This study presents a comprehensive framework for evaluating the development of digital trade, encompassing six key dimensions: digital innovation capacity, talent cultivation, trade volume, infrastructure, business environment, and security. Utilizing the entropy method, we quantify the digital trade development of 51 economies worldwide from 2010 to 2020. Through kernel density estimation, we trace the progression of digital trade and identify its determinants. Our research uncovers an overall upward trajectory in digital trade development among the surveyed countries, with a pronounced disparity between developing and developed nations. China emerges as a frontrunner in innovation capacity, trade volume, and infrastructure but lags in nurturing digital talent and refining the business environment and security measures. The study pinpoints economic prosperity, trade composition, and technological advancement as crucial to digital trade progression, with the impact of these factors exhibiting distinct patterns across developed and developing economies.

Utilizing data from the China Household Finance Survey (CHFS), this research constructs an index to measure the level of digital economy engagement among rural households and investigates its correlation with rural household income. The findings reveal that digital economy participation significantly diversifies rural income streams, notably elevating the share of non-agricultural earnings and bolstering overall household revenue. While digital engagement has a negative impact on the proportion of agricultural income, it has a significant positive effect on agricultural earnings in households with substantial agricultural dependence. Income growth through digital participation is facilitated by improvements in human capital, increased financial market involvement, entrepreneurial ventures, and broader employment opportunities for family members. The digital economy's contribution to income growth is particularly pronounced among vulnerable and economically lagging rural households, reflecting its inclusive nature. The findings advocate for strategic initiatives to encourage rural households' involvement in the digital economy, positioning it as a crucial strategy for promoting common prosperity.

Digital technological innovation provides new momentum for ideological and political education. Digital empowerment of ideological and political education is a new requirement for fulfilling the fundamental task of moral cultivation in the new era, as well as a new task to adapt to the digital transformation of education and promote educational modernization. By deeply integrating the traditional advantages of ideological and political education with modern information technology, digital technology empowerment leads to profound changes in the components and internal structure of ideological and political education. This transformation brings updates in educational concepts, expansion of educational space, sharing of educational resources, and improvement in educational quality and effectiveness, thus forming a new model of ideological and political education in the digital age. To promote the digital empowerment of ideological and political education, it is essential to accurately identify changes, scientifically respond to them, and proactively seek changes. This includes enriching digital resources for ideological and political education, promoting the co-construction and sharing of high-quality resources, optimizing the educational environment, constructing a blended ecosystem of virtual and physical education, focusing on the cultivation of digital capabilities, enhancing the professional quality of the ideological and political education workforce, and innovating teaching methods to establish a new model of “teaching, learning, and evaluation unity” empowered by digital technologies.

Utilizing research papers in the domain of educational trust indexed in the China National Knowledge Infrastructure (CNKI) database as our sample, this study analyzes the hot topics and evolutionary trends within the field through the application of the knowledge visualization software, CiteSpace. The identified research hotspots include social capital, college students, social trust, teacher-student relationships, educators, trust crises, and interpersonal trust. Notably, the research perspectives on educational trust have progressively transitioned from micro-level analyses to meso- and macro-level investigations. Moreover, the comprehension of educational trust is increasingly being situated within a broader societal framework. Blockchain technology stands out as a cutting-edge area of research within educational trust. In addressing the prevalent challenges in educational trust research, we propose enhancing research efforts on educational trust within the context of digital education environments, incorporating diversified empirical methodologies, and fostering deeper advancements in the field.

Precise governance is the guiding objective of cyber ideological security governance, and the powerful empowerment of big data provides both technological support and opportunities for the era of precise governance in cyber ideological security. There is an inherent alignment between big data and the precise governance of cyber ideological security, characterized by shared subjectivity, technological connectivity, and content interoperability, which forms the basis for such governance. However, challenges still exist in the process of big data empowerment for precise governance, including the absence of diverse governance subjects, the broadening of digital risks in governance, and insufficient understanding of the governance concept. To address these challenges, it is necessary to focus on three key aspects: innovating new paradigms for multi-party participation in governance, exploring new paths for risk warning and governance, and establishing a comprehensive strategic governance concept. These efforts will work synergistically to promote the development and construction of ideological security in cyberspace.

