Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

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.

A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

Evaluating ChatGPT’s Consciousness and Its Capability to Pass the Turing Test: A Comprehensive Analysis

This study explores the capabilities of ChatGPT, specifically in relation to consciousness and its performance in the Turing Test. The article begins by examining the diverse perspectives among both the cognitive and AI researchers regarding ChatGPT’s ability to pass the Turing Test. It introduces a hierarchical categorization of the test versions, suggesting that ChatGPT approaches success in the test, albeit primarily with na?ve users. Expert users, conversely, can easily identify its limitations. The paper presents various theories of consciousness, with a particular focus on the Integrated Information Theory proposed by Tononi. This theory serves as the framework for assessing ChatGPT’s level of consciousness. Through an evaluation based on the five axioms and theorems of IIT, the study finds that ChatGPT surpasses previous AI systems in certain aspects;however, ChatGPT significantly falls short of achieving a level of consciousness, particularly when compared to biological sentient beings. The paper concludes by emphasizing the importance of recognizing ChatGPT and similar generative AI models as highly advanced and intelligent tools, yet distinctly lacking the consciousness attributes found in advanced living organisms.

Smart Design and Manufacturing the Welded Q350 Steel Frames via Lifecycle Management Strategy of Digital Twin

Artificial intelligent aided design and manufacturing have been recognized as one kind of robust data-driven and data-intensive technologies in the integrated computational material engi-neering(ICME)era.Motivated by the dramatical developments of the services of China Railway High-speed series for more than a decade,it is essential to reveal the foundations of lifecycle man-agement of those trains under environmental conditions.Here,the smart design and manufacturing of welded Q350 steel frames of CR200J series are introduced,presenting the capability and opportu-nity of ICME in weight reduction and lifecycle management at a cost-effective approach.In order to address the required fatigue life time enduring more than 9×10^(6)km,the response of optimized frames to the static and the dynamic loads are comprehensively investigated.It is highlighted that the maximum residual stress of the optimized welded frame is reduced to 69 MPa from 477 MPa of previous existing one.Based on the measured stress and acceleration from the railways,the fatigue life of modified frame under various loading modes could fulfil the requirements of the lifecycle man-agement.Moreover,our recent developed intelligent quality control strategy of welding process mediated by machine learning is also introduced,envisioning its application in the intelligent weld-ing.

Decision Making Support System(DSS)——A Synthesized and Integrated Crystallization of Systems Engineering, Artificial Intelligence and Electronic Technologies

I firmly believe that of systems engineering is the requirement-driven force for the progress ofsoftware engineering, artificial intelligence and electronic technologies. The development ofsoftware engineering, artificial intelligence and electronic technologies is the technical supportfor the progress of systems engineering. INTEGRATION can be considered as "bridging" the ex-isting technologies and the People together into a coordinated SYSTEM.

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.

Key technologies and applications of intelligent dispatching command for high-speed railway in China

Purpose–The intelligent Central Traffic Control(CTC)system plays a vital role in establishing an intelligent high-speed railway(HSR)system.As the core of HSR transportation command,the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching.This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.Design/methodology/approach–This paper first briefly introduces the functions and configuration of the intelligent CTC system.Some new servers,terminals and interfaces are introduced,which are plan adjustment server/terminal,interface for automatic train operation(ATO),interface for Dynamic Monitoring System of Train Control Equipment(DMS),interface for Power Supervisory Control and Data Acquisition(PSCADA),interface for Disaster Monitoring,etc.Findings–The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans,safety control of train routes and commands,traffic information data platform,integrated simulation of traffic dispatching and ATO function.These technologies have been applied in the Beijing-Zhangjiakou HSR,which commenced operations at the end of 2019.Implementing these key intelligent functions has improved the train dispatching command capacity,ensured the safe operation of intelligent HSR,reduced the labor intensity of dispatching operators and enhanced the intelligence level of China’s dispatching system.Originality/value–This paper provides further challenges and research directions for the intelligent dispatching command of HSR.To achieve the objectives,new measures need to be conducted,including the development of advanced technologies for intelligent dispatching command,coping with new requirements with the development of China’s railway signaling system,the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

An intelligent active probing and trace-back scheme for IoT anomaly detection

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

Robot and its living space:A roadmap for robot development based on the view of living space

Robot’s living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society Robots,as the creation of humans,became an irreplaceable component in human society.With the advancement of technologies,robots have become more and more intelligent and have been widely used in many fields,such as disease diagnosis,customer services,healthcare for the older people,and so on.As robots made our lives much more convenient than ever before,they also brought many potential risks and challenges in technology,security,and ethic.To better understand the development of robots,we proposed a concept of a robot’s living space and analyzed the role of robots in our society.In this paper,we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot relationship.The research in this paper contains three central aspects.First,we interpret the concept of the robot’s living space and the functions of each space.Second,we analyze and summarize the relative technologies which support robots living well in each space.Finally,we provide advice and improvement measures based on a discussion of potential problems caused by the developments of robots.With the trend of robots humanization and human-robot society integration,we should seriously consider how to collaborate with intelligent robots to achieve hybrid intelligence.To build a harmonious human-robot integrated society,studying the robot’s living space and its relationship with humans is the prerequisite and roadmap.

