A graphical model for haloanhydrite components and P-wave velocity:A case study of haloanhydrites in Amu Darya Basin

Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constructed a template of the relation between haloanhydrite mineralogy （anhydrite, salt, mudstone, and pore water） and wave velocities. We used the relation between the P-wave rnoduli ratio and porosity as constraint and constructed a graphical model （petrophysical template） for the relation between wave velocity, mineral content and porosity. We tested the graphical model using rock core and well logging data.

Predictive factors and model validation of post-colon polyp surgery Helicobacter pylori infection

BACKGROUND Recently,research has linked Helicobacter pylori(H.pylori)stomach infection to colonic inflammation,mediated by toxin production,potentially impacting colorectal cancer occurrence.AIM To investigate the risk factors for post-colon polyp surgery,H.pylori infection,and its correlation with pathologic type.METHODS Eighty patients who underwent colon polypectomy in our hospital between January 2019 and January 2023 were retrospectively chosen.They were then randomly split into modeling(n=56)and model validation(n=24)sets using R.The modeling cohort was divided into an H.pylori-infected group(n=37)and an H.pylori-uninfected group(n=19).Binary logistic regression analysis was used to analyze the factors influencing the occurrence of H.pylori infection after colon polyp surgery.A roadmap prediction model was established and validated.Finally,the correlation between the different pathological types of colon polyps and the occurrence of H.pylori infection was analyzed after colon polyp surgery.RESULTS Univariate results showed that age,body mass index(BMI),literacy,alcohol consumption,polyp pathology type,high-risk adenomas,and heavy diet were all influential factors in the development of H.pylori infection after intestinal polypectomy.Binary multifactorial logistic regression analysis showed that age,BMI,and type of polyp pathology were independent predictors of the occurrence of H.pylori infection after intestinal polypectomy.The area under the receiver operating characteristic curve was 0.969[95%confidence interval(95%CI):0.928–1.000]and 0.898(95%CI:0.773–1.000)in the modeling and validation sets,respectively.The slope of the calibration curve of the graph was close to 1,and the goodness-of-fit test was P>0.05 in the two sets.The decision analysis curve showed a high rate of return in both sets.The results of the correlation analysis between different pathological types and the occurrence of H.pylori infection after colon polyp surgery showed that hyperplastic polyps,inflammatory polyps,and the occurrence of H.pylori infection were not significantly correlated.In contrast,adenomatous polyps showed a significant positive correlation with the occurrence of H.pylori infection.CONCLUSION Age,BMI,and polyps of the adenomatous type were independent predictors of H.pylori infection after intestinal polypectomy.Moreover,the further constructed column-line graph prediction model of H.pylori infection after intestinal polypectomy showed good predictive ability.

Graphical model construction based on evolutionary algorithms

Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well.

VIDEO MULTI-TARGET TRACKING BASED ON PROBABILISTIC GRAPHICAL MODEL

In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.

Systemic Risk of Conventional and Islamic Banks: Comparison with Graphical Network Models

The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on graphical Gaussian distributions, which allows us to capture the contagion effects that move along countries. We also consider Bayesian graphical models, to account for model uncertainty in the measurement of financial systems interconnectedness. Our proposed model is applied to the Middle East and North Africa (MENA) region banking sector, characterized by the presence of both conventional and Islamic banks, for the period from 2007 to the beginning of 2014. Our empirical findings show that there are differences in the systemic risk and stability of the two banking systems during crisis times. In addition, the differences are subject to country specific effects that are amplified during crisis period.

Graphical Simulator for Chinese Ink-Wash Drawing

Simulating the traditional painting art by computer graphics is a challenging and attractive subject. Basing on the experience in the ink wash drawing, in this paper, we expound the artistic characters of ink wash painting and particularly analyze the characteristics of the materials used in the ink wash drawing and the relationships between them. A simulation model is presented and some typical visual effects of the ink wash painting are realized.

Optimization of a precise integration method for seismic modeling based on graphic processing unit

General purpose graphic processing unit （GPU） calculation technology is gradually widely used in various fields. Its mode of single instruction, multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps. In this study, we introduce a GPU-based parallel calculation method of a precise integration method （PIM） for seismic forward modeling. Compared with CPU single-core calculation, GPU parallel calculating perfectly keeps the features of PIM, which has small bandwidth, high accuracy and capability of modeling complex substructures, and GPU calculation brings high computational efficiency, which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.

Development of a building information model-guided post-earthquake building inspection framework using 3D synthetic environments

Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.

