中国人民解放军军事政治大学

Abstract To enhance the mechanical properties of concrete under impact loading, this study prepared nickel-plated carbon fiber reinforced concrete (NCFC) by modifying carbon fibers with nickel plating to improve interfacial bonding with the cement-based matrix. Split Hopkinson pressure bar (SHPB) impact tests were conducted systematically. Results indicate that incorporating nickel-plated carbon fibers (NCF) significantly enhances concrete’s dynamic compressive strength, peak strain, and energy dissipation density, with a pronounced strain-rate strengthening effect. The most pronounced reinforcement occurs at a fiber volume fraction of 0.3%, where dynamic compressive strength and energy dissipation density increase by approximately 58.58% and 157.01%, respectively, compared to plain concrete. Nickel-plated carbon fibers effectively suppressed crack propagation and delayed failure by enhancing interfacial bonding and bridging crack resistance, significantly improving the dynamic deformation capacity and energy absorption performance of concrete. This study elucidates the micro-mechanism by which nickel-plated carbon fibers enhance concrete’s impact resistance, providing a basis for developing high-toughness protective engineering materials.

Machine learning algorithm plays a key role in intelligent logistics and equipment support. The basic principles and different characteristics of typical shallow machine learning support vector machine and typical deep learning convolutional neural networks are analyzed. The two kinds of model algorithms are used to model the data of people’s daily activities. Among them, the sample data distinguishes the big sample and the small sample, the four models are used to classify and identify people’s daily activities. The results show, for large sample data, the classification effect of deep learning is obviously better than that of traditional machine learning algorithm, for small sample data, traditional machine learning algorithm is better than deep learning algorithm. The ability of deep learning to deal with big data is obviously better than that of dealing with small sample data. Shallow website support vector machine is better at handling big data than small samples, but not much. The results provide important reference for the choice of algorithm.

In recent years, the continuous application and expansion of unmanned intelligent technology in military maritime equipment have spurred the rapid development of military unmanned maritime vehicles, represented by Unmanned Surface Vehicles and Unmanned Underwater Vehicles. Their accelerating deployment across various maritime domains is profoundly reshaping patterns of maritime military competition and the maritime security order, making them an urgent topic for discussion concerning international maritime peace and development. Furthermore, with their unique advantages such as low cost, potential for mass deployment, high concealment, long endurance, and the avoidance of personnel casualties, unmanned maritime vehicles are redefining maritime situational awareness capabilities for nations, especially small and medium-sized states, to an unprecedented degree. Their large-scale application not only poses severe challenges to traditional maritime rules based on the United Nations Convention on the Law of the Sea but also creates uncertainty for the maintenance of the existing maritime security order. Military unmanned maritime vehicles will drive nations toward a re-balance of power. This paper aims to analyze the historical progression and practical application of military unmanned maritime vehicles. It will systematically discuss how they can constructively perform this re-balance function across dimensions such as peacetime, crises, and wartime, and re-examine their potential contributions to the international maritime security order.

BackgroundBioterrorism involves the intentional release of biological agents capable of causing mass casualties, public health emergencies, and societal disruption. As frontline healthcare providers, nurses play a critical role in early detection, isolation, emergency care, and surveillance. However, substantial gaps exist in nursing bioterrorism preparedness across healthcare settings, and implementation determinants influencing preparedness remain insufficiently synthesised.ObjectiveTo systematically synthesise quantitative and qualitative evidence on barriers and facilitators influencing nurses’ bioterrorism preparedness using the Consolidated Framework for Implementation Research (CFIR 2.0).MethodsThis mixed-methods systematic review will follow the Joanna Briggs Institute (JBI) methodology for mixed-methods systematic reviews and adhere to PRISMA-P guidelines, with CFIR 2.0 serving as the guiding analytical framework. A comprehensive literature search will be performed across CINAHL, PubMed, Embase, Scopus, Web of Science, ProQuest, Cochrane Library, and Medline electronic databases from their inception until March 2026, including English and Chinese publications. Two independent reviewers will screen identified records against predefined eligibility criteria and appraise the methodological quality of included studies using the Mixed Methods Appraisal Tool (MMAT). Data synthesis will be thematically mapped to CFIR 2.0 constructs and integrated using a convergent integrated approach.Anticipated contributionsThis review is expected to provide the first synthesis informed by implementation science of determinants influencing nurses’ bioterrorism preparedness. By integrating evidence across methodological traditions, the review will generate a comprehensive understanding of individual, organisational, and contextual factors shaping preparedness implementation.DiscussionThis protocol describes a mixed-methods systematic review informed by implementation science and guided by the Consolidated Framework for Implementation Research (CFIR 2.0) to examine barriers and facilitators influencing nurses’ bioterrorism preparedness. The findings are expected to support a more comprehensive understanding of factors shaping preparedness in healthcare settings and to inform evidence-informed preparedness planning, nursing education, and organisational support strategies aimed at strengthening healthcare system resilience to biological threats.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251273403, Identifier CRD420251273403.

Addressing the cargo loading task planning problem in environments with multiple service desks of heterogeneous efficiency, this paper proposes an intelligent optimization method based on Deep Q-Networks(DQN). Traditional logistics scheduling methods face challenges in high-dimensional state representation and local optima traps when handling the strong coupling relationships among service desk efficiency differences, cargo compatibility constraints, and dynamic task allocation. This study quantifies efficiency disparities by constructing a service desk-cargo efficiency matrix, designs a composite state space integrating remaining cargo quantities and service desk timelines, and innovatively introduces an adaptive reward function that combines time cost, load balancing, and constraint penalties. Experimental results demonstrate that the proposed method reduces the total completion time of parallel operations across multiple service desks by 8.2% compared to traditional heuristic algorithms, while adhering to transport vehicle capacity and cargo compatibility constraints. This approach provides a novel theoretical framework and technical implementation pathway for dynamic task scheduling in complex logistics scenarios.