Characterizing three-dimensional features of Antarctic subglacial lakes from the inversion of hydraulic potential——Lake Vostok as a case study

To estimate basal water storage beneath the Antarctic ice sheet, it is essential to have data on the three-dimensional characteristics of subglacial lakes. We present a method to estimate the water depth and surface area of Antarctic subglacial lakes from the inversion of hydraulic potential method. Lake Vostok is chosen as a case study because of the diverse and comprehensive measurements that have been obtained over and around the lake. The average depth of Lake Vostok is around 345±4 m. We estimated the surface area of Lake Vostok beneath the ice sheet to be about 13300±594 km^2. The lake consists of two sub-basins separated by a ridge at water depths of about 200–300 m. The surface area of the northern sub-basin is estimated to be about half of that of the southern basin. The maximum depths of the northern and southern sub-basins are estimated to be about 450 and 850 m, respectively. Total water volume is estimated to be about 4658±204 km^3. These estimates are compared with previous estimates obtained from seismic data and inversion of aerogravity data. In general, our estimates are closer to those obtained from the inversion of aerogravity data than those from seismic data, indicating the applicability of our method to the estimation of water depths of other subglacial lakes.

Exploring evolutionary features of directed weighted hazard network in the subway construction

A better understanding of previous accidents is an effective way to reduce the occurrence of similar accidents in the future. In this paper, a complex network approach is adopted to construct a directed weighted hazard network(DWHN) to analyze topological features and evolution of accidents in the subway construction. The nodes are hazards and accidents, the edges are multiple relationships of these nodes and the weight of edges are occurrence times of repetitive relationships. The results indicate that the DWHN possesses the property of small-world with small average path length and large clustering coefficient, indicating that hazards have better connectivity and will spread widely and quickly in the network. Moreover,the DWHN has the property of scale-free network for the cumulative degree distribution follows a power-law distribution.It makes DWHN more vulnerable to target attacks. Controlling key nodes with higher degree, strength and betweenness centrality will destroy the connectivity of DWHN and mitigate the spreading of accidents in the network. This study is helpful for discovering inner relationships and evolutionary features of hazards and accidents in the subway construction.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Evolutionary Computation Based Optimization of Image Zernike Moments Shape Feature Vector

The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.

Surface characteristics analysis of fractures induced by supercritical CO_(2)and water through three-dimensional scanning and scanning electron micrography

Morphology of hydraulic fracture surface has significant effects on oil and gas flow,proppant migration and fracture closure,which plays an important role in oil and gas fracturing stimulation.In this paper,we analyzed the fracture surface characteristics induced by supercritical carbon dioxide(SC-CO_(2))and water in open-hole and perforation completion conditions under triaxial stresses.A simple calculation method was proposed to quantitatively analyze the fracture surface area and roughness in macro-level based on three-dimensional(3D)scanning data.In micro-level,scanning electron micrograph(SEM)was used to analyze the features of fracture surface.The results showed that the surface area of the induced fracture increases with perforation angle for both SC-CO_(2)and water fracturing,and the surface area of SC-CO_(2)-induced fracture is 6.49%e58.57%larger than that of water-induced fracture.The fractal dimension and surface roughness of water-induced fractures increase with the increase in perforation angle,while those of SC-CO_(2)-induced fractures decrease with the increasing perforation angle.A considerable number of microcracks and particle peeling pits can be observed on SC-CO_(2)-induced fracture surface while there are more flat particle surfaces in water-induced fracture surface through SEM images,indicating that fractures tend to propagate along the boundary of the particle for SC-CO_(2)fracturing while water-induced fractures prefer to cut through particles.These findings are of great significance for analyzing fracture mechanism and evaluating fracturing stimulation performance.

Feature Selection Method by Applying Parallel Collaborative Evolutionary Genetic Algorithm

Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.

