In-depth Exploration Research Articles

Neuroinflammation, Blood–Brain Barrier, and HIV Reservoirs in the CNS: An In-Depth Exploration of Latency Mechanisms and Emerging Therapeutic Strategies

Neuroinflammation, Blood–Brain Barrier, and HIV Reservoirs in the CNS: An In-Depth Exploration of Latency Mechanisms and Emerging Therapeutic Strategies

Darboux Transformations of Nonlinear Coupled Equations and Their Solutions

This study is dedicated to an in-depth exploration of the integrable structure inherent in the coupled KdV equation, with particular emphasis on solving Lax pairs and their application in constructing Darboux Transformation and Bäcklund Transformation. By leveraging these transformations, we have derived a series of superposition formulas that offer novel insights into understanding the interactions among solitary waves within coupled systems. Furthermore, our successful identification of a recursive operator for this system not only enhances our comprehension of the dynamical behaviors exhibited by the coupled KdV equations but also establishes a theoretical cornerstone for future research endeavors in this field.

A network analysis of moral distress among clinical internship nursing students: A cross-sectional study.

BackgroundMoral distress is a significant challenge faced by clinical internship nursing students. Most current studies investigate the impact of external factors on nursing students' moral distress. However, there is a lack of in-depth exploration of the interactions between various moral distress items.Research objectiveThis study aims to construct a network structure of moral distress among clinical internship nursing students, while exploring the touchpoints of moral sensitivity and moral resilience on moral distress, to identify potential targets for moral distress intervention.Research designA cross-sectional design utilized acceptable validity scales. Network analysis was conducted using R (Version 4.4.0). Regularized partial correlation was utilized to describe the associations between different nodes in the network. Central nodes were identified through centrality indices.Participants and research contextThe participants in this study are 372 nursing students undergoing clinical internships at a comprehensive teaching hospital in Changsha, China.Ethical considerationsThis study was approved by the hospital ethics committee (Review Number: Express 241098). Written informed consent was obtained from all participants.ResultsIn the moral distress network, the connections between MD3 "I provide life-sustaining treatment per the family's wishes" and MD4 "Despite only extending survival, I implement life-saving measures" are the strongest. MD18 "Decline in service quality due to poor team communication," MD19 "I ignore situations lacking adequate information for informed consent," and MD17 "I collaborate with colleagues unable to meet the patient's treatment needs" are the central nodes of the moral distress network. In the relational network, MD19 is a critical connection point linking moral distress with moral sensitivity and moral resilience.ConclusionFuture interventions for moral distress could focus on the strongly related and significant distress (MD3, MD4, MD18, MD19, MD20, and MD17) mentioned in this study, and develop scientific and targeted interventions to reduce clinical internship nursing students' moral distress.

