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.”

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.

In the context of China's new era, college students' values exhibit a range of complex and sometimes contradictory features: political beliefs marked by both alignment and tension; moral perspectives shaped by a mix of dedication and confusion; life attitudes characterized by both striving and passivity; learning motivations balancing idealism with utilitarianism; and interpersonal interactions that blend real and virtual dimensions. These characteristics are closely tied to the broader conditions of the new era, including the interplay of domestic and global changes (“two overarching contexts”), cultural diversification, the digitalization of lifestyles, and the widespread expansion of higher education. To enhance the effectiveness and relevance of values education for college students, it is essential to tailor guidance strategies to their specific value orientations and evolving socio-temporal contexts. This can be achieved through theoretical clarification, curriculum integration, platform development, supportive environments, and need-based approaches.

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.

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.

As a soft institutional environment, can an optimal business environment enhance firm resilience to uncertainty shocks, serving as fertile ground for nurturing their adaptability? This study explores the impact of the business environment on firm resilience and the underlying mechanisms, focusing on the COVID-19 shock situation. Findings indicate an inverted U-shaped relationship between the business environment and firm resilience, where firms' risk-taking propensity positively moderates this relationship. Mechanism testing, based on a “resource-relationship-capability” framework, demonstrates that the business environment enhances firm resilience by optimizing resource allocation, improving government-enterprise relations, and stimulating innovation capacity. Heterogeneity analysis further indicates that the effect of the business environment on firm resilience varies significantly across different levels of regional development, industry factor intensity, and firm-specific characteristics (ownership structure, lifecycle stage, and scale). This study offers theoretical insights and practical recommendations to help enterprises fully leverage policy dividends from an optimized business environment to enhance firm resilience. It also guides government policymakers in designing more balanced and effective policies to improve the business environment.

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.

To develop a band-pass filter with a favorable shape factor (SF), based on the substrate integrated waveguide (SIW) structure, a method is proposed that involves etching two asymmetric circular complementary split-ring resonators (CSRRs) on the top metal layer of SIW. The proposed method introduces two transmission zeros, effectively improving out-of-band suppression and enhancing the frequency selectivity required for the band-pass filter. Simultaneously, the design employs integrated passive device (IPD) technology to achieve miniaturization and high integration of the CSRR-SIW band-pass filter. The measurement results indicate that the designed band-pass filter achieves a minimum insertion loss of 3.18 dB within the pass band, with a shape factor of 2.1, a relative bandwidth of 18%, and dimensions of 2.74 mm×1.50 mm×0.54 mm. For a miniaturized band-pass filter using IPD technology, this design exhibits good shape factor.

In view of the characteristics of short duration and subtle facial muscle movements of micro-expressions, a dual-stream Transformer-based micro-expression sequence recognition network (TSTMER) is proposed. Firstly, the TV-L1 optical flow method is used to calculate the optical flow information between the micro-expression sequence frames and the starting frame, in order to obtain the motion characteristics of facial muscles in micro-expressions. Secondly, a micro-expression feature extraction module is designed, which consists of two Transformer networks and a cross-attention mechanism. The two Transformer networks are used to extract spatial features of optical flow images and micro-expression images respectively, and the cross-attention mechanism fuses their spatial features into new ones. Then, a spatio-temporal attention mechanism is designed to enhance the weights of micro-expression local regions and sequence frames. Finally, Bi-LSTM is used to learn both the forward and the backward dependencies of micro-expression sequences in time dimension. Experiments on multiple micro-expression datasets demonstrate that the TSTMER network has significant advantages in UAR and UF1.

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.

China's reform and opening-up policies have brought profound changes in the social sphere.The living environment and socialization factors influencing contemporary youth have undergone dramatic transformations.As a result,young people's perception and evaluation of social issues or general phenomena have also changed significantly across generations,which in turn has considerable implications for their values and behavioral patterns.Based on longitudinal data from the 2013 and 2021 waves of the Chinese Social Survey(CSS),this study systematically analyzes the dynamic changes in Chinese youth's perceptions of social fairness,attention to social hot issues,social trust,and their evaluations over the past decade.The findings reveal that youth have become markedly more attentive and sensitive to these issues,though the implications of various indicators differ substantially.Their evaluations tend to be more rational and diversified,with increased inclusiveness,reflecting a trend toward value pluralism among today's youth.

