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Capillary barrier effects (CBEs) have been applied in capillary barrier systems as effective means of protecting underground regions from wetting. However, the microscale behavior of CBEs and related influencing factors are not well understood. This study utilized microfluidics to investigate effects of flow rate and pore size variability on CBEs at the microscale. Imbibition processes of water displacing air were imposed on three water-wet microfluidic chips with different degrees of pore size variability and injection rates. The obtained results demonstrated that the increasing injection rate changed water invasion pattern from finger growth to compact displacement. The CBEs could increase [S.sub.wf] (water saturation of fine sections at the onset of breakthrough) by up to 44%. The increase in [S.sub.wf] became smaller at a higher injection rate but was insensitive to the pore size variability within the investigation range. This study also elaborated the specific effect of inertia on pore body invasions under different pore characteristics. Among the materials with close average pore size, those with more uniform pore sizes are recommended for the construction of capillary barrier systems. For capillary barrier systems with robust CBEs, increasing the thicknesses of coarse layers has insignificant effect on reducing deep percolation. Key words: microfluidics, capillary barrier effect, favorable imbibition, flow rate, pore size variability, 1. Introduction Capillary barrier effects (CBEs) can be formed at the interfaces between fine- and coarse-grained soils in unsaturated conditions. Briefly, owing to their smaller pore sizes, the fine-grained soils [...]

As a new technology,blockchain has attracted the attention of all walks of life since its birth.Consensus algorithm is one of the core technologies of blockchain technology,and the research of consensus algorithms is also the top priority of blockchain development.Aiming at the problems of PBFT,which is widely used in consortium blockchain,such as the random selection of primary node and the inability of nodes to join and exit dynamically,an dynamic PBFT algorithm(DPBFT) is proposed.Firstly,the selection method of primary node of PBFT is improved,and the trust score is set for every node.The trust score is dynamically updated according to the behavior of the nodes in every round of consensus,and the primary node is selected according to the integral value,which improves the probability that an honest node is elected as the primary node.Secondly,four sub-protocols(JOIN,EXIT,PCLEAR,RCLEAR) are set for PBFT algorithm to solve the problem of joining and exiting nodes respectively and punish the offending nodes,so that the system has a dynamic network structure.It can be proved that the newly added four sub-protocols have good safety and liveness without affecting the safety and liveness of the original PBFT algorithm.At last,experimental results show that DPBFT algorithm has better consensus efficiency than traditional PBFT algorithm.

With the continuous development of the futures market,its transaction volume keeps increasing.Behind the massive transactions,some traders use related transaction behaviors to manipulate the futures market and disrupt the order of transactions,which has brought severe challenges to market supervision and risk control.How to mining potential related transaction behaviors from massive transaction data is an important task for maintaining fair transactions in the futures market.In response to this problem,this paper proposes a bidirectional long short-term memory(Bi-LSTM) network model with multi-feature information fusion,which extracts multiple dimensions of shallow feature information such as trading time,trading volume,position changes,and futures varieties from the original transaction data.The Bi-LSTM network model learns deep features from the contextualrelationship in the forward and backward directions of the time series and realizes the detection of related transaction behavior.For shallow features extraction,a multi-granularity window feature extraction method based on transaction behavior is proposed,to captures the correlation of transactions between accounts from the levels of day,hour,minute,second,etc.It solves the problems of high data dimension,large amount of data,and weak correlation of original transaction data.The model introduces the Dropout strategy to alleviate problems of slow convergence and over-fitting.Experimental results on the real data of Zhengzhou Commodity Exchange show that the proposed method evidently improves the classification precision and recall compared with some traditional classification models and RNN and LSTM network.At the same time,the ablation experiment of each dimension information proves the effectiveness of the multi-feature fusion method and the multi-granularity window strategy.In addition,the transaction data of two futures varieties are extracted for testing,and the results show that the proposed model has good generalization ability.

Diffusion is an important phenomenon that governs contaminant transport through compacted clay liner at the bottom of landfill. The diffusion process is influenced by many factors, i.e., type, valence and concentration of ions. Therefore, an accurate diffusion model is of great importance for the design of liner system of landfill. The simplified Guntelberg activity coefficient was introduced to indicate concentration difference between ideal solution and actual solution. The driving force of diffusion usually considered combined action of chemical and diffusion potential. The semipermeable membrane effect of clay was considered. A diffusion model describing simultaneous movement of multi-ions through clay barrier was proposed. Moreover, the multi-physical field simulation software COMSOL Multiphysics was employed for conducting simulations for the proposed diffusion model. The results indicate that the factors such as solution non-ideality, diffusion potential, and semipermeable membrane effect of clay play an important role in the diffusion process of multiple ions. A remarkable retarded behavior was captured, when the solution non-ideality or semipermeable membrane effect was considered in the proposed model. Further, an accelerating effect on cations was observed taking diffusion potential into consideration. The multi-ion diffusion process under combined action is not a simple superposition of single influence mechanism, but rather, each mechanism restricts each other and affects the multi-ion diffusion behavior.

Summary Edible mushrooms have become an important global ingredient due to their rich nutrients and delicious taste. However, their high moisture content, abundant bioactive components and susceptibility to microbial contamination led to a relatively short shelf‐life. Therefore, extensive research on the active ingredients, bioactivity and preservation techniques of mushrooms is crucial for enhancing the rapid development and increasing acceptance of the mushroom industry. Furthermore, the comprehensive utilisation of by‐products from mushroom processing and the diversified development of mushroom products are important means to increase the added value of the mushroom industry. Therefore, through the aforementioned research, the sustainable and healthy development of the mushroom industry can be promoted. [ABSTRACT FROM AUTHOR]

Faced with a large number of IoT transactions,efficient consensus algorithm plays a key role in the application of blockchain technology into IoT.In this paper,according to the problems of long consensus time delay and low throughput in practical Byzantine fault tolerant algorithm (PBFT),we propose a practical Byzantine fault tolerant algorithm based on clustering (C-PBFT).Firstly,the nodes are clustered according to location features to form a network structure with multiple centers and layers.Then,consensus tasks are divided to conduct consensus in bottom and top network,thereby reducing the communication cost needed by consensus.Finally,credibility of dynamic credit model evaluation node is introduced to reduce the participation of abnormal nodes and increase the security and reliability of the system.Experimental results show that the C-PBFT algorithm can effectively reduce communication overhead,consensus delay and improve throughput.