In the context of globalization and digitalization, contemporary Western left-wing scholars have shifted their critical focus to the digital network space, offering an in-depth analysis of the phenomenon of digital cultural hegemony. They reveal the underlying causes of its formation, provide a thorough explanation of the social harm it causes, and actively seek countermeasures. However, these critical theories are constrained by limited perspectives, incomplete critiques, and unrealistic solutions. The root of these dilemmas lies in the narrative context of Western capitalist societies. To break through this theoretical impasse, it is essential for academia to reconstruct the theoretical framework, value orientation, and practical approaches, thereby promoting the development and innovation of digital cultural hegemony critique theory.

Empirical studies on the influencing factors of false information verification behavior on social media have shown inconsistencies. This study systematically integrates relevant empirical research from both domestic and international sources to identify the key factors influencing users' false information verification behavior, providing insights for false information governance. Using a meta-analysis approach,the study extracts 98 effect sizes, a total sample size of 66 482, and 17 influencing factors from 35 empirical studies.Publication bias tests, heterogeneity tests, overall effect tests, and moderator effect analyses were conducted. The findings indicate that information-related, cognitive, emotional, and individual factors all exert varying degrees of influence on false information verification behavior. Specifically, information relevance, information importance, and information literacy have a significant impact on users' verification behavior. Additionally, social background, gender, discipline type, and education level moderate the relationships between related variables and false information verification behavior.

Constructing appropriate internal governance mechanisms to enhance organizational resilience is an important approach for companies to cope with uncertain shocks. This paper divides internal governance mechanisms into three levels: “shareholders, board of directors and incentives.” Using dynamic qualitative comparative analysis (QCA) and panel data from 64 AI A-share listed companies from 2018 to 2022, this study explores the impact of the internal governance mechanism configurations on organizational resilience under time effects. The findings indicate that the internal governance mechanisms need to be synergistic in order to effectively improve organizational resilience. Four internal governance mechanism configurations for companies with high organizational resilience can be summarized as technological, supervisory, and centralized models, with high compensation incentives being a common factor across all configurations. These configurations show time effects, and internal governance mechanisms need to be dynamically adjusted to enhance organizational resilience. This study enriches the understanding of factors affecting organizational resilience and deepens the research on internal governance mechanisms from the “governance bundle” perspective, providing practical insights for the management of AI companies.

Against the backdrop of new engineering education, this study focuses on the alignment between university big data talent cultivation and enterprise demands. The aim is to reveal the matching degree between academic training models and industry needs, providing theoretical support and practical guidance for optimizing talent development systems. First, a co-occurrence network of academic papers and patent technologies is constructed using social network analysis to explore the relationship between academic research and enterprise technological needs. Second, a topic mining model based on Word2Vec-K-means-LDA is employed to extract core themes across different time periods. Finally, cosine similarity is used to measure the semantic alignment between topics. The results indicate that big data technology follows an evolutionary path from theoretical research to industrial applications, with strong alignment in areas such as knowledge graphs and data mining. However, university curricula still exhibit shortcomings in interdisciplinary integration and practical skill development, making it challenging to fully meet the demand for interdisciplinary talent in enterprises. In response, this study proposes strategies such as constructing interdisciplinary training systems and deepening university-enterprise collaboration to enhance talent development effectiveness and support the high-quality development of the big data industry.

Artificial intelligence (AI) has emerged as a new agent in the realm of organizational knowledge creation. Despite the growing interest in AI-driven knowledge creation research, the field still lacks a coherent and comprehensive framework to guide this field. Additionally, scholars have a limited understanding of the shortcomings of existing research and future research directions. To address this gap, the present paper initiates with a bibliometric analysis that visually maps the research themes within this domain. Building upon this foundation, we propose an integrative framework for AI-driven knowledge creation, grounded in the “input-process-output” paradigm. Concludingly, we offer forward-looking insights into potential research directions, focusing on the intricacies of the process, antecedents, and consequences of AI-driven knowledge creation. This paper serves as a foundational resource and provides valuable guidance for future scholars engaged in AI-driven knowledge creation research.

