Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

To classify DNA sequences, k-mer frequency is widely used since it can convert variable-length sequences into fixed-length and numerical feature vectors. However, in case of fixed-length DNA sequence classification, subsequences starting at a specific position of the given sequence can also be used as categorical features. Through the performance evaluation on six datasets of fixed-length DNA sequences, our algorithm based on the above idea achieved comparable or better performance than other state-of-the art algorithms.

Numerical Classification of Brevibacterium and Related Genera Using Linocin M18 Bacteriocin

Fifty bacterial strains isolated from dairy product, skin and blood from cancer and kidney failure dialysis patients were identified to 22 species and the following genera： Brevibacterium, Corynebacterium, Arthrobacter, Actinomyces, Exiguobacterium, Kocuria, Micrococcus, Rothia, Rhodococcus and classified numerically using a set of 52 phenetic characteristics, using simple matching coefficient （Ssm） and clustering method of unweighted average linkage between groups by SPSS program. They were also grouped to 7 main clusters and 29 sub-clusters in the hierarchical tree. Twelve isolates of the different species from the genera Brevibacterium, Arthrobacter, Corynebacterium, Kocuria, Rhodococcus, Rothia were selected from the taxonomic clusters and probed for lin gene by peR. One species Kocuria rhizophila which inhibited most of the test organism did not have lin gene in the chromosome while the species Corynebacterium glucuronolyticum, Arthrobacter comminsii, Arthrobacter oxydans have the lin gene. Our results establish a wide distribution of the structural gene encoding this Iinocin M 18 within coryneform bacteria and also in the genus Kocuria.

Comprehensive analysis of slope stability and determination of stable slopes in the Chador-Malu iron ore mine using numerical and limit equilibrium methods

One of the critical aspects in mine design is slope stability analysis and the determination of stable slopes. In the Chador- Malu iron ore mine, one of the most important iron ore mines in central Iran, it was considered vital to perform a comprehensive slope stability analysis. At first, we divided the existing rock hosting pit into six zones and a geotechnical map was prepared. Then, the value of MRMR （Mining Rock Mass Rating） was determined for each zone. Owing to the fact that the Chador-Malu iron ore mine is located in a highly tectonic area and the rock mass completely crushed, the Hoek-Brown failure criterion was found suitable to estimate geo-mechanical parameters. After that, the value of cohesion （c） and friction angle （tp） were calculated for different geotechnical zones and relative graphs and equations were derived as a function of slope height. The stability analyses using numerical and limit equilibrium methods showed that some instability problems might occur by increasing the slope height. Therefore, stable slopes for each geotechnical zone and prepared sections were calculated and presented as a function of slope height.

Weighted Sparse Image Classification Based on Low Rank Representation

The conventional sparse representation-based image classification usually codes the samples independently,which will ignore the correlation information existed in the data.Hence,if we can explore the correlation information hidden in the data,the classification result will be improved significantly.To this end,in this paper,a novel weighted supervised spare coding method is proposed to address the image classification problem.The proposed method firstly explores the structural information sufficiently hidden in the data based on the low rank representation.And then,it introduced the extracted structural information to a novel weighted sparse representation model to code the samples in a supervised way.Experimental results show that the proposed method is superiority to many conventional image classification methods.

Numerical Approaches to Identification of Characteristic Soil Layers in an Urban Environment

Pedogenetic soil horizons are one of the fundamental building blocks of modern soil classification; however, in soils of urban areas which are often strongly disturbed by human activities, horizons are difficult to distinguish but substitutive morphological layers may be identified. To identify the characteristic soil layers in an urban environment, 224 soil layers of 36 in-situ pedons were examined and described in urban and suburban Nanjing, and 27 variables were extracted for multivariate analysis. Three groups and six subdivisions were identified by TwoStep cluster analysis combined with hierarchical cluster analysis based on factor scores. Soil forming factors and soil forming processes could be interpreted from the principal component analysis （PCA） of variables, cluster analysis of soil layers, and discriminant analysis of soil layer groups and their subdivisions. Parent materials, moisture regimes, organic matter accumulation, and especially nutrient accumulation were the main causes of characteristic soil layer formations. The numerical approaches used in this study were useful tools for characteristic soil layer identification of urban soils.

Classification of Hailstone Trajectories in a Hail Cloud over a Semi-Arid Region in China

The growth trajectory of hailstones in clouds determines the ground intensity and spatial distribution of hailfall.A systematic study of hail trajectories can help improve the current scientific understanding of the mechanisms by which hail forms in semi-arid regions of China and,in doing so,improve the quality of hail forecasts and warnings and help to prevent and mitigate disasters.In this study,the WRFv3.7.1 model was employed to provide the background field to drive the hailstone trajectory model.Cluster analysis was then used to classify hail trajectories to investigate the characteristics of different types of hail trajectories and the microphysical characteristics of hail formation.The differences in hail trajectories might be mainly due to differences in the background flow fields and microphysical fields of hail clouds in different regions.Comparative analysis revealed that as the maximum particle size of ground hailfall increased,the maximum supercooled cloud water content and the maximum updraft velocity for the formation and growth of hailstone increased.The larger the size when the hailstone reaches its maximum height,the larger the ground hailstone formed.Overall,the formation and growth of hailstone are caused by the joint action of the dynamical flow field and cloud microphysical processes.The physical processes of hailstone growth and main growth regions differ for different types of hail trajectories.Therefore,different catalytic schemes should be adopted in artificial hail prevention operations for different hail clouds and trajectories due to differences in hail formation processes and ground hailfall characteristics.

A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification

Complex proteins are needed for many biological activities.Folding amino acid chains reveals their properties and functions.They support healthy tissue structure,physiology,and homeostasis.Precision medicine and treatments require quantitative protein identification and function.Despite technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved.Protein function inference from amino acid sequences is the main biological data challenge.This study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family discrimination.Training and validation employed 70%of the dataset,while 30%was used for testing.This paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all phases.The initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning classification.The single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%F1-score.The two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%F1-score.This work provides balanced,reliable,and precise forecasts for all families in all measures.It ensured that the new model was resilient to family variances and provided high-scoring results.

An Innovative Bispectral Deep Learning Method for Protein Family Classification

Proteins are essential for many biological functions.For example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and homeostasis.Note that quantifiable protein characteristics are vital for improving therapies and precision medicine.The automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence data.Inferring protein function from amino acid sequences is crucial in biology.This study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector representation.The power of two representations was used to identify each amino acid,and a coding technique was established for each sequence family.Subsequently,the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding techniques.Initially,the dataset was split 70/30 for training and testing.Correspondingly,the dataset was utilized for 70%training,15%validation,and 15%testing.The suggested methods are evaluated using accuracy,precision,and recall.The non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after validation.Meanwhile,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%recall.Validation showed 90%accuracy,91.2%precision,and 89.7%recall.Note that both algorithms outperform the rest.The paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning methods based on convolutional architecture.They offered the best inference performance as the proposed approach improves categorization and prediction.Several instances show successful multi-class prediction in molecular biology’s massive data.

Classification and Translation of Chinese Abbreviations

Chinese abbreviation,containing fewer words and delivering a wealth of information,is a vital component of Chinese language.But the tremendous differences between Chinese and English make it an arduous task to translate Chinese abbreviations into English.Based on the analyses of the structure and patterns of word–formation of Chinese abbreviations,it makes a classification of Chinese abbreviations,summarize the translation methods,and point out some attention points in translation.A systematic analysis on the structure and classification of Chinese abbreviations will be beneficial to reduce the mistakes in its translation.