Compositional Zero-Shot Learning (CZSL) defines categories as combinations of attributes and objects, requiring models to accurately recognize novel combinations not encountered during training. CZSL not only reduces dependence on labeled data but also explores the compositional generalization capabilities of deep learning models. Existing methods often neglect the appearance variability of attributes and objects in different compositional contexts, resulting in insufficient generalization. To address this, we propose a Context-Aware Decision Mechanism for CZSL. Leveraging the multimodal pretrained CLIP model as an encoder, our approach incorporates textual soft prompts, adapter fine-tuning, and feature denoising mechanisms to independently extract attributes and object features. Furthermore, we design a context-related gating network to adaptively fuse the predicted scores of attributes and objects during the decision-making phase. Experiments on four standard benchmark datasets demonstrate that our method significantly enhances CZSL performance, validating the effectiveness of context-aware decision-making in handling attribute-object combinations.

Aiming at the problems of current gait recognition methods in the recognition accuracy under cross-view conditions and performance under complex wearing conditions, this paper proposes a cross-view gait robot with global and local feature fusion (GLFF), which combines with the introduction of neural network hyperparameters to form the brain neural network for enhancing the network capabilities. Firstly, the power segmentation camera captures the movement information of the limbs and enhances the fine-grained characteristics of the network block space; and then, uses the set pooling layer (SP) to extract the color features with high viewing angle robustness, aggregate the gait at the time point of the asynchronous state, integrate a series of features and use the micro-action template builder to extract the micro-action features, and train to obtain GLFF. The CASIA-B data set of the Institute of Automation of the Chinese Academy of Sciences was used for experimental verification. The average recognition accuracy of this software under cross-view conditions reached 88.9%, and the recognition rate under simulated wearing conditions (CL) reached 79.5 %. This result can verify that fusion with local components can effectively reduce the influence of wearing factors on cross-view gait recognition.

Aiming at the problemof excessive computational load in commonly used neural network classification models, this paper proposes a lightweight Convolutional Neural Network model that can be deployed in low-end embedded devices-a lightweight convolutional neural network model based on MobileNet and ShuffleNet. This model combines the characteristics of MobileNet and ShuffleNet, introduces Squeeze-and-Excitation channel attention mechanism and shortcut branch, and uses improved h-swish activation function to replace the original ReLU activation function, so as to improve both the recognition accuracy and the computational efficiency. Test results verify that the proposed lightweight convolutional neural network model based on MobileNet and ShuffleNet outperforms traditional lightweight convolutional neural network models such as MobileNet, ShuffleNet, EfficientNet, etc.

The legal functions of the cross-border data“whitelist”mechanism include:expanding the“circle of trusted partners”for cross-border data flows,supporting data circulation under the Belt and Road Initiative,and facilitating equal,mutually beneficial,and progressive dialogue and negotiation between nations.In general,the mechanism follows an exploratory path characterized by a“from near to far”and“point-to-surface”approach.The“whitelist”mechanism should be positioned alongside other regulatory frameworks—such as security assessments,standard contractual clauses,and professional certifications—rather than serving as a substitute.The criteria for applying the“whitelist”should take into account several factors,including:the authority of public institutions in the recipient country to access personal data,the country's personal data protection policies and laws,mechanisms for implementing personal data protection,and relevant data protection treaties,agreements,or international commitments.

With the increasing size of financial assets , countries around the world are gradually becoming more transparent in their monetary policies, using central bank monetary policy communication as a new type of monetary policy instrument. To measure central bank communication more accurately, this paper proposes a dynamic topic model based branching processes, and systematically constructs a central bank communication index by combining the key wording method. At the same time, the paper uses the constructed communication index as a new type of monetary policy instrument to build a TVP-FAVAR model, which can extract potential macroeconomic information from a large number of macroeconomic variables, and its time-varying nature can also better reflect the dynamic regulatory effect of monetary policy. Experimental results demonstrate the effectiveness of central bank monetary policy communication,which can capture timely information on conventional monetary policy instruments and immediately respond to changes in interest rates and money supply, which can effectively smooth out output volatility and have a longer-term impact on the macroeconomy.