Clinical observation of nivolumab combined with cabozantinib in the treatment of advanced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a particularly serious kind of liver cancer. Liver cancer ranks third in terms of mortality rate worldwide, putting it among the leading causes of deaths from cancer. HCC is the primary kind of liver cancer and makes up the vast majority of cases, accounting for approximately 90% of occurrences. Numerous research have verified this information. the progress of fatty liver, alcohol induced cirrhosis, smoking habits, obesity caused by overweight, and metabolic diseases such as diabetes. The treatment strategies for HCC can be divided into two categories: One is curative treatment, including liver transplantation, surgical resection, and ablation therapy or selective arterial radiation embolization, aimed at completely eliminating the lesion; Another type is non curative treatment options, including transarterial chemoembolization and systemic therapy, which focus on controlling disease progression and prolonging patient survival. The majority of HCC patients are found to be in an advanced stage and need systemic therapy. Sorafenib and lenvatinib are frequently used as first-line medications in traditional HCC treatment to slow the disease's progression. For second-line treatment, regorafenib, cabozantinib, or remdesizumab are used to inhibit tumors through different mechanisms and prolong survival. In recent years, with the in-depth exploration of the pathogenesis and progression mechanism of HCC, as well as the rapid progress within the domain of tumor immunotherapy, the treatment prospects for advanced HCC patients have shown a positive transformation. This transformation is reflected in the fact that more and more patients are gradually gaining significant and considerable therapeutic advantages from advanced immunotherapy regimens, bringing unprecedented improvements to their treatment outcomes. In order to enable activated T cells to attack tumor cells, immune checkpoint inhibitors interfere with the inhibitory. To evaluate the effects of nivolumab in combination with cabozantinib on patient tumor markers and immune function, as well as the therapeutic efficacy of this combination in treating advanced HCC, a study was conducted. In all, 100 patients with advanced HCC who were brought to our hospital between July 2022 and July 2023 and who did not match the requirements for surgical resection had their clinical data thoroughly analyzed retrospectively in this study. Among them, half of the patients (50 cases) only received oral cabozantinib as a single treatment regimen (set as the control group), while the other half of the patients (50 cases) received intravenous infusion of nivolumab in addition to oral cabozantinib (set as the observation group). The objective of the probe is to examine the variations in disease control rate (DCR) and objective response rate (ORR) between two groups; At the same time, changes in the levels of T lymphocyte subsets (CD3+, CD4+, CD8+) and tumor markers, including AFP, GP-73, and AFP-L3, were evaluated; In addition, changes in liver and kidney function indicators and adverse reactions during treatment were also monitored. For patients with advanced HCC, this research also calculated and analyzed the progression free survival of two patient groups throughout the course of a 12-month follow-up to assess the effectiveness and safety of this therapeutic approach. Upon comparing baseline information for both groups of subjects before treatment, it was found that no statistically significant alterations had occurred (P > 0.05). After the therapeutic intervention, the observation group and control group's ORR and DCR differed statistically significantly (P < 0.05). The observation group's scores significantly improved. Subsequent examination revealed that the observation group's T lymphocyte subset levels had significantly changed, mostly exhibiting an increase in CD3+, CD4+, and CD4+/CD8+ levels while CD8+ levels had comparatively dropped. There was a significant difference (P < 0.05) between these changes and those in the control group. The observation group also showed positive improvements in tumor markers; AFP, GP-73, and AFP-L3 levels were considerably lower in the group under observation than in the control group, with statistically significant differences (P < 0.05). When liver function was assessed, total bilirubin and alanine aminotransferase were found to be considerably lower in the observation group than in the control group (P < 0.05). The incidence of adverse responses was not statistically significant (P > 0.05), indicating that the incidence of adverse responses did not differ significantly between the two groups. When treating advanced HCC, nivolumab and cabozantinib together have the ability to increase T lymphocyte numbers, reduce tumor marker levels, effectively prolong survival time, and have better efficacy than simple control treatment, with good safety.

Evolutionary game analysis of data value co-creation in construction projects

PurposeConstruction projects are characterized by large construction scale, long construction period, high uncertainty, etc. and a large amount of data is generated in the construction process. Through an in-depth exploration of data value, value addition of the projects can be achieved based on data value co-creation. The purpose of this study is to analyze the laws of the strategic choices of participating subjects in the process of data value co-creation and the factors that influence the stability of the system.Design/methodology/approachBased on prospect theory and evolutionary game theory, this study constructs an evolutionary game model of data value co-creation among owner, constructor and designer and analyzes the dynamic evolution law of data value co-creation behavior in construction projects and the influence of key elements on system stability.FindingsThe study shows that the effort cost, profit value sensitivity, participation willingness and the owner’s punishment for non-participating subjects have a significant impact on data value co-creation; adjusting the punishment, reducing the effort cost and improving the subject’s benefit perception can effectively promote the stability of the data value co-creation system; meanwhile, the subjects’ willingness to data value co-creation shows complementarity and interdependence.Originality/valueThe research results can reveal the evolution law of data value co-creation in construction projects and provide support for the optimization of data value co-creation practice and mechanism.

Decoding the Chemical Language of Ribosomally Synthesized and Post-Translationally Modified Peptides from the Untapped Archaea Domain.