基于果实表型性状的山楂种质资源遗传多样性分析

为保护山楂种质资源,评价及筛选利用优异山楂种质资源,对国家果树种质沈阳山楂圃保存的234份山楂种质资源的26个果实表型性状进行鉴定评价,分析表型性状的分布频率、变异系数和遗传多样性指数,通过聚类分析对种质资源进行分类,并基于果实性状进行主成分分析。结果表明:山楂种质的果实描述性状多样性丰富,以果点显著、果皮红色、果实近方形、果点多、果皮有光泽、萼片姿态开张反卷、萼片有绒毛、萼片有锯齿、萼片三角形、萼片着生状态显著、种核有凹痕、萼筒漏斗形、梗洼广浅和梗基膨大状居多,分别占97.86%、90.17%、66.67%、65.95%、97.44%、56.84%、96.58%、97.86%、98.72%、89.32%、86.32%、65.81%和98.29%。数量性状中种仁率变异系数(43.88%)最大,其次为单果重(41.38%);Shannon-Wiener指数范围0.68~2.02。描述性状Shannon-Wiener指数范围0.07~1.67,其中果肉质地和颜色较高,多样性最丰富,而数量性状Shannon-Wiener指数范围达0.68~2.02,也表现出丰富的多样性。聚类分析在欧式距离为7时将供试种质资源分为4个类群,第Ⅰ类群和第Ⅱ类群均是大果山楂,羽裂山楂资源大部分在第Ⅳ类群,其余的种主要分布在第Ⅲ类群和第Ⅳ类群,没有形成单独的类群,但这些资源在聚类图中是聚在一起的。主成分分析结果将26个表型性状简化为9个主成分,包含果实形状等17个表型性状。

基于岩体质量的井筒保安矿柱优化与应用

为兼顾井筒稳定和减少资源的不必要浪费,在常规的岩层移动角井筒保安矿柱圈定法基础上,采用工程类比法,将Ⅱ级岩体质量围岩部分的井筒保安矿柱尺寸优化为80m,形成上部台体下部圆柱体的保安矿柱。同时对优化后的保安矿柱周边压覆资源采取控制采场顶板暴露面积和分层充填的保护性开采措施。数值模拟结果表明:按优化后的保安矿柱范围开采周边矿体后,地表井架、卷扬机房及井筒内壁变形量均未超过最大允许变形值,说明优化方案具有可行性。

基于拓展的Mathews稳定图法和数值模拟的采空区稳定性分析

某铅锌矿经多年开采遗留下一些未处理采空区,为进行后续开采,现需对采空区进行稳定性研究。通过拓展的Mathews稳定图法、FLAC3D数值模拟软件进行研究,并基于Mathews稳定图法分析暴露时间对采空区稳定性的影响。以矿山三个未处理采空区作为研究对象,利用RMR和Q′值进行岩体质量分级,根据获得的岩体力学参数,使用增加了稳定性等概率线的Mathews稳定图,计算出采空区顶板及上盘的稳定性及稳定概率;计算考虑暴露时间的采空区稳定性概率,进行比较;利用FLAC3D数值模拟软件对采空区进行稳定性数值模拟分析,并与Mathews合成图得出的结果进行对比。结果表明:综合Mathews稳定图法和FLAC3D数值模拟,6204采空区顶板评价结果为不稳定,其他采空面均为稳定;随着暴露时间的增加,采空区稳定性随之降低,且对岩性较差的采空区有较大影响;拓展的Mathews稳定图法与数值模拟可以相互验证,综合分析采空区的稳定性。

求解约束优化问题的改进蛇优化算法

结合外点罚函数法与改进蛇优化算法求解约束优化问题,得到一种新的求解约束优化问题的算法WDFSO。算法WDFSO首先通过外点罚函数法将约束优化问题转化为一系列界约束优化问题,然后运用基于变异质心的对立学习策略与种群分类策略改进的蛇优化算法对所得界约束优化问题进行求解,进而获得所求约束优化问题的解。为验证算法WDFSO的有效性,选取CEC2006中19个标准约束优化问题进行数值实验,并使用Wilcoxon秩和检验来证明算法的显著性。实验结果表明,与对比算法相比,算法WDFSO求解约束优化问题具有更高的收敛精度和更好的稳定性。最后应用算法WDFSO求解两个工程约束优化问题,结果表明算法WDFSO求解性能更好。