This study addresses the uncertainties in knowledge integration and the complexities of collaboration in the context of new energy vehicle (NEV) enterprises. From a risk prevention perspective, we develop a tripartite game model involving the government, vehicle manufacturers, and battery suppliers. The main findings are as follows: First, NEV manufacturers are more concerned with the uncertainty of knowledge integration, while battery suppliers focus on the complexity of collaboration. This highlights both the shared and distinct risks associated with collaborative innovation across different sectors. Second, in the NEV industry, as the capacity for knowledge absorption and the existing knowledge stock increase, the risks associated with knowledge integration uncertainty can be significantly reduced. The NEV credit policy encourages enterprises to enhance collaboration and invest more in R&D, which further mitigates these risks. Third, technological intensity and the diversification of the industrial chain increase the difficulty of collaboration. A well-structured first-stage payment ratio and effective government oversight can help address the risks associated with collaboration complexity. In particular, battery suppliers are more reliant on government regulation to promote knowledge sharing and safeguard intellectual property.

Fred Magdoff, adopting an ecological agricultural lens, critically examines the non-ecological practices of capitalist agriculture and the multitude of issues it engenders, including metabolic disruptions between society and nature, the perils of chemical agriculture, and escalating food safety and grain crises. His inquiry transcends the confines of ecological agriculture, extending into the realms of human-society and human-to-human relationships, ultimately leading him to explore deeper into the study of ecological civilization. This process has shaped a distinctive formative logic that underpins his vision of ecological civilization. Magdoff’s ecological civilization perspective, along with its formative logic, offers a unique vantage point for launching ecological critiques of capitalism and for elucidating an ecological civilization that could supersede capitalist civilization. Furthermore, it provides valuable insights for advancing the historical trajectory of constructing an ecological civilization with Chinese characteristics.

This study constructs a new economic geography model to explore the internal mechanisms by which Foreign Direct Investment (FDI) influences technological innovation, environmental governance, and high-quality economic development. A spatial econometric model was then applied using a sample of 279 cities in China from 2011 to 2020. The findings indicate that FDI promotes high-quality economic development in cities during the study period. Technological innovation and environmental governance serve as effective mediating variables through which FDI drives urban economic development. The mediating effect of technological innovation accounts for about 9%, while the mediating effect of environmental governance accounts for around 16%. The impact of FDI on high-quality urban economic development is heterogeneous: in cities with low FDI proportions and non-resource-based cities, FDI significantly enhances the level of high-quality economic development, whereas in cities with high FDI proportions and resource-based cities, the impact is not significant.

Smart manufacturing not only fosters industrial upgrading at the macro level but also supports the sustainable development of enterprises at the micro level. This study empirically investigates the relationship between smart manufacturing and corporate “greenwashing” using data from A-share listed manufacturing firms in China from 2013 to 2022. The findings indicate that smart manufacturing plays a significant role in reducing corporate “greenwashing,” with this result remaining robust across various tests. Additional analysis through a quasi-natural experiment, adopting the smart manufacturing pilot policy (difference-in-differences, DID), further corroborates the robustness of these findings. Mechanism tests reveal that smart manufacturing mitigates “greenwashing” by reducing information asymmetry, easing financing constraints, and fostering green innovation. Heterogeneity analysis shows that the impact of smart manufacturing in curbing “greenwashing” is particularly pronounced in firms that voluntarily disclose environmental information, in heavily polluting industries, in companies located in inland cities, and in those with executives having international experience.

Autonomous driving is a complex and disruptive technology, and public acceptance plays a significant role in its application and development. This study explores the acceptance of autonomous vehicles among young people through a mixed analysis of big data and statistical models. The research finds that young people's attitudes toward autonomous vehicles are mixed, with slightly more negative than positive attitudes. On one hand, young people recognize the safety, efficiency, and comfort brought by the technology; on the other hand, they are concerned about risks such as data leakage and system security. Perceived risk, perceived ease of use, perceived usefulness, and trust all significantly affect young people's acceptance of autonomous vehicles, with trust serving as a mediating factor. Compared to older generations, young people have a more positive attitude toward autonomous vehicles and are more willing to embrace the technology, though they are also more concerned about risks. The attitudes of young people toward autonomous vehicle technology are neither blindly optimistic nor fearfully pessimistic, but rather objectively skeptical. In the future development of autonomous vehicles, it is necessary to reduce risks at the software, hardware, environmental, and data levels to improve public trust, while addressing the issue of responsibility allocation through “meaningful human control.”

