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The purpose of this paper is to analyze the cable-supported bridges more efficiently by building the finite element model with the spatial combined cable element.

The spatial combined cable element with rigid arms and elastic segments was derived. By using the analytical solution of the elastic catenary to establish the flexibility matrix at the end of the cable segment and adding it to the flexibility matrix at the ends of the two elastic segments, the flexibility matrix at the end of the cable body is obtained. Then the stiffness matrix of the cable body is established and the end force vector of cable body is given. Using the displacement transformation relationship between the two ends of the rigid arm, the stiffness matrix of the combined cable element is derived. By assigning zero to the length of the elastic segment(s) or/and the rigid arm(s), many subdivisions of the combined cable element can be obtained, even the elastic catenary element.

The examples in this field and specially designed examples proved the correctness of the proposed spatial combined cable element.

The combined cable element proposed in this study can be used for the design and analysis of cable-stayed bridges. Case studies show that it is able to simulate cable accurately and could also be used to simulate the suspenders in arch bridges as well in suspension bridges.

The purposes of this study are to contribute to the limited green growth (GG) literature in emerging markets, to analyze GG from a financial economy perspective and to determine the contribution of financial development and innovation to GG in Brazil, Russian Federation, India, China and South Africa and Türkiye (BRICS-T). BRICS-T countries significantly impact the world population, international politics, energy resources and economy. In addition, BRICS-T countries are one of the leading countries in the world with their sustainability efforts. Investigating the GG model in these countries may contribute to structuring emerging economies around the principles of GG and advancing global green transformation efforts.

The authors applied panel data analysis from 2001 to 2019. GG is economic growth free from environmental depletion in the model. National income, personnel expenditure and foreign direct investments are macroeconomic variables. These variables measure economic development and promote economic and social progress, which is essential for GG. Capital accumulation and innovation are essential tools in GG transformation. Therefore, financial development and patent applications represent the moderating variables. The authors estimate the fixed effect model with Parks-Kmenta robust.

Empirical results show that national income growth and foreign direct investments positively affect GG. Personnel expenditure negatively affects GG. On the contrary, financial development and patent growth have little moderating role.

This study contributes to the literature on creating a GG model in emerging countries. The study is original in its model and sample.

This study aims to manufacture cotton fabric with thermal regulation performance by using the composite phase change material (CPCM) prepared by coating paraffin doped with expanded graphite (EG), and the thermal effect of the fabric material was evaluated and characterized.

EG/paraffin CPCM with shape stability and enhanced thermal conductivity were prepared by the impregnation method and then finished on the surface of cotton fabric with coating technology. The microstructure, crystal structure, chemical composition, latent heat property and thermal conductivity were analyzed by scanning electron microscope, x-ray diffraction, Fourier transform infrared spectroscopy, differential scanning calorimeter and thermal constant analyzer. The photo-thermal effect of the coated fabric was studied by a thermal infrared imager.

CPCM prepared with a mass ratio of EG to paraffin of 1:8 showed excellent shape stability and low paraffin leakage rate. The latent heat of the CPCM was 51.6201 J/g and the thermal conductivity coefficient was increased by 11.4 times compared with the mixed paraffin. After the CPCM was coated on the surface of the cotton fabric, the light-to-heat conversion rate of the C-EG/PA3 sample was improved by 86.32% compared with the original fabric. In addition, the coated fabric showed excellent thermal stability and heat storage performance in the thermal cycling test.

EG can improve the shape stability and thermal conductivity of paraffin but will reduce the latent heat energy.

The method developed provided a simple and practical solution to improving the thermal regulation performance of fabrics.

Combining paraffin wax with fabrics in a composite way is innovative and has certain applicability in improving the thermal properties of fabrics.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

The purpose of this paper is to integrate distributed ontologies on the web system and clarify the structure of the integrated one.

A formal method based on concept lattices is introduced as a mechanism to form more general semantic level. By checking the extension and the intension of concept, this method extracts the concept pairs satisfying inclusion relations from descartes' set of concepts in distributed ontologies first, and then constructs a concept lattice according to these concept pairs. An algorithm to reduce redundant relations is also proposed to clarify the structure of integrated ontology.

The experiments demonstrate the effectiveness of the proposed method to reduce redundant relations, and the Nir‐to‐Ncr ratio inclines to 1.05 from 3.13.

Instances of certain concept are not given completely on the web, so it is difficult to check extension of different concepts.

A very useful method of integrating distributed ontologies on the web.

Compared with existing methods, this formal method can be performed by program automatically without any human intervening, and can extract the inclusion relations between concepts from distributed ontologies completely.

This paper aims to present an underwater climbing robot for wiping off marine life from steel pipes (e.g. jackets of oil platforms). The self-adaption mechanism that consists of a passive roll joint and combined magnet adhesion units provides the robot with better mobility and stability.

Adhesion requirements are achieved by analyses of falling and slipping. The movement status on pipes is analyzed to design the passive roll joint. The optimized structure parameters of the combined magnet adhesion unit are achieved by simulations. An approximation method is established to simplify the simulations conditions, and the simulations are conducted in two steps to save time effectively.

The self-adaption mechanism has expected performance that the robot can travel on pipes in different directions with high mobility. Meanwhile, the robot can clean continuous region of underwater pipes’ surface of offshore platforms.

The proposed underwater robot is needed by offshore oil platforms as their jackets require to be cleaned periodically. Compared with traditional maintenance by divers, it is more efficient, economic and safety.

Due to the specific self-adaption mechanism, the robot has good mobility and stability in any directions on pipes with different diameters. The good performance of striping attachments from pipes makes the underwater robot be a novel solution to clean steel pipes.

The aim of this study is to investigate the competitiveness of the selected services in Turkey in comparison with the EU and the selected EU countries based on three comparative advantage indices.

Three different revealed comparative advantage indices were utilised in a combined way. Import and export figures of six service sectors were taken into account for the period of 2000-2010. The selected services are: transportation, travel, construction, financial services and insurance, communications and IT services, and personal, cultural and recreational services. Consistency of the results was achieved through correlation analyses.

Strong comparative advantages exist for Turkey in construction, tourism and transportation sectors. Although Turkish financial and insurance and communication and computer-information sectors appear to be weak compared to EU, there is a substantial potential for improvement.

A detailed comparative investigation of services' competitiveness for Turkey was provided. The policy decision makers in Turkey and in Europe's selected countries can utilise the findings and recommendations of the study for projection of the investigated sectors.