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The purpose of this study is to identify the causative factors of metro construction safety accidents, analyze the correlation between accidents and causative factors and assist in developing safety management strategies for improving safety performance in the context of the Chinese construction industry.

To achieve these objectives, 13 types and 48 causations were determined based on 274 construction safety accidents in China. Then, 204 cause-and-effect relationships among accidents and causations were identified based on data mining. Next, network theory was employed to develop and analyze the metro construction accident causation network (MCACN).

The topological characteristics of MCACN were obtained, it is both a small-world network and a scale-free network. Controlling critical causative factors can effectively control the occurrence of metro construction accidents. Degree centrality strategy is better than closeness centrality strategy and betweenness centrality strategy.

In practice, it is very difficult to quantitatively identify and determine the importance of different accidents and causative factors. The weights of nodes and edges are failed to be assigned when constructing MCACN.

This study provides a theoretical basis and feasible management reference for construction enterprises in China to control construction risks and reduce safety accidents. More safety resources should be allocated to control critical risks. It is recommended that safety managers implement degree centrality strategy when making safety-related decisions.

This paper establishes the MCACN model based on data mining and network theory, identifies the properties and clarifies the mechanism of metro construction accidents and causations.

This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.

A novel type of heat transfer fluid known as hybrid nanofluids is used to improve the efficiency of heat exchangers. It is observed from literature evidence that hybrid nanofluids outperform single nanofluids in terms of thermal performance. This study aims to address the stagnation point flow induced by Williamson hybrid nanofluids across a vertical plate. This fluid is drenched under the influence of mixed convection in a Darcy–Forchheimer porous medium with heat source/sink and entropy generation.

By applying the proper similarity transformation, the partial differential equations that represent the leading model of the flow problem are reduced to ordinary differential equations. For the boundary value problem of the fourth-order code (bvp4c), a built-in MATLAB finite difference code is used to tackle the flow problem and carry out the dual numerical solutions.

The shear stress decreases, but the rate of heat transfer increases because of their greater influence on the permeability parameter and Weissenberg number for both solutions. The ability of hybrid nanofluids to strengthen heat transfer with the incorporation of a porous medium is demonstrated in this study.

The findings may be highly beneficial in raising the energy efficiency of thermal systems.

The originality of the research lies in the investigation of the Darcy–Forchheimer stagnation point flow of a Williamson hybrid nanofluid across a vertical plate, considering buoyancy forces, which introduces another layer of complexity to the flow problem. This aspect has not been extensively studied before. The results are verified and offer a very favorable balance with the acknowledged papers.

The purpose of the paper is to analyse the performances of closures and compressibility corrections classically used in turbulence models when applied to highly-compressible turbulent boundary layers (TBLs) over flat plates.

A direct numerical simulation (DNS) database of TBLs, covering a wide range of thermodynamic conditions, is presented and exploited to perform a priori analyses of classical and recent closures for turbulent models. The results are systematically compared to the “exact” terms computed from DNS.

The few compressibility corrections available in the literature are not found to capture DNS data much better than the uncorrected original models, especially at the highest Mach numbers. Turbulent mass and heat fluxes are shown not to follow the classical gradient diffusion model, which was shown instead to provide acceptable results for modelling the vibrational turbulent heat flux.

The main originality of the present paper resides in the DNS database on which the a priori tests are conducted. The database contains some high-enthalpy simulations at large Mach numbers, allowing to test the performances of the turbulence models in the presence of both chemical dissociation and vibrational relaxation processes.