Utilizing a dataset encompassing State-Owned Enterprises(SOEs) listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2021, this study explores the impact of the Party Committee's participation in corporate governance on firm value and analyzes the influence and mechanism of the executive team's information fragmentation. The study finds that the degree of the Party Committee's involvement in corporate governance has a positive effect on firm value. Furthermore, when the Party Committee participates in the company's governance by entering the board of directors, the supervisory board, and the executive layer, it significantly affects firm value. The executive team's information fragmentation exhibits an inverse U-shaped moderating effect between the degree of involvement and firm value. When the intensity of the executive team's information fragmentation is low or high, it suppresses the positive impact of the Party Committee's participation in corporate governance on firm value. Only when the intensity is moderate can the Party Committee's involvement significantly enhance firm value.

When OTFS modulation is used for actual data transmission, the Doppler resolution is usually low, leading to fractional Doppler frequency shifts and causing Doppler inter-symbol interference in fading channels, which reduces the accuracy of channel estimation. To address this issue, a deep learning-based fractional Doppler channel estimation method for OTFS systems is proposed. Firstly, the cross-correlation algorithm is used for the initial estimation of fractional Doppler channels in the method; and then, a deep convolutional neural network is built and trained to optimize the preliminary channel estimation results, thus effectively improving the accuracy of OTFS fractional Doppler channel estimation. Simulation experiments show that by combining the advantages of traditional algorithms and deep learning, the proposed method achieves an approximate 6 dB performance gain, effectively enhancing the accuracy of OTFS fractional Doppler channel estimation. Moreover, the method is capable of effectively addressing channel mismatch, with performance differences in various high-mobility scenarios being less than 30%, demonstrating a certain level of robustness.

The quintessential hallmark of the modern era of big data transcends the notions of “magnitude” and “quantities”, embracing the concept of “factual numbers”. This epoch is, in essence, an era where discourse is shaped by “factual numbers”, which are distinct from both data and numerals. “Factual numbers” are construed as the empirical substantiation of reality, whereas data are the metrics by which the world is quantified, and numerals represent the foundational manifestation of existence. The historical progression from ancient numerals to contemporary data and, ultimately, to the current vogue of “factual numbers”, metaphorically mirrors the humankind’s journey through a metaphorical “three caverns” paradigm: Numbers are the product of those dwelling in the “first cavern”, striving to comprehend the natural world; data are the creation of those in the “second cavern”, attempting to quantify the man-made world; “Factual numbers” are the artifacts of those in the “third cave”, endeavoring to construct a virtual reality. Dataism, as the ideology of this “third cavern”, is ultimately revealed as a mere ideological adjunct to the age of big data.

Driven by new-quality productive forces, the value connotation of the “Two Mountains” concept is continuously expanding. Centered on technological innovation and characterized by data, intelligence, and green development, new-quality productive forces promote the synergy of ecological industrialization, digital transformation, and sustainable green growth. This transformation not only broadens the value subjects of the “Two Mountains” concept by integrating technology enterprises, social organizations, and the public into the ecological economy but also enriches its value objects by facilitating the capitalization of ecological resources, the explicit recognition of ecosystem services, and the assetization of data. Meanwhile, new-quality productive forces optimize value measurement by making ecological benefits a key factor in economic decision-making, thereby fostering high-quality economic growth, sustainable social progress, and advancing ecological civilization to a higher level. Based on this value expansion, this study proposes practical pathways, including leveraging technological innovation to integrate ecological and economic development, promoting industrial upgrading to enhance the value of ecological resources, and cultivating ecological consciousness through social and cultural development. By deepening the value logic and practical model of the “Two Mountains” concept from the perspectives of human-nature, human-society, and self-environment relationships, this research contributes to the modernization of ecological civilization and provides an innovative perspective on Xi Jinping's ecological civilization thought.

Digital technologies have transformed the elements, processes, and forms of participation in traditional social innovation, making digital social innovation a popular topic in academic research. However, its disciplinary nature and conceptual definition remain unclear, resulting in the lack of a unified paradigm for digital social innovation research both domestically and internationally. By reviewing existing literature, this paper provides a comprehensive overview of the connotations, structural dimensions, and practical mechanisms of digital social innovation. Based on this, an analytical framework for digital social innovation is constructed, aiming to clarify and structure the research in this field and explore a new research paradigm for digital social innovation in the Chinese context.

