With the acceleration of urbanisation, the problem of ground settlement in tunnel construction has become a serious challenge. Aiming at the existing ground settlement monitoring and resilience management problems such as limited monitoring range, neglected spatio-temporal characteristics, and poor combination of prediction results and resilience management, this paper proposes a two-stage spatio-temporal prediction and resilience optimization for tunnel-induced ground settlement. Specifically, firstly, this paper constructs a comprehensive monitoring architecture integrating SBAS-InSAR technology and Internet of Things (IoT) technology, which realises accurate and comprehensive monitoring of ground settlement. Secondly, a two-stage spatio-temporal prediction method of ground settlement is proposed: based on the wide-area spatio-temporal data acquired by SBAS-InSAR technology, the spatio-temporal transformer model is used to make the preliminary prediction. Then, with the small-area variables collected by IoT, the parameter-seeking optimisation algorithm based on the Grid Search-Particle Swarm is used in conjunction with the Time Convolutional-Bidirectional Long and Short-Term Memory Network (GR-PSO-TCN-BiLSTM) model to correct the prediction error. In terms of resilience optimisation, this paper proposes a multi-stage resilience enhancement strategy based on ground settlement prediction, which combines prevention importance, degradation importance and recovery importance, aiming to maximise the resilience of tunnel-induced ground settlement area. Finally, an empirical analysis using the traffic along the Zhengzhou Metro as an example verifies the effectiveness of the proposed method. These results indicate that coupling wide-area remote sensing with local IoT correction can substantially improve settlement prediction accuracy and provide actionable guidance for maintenance prioritization, thereby enhancing the robustness and recovery capability of metro systems.

Within the context of capability-based system-of-systems (SoS), the technology selection involves multiple stakeholders interactively participating in decision-making. In this article, a novel approach based on graph model for conflict resolution (GMCR) is proposed to handle the interactive technology selection decision across capability domains. First, a GMCR methodology based decision analysis framework for technology selection is presented, which allows decision-makers (DMs) with distinct risk attitudes, preference knowledge, and degrees of foresight to independently and interactively participate in technology selection. Then, the technology selection model is established by a four-step procedure that incorporates improved fuzzy option prioritization, followed by systematical technology selection analysis to provide strategic insights for identifying potential mutually accepted technology portfolios. Finally, an illustrative example is used to demonstrate the applicability and effectiveness of the proposed approach.

With the increasing level of machine intelligence, the problem of collaboration among intelligent individuals in heterogeneous networks has become a focal point of research. Taking the task allocation of heterogeneous Unmanned Aerial Vehicle (UAV) swarms as an example, we study the game behavior of heterogeneous network based on task traction. To align with the autonomous and collaborative decision-making process of unmanned swarms, we construct a multi-party multi-strategy evolutionary game framework on heterogeneous networks, define local and global game payoff functions, and innovatively propose an Enhanced Moran Rule that integrates “Pairwise updating” and “Virtual Game” (PVG-EMR algorithm) to improve the task collaboration of UAV swarm. Experiments on various underlying communication network models show that the PVG-EMR algorithm proposed in our study can effectively plan the collaborative object of UAVs and ensure the appropriate assignment of tasks in the heterogeneous network, optimizing both local and global payoffs. Moreover, the algorithm exhibits robust performance.

Considering the current “Digital Finance- Commercial Bank Risks” is not clear and rarely studied for rural commercial banks. This study builds a two-way fixed effects model based on the annual data of 122 rural commercial banks from 2014-2020year in China and found that digital finance development has an “inverted U-shaped” relationship with the risk-taking of rural commercial banks. Further studies have found that market competition and risk preference for rural commercial banks have moderating and mediating effects. We have found that: (1) During the early stages, there is an increase in competition among rural commercial banks, leading them to adopt more stable operating strategies to ensure operations. which passively increases their risk-taking, thus verifying the left half of the “inverted U-shaped (2) In the future, digital finance is expected to break through an inflection point and competition will weaken. Rural commercial banks will increase their risk preferences to make up for previous profit losses. At this time, they will be better able to accurately identify risks, ultimately reducing their level of risk-taking. This verifies the right half of the “inverted U-shaped Overall, the Internal Mechanism among “Digital Finance Development, Market Competition, Risk Preferences, and Risk-Taking” has been confirmed.