The New Integrated Management of Efficient Buildings: An Intelligent Method Approach and AI

Building process innovation highlights building systems and products that denounce both an incremental and radical development,indicating the technological sector as the driver for energy efficiency.The prototypes of this innovation stand out both in the tertiary and residential sector and are aimed at sustainable development with the use of renewable resources,intelligent technologies,with a view to contextualizing and environmental energy requalification.The objectives are those of a new integrated building management,intended as an increase in energy efficiency and an efficient digitalized connection with users and smart grids,through control platforms,IoT(internet of things),security throughout the building’s life cycle and use of AI(artificial intelligence)devices and materials.The approach is a new methodology,with the application of BMS(building management system)and BEMS(building energy management system)systems integrated with other systems,with an interface of protocols open to different manufacturers and multiple functions that invest both the technological and environmental system of the building,with advantages in electrical energy management and thermoelectric like intelligent HVAC systems,with intelligent monitoring and user comfort.Both the new Artificial Intelligence and Machine Learning platforms contribute to a predictive use of energy efficiency with plant optimization.Therefore efficiency and energy saving strategies with intelligent integrated management and medium-long term maintenance programs.The challenge is a new intelligent method approach with the use of artificial intelligence and the development of new technologies in sustainable buildings.

The Theory of Biomedical Knowledge Integration(Ⅳ)

This paper presented some philosophic viewpoints of the Theory of BMKI (The Theory of Biomedical Knowledge Integration), a new exploration in BioMedica l Informatics. It discussed an evolutional relation from propositional calculus, predicate calculus, through framework, to neural network.. The differences in e xclusivity and other natures were explored for physical systems(the real world), quasi-physical systems(the copies of the real world) and mental systems(the abs tracts of the real world). Based on their behaviours in cognitive sciences and k nowledge engineering, the new concepts quasi-infinity or -infinitesimal,potentia l knowledge,dynamic knowledge were introduced. This paper has also described so called "big-or" space which is the base of scientific understanding or associati on. Furthermore the paper put forward the viewpoint that "reasoning only can imp lement in an axiomatic space" and then outlined the building processes of such k ind of space. At last so called "beacon-and-compass strategy" in BMKI was introd uced.

Research on Interdisciplinary Project-Based Teaching in Primary Schools Catalyzed by Digital Intelligence Empowerment

With the rapid development of information technology,digital intelligence empowerment has gradually become an important direction of educational innovation.This paper uses the case analysis method to explore in depth how digital intelligence empowerment and project-based teaching can promote the integration of primary school curricula.Taking the teaching of intelligent patrol cars as an example,this paper analyses the positive role of digital intelligence empowerment in improving teaching effectiveness and cultivating students’comprehensive ability.The research results show that digital intelligence empowerment not only enriches teaching resources but also optimizes the teaching process.Combined with project-based teaching methods,it can effectively improve students’interest in learning and performance.This study provides a useful reference and inspiration for the project-based teaching of curriculum integration in primary schools and has certain practical significance and theoretical value for promoting the process of educational informatization.

Teaching Reform and Practice of Animal Products Processing under the Background of Intelligent Agriculture

Effective food professional personnel training strategies are explored and implemented,and interdisciplinary talents of food science and engineering in accordance with the background of intelligent agriculture are cultivated from the aspects of construction of teaching staff,reform of teaching content,upgrading of teaching model,construction of industry-education integration platform,which is of great significance to the modernization development of Chinese animal products processing industry.

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

The Theory of Biomedical Knowledge Integration (Ⅶ)──The Non-Euclid Macro-Micro Transform Law

This article continued to do the scholastic pursuits on some profound mechanisms in the life systems, which are believed to be related to the further development of Medical Informatics. It discussed at first the structural nature of things, then probed a principle which is a basis for both of the fractal theory and the wavelet analysis, being called the shape-constancy law of the basic constructors at the different scale levels. And the paper also ventured the equivalency between the shape of wave and matrix, thus presented a new concept “shaped-number”, being expected to work in the operations of some bio-medical functions or shapes.