At present, computer vision based methods for measuring the body size of livestock and poultry mainly use image processing technique to analyze and process a single complete side image of livestock and poultry, calculate the required measurement point coordinates, and calculate the required body size data based on calibration parameters. However, such methods have strict requirements for the quality of side body images of livestock and poultry. When local side parts of livestock and poultry are missing in the images, it will make it difficult to apply such body size measurement methods. This paper proposes a visual triangle height measurement algorithm and a similar triangle length measurement algorithm based on dual camera information complementarity. Firstly, the proposed algorithm compensates for the lack of depth of field information in a single two-dimensional image by using the dual view image information of the overhead camera and the side camera to complement each other. At the same time, the YOLOV5 algorithm and convex hull corner detection algorithm are used for shoulder and tail measurement points extraction with the top view of the cow's back. The proposed algorithm obtains the relative horizontal distance between the shoulder blade of the cow and the side camera through the upper camera, and calculates the cow's height based on the principle of similar triangles, combined with the actual height of the lower coordinate of the auxiliary scale in the side camera. The proposed algorithm for measuring the straight length of similar triangles uses cow height data to calculate the depth of field corresponding to the cow's back in the directly above camera image. Then, it combines the depth of field information, the Euclidean distance between the shoulder and tail measurement points, and the spatial resolution of the above camera to the ground to calculate the straight length of the cow. It solves the measurement problem problem of body size in the case that the complete side image of cattle can not be obtained. The body size of 30 cattle is measured. Under normal circumstances, the average error of cattle height measurement is 2.25%, and the average error of body length measurement is 3.33%; the average error in height measurement of cows under relatively crowded conditions is 3.03%, and the average error in body length measurement is 3.68%.

Network pruning is one of the main methods for compressing deep neural network models. To address the issue of low efficiency in existing genetic algorithm-based network pruning methods, we propose a structured pruning method based on an improved adaptive genetic algorithm. Firstly, we design a new fitness function that balances the impact of model loss and parameter quantity of the final result. Secondly, adaptive crossover and mutation probabilities are used instead of fixed hyperparameters to improve pruning efficiency and model accuracy. Finally, the feasibility of the method is verified through experiments, obtaining network models with higher accuracy and fewer parameters.

Cattle target detection is a prerequisite for individual registration and recognition of cattle based on deep learning. The differences in lighting, color and breed in different actual scenarios make low-level features of cattle images diverse, while semantic information in high-level features cannot fully match the diverse low-level features, resulting in poor detection accuracy. In order to solve the problem of insufficient high-level feature semantics of the detection model, this paper designs a new cattle feature extraction backbone network ResMO Backbone and feature fusion network Dense Neck, and proposes a cattle detection algorithm based on ResMO Sense YOLO. In the backbone network, the ResMO module (ResBlock MHSA ODConv) is used to focus on the characteristics of cattle high-level features at multi-semantic level to enrich semantic information, and the SPPF structure and multi-layer convolution structure are combined to expand the receptive field, so that the model can better extract cattle high-level features; then, a feature pyramid based on DenseBlock and a feature fusion network cascaded with a path aggregation network based on DenseBlock are proposed, which utilize the feature reuse feature of DenseBlock and combine the multi-scale fusion feature of the feature pyramid and path aggregation network to further integrate the low-level feature position information and high-level feature semantic information of cattle, improving model detection accuracy. Compared with the FLYOLOv3, SSD and YOLOv5s, the model in this paper shows an average accuracy improvement of 40.1%, 30.3%, and 4.0% in the data sets of cow channels, cow sheds, and beef sheds collected in the laboratory. The recall rate increased by 34.9%, 23.1%, and 6.8%, respectively, and the mAP increased by 49.2%, 35.3%, and 5.0%.

The mirror of speed culture, characterized by technological acceleration, societal transformation, and a heightened pace of life, has emerged as a vibrant cultural phenomenon. This multifaceted acceleration, coupled with intense competition and involution, has fundamentally altered the forms of self-relationship dominated by social time. Consequently, we observe the fragmentation of happiness scales amidst the disintegration of time and space, the reconfiguration of personal social relationships in an increasingly accelerated society, and the dissemination of multi-layered self-forms within the competitive logic of speed. These phenomena contribute to a dearth of temporal-spatial experience, desynchronization between societal and individual velocities, the atrophy and illusion of subjective self-awareness, and the conflict between self-awareness of time and the coercion of social life rhythms, collectively posing novel dilemmas for youth happiness. To surmount these dilemmas reflected in the mirror of speed culture, it is imperative to foster a culture of happiness by reshaping time experience as a pivotal element, harness the potential of moral happiness as a catalyst for transformation, and broaden the horizons of happiness by embracing value rationality as the guiding axis.

