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.

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.

This study analyzes the central government budget execution audit rectification reports for the years 2021 to 2023, from the perspective of collaborative governance. Based on the SFIC model, it systematically investigates the pathways to enhance both audit rectification and budget execution performance. The findings reveal that, while significant progress has been made in establishing a multi-dimensional collaborative supervision mechanism and advancing governance logic, several key challenges persist. These include structural flaws in institutional design, inefficient information and feedback channels, the absence of a shared responsibility mechanism, and the breakdown of vertical collaboration transmission. To address these challenges, the study proposes a solution based on the dynamic cyclical logic of the SFIC model. It outlines a four-pronged approach to improving collaborative governance: activating initial conditions, strengthening multi-dimensional catalytic leadership, innovating institutional design, and streamlining collaborative processes. This framework offers actionable recommendations for enhancing the systemic collaborative governance of budget performance improvement within the context of audit rectification.

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.

In order to meet the requirements of high precision and high reliability clock for time-consuming equipment, according to the characteristics of high long-term stability of global navigation satellite system(GNSS) timing and high short-term stability of crystal oscillator clock(COC), a scheme of taming crystal oscillator by GNSS second pulse is proposed, which realizes the complementary advantages of GNSS and COC, and improves the stability and accuracy of crystal oscillator output frequency. Based on the system of GNSS taming the constant temperature crystal oscillator, the singular value truncation algorithm is used to filter out the outliers in the system in real time; combined with the fading filtering algorithm, the system error is effectively reduced and the stable output is within a certain range. Experimental results show that the frequency accuracy of the constant temperature crystal oscillator can reach 3.4×10^-12, and the timing accuracy is better than 20 ns.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

Precise segmentation of brain tumors is a crucial research focus in clinical medicine. Currently, magnetic resonance imaging (MRI) is widely used for brain tumor segmentation; however. Leveraging multimodality MRI image information can significantly enhance segmentation performance. This paper aims to provide a comprehensive review of the various methods used for multimodality brain tumor segmentation. It explores the application of deep learning technology in this context, and discusses the unique advantages and limitations of methods based on convolutional neural networks(CNN), fully CNNs, and Transformers. The review also summarizes three commonly employed strategies: modal stacking, network improvement, and modal feature fusion. It also covers additional methodologies, including those utilizing generative adversarial networks, feature, correlation, network improvement,and cross-modality self-supervised learning. Furthermore, the paper addresses the challenges and potential solutions for multimodality brain tumor segmentation in the absence of certain modalities. Finally, it provides insights into the future development direction of multimodality brain tumor segmentation.

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.

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.

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.

Based on the 40 nm CMOS process, a fractional-N phase-locked loop with frequency range of 19~20.25 GHz for frequency-modulation continuous-wave chirp is proposed. An improved pre-charged frequency-phase detector is used to alleviate the blind zone problem in the frequency-phase detector and quickly captures the frequency difference of the input signals for achieving fast locking in the phase-locked loop and meeting the requirements of slope rate of chirp. An improved multistage noise-shaping structure is used in the Σ-Δ modulator to effectively increase the length of the output sequence and reduce fractional spurs. Simulation results show that with an output frequency of 19 GHz, the phase noise of the phase-locked loop is -100 dBc@1 MHz, and the maximum modulation rate of the linear wave is 310 MHz/μs, which has much faster modulation speed by comparing with traditional phase-locked loop. The overall power consumption is 65-105 mW under 1.8 V supply voltage for VCO and 0.9 V supply voltage for the rest circuits.