Chemical communication is crucial in ecosystems with complex microbial communities. However, the difficulties inherent to the cultivation of archaea have led to a limited understanding of their chemical language, especially regarding the structure diversity and function of secondary metabolites (SMs). Our in-depth exploration into the biosynthetic potential of archaea has unveiled the previously unexplored biosynthetic capabilities and chemical diversity of archaeal ribosomally synthesized and post-translationally modified peptides (RiPPs). Through the first application of heterologous expression in archaeal SM discovery, we have identified 24 lanthipeptides, including a distinctive type featuring diamino-dicarboxylic termini. It highlights the uniqueness of archaeal biosynthetic pathways and significantly expands the chemical landscape of archaeal SMs. Additionally, archaeal lanthipeptides demonstrate antagonistic activity against haloarchaea, mediating the unique biotic interaction in the halophilic niche. They showcase a new ecological role of RiPPs in enhancing the host's motility by inducing the rod-shaped cell morphology and upregulating the archaellin gene expression, facilitating the archaeal interaction with abiotic environments. These discoveries broaden our understanding of archaeal chemical language and provide promising prospects for future exploration of SM-mediated interaction.

The Role of the Government and Innovation of Policy Tools in the Governance of Smart Education: An Interdisciplinary Analysis Based on Public Administration and Education Studie

In the current era of rapid advancement in information technology, smart education has become a key trend in the transformation of the education field. This study ingeniously integrates the unique perspectives of public administration and education science to conduct in-depth exploration of the roles played by the government and the innovation of policy tools in the process of smart education governance. Firstly, it comprehensively reviews the theoretical foundation upon which smart education governance is based, and then clearly defines the core functions of the government as a policy maker, resource allocator, and supervisor and evaluator. During the research process, it was keenly observed that in the current practice of smart education governance, there are a series of problems such as deviations in role positioning, obstacles in policy implementation, and low efficiency of collaborative governance. In terms of policy tool application, there are also many difficulties such as unreasonable structure, poor adaptability to actual situations, and insufficient innovation motivation. By using the literature research method, a large amount of materials were extensively consulted, and at the same time, the case analysis method was employed to deeply analyze the typical successful cases of smart education governance both at home and abroad, absorbing valuable experiences from them. Based on this, it is proposed that the government should reshape its role, transforming from an all-encompassing comprehensive type to a service-oriented one, strengthening policy implementation efforts, and actively promoting the collaborative governance model. At the same time, efforts should be made to innovate policy tools, optimize their structure, and enhance their compatibility with actual needs, constantly promoting innovation at the form and content levels.

On Common Values in the Community of a Shared Future

This study employs a multimodal analytical framework combining textual analysis of dramatic literature, cross-examination of primary and secondary sources, and the theoretical framework of the “community with a shared future for mankind” concept. Through this interdisciplinary approach, the research aims to excavate the inherent values and philosophical implications within literary texts. Taking Shakespeare's The Merchant of Venice as a case study, the investigation first establishes the socio-historical context of the Renaissance drama. Subsequently, it conducts an in-depth exploration of the "community with a shared future" paradigm, systematically analyzing its theoretical dimensions while establishing substantive connections with the play's narrative architecture. The conceptual correlation not only validates the contemporary relevance of this humanistic philosophy but also reveals its interpretative potential for classical texts. The study culminates in proposing actionable recommendations for fostering harmonious development in contemporary global society, thereby bridging literary scholarship with practical socio-political discourse.

Photometric Calibration of Spectral Imaging CoronaGraph with Dual-Wavelength Observation

Abstract The Spectral Imaging CoronaGraph (SICG) serves as the optical observation equipment of E-corona in the Chinese Meridian Project Phase II, which aims at monitoring the initial source of solar activities. For the purpose of in-depth exploration and space weather forecast in the full chain of Sun-Earth space, SICG is designed to work at two wavelengths of 637.4 nm and 530.3 nm in quasi-simultaneous observation mode. Thus the photometric calibration is more challenging to guarantee accurate scientific data of SICG. Two solar photometers are specially developed to match the observing wavelength and make the photoelectronic conversion traceable. Correspondingly, the calibration process selects the solar disk center as the brightness reference, which compensates for the photometric losses along the atmospheric transmission path. This study derives the calibration coefficient from the two photometers for the E-coronal brightness process in real time. By modeling aerosol absorption and scattering and comparing with continuous flat-field observation, the photometric calibration of SICG is evaluated with deviations of 2.1% and 2.3% at 637.4 nm and&amp;#xD;530.3 nm, respectively. Based on this, the evolution speed of a multi-temperature coronal loop was analyzed, facilitating further research into the physical mechanisms of CMEs.