Based on panel data from 30 Chinese provinces between 2013 and 2022,this study employs the entropy method,kernel density estimation,Dagum Gini coefficient,spatial Markov chain,and standard deviation ellipse model to measure the development level and spatio-temporal evolution characteristics of new-quality productive forces in China.The findings are as follows:(1)Green and low-carbon development is a key indicator in assessing the level of new-quality productive forces.(2)Overall,the development level remains low and shows a decreasing spatial gradient from east to central,northeast,and then west China.(3)In terms of regional disparities,the overall inter-regional difference first narrowed and then widened.Intra-regional differences follow the pattern:west > east > central > northeast.(4)Regarding dynamic trends,the development of new-quality productive forces is on the rise nationwide and across all regions.Regional polarization has eased,although absolute disparities have widened.(5)From the perspective of spatial dynamics,the spatial distribution of development levels demonstrates a diminishing trend from the eastern coastal regions to the inland areas,with high-performing provinces primarily concentrated in southeastern coastal regions.

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.

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.

As a new economic paradigm following the agricultural and industrial economies,the digital economy has rapidly expanded,driving the growth of service sectors such as digital consulting,IT advisory,and demand analysis.This expansion has had a profound impact on China's economic structure and societal operations.Drawing on provincial panel data from 2014 to 2021,this study empirically investigates the relationship between digital economy development and the tertiarization of industrial structure,focusing on both output and efficiency dimensions.The results show that a one-standard-deviation increase in the digital economy index corresponds to average increases of 0.216 and 0.503 standard deviations in tertiarization output and efficiency levels,respectively.Mechanism analyses further reveal that digital economy development promotes industrial tertiarization indirectly through enhanced market integration and improved capital-technology efficiency.Heterogeneity analysis indicates that in central regions and highly urbanized areas,the digital economy significantly boosts both output and efficiency.In contrast,in western regions and less urbanized areas,the digital economy primarily drives output growth,while in eastern regions,it predominantly enhances efficiency

Based on the SMIC 55 nm process, a 2.4 GHz phase-locked loop (PLL) with sub-sampling technology was designed to achieve low spurious and low jitter performance. In response to the issues of significant jitter and high in-band noise in traditional charge pump PLLs, the sub-sampling technique was introduced. Additionally, the three mechanisms affecting reference spurs in the sub-sampling PLL were investigated, and methods such as the Dummy sampler, isolation buffer, and a novel sub-sampling charge pump were proposed to reduce the level of reference spurs in the output signal. To avoid frequency locking errors and reduce lock-in time, a frequency locking loop incorporating a novel dead-zone circuit was employed. Considering the influence of the input signal on output noise, a variable duty cycle input signal buffer was designed to further decrease reference spurs. Simulation results demonstrate that under a 1.2 V power supply voltage, the sub-sampling PLL achieves a reference spur level of -87.72 dBc at a 50MHz frequency offset and an in-band phase noise of -126.3 dBc/Hz at 1 MHz. The overall power consumption of the system is 11.72 mW.

The neo-sensationalist fiction was a striking literary genre in the 1930s, which fervently depicted the urban modernity of Shanghai. Its works predominantly focus on fragmented urban landscapes, fluid consumer trends, and modern men and women entangled in life, death, love, and desire, while giving insufficient attention to the underclass—particularly the laboring masses. This stands in stark contrast to the proletarian literature advocated by the left-wing literary sphere. The literary inclination of the neo-sensationalist writers was, on the one hand, influenced by the burgeoning Western modernist movements of the time, and on the other hand, it was also related to their unique understanding of literature. Consequently, their creative works embodied a narrative ethics distinct from traditional literature.

