The macular microstructure repair and predictive factors of surgical outcomes after vitrectomy for idiopathic macular hole

To evaluate the macular microstructure repair and explore the factors related to those changes and visual improvement after vitrectomy for idiopathic macular hole(IMH). Totally 19 eyes of 18 IMH patients who underwent macular hole(MH) surgery were evaluated with bestcorrected visual acuity(BCVA) and spectral-domain optical coherence tomography(SD-OCT) images. All 19 eyes closed at 6 mo postoperatively. BCVA was observed gradually improved(P<0.001), with subretinal fluid(SRF) gradually absorbed(P=0.021) and the rate of external limiting membrane(ELM) defects gradually decreased(P=0.011) with follow-up time. Poorer postoperative logMAR BCVA correlated with larger MH minimum diameter(P<0.001), larger MH basal diameter(P=0.008), longer symptom duration(P=0.002) and poorer preoperative logMAR BCVA(P=0.010). More improvement in BCVA correlated only with poorer preoperative in logMAR BCVA(P=0.002). The earlier reconstruction of ELM was associated with smaller MH basal diameter(P=0.022) and shorter symptom duration(P=0.008). In conclusion, smaller basal diameter of MH and shorter symptom duration were key factors in earlier reconstruction of ELM.

A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

The Wireless Sensor Networks(WSN)are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the inter-net.A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network.Additionally,the nodes are dis-carding and modifying the data packets according to the requirements of the sys-tem.The assault modifies the fundamental concept of the WSNs,which is that different devices should communicate with one another.In the proposed system,there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices.The currently available model is unable to recognise the myriad of different kinds of attacks.The fuzzy engine identified suspicious actions by utilising the rules that were gen-erated to make a prediction about the malicious node that would halt the process.Experiments conducted using simulation are used to determine delay,accuracy,energy consumption,throughput,and the ratio of packets successfully delivered.It stands in contrast to the model that was suggested,as well as the methodologies that are currently being used,and analogue behavioural modelling.In comparison to the existing method,the proposed model achieves an accuracy rate of 45 per-cent,a packet delivery ratio of 79 percent,and a reduction in energy usage of around 35.6 percent.These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the net-work’s capability of detecting grey hole assaults.

BP neural network model on the forecast for blasting vibrating parameters in the course of hole-by-hole detonation

According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the deep hole stair demolition in a mine asan experimental object and using the raw information and the blasting vibration monitoringdata collected in the process of the hole-by-hole detonation, carried out some training andapplication work on the established BP network model through the Matlab software, andachieved good effect.Also computed the vibration parameter with the empirical formulaand the BP network model separately.After comparing with the actual value, it is discoveredthat the forecasting result by the BP network model is close to the actual value.

Prediction and Analysis of O3 Based on the ARIMA Model

Despite of the small amount in the atmosphere,ozone is one of the most critical atmospheric component as it protects human beings and any other life on the earth from the sun's high frequency ultraviolet radiation. In recent decades,the global ozone depletion caused by human activities is w ell know n and produces an " ozone hole",the most direct consequence of w hich is the increase in ultraviolet radiation,w hich w ill affect human survival,climatic environment,ecological environment and other important adverse impacts. Due to the implementation of the M ontreal protocol and other agreement,the total amount of ozone depleting substance in the atmosphere has been prominent reduced,w hich w ill lead to a new round of regional climate change.Therefore,predicting the changes of the total ozone in the future w ill have an important guiding significance for predicting the future climate change and making reasonable measures to deal w ith the climate change. In this paper,based on the ozone data of 1979 to 2016 in the southern hemisphere and ARIM A model algorithm,using time series analysis,w e obtain prediction effect of ARIM A model is good by Ljung-Box Q-test and R^2,and the model can be used to predict the future ozone change. With the help of SPSS softw are,the future trend of the total ozone can be predicted in the future 50 years. Based on the above experiment results,the global ozone change in the future 50 years can be forecasted,namely the atmospheric ozone layer w ill return to its 1980's standard by the middle of this century at the global scale.