Using China's provincial panel data from 2010 to 2023, this study employs a fixed-effects model and the Bootstrap method to establish a multiple mediating effect model. A comprehensive technological innovation index is developed through principal component analysis, covering three dimensions: input, output, and carrier/performance. This framework is used to systematically examine the pathways through which population aging influences technological innovation. The empirical findings reveal that population aging exerts a significant negative indirect effect on technological innovation through the health productivity pathway. This is manifested in aging suppressing innovation by squeezing fiscal healthcare expenditures and reducing residents' health investments. Regional heterogeneity analysis indicates a “gradient increase” pattern in the negative impact of aging on innovation. While the eastern region alleviates some adverse effects through talent agglomeration, central and western regions confront the dual challenges of “aging and human capital drain.” The study demonstrates that China's characteristic of “growing old before becoming affluent” causes population aging to primarily inhibit innovation by undermining human capital quality and diverting public health resources. Accordingly, we recommend prioritizing the optimization of educational and healthcare resource allocation, enhancing the cultivation of high-skilled talent, and establishing region-specific innovation support policies to address aging challenges and stimulate innovation momentum.

In complex scenarios, Chinese character recognition encounters challenges. Traditional feature extraction or classification methods often fail to extract sufficient features due to potential character occlusion or deformation, leading to recognition errors. Furthermore, existing models that integrate visual and language information overlook the importance of hidden character features in the language model, resulting in inaccurate error correction. To tackle this issue, we propose a multi-dimensional representation recognition algorithm based on characters, radicals, and key pen strokes. This algorithm utilizes self-attention mechanisms to extract multi-level character features, effectively addressing the problem of insufficient feature extraction and reducing recognition errors. Additionally, a multi-dimensional representation fusion mechanism is developed to connect the visual model with the language model, effectively conveying hidden character features to the language model. This algorithm accomplishes text recognition, character recognition, and error correction in three stages. Experimental results demonstrate that compared to state-of-the-art Transformer-based models, the algorithm in this paper achieved performance improvements of 1.81%, 1.11%, 0.25%, and 2.27% on scene text, web datasets, printed text and handwritten datasets, respectively.

The impacts of anisotropic ocean turbulence on the propagation of anomalous vortex beams through an ocean channel are examined. Using Rytov theory, mathematical expressions for the orbital angular momentum mode detection probability and channel capacity of an anomalous vortex beam are derived. Numerical simulations are used to analyze the effects of light source parameters and anisotropic ocean turbulence parameters on the mode detection probability and channel capacity, which indicate that higher radial index values of the anomalous vortex beam result in better resistance to turbulence. In addition, an increase in most turbulence parameters, with the exception of the anisotropy factor and turbulent kinetic energy dissipation rate, leads to a decrease in the detection probability and channel capacity. Furthermore, the propagating performance of an anomalous vortex beam is compared with that of the Hankel-Bessel beam and the Laguerre-Gauss vortex beam in anisotropic ocean turbulence. It has been found that anomalous vortex beams have superior transmission abilities in anisotropic ocean turbulence under certain conditions.

The modern environmental governance system integrating “digital-intelligent empowerment + interest coordination” can accurately identify stakeholders in water environment collaborative governance and effectively enhance the modernization level of China's “AI+” water environment governance. Drawing upon Zhejiang's digital-intelligent water governance practices, this paper constructs a tripartite evolutionary game model involving “government-enterprise-public” collaborative governance. It reveals the evolutionary pathway of interest coordination and governance strategies in digital-intelligence-empowered water environment governance. The results show that: (1) Government incentive governance, enterprise contractual governance, public collaborative governance constitutes the optimal and ideal stable point for achieving interest-coordinated governance in digital-intelligence-empowered water environments. (2) Stakeholders' recognition of the effectiveness of digital-intelligence-empowered water governance and the evolutionary pattern of public collaborative participation are key drivers for interest coordination within the digital water governance system. (3) The government's incentive regulatory policies with well-defined rewards and punishments can not only strengthen enterprise investment but also effectively enhance public enthusiasm for participating in water governance. (4) Blockchain and AI technologies, as key nexuses of digital-intelligent empowerment, elevate the level of interest-coordinated governance by building intelligent monitoring platforms, thereby propelling water environment governance strategies toward stable equilibrium. The findings of the study not only expand the scope of collaborative value co-creation theory in the AI era but also provide significant reference value for constructing China's modern environmental governance system.

