At present, the refined damage assessment of reinforced concrete structures subjected to penetration explosion mainly relies on numerical simulations and experimental data analysis, both of which suffer from the drawbacks of time-consuming processes and high economic costs. To address these issues, a novel digital-intelligent prediction model for penetration-explosion damage is proposed to simulate the entire dynamics of the penetration-explosion process. Based on the graph neural network and a multi-task learning paradigm, this model can achieve rapid and accurate predictions of building damage morphology and node failures under various penetration positions and explosion yields, with a single penetration-explosion prediction taking only 0.2 seconds. Specifically, during the penetration phase, the structural response field prediction error is less than 2.9%, and the node failure prediction accuracy exceeds 99.5%; during the explosion phase, the structural response field prediction error is lower than 6.5%, and the failure prediction accuracy surpasses 98.8%. The evaluation of 100 penetration-explosion damage scenarios takes only 30 s.The proposed digital-intelligent prediction model demonstrates excellent generalization performance and highly efficient, accurate prediction capability, thus providing a new tool for building structure damage assessment and safety protection design.

Accurate determination of extreme boundary conditions in the forward design of a fuse is a robust guarantee for its safety. The safety margins hold paramount significance in the fuze forward design, where environmental excitations, detonation control components, and other safety factors directly impact fuze reliability. To address these challenges, this study proposes a conceptual framework for the fuze safety margins and establishes an analytical evaluation method for the electromagnetic environment safety margins in the fuze systems. The method specifically addresses two key scenarios: (1) voltage, current, and energy safety margin calculations under known electromagnetic environments, and (2) transient/pulse electromagnetic conditions. Furthermore, the paper systematically elaborates on experimental principles for safety margin verification in fuze detonation control circuits, principles for test point selection, and methods for component parameter selection.These contributions provide references and support for the theoretical analysis and engineering application of fuse safety margins.

With regard to the new development trends of artillery technology with the support of informationized and intelligent technologies, this paper summarizes the technological progress of artillery in aspects such as dynamic kill chains, long-range high-precision strikes, anti-jamming reliable strikes, high-density firepower strikes, and data-driven intelligent firepower. Furthermore, in combination with typical battlefield applications and new directions of equipment development, it analyzes the positive effects of these advancements on enhancing artillery performance. The research indicates that modern artillery has achieved severalfold increases in performance metrics concerning closed-loop strike time, range, accuracy, and rate of fire. It has also laid the foundation for artillery to adapt to the requirements of intelligent battlefield confrontation in terms of anti-jamming capabilities in complex battlefields and self-sensing/self-controlling functions of artillery systems. On this basis, this paper further explores the future development trends of artillery technology and proposes cutting-edge technological directions such as new-quality launching methods, extended-range technologies, and group firepower capabilities for artillery systems. It also preliminarily analyzes the significant implications of technologies such as high-initial-velocity launching through hydro-oxygen combustion, cannon-launched scramjet propulsion, and intelligent firepower coordination for the new-quality development of artillery, providing a preliminary vision for the future evolution of artillery systems.

Radar signal sorting is a key technology in the electronic warfare system and an essential part of battlefield situational awareness. The development of new system radar technology brings a serious challenge to radar signal sorting in the current complex battlefield electromagnetic environment. For the problem that the traditional K-means clustering algorithm is sensitive to the initial cluster number K and the initial points when performing signal sorting on radar full pulse data, an optimized K-means-based radar signal sorting algorithm is proposed. The combination of water wave center diffusion(WWCD) optimization algorithm and the Canopy algorithm realizes the optimal selection of the distance threshold of the Canopy algorithm, and optimizes the selection of the cluster number K for K-means clustering, effectively reducing the sensitivity of the K-means algorithm to the selection of the initial number of clusters. Three kinds of UCI data and three kinds of frequency-hopping radar pulse data are used to verify the sorting performance of the proposed method, and the clustering effects are also compared with common clustering algorithms such as DBSCAN, OPTICS, and Canopy-K-means. The results show that the proposed method is insensitive to the setting of initial parameters and has a high clustering and sorting accuracy.

Mobile edge computing (MEC) can effectively address the issues of increasing business demands and greater processing difficulties for mobile users. However, users' service demands have differentiated characteristics in time and space. At the same time, network characteristics such as deep uncertainty in the network environment also restrict the efficiency of network edge task processing and service caching. In response to these challenges, this paper studies the users' service offloading and service caching issues in a multi-user and multi-edge server MEC system. To address the differentiated service requirements of users in both time and space, with the optimization goal of minimizing the average delay and energy consumption cost for all users under the base station, a joint optimization of task offloading and service caching based on the differentiated needs in time and space is established.Taking into consideration the characteristics of the network environment with deep uncertainty, a multi-agent deep reinforcement learning algorithm based on graph neural networks and long short-term memory networks is further proposed for autonomous learning and decision-making of user task offloading, resource allocation, and service caching strategies. Finally, simulations have verified that the proposed algorithm has good convergence and energy efficiency.

