Exploring the mechanisms of calcium carbonate deposition on various substrates with implications for effective anti-scaling material selection

The unexpected scaling phenomena have resulted in significant damages to the oil and gas industries,leading to issues such as heat exchanger failures and pipeline clogging.It is of practical and fundamental importance to understand the scaling mechanisms and develop efficient anti-scaling strategies.However,the underlying surface interaction mechanisms of scalants(e.g.,calcite)with various substrates are still not fully understood.In this work,the colloidal probe atomic force microscopy(AFM)technique has been applied to directly quantify the surface forces between calcite particles and different metallic substrates,including carbon steel(CR1018),low alloy steel(4140),stainless steel(SS304)and tungsten carbide,under different water chemistries(i.e.,salinity and pH).Measured force profiles revealed that the attractive van der Waals(VDW)interaction contributed to the attachment of the calcium carbonate particles on substrate surfaces,while the repulsive electric double layer(EDL)interactions could inhibit the attachment behaviors.High salinity and acidic p H conditions of aqueous solutions could weaken the EDL repulsion and promote the attachment behavior.The adhesion of calcite particles with CR1018 and4140 substrates was much stronger than that with SS304 and tungsten carbide substrates.The bulk scaling tests in aqueous solutions from an industrial oil production process showed that much more severe scaling behaviors of calcite was detected on CR1018 and 4140 than those on SS304 and tungsten carbide,which agreed with surface force measurement results.Besides,high salinity and acidic p H can significantly enhance the scaling phenomena.This work provides fundamental insights into the scaling mechanisms of calcite at the nanoscale with practical implications for the selection of suitable antiscaling materials in petroleum industries.

On Definition of Geograghic Variable Scaling

According to the description effect range, the method of scaling system in cartography is with the precision of four grades： nominal scaling, ordinal scaling, interval scaling and ratio scaling. The authors have researched their innate character and inherent relations. The essence of evaluation partialordering set （A ,≤） is the mapping of evaluation object （X, ≤） under fixed condition. The collection and classification of xi ∈ X corresponds how to express Aj ∈ A, i.e. , V xi ∈ X→f（xi ） = Aj → A, Aj is the image of xi under mapping f. The function relation between evaluation partial-ordering set （A, ≤） and evaluation object （X, ≤） is decided by the space character and the way of collection and classification. The different space character and way of collection and classification will produce the different express method and evaluation result, accordingly the authors give out the mathematical definitions for nominal scaling, ordinal scaling, interval scaling and ratio scaling respectively. These results have been proved through the examples.

Generating highly active oxide-phosphide heterostructure through interfacial engineering to break the energy scaling relation toward urea-assisted natural seawater electrolysis

Urea-assisted natural seawater electrolysis is an emerging technology that is effective for grid-scale carbon-neutral hydrogen mass production yet challenging.Circumventing scaling relations is an effective strategy to break through the bottleneck of natural seawater splitting.Herein,by DFT calculation,we demonstrated that the interface boundaries between Ni_(2)P and MoO_(2) play an essential role in the selfrelaxation of the Ni-O interfacial bond,effectively modulating a coordination number of intermediates to control independently their adsorption-free energy,thus circumventing the adsorption-energy scaling relation.Following this conceptual model,a well-defined 3D F-doped Ni_(2)P-MoO_(2) heterostructure microrod array was rationally designed via an interfacial engineering strategy toward urea-assisted natural seawater electrolysis.As a result,the F-Ni_(2)P-MoO_(2) exhibits eminently active and durable bifunctional catalysts for both HER and OER in acid,alkaline,and alkaline sea water-based electrolytes.By in-situ analysis,we found that a thin amorphous layer of NiOOH,which is evolved from the Ni_(2)P during anodic reaction,is real catalytic active sites for the OER and UOR processes.Remarkable,such electrode-assembled urea-assisted natural seawater electrolyzer requires low voltages of 1.29 and 1.75 V to drive 10 and600 mA cm^(-2)and demonstrates superior durability by operating continuously for 100 h at 100 mA cm^(-2),beyond commercial Pt/C||RuO_(2) and most previous reports.

