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.

In order to outline the dynamic evolution and the development trends of digital native studies, the article uses CiteSpace as a research tool to analyze the previous relevant studies at home and abroad. It is found that the research institutions that have made great contributions to the field of digital natives at home and abroad come from universities in Australia and the United Kingdom where information technology and teaching activities are highly integrated. In the early stage, the foreign research lays the theoretical foundation, while the domestic research attaches importance to the empirical examination; in the later stage, foreign research conducted the model of digital education reform, and domestic research analyzed the causes and solutions of digital divide. In a whole, domestic research is the refinement and perfection of foreign research. In the future, the focus of domestic research will be on concretizing education reform models and comparing the feasibility and acceptability of various teaching strategies with empirical research.

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.

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.

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.

The school class is an important place for adolescent social interaction, and it is of great value to study the influence of the class peer group on adolescent mental health. Based onthe data from the China Education Panel Survey (CEPS), this study explores the impact of the class peer group on adolescent mental health and its heterogeneity. It is found that under the influence of social conformity function, the average level of mental health of the class peer group has a health-promoting effect on all the adolescents. Under the influence of social comparison function, the greater the degree of heterogeneity of the mental health of the class peer group, the more obvious the adverse effect on all the adolescents. The results of heterogeneity analysis showthat social conformity is more beneficial to individuals with medium and high levels of mental health. And social comparison is more unfavorable for individuals with medium and low levels of mental health. This research provides a possible way for us to further strengthen the prevention and intervention of adolescent mental health problems.

This research examines the influence of blockchain technology on corporate production efficiency, utilizing a sample of non-ST manufacturing companies listed on the A-share market from 2019 to 2021. The study employs text analysis and the machine learning word2vec method to quantify the extent of blockchain application in enterprises, as reported in their annual reports. The empirical analysis investigates the relationship between blockchain technology adoption and the total factor productivity (TFP) of these manufacturing firms. The findings indicate a positive correlation between the degree of blockchain technology implementation and the TFP of the companies. Mechanistic insights reveal that trust construction and the dual objectives of cost reduction and efficiency enhancement are pivotal pathways through which blockchain application contributes to the improvement of TFP. Additional analysis suggests that the sub-industry classification of businesses and the agency costs associated with corporate governance can influence the extent to which blockchain application boosts productivity. This study contributes to the understanding of blockchain technology's impact and its operational mechanisms within microenterprises, offering valuable insights for the broader adoption of blockchain in enterprises and the advancement of high-quality economic growth.

National auditing plays a crucial role in the evolution towards a modernized national governance framework. Within the context of the digital economy, accurately grasping the confluence of “data factorization” and “modernization of governance” and exploring the impact of data factorization on national audit quality is of significant importance for enhancing the supervisory effectiveness of national audits. This study utilizesprovincial panel data from Chinaover the period from 2009 to 2020 to empirically investigate the impact of data factorization on the quality of national audits and the underlying mechanisms at work. Our findings underscore the positive influence of data factorization on enhancing national audit quality, a result that is further substan-tiated through rigorous robustness testing. The study reveals that the positive effect of data factorization is mediated through its ability to elevate the level of informatization and alleviate the pressures of economic growth. Notably, the study identifies that the beneficial impact of data factorization is particularly pronounced in the eastern regions, within jurisdictions enjoying a robust institutional environment, and in contexts characterized by higher degrees of fiscal decentralization.

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.

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.

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.

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.

Aiming at the characteristic of a certain dependency relationship existing among multiple tasks in the edge computing system, a resource allocation strategy for minimizing the total computing time is investigated in this paper. Sequential dependency relationship among multiple tasks is taken into account. Multiple tasks of the user are offloaded in sequence. When the current task completes offloading, the next task can be offloaded without waiting for the current task to finish computing. By using a two-tier offloading strategy, user can first offload task to small base station (SBS), and when the edge server in SBS has insufficient computing capacity, SBS will offload the part of task to the edge server in macro base station. The joint optimization of user association, resource allocation of computation resources and the transmitting power of user are formulated to minimize the total computation time of the multi-task edge computing(MEC) system. A suboptimal solution is obtained by adopting a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the QPSO algorithm has less total computation time compared with the standard particle swarm optimization algorithm and the other benchmark strategies.