Brief Analysis of the Comparison of the Connotation of National Defense Thought in Buddhism, Taoism and Confucianism

This study analyzes the thoughts of defending the nation in Buddhism, Taoism and Confucianism, and takes "Golden Light Sutra", "Tao Te Ching" and "The Analects of Confucius · Wei Zheng" as the core basis. Buddhism explains the relationship between the rise and fall of a country and the behavior of sentient beings through "cause and effect co-karma", and emphasizes that defending the country is a collective practice of purifying co-karma; Taoism upholds the principle of "Governance by Non-Action"and "The Dao is Spontaneous", advocating reducing human intervention; Confucianism takes "rule by virtue"as its core and attaches importance to the construction of ideology and social ethics. Through comparison, it is found that the three have their own characteristics and complement each other in the construction of rule legitimacy, the transformation of sacred narrative and the way of caring for sentient beings. In-depth exploration of these thoughts is of great significance to excavate ancient Chinese governance wisdom and provide enlightenment for modern social governance.

Emphasizing Grey Systems Contribution to Decision-Making Field Under Uncertainty: A Global Bibliometric Exploration

Grey systems are applied in numerous domains, proving a high efficiency in predicting and investigating complex systems, where data is insufficient, unknown, or partially known. The systems have a strong contribution in the decision-making field under uncertainty, by identifying the connection between variables and optimizing the process of choosing the strategies. With time, the methods offered by the grey systems theory have faced a continuous adoption process in various research fields associated with decision-making. In this context, this paper aims to provide an in-depth bibliometric exploration, focusing on a filtered dataset, gathered from Clarivate Analytics’ Web of Science Core Collection database (WoS) for the purpose of better highlighting the adoption process faced by grey systems theory in the decision-making field under uncertainty. Based on the extracted dataset, the value registered for the annual growth rate is 17.1%, proving that the scientific community’s focus in this field is significant, and it has maintained academics’ interest for a long time. Also, the results of the bibliometric analysis showed that the Journal of Grey System was the most relevant source, while Sifeng Liu provided the greatest contribution to the field based on the number of published papers. Nanjing University of Aeronautics and Astronautics is ranked first in the top of most relevant affiliation based on the number of published papers, while China—the homeland of grey systems theory—assumes the leading contributor country place. The review of the top 10 most cited papers revealed the advantages of using grey systems theory in decision-making field under uncertainty.

What Is a Good Question? Assessing Question Quality via Meta-Fact Checking

Knowledge-based questions are typically employed to evaluate LLM's knowledge boundaries; meanwhile, numerous studies focus on question generation as a means to enhance the capabilities of both models and individuals. However, there is a lack of in-depth exploration about what constitutes a good question from the perspective of knowledge cognition. This paper proposes aligning the complete knowledge underlying questions with educational criteria effectively employed in physics courses, thereby developing novel knowledge-intensive metrics of question quality. To this end, we propose Meta-Fact Checking (MFC), which transforms questions into knowledge graph (KG) triples utilizing LLMs through few-shot prompting, thereby quantifying question quality based on the patterns observed within these triples. MFC introduces a novel interaction mechanism for KGs that communicates meta-facts, illustrating the types of knowledge that KGs can offer to the LLM for reasoning questions, rather than relying solely on the original triples. This strategy ensures that MFC remains unaffected by unexplored triples that LLM has not yet encountered within KGs compared to the retrieve-while-reasoning routine. Experiments across multiple datasets and LLMs demonstrate that MFC significantly improves the accuracy and efficiency of both question answering and assessing. This research marks a pioneering effort to automate the evaluation of question quality based on cognitive capabilities.

Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution

Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective. Their theoretical connections and differences warrant in-depth exploration. This work conducts comprehensive analyses of these losses, yielding significant insights: 1) Common strengths --- both can be viewed as augmentations of traditional losses with Distributional Robust Optimization (DRO), enhancing robustness to distributional shifts; 2) Respective limitations --- stemming from their use of different distribution distance metrics in DRO optimization, SL exhibits high sensitivity to false negative instances, whereas CCL suffers from low data utilization. To address these limitations, this work proposes a new loss function, DrRL, which generalizes SL and CCL by leveraging Rényi-divergence in DRO optimization. DrRL incorporates the advantageous structures of both SL and CCL, and can be demonstrated to effectively mitigate their limitations. Extensive experiments have been conducted to validate the superiority of DrRL on both recommendation accuracy and robustness.