Scaling Laws Behind Penetrative Turbulence:History and Perspectives

An unstably stratified flow entering into a stably stratified flow is referred to as penetrative convection,which is crucial to many physical processes and has been thought of as a key factor for extreme weather conditions.Past theoretical,numerical,and experimental studies on penetrative convection are reviewed,along with field studies providing insights into turbulence modeling.The physical factors that initiate penetrative convection,including internal heat sources,nonlinear constitutive relationships,centrifugal forces and other complicated factors are summarized.Cutting-edge methods for understanding transport mechanisms and statistical properties of penetrative turbulence are also documented,e.g.,the variational approach and quasilinear approach,which derive scaling laws embedded in penetrative turbulence.Exploring these scaling laws in penetrative convection can improve our understanding of large-scale geophysical and astrophysical motions.To better the model of penetrative turbulence towards a practical situation,new directions,e.g.,penetrative convection in spheres,and radiation-forced convection,are proposed.

Integer multiple quantum image scaling based on NEQR and bicubic interpolation

As a branch of quantum image processing,quantum image scaling has been widely studied.However,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantum version of bicubic interpolation has not yet been studied.In this work,we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation(NEQR).Our scheme can realize synchronous enlargement and reduction of the image with the size of 2^(n)×2^(n) by integral multiple.Firstly,the image is represented by NEQR and the original image coordinates are obtained through multiple CNOT modules.Then,16 neighborhood pixels are obtained by quantum operation circuits,and the corresponding weights of these pixels are calculated by quantum arithmetic modules.Finally,a quantum matrix operation,instead of a classical convolution operation,is used to realize the sum of convolution of these pixels.Through simulation experiments and complexity analysis,we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm,and has better effect than the quantum version of bilinear interpolation.

Direct scaling of residual displacements for bilinear and pinching oscillators

The estimation of residual displacements in a structure due to an anticipated earthquake event has increasingly become an important component of performance-based earthquake engineering because controlling these displacements plays an important role in ensuring cost-feasible or cost-effective repairs in a damaged structure after the event.An attempt is made in this study to obtain statistical estimates of constant-ductility residual displacement spectra for bilinear and pinching oscillators with 5%initial damping,directly in terms of easily available seismological,site,and model parameters.None of the available models for the bilinear and pinching oscillators are useful when design spectra for a seismic hazard at a site are not available.The statistical estimates of a residual displacement spectrum are proposed in terms of earthquake magnitude,epicentral distance,site geology parameter,and three model parameters for a given set of ductility demand and a hysteretic energy capacity coefficient in the case of bilinear and pinching models,as well as for a given set of pinching parameters for displacement and strength at the breakpoint in the case of pinching model alone.The proposed scaling model is applicable to horizontal ground motions in the western U.S.for earthquake magnitudes less than 7 or epicentral distances greater than 20 km.

Universal Scaling Laws in Quantum-Probabilistic Machine Learning by Tensor Network: Toward Interpreting Representation and Generalization Powers

The interpretation of representations and generalization powers has been a long-standing challenge in the fields of machine learning(ML)and artificial intelligence.This study contributes to understanding the emergence of universal scaling laws in quantum-probabilistic ML.We consider the generative tensor network(GTN)in the form of a matrix-product state as an example and show that with an untrained GTN(such as a random TN state),the negative logarithmic likelihood(NLL)L generally increases linearly with the number of features M,that is,L≃kM+const.This is a consequence of the so-called“catastrophe of orthogonality,”which states that quantum many-body states tend to become exponentially orthogonal to each other as M increases.This study reveals that,while gaining information through training,the linear-scaling law is suppressed by a negative quadratic correction,leading to L≃βM−αM^(2)+const.The scaling coefficients exhibit logarithmic relationships with the number of training samples and quantum channelsχ.The emergence of a quadratic correction term in the NLL for the testing(training)set can be regarded as evidence of the generalization(representation)power of the GTN.Over-parameterization can be identified by the deviation in the values ofαbetween the training and testing sets while increasingχ.We further investigate how orthogonality in the quantum-feature map relates to the satisfaction of quantum-probabilistic interpretation and the representation and generalization powers of the GTN.Unveiling universal scaling laws in quantum-probabilistic ML would be a valuable step toward establishing a white-box ML scheme interpreted within the quantum-probabilistic framework.

Complete Universal Scaling in First-Order Phase Transitions

Phase transitions and critical phenomena are among the most intriguing phenomena in nature and society.They are classified into first-order phase transitions(FOPTs)and continuous ones.While the latter shows marvelous phenomena of scaling and universality,whether the former behaves similarly is a long-standing controversial issue.Here we definitely demonstrate complete universal scaling in field driven FOPTs for Langevin equations in both zero and two spatial dimensions by rescaling all parameters and subtracting nonuniversal contributions with singular dimensions from an effective temperature and a special field according to an effective theory.This offers a perspective different from the usual nucleation and growth but conforming to continuous phase transitions to study FOPTs.

Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for Linear and Set Constrained Convex Optimization Problems

The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization problems.In this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual vari-ables.By satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence rate.This only requires assuming convexity of the objective function.We validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

基于分数阶傅里叶变换和图像加权熵的chirp scaling算法

针对传统的基于傅里叶变换和匹配滤波实现的chirp scaling(CS)成像算法中多普勒参数随斜距变化以及成像分辨率低的问题,提出利用分数阶傅里叶变换(FRFT)对CS成像算法进行优化。首先建立斜视合成孔径雷达(SAR)回波信号模型,理论推导利用FRFT代替匹配滤波进行信号压缩。针对方位向最优旋转角的搜索问题,对得到的图像基于加权最小熵建立代价函数,利用动量法的梯度下降优化算法进行迭代计算,最终得到分辨率更高的SAR图像。为验证算法的有效性,分别在点目标仿真数据和实测SAR数据集上进行实验。结果表明,与传统CS成像算法相比,该算法的成像结果成像主瓣宽度更窄、旁瓣更低、成像更加清晰。

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

Assessment of Groundwater Stability Using Corrosion and Scaling Tendency Indices on Selected Springs in the Manga Region in Nyamira County, Kenya

We present the result of groundwater stability assessment on three major springs in the Manga region in Nyamira County found in Kenya in 2018. These springs are Kiangoso (SP1), Kerongo (SP2) and Tetema (SP3). The corrosion and scaling tendency indices were obtained using the Langelier saturation index (LSI), Ryznar stability index (RSI), and Puckorius scaling index (PSI). The LSI values obtained for SP1, SP2, and SP3 are −3.93, −4.71, and −4.17, respectively, while using RSI, the values obtained for SP1, SP2, and SP3 are 14.15, 14.53, and 13.74, respectively. Using PSI, the values of SP1, SP2, and SP3 are 5.58, 5.45, and 5.58, respectively. From the interpretation of the indices, the groundwater from the three springs in the Manga region using LSI and RSI showed intolerable corrosion;hence, it is unlikely to scale as obtained from PSI.

Dendritic spine density changes and homeostatic synaptic scaling:a meta-analysis of animal studies

Mechanisms of homeostatic plasticity promote compensatory changes of cellular excitability in response to chronic changes in the network activity.This type of plasticity is essential for the maintenance of brain circuits and is involved in the regulation of neural regeneration and the progress of neurodegenerative disorders.One of the most studied homeostatic processes is synaptic scaling,where global synaptic adjustments take place to restore the neuronal firing rate to a physiological range by the modulation of synaptic receptors,neurotransmitters,and morphology.However,despite the comprehensive literature on the electrophysiological properties of homeostatic scaling,less is known about the structural adjustments that occur in the synapses and dendritic tree.In this study,we performed a meta-analysis of articles investigating the effects of chronic network excitation(synaptic downscaling)or inhibition(synaptic upscaling)on the dendritic spine density of neurons.Our results indicate that spine density is consistently reduced after protocols that induce synaptic scaling,independent of the intervention type.Then,we discuