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In the current research of debris flow geological disaster prediction, determining reasonable disaster-inducing factors and ensuring the accuracy and rapidity of the prediction model are considered vital issues, and also, essential foundations for disaster early warning and disaster prevention and mitigation. Aiming at the problems of low prediction accuracy and long prediction time in the current debris flow research, firstly, six debris flow impact factors were selected relying on the fast multiple principal component extraction (FMPCE) algorithm, including rainfall, slope gradient, gully bed gradient, relative height difference, soil moisture content and pore water pressure. Next, based on the broad learning (BL) algorithm, the debris flow prediction model based on FMPCE and the optimized BL is established with the input of debris flow-inducing factors and the output of debris flow probability. Then the model is optimized using matrix stochastic approximate singular value decomposition (SVD), and the debris flow disaster prediction model, based on SVDBL, is constructed. The prediction results of the optimized model are compared with those of the gradient descent optimized the BP neural network model(GD-BP), Support Vector Machines model(SVM) based on grid search and BL model. The results show that the accuracy of SVDBL is 7.5% higher than that of GD-BP, 3% higher than that of SVM and 0.5% higher than that of BL. The RMSE sum of SVDBL was 0.05870, 0.0478 and 0.0227 less than that of GD-BPSVM and BL, respectively; the MAPE sum of SVDBL was 1.95%, 1.66% and 0.49% less than that of GD-BPSVM and BL; the AUC values of SVDBL were 12.75%, 7.64% and 2.79% higher than those of the above three models, respectively. In addition, the input dataset is expanded to compare the training time of each model. The simulation results show that the prediction accuracy of this model is the highest and the training time is the shortest after the dataset is expanded. This study shows that the BL can be used for debris flow prediction, and can also provide references for disaster early warning and prevention.

As an efficient, eco-friendly and economical solution for connecting precast concrete components, grouted sleeve connectors aroused widespread interest in engineering site. However, as the key material of grouted sleeve connection, grouting material needs to provide enough good performance to meet the engineering requirements. In order to better design the mix proportion of grouting materials, this paper studies the influence of water binder ratio, carbon fibre content, expansion agent addition and other factors on the performance of grouting materials, and optimizes the mixture ratio of grouting materials by analyzing the workability, compressive strength and expansibility, and analyzes the bonding performance of grouting materials by using the half-grouting steel sleeve connector.

Around the world, soft soils can be found in many areas close to seas and rivers. These areas play an indispensable and crucial role in the development of government plans, especially in the population growth sector. Due to maintaining a weak shear power and vast settlement under the buildings, soft soils are considered problematic soil. The significant risks associated with building structures and infrastructures in soft soil are high, requiring engineers' extreme attention. It depends on undrained shear strength (USS) that the foundation of structures can bear in soft soil, and this factor vigorously controls the selection of soil improvement techniques. In recent years, there have been enhancements and extensions in the methodologies employed for estimating soil characteristics, including USS. These methods are divided into three main sections: Laboratory Testing, Field Testing, and Correlation with Other Soil Parameters. In recent research, data science techniques have created more reliable and accurate models for predicting USS. This study aims to apply the K-Nearest Neighbors (KNN) classifying method for predicting USS. Mountain Gazelle Optimizer (MGO) and Coronavirus Herd Immunity Optimizer (CHIO) appeal for developing hybrid models with KNN and facilitating accuracy enhancement. The dataset which utilized in this study contains four input variables including liquid limit (LL), plastic limit (PL), and sleeve friction (SF), overburden weight (OBW). Comparative analysis across all data phases reveals that the KNCH model, optimized using the CHIO, achieved superior predictive performance with the highest coefficient of determination (R² = 0.993), and the lowest values in root mean square error (RMSE = 85.19), mean squared error (MSE = 7256.15), normalized RMSE (NRMSE = 0.470), and scatter index (SI = 0.065). In contrast, the KNN model without optimization reported R² = 0.971, RMSE = 168.17, and SI = 0.132, while the KNMG model—optimized using the MGO—resulted in R² = 0.983, RMSE = 128.15, and SI = 0.101.

Reactive powder concrete (RPC) is a new cement-based material with ultra-high strength, high durability, high toughness and good volume stability. The study of chloride corrosion behavior is of great significance to the application of RPC in complex service environment. In this paper, the chloride ion immersion experiment is carried out to investigate the change rule of chloride ion concentration on the surface of RPC with soaking time under different loads. The results show that with the extending of soaking time, the chloride ion concentration of RPC surface gradually increases and tends to be stable, and the different depths from RPC surface also show similar rules. The results also show that the chloride ion diffusion characteristics of RPC under different loads are different, and the chloride ion diffusion rate under bending stress is higher than that under compressive stress.

This paper presents a comprehensive investigation regarding the carbonation, chloride ions penetration and freeze-thaw durability test of Reactive Powder Concretes (RPCs). Experimental results demonstrate that the depth of carbonization deepens in RPCs with the extension of carbonization time, but the internal carbonization reaction gradually weakens. The wetting-drying cycles accelerates chloride ion migration in concrete. Freeze-thaw cycles reduce the mechanical properties of concrete.

This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is extremely useful for future research in this area. The examples of regression analysis using the Statistical Application System (SAS) are also included. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject fields.

目的：报道高迁移率族蛋白N2（HMGN2）对大肠埃希菌（Escherichia coli）K12的抗菌功能,并对其抗菌机制进行探讨,同时检验HMGN2对中性粒细胞是否具有趋化活性。创新点：从分子水平上探讨了HMGN2对大肠埃希菌的抗菌机制。方法：用反相高效液相色谱法从人类子宫纤维囊腺瘤中提取组织细胞的HMGN2分子（tH MGN2）。诱导重组表达质粒PET-32a-c（＋）-HMGN2表达重组蛋白HMGN2（rH MGN2）。用琼脂糖凝胶弥散法对HMGN2的抗菌活性进行检测,并用微量肉汤稀释法测定HMGN2的最小抑菌浓度（MIC）。通过膜通透性实验和凝胶阻滞实验检测HMGN2对细菌菌膜和核酸的作用。通过结晶紫实验和电镜扫描验证HMGN2的抗生物被膜形成作用。通过氮蓝四唑（NBT）法和Transwell趋化法分别验证HMGN2的活化效应和对中性粒细胞的趋化活性。结果：我们分离纯化获得了高质量的天然和重组HMGN2分子,同时验证了HMGN2对革兰氏阴性大肠埃希菌具有较强的抗菌活性,MIC为16.25μg/ml。细菌膜通透性实验发现HMGN2使大肠埃希菌膜渗透性明显增大。HMGN2分子与大肠埃希菌K12染色体DNA和质粒DNA的结合均呈浓度依赖效应。银染和扫描电镜结果显示,HMGN2与大肠埃希菌共培养可干扰细菌生物被膜形成,并破坏已形成的早期和成熟生物被膜。然而HMGN2对中性粒细胞没有活化作用和趋化作用。结论：作为抗菌肽,HMGN2对大肠埃希菌有良好的抗菌活性。该活性可能通过影响细胞膜的通透性和干扰细菌DNA转录以及干扰生物被膜而发挥作用。

Aim： To clarify whether CFTR is a molecular target of intestinal fluid secretion caused by the anthraquinone compounds from laxative herbal plants. Methods： A cell-based fluorescent assay to measure I- influx through CFTR chloride channel. A short-circuit current assay to measure transcellular CI- current across single layer FRT cells and freshly isolated colon mucosa. A closed loop experiment to measure colon fluid secretion in vivo. Results： Anthraquinone compounds rhein, aloe-emodin and 1,8-dihydroxyanthraquinone （DHAN） stimulated I- influx through CFTR chloride channel in a dose-dependent manner in the presence of physiological concentration of cAMP. In the short-circuit current assay, the three compound enhanced CI- currents in epithelia formed by CFTR-expressing FRT cells with ECso values of 73±1.4, 56±1.7, and 50±0.5 μmol/L, respectively, and Rhein also enhanced CI- current in freshly isolated rat colonic mucosa with a similar potency. These effects were completely reversed by the CFTR selective blocker CFTRinh-172. In in vivo closed loop experiments, rhein 2 mmol/L stimulated colonic fluid accumulation that was largely blocked by CFTR~oh-172. The anthraquinone compounds did not elevate cAMP level in cultured FRT cells and rat colonic mucosa, suggesting a direct effect on CFTR activity. Conclusion： Natural anthraquinone compounds in vegetable laxative drugs are CFTR potentiators that stimulated colonic chloride and fluid secretion. Thus CFTR chloride channel is a molecular target of vegetable laxative drugs.

Differentiation from preadipocytes into mature adipocytes is a complex biological process in which miRNAs play an important role. Previous studies showed that miR-214-3p facilitates adipocyte differentiation of bone marrow-derived mesenchymal stem cells (BMSCs) in vitro. The detailed function and molecular mechanism of miR-214-3p in adipocyte development is unclear. In this study, the 3T3-L1 cell line was used to analyze the function of miR-214-3p in vitro. Using 5-Ethynyl-2′-deoxyuridine (EdU) staining and the CCK-8 assay, we observed that transfection with the miR-214-3p agomir visibly promoted proliferation of 3T3-L1 preadipocytes by up-regulating the expression of cell cycle-related genes. Interestingly, overexpression of miR-214-3p promoted 3T3-L1 preadipocyte differentiation and up-regulated the expression of key genes for lipogenesis: PPARγ, FABP4, and Adiponectin. Conversely, inhibition of miR-214-3p repressed 3T3-L1 preadipocyte proliferation and differentiation, and down-regulated the expression of cell cycle-related genes and adipogenic markers. Furthermore, we proved that miR-214-3p regulates 3T3-L1 preadipocyte differentiation by directly targeting the 3′-untranslated regions (3′UTR) of Ctnnb1, which is an important transcriptional regulatory factor of the Wnt/β-Catenin pathway. Taken together, the data indicate that miR-214-3p may positively regulate preadipocyte proliferation and enhance differentiation through the Wnt/β-Catenin signaling pathway.