When the conventional feature matching color transfer method is applied to the fusion of ultra-wide field-of-view infrared and low-light images, the problems such as high mismatch rate and poor fusion effect may occur. Thus, A natural color fusion method based on superpixel features and color clustering joint constraints is proposed. First of all, the superpixel segmentation is carried out on low-light image and space of color reference image, and  low-level, mid-level, and high-level superpixel feature sets are constructed sequentially. Secondly, the superpixel color clustering of space of color reference image is processed by the improved Fuzzy ART network. Thirdly, the initial superpixel matching set is established based on the similarity of superpixel features. Additionally, with the adaptive color transfer, an initial colorized low-light image is generated and then fused with an ultra-wide field-of-view infrared image to obtain an initial color-fused image. Finally, based on the similarity between infrared-only information and infrared information with color, color transfer is applied to the infrared-only information, ultimately obtaining a naturally colored fused image. The experiment results indicate that compared with traditional methods, the proposed method improves fusion metrics and color metrics by over 19.5% and 9.4%, respectively. The fused images obtained not only retain the significant feature information of both infrared and low-light images well, but also exhibit natural and continuous colors. 

Currently, human society is entering an era of Intelligent Internet of Everything (IoE), characterized by the integration of humans, machines, and objects. Information and communication networks, as the crucial information infrastructure supporting the "Intelligent IoE," are undergoing unprecedented transformations. The development of these networks has a profound impact on people's lifestyles, economic growth, and social progress. The deep integration of artificial intelligence and information communication technologies makes intelligent information networks an inevitable trend in the evolution of network intelligence. In response to the diverse application demands of complex scenarios in the intelligent era, networks should have the ability to proactively adapt to changes in both internal and external conditions. Key features such as autonomous learning, optimization, management, and evolution enable networks to enhance the timeliness, effectiveness, and personalization of services, thus meeting the demands of the intelligent age. 

To address urban traffic congestion and the high demands for traffic collaboration, this study proposes a communication cooperation model-multi-agent deep deterministic policy gradient (CCM-MADDPG) algorithm based on a communication cooperation module (CCM). The core of this algorithm lies in the design of the CCM, which explores the deep-level relationships among neighboring agents through an embedding layer and an information extraction module. It dynamically weights and fuses neighbor information with an attention mechanism to avoid redundancy and information averaging. By embedding the CCM into the Actor network, agents integrate neighborhood information for decision-making, leveraging the locality principle to alleviate joint action space challenges while enhancing system stability and collaborative efficiency. Experiments demonstrate that the CCM-MADDPG significantly outperforms baseline models, such as Qmix and FRAP, on both synthetic and real-world dataset, exhibiting exceptional scalability and adaptability in complex road networks. Specifically, in the Manhattan road network, it reduces average delay by approximately 28.8% and 26.3% compared with Qmix and FRAP, respectively. The ablation studies confirm the necessity of CCM's information extraction and attention mechanisms. This algorithm provides an efficient solution for multi-agent collaborative control, with dual potential for urban traffic optimization and applications. Its core contribution resides in the successful integration of the CCM design with the MADDPG framework.

Object detection technology has great potential for progress in the field of UAV remote sensing images. In order to design object detection algorithms suitable for remote sensing images, it is necessary to accurately grasp the advantages and disadvantages of the current algorithms and explore new solutions. Firstly, this paper conducts a comprehensive and systematic analysis of object detection detection methods based on traditional techniques and deep learning, and compares the various algorithms. Secondly, it summarizes the commonly used remote sensing image datasets and evaluation metrics for object detection detection algorithms. Thirdly, to address the key challenges in remote sensing image object detection, such as small target size, occlusion, and complex background environments, the paper deeply analyzes the difficulties faced by the existing methods in handling these issues. Finally, considering the background environments and characteristics of remote sensing images, the paper discusses  the potential improvement directions for the current algorithms from five aspects, providing a reference for the development of object detection technology in unmanned aerial vehicle (UAV) remote sensing images.

