Principal Equatorial Null Geodesic Congruences in the Kerr Metric, and Their Quantum Propagators

Using the Raychaudhuri equation, we associate quantum probability amplitudes (propagators) to equatorial principal ingoing and outgoing null geodesic congruences in the Kerr metric. The expansion scalars diverge at the ring singularity;however, the propagators remain finite, which is an indication that at the quantum level singularities might disappear or, at least, become softened.

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Robust Principal Component Analysis Integrating Sparse and Low-Rank Priors

Principal Component Analysis (PCA) is a widely used technique for data analysis and dimensionality reduction, but its sensitivity to feature scale and outliers limits its applicability. Robust Principal Component Analysis (RPCA) addresses these limitations by decomposing data into a low-rank matrix capturing the underlying structure and a sparse matrix identifying outliers, enhancing robustness against noise and outliers. This paper introduces a novel RPCA variant, Robust PCA Integrating Sparse and Low-rank Priors (RPCA-SL). Each prior targets a specific aspect of the data’s underlying structure and their combination allows for a more nuanced and accurate separation of the main data components from outliers and noise. Then RPCA-SL is solved by employing a proximal gradient algorithm for improved anomaly detection and data decomposition. Experimental results on simulation and real data demonstrate significant advancements.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Principals’and Teachers’Awareness,Knowledge,and Differentiation of Privatization-A Secondary Publication

Based on the keynote report by Professor Martin Thrupp,this paper discusses the hollowing out of education provision by the state and the permeation of managerialism.It was pointed out that principals and boards of trustees in socioeconomically advantaged areas may not be willing to share their benefits with schools in less advantaged areas.The new liberal policies have hollowed out state provision of education,so the education system has come to rely heavily on private actors.This paper also presents the current stage of privatization in Japan and the principals’and teachers’perceptions of privatization.

分组密码算法uBlock积分攻击的改进

积分攻击是由Daemen等人(doi:10.1007/BFb0052343)于1997年提出的一种密码分析方法,是继差分分析和线性分析之后最有效的密码分析方法之一。作为2018年全国密码算法设计竞赛分组算法的获胜算法,uB-lock抵抗积分攻击的能力受到较多的关注。为了重新评估uBlock家族密码算法抵抗积分攻击的安全性,该文利用单项式传播技术,结合混合整数线性规划(MILP)工具搜索积分区分器,并利用部分和技术进行密钥恢复攻击。对于uBlock-128/128和uBlock-128/256,基于搜索到的9轮积分区分器分别进行了首个11轮和12轮攻击,数据复杂度为2~(127)选择明文,时间复杂度分别为2~(127.06)和2~(224)次加密,存储复杂度分别为2~(44.58)和2~(138)字节;对于uBlock-256/256,基于搜索到的10轮积分区分器进行了首个12轮攻击,数据复杂度为2~(253)选择明文,时间复杂度为2~(253.06)次加密,存储复杂度为2~(44.46)字节。与之前uBlock的最优积分攻击结果相比,uBlock-128/128和uBlock-256/256的攻击轮数分别提高2轮,uBlock-128/256的攻击轮数提高3轮。本文的攻击说明,uBlock针对积分攻击依然有足够的安全冗余。

Experimental study on failure characteristics of single-sided unloading rock under different intermediate principal stress conditions

Investigation of unloading rock failure under differentσ_(2)facilitates the control mechanism of excavation surrounding rock.This study focused on single-sided unloading tests of granite specimens under true triaxial conditions.The strength and failure characteristics were studied with micro-camera and acoustic emission(AE)monitoring.Furthermore,the choice of test path and the effect ofσ_(2)on fracture of unloading rock were discussed.Results show that the increasedσ_(2)can strengthen the stability of single-sided unloading rock.After unloading,the rock’s free surface underwent five phases,namely,inoculation,particle ejection,buckling rupture,stable failure,and unstable rockburst phases.Moreover,atσ_(2)≤30 MPa,the b value shows the following variation tendency:rising,dropping,significant fluctuation,and dropping,with dispersed damages signal.Atσ_(2)≥40 MPa,the tendency shows:a rise,a decrease,a slight fluctuation,and final drop,with concentrated damages signal.After unloading,AE energy is mainly concentrated in the micro-energy range.With the increasedσ_(2),the micro-energy ratio rises.In contrast,low,medium and large energy ratios drop gradually.The increased tensile fractures and decreased shear fractures indicate that the failure mode of the unloading rock gradually changes from tensile-shear mode to tensile-split one.The fractional dimension of the rock fragments first increases and then decreases with an inflection point at 20 MPa.The distribution of SIF on the planes changes asσ_(2)increases,resulting in strengthening and then weakening of the rock bearing capacity.

Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network

The composition control of molten steel is one of the main functions in the ladle furnace(LF)refining process.In this study,a feasible model was established to predict the alloying element yield using principal component analysis(PCA)and deep neural network(DNN).The PCA was used to eliminate collinearity and reduce the dimension of the input variables,and then the data processed by PCA were used to establish the DNN model.The prediction hit ratios for the Si element yield in the error ranges of±1%,±3%,and±5%are 54.0%,93.8%,and98.8%,respectively,whereas those of the Mn element yield in the error ranges of±1%,±2%,and±3%are 77.0%,96.3%,and 99.5%,respectively,in the PCA-DNN model.The results demonstrate that the PCA-DNN model performs better than the known models,such as the reference heat method,multiple linear regression,modified backpropagation,and DNN model.Meanwhile,the accurate prediction of the alloying element yield can greatly contribute to realizing a“narrow window”control of composition in molten steel.The construction of the prediction model for the element yield can also provide a reference for the development of an alloying control model in LF intelligent refining in the modern iron and steel industry.

A blast furnace fault monitoring algorithm with low false alarm rate:Ensemble of greedy dynamic principal component analysis-Gaussian mixture model

The large blast furnace is essential equipment in the process of iron and steel manufacturing. Due to the complex operation process and frequent fluctuations of variables, conventional monitoring methods often bring false alarms. To address the above problem, an ensemble of greedy dynamic principal component analysis-Gaussian mixture model(EGDPCA-GMM) is proposed in this paper. First, PCA-GMM is introduced to deal with the collinearity and the non-Gaussian distribution of blast furnace data.Second, in order to explain the dynamics of data, the greedy algorithm is used to determine the extended variables and their corresponding time lags, so as to avoid introducing unnecessary noise. Then the bagging ensemble is adopted to cooperate with greedy extension to eliminate the randomness brought by the greedy algorithm and further reduce the false alarm rate(FAR) of monitoring results. Finally, the algorithm is applied to the blast furnace of a large iron and steel group in South China to verify performance.Compared with the basic algorithms, the proposed method achieves lowest FAR, while keeping missed alarm rate(MAR) remain stable.

Implications for identification of principal stress directions from acoustic emission characteristics of granite under biaxial compression experiments

The rock fracture characteristics and principal stress directions are crucial for prevention of geological disasters.In this study,we carried out biaxial compression tests on cubic granite samples of 100 mm in side length with different intermediate principal stress gradients in combination with acoustic emission(AE)technique.Results show that the fracture characteristics of granite samples change from‘sudden and aggregated’to‘continuous and dispersed’with the increase of the intermediate principal stress.The effect of increasing intermediate principal stress on AE amplitude is not significant,but it increases the proportions of high-frequency AE signals and shear cracks,which in turn increases the possibility of unstable rock failure.The difference of stress in different directions causes the anisotropy of rock fracture and thus leads to the obvious anisotropic characteristics of wave velocity variations.The anisotropy of wave velocity variations with stress difference is probable to identify the principal stress directions.The AE characteristics and the anisotropy of wave velocity variations of granite under two-dimensional stress are not only beneficial complements for rock fracture characteristic and principal stress direction identification,but also can provide a new analysis method for stability monitoring in practical rock engineering.

Model of rigid and elastic-plastic motion in intraplate blocks and strain status of principal blocks in Chinese mainland

The model of rigid and elastic-plastic motion and strain in intraplate blocks is established in the paper. The unique of strain parameters and minimum root-mean-square error of velocity residual of blocks are tested in the model. Based on the velocity fields in Chinese mainland and its peripheral areas, the strain parameters of 8 blocks are estimated and their strain status analyzed. The estimated strain status of each block is well consistent with those derived by the methods of geology and geophysics. The principal direction of collision force from India plate to Eurasia plate estimated from the azimuth of principal compressive strain of Himalaya block might be N7.1°E.

