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A prominent argument as to why countries sign “deep” preferential trade agreements (PTAs) is to foster global value chains (GVCs) operations. By exploiting a new dataset on the content of PTAs, this paper quantifies the positive impact of deep PTAs on GVC participation, mostly driven by value-added trade in intermediate rather than in final goods and services. On average, each additional policy areas increases the domestic and the foreign value added of intermediates by 0.48 and 0.38%. Deep PTAs facilitate integration in industries with higher levels of value added. Their content also matters for GVC integration by income group.

One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of their benign or malignant nature is important for the long-term survival of patients. With the development of artificial intelligence, deep learning has become the main method for lung nodule classification. However, successful deep learning models usually require large number of parameters and carefully annotated data. In the field of medical images, the availability of such data is usually limited, which makes deep networks often perform poorly on new test data. In addition, the model based on the linear stacked single branch structure hinders the extraction of multi-scale features and reduces the classification performance. In this paper, to address this problem, we propose a lightweight interleaved fusion integration network with multi-scale feature learning modules, called MIFNet. The MIFNet consists of a series of MIF blocks that efficiently combine multiple convolutional layers containing 1 × 1 and 3 × 3 convolutional kernels with shortcut links to extract multiscale features at different levels and preserving them throughout the block. The model has only 0.7 M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. The proposed MIFNet conducted exhaustive experiments on the reconstructed LUNA16 dataset, achieving impressive results with 94.82% accuracy, 97.34% F1 value, 96.74% precision, 97.10% sensitivity, and 84.75% specificity. The results show that our proposed deep integrated network achieves higher performance than pre-trained deep networks and state-of-the-art methods. This provides an objective and efficient auxiliary method for accurately classifying the type of lung nodule in medical images.

With the continuous growth of the number of college graduates, the employment situation of college graduates is facing more and more challenges and pressure. In this context, the importance of innovative & venture education is constantly highlighted. Based on this, this paper first analyses the current situation of the integration of multimedia tech and innovative & venture education, then studies the necessity of the integration of innovative & venture education and computer multimedia tech, and finally gives the specific implementation strategy of the deep integration of innovative & venture education and multimedia tech.

[Purpose/Significance] Against the backdrop of an accelerating population aging trend, the integration of big data and intelligent services in multimodal healthcare and eldercare has become pivotal for enhancing the quality of medical and eldercare services. However, existing knowledge service systems for big data in healthcare and eldercare face challenges such as difficulty of integrating multi-source heterogeneous data, the absence of cross-organizational sharing mechanisms, and passive service models. [Method/Process] First, a cross-domain aggregation method is proposed for multi-source heterogeneous medicare big data, including: 1) A method for constructing a clinical, key-feature-based medical case knowledge database. It extracts and categorizes critical features from electronic medical records using natural language processing (NLP). 2) A natural language processing-based cross-domain disease risk factor mining framework. It identifies risk factors from social media via topic-enhanced word embeddings and clustering techniques. 3) An adaptive pointer-constrained generation method for medical text-to-table tasks. It leverages the BART architecture to transform unstructured medical text into structured tables. Next, a knowledge discovery method based on multimodal medicare big data is developed, including: 1) A medical decision support approach integrating case-based reasoning (CBR) and explainable machine learning. It aims to enhance diagnostic interpretability through ensemble learning and case similarity analysis. 2) A large-scale medical model-driven knowledge system. It utilizes multimodal data pretraining and domain adaptation to support the entire diagnosis-treatment process. 3) A personalized recommendation method based on temporal warning signals, generating precise intervention plans via collaborative filtering and dynamic updates. Finally, a smart service model for full-cycle evolving needs is constructed, including: 1) A health information supply-demand consistency matching framework combining deep learning and clustering techniques; 2) A multi-level, cross-scenario health demand and behavior dynamic modeling approach. [Results/Conclusions] The proposed methodological framework significantly improves the efficiency with which medicare big data is integrated and the capabilities of its knowledge services. Key outcomes include: 1) Enabling disease risk prediction and personalized interventions through deep integration of cross-organizational, cross-scenario medicare data via multimodal aggregation and semantic alignment. 2) The CBR-ECC model and WiNGPT large medical models enhance the interpretability and full-process coverage of medical decision-making. These models improve the accuracy of diagnoses made by primary care physicians by over 30%. 3) The temporal warning-based recommendation method increases the dynamic update efficiency of health interventions by 40% and user satisfaction by 25%; 4) Dynamic health demand modeling reveals core pain points for chronic disease patients, providing a basis for precision service strategies. This research provides the theoretical and technical support for developing a proactive health service system that is both data-driven and human-machine collaborative. This system will, advance the implementation of the Healthy China strategy and innovation in aging population governance.

