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Since smartphones became ubiquitous, online grocery and food purchases through take-away delivery platforms have steadily increased in China. Nevertheless, whether the development of take-away delivery can ameliorate urban–rural wage inequality still requires further analysis. The purpose of this paper is to clarify whether this positive effect exists.

This paper makes estimations based on city and individual levels combining the Chinese Household Income Project (CHIP) 2008, CHIP 2013, CHIP2018 survey data and the take-away delivery site data. At the city level, the Oaxaca-Blinder (O-B) decomposition method is employed to construct wage inequality index of urban and rural labors. At the individual level, this paper analyzes urban–rural wage differentials with high or low formal education level.

The rapid establishment of take-away delivery sites has resulted in an increase of 52.425 yuan on average in the annual wage of rural labors with low formal education level. When the cumulative number of sites increases by 1 unit, the annual wage inequality index decreases by 0.007 on average. Labors with the characteristics of rural household registration and low education can enjoy more dividends. Through inter-/within-industry decomposition, this paper elaborates formal education, age and cross-industry transfer as the main factors for the improvement of urban–rural wage inequality. Narrowing effect of wage differences between different groups in multiple sample slices also contributes to the mechanism analyses.

To the best of the author’s knowledge, this paper is the first to analyze the impact of take-away delivery development on the urban–rural wage inequality from the perspective of the establishment of take-away delivery sites. This empirical study will enrich the existing theoretical perspectives on urban–rural divide under the emergence of new forms of employment. The results indicate that new forms of employment represented by take-away delivery can not only promote economic growth, but also eliminate urban–rural inequality.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently.

First, local and global features of images are extracted, and the visual dictionary is generated by the k-means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model.

The findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions.

Dunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs.

The mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

The paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant departments in formulating public opinion control measures for specific time and space contexts.

The spatiotemporal situational awareness framework comprises situational element extraction, situational understanding and situational projection. In situational element extraction, the data on the COVID-19 vaccine, including spatiotemporal tags and text contents, is extracted. In situational understanding, the bidirectional encoder representation from transformers – latent dirichlet allocation (BERT-LDA) and bidirectional encoder representation from transformers – bidirectional long short-term memory (BERT-BiLSTM) are used to discover the topics and emotional labels hidden in opinion texts. In situational projection, the situational evolution characteristics and patterns of online public opinion are uncovered from the perspective of time and space through multiple visualisation techniques.

From the temporal perspective, the evolution of online public opinion is closely related to the developmental dynamics of offline events. In comparison, public views and attitudes are more complex and diversified during the outbreak and diffusion periods. From the spatial perspective, the netizens in hotspot areas with higher discussion volume are more rational and prefer to track the whole process of event development, while the ones in coldspot areas with less discussion volume pay more attention to the expression of personal emotions. From the perspective of intertwined spatiotemporal, there are differences in the focus of attention and emotional state of netizens in different regions and time stages, caused by the specific situations they are in.

The situational awareness framework can shed light on the dynamic evolution of online public opinion from a multidimensional perspective, including temporal, spatial and spatiotemporal perspectives. It enables decision-makers to grasp the psychology and behavioural patterns of the public in different regions and time stages and provide targeted public opinion guidance measures and offline event governance strategies.

Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images.

The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results.

The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved.

This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.

The purpose of this paper is to prepare carbon fiber-reinforced polyimide matrix composites and to investigate the single role of carbon fiber in polyimide composites on tribological performance under distilled water condition.

Three carbon fiber-reinforced polyimide matrix composites were fabricated by using a hot press molding technique. The tribological behaviors of carbon fiber-reinforced polyimide matrix composites sliding against steel ball were evaluated with a ball-on-disk tribotester under distilled water condition. Meanwhile, the effect of different length of carbon fiber on the wear resistance of polyimide matrix composites was investigated during the sliding process.

The friction coefficients and specific wear rates of polyimide composites containing 100 μm carbon fibers were lower than those of other specimens. The wear mechanism of carbon fiber-reinforced composites was delamination under distilled water condition. The interfacial combination between the carbon fiber and matrix became worse with the increase of length of carbon fiber.

This paper reported the effect of the different length of carbon fiber on polyimide matrix composites to prepare mechanical parts in mining industrial fields.

The purpose of this paper is to investigate a cylindrical flow insert for a parabolic trough solar collector. Centrally placed and eccentric placed inserts are investigated in a systematic way to determine which configuration leads to the maximum thermal enhancement.

The analysis is performed in SolidWorks Flow Simulation with a validated computational fluid dynamics model. Moreover, the useful heat production and the pumping work demand increase are evaluated using the exergy and the overall efficiency criteria. The different scenarios are compared for inlet temperature of 600 K, flow rate of 100 L/min and Syltherm 800 as the working fluid. Moreover, the inlet temperature is examined from 450 to 650 K, and the diameter of the insert is investigated up to 50 mm.

According to the final results, the use of a cylindrical insert of 30 mm diameter is the most sustainable choice which leads to 0.56 per cent thermal efficiency enhancement. This insert was examined in various eccentric positions, and it is found that the optimum location is 10 mm over the initial position in the vertical direction. The thermal enhancement, in this case, is about 0.69 per cent. The pumping work demand was increased about three times with the insert of 30 mm, but the absolute values of this parameter are too low compared to the useful heat production. So, it is proved that the increase in the pumping work is not able to eliminate the useful heat production increase. Moreover, the thermal enhancement is found to be greater at higher temperature levels and can reach up to 1 per cent for an inlet temperature of r650 K.

The present work is a systematic investigation of the cylindrical flow insert in a parabolic trough collector. Different diameters of this insert, as well as different positions in two dimensions, are examined using a parametrization of angle-radius. To the authors’ knowledge, there is no other study in the literature that investigates the presented many cases systematically with the followed methodology on parabolic trough collectors. Moreover, the results of this work are evaluated with various criteria (thermal, exergy and overall efficiency), something which is not found in the literature.