Utilizing provincial panel data from China over the period from 2013 to 2020, this study measures the level of coordinated development between the digital economy and “environment-economy” by employing the entropy weight method and the coupling coordination model. It further applies panel fixed-effects models, mediating effect models, and threshold effect models to empirically test the effects and mechanisms through which the digital economy influences the coordinated development of “environment-economy”. The findings indicate: Firstly, the digital economy significantly promotes the coordinated development of “environment-economy” in urban and rural areas. Specifically, urban areas exhibit a “reverse U-shaped” pattern with pronounced impacts in central, western, and northeastern regions, contrasting with the eastern region. Conversely, rural areas display a “U-shaped” pattern, with notable effects in the east and northeast, but not in the central and western regions. Secondly, in urban contexts, the advancement of industrial structures and technological innovation emerge as crucial factors in mediating the digital economy's role in “environment-economy” coordination, with industrial upgrading presenting threshold effects. In rural scenarios, industrial upgrading, technological innovation, and regulatory oversight collectively underpin the digital economy's impact, devoid of threshold effects. The study emphasizes the necessity for tailored digital economy strategies that capitalize on local competitive edges, foster ongoing industrial evolution and technological progress, and reinforce regulatory frameworks. It advocates for the holistic development of “digital villages” along with “smart cities,” exploiting the digital economy's potential to steer coordinated “environment-economy” development via urban-rural integration.

Digital social innovation(DSI) brings new opportunities for integrating the dual advantages of technological and social innovation. This study commences with a precise definition of DSI, subsequently utilizing bibliometric techniques to meticulously review the pertinent literature. Organized around nine key clusters, the research constructs a two-dimensional knowledge framework centered on social orientation and the innovation process. Firstly, DSI evolves from an initial phase characterized by digital empowerment across various sectors such as business, politics, society, and daily life, to its current paradigm, underpinned by a robust digital innovation ecosystem and oriented towards digital social entrepreneurship. This evolution is marked by an expansive social orientation, which is directed towards the realization of a complex array of values. Secondly, the innovation process is depicted as a cyclical, interactive development centered on digital technology, where various actors engage actively or passively with the social environment. The proposed knowledge framework offers a novel theoretical lens for deepening our understanding of social innovation phenomena propelled or enabled by digital technology, thereby fostering the growth of domestic research in digital social innovation theory and bolstering the implementation of digital transformation strategies.

A modeling method based on BSIM-BULK for body contact SOI devices is proposed. Based on the original BSIM-BULK, a new bulk port required by SOI device is added to the model to characterize the structure of bulk and substrate, and the RF parasitic effect of SOI is characterized by the peripheral parasitic circuit model. A definite parameter extraction method is proposed. The SOI model was obtained by fitting the test data curve by adjusting the parameters of the peripheral parasitic element and the internal model. The model can accurately characterize the characteristics of the operating area of SOI devices, so that BSIM-BULK can be applied to SOI devices. After extracting the parameters of RF model, the simulation data and test data are in good agreement.

The pathological analysis of breast tissue biopsy has important clinical application value. Aiming at the problems of time-consuming, labor-intensive, and incomplete extracted features in the manual extraction feature classification algorithm, this study combines with deep learning and proposes a model fusion method based on multi-stage migration and attention mechanism for benign and malignant breast pathological images classification. In order to speed up the training convergence speed and use the image features of different pathological image datasets, this paper adopts multi-stage transfer learning, and at the same time adds an attention mechanism to the network, and suppresses unnecessary features by learning important features in image channels and space classification accuracy. Finally, in order to utilize the features of images of different multiples of the dataset at the same time, a model fusion network is established for classification. The network achieves an AUC of 0.946 for classifying benign and malignant images. The experimental results show that the model fusion method based on multi-stage transfer and attention mechanism has achieved high accuracy in the classification of breast pathological images, which has positive guiding significance for breast cancer diagnosis.