The creation of a positive educational ecology requires a break with the subject-object dichotomy and a transformation from a single individual perspective centered on teachers or students to a relational perspective of building a dialogue-based, co-subjective relationship between teachers and students. Grounded in humanistic principles, the teacher-student co-presence dialogue is an emerging educational proposition, which examines the teacher-student relationship from the perspective of mutual development and views teachers and students as a unified community. It demonstrates the key characteristics of relationality, collaborativity, and practicality as a matter of priority, all of which together highlight the status of teachers and students as co-subjects, establish the goal of their shared development, and form the concept of joint creation through collaboration. In this sense, constructing a path for the co-presence dialogue entails shifting from one-sided inclination to bidirectional equality in educational roles, transforming external impetus into internal generation as the driving force for growth, and evolving from clear-cut distinctions to co-present openness in the teacher-student relationship. Ultimately, these shifts will contribute to achieving the goal of mutual enhancement in teaching and learning.

The extraction of buildings from remote sensing images faces multiple challenges such as diverse shapes, scale changes, complex backgrounds, and pixel blurring. To address these issues, this paper proposes a dual path network called DBCTNet, which combines the advantages of CNN and Transformer. This network integrates local detail paths with global context paths, fully utilizing the local feature extraction capabilities of CNN and the global information processing capabilities of Transformer, thereby achieving more efficient building extraction. In order to further improve the performance of the model, a feature refinement fusion module is proposed to reduce the feature differences between different paths and enhance the adaptability of the model in complex scenes. At the same time, a multi-level feature aggregation module is proposed to improve the robustness and accuracy of the model when dealing with buildings of different scales by aggregating multi-scale feature information. Experimental results showthat DBCTNet achieves excellent performanceon the Massachusetts building dataset and the Inria aerial image labeling dataset.

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.

Predicting effluent quality is crucial for early warning and monitoring of sewage plant water quality, enabling dynamic regulation of sewage plants. However, industrial wastewater quality data often exhibit high volatility and nonlinearity. To address these challenges, this paper proposes a time-convolutional network(TCN) model based on the efficient channel attention(ECA) mechanism, bidirectional LSTM(BiLSTM), and CEEMDAN optimization. Firstly, the CEEMDAN decomposition technique effectively reduces the fitting difficulty of the input sequence. The ECA module captures local channel feature information, which the BiLSTM module extracts bi-directional temporal features and enables accurate prediction of industrial wastewater effluent quality time series by combining with the TCN network Finally, the output of each component is reconstructed. Experimental results demonstrate that the proposed TCN model achieves better accuracy in predicting the chemical oxygen demand COD of industrial wastewater effluent quality, with an R² index of 0.90, surpassing other benchmark models. This study provides a basis for efficient dynamic regulation in industrial wastewater treatment plants.

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.

The light field camera is a novel imaging device that has garnered considerable attention due to its ability to capture both light intensity and direction information in a single shot. Such capability holds immense promise for image processing applications. However, the current state-of-the-art super-resolution methods tend to overlook the critical occlusion relationship, leading to suboptimal reconstruction performance in complex occlusion scenarios. Moreover, prevailing approaches lack refinement in their exploration of structural and parallax information. To address these limitations, this paper proposes a novel multilevel feature fusion-based light field image super-resolution network. The network begins by extracting viewpoint maps from multiple viewing angles, allowing for a more comprehensive understanding of the occlusion relationship. Subsequently, by treating two diagonal views as a sequence, the network extracts gradient maps to mine crucial structural information. Finally, we convert the subaperture image into a lens images to extract valuable parallax information. Experimental results on open datasets demonstrate superior reconstruction performance of the proposed method, surpassing state-of-the-art approaches.

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.

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.

A numerical method for solving fractional-order integro-differential equations with weak singularity is proposed based on neural network and Jacobi polynomials. Firstly, the existence and uniqueness of the solution of the equation are proved theoretically. Then, the equation is discretized based on the definition of Caputo fractional-order derivative and the Gauss-Jacobi numerical integration formula. Meanwhile, the neural network based on Jacobi polynomials is constructed to approximate the numerical solution of the equation. Finally, numerical experiments show that the method is effective.

