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The purpose of this paper is to explore the factors that affect the compactness of the mud filter cake, so as to prepare diaphragm wall slurry with good uniformity, small filtration loss and excellent recycling performance.

In this paper, the thickness, filtration loss and slurry viscosity of the filter cake are used as the characterization methods. The effects of pore depth, slurry specific gravity, intercalated metal ions, bridging polymer and water-soluble polymer on the compactness of the filter cake were studied.

The experimental results showed that the slurry's own pressure (pore depth) and specific gravity have little influence on the compactness of the filter cake and K⁺ can be considered as an auxiliary filtration loss reduction factor. Both the sulfonate copolymer and the potassium polyacrylate particle can significantly reduce the filtration loss of the slurry, which can effectively improve the filter cake compactness. Moreover, the composite application of potassium polyacrylate particles in the sizes of 80–100 and 150–200 meshes can exhibit a better filter cake compaction effect.

It solves the problems of high pulping cost, serious pollution of the environment, poor quality of filter cake formation and large filtration loss during the construction of the diaphragm wall, which improved the construction quality of the diaphragm wall.

A novel ternary flocculant was prepared by a simple compounding method to achieve efficient and rapid mud-water separation. This paper aims to discuss the possible mud-water separation mechanism.

This experimental study aims to investigate the effects of different types of flocculants on the separation of waste mud water and the degradation of flocculants in the supernatant. The flocculating component, the ratio of the flocculating accelerator to the flocculant and the addition amount of the novel ternary flocculant were optimized.

The experimental results show that the composition of the new ternary flocculant is cationic polyacrylamide (CP-02), grafted starch (GS-501) and flocculation sedimentation accelerator, the best effect, the mass ratio is 1:0.5: 0.75. According to 0.25:1 (volume ratio), the new ternary flocculant is pre-configured into a solution with a concentration of 3 kg/m³ to achieve efficient and rapid mud-water separation.

The new ternary flocculant is used for the separation of mud and water in the underground continuous wall waste mud, improving the level of civilized construction.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Although researchers have demonstrated a keen interest in knowledge collaboration in online encyclopedias, previous studies have seldom explored the dynamic interrelations in online encyclopedias over time that involve the iteratively melding of individual cognitive system and knowledge collaboration system. Therefore, this paper aims to reveal the structure and dynamics of knowledge collaboration in online encyclopedias from a perspective of system dynamics (SD).

This paper proposes a general activity system of knowledge collaboration in online encyclopedias based on Engeström’s activity theory. According to the SD methodology proposed by Forrester, this study develops a holistic SD model by identifying interactions of knowledge collaboration factors based on behavioral theories; validating the SD model by structural tests and behavior tests involving historical data of English Wikipedia; and conducting simulation to capture the interactive dynamics of the salient factors of knowledge collaboration.

According to the SD methodology, this study develops and validates an SD model to explore interesting dynamic interrelations among core factors (contributors, conflicts, discussions, entries quantity and entries quality) that are neglected by previous research. The results show that there is a significant negative feedback relationship between inactive contributors and entries quality, between contributors and conflicts and between edit conflicts and entries quality. There is a complicated nonlinear feedback relationship between active contributors and entries quality, and between edit conflicts and discussions.

Different from prior empirical studies that normally investigate the unidirectional linear relationships among prominent factors of knowledge collaboration in online encyclopedias from a static perspective, this study captures a dynamic picture of their interrelations by unfolding their behavior patterns over time. The main contribution of this study is to develop a holistic SD model and to reveal and elaborate on the complex dynamics involved online encyclopedias based on activity theory.