With the continuous development of science and technology, navigation and positioning technology has been applied to all aspects of society. The ultra-wide band (UWB) based positioning technology has real-time indoor and outdoor accurate tracking ability and high positioning accuracy, which has a wide range of military and civilian applications. Despite that, the data will have abnormal fluctuations in the case of strong interference due to the complex and changeable indoor environment, which may affect the accuracy of positioning and even cause serious accidents. In this paper, UWB precise positioning under signal interference is studied. A two-stream 1D residual network (TS-1DRN) model learning location features from multimodal data is proposed where the main network structure is based on ResNet2D, and a precise positioning model based on the two-stream deep residual network with fusion utilization of multimodal data is applied to accurate positioning in abnormal scenarios. Considering that the anchor coordinates and distance can be used to obtain the tag coordinates in physical model, distance data are further added with anchor coordinates as the neural network training inputs into the two-stream network compared with previous studies. The effectiveness of the proposed model is verified by comparing with the classical algorithms commonly used for UWB positioning. The positioning accuracy under NLOS is improved by about 150% in the 3D space, and it also performs well in other dimensions, with the minimum positioning error reduced to 34.9952mm. Furthermore, the data in normal scenarios were also used for training and testing, and the experimental results are also significantly improved, indicating the robustness of the proposed model.

Time series is a common type of data that widely exists in various real-world scenarios, such as traffic flow data, network KPI, financial data, which can be regarded as time series. Anomaly detection in time series is an interesting research topic with a wide range of real-world applications, such as network intrusion detection, traffic situation monitoring and sensor error detection. In the real-world scenario, the frequency of anomalies is very low and little anomalous sample is available for analysis. Therefore, unsupervised methods are usually used for anomaly detection. In this paper, based on spectrum analysis and time series decomposition, an unsupervised deep framework for anomaly detection in time series data is designed. First, we decompose the original time series into trend series, seasonal series and residual series based on spectrum analysis. Then, prediction models based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) are designed to predict the trend series and seasonal series, respectively, and the residual series is reconstructed based on a Gaussian distribution. Next, the time series data are reconstructed by superimposing the predicted trend series and seasonal series and reconstructed residual series. Finally, we compare the original time series with the reconstructed time series and detect anomalies according to a certain threshold. The method proposed in this paper integrates prediction-based and reconstruction-based methods, and the experimental results on four datasets demonstrate the excellent performance of our method.

A weapon system-of-systems in integrated joint operations under the condition of information can be abstracted as a heterogeneous combat network(HCN). In the complicated battlefield environment, the enemy information obtained by our side lacks completeness and with great uncertainty. To solve the problem, this paper puts forward a framework for the disintegration of enemy combat networks under incomplete information. First, a weapon system of systems is modeled as a HCN by abstracting diversified systems and information flows into multiple types of nodes and edges. Next, the HCN is reconstructed with the link prediction method based on representation learning. Then, based on the conception of killing chain, a new index is proposed to evaluate the effect of disintegration considering the capability attribute of weapons and its attack cost. Last, a real-world HCN is taken as a case study and extensive experiments are conducted to demonstrate the feasibility and effectiveness of the proposed framework. The results show that the framework proposed in this paper can provide decision-making support for battlefield command under incomplete information.

