Search results

This study aims at investigating the heat transfer characteristics of a nonsquare enclosure when hydrodynamic resistance is altered discontinuously along its inner surface. Particularly, it focuses on investigating how several essential factors collaboratively influence the natural convection, including the Rayleigh number (Ra), the aspect ratio (AR), the nanoparticle volume fraction (ϕ) and the locations of changing hydrodynamic resistance.

To achieve these objectives, an L-shaped enclosure of various AR is adopted, while zero local shear resistance is applied and modeled by stress-free (SF) patches of four distinct arrangements (corresponding to Cases 1–4). The nanofluid is modeled by Buongiorno’s two-phase model. The effects are explored using an in-house numerical framework based on a hybrid lattice Boltzmann-finite difference method with the total variation minimization scheme.

The results show that when Ra is sufficiently large, i.e. Ra = 10⁵, SF patches can generally enhance the heat transfer performance regardless of other factors. However, the ways of achieving those enhancements are different, which mainly depend on the arrangement of the SF patches and AR but are nearly independent of ϕ. The maximum improvement of heat transfer can be achieved in Case 3 with AR = 0.6, Ra = 10⁵ and ϕ = 0.04, where the averaged Nusselt number is enhanced by 8.89%.

This study presents a new scenario where the SF patches of various arrangements are applied to enhance the nanofluid natural convection of a nonsquared enclosure, and it reveals how the improvement is achieved and cooperatively affected by several important factors.

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews.

Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper.

The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product.

This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms.

This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

The purpose of this paper is to explore which structures of academic articles referees would pay more attention to, what specific content referees focus on, and whether the distribution of PRC is related to the citations.

Firstly, utilizing the feature words of section title and hierarchical attention network model (HAN) to identify the academic article structures. Secondly, analyzing the distribution of PRC in different structures according to the position information extracted by rules in PRC. Thirdly, analyzing the distribution of feature words of PRC extracted by the Chi-square test and TF-IDF in different structures. Finally, four correlation analysis methods are used to analyze whether the distribution of PRC in different structures is correlated to the citations.

The count of PRC distributed in Materials and Methods and Results section is significantly more than that in the structure of Introduction and Discussion, indicating that referees pay more attention to the Material and Methods and Results. The distribution of feature words of PRC in different structures is obviously different, which can reflect the content of referees' concern. There is no correlation between the distribution of PRC in different structures and the citations.

Due to the differences in the way referees write peer review reports, the rules used to extract position information cannot cover all PRC.

The paper finds a pattern in the distribution of PRC in different academic article structures proving the long-term empirical understanding. It also provides insight into academic article writing: researchers should ensure the scientificity of methods and the reliability of results when writing academic article to obtain a high degree of recognition from referees.

This paper aims to establish a theoretical search model on academic social networking sites (ASNSs).

Based on the characteristics of ASNSs and a previous extended sense-making model, this paper first presented an initial model of searching on ASNSs. Next, an online survey was conducted on ResearchGate to understand the search processes and outcomes with the help of a survey questionnaire. In total, 359 participants from 70 countries participated in this online survey. The survey results provided a basis for modifying the initial model.

Results showed that the theoretical model of searching on ASNSs included motives for searching on ASNSs, identification of needs, search triggered by information needs, search triggered by social needs and outcomes. The search triggered by information needs was significantly positively correlated with learning outcomes. Besides learning outcomes, searching on ASNSs could help user amplify their social networks and promote research dissemination.

Understanding users’ search habits and knowledge acquisition can provide insights for ASNSs to design an interface to support searching and enhance learning. Moreover, the proposed model can help users recognize their knowledge status and learning effects and improve their learning efficiency.

This paper contributes to establishing a theoretical model to understand users’ search process and outcomes on ASNSs.

The purpose of this paper is to classify Chinese word semantic relations, which are synonyms, antonyms, hyponyms and meronymys.

Basically, four simple methods are applied, ontology-based, dictionary-based, pattern-based and morpho-syntactic method. The authors make good use of search engine to build lexical and semantic resources for dictionary-based and pattern-based methods. To improve classification performance with more external resources, they also classify the given word pairs in Chinese and in English at the same time by using machine translation.

Experimental results show that the approach achieved an average F1 score of 50.87 per cent, an average accuracy of 70.36 per cent and an average recall of 40.05 per cent over all classification tasks. Synonym and antonym classification achieved high accuracy, i.e. above 90 per cent. Moreover, dictionary-based and pattern-based approaches work effectively on final data set.

For many natural language processing (NLP) tasks, the step of distinguishing word semantic relation can help to improve system performance, such as information extraction and knowledge graph generation. Currently, common methods for this task rely on large corpora for training or dictionaries and thesauri for inference, where limitation lies in freely data access and keeping built lexical resources up-date. This paper builds a primary system for classifying Chinese word semantic relations by seeking new ways to obtain the external resources efficiently.