考虑钻杆接头的小井眼环空压耗计算模型

小井眼钻井作为开发深部油气资源的重要技术手段越来越受到重视。小井眼往往具有更高的环空压耗,这给环空压力控制带来了一定挑战。准确预测钻柱偏心旋转情况下的环空压耗是小井眼钻井的重要理论与实践基础,但常规预测模型适用性非常有限,往往忽略了钻杆接头的影响,无法满足现场的精度需求。为此,利用数值模拟技术,分析了钻杆接头对环空流场、压耗的影响规律,并据此确定了压耗修正因子经验模型的构建方式。分析结果表明:钻杆接头所产生的额外压耗受钻井液类型、钻杆转速、偏心和环空返速的影响,压耗修正因子的构建应考虑多种因素的耦合关系。利用152组数模结果,建立了针对新疆玛湖油田的小井眼环空压耗预测模型,该模型计算结果表明,在钻杆低偏心度情况下存在临界转速,使得环空压耗达到最大,而钻杆处于高偏心度时,环空压耗会随转速增大而增大;而偏心度对环空压耗的影响规律也会因转速不同而变得更加复杂。利用环空压耗预测模型,计算了玛湖油田MHHW-X井的当量循环密度,结果与PWD数据进行对比,平均误差仅为1.18%,模型具有较高的准确性。研究结果表明,利用数模结果建立的考虑钻杆接头的小井眼环空压耗模型可以满足现场预测精度需要,能够为现场环空压力控制提供指导。

气膜孔对DD6镍基单晶高温合金高周疲劳强度的影响研究

气膜孔因其特殊的几何构型,在提升涡轮叶片冷却效率的同时,孔边往往存在较为明显的应力集中现象,导致叶片的高周疲劳(High Cycle Fatigue,HCF)失效。因此,本文开展了900℃下振动疲劳试验,以评估圆柱型和簸箕型气膜孔对DD6镍基单晶高温合金高周疲劳强度的影响,并通过扫描电子显微镜(Scanning Electron Microscope,SEM)和能谱仪(Energy Dispersive Spectroscopy,EDS)分析气膜孔模拟件HCF失效机理。研究发现孔边氧化层在交替载荷作用下存在均布开裂现象,形成尖角,最终导致裂纹萌生,并沿着{111}滑移面扩展。通过有限元仿真分析了孔边应力应变分布,结合临界距离理论,建立了气膜孔高周疲劳强度评估方法和基于疲劳缺口系数经验公式的气膜孔高周疲劳强度预测方法。结果表明:簸箕型气膜孔的高周疲劳强度为298.03 MPa,圆柱型气膜孔高周疲劳强度为321.35 MPa;气膜孔高周疲劳强度预测误差在10%以内。所建立的含异型气膜孔的DD6单晶高温合金高周疲劳强度评估、预测方法可为航空发动机涡轮叶片膜冷却结构疲劳性能的设计和评估提供技术支持。

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

塔筒安装条件下法兰盘受力变形分析研究

法兰盘是塔筒的重要部件,起到连接风机塔筒的作用。塔筒翻身吊装施工过程中,法兰盘及螺栓孔容易发生损伤变形,导致螺栓连接失效,严重影响塔筒的安全稳定性。因此,研究吊装条件下塔筒法兰盘的受力变形特征有着重要意义。采用有限元数值分析软件,对吊装条件下法兰盘及螺孔受力及变形特征进行分析研究,结果表明:塔筒吊装翻身过程中,当塔筒处于水平状态下法兰盘螺孔变形最大;法兰盘螺孔最大变形随法兰盘厚度增大而减小,随塔筒壁厚以及长度增大而增大。基于敏感性分析数据,提出了基于非线性拟合方法的法兰盘螺孔最大变形预测公式,平均误差仅为6.79%,可以为吊装条件下法兰盘螺孔最大变形预测提供有效的理论支持。

基于深度学习的复合材料开孔板拉伸失效行为预测

为研究复合材料开孔板在拉伸载荷下的失效行为,基于开孔板的拉伸试验建立了高精度的有限元仿真模型,并批量生成了拉伸载荷-位移曲线的数据集。提出了一种双长短时记忆(Long short-term memory,LSTM)神经网络模型用于预测载荷-位移曲线,其中第1个LSTM模型进行输入特征的提取,第2个LSTM模型直接给出载荷-位移曲线的预测。结果表明:这一模型能够高效、准确地预测开孔板的拉伸载荷-位移曲线,在测试集上的决定系数R2可以达到0.9755,关键特征如初始刚度E0的预测误差仅为1.85%,极限载荷Fmax的预测误差仅为2.16%。