Molecular modelling and gene expression analysis to probe the GT-γ trihelix transcription factors in Solanum tuberosum under drought stress

GT-γ transcription factors, a subfamily known for their involvement in stress responses, remain uncharacterized in Solanum tuberosum under drought stress. This study employed in-silico approaches and in-vitro expression profiling in differential tissues to investigate StGTγ-1, StGTγ-2, StGTγ-3, and StGTγ-4 potential role in the potato’s drought tolerance mechanisms. Analysis of cis-regulatory elements showed complex networks controlling stress response. Alpha helices were prevalent in their structures, possibly aiding protein stability and interaction. Additionally, intrinsically disordered regions were observed in some StGT-γ proteins, suggesting their role in stress adaptation through flexibility. Protein structure modeling and validation revealed structural diversity within the GT-γ family, potentially reflecting variations in functionalities. Physicochemical analysis highlighted differences in protein properties that could influence their nuclear function. Post-translational modifications further diversified their functionalities. Subcellular localization prediction and topology analysis confirmed their nuclear localization, aligning with the anticipated role in transcriptional regulation. GT-γ proteins likely regulate genes due to structural variations. This is based on the presence of DNA-binding domains and functional annotation suggesting roles in metabolism, gene expression, and stress response. Molecular docking predicted partners involved in drought response, indicating GT-γ proteins’ role in drought tolerance networks. Identified StGT-γ genes were highly expressed in leaves after 14 days of drought stress, indicating their key role in protecting this vulnerable tissue during drought. This study enhances understanding of GT-γ factors and provides a foundation for the functional characterization and in-depth exploration of the role and regulatory mechanisms of GT-γ genes in potato’s response to drought stress.

Discrete Curvature Graph Information Bottleneck

Graph neural networks(GNNs) have been demonstrated to depend on whether the node effective information is sufficiently passing. Discrete curvature (Ricci curvature) is used to study graph connectivity and information propagation efficiency with a geometric perspective, and has been raised in recent years to explore the efficient message-passing structure of GNNs. However, most empirical studies are based on directly observed graph structures or heuristic topological assumptions, and lack in-depth exploration of underlying optimal information transport structures for downstream tasks. We suggest that graph curvature optimization is more in-depth and essential than directly rewiring or learning for graph structure with richer message-passing characterization and better information transport interpretability. From both graph geometry and information theory perspectives, we propose the novel Discrete Curvature Graph Information Bottleneck (CurvGIB) framework to optimize the information transport structure and learn better node representations simultaneously. CurvGIB advances the Variational Information Bottleneck (VIB) principle for Ricci curvature optimization to learn the optimal information transport pattern for specific downstream tasks. The learned Ricci curvature is used to refine the optimal transport structure of the graph, and the node representation is fully and efficiently learned. Moreover, for the computational complexity of Ricci curvature differentiation, we combine Ricci flow and VIB to deduce a curvature optimization approximation to form a tractable IB objective function. Extensive experiments on various datasets demonstrate the superior effectiveness and interpretability of CurvGIB.

Exploring Quantum Supremacy: A Review Of Quantum Technologies And Algorithms

This review paper provides an in-depth exploration of quantum computing and the algorithms that drive its potential. It begins by introducing quantum computers and explores various types of quantum computers, focusing on the most prominent architectures. This paper also explains the concept of qubits, highlighting their unique properties such as superposition and entanglement, which differentiate quantum systems from classical computing. It discusses the specific properties of quantum computers that make them particularly suited to solving certain classes of problems, such as optimization, cryptography, and simulation. Additionally, the paper provides a detailed overview of key quantum algorithms, including Shor’s algorithm for integer factorization, Grover’s algorithm for unstructured search, and the use of quantum simulation in modelling complex physical systems. Through this review, we aim to showcase the evolving landscape of quantum computing, its capabilities, and the algorithms driving its future impact across various fields.

