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As the information asymmetry and credit rationing are existing, SMEs are finding it difficult to gain bank credit. Trade credit, as a one off substitution, gives another access to SME finance. The purpose of this paper is to examine the effects between inter‐corporate relationships (including the direct‐relationship and indirect‐relationship) and trade credit.

Following the mainstream of qualitative and quantitative research, this paper examines the relationship between SMEs and their analysis of the commercial credit financing. In the empirical research, through text‐analysis to build the variable of “the number of unions that enterprises take part in”. First, find the relate union through “baidu and googel” by the keywords of enterprise's name and the Union; then two persons select and determine which the enterprise may take part in and calculate the number. For that which cannot make sure, ask the third person. Learning from the HHI‐index, the paper calculates according to the amount and times of the enterprise related transactions to build the variable of “the concentration of enterprises related transactions”. Based on three years panel data (from 2007 to 2009) of 196 small and medium listed companies, this paper establishes the empirical models and examines the effects between inter‐corporate relationship and trade credit through the random effect model.

The paper finds that: SMEs must pay attention to inter‐enterprise relationship management. Without the power and status owned by large enterprises, SMEs have to learn how to survive in the complex and changing environment. The managers of SMEs have to develop their skills to manage the inter‐enterprise relationship. It finds the effects between inter‐enterprise relationship and trade credit seem like a “U” shape. SMEs should take part in associations wittingly and establish the relationship with the others, as all economic activities are embedded in the social network. This research shows that participating in the business associations, especially provincial associations, has a positive impact to gain trade credit.

This paper breaks through the traditional SMEs' financing theories. In this paper, the individual level theories have been extended to the organizational level. This paper also expands the study of the social capital theory and gives a more tolerable empirical test.

This study aims to understand the epistemic foundation of the classification applied in the first Chinese library catalogue, the Seven Epitomes (Qilue).

Originating from a theoretical stance that situates knowledge organization in its social context, the study applies a multifaceted framework pertaining to five categories of textual data: the Seven Epitomes; biographical information about the classificationist Liu Xin; and the relevant intellectual, political, and technological history.

The study discovers seven principles contributing to the epistemic foundation of the catalogue's classification: the Han imperial library collection imposed as the literary warrant; government functions considered for structuring texts; classicist morality determining the main classificatory structure; knowledge perceived and organized as a unity; objects, rather than subjects, of concern affecting categories at the main class level; correlative thinking connecting all text categories to a supreme knowledge embodied by the Six Classics; and classicist moral values resulting in both vertical and horizontal hierarchies among categories as well as texts.

A major limitation of the study is its focus on the main classes, with limited attention to subclasses. Future research can extend the analysis to examine subclasses of the same scheme. Findings from these studies may lead to a comparison between the epistemic approach in the target classification and the analytic one common in today's bibliographic classification.

The study is the first to examine in depth the epistemic foundation of traditional Chinese bibliographic classification, anchoring the classification in its appropriate social and historical context.

When a vehicle passes through a long highway tunnel, the smoke it discharges accumulates in the tunnel. High smoke concentration has an important influence on the driver’s health and driving safety. The use of numerous jet fans to diffuse the smoke causes excessive energy consumption, so it is of significant practical value to design suitable tunnel ventilation.

The study is based on the continuum hypothesis, incompressible hypothesis, steady flow hypothesis and similar hypothesis of gas in a long highway tunnel. These hypotheses calculate the gas emissions and wind demand in a long highway tunnel given the deployment of the jet fan program.

This program selects each of the two 1120-type jet machine group and sets up 13 groups; each group has an interval of 184.5 m in the end. The analysis of air test results when the tunnel is in operation shows that CO and smoke concentrations meet the design requirements, which can provide reference for a similar engineering design later.

At present, a highway tunnel is recognized at home and abroad by means of clearance of longitudinal ventilation, which is 2,000 m. In view of this, this paper is based on the theory of longitudinal jet ventilation of a highway tunnel, whose length is more than 2,000 m, to calculate the ventilation and apply it to a tunnel’s ventilation design.

In recent years, deep learning and extended reality (XR) technologies have gained popularity in the built environment, especially in construction engineering and management. A significant amount of research efforts has been thus dedicated to the automation of construction-related activities and visualization of the construction process. The purpose of this study is to investigate potential research opportunities in the integration of deep learning and XR technologies in construction engineering and management.

This study presents a literature review of 164 research articles published in Scopus from 2006 to 2021, based on strict data acquisition criteria. A mixed review method, consisting of a scientometric analysis and systematic review, is conducted in this study to identify research gaps and propose future research directions.

The proposed research directions can be categorized into four areas, including realism of training simulations; integration of visual and audio-based classification; automated hazard detection in head-mounted displays (HMDs); and context awareness in HMDs.

This study contributes to the body of knowledge by identifying the necessity of integrating deep learning and XR technologies in facilitating the construction engineering and management process.