The contribution rate assessment of equipment system of systems capability is a basic problem in the development planning of equipment system of systems which provides reliable basis for grasping the development direction of Tracking, Telemetry, and Control (TT&C) equipment system of systems accurately. From the perspective of system of systems, based on the characteristics and functions of TT&C equipment system of systems, this paper puts forward an assessment method of TT&C equipment system of systems capability contribution rate based on Analytic Hierarchy Process (AHP).Firstly, constructing the TT&C equipment system of systems capability contribution rate assessment system of systems according to the characteristics of this system of systems. Secondly, the capability of the equipment system of systems is assessed based on the AHP. Finally, a TT&C equipment system of systems is taken as an example to analyze and calculate the contribution of each equipment to the whole system of systems, and verify the feasibility and effectiveness of the method proposed in this paper. The method proposed in this paper provides a reference for the contribution rate assessment of TT&C equipment system of systems capability.

Modern military operations in high-tech information warfare settings are dynamic processes involving various types of combat systems connected via multiple channels, which can be abstracted as a type of complex temporal combat network (TCN). The equipment contribution analysis in TCNs is of significant military value for optimizing operation process planning and improving network resilience in complex electromagnetic battlefields. This paper presents an integrated framework called capability oriented equipment contribution analysis (CECA) to analyze key equipment in TCNs. Specifically, a temporal network model is proposed to characterize TCNs by considering their dynamic nature and the heterogeneity capabilities of different types of functional entities during military operations. Based on this model, an operation capability contribution index is proposed to measure the contribution of each piece of equipment involved in executing operational tasks. Finally, the reliability and effectiveness of the CECA are demonstrated based on a TCN case study. This paper provides a detailed and precise quantitative analysis of the equipment contributions in TCNs, which yields useful insights for operation guidance and designing a more resilient combat system-of-systems.

The identification and selection of emerging technologies has always been a hot field concerned by countries, armed forces and enterprises. Selecting emerging technologies from huge amounts of data is helpful to grasp technological frontiers and technological advantages. We use the unmanned system papers collected in Web of Science (WoS) database as datasets. Firstly, the bibliographic coupling network is constructed. And then the key technologies in the field of unmanned systems are identified by using the complex network community detection algorithm. Finally, the emerging technologies in the field of unmanned systems are screened according to the four indicators of novelty, popularity, influence and growth. We have successfully identified 113 key technologies in the field of unmanned systems and selected 10 of them as emerging technologies. The effectiveness and feasibility of the method have been verified by the evaluation of the research team, which is of great significance for the identification, assessment and prediction of technology.

The combat system-of-systems (CSoSs) in high-tech information warfare, composed of different types of combat systems connected through multiple interconnections, can be abstracted as a type of complex heterogeneous network. Research on the disintegration of the operational capability of a heterogeneous combat network (HCN) is of significant military value for optimizing the planning of operation process and improving network survivability in a complex electromagnetic battlefield. Accordingly, this paper presents an integrated framework called HCN operational capability disintegration based on link prediction (OCDLP) for modeling and solving the disintegration problem regarding the operational capability of an HCN. More specifically, a heterogeneous network model of a combat network under incomplete information is first established, considering different types of functional entities and information flows. Based on this model, a link prediction model is then applied to recover the original combat network structure while taking advantage of the observed information. Next, a disintegration evaluation metric of the operational capability of an HCN is elicited to evaluate the attack efficiency after a link prediction. Last, the reliability and effectiveness of the OCDLP are illustrated through a case study. The results provide useful insight into the operation guidance and design of a more resilient CSoSs.

The Air and Missile Defense System (AAMDS) is a multiple weapon platform coordinating combat system which plays a critical role in the whole anti-aircraft combat system-of-systems. The AAMDS has great advantages in striking time-sensitive targets, which is the main task in anti-aircraft combat. In this paper, we put forward a temporal constraint modeling and conflict resolving method to illustrate the combat process of the AAMDS based on temporal constraint network(TCN). First, the structure of the AAMDS is analyzed to model it as a heterogeneous network. Next, we construct a mapping framework to guide the mapping from AAMDS to TCN. Then, a new conflict resolving strategy is proposed under the background of anti-missile combat. Finally, the reliability and effectiveness of the proposed method are demonstrated by a case study. The results show that our method proposed in this paper performs well.