Analysis of Complex Network Attack and Defense Game Strategies Under Uncertain Value Criterion

The study of attack–defense game decision making in critical infrastructure systems confronting intelligent adversaries, grounded in complex network theory, has emerged as a prominent topic in the field of network security. Most existing research centers on game-theoretic analysis under conditions of complete information and assumes that the attacker and defender share congruent criteria for evaluating target values. However, in reality, asymmetric value perception may lead to different evaluation criteria for both the offensive and defensive sides. This paper examines the game problem wherein the attacker and defender possess distinct target value evaluation criteria. The research findings reveal that both the attacker and defender have their own “advantage ranges” for value assessment, and topological heterogeneity is the reason for this phenomenon. Within their respective advantage ranges, the attacker or defender can adopt clear-cut strategies to secure optimal benefits—without needing to consider their opponents’ decisions. Outside these ranges, we explore how the attacker can leverage small-sample detection outcomes to probabilistically infer defenders’ strategies, and we further analyze the attackers’ preference strategy selections under varying acceptable security thresholds and penalty coefficients. The research results deliver more practical solutions for games involving uncertain value criteria

Microstructure Evolution and Mechanical Properties of X6CrNiMoVNb11-2 Stainless Steel after Heat Treatment

X6CrNiMoVNb11-2 supermartensitic stainless steel, a special type of stainless steel, is commonly used in the production of gas turbine discs in liquid rocket engines and compressor disks in aero engines. By optimizing the parameters of the heat-treatment process, its mechanical properties are specially adjusted to meet the performance requirement in that particular practical application during the advanced composite casting-rolling forming process. The relationship between the microstructure and mechanical properties after quenching from 1040 °C and tempering at 300–670 °C was studied, where the yield strength, tensile strength, elongation and impact toughness under different cooling conditions are obtained by means of mechanical property tests. A certain amount of high-density nanophase precipitation is found in the martensite phase transformation through the heat treatment involved in the quenching and tempering processes, where M23C6 carbides are dispersed in lamellar martensite, with the close-packed Ni3Mo and Ni3Nb phases of high-density co-lattice nanocrystalline precipitation created during the tempering process. The ideal process parameters are to quench at 1040 °C in an oil-cooling medium and to temper at 650 °C by air-cooling; final hardness is averaged about 313 HV, with an elongation of 17.9%, the cross-area reduction ratio is 52%, and the impact toughness is about 65 J, respectively. Moreover, the tempered hardness equation, considering various tempering temperatures, is precisely fitted. This investigation helps us to better understand the strengthening mechanism and performance controlling scheme of martensite stainless steel during the cast-rolling forming process in future applications

Microstructure Evolution and Mechanical Properties of X6CrNiMoVNb11-2 Stainless Steel after Heat Treatment

X6CrNiMoVNb11-2 supermartensitic stainless steel, a special type of stainless steel, is commonly used in the production of gas turbine discs in liquid rocket engines and compressor disks in aero engines. By optimizing the parameters of the heat-treatment process, its mechanical properties are specially adjusted to meet the performance requirement in that particular practical application during the advanced composite casting-rolling forming process. The relationship between the microstructure and mechanical properties after quenching from 1040 °C and tempering at 300–670 °C was studied, where the yield strength, tensile strength, elongation and impact toughness under different cooling conditions are obtained by means of mechanical property tests. A certain amount of high-density nanophase precipitation is found in the martensite phase transformation through the heat treatment involved in the quenching and tempering processes, where M23C6 carbides are dispersed in lamellar martensite, with the close-packed Ni3Mo and Ni3Nb phases of high-density co-lattice nanocrystalline precipitation created during the tempering process. The ideal process parameters are to quench at 1040 °C in an oil-cooling medium and to temper at 650 °C by air-cooling; final hardness is averaged about 313 HV, with an elongation of 17.9%, the cross-area reduction ratio is 52%, and the impact toughness is about 65 J, respectively. Moreover, the tempered hardness equation, considering various tempering temperatures, is precisely fitted. This investigation helps us to better understand the strengthening mechanism and performance controlling scheme of martensite stainless steel during the cast-rolling forming process in future applications.</jats:p

The current situation and the countermeasures of football teaching in university campus under the new environment – Taking Jiangxi science and technology normal university and YuZhang normal college as examples

Football course teaching is the key project of college physical education course, which has the nature of unity and entertainment and attracts the interest of college students. However, there are still many problems in football education in our country, so in fact, the football teaching system in university campus is not perfect, which makes students’ interest in sports reduced. Based on this, this paper first analyzes the current situation of football teaching on campus in the world, and then discusses the effective measures to do well in the new environment. So as to provide a reference for people from all walks of life. </jats:p

DreamDA: Generative Data Augmentation with Diffusion Models

The acquisition of large-scale, high-quality data is a resource-intensive and time-consuming endeavor. Compared to conventional Data Augmentation (DA) techniques (e.g. cropping and rotation), exploiting prevailing diffusion models for data generation has received scant attention in classification tasks. Existing generative DA methods either inadequately bridge the domain gap between real-world and synthesized images, or inherently suffer from a lack of diversity. To solve these issues, this paper proposes a new classification-oriented framework DreamDA, which enables data synthesis and label generation by way of diffusion models. DreamDA generates diverse samples that adhere to the original data distribution by considering training images in the original data as seeds and perturbing their reverse diffusion process. In addition, since the labels of the generated data may not align with the labels of their corresponding seed images, we introduce a self-training paradigm for generating pseudo labels and training classifiers using the synthesized data. Extensive experiments across four tasks and five datasets demonstrate consistent improvements over strong baselines, revealing the efficacy of DreamDA in synthesizing high-quality and diverse images with accurate labels. Our code will be available at https://github.com/yunxiangfu2001/DreamDA.Comment: 14 pages, 8 tables, 3 figure