An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The research proposes a hybrid algorithm model that combines model-driven and data-driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi-objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi-source heterogeneous data, selection of key sub-parameters using Principal Component Analysis (PCA), enhanced K-means clustering analysis, determination of structural component target thresholds, time-dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double-layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub-parameters that have significant impacts on the performance of structural components. Enhanced K-means clustering analysis is carried out to effectively group and classify the identified key sub-parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi-parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan-Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double-layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and the bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.
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Environmental information disclosure (EID) is integral to government environmental policies and corporate social responsibilities. The current research presents a theoretical model that analyses the connection between EID, green finance, and green technological innovation (GTI). The required data was collected through a structured questionnaire, and final data analysis was performed using 230 valid responses. Structural equation modeling (SEM) combined with artificial neural networks (ANN) is used in the present framework to analyze constructs’ linear and non-linear relationships. The empirical analysis found that government EID significantly improved the value of green securities (GS) and green credit (GC), aided considerably by enterprises’ openness about environmental practices. Green securities and GC are also used, which has a good impact on the development of GTI. Green financing is critical when linking environmental disclosure with green technologies in businesses. The results reveal the mediating role of GC and GS in the relationship between the two aspects of EIDs (EEID and GEID) and GTI, providing a new perspective on how EID influences GTI through financial mechanisms. The findings contribute to a more comprehensive understanding of the intricate interplay between EID, green finance, and GTI, providing valuable insights for policymakers, businesses, and investors working toward sustainable development.
Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource scenarios, where the available training data is scarce, previous prompt-based methods may still perform poorly for prompt-based representation learning due to a superficial understanding of the relation. To this end, we highlight the importance of learning high-quality relation representation in low-resource scenarios for RE, and propose a novel prompt-based relation representation method, named MVRE (Multi-View Relation Extraction), to better leverage the capacity of PLMs to improve the performance of RE within the low-resource prompt-tuning paradigm. Specifically, MVRE decouples each relation into different perspectives to encompass multi-view relation representations for maximizing the likelihood during relation inference. Furthermore, we also design a Global-Local loss and a Dynamic-Initialization method for better alignment of the multi-view relation-representing virtual words, containing the semantics of relation labels during the optimization learning process and initialization. Extensive experiments on three benchmark datasets show that our method can achieve state-of-the-art in low-resource settings.
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