The theoretical and experimental studies on the generation of asymmetrical transverse mode beams are carried out by employing an off-axis pumped Nd: YVO₄ laser with adjustable output reflectivities and an extra-cavity single-cylindrical lens. Numerical results show that the mode order of the output transverse modes is positively correlated with the off-axis displacement under the same intracavity loss. In addition, with the increasing of the cavity loss, the output modes become more asymmetrical. A corresponding experimental device is established, the experimental results show that as the output mirror reflectivities decrease from 95% to 81%, 60% and 50%, the transverse modes symmetries of 93%, 70%, 43% and 14%, are realized, respectively, which are consistent with the numerical simulation results. Furthermore, when the output mirror reflectivity is greater than 60%, the output power is positively correlated with the laser mode order number, otherwise the output power is negatively correlated with the mode order number. When the output mirror reflectivity is 60%, the output powers of the asymmetrical transverse modes exceed 1 W with a pump power of 6.36 W, and the maximum output mode can reach TEM_9,0. The experimental results are in good agreement with the numerical simulation results.

In this paper, a low noise amplifier (LNA) circuit with an operating frequency of 4～8 GHz is developed. A lossy matching network is used to achieve the broadband characteristics and in-band flatness of the LNA, both gain and noise performance of the LNA are taken into account by reasonably distributing the gain of the two stages. The LNA is implemented by a localized GaAs pHEMT process, and the measurement results show that the gain is 20.5 dB, the input return loss is better than 14 dB, and the output return loss is better than 12 dB in the operating frequency band.Based on the wafer-level heterogeneous packaging process, the package design for this LNA was developed, and a co-simulation method integrating wafer-level packaging process with GaAs process was proposed to evaluate the performance of the packaged LNA.The proposed co-simulation method provides a reference for building heterogeneous microsystems based on wafer-level processes and microwave solid-state integrated circuits.

Mined information from massive medical texts and extracting key named entities is crucial to improving the efficiency of medical information processing and dialogue understanding.Aiming at the difficulty of recognizing complex medical terms in named entity recognition tasks in the medical field, and the difficulty of comprehensive and accurate capture by a single feature method, a medical text named entity recognition method is proposed, which integrates multiple features of Pinyin, glyphs and lexicon information.This method employs pre-trained models to extract semantic vectors, constructs glyph vectors based on Chinese character strokes and radicals, along with tonal Pinyin vectors. It aligns medical dictionaries through bidirectional matching, dynamically fuses multi-modal features, and ultimately inputs them into a BiLSTM-CRF model to enhance entity recognition accuracy. The experimental results demonstrate that the proposed method significantly outperforms other comparative experiments in recognizing online doctor-patient conversation texts from the IMCS internet platform. The highest F1 score achieved on the test set reaches 92.5%, representing an improvement of up to 8.6 percentage points compared to the F1 scores of the other five comparative experiments (Base-BERT, MC-BERT, RoBERTa-wwm-ext, Medbert-Kd-Chinese, and Albert-Base-Chinese). Furthermore, ablation experiments also further verified the effectiveness of the fusion features in improving the recognition performance.

Aiming at the problem of limited energy stored in unmanned aerial vehicle (UAV), a resource allocation strategy for UAV-assisted edge computing in wireless powered communication networks is investigated. By deploying a laser beam director on the ground, sufficient energy can be provided for the UAV in a short period of time. Then, multiple ground terminals obtain energy from this UAV by radio frequency energy harvesting method and offload their computing tasks to the UAV with edge servers. The resource allocation problem is modeled as an optimization problem whose objective is to minimize the total energy consumption of the UAV subject to the constraints of energy and data causality, computational resources, and transmitting power. The suboptimal solution is obtained by introducing an imperialist competitive algorithm. Simulation results show that the imperialist competitive algorithm consumes less energy compared with the particle swarm optimization algorithm and the equal upload time allocation method.

In order to construct generalized barycentric coordinates with non-negativity and locality, this paper proposes a method for constructing visible natural neighbor coordinates based on minimizing Dirichlet energy. By computing the second-order Voronoi diagram and visible vertices, the visible natural neighbors are obtained, ensuring the locality of the coordinates. Subsequently, the coucept of Dirichlet evergy is induced, and the smoothness of the coordinates is ensured by minimizing the Dirichlet energy. Numerical examples demonstrate that the proposed method can construct barycentric coordinates with non-negativity and stronger locality within any simple polygon.