A Study on the Nonlinear Caputo-Type Snakebite Envenoming Model with Memory

In this article,we introduce a nonlinear Caputo-type snakebite envenoming model with memory.The well-known Caputo fractional derivative is used to generalize the previously presented integer-order model into a fractionalorder sense.The numerical solution of the model is derived from a novel implementation of a finite-difference predictor-corrector(L1-PC)scheme with error estimation and stability analysis.The proof of the existence and positivity of the solution is given by using the fixed point theory.From the necessary simulations,we justify that the first-time implementation of the proposedmethod on an epidemicmodel shows that the scheme is fully suitable and time-efficient for solving epidemic models.This work aims to show the novel application of the given scheme as well as to check how the proposed snakebite envenoming model behaves in the presence of the Caputo fractional derivative,including memory effects.

Enhancing Feature Discretization in Alarm and Fire Detection Systems Using Probabilistic Inference Models

Sensors for fire alarms require a high level of predictive variables to ensure accurate detection, injury prevention, and loss prevention. Bayesian networks can aid in enhancing early fire detection capabilities and reducing the frequency of erroneous fire alerts, thereby enhancing the effectiveness of numerous safety monitoring systems. This research explores the development of optimized probabilistic graphic models for the discretization thresholds of alarm system predictor variables. The study presents a statistical model framework that increases the efficacy of fire detection by predicting the discretization thresholds of alarm system predictor variable fluctuations used to detect the onset of fire. The work applies the Bayesian networks and probabilistic visual models to reveal the specific characteristics required to cope with fire detection strategies and patterns. The adopted methodology utilizes a combination of prior knowledge and statistical data to draw conclusions from observations. Utilizing domain knowledge to compute conditional dependencies between network variables enabled predictions to be made through the application of specialized analytical and simulation techniques.

工程图学虚拟仿真模型系统设计及其教学分析

针对工科类专业工程测绘课程实验需求,基于云服务、虚拟模型技术和可视化仿真体验,开发了工程测绘虚拟仿真实验系统。该系统对工程测绘实验方案、互动环节和考核评价等内容的教学体系重构和数字化重建,以测绘对象三维建模代替零部件实物,降低实验教学成本投入;以虚拟实验室代替实际测绘实验室,减少实验室资源占用;以在线实验代替固定时间课内实验,提高课程教学灵活度。为校内外师生提供灵活交互、强沉浸感和高用户黏度的在线实验教学服务,驱动学生自主完成实验学习和在线互动等活动,从而使学生的空间思维、图感表达能力得到充分训练,有效提高学生的工程创新素质。

改进PCA方法的牙颌特征提取和数据集构建

为了提高牙颌数字模型特征提取的准确性和牙齿分割的效率,提出一种几何变换和主成分分析相结合的坐标系标准化方法,并以离散曲率、法向量、形状直径函数和离散测地距离为基础特征对牙颌数字模型进行了特征提取,在此基础上进一步扩展构建了76个特征数据集。采用提出的坐标系标准化方法和数据集对上颌进行了牙齿分割实验。结果表明:改进主成分分析方法能够快速准确地实现牙颌数字模型坐标系对齐,能够准确识别牙齿的特征信息并作出标记,牙齿分割完整,平均分割准确率达到99.74%。基于改进主成分分析方法的牙颌模型特征提取方法能够极大地提高特征对牙齿的区分性,从而降低位姿对牙颌特征提取带来的负面影响,实现在特征数据较少的情况下准确分割牙齿,可为数字化口腔诊疗提供一定的参考。

与矿山三维地质模型自动关联的剖面图更新方法

露天煤矿开采过程中矿山三维地质模型不断变化,需要随之更新地质剖面图以真实反映当前矿山的地质构造与地层性质。针对目前剖面图更新方法存在剖面图内部图元属性信息与空间信息交互不畅导致剖面图更新不够准确的问题,提出了一种与矿山三维地质模型自动关联的剖面图更新方法。通过设计矿山三维地质模型与剖面图元关联关系的概念模型与逻辑模型,实现模型图元与剖面图元对象名称的关联;通过在剖面图上设置水平和高程位置的定位坐标,将剖切所得对象关联至相应的剖面图位置,实现模型图元与剖面图元空间位置坐标的关联,以达到矿山三维地质模型与剖面图属性信息、空间信息等图元数据关联的目的;根据矿山三维地质模型中发生改变的图元确定需要更新的区域,提取区域内各剖面线并根据关联关系得到相应的剖面图,进一步通过遍历判断图元属性更新标志位和几何更新标志位的状态,自动更新各剖面图中属性或几何形状发生变化的剖面图元。应用结果表明,该方法实现了剖面图与矿山三维地质模型的关联,以及剖面图的自动更新;利用该方法自动更新后的剖面图中定位坐标数据与更新前一致,验证了剖面图更新方法的准确性。