In the context of modern military operations, the network collaboration capability of distributed unmanned combat clusters is of vital importance. Focusing on such cluster collaborative networks, an inductive graph reinforcement learning framework is established, and a heterogeneous network disintegration method integrating inductive graph representation learning and deep reinforcement learning techniques is proposed. The inductive graph representation learning empowers the method with the ability to rapidly generate disintegration effects on the strategies of emerging networks, and by modeling network disintegration as a Markov decision process and combining it with deep reinforcement learning for solving, the disintegration efficacy is significantly improved while reducing the complexity of the problem. Experiments show that the inductive approach is able to transfer among networks of different scales and achieves a better balance between disintegration effectiveness and time consumption; especially in scenarios where node-attack cost constraints are introduced, it exhibits excellent disintegration efficacy and strategy scalability. This research has significant military application value in reducing the complexity of battlefield cognition and improving the timeliness of dynamic decision-making.

Deep transportation tunnels and coal mine roadways in high geostress and complex geological environments are prone to engineering disasters such as large deformation of surrounding rock, coal bump and rockburst. The traditional cable with insufficient deformation capacity and limited impact resistance cannot adapt to the nonlinear large deformation damage characteristics of the deep buried surrounding rocks. A new type of energy-absorbing anchor cable with a shrinkable tube structure is proposed. This design leverages the plastic deformation energy absorption properties of steel to allow for greater deformation while providing high-strength, stable, and pressure-yielding bearing capacity. To explore its impact resistance characteristics, a series of drop hammer impact tests were conducted on the core pressure-yielding component of the anchor cable under varying impact heights, drop hammer weights, and numbers of impacts. The test results show that the shrinkable-tube energy-absorbing anchor cable can absorb energy up to 103 kJ in a single impact, and the working resistance is basically stabilized between 240~280 kN, which has good impact resistance performance. A field application test was carried out in the roadway of 8302 mining face in Xinjulong coal mine. The results indicate that the shrinkable-tube energy-absorbing anchor cable can achieve 120 mm of pressure-yielding deformation without fracturing. This research provides a new technical approach for controlling large deformation and impact disasters in deep rock masses.

The penetration depth of earth-penetrating projectiles into layered protective structures is a key issue in protective engineering design. In practice, the projectile usually penetrates the target with an incidence angle, and the protective structure is typically composed of multi-layered materials. The relevant theories and empirical formulas for penetration depth are relatively complex, which are not conducive to engineering applications. Based on the motion equation of the projectile, the motion deflection law of the projectile during oblique penetration was analyzed and a safety-biased calculation formula for oblique penetration was presented. The concept of equivalent thickness was introduced to convert the equivalent thickness of multi-layered media through wave impedance, then a calculation method for the penetration depth of multi-layered media was obtained. The results show that the oblique penetration depth can be estimated by multiplying the normal penetration depth by the cosine of the angle of incidence, yielding a conservative result; the penetration depth in different media is inversely proportional to the medium's impedance. The derived formulas for oblique and layered media penetration depths are consistent in form with current specifications, elucidating the physical significance of the empirical formulas in the specifications. Compared with the oblique penetration test in reinforced concrete, the maximum relative error of the theoretical prediction is 21.6% in the range of incidence angle from 0° to 50°. Compared with the numerical simulation of penetration to layered media , the theoretical prediction of the number of penetration layers is consistent with the simulation, and the relative error of penetration depth is 3%, which verifies the applicability of the theoretical calculation method.

The mechanical behavior of structural planes at various scales within a rock mass determines the stability of the surrounding rock in engineering projects. With the continuous increase in the burial depth of underground engineering projects, the threat posed by time-delay engineering disasters under disturbed environments is becoming more and more prominent. Based on a systematic review of research from field observations, laboratory experiments, and theoretical models, it is concluded that the remote induction of tectonically controlled time-delayed rockbursts and engineering seismic events is essentially the delayed instability of rock mass structural planes triggered by disturbances.A new concept termed the disturbance aftereffect of shear creep on structural planes is proposed. This concept refers to the phenomenon that, when a structural plane undergoing shear creep is subjected to a disturbance, the influence of the disturbance persists even after the disturbance has ceased. As a result, the subsequent creep process of the disturbed structural plane exhibits different states and nonlinear behaviors compared to its undisturbed condition. Based on the typical manifestations of the disturbance aftereffect, the critical transition behavior of shear creep in structural planes and its influencing factors are explored. In addition, this study highlights that the scientific characterization of disturbance intensity, the conditions and mechanisms of delayed instability critical transition, theoretical models of the critical transition process, and cross-scale research of rock-structural plane-rock mass systems are key issues that urgently need to be resolved. The solution of these problems will provide a solid foundation for understanding the mechanisms of remote triggering of earthquakes and the effective prevention of time-delay engineering disasters.