UAV kinematics and sensor performance models,optimal trajectory trends of UAVs are analyzed theoretically.Then,feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage,producing trajectories with far less computational complexity and performance very close to the best-known trajectories.Next,the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented,which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage.Finally,the feedback guidance method is numerically simulated.Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field(LGVF)method and verify the effectiveness of the proposed method.Additionally,compared with the receding horizon optimization(RHO)method,the proposed method has the same optimization ability as the RHO method and better real-time performance.

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment,photographing habits,camera resolution and other factors,resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits.Developing an anticounterfeiting code authentication algorithm based on mobile phones is of great commercial value.Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes,the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy.To address the small differences in texture features,low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios,we propose a feature-guided double pool attention network(FG-DPANet)to solve the reprinting forgery problem of printing anti-counterfeiting codes.To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes,we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity.The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes.The double pool attention network(DPANet)is a lightweight double pool attention residual network.Under the condition of ensuring detection accuracy,DPANet can simplify the network structure as much as possible,improve the network reasoning speed,and run better on mobile devices with low computing power.We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper.Experimental results show that the proposed FG-DPANet can resist highquality and small-size anti-counterfeiting code reprint forgery.By comparing with the existing algorithm based on texture,it is shown that the proposed method has a higher authentication accuracy.Last but not least,the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene,and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images.

Bee Colony Optimization Algorithm for Routing and Wavelength Assignment Based on Directional Guidance in Satellite Optical Networks

With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.

Hybrid gradient vector fields for path-following guidance

Guidance path-planning and following are two core technologies used for controlling un-manned aerial vehicles(UAVs)in both military and civilian applications.However,only a few approaches treat both the technologies simultaneously.In this study,an innovative hybrid gradient vector fields for path-following guidance(HGVFs-PFG)algorithm is proposed to control fixed-wing UAVs to follow a generated guidance path and oriented target curves in three-dimensional space,which can be any combination of straight lines,arcs,and helixes as motion primitives.The algorithm aids the creation of vector fields(VFs)for these motion primitives as well as the design of an effective switching strategy to ensure that only one VF is activated at any time to ensure that the complex paths are followed completely.The strategies designed in earlier studies have flaws that prevent the UAV from following arcs that make its turning angle too large.The proposed switching strategy solves this problem by introducing the concept of the virtual way-points.Finally,the performance of the HGVFs-PFG algorithm is verified using a reducedorder autopilot and four representative simulation scenarios.The simulation considers the constraints of the aircraft,and its results indicate that the algorithm performs well in following both lateral and longitudinal control,particularly for curved paths.In general,the proposed technical method is practical and competitive.

An Investigation Into the Needs and Challenges of County-Level Family Education Guidance Services and Corresponding Improvement Measures

The delivery of targeted family education guidance services is essential for establishing a three-in-one education model that encompasses families,schools,and communities.It is also the key to creating a favorable educational environment that promotes the comprehensive and healthy development of individual students.To gain a comprehensive understanding of family education characteristics and trends in Chinese counties,accurately tap into the educational needs of parents,and effectively identify strengths and weaknesses in family education guidance services,this paper employed a quantitative research method based on a questionnaire survey and interview data from J district of C city after years of continuous monitoring of the quality of family education in the area,aiming to explore these issues and put forward several suggestions for improvement.The results indicate that the family learning environment and atmosphere in the district are generally favorable,while parental educational competence requires further improvement.Parents express a strong desire for interactive,systematic,and fragmented family education guidance from schools.Most parents acknowledge the significance of receiving guidance on family education,yet they are disinclined to actively engage in learning-related activities.While the operational mechanisms of county-level family education guidance services have been continuously improved,there is a need for greater overall coordination.Furthermore,the potential of school-led family education guidance services has yet to be fully tapped,and the scientific and professional standards of such guidance need to be further enhanced.Based on the aforementioned findings,we propose recommendations for improvement.Using J district’s effective improvement practices in management,curriculum,and teams as an example,we intend to explore practical ways to promote county-level family education guidance services and provide a valuable reference for the establishment of a comprehensive and effective system to improve such services.

Shanghai mapped out the guidance to“Cosmetics Samples”for the first time

On April 7th,2023,the supervisory department of Xuhui district published the first Guidance to“Cosmetics Samples”for the First Time(Trial).It unified the management requirement regarding cosmetics samples,guided managers to develop responsibility awareness and prevent and advert risks beforehand.Besides,it identified the definition of“cosmetics samples”.

Train post‑derailment behaviours and containment methods:a review

Railway accidents,particularly serious derailments,can lead to catastrophic consequences.Therefore,it is essential to prevent derailment escalation to reduce the likelihood of severe derailments.Train post-derailment behaviours and containment methods play a critical role in preventing derailment escalation and providing passive safety protection and accident prevention in the event of a derailment.However,despite the increasing attention on this field from academia and industry in recent years,there is a lack of systematic exploration and summarization of emerging applications and containment methods in train post-derailment research.For this reason,this paper presents a comprehensive review of existing studies on train post-derailment behaviours,encompassing various topics such as post-derailment contact-impact models,dynamic modelling and simulation techniques,and the primary factors influencing post-derailment behaviours.Significantly,this review introduces and elucidates substitute guidance mechanisms(SGMs),which serve as railway-specific passive safety protection and accident prevention measures.The various types of SGMs are depicted,and their ongoing developments and applications are explored in depth.The review additionally points out several unresolved challenges including the adverse effects of SGMs,and proposes future research directions to advance the theoretical understanding and practical application of train post-derailment behaviours and containment methods.This review seeks to be a valuable reference for railway industry professionals in preventing catastrophic derailment consequences through post-derailment containment methods.

Transcriptomic and bioinformatics analysis of the mechanism by which erythropoietin promotes recovery from traumatic brain injury in mice

Recent studies have found that erythropoietin promotes the recovery of neurological function after traumatic brain injury.However,the precise mechanism of action remains unclea r.In this study,we induced moderate traumatic brain injury in mice by intrape ritoneal injection of erythro poietin for 3 consecutive days.RNA sequencing detected a total of 4065 differentially expressed RNAs,including 1059 mRNAs,92 microRNAs,799 long non-coding RNAs,and 2115circular RNAs.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses revealed that the coding and non-coding RNAs that were differentially expressed after traumatic brain injury and treatment with erythropoietin play roles in the axon guidance pathway,Wnt pathway,and MAPK pathway.Constructing competing endogenous RNA networks showed that regulatory relationship between the differentially expressed non-coding RNAs and mRNAs.Because the axon guidance pathway was repeatedly enriched,the expression of Wnt5a and Ephb6,key factors in the axonal guidance pathway,was assessed.Ephb6 expression decreased and Wnt5a expression increased after traumatic brain injury,and these effects were reversed by treatment with erythro poietin.These findings suggest that erythro poietin can promote recove ry of nerve function after traumatic brain injury through the axon guidance pathway.