In the application of target tracking in complementary field-of-view sensor networks, when a target crosses the field-of-view of multiple nodes, it can only be observed and return measurement by a subset of nodes at each time step. This leads to disparate accuracy in the state estimation of the same target among different nodes, causing a mismatch in the gain weights for adaptive consensus information fusion between nodes and resulting in degraded target tracking performance in the sensor network. In this paper, we propose a confidence-based adaptive consensus filter algorithm (CB-ACF) based on the existing ACFr algorithm. The proposed algorithm utilizes the historical statistical measures of target observability by nodes to categorize the target's state across nodes into two situations: entering the field-of-view and exiting the field-of-view. It then designs an adaptive confidence calculation rule that reflects the trend of changes in the accuracy of target state estimation by each node. By using node confidence, it designs consistency gain weights and performs adaptive consensus filtering fusion of state estimation values among nodes. Simulation results demonstrate that compared with the prevailing adaptive consensus filter algorithm, the proposed algorithm significantly improves the target state estimation accuracy in the sensor network and enhances the consistency of target state estimation among nodes.

This paper proposes a method for studying the cylindrical vector wave functions(CVWFs) of electromagnetic scattering by the two-dimensional bianisotropic media. Based on the two-dimensional CVWFs, the electromagnetic field in the two-dimensional bianisotropic media can be expanded into the integral expression of the CVWFs, while the scattering field and incident field outside the bianisotropic media can also be expanded into the form of the CVWFs in media. By using the continuity of the tangential component of the electromagnetic field on the surface of the bianisotropic media, the extended boundary condition method and the orthogonality of the CVWFs, the scattering coefficients of the scattering field in terms of CVWFs in free space can be derived, and then the radar cross section of the two-dimensional bianisotropic media can be obtained. Numerical results comparing with existing literature coincide enough, and discussions are drawn at the end.

Taking the active material of waste lithium battery cathode as the research object, a mild citric acid leaching system is selected to investigate the effects of acid concentration, reducing agent, solid-liquid ratio, reaction temperature, reaction time and other factors on cobalt leaching rate. Furthermore, the thermodynamics and the kinetics of cobalt leaching are analyzed. In terms of the selection of reducing agents, both the subsequent recovery of cobalt in the leaching solution (synthesis of cobalt ferrite) and the use of iron based reducing agents have achieved efficient cobalt leaching, while providing an ion foundation for subsequent cobalt recovery. Experimental results show that under the conditions of reaction temperature of 80 ℃, initial acid concentration of 1.25 mol/L, addition of reduced iron powder of 6.67 mmol/g, and solid-liquid ratio of 40 g/L, cobalt leaching rate can reach 99.95% after 120 minutes of leaching. In the range of pH<6.80 and (0.65~1.61)V＞E＞-0.31 V, reduction helps to accelerate the leaching of cobalt from the positive electrode active material of lithium batteries. The cobalt leaching process can be described by an unreacted core contraction model without a solid product layer. The reaction rate is controlled by a combination of surface chemical reactions and diffusion. The calculated apparent activation energy is 24.55 kJ/mol, and the expression for the prediction model of cobalt leaching rate in this system is obtained.

Rivets are often used to connect different metal parts or structures in the industrial field, and some serious safety problems may occur once assembly errors or missing installation occur. In order to solve the above problems, a LRD-DETR (Limited Ranged-Deformable-DETR) model for detecting rivet is proposed. After feature extraction in the feature extraction network, a multi-scale fusion module DSPANet is added to enhance the semantic information of feature maps. A range-limiting deformable attention mechanism is designed in the encoder, which limits the sampling range of each reference point. The DAB-DETR is used in the decoder; and based on it, a new MLP module is designed to update the 4D anchor frame information every time. The experimental results showed that the model in the self-built rivet dataset achieved a satisfactory result of 59.2% mAP value, which was better than prevailing target detection algorithms.