An embedded vertical‐federated feature selection algorithm based on particle swarm optimisation

In real life,a large amount of data describing the same learning task may be stored in different institutions(called participants),and these data cannot be shared among par-ticipants due to privacy protection.The case that different attributes/features of the same instance are stored in different institutions is called vertically distributed data.The pur-pose of vertical‐federated feature selection(FS)is to reduce the feature dimension of vertical distributed data jointly without sharing local original data so that the feature subset obtained has the same or better performance as the original feature set.To solve this problem,in the paper,an embedded vertical‐federated FS algorithm based on particle swarm optimisation(PSO‐EVFFS)is proposed by incorporating evolutionary FS into the SecureBoost framework for the first time.By optimising both hyper‐parameters of the XGBoost model and feature subsets,PSO‐EVFFS can obtain a feature subset,which makes the XGBoost model more accurate.At the same time,since different participants only share insensitive parameters such as model loss function,PSO‐EVFFS can effec-tively ensure the privacy of participants'data.Moreover,an ensemble ranking strategy of feature importance based on the XGBoost tree model is developed to effectively remove irrelevant features on each participant.Finally,the proposed algorithm is applied to 10 test datasets and compared with three typical vertical‐federated learning frameworks and two variants of the proposed algorithm with different initialisation strategies.Experi-mental results show that the proposed algorithm can significantly improve the classifi-cation performance of selected feature subsets while fully protecting the data privacy of all participants.

党的十八大以来我国金融支农政策的演进特征与发展趋势

以党的十八大以来中央层面颁布的324份政策文件为研究对象,基于政策文献计量和内容分析法,从政策发文变迁、发文类型、发文部门、主题演化、作用对象共五个维度分析我国金融支农政策的演进特征与发展趋势,发现:党的十八大以来金融支农政策文件在数量上整体呈现递增趋势,绝大多数以意见和通知的形式颁发;政策发文主体呈现多元化特征,跨部门联合决策程度较高,但部门间协同性有待提高;政策着力于促进农村产业发展。政策演进具有明显的阶段性特征:精准脱贫阶段,政策聚焦于创新金融扶贫机制,以促进农户脱贫增收;乡村振兴阶段,政策聚焦于深化农村金融供给侧改革、健全农村金融服务体系,以巩固拓展脱贫攻坚成果同乡村振兴有效衔接。

黄河流域山东段国土空间格局演变及驱动因素研究

以黄河流域山东段为研究区,结合2000—2020年土地利用数据和社会经济统计数据,从规模特征、动态变化和分布特征等方面对国土空间时空演变格局进行多维度定量分析,并采用地理探测器探析其驱动因素。结果表明:(1)规模特征方面,除乡村生活空间规模相对稳定外,其他各类国土空间增减特征显著;(2)动态变化方面,不同国土空间类型间的动态变化差异明显,且各区县单元综合动态度呈现东北高、西南低、中部突出的特征;(3)分布特征方面,各国土空间面积变化具有显著的空间集聚特征,这一特征在2010—2020年相对减弱;(4)驱动因素方面,研究区国土空间面积变化受到地形地貌、水热状况、区位情况以及社会经济等多种因素的共同影响,自然环境因素是国土空间格局演变的重要地理基础,社会经济发展则发挥着关键的推动作用。

进化计算在大规模高维特征选择中的应用综述

随着大数据时代的到来,数据的规模和特征维度呈现爆炸式增长,这给数据处理带来了前所未有的挑战。特征选择作为数据预处理的关键环节,在处理大规模高维数据时显得尤为重要。而进化计算方法因其出色的全局搜索能力和高效的优化性能,越来越多的研究者开始对其进行研究,其在大规模高维特征选择中得到了广泛的应用。本文首先介绍了大规模高维数据处理的重要性;然后简单介绍了部分经典和较新的进化计算方法,并详细介绍了其在大规模高维特征选择中的应用情况;最后对目前进化计算在大规模高维特征选择中存在的问题进行总结,并展望了其未来的发展方向。

基于演化博弈的移动通信数据拟态防御方法

通信数据在拟态防御时,若未能及时处理数据中的噪声,会直接影响数据的拟态防御效果,给数据传输带来安全威胁。为提升数据的拟态防御效果,提出基于演化博弈的移动通信数据拟态防御方法。针对数据中的噪声问题,使用阈值去噪方法对数据实施去噪处理,并根据去噪结果提取数据的通信行为特征;以上述操作为基础,结合演化博弈理论建立移动通信数据拟态防御模型,通过获取模型动态调度值制定出最佳调度策略;将提取的数据行为特征输入到构建的模型内,利用调度策略实现移动通信数据的拟态防御。实验结果表明,使用上述方法开展数据拟态防御时,防御效果较好。