Monetary rewards or comment recognition? The difference between the two types of feedback in online Q&amp;A community

PurposeAn in-depth exploration of difference in the motivational effects of various types of feedback can better address the challenges faced by knowledge contributions. Drawing upon the self-determination theory (SDT), this study examines the relative effects of asker monetary rewards and peer comment recognition in the short term, the heterogeneous effects across different social status levels and their long-term impact over time.Design/methodology/approachBased on panel data from an online financial knowledge Q&amp;A community, this study employs a fixed-effects Poisson regression for empirical analysis. In addition, it also conducts instrumental variable regression and a series of robustness tests.FindingsThe results indicate the following: (1) Peer comment recognition has a positive effect on knowledge contributions, whereas asker monetary rewards have a negative impact. (2) In the short-term, the relative effect of asker monetary rewards is stronger, but this effect changes as the social status of the user increases. (3) There is no evidence of an offsetting effect between asker monetary rewards and peer comment recognition. (4) The impact of both asker monetary rewards and peer comment recognition on continuous knowledge contributions decreases over time, but the influence of the latter lasts longer.Originality/valuePrevious studies on monetary rewards and peer recognition have focused primarily on their direct effects. This research explores the differences in external incentives by examining the sources, types and timing of feedback, providing a more comprehensive understanding of the mechanisms that drive continuous knowledge contribution. It also offers a more valuable insights for the management of knowledge Q&amp;A communities.

Does Monocentric Spatial Structure Narrow the Urban-Rural Income Gap? A Case Study of Northeast China

The impact of spatial structure on the Urban-Rural income gap varies across regions and scales. Much of the current literature focuses on urban agglomerations, highlighting the need for more in-depth exploration of specific regions. Using LandScan population data, this paper investigates urban spatial structure from the perspective of monocentricity and polycentricity, investigating their respective impacts and mechanisms on the Urban-Rural income gap in Northeast China. The findings are as follows: (1) The development of a monocentric spatial structure in Northeast China significantly reduces the Urban-Rural income gap, a conclusion verified through robustness testing. (2) A mediation effect test confirms that this spatial structure reduces the gap by increasing urban labor productivity. (3) The urban spatial structure of Northeast China also has positive spillover effects, reducing income inequality between urban and rural areas in neighboring regions. Further exploration of the relationship between Urban-Rural income inequality and spatial structure is crucial for achieving shared prosperity. Additionally, this research provides policy support for developing a novel framework for land use planning and conservation.

Unlocking The Potential of Carbon Nanomaterials in Biomedicine: From Synthesis to Therapeutic Applications

This paper presents an in-depth exploration of the transformative potential of carbon nanomaterials in the biomedical field, underscoring groundbreaking advancements and the broad spectrum of their applications. By categorizing carbon nanomaterials into graphene, carbon nanotubes, fullerenes and nanodiamonds, we elucidate their distinct synthesis processes and unique physicochemical properties, which underpin their utility in novel biomedical applications. The forefront of our review highlights the integration of these materials into cutting-edge therapeutic strategies, including targeted drug delivery systems, molecular diagnostics, precision oncology, advanced bioimaging and scaffold-based tissue regeneration. A critical aspect of our discussion is the examination of biocompatibility and toxicity challenges, where we showcase the latest research aimed at enhancing the safety profiles of carbon nanomaterials for clinical translation. Our analysis advocates for a multidisciplinary approach to harness the full potential of carbon nanomaterials, aiming to pave the way for next-generation healthcare innovations. The convergence of carbon nanomaterial science and biomedicine heralds a new era of solutions to complex medical challenges, promising significant impacts on patient care and therapeutic outcomes.

Exploring the Legal Protection Path of Commercial Data

With the rapid development of digital economy, commercial data has become the core element of enterprise competition, but its legal protection path has not yet formed a unified system.An in-depth exploration of the definition and characteristics of commercial data, analyzing the differences between it and public and corporate data, and highlighting the unique attributes of commercial data in terms of aggregation, value and dynamism are the basis for understanding its legal protection.On this basis, the existing legal protection paths are systematically sorted out, covering the paradigm of empowering legislation, anti-unfair competition law and intellectual property protection, etc. However, these paths still face many limitations in practical application, such as the tendency of pan-interventionism, the difficulty of identifying competitive relationships and the ambiguity of business ethics standards.The existence of these problems highlights the need to optimize the path of legal protection of commercial data, for which it is proposed to build a multi-level, synergistic and comprehensive protection system, optimize the application of the anti-unfair competition law, and create a dual security protection system for secret and public data through legislation. At the same time, the proposal to construct a special article on commercial data can better promote data sharing and the development of artificial intelligence.