一种面向方面编程的图示化分析与实现

图示化地展现面向方面编程的组成、技术架构、编程和测试过程,有助于对AOP思想的理解和技术实现的掌握。以多年实际的教学案例实践,结合UML分析建模技术,使用用例图完成功能需求分析,引入AOP切面的建模分析,建立横切关注方面的立体概念;采用组件图完成技术架构图,进而完成了AOP的技术实现和文件结构图,并详细给出了相关切面织入的测试过程图。探索一种图示化的面向方面的分析与实现过程,解决了面向方面编程认知思想和技术实现的难点。在实际教学中,收到一定效果。

以图查房时空数据模型研究与应用

目前不动产登记信息查询应用主要是面向权利人和利害关系人且是有条件的查询,其他广大群众还难以快速获取不动产登记基本信息,导致在房屋交易中存在信息不对称风险,有必要从技术层面研究加强不动产登记信息公开查询的办法。本文在分析省级不动产登记数据现状、顾及信息安全的基础上,研究设计了以图查房时空数据模型。基于此模型,开发了面向自然人的图查房服务,在广东省域范围进行了应用,验证了本文设计的以图查房时空数据模型有效可行。

计算机图形建模与3D打印技术要点探讨

在如今的工作生活中,计算机图形建模与3D打印技术的应用愈发普遍。图形建模技术可依托于三维空间对物体进行精确描述、模拟,为设计师、工程师等人士提供强大工具,帮助其直观、高效地进行设计和创新。3D打印技术则能将数字模型转化为真实物理产品,实现从虚拟到实体的转变,为各领域的行业革新提供助力。

GNNSched:面向GPU的图神经网络推理任务调度框架

由于频繁的显存访问,图神经网络GNN在GPU上运行时往往资源利用率较低。现有的推理框架由于没有考虑GNN输入的不规则性,直接适用到GNN进行推理任务共置时可能会超出显存容量导致任务失败。对于GNN推理任务,需要根据其输入特点预先分析并发任务的显存占用情况,以确保并发任务在GPU上的成功共置。此外,多租户场景提交的推理任务亟需灵活的调度策略,以满足并发推理任务的服务质量要求。为了解决上述问题,提出了GNNSched,其在GPU上高效管理GNN推理任务的共置运行。具体来说,GNNSched将并发推理任务组织为队列,并在算子粒度上根据成本函数估算每个任务的显存占用情况。GNNSched实现了多种调度策略来生成任务组,这些任务组被迭代地提交到GPU并发执行。实验结果表明,GNNSched能够满足并发GNN推理任务的服务质量并降低推理任务的响应时延。

基于CGRSNet的残缺油桃外形点云补全方法

[目的]利用深度相机采集到的油桃数据在进行三维重建时,存在摄像头视野限制,导致油桃点云出现缺失的现象,严重影响油桃表型分析的准确度。为了提高油桃分级分类的准确率和工作效率,提出一种基于三维重建的油桃片外形参数估测方法。[方法]提出了一种基于真实结构的粗粒度点云生成网络(coarse-grained realistic structure with point cloud generation network,CGRSNet)。该网络首先通过编码、解码机制构建点云补全网络,通过新的采样算法对原始点云提取不同维度下的特征信息,提升油桃表型的特征提取能力,然后自解码器自上而下进行多维度点云补全,最终得到完整的油桃形状,最后用模型的参数预估模块预估油桃体积。利用多组参数构建的油桃几何模型离散成点云数据输入CGRSNet网络,得到油桃几何模型外形补全的预训练模型,再利用真实油桃点云数据对预训练模型CGRSNet做模型迁移的参数微调。[结果]该补全网络对油桃数据集补全结果的倒角距离为0.196 cm。经线性回归分析,得出体积估测的RMSE和R^(2)分数为2.47 cm^(3)和0.94,相较于PF-Net的RMSE和R^(2)分数提升约0.88 cm^(3)和0.01。[结论]本文提出的基于CGRSNet的油桃外形参数估测算法具有较好的实用性和精确度。

融合多源异构在线评论的开放式创新社区创意采纳预测研究

整合内外部创新资源以获得市场优势一直是企业关注的问题,这是因为企业的创新能力直接决定了企业的兴衰成败。已有的以在线评论为数据源预测开放式创新社区中创意采纳的研究主要依靠单一的文本特征。基于启发式系统性说服理论,本文提出了一种新的开放式创新社区创意采纳预测模型。该模型在基于信任转移理论和价值共创理论基础上,归纳了多源异构在线评论的定义,并从启发式评论者特征、启发式评论特征和系统性评论特征3个层次特征来描述创意。此外,还引入图注意力网络实现了3个层次创意特征的融合。本文从真实的开放式创新社区收集了数据集,验证了本文模型及其各个特征,该模型准确预测创意采纳的综合性能大约在97%,图模型特征融合后的分类预测结果优于传统的机器学习分类算法。研究结果不仅证明了图模型在特征融合中的有效性,而且从融合评论特征和评论者特征的角度,为创意采纳预测提供了方法和理论上的支持。