Curve approximation is a fundamental problem in Computer Aided Geometric Design (CAGD), and has wide applications in data compression, path planning and trajectory generation. Challenges such as high computational complexity and poor approximation results are often encountered when approximating complex intersecting curves. To address these challenges,a new curve approximation algorithm is proposed, which minimizes the integral of squared distance between the original curve and its approximation one for determining the control points of the approximating curve. Unlike traditional integral minimization methods, the proposed algorithm employs the Gauss-Kronrod quadrature to efficiently compute complex integrals, improving both accuracy and computational efficiency. Experimental results demonstrate that the proposed algorithm achieves higher approximation accuracy and reduced data volume compared to prevailing approximation methods.

In the context of the new era,this paper systematically elaborates on the dialectical unity between ecological civilization construction and the development of new-quality productive forces,based on the ecological connotations of the“Two Mountains” concept.By analyzing the paradigm of coordinated development—“We want both lucid waters and lush mountains as well as gold and silver mountains”—the paper demonstrates how green technological innovation serves as a medium to promote mutual reinforcement between ecological conservation and economic growth.It focuses on the value principle of ecological prioritization reflected in the idea“We would rather have lucid waters and lush mountains than gold and silver mountains,” revealing the mechanisms by which green lifestyles drive the development of new-quality productive forces.Furthermore,by deconstructing the value transformation logic embedded in“Lucid waters and lush mountains are invaluable assets,”the paper clarifies the internal logic by which green ecological consensus guides the capitalization of ecological resources and facilitates the development of new-quality productive forces.The aim is to construct a theoretical framework for transforming ecological advantages into development advantages of new-quality productive forces,thus providing a solution with both theoretical depth and practical significance to the ecological paradox in the modernization process.

In order to solve the problem of high capacity idle rate and uneven interests of multi-parties in the production capacity sharing platform, the service fee input and order acquisition income of the manufacturers, the requirements for product quality and delivery of the costumers, the production coordination and cost control of the platform, a model is constructed with the objective of capacity utilization fairness and order value fairness, which can get the strategies of order splitting, order allocation and production scheduling. An improved discrete cuckoo search algorithm is proposed to solve the model. A random walk mechanism is revised to avoid the algorithm from falling into local optima. An adaptive discard probability is designed for cuckoo to get more random walks in the early stage, which increases the diversity of solutions. Meanwhile, a special method of encoding mapping is developed to prevent Levy flight from degrading to random search. Numerical experimental results show that the optimization performance of the improved discrete cuckoo search algorithm is better than that of classic cuckoo search algorithm and genetic algorithm within the same runtime.

The traditional strawberry maturity prediction method mainly relies on the professional knowledge and experience of planting personnel to observe and detect the growth stages of different varieties of strawberries, and estimate the maturity of strawberries. However, this method is inefficient. In response to such issues, this paper is based on the national standard for strawberry maturity, and further refines the maturity of strawberries under this standard. We established a strawberry dataset, trained the YOLOv5 object detection model, and used this model to numerically analyze the maturity of strawberries. Numerical results were statistically analyzed, and the data was processed to obtain sample points. Finally, univariate polynomial regression, neural network regression, and DoubleBoltzmann fitting methods were used to regress the growth curve of red strawberry under specified growth conditions, and the results were compared and evaluated. Experimental results indicate that the proposed growth model for strawberries during their ripening period can predict and analyze the growth trend of strawberries throughout the entire process from fruiting to fruit ripening.

In recent years, Chinese online literature has experienced rapid growth in overseas markets, evolving through stages of publication licensing, translation-based export, and platform-based expansion, and has now entered a new phase of global co-creation. At this stage, the market scale, ecosystem, and strategic orientation of Chinese online literature abroad have reached new heights. However, the field is also facing increasingly intense global competition. This study analyzes the competitive landscape of Chinese online literature from the perspectives of market dynamics, cultural factors, and industrial structure. It addresses key challenges such as content homogenization, cultural barriers, and the overload of entertainment content. To overcome these obstacles, the paper explores strategic solutions in content selection, translation mechanisms, and IP development, aiming to provide viable pathways for the global dissemination and sustainable development of Chinese online literature.