简谐激励下声学黑洞梁的疲劳裂纹扩展分析

针对含有裂纹的声学黑洞(ABH)梁,基于假设振型法建立振动力学方程,采用包含裂纹位置及深度信息的等效弯曲刚度模型描述裂纹对ABH梁振型的影响.利用Paris公式模拟疲劳裂纹的扩展速率,提出ABH梁的振动疲劳寿命预测流程图.MATLAB数值模拟与COMSOL有限元分析结果表明,基于等效弯曲刚度模型的数值模型与有限元仿真结果吻合.裂纹扩展分析表明,ABH梁特征频率的阶数与疲劳寿命没有内在关系.振动疲劳模拟表明ABH结构的特征参数会影响疲劳寿命分布.

基于延时微分的旋转导向工具造斜率预测方法研究

【目的】旋转导向工具造斜率的准确预测是实现工具精准控制的前提,为掌握旋转导向底部钻具组合造斜率变化规律。【方法】根据工具结构特点,采用欧拉-伯努利梁方程法对复合式旋转导向工具底部钻具组合进行受力分析。通过结合底部钻具几何关系,得到各节工具转角与钻头预钻进偏角计算方法,并将旋转导向工具各节点与钻头间距离作为空间延时周期,最终建立基于延时微分的旋转导向底部钻具组合的造斜率预测模型。该模型综合考虑了工具本身几何尺寸与结构、与地层有关的钻头切削各向异性、钻压以及工具材料及内外径有关的抗弯刚度等参数对造斜率的定量影响。【结果和结论】研究表明:(1)该模型预测结果与斯伦贝谢成熟应用的基于有限元分析的钻具造斜率预测软件仿真结果具有良好的吻合度,预测结果差值均保持在0.5(°)/30 m以内,符合工程要求。(2)同时该方法还可在钻具姿态测量短节的实际测量数据基础上补偿钻头与测量单元间距带来的测量误差问题,为解决测量盲区提供一种新的途径。基于空间延时微分的造斜率预测方法为旋转导向系统底部钻具组合的结构优化及钻井参数优选提供理论依据和快速分析手段。建立的模型易于仿真和移植,在实际工程应用中可获取较好的实时响应,为基于数字孪生的旋转导向工具研发提供准确的钻具运动模型基础。

基于误差预测的矿山贯通测量质量控制优化方法研究

针对矿井贯通测量误差影响较大的问题,为使加测陀螺定向边数量和位置都达到最优效果,分析了陀螺仪的工作原理,研究了贯通测量误差预测方法,按照麻家梁煤业(二号井)13采区和14采区测量需求和相关规定提出两套优化方案:方案一加测8条间距分布均匀的陀螺定向边,13采区和14采区在支导线终边进行加测;方案二对13采区和14采区加测位置和大巷中原陀螺定向边位置不发生改变,调整其他陀螺定向边位置。通过实测,两套优化方案均能满足矿井贯通测量需求和相关设计要求,其中方案二能实现陀螺定向边最大限度优化,误差分布更均匀,中误差减小到12.5 mm。

Application of artificial intelligence in predicting the dynamics of bottom hole pressure for under-balanced drilling:Extra tree compared with feed forward neural network model

This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point.Thus,the application of this study proposed models for predicting bottomhole pressure trends.

Quantum Mechanics and General Relativity Identify Standard Model Particles as Black Holes

The Standard Model of particle physics does not account for charged fermion mass values and neutrino mass, or explain why only three particles are in each charge state 0, -e/3, 2e/3, and -e. These issues are addressed by treating Standard Model particles with mass m as spheres with diameter equal to their Compton wavelength l =&#295;/mc, where &#295;is Planck’s constant and c the speed of light, and any charge in diametrically opposed pairs ±ne/6 with n = 1, 2, or 3 at the axis of rotation on the sphere surface. Particles are ground state solutions of quantized Friedmann equations from general relativity, with differing internal gravitational constants. Energy distribution within particles identifies Standard Model particles with spheres containing central black holes with mass m, and particle spin resulting from black hole angular momentum. In each charge state, energy distribution within particles satisfies a cubic equation in l, allowing only three particles in the charge state and requiring neutrino mass. Cosmic vacuum energy density is a lower limit on energy density of systems in the universe, and setting electron neutrino average energy density equal to cosmic vacuum energy density predicts neutrino masses consistent with experiment. Relations between charged fermion wavelength solutions to cubic equations in different charge states determine charged fermion masses relative to electron mass as a consequence of charge neutrality of the universe. An appendix shows assigning charge ±e/6 to bits of information on the event horizon available for holographic description of physics in the observable universe accounts for dominance of matter over anti-matter. The analysis explains why only three Standard Models are in each charge state and predicts neutrino masses based on cosmic vacuum energy density as a lower bound on neutrino energy density.

露天矿台阶爆破优化设计软件系统

为了改善目前台阶爆破主要依赖人工经验设计的现状,基于VC++平台开发台阶爆破优化设计软件系统。研发基于Delaunay三角网格的迭代算法,实现台阶面自适应布孔,运用Voronoi随机网格技术确定起爆顺序。通过非线性预测理论对软件设计爆破方案的振动效应进行预测,从而优化爆破参数。爆破完成后,采用图像阈值化分割方法分析现场爆堆照片的块度分布,根据分析结果并综合考虑安全性、经济性等因素,对爆破效果进行评价,评价结果可以为后续爆破施工提供指导。工程实际应用结果表明:软件实现了台阶爆破自适应布孔,显著提升爆破设计效率,有效提高爆破效果,减少爆破振动有害效应。

基于数据预处理技术并考虑围岩应力梯度影响的隧洞岩爆预测

针对目前岩爆预测研究通常忽视岩爆数据集存在离群样本、缺失值与样本不平衡性问题以及围岩应力梯度的影响,提出一套完备的岩爆数据预处理流程,引入可间接表征围岩应力梯度的洞径指标,建立了隧洞岩爆多因素综合预测模型。在数据采集阶段,考虑隧道与采场及隧洞群受力条件差异,从岩爆数据库中分离出隧洞岩爆样本共306例。在岩爆预测指标选取阶段,选取隧洞洞径D_(0)、围岩最大切向应力σ_(θmax)、岩石单轴抗压强度σ_(c)、岩石抗拉强度σ_(t)、弹性能变形指数W_(et)共5个指标。在数据预处理阶段:针对缺失值,引入随机森林多重插补法(MI-RF)对岩爆样本进行补全;针对离群样本,引入最近邻(KNN)、孤立森林(IsolationForest)、局部异常因子(LOF)3种无监督算法综合评估岩爆数据集并剔除离群样本;针对样本不平衡,引入自适应综合过采样(ADASYN)算法扩容少数类样本。在模型验证阶段:采用支持向量机(SVM)、随机森林(RF)、梯度提升树(GBDT)、自适应提升树(AdaBoost)、极限梯度提升树(XGBoost)5类算法构建岩爆预测模型。模型预测结果表明:基于数据预处理并考虑洞径指标的5类模型皆为同类算法模型中的最优;在不进行数据预处理的条件下,考虑洞径指标模型要优于不考虑洞径指标的同类算法模型。

兰陵县会宝岭铁矿变质岩裂隙含水层放水试验技术探讨

本文采用井下水文地质钻探及放水试验技术查明了会宝岭铁矿-410m~-760m标高井筒施工中出水点位置、岩性特征、水压、地下水导水通道及补给来源等水文地质成果;根据放水试验成果计算各涌水段水文地质参数,开展井筒施工涌水量预测,为井筒施工提出防治水建议,并为深部矿体开采提供重要的水文地质资料,也为沉积变质岩型铁矿床深部探水技术进行了讨论。

工程勘察近水平定向钻探技术解读和规范编制的建议

工程勘察技术方法向多元化方向发展,依托小口径定向钻进和孔内测试技术的工程勘察技术已成为近年来一个新的聚焦点。工程勘察定向钻探技术可有效地应用于非常复杂地表环境下的勘察施工、超前地质预报以及地质灾害防控、环境监控与治理等领域。为进一步发挥该技术的优势和规范市场,目前多个行业协会或团体开始编制与工程勘察定向钻探相关的技术规程或标准。本文系统总结了工程勘察近水平定向钻探技术的应用场景,分析了其技术内涵和关键技术,提出并讨论了规程编制过程中应重点关注的方向和关键技术内容,从轨迹设计、设备选择、工艺技术、钻具组合、取心工具、冲洗液体系、定向仪器、孔内测试等方面提出建议,对相关标准的编制具有参考意义。

无线传感网络最佳候选节点空洞攻击抑制方法

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