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.

Cardiovascular disease is one of the most deadly diseases in the world, and its early screening and accurate diagnosis have important social value for reducing morbidity and mortality. As an important physiological signal of human body, phonocardiogram (PCG) contains rich cardiac pathological information, can objectively reflect the health status of heart and cardiovascular system, and is an important data source for diagnosis of cardiovascular diseases. Heart sound segmentation is the basis of heart sound signal pathological analysis, and plays a decisive role in the extraction of pathological features and disease diagnosis. However, prevailing heart sound segmentation algorithms often need to cooperate with the synchronous input ECG signal to achieve accurate segmentation effect, and the segmentation effect of pure heart sound signal is not good. Therefore, a heart sound segmentation algorithm based on Hidden Markov Cardiac Cycle (HMCC) is proposed to achieve accurate segmentation of pure PCG signals. Firstly, baseline calibration and wavelet denoising are used to realize signal preprocessing. Secondly, the heart sound envelope extraction based on Hilbert transform is proposed, and the corresponding relationship between peak value and S1 and S2 is located by combining with the law of cardiac cycle. An improved hidden Markov algorithm is further proposed to update the initial state distribution of heart sounds and optimize the Viterbi algorithm to calculate the duration of heart sound interval. Through the same benchmark of 244 heart sound data and comparative experiments at home and abroad, the segmentation positioning with accuracy score of 97.23% and segmentation accuracy rate of 97.32% has been achieved, providing a high-quality segmentation data source for PCG-based intelligent auxiliary diagnosis and analysis of cardiovascular diseases.

In order to maximize the efficiency of lithium-ion battery, it is necessary to design a reasonable battery thermal management scheme for the current temperature rise and temperature uniformity of electric vehicle batteries. Firstly, compared with air cooling, liquid cooling has gradually become the mainstream cooling method due to its irreplaceable advantages in the practical application of lithium ion battery thermal management scheme. Secondly, from the four angles of structure design, cooling medium, direct cooling and liquid cooling-PCM composite cooling, the research status of liquid cooling mode is summarized, and the respective cooling characteristics and comparisons of various parameters are summarized, which provides a reference for the model design and structural parameters of liquid cooling in the future. Finally, the measures taken in the follow-up study are proposed for the lack of liquid cooling.

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.

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%.

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.”

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.

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.

An interpolatory subdivision method based on type 2 Coons surface is proposed, which is defined on quadrilateral meshes of any topology. Step 1, set the tangent vector of the initial grid point on each edge. Step 2, in each layer of subdivision, the curve boundary is constructed from two points and tangent vectors on the mesh edge by combining with the De Casteljau’s algorithm. The center point of curve parameter is set as the new edge point, and the new tangent vector of two endpoints and new edge point is calculated. And then, set the boundary derivative on the boundary of four curves, and give the endpoint torsion vector to construct type 2 Coons surface. The parameter center point of the sampling surface is taken as the new surface point, and the new tangent vector on the connection edge of the surface point and the edge point is calculated. Step 3, set the shape parameters to adjust the tangent vector of the regular points, and finally, the limit surface is iteratively obtained. Numericals examples shows that, compared with Kobbelt subdivision method and 4-point surface subdivision method, the proposed method which can obtain interpolatory surfaces with higher smoothness is controllable easily. Convergence and continuity analysis prove that the interpolation surface is at least G¹ continuous.

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.

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.

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.

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.

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%.

Cyclic codes are an important subclass of linear codes. They are widely used in data storage systems and communication systems, because they have clear algebraic structure, simple encoding and decoding algorithms, and easy implementation. In this paper, a class of ternary cyclic codes is studied. By analyzing the solutions of some equations, the parameters of such ternary cyclic codes are determined. The optimality of the codes is proved by the Sphere Packing bound.