Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.     

草原石林：东北亚早白垩世花岗岩伸展穹隆和岩浆成矿爆发的地质遗迹及其形成过程

东北亚地区至少发育9个大型草原石林,属花岗岩石林的一种,是东北亚早白垩世巨量伸展和第四系冰川、风化作用的共同产物,主要特点是发育密集的近水平节理。本文通过整理岩浆岩专题填图和构造研究发现,这些石林的主体是早白垩世东北亚地区巨量伸展过程中形成众多的不对称花岗岩穹隆或同减薄岩体,岩体边部发育韧性剪切带,核部发布近水平岩浆流动面理,伸展和岩浆多期侵位导致与成矿密切相关的岩浆热液大量上涌,岩体抬升至地表,沿岩浆面理发育密集的近水平节理和部分垂直节理,后期沿这些构造面理发生的物理风化和化学作用(包括风蚀、冰川以及盐风化等)造就了千奇百怪的形态。草原石林是花岗岩地貌中的“另类”,不仅具有指示东北亚地区花岗岩形成时代(早白垩世)的特点,也是寻找晚中生代中国北方大规模成矿的良好标志体,而且风化形成的千姿百态的景色极具观赏价值,可以作为整个蒙古草原地区和大兴安岭南段地区特色的地学科普和生态旅游资源。     

一种基于深度森林的恶意代码分类方法

针对当前恶意代码静态分析方法精度不足的问题，将恶意代码映射为无压缩的灰度图像，然后根据图像变换方法将图像变换为恒定大小的图像，使用方向梯度直方图提取图像的特征，最后提出一种基于深度森林的恶意代码分类方法.实验中选择不同家族的多个恶意代码样本进行分类，验证了该方法的有效性，并且实验结果优于近期提出的SPAM-GIST方法.     

Hierarchical learning using deep optimum-path forest

Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements.     

High efficiency of methylene blue removal using a novel low-cost acid treated forest wastes,Cupressus semperirens cones: Experimental results and modeling

The removal of methylene blue (MB) from aqueous solutions using sulfuric acid modified Cupressus semperirens cones (H₂SO₄-CSC), was investigated. Results showed that a pH value of 12 was favorable for the adsorption of MB and that the moisture and ash yields are suitable for industrial exploitation. A high porosity value was found, 68.1%. In agreement with its low content of basicity compared to its acidity, the H₂SO4-CSC absorbent had an acidic behavior. Rate constants of pseudo-first-order, pseudo-second-order, and nth kinetic model were determined to analyze the dynamic of the biosorption process; they showed that adsorption kinetics followed a pseudo-second-order and nth kinetic models. Ionic strength was shown to have a negative impact on the biosorption of MB onto H₂SO₄-CSC. The Sips isotherm model was found to be the most relevant to describe MB biosorption onto H₂SO₄-CSC with a correlation factor R²?>?0.999. The biosorption capacity of H₂SO₄-CSC was found to be 460?mg?g^?1 at 10°C and 590?mg?g^?1 at 25°C, confirming biosorbent efficiency for the removal of MB dye from aqueous solutions. Thermodynamic parameters indicated that the biosorption process of MB was endothermic and more effective at high temperatures. The values of ΔG° and ΔH° confirmed that the biosorption of MB onto H₂SO₄-CSC was spontaneous and endothermic in nature. An irregular increase in randomness at the H₂SO₄-CSC–solution interface during the biosorption process was suggested by the positive values of ΔS°.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

Quantifying the efficiency and biases of forest Saccharomyces sampling strategies

Saccharomyces yeasts are emerging as model organisms for ecology and evolution, and researchers need environmental Saccharomyces isolates to test ecological and evolutionary hypotheses. However, methods for isolating Saccharomyces from nature have not been standardized, and isolation methods may influence the genotypes and phenotypes of studied strains. We compared the effectiveness and potential biases of an established enrichment culturing method against a newly developed direct plating method for isolating forest floor Saccharomyces spp. In a European forest, enrichment culturing was both less successful at isolating Saccharomyces paradoxus per sample collected and less labour intensive per isolated S. paradoxus colony than direct isolation. The two methods sampled similar S. paradoxus diversity: The number of unique genotypes sampled (i.e., genotypic diversity) per S. paradoxus isolate and average growth rates of S. paradoxus isolates did not differ between the two methods, and growth rate variances (i.e., phenotypic diversity) only differed in one of three tested environments. However, enrichment culturing did detect rare Saccharomyces cerevisiae in the forest habitat and also found two S. paradoxus isolates with outlier phenotypes. Our results validate the historically common method of using enrichment culturing to isolate representative collections of environmental Saccharomyces. We recommend that researchers choose a Saccharomyces sampling method based on resources available for sampling and isolate screening. Researchers interested in discovering new Saccharomyces phenotypes or rare Saccharomyces species from natural environments may also have more success using enrichment culturing. We include step-by-step sampling protocols in the supplemental materials.     

Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.