Twin-Block双期矫治与单纯直丝弓矫正技术在早期下颌后缩矫正患儿中的疗效对比

目的探讨Twin-Block双期矫治与单纯直丝弓矫正技术在替牙及恒牙早期错(牙合)畸形矫正患儿中的疗效对比。方法选取我院2020年9月到2023年9月收治的60例恒牙早期错(牙合)畸形患儿作为研究对象,按照治疗方法不同分为观察组与对照组,各组均为30例。所有患儿均采取矫正治疗,对照组患儿采取直丝弓矫正技术进行治疗,观察组患儿采取Twin-Block矫治器及直丝弓矫治技术进行双期治疗。对比两组患儿临床疗效,及矫治时间,治疗前头影测量及关节改建情况,采用口腔锥形束扫描成像进行检测两组患儿关节窝宽径、关节前间隙、关节后间隙、关节上间隙、髁突前后径、髁突高度相关参数表达水平,最后对比两组患儿治疗前后上下齿槽座(ANB)角、下齿槽座(SNB)角、上齿槽座(SNA)角、下颌平面角(Mp-SN)、下颌综合长度(Ar-Po)、下颌升支高度(Ar-Go)、下颌体长(Go-Gn)、上中切牙-SN平面角(U1-SN)、下中切牙-下颌平面角(L1-Mp)、上唇审美平面距(Ls-E)、下唇审美平面距(Li-E)、鼻唇角。结果观察组治疗总有效率96.67%明显高于对照组76.67%(P<0.05);治疗后,观察组矫治时间长于对照组(P<0.05);治疗前,两组患儿关节窝宽径、关节前间隙、关节后间隙、关节上间隙、髁突前后径、髁突高度对比无明显差异(P>0.05),治疗后,两组患儿关节窝宽径对比无明显差异,关节前间隙减小,观察组(1.21±0.28)mm高于对照组,关节后间隙、关节上间隙、髁突前后径、髁突高度均增加,且观察组高于对照组(P<0.05);治疗前,两组患儿ANB角、SNB角、SNA角、Mp-SN、Ar-Po、Ar-Go、Go-Gn、U1-SN、L1-Mp、Ls-E、Li-E、鼻唇角对比无明显差异(P>0.05),治疗后,两组患儿SNB、Mp-SN、Ar-Po、Ar-Go、Go-Gn、鼻唇角均增大,ANB、U1-SN、L1-Mp、Li-E、Ls-E减小,且观察组变化大于对照组(P<0.05)。结论Twin-Block矫治器配合直丝弓矫治技术双期矫治器与单纯直丝弓矫正技术相比治疗替牙及恒牙早期错(牙合)畸形疗效显著,可作为儿童恒牙早期错(牙合)畸形的首选治疗方案。

TOC estimation from logging data using principal component analysis

Total organic carbon(TOC)content is one of the most important parameters for characterizing the quality of source rocks and assessing the hydrocarbon-generating potential of shales.The Lucaogou Formation shale reservoirs in the Jimusaer Sag,Junggar Basin,NW China,is characterized by extremely complex lithology and a wide variety of mineral compositions with source rocks mainly consisting of carbonaceous mudstone and dolomitic mudstone.The logging responses of organic matter in the shale reservoirs is quite different from those in conventional reservoirs.Analyses show that the traditional△logR method is not suitable for evaluating the TOC content in the study area.Analysis of the sensitivity characteristics of TOC content to well logs reveals that the TOC content has good correlation with the separation degree of porosity logs.After a dimension reduction processing by the principal component analysis technology,the principal components are determined through correlation analysis of porosity logs.The results show that the TOC values obtained by the new method are in good agreement with that measured by core analysis.The average absolute error of the new method is only 0.555,much less when compared with 1.222 of using traditional△logR method.The proposed method can be used to produce more accurate TOC estimates,thus providing a reliable basis for source rock mapping.

基于Group-Res2Block的智能合成语音说话人确认方法

针对现有说话人确认任务基于自然语音条件下并不适用于智能合成语音的问题,提出一种基于Group-Res2Block的智能合成语音说话人确认方法。首先,设计了Group-Res2Block结构,在Res2Block的基础上将当前分组与相邻前后分组进行合并形成新的分组,以增强说话人局部特征的上下文联系;其次,设计了并行结构的多尺度通道注意力特征融合机制,利用不同大小卷积核实现同一层级的特征在通道维度的特征选择,以获取更具表现力的说话人特征,避免信息冗余;最后,设计了串行结构的多尺度层注意力特征融合机制,构建层结构,将深浅层特征整体进行融合并赋予不同权重,以获取最优的特征表达。为验证所提出特征提取网络的有效性,构建了中英文两种智能合成语音数据集进行消融实验和对比实验。结果表明本文方法在该任务的评价指标精确度(accuracy,ACC)、等错误率(equal error rate,EER)和最小检测代价函数(minimum detection cost function,minDCF)上是最优的。此外,通过对模型泛化性能进行测试,验证了本文方法对未知智能语音算法的适用性。

Functionality of Covalent Organic Framework (COF) in Gas Storage Application: First Principal Study

Industrial growth in recent years led to air pollution and an increase in concentration of hazardous gases such as O3 and NO. Developing new materials is important to detect and reduce air pollutants. While catalytic decomposition and zeolites are traditional ways used to reduce the amount of these gases. We need to develop and explore new promising materials. Covalent organic framework (COF) has become an attractive platform for researcher due to its extended robust covalent bonds, porosity, and crystallinity. In this study, first principal calculations were performed for gases adsorption using COFs containing nitrogen and π-bonds. Different building blocks (BBs) and linkers (LINKs/LINK1 & LINK2) were investigated by means of density functional theory (DFT) calculations with B3LYP and 3-21G basis sets to calculate the binding energies of gases @COF systems. Electrostatic potential maps (ESPM), Mulliken charges and non-covalent interaction (NCI) are used to understand the type of interactions between gas and COFs fragments. O3 was found to bind strongly with COF system in comparison with NO which could make COF a useful selective material for mixed gases environment for sensing and removal application.