NRS分型及椎旁肌的MRI影像学与腰椎管狭窄症术后改善率的关系

目的 探讨数字评分量表(NRS)分型及椎旁肌的MRI影像学与腰椎管狭窄症术后改善率的关系。方法 选取本院收治的220例腰椎管狭窄患者作为观察组,选取同期健康体检者100例作为对照组;对受试者NRS分型进行判断,并分析患者竖脊肌横截面积、多裂肌横截面积、多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度;采用日本脊柱外科学会(JOA)下腰痛评分系统对腰椎管狭窄症术后改善率进行分析。结果 对照组NRS分型明显优于观察组,差异有统计学意义(P <0.05);观察组患者竖脊肌横截面积、多裂肌横截面积均明显低于对照组,多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度均明显高于对照组,且差异有统计学意义(P <0.05);术后改善率良好组患者NRS分型、多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度明显低于不良组,术后改善率良好组患者竖脊肌横截面积、多裂肌横截面积均明显高于不良组,差异有统计学意义(P <0.05);腰椎管狭窄症术后改善率与NRS分型、竖脊肌横截面积、多裂肌横截面积呈明显正相关关系,与多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度呈明显负相关关系,差异有统计学意义(P <0.05);腰椎管狭窄症患者NRS分型、竖脊肌横截面积、多裂肌横截面积、多裂肌脂肪浸润程度、竖脊肌脂肪浸润程度均是影响其术后改善率的独立性因素,且差异有统计学意义(P <0.05)。结论 NRS分型及椎旁肌的MRI表现与腰椎管狭窄症术后改善率呈显著相关性关系。

基于主客观环流分型的强降水数值预报MODE检验方法及其在2019年暖季东北地区的应用

本文构建了基于主客观环流分型的强降水数值预报空间检验(MODE)方法框架,并利用该框架对欧洲中期天气预报中心全球模式(ECMWF)和中国气象局区域中尺度数值天气预报模式(CMA_MESO)的2019年暖季东北地区强降水预报进行检验。结果表明,2019年暖季东北地区54个强降水日的环流型可分为:西风槽型(15个)、副热带高压影响型(13个)、急流型(5个)、西部(12个)和东部冷涡型(9个)。其中,西风槽型和急流型以区域性强降水为主,模式对其强降水发生与否的预报能力强,TS评分较高;西部、东部冷涡型强降水的局地性强,模式对其强降水发生与否的预报能力差,TS评分低;副热带高压影响型也以区域性强降水为主,模式对其强降水发生与否的预报能力也比较强,但是对其强降水质心位置、强度、面积等属性预报偏差较大,TS评分也相对较低。另外,从两种模式预报性能对比看,CMA_MESO强降水强度和面积预报较实况普遍偏强,虽然其预报的TS评分一般高于ECMWF,但其对强降水预报的空报率也都比ECMWF大,对强降水的属性预报偏差一致性一般也低于ECMWF,其预报的可订正性整体上不及ECMWF。

基于岩体质量分级的某金矿深部采场支护技术研究

深部矿岩支护参数选择的科学性、合理性是保障其长期稳定的关键。以山东黄金某金矿为研究背景,采用三维数字摄影测量系统对矿区深部-620 m中段17线穿脉岩体进行结构面测量,获取岩体结构面产状、节理间距、密度等参数,运用Q系统和RMR对岩体质量进行分级,得出-620 m中段17线穿脉岩体为Ⅳ级,岩体质量较差。在此基础上,采用理论分析方法提出采场围岩支护方案,并运用FLAC3D软件对支护方案进行模拟,分析支护前后采场围岩位移、应力的变化和塑性区分布情况。结果表明:对于稳定性为Ⅳ级的围岩,采用“锚索+锚杆联合混凝土喷浆”的支护方式,可有效控制顶板和两帮的变形,并降低应力集中,保障采场围岩的稳定性。本研究确定了合理的支护方案,为矿山安全生产和支护设计提供了科学依据。