我国近代博览建筑的起源与发展——以天津为例

近代天津开埠以后,尤其北洋新政的推行,促进了城市博览事业的发展。文章梳理了近代天津自北洋新政至民国政府时期博览建筑的设计与建设,探讨了近代天津博览建筑从沿用传统、效仿西法到走向现代三个不同阶段的特征,揭示了在政权更迭、文化冲击、社会发展等各方面因素影响下近代天津博览建筑的产生与转型。

采用进化计算的高维多模态特征选择

针对特征选择问题中的多模态特性和高维特性,引入新的种群初始化策略对大规模多模态多目标优化算法进行改进,提出了一种基于进化多目标优化的高维多模态特征选择算法。对原始的连续优化算法进行离散化处理,用于评价离散优化问题中的个体,并在6个高维特征选择数据集上进行验证。结果表明:所提算法提升了初始种群的质量并加快了算法的收敛;相比于其他同类算法,所提算法获得了更优的帕累托前沿,其超体积指标值整体最优,并且在不影响分类精度的前提下可获得平均2.53个等效特征子集,表明所提算法具有最好的分类精度和最多样化的等效特征子集。

断陷湖盆陡坡带早期斜向扇三角洲形成机制、发育规律及勘探启示:以东营凹陷北带为例

断陷湖盆作为全球最重要的含油气盆地类型之一,在盆地早期的充填演化过程中,其陡坡带往往能够形成厚达上千米的砂砾岩沉积建造,成为油气勘探的重要领域。目前对这些巨厚层砂砾岩的沉积成因认识仍存在较多的争议。本次研究以济阳坳陷典型的断陷湖盆——东营凹陷陡坡带盐家地区为例,基于高分辨率三维地震数据体、岩心、录井、测井等地质资料,综合运用地震地层学、构造地质学、沉积学和流变学分析方法,对前期在断陷湖盆陡坡带早期发现的斜向扇三角洲的发育特征及其形成演化机制进行了研究,取得了以下主要认识:(1)斜向扇三角洲沉积物中砾石含量相对较高,由牵引流和重力流共同形成,石英含量平均40%~55%,发育扇三角洲平原、扇三角洲前缘和前扇三角洲亚相,其地震响应特征在顺物源方向表现为(斜交、叠瓦和帚状)前积相,在垂直物源方向表现为充填相;(2)斜向扇三角洲主要发育于断陷早期,并于断陷中期大量消亡,扇体与边界断层以低角度斜交,角度通常不会超过45°,扇体的发育规模受到其根部所处位置的影响,扇体的消亡呈非等时性;(3)断陷湖盆陡坡带早期大型斜向扇三角洲的形成演化主要受控于4个因素:边界断层分段特征形成的转换构造为斜向扇三角洲的发育提供输送通道,断陷早期边界断层控制下的古凹槽为斜向扇三角洲的发育提供可容纳空间,断块间相互作用形成的破碎带为斜向扇三角洲的发育提供物质来源以及边界断层的硬连接导致斜向扇三角洲的消亡并向近岸水下扇转换;(4)已发现断陷湖盆陡坡带深层砂砾岩油气藏多属于斜向扇三角洲。本次研究指出陡坡带各级别转换带可作为勘探的重点靶区,布井方向应沿斜交边界断层。本研究不仅丰富了断陷湖盆陡坡带沉积理论,同时能够为中国陆相断陷盆地深层勘探提供新的思路和理论依据。

我国高等教育实践教学研究热点与进展分析

基于中国知网数据库1998—2022年CSSCI引文数据库,借助CiteSpace,绘制我国高等教育实践教学研究现状、热点和演进特征的科学知识图谱。研究发现,该领域研究仍具较高关注度,但研究机构之间合作密度稀疏。研究内容呈现多元学科特征,从新工科延伸到新文科、课程思政和人工智能。研究热点集中在实践教学制度、人才培养方案、课程体系、实践教学模式、评估与监督体系等方面。但现有研究多强调实践教学对人才培养的作用而缺乏对具体实现路径的系统分析,强调多维度、多层次构建实践教学体系而缺乏对影响因素权重的研究,强调从学科特征出发改革实践教学模式而缺乏对各学科教学内容的比较和分类研究,这些为进一步构建满足教育现代化征程要求和经济高质量发展的高等教育实践教学体系留下空间。