Based on the weak signal processing requirements of silicon carbide (SiC) high-performance neutron detection system, a low-noise and low-power SiC neutron detector readout circuit is designed. The readout circuit reads, shapes and amplifies the weak signal output by the SiC neutron detector, and forms a neutron detection system with the front-end detector and the back-end counter, which can realize the needs of neutron count measurement. The designed readout circuit integrates a low-voltage power management module, preamplifier, main amplifier and discriminator. In order to verify the effectiveness of the circuit design, the readout circuit is experimentally tested. The test results indicate that the single board power consumption is about 300 mW, the equivalent input noise is about 1 fC, where the preamplifier linearity is 99.72%, the output dynamic range is about ±3 V, and the total amplification block gain is about 19.28 mV/fC. The designed circuit has the characteristics of low noise, low power consumption, high gain and good stability.

The development of information technology has led to a tremendous demand for 3D assets. Recently, text-guided 3D generation models have gained widespread attention due to their ease of use. To meet the growing demand for fast and diverse high-quality 3D shape generation, we propose a 3D shape generation method, named Re3Diffusion, based on a multi-modal retrieval system and latent space diffusion model. Given a text prompt, Re3Diffusion can access an external multi-modal knowledge base to retrieve relevant shapes, using them as references for generating 3D models. To generate 3D shapes more aligned with the text description, we first use contrastive learning pretraining to narrow the gap between different modalities through parameter learning, and then combine the retrieval system during inference to reduce the distance between the generated shape and the target distribution via non-parameter learning. To avoid misleading generation results caused by irrelevant information retrieved with low correlation to the text condition, we further design an adaptive weighting scheme to selectively utilize the retrieved shape information. This method also supports text-guided shape editing. Experimental results show that the method significantly improves the semantic matching between the generated 3D shape and the text condition while maintaining fast generation speed, and it also demonstrates novel out-of-domain generalization ability.

China's current legislative framework for cross-border data transfer falls short of fully meeting practical needs,and the security assessment mechanism for outbound data still requires further refinement.On the regulatory front,the fragmented supervisory model currently in place has led to regulatory overlaps and inefficiencies.The dual challenges of legislation and supervision have also created difficulties in aligning with international data governance standards.In response,it is recommended that the principle of data sovereignty serves as the guiding framework,supported by a graded and classified data management system.The legal framework governing cross-border data transfers should be improved,and a coordinated regulatory mechanism should be established to strengthen China's data security governance system.Additionally,China should actively align with high-standard international trade agreements on data flows and contribute to the construction of a global community with a shared future in cyberspace.

Methane (CH₄) is an important greenhouse gas, and it is critical to investigate the mechanisms of CH₄ microleakage and thus to evaluate the atmospheric CH₄ sources and sinks. This paper reviews the research progress on CH₄ migration mechanisms in underground porous medium and focuses on the application of Computational Fluid Dynamics (CFD) in this field. The physical properties of underground porous medium are complex and diverse, and the CH₄ microleakage process is affected by multiple factors. Diffusion, convection and buoyancy have influences on CH₄ migration, but the dominant mechanism is still controversial. As CFD technique is easy to quantitatively control, has predictive functions, and can make up for the limitations of field monitoring and experimental physical simulation, it has been widely applied to the research of CH₄ migration mechanisms in various underground porous medium conditions. This paper summarizes the application of CFD technique in the modeling of CH₄ migration process in the scenarios of buried natural gas pipeline leakage, coal bed methane, shale gas, landfill gas and seabed gas hydrate exploitation, and identifies the main mechanisms and influencing factors of CH₄ migration. This paper will provide a reference for the application of CFD technique in the research of microleakage CH₄ migration mechanisms of underground oil and gas reservoirs.

A high-gain low-noise amplifier operating in the X-band has been designed based on the 0.18 μm CMOS technology. The circuit employs a two-stage differential common-source structure and utilizes a cross-coupled capacitance neutralization to counteract the coupling effects from the Miller effect of the transistor gate-drain parasitic capacitance, and improves the circuit gain, stability, and reverse isolation. Transformer baluns are used for input, inter-stage, and output matching networks to achieve a compact impedance matching structure and conversion from single-ended to differential signal at ports. Testing results show that the low-noise amplifier has a 3 dB gain bandwidth of 9-11 GHz, a maximum gain of 25.1 dB, a noise figure of (3.6±0.1) dB, and a core chip area of 0.34 mm² with a DC power consumption of 35 mW.