School-enterprise cooperation and innovation is an essential support for the implementation of an innovation-driven development strategy. This study examines the stages of school-enterprise cooperation and innovation, underpinned by integrated media, through the lens of deep coupling theory. It categorizes the process into pre-, mid-, and post-stages. The pre-stage involves analyzing university agricultural programs to develop a credit network database identifying optimal school-enterprise pairings. The mid-stage focuses on enhancing knowledge exchange mechanisms to tightly integrate participating entities. The post-stage assesses the impact of these integrations, revealing a coordination degree of 0.416 among agricultural majors, suggesting that effective interaction mechanisms are yet to be established, occasionally leading to mutual constraints. This research informs strategies to foster robust school-enterprise collaborations in agricultural education.

In the “Internet+” era, the smart classroom has emerged as a significant focal point in the research on educational informatization. This advancement represents a synergistic melding of novel technologies and academic practices. Accordingly, this study proposes a smart classroom teaching model tailored for accounting courses in higher education institutions. Within this framework, we have developed a specialized model to evaluate teaching efficacy in such environments. This evaluation model incorporates an enhanced version of the basic YOLOv5s model, which has been modified in three key areas to facilitate the detection of students’ facial expressions during lectures. Furthermore, to accommodate low-resolution images, the model integrates an advanced DRN-F face super-resolution reconstruction algorithm, thereby augmenting the accuracy of face recognition. This paper introduces a composite model for assessing the effectiveness of smart classroom teaching by amalgamating the two refined models. Comparative analysis with traditional manual recognition methods reveals that our model aligns more closely with the actual attentive responses of students when their faces are unobscured. A practical study on the teaching application further substantiates the efficacy of the smart classroom approach. Employing an independent sample t-test, the findings demonstrate a statistically significant difference in the post-test scores between the experimental and control groups, with a Sig. Value of 0.001 (p<0.05). This indicates that the learning achievements of students under the smart classroom model are significantly enhanced compared to those in conventional teaching settings.

The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.

Many studies use the same factors to explain attitudes toward specific trade agreements and attitudes toward the principle of free trade and thus treat both objects as interchangeable. Contemporary trade agreements, however, often reach beyond trade in the narrow sense. Consequently, factors unrelated to free trade may affect citizens' evaluations of these agreements. We propose a model of attitude formation toward specific trade agreements that includes the societal context as a constitutive feature. We expect salient aspects of an agreement to activate corresponding predispositions. Empirically, we compare how this contextual model and a standard model perform in explaining German citizens' attitudes toward free trade and the Transatlantic Trade and Investment Partnership (TTIP). The results show that the standard model performs well in explaining public opinion on the principle of free trade but is less useful in explaining attitudes toward TTIP. The latter were driven by postures toward transatlantic cooperation, predispositions toward the role of interest groups in politics, and market regulation – aspects salient in German public discourse about TTIP. In sum, we find ample evidence for the need to differentiate between the two attitude objects and for our contextual model of attitude formation.

Against the backdrop of deep integration of technological innovation and industrial innovation, this study constructs a theoretical model incorporating market and government behavior. Utilizing panel data from 284 prefecture-level cities across China from 2013 to 2023, it empirically analyzes the roles and mechanisms of efficient market and proactive government in facilitating the deep integration. Findings indicate that the market’s “push–pull mechanism” promotes the deep integration of technological and industrial innovation, enhancing output performance. However, market mechanism exhibits diminishing marginal output performance: beyond a certain threshold, the force to drive further performance improvements weakens. The government’s role can positively influence the market mechanism’s ability to drive deep integration of technological innovation and industrial innovation, and growth in output performance. In coordinating government and market efforts, the following principles should be observed: as market mechanism matures, gradually enhance technological innovation by allocating human capital to research activities, particularly basic research; formulate industrial policies that progressively prioritize science and technology innovation activities; and advance the development of public goods, such as technology transfer and commercialization platforms. To advance the deep integration of technological and industrial innovation, policy implications include consistently leveraging market mechanisms, coordinating government and market efforts to design policies for capital and talent mobility, and promoting the construction of conversion platforms like concept-validation centers and science incubators.

Combining for the first time a new dataset of non-tariff measures (NTMs) in 65 countries with the CEPII's unit values database, we estimate average ad-valorem equivalents (AVEs) for sanitary and phytosanitary, technical-barriers-to trade and other measures by section of the Harmonized System of product classification. While most existing AVEs are obtained from indirect quantity-based estimation, ours are obtained from direct price-gap estimation. They lie in a single-digit range, i.e. substantially lower than previous estimates based on older data. Our results may reflect the progressive phasing out of command-and-control instruments such as quantitative restrictions in many countries; they also suggest that sanitary and technical regulations have not substituted for them as trade-restrictive interventions. Most interestingly, we show that deep-integration clauses in regional trade agreements, in particular the mutual recognition of conformity-assessment procedures, substantially reduce the price-raising effect of NTMs, possibly reflecting lower compliance costs.