In order to solve the problem of low matching accuracy and redundant feature points of SURF algorithm under complex environment changes, the SURF algorithm is optimized by combining the quadtree method for eliminating redundant feature points and the improved RANSAC algorithm for quickly obtaining the homography matrix. Firstly, the feature points generated by SURF algorithm are segmented by the quadtree method, redundant feature points are eliminated according to the maximum threshold of division depth and number of feature points, and rough matching is performed. Then, the feature points are sorted according to the distance between feature vectors, and RANSAC iteration is performed within the limited range, so as to obtain the optimized image matching result.Experimental results show that the average precision rate of the improved SURF algorithm is 96.47% and the average consumption time is 649.33 ms under a certain degree of rotation, zoom, blur, and illumination intensity variation. The improved SURF algorithm has good robustness, accuracy, and timeliness under complex environment changes.

Chinese-style modernization distinctly emphasizes a humanistic orientation, with the modernization of individuals serving as its core and the modernization of ideological perspectives as its essence. Ideological and political education, as a purposeful and deliberate social practice aimed at transforming individuals' thoughts and behaviors, holds a vital role in advancing the modernization of individuals. In the face of new circumstances and challenges emerging in the process of individual modernization, the development of ideological and political education is confronted with fresh demands, such as the establishment of a novel educational paradigm, the reinforcement of value orientation, and the augmentation of spiritual motivation. To address these demands, a systematic approach should be adopted to comprehensively propel the advancement of the ideological and political education system. With content development as the cornerstone, the quality of ideological and political education should be enhanced. Moreover, the dual paths of tradition preservation and innovation should be upheld to propel the development of ideological and political education methods. By fostering a more robust service capability, the modernization of individuals can be facilitated, thereby driving forward Chinese-style modernization.

For the purpose of solving the problem that particle swarm optimization has precocious convergence and will fall into local optimal solution with little hindrance, a particle swarm optimization algorithm with nonlinear inertial weight and t-distribution mutation is proposed. By improving the inertial weight and t-distribution mutation operators, the algorithm enhances the global search capability of the particles. Simulation results reveal that the improved algorithm has strong global optimization ability and effectively overcomes the premature convergence phenomenon.

The detection of cattle targets in complex environments is a key issue in precise counting of cattle numbers based on machine vision. Due to factors such as occlusion caused by overcrowding of cattle and incomplete cattle individuals caused by their position at the edge of the camera, existing cattle target detection methods are not suitable for complex environments in breeding farms. This paper proposes a cattle detection algorithm in complex environments based on Swin-Transformer(SWT)-YOLOV5s network: first, we propose a backbone network SWT Backbone for cattle feature extraction, which is cascaded by double-layer shortcut(SC)-SWT and multi-layer convolution. Using the characteristics of SC-SWT module that focuses on global features, combined with the residual multi-layer convolution module that focuses on local features, we increase the depth and receptive field of the network, so that the model can fully extract both global and local features of cattle. Then, the feature fusion target detection head SWT Head, which is cascaded by not shortcut(NSC)-SWT and pyramid network, is proposed. Through the pyramid network, the feature pyramid of multi-scale fusion of global features and local features extracted from the SWT Backbone backbone network is constructed. Combined with the global receptive field of the NSC-SWT module Transformer and the CNN local receptive field of the C3 module, it enables the model to more accurately detect and filter the global features of cattle at high semantic levels and local features that represent the details of cattle in the feature pyramid, while efficiently removes feature interference from the background environment, and improves the detection accuracy of cattle targets in complex environments. Simulation experiments were conducted on the COWYCTC-1480 dataset collected in the laboratory. Compared with the widely used YOLOV5s and SSD algorithms, the accuracy, recall, and mAP of our method on the test set were 7.0%, 2.0%, and 11.1% points higher than the YOLOV5s algorithm,20.0%,8.0%,32.0%, and 29.3 points higher than the SSD algorithm, respectively.