Greatly enhanced corrosion/wear resistances of epoxy coating for Mg alloy through a synergistic effect between functionalized graphene and insulated blocking layer

The poor corrosion and wear resistances of Mg alloys seriously limit their potential applications in various industries.The conventional epoxy coating easily forms many intrinsic defects during the solidification process,which cannot provide sufficient protection.In the current study,we design a double-layer epoxy composite coating on Mg alloy with enhanced anti-corrosion/wear properties,via the spin-assisted assembly technique.The outer layer is functionalized graphene(FG)in waterborne epoxy resin(WEP)and the inner layer is Ce-based conversion(Ce)film.The FG sheets can be homogeneously dispersed within the epoxy matrix to fill the intrinsic defects and improve the barrier capability.The Ce film connects the outer layer with the substrate,showing the transition effect.The corrosion rate of Ce/WEP/FG composite coating is 2131 times lower than that of bare Mg alloy,and the wear rate is decreased by~90%.The improved corrosion resistance is attributed to the labyrinth effect(hindering the penetration of corrosive medium)and the obstruction of galvanic coupling behavior.The synergistic effect derived from the FG sheet and blocking layer exhibits great potential in realizing the improvement of multi-functional integration,which will open up a new avenue for the development of novel composite protection coatings of Mg alloys.

Mantle sources of Cenozoic volcanoes around the South China Sea revealed by geochemical and isotopic data using the principal component analysis

Principal component analysis(PCA)was employed to determine the implications of geochemical and isotopic data from Cenozoic volcanic activities in the Southeast Asian region,including China(South China Sea(SCS),Hainan Island,Fujian-Zhejiang coast,Taiwan Island),and parts of Vietnam and Thailand.We analyzed 15 trace element indicators and 5 isotopic indicators for 623 volcanic rock samples collected from the study region.Two principal components(PCs)were extracted by PCA based on the trace elements and Sr-Nd-Pb isotopic ratios,which probably indicate an enriched oceanic island basalt-type mantle plume and a depleted mid-ocean ridge basalt-type spreading ridge.The results show that the influence of the Hainan mantle plume on younger volcanic activities(<13 Ma)is stronger than that on older ones(>13 Ma)at the same location in the Southeast Asian region.PCA was employed to verify the mantle-plume-ridge interaction model of volcanic activities beneath the expansion center of SCS and refute the hypothesis that the tension of SCS is triggered by the Hainan plume.This study reveals the efficiency and applicability of PCA in discussing mantle sources of volcanic activities;thus,PCA is a suitable research method for analyzing geochemical data.

Novel approach of ultrasound-guided lateral recess block for a patient with lateral recess stenosis: A case report

BACKGROUND Ultrasound guide technology,which can provide real-time visualization of the needle tip and tissues and avoid many adverse events,is widely used in mini-mally invasive therapy.However,the studies on ultrasound-guided Lateral recess block(LRB)are limited,this is probably because there is no recognized standard method for ultrasound scanning.This study aimed to evaluate the effect of ultrasound-guided LRB in patients with lateral recess stenosis(LRS).CASE SUMMARY A 65-year-old patient complained of low back pain accompanied occasionally by pain and numbness in the left lower limb.Physical examination showed ten-derness on the spinous process and paraspinal muscles from L1 to S1,extensor hallucis longus and tibialis anterior weakness(muscle strength:4-),and a positive straight leg raising test in the left lower limb(60°).Magnetic resonance imaging showed L4–L5 disc degeneration with left LRS and nerve root entrapment.Subsequently,the patient was diagnosed with LRS.This patient was treated with a novel ultrasound-guided LRB approach.The patient’s symptoms significantly improved without any complications at 1 wk postoperatively and at the 3-month follow-up.CONCLUSION This is the first report on the LRS treatment with ultrasound-guided LRB from the contralateral spinous process along the inner side of the articular process by out-plane technique.Further studies are expected to investigate the efficacy and safety of ultrasound-guided LRB for patients with LRS.

Integrated classification method of tight sandstone reservoir based on principal component analysise simulated annealing genetic algorithmefuzzy cluster means

In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.