基于进化集成学习的用户购买意向预测

在电子商务时代背景下,精准预测用户的购买意向已经成为提高销售效率和优化客户体验的关键因素。针对传统集成策略在模型设计阶段往往受人为因素限制的问题,构建了一种自适应进化集成学习模型用于预测用户的购买意向。该模型能够自适应地选择最优基学习器和元学习器,并融合基学习器的预测信息和特征间的差异性扩展特征维度,从而提高预测的准确性。此外,为进一步优化模型的预测效果,设计了一种二元自适应差分进化算法进行特征选择,旨在筛选出对预测结果有显著影响的特征。研究结果表明,与传统优化算法相比,二元自适应差分进化算法在全局搜索和特征选择方面表现优异。相较于六种常见的集成模型和DeepForest模型,所构建的进化集成模型在AUC值上分别提高了2.76%和2.72%,并且能够缓解数据不平衡所带来的影响。

极端梯度提升改进的森林优化特征选择算法

针对传统的森林优化特征选择算法(FSFOA)处理分类任务时,存在初始化盲目和忽视降维率的问题,利用集成学习算法能够高效地对特征进行评价,提出一种用集成学习启发的,并带有重要性度量的初始化策略的改进方式,结合提出的后向删除最优子集选择策略得到一个新的特征选择算法,极端梯度提升改进的森林优化特征选择算法(FSFOAX)。通过在来自7个不同维度的UCI数据库的数据集上做对比实验,可以发现FSFOAX的性能优于FSFOA,即便是对比近年来性能优异的包裹式特征选择算法,FSFOAX在重要的分类准确率这一指标上也十分具有竞争力,说明FSFOAX在改进FSFOA的同时,更好地适用于特征选择任务。

基于特征选择和进化神经网络的网络异常入侵检测方法

由于网络信息数量庞大,内部存在大量冗余特征信息,异常检测时容易受其影响,导致检测效率降低,无法保障网络运行安全。为此提出基于特征选择和进化神经网络的网络异常入侵检测方法。应用主成分分析法选择合适的网络运行数据特征,基于进化神经网络构建异常入侵检测模型,阐述网络异常入侵检测过程,并制定特征数据提取模式与异常入侵判定规则,从而获取最终网络异常入侵检测结果。实验数据显示,提出方法获得异常入侵检测特征数量与最佳特征数量相同,网络异常入侵检测相对准确率最大值为98%,以此证明所提方法检测异常入侵精准性高。

大型水利水电工程锚固系统运行状况分析

为了保证大型水利水电工程锚固系统长期有效运行,通过对三峡集团所属6个水电站高边坡和地下洞室锚固系统监测数据统计分析,阐明锚索荷载、锚杆应力的时程演化规律,分析影响锚索荷载、锚杆应力变化特征的因素。结果表明:①各水电站高边坡和地下洞室锚索荷载、锚杆应力多数在设计范围内,其显著变化主要发生在施工期,在开挖支护完成后逐渐趋于稳定。②边坡锚索荷载随时间呈现出显著的3阶段,首先急速下降,然后缓慢下降,最后逐渐稳定或呈周期性变化。地下洞室锚索荷载也呈现出类似特征,但其变化特征更为复杂。③锚杆应力随温度变化呈现显著的负相关关系。④地下洞室锚索荷载存在洞径效应,洞室尺寸越大,锚索荷载均值越大,荷载损失率变化幅度更为显著。⑤地下洞室群交叉洞口、块体及保留岩墩等卸荷充分部位锚杆应力较大,位于块体部位的锚索荷载损失率一般大于其他工程部位。⑥锚固于断层部位的锚索,锚索荷载损失率偏大;岩体质量越低,锚索荷载损失率越大。锚索工程建设期失效特征有钢绞线拉断、锚索击穿锚罩、钢绞线缩孔等,运营长期失效特征有锚墩头锈蚀或锚墩头有碳酸钙析出。

居民体育消费结构特征及演化趋势研究——以陕西省为例

在新的发展阶段背景下,在健康中国战略以及以消费刺激内需、构建经济内部循环的战略框架引领下,体育消费作为推动经济增长的新动能,促进了居民的身心健康与精神愉悦,也得以成为提高民众幸福感的关键力量,展现出巨大的发展潜力。然而,在快速发展的同时,体育消费领域亟须优化的结构问题也日益显现。对此,本研究围绕国家战略机遇及体育消费结构优化的迫切需求,选取陕西省作为案例,对居民体育消费的结构特征及演化趋势进行总结,并提出针对性的有效策略,以促进居民体育消费结构的持续优化,进而为我国体育产业的振兴、健康中国战略以及经济内循环战略的落实提供支持。