In this paper, a P-band burst signal modulation mode identification algorithm based on signal preamble image and multi-semantic feature fusion network is proposed, which makes full use of the preamble features of P-band burst signals and solves the problem that the signal information segment features are difficult to distinguish in low signal-to-noise ratio environment, and at the same time, it avoids the dependence of traditional signal preamble autocorrelation recognition algorithm on signal preamble a priori information and the influence of frequency bias. Firstly, the proposed algorithm uses the signal preamble frequency spectrum to complete the image construction, and uses the spectral features formed by different burst signal leading regular code words to distinguish; secondly, the proposed multi-semantic feature fusion network makes full use of the texture features such as edge contours of the signal preamble spectrum extracted by the low-level residual network, and also incorporates the abstract and complex semantic features of the signal spectrum extracted by the high-level residual network. The proposed multi-semantic feature fusion network can solve the problem that the residual network only uses the high-level abstract semantic features but ignores the low-level features, and improve the recognition performance of burst signal modulation mode. Experimental results show that, compared with the signal preamble autocorrelation algorithm, the recognition algorithms based on the information segment frequency spectrum image and the ResNet50 network, and the recognition algorithms based on the information segment time-frequency map and the ResNet50 network, the proposed algorithm improves recognition performance by 20.84%, 30.83%, and 60.39% respectively in a -15 dB signal-to-noise ratio environment.

Technology licensing is a crucial means for firms to accelerate the diffusion of new technologies and realize their commercial value. To explore how firms implement technology licensing strategies in a competitive environment, this study constructs a duopoly competition model with two manufacturers, where one firm owns the innovative technology. By comparing the profits and outputs of both firms under three licensing modes—no licensing, fixed-fee licensing, and per-unit licensing—this study examines the technology owner's strategic choice of licensing and the diffusion of technology in a competitive market. The results indicate that when the attractiveness of the new technology market is low, technology licensing does not necessarily generate higher profits for the technology owner. While per-unit licensing can mitigate competition between firms, it is not always the optimal choice for the technology-owning firm. Additionally, the study finds that the greater the market attractiveness of the new technology, the more likely the technology owner is to adopt a fixed-fee licensing model.

This work presents 2-D numerical simulation results of Single-Event Burnout (SEB) for hardened 1.7 kV 4H-silicon carbide (SiC) power VDMOSFET. Research results prove that, compared with the conventional VDMOSFET with five-buffer layers (FB-VDMOSFET), the modified VDMOSFET has achieved better SEB performance under high Liner Energy Transfer (LET) value range. After reinforcement, the on-resistance of the VDMOSFET is lower under the action of the current spreading layer compared to the traditional structure. In addition, the integrated PN junction in the JFET region reduces the peak electric field under breakdown voltage from 3.66 MV/cm to 2.94 MV/cm. When the drain-source voltage (V_DS) is 1400V and the Linear Energy Transfer (LET) value is 0.5 pC/μm, the FB-VDMOSFET has burned out, while the improved VDMOSFET, due to the stepped source metal electrode contact and integrated PN junction, reduces collision ionization, lowering the maximum lattice temperature to 2 070 K. This reinforced structure can be used in the aerospace industry.

The rapid advancement of digital technology has introduced new challenges to anti-fraud education in universities. As a new paradigm of public administration and governance, collaborative governance is increasingly being applied as a reference framework in the modernization of university education. However, current anti-fraud education in universities faces several issues, including the absence of governance entities, insufficient objective linkage mechanisms and subjective domain awareness, and limited effectiveness of educational campaigns. University-based anti-fraud education operates within a stable and open system where governance resources, anti-fraud information, and co-governance dynamics circulate among multiple actors. The willingness of these actors to engage in collaborative governance is shaped by a four-dimensional structure of subjective driving forces, ultimately aiming to enhance students' “three senses” (security, participation, and gain), build safe campuses, and contribute to broader societal governance. By incorporating collaborative governance theory into anti-fraud education, universities can establish a multi-actor interactive governance network, explore effective points of integration for coordinated anti-fraud initiatives, and achieve a dynamic integration of traditional resources, outcome feedback, and accumulated experiences. This approach fosters a unified awareness of the anti-fraud education community in universities. Ultimately, a comprehensive and multi-layered governance network for anti-fraud education should be constructed, founded on “actor collaboration,” driven by “mechanism collaboration,” secured through “resource collaboration,” and oriented toward “awareness collaboration.”

In the ongoing process of agricultural industrialization, distinct cultivation models for new-type professional farmers have emerged, categorized primarily as “leading enterprise-driven,” “farmer cooperative-centered,” and “powerful village company-led,” based on the different entities involved in the cultivation process. Specifically, the leading enterprise-driven model focuses on leading enterprises, adopting organizational forms such as “leading enterprise+farmer households” and “leading enterprise+base+farmer households.” The farmer cooperative-centered model revolves around farmer cooperatives, employing the organizational form of “cooperative+farmer households.” Meanwhile, the powerful village company-led model emphasizes powerful village companies, adopting the organizational form of “powerful village company+farmer households.” In anticipation of the future trends in agricultural industrialization, there is an urgent need to optimize organizational structures to achieve integrated cultivation of new-type professional farmers, strengthen contract governance to establish incentive and restraint mechanisms, implement relational governance to foster trust and reputation among farmers, and refine policy and institutional frameworks to construct robust policy support systems for cultivating new-type professional farmers.

This paper introduces the problem that a trained fault diagnosis model for a power grid cannot be used when the grid topology is changed. In order to address this issue, a method based on convolutional neural network (CNN) and transfer learning is proposed. The model uses the voltage and current phase data collected by the synchronous phasor measurement unit (PMU) for the training of CNN. After the training process, the CNN model can accurately locate faults in the grid and determine fault types when the PMU equipment is limited. When the topology of the grid is changed, a new fault diagnosis model can be quickly trained by model-based transfer learning and a small number of fault samples under the new grid will be required. Moreover, the performance of the model is as good as the model trained by enough samples. The proposed method reduces the need of new samples in the training process of an untrained CNN model after the topology of the grid is changed.

To evaluate the effectiveness of science and technology talent policies, this paper selects 327 policy documents related to science and technology talent from the Yangtze River Delta and the northeast China regions over the past 12 years. Using Nvivo software, the study employs the “Projection-Post” comparative analysis method and entropy weight method to compare the policies in terms of policy tools, policy goals, and policy implementation, analyzing the mechanisms behind these policies. The findings show that the policy tool structure in northeast China is more imbalanced, while the Yangtze River Delta demonstrates a more complementary structure of policy tools. The implementation of science and technology talent policies in the Yangtze River Delta is more effective, whereas northeast China's implementation shows less sustainability and stability. Both regions perform best in demand-driven tools, though the internal contribution rates of policy tools differ. Over time, both regions have shifted from a substitutionary to a complementary use of supply-side and demand-side policy tools, reflecting an emphasis on the organic integration of policy tools. The policy goals of each region and phase are distinct, avoiding a “one-size-fits-all” approach. Both regions emphasize the cohesion between policy goals, forming a unified regional strategy, though mismatches between tool-goal combinations exist.

This paper compares the family policies of East and West Germany, analyzing their effects on female employment rates and birth rates under different models. The study finds that West Germany's family policy tends toward the “re-familization” of childrearing, which has forced some professional women to delay or forgo childbirth. As a result, while female employment rates have increased, they have fallen into the trap of low fertility rates. In contrast, East Germany's family policy emphasized the “de-familization” of childrearing, which led to a simultaneous increase in both female employment rates and birth rates. Drawing on the experiences and lessons from both East and West Germany, China could build a “work-family friendly” family policy system to promote the long-term balanced development of its population structure.

Southern Fujian was a major hub for the study of the Four Books, and the rise and development of Four Books scholarship in the region are closely tied to the imperial examination system. Existing research has overlooked the late Ming period, a time when the imperial examination flourished and a large number of Confucian classics were produced. A linear historical narrative fails to capture the richness of the historical context, leading to an incomplete understanding of the evolution of Four Books scholarship in Ming Southern Fujian. In the Ming dynasty, while Southern Fujian scholars predominantly upheld Zhu Xi's teachings, they also engaged in confrontations with Wang Yangming's school, with some even directly shifting to Wang's philosophy. However, regardless of their academic stance, these scholars' primary aim remained serving the imperial examination system. Wu Hanqi's Detailed Explanation of the Four Books lacks a clear academic position; instead, it is a sermon-like work that synthesizes various schools of thought through his understanding of contemporary literature. Detailed Explanation of the Four Books reflects the diverse perspectives of late Ming Four Books scholarship and provides insight into the dynamic intellectual climate of the time. This offers direct evidence for constructing a more nuanced and multi-dimensional narrative of Four Books scholarship in late Ming Southern Fujian.