基于多因素分析法的巷道支护分级与支护参数优化

为解决金川三矿区东部贫矿巷道支护破坏变形问题,通过优化的层次分析法(analytic hierarchy process,AHP)构建支护影响因素层次结构分析模型,实现巷道分级可视化应用;针对巷道支护破坏变形严重区域开展巷道支护破坏变形特征及机理分析,提出巷道支护优化方案;并利用FLAC3D构建数值模型,开展了支护方案支护效果对比分析。结果表明:破碎岩体在外部水平构造应力为主的高应力环境下,采用“锚网喷”支护可以有效改善巷道周围塑性区、应力场和位移的分布特征;相比螺纹钢锚杆,玻璃钢锚杆所受轴力较低,可以有效改善锚固水泥浆的应力状态,进而减小锚固段长度,在支护效果、材料成本上具有一定优势。

广东省暴雨型浅层滑坡灾害动力预警模型与气象风险预警研究

针对县级地质灾害气象风险预警面临的精度及模型建设问题,根据广东省地质灾害主要发生在坡面残坡积浅表层的突出特点,通过对典型地质灾害进行物理模拟试验和数值模拟,研究广东省浅表层斜坡失稳发生机理。研究表明:边坡在暴雨条件下,斜坡岩土体容易在浅表层首先造成失稳,影响因素主要有降雨量、降雨历时、土体类别和坡体结构等因素。由此,对研究区划分斜坡单元,按各斜坡单元的坡长、坡度、岩土类型、分层及其关键物理力学参数开展斜坡单元概化分类,并将Green-Ampt降雨入渗模型和无限边坡稳定性评价方法相结合,优化构建了动力学斜坡稳定性评价模型。结合龙川县贝岭镇流域应用实例,初步探索了坡面单元尺度下地质灾害气象风险预警斜坡失稳动力学预警技术,可为广东省开展以斜坡单元预警为主要方式的县级地质灾害气象风险预警提供支撑。

静动载条件下深部破碎巷道支护设计及稳定性分析

深部破碎围岩稳定性控制是制约深部金属矿安全高效开采的难题之一。以金川二矿区深部某中段破碎围岩条件下的采准巷道稳定性控制为工程背景,应用Q、RMR、GSI进行岩体质量分级与岩体力学参数估算,分别应用经验法和工程类比法进行采准巷道支护设计,并依据收敛约束法确定合理的释能支护时机。在此基础上,应用UNWEDGE软件对释能支护设计进行安全性评价,并通过RS2数值方法分析支护效果。研究结果表明:对于深部破碎采准巷道一次支护采用锚网喷支护,二次支护采用混凝土衬砌,最佳衬砌支护时机应满足巷道帮位移小于38 mm且顶板位移小于15 mm;静动载下有无支护数值分析结果表明,锚网喷支护满足巷道安全要求,同时对采准巷道围岩结构控制型、应力控制型破坏以及动力失稳破坏等起到良好的控制效果。研究结果为金川二矿区深部破碎采准巷道围岩稳定性控制提供了理论依据与技术支撑。

金川某矿区东部贫矿开采区域支护工艺优化研究

为解决金川某矿区东部贫矿巷道支护破坏变形普遍、返修频繁、开采经济效益低的问题,通过系统开展巷道收敛变形及围岩体质量现场调查,依据围岩稳定性分级指标对开采区域支护难度进行分级;基于巷道变形破坏严重区域的变形特征分析,提出了巷道支护优化方案;利用FLAC3D构建数值模型,开展了不同支护方案支护效果的对比分析。研究结果表明:在外部水平构造应力为主的高应力环境下,采用“锚网喷”支护可以有效控制巷道变形,增加钢筋条及锚杆施加预紧力可以有效提高围岩的稳定性;相比较于普通螺纹钢锚杆支护,锚杆采用梅花形布置方式可以大大减少使用量和支护工作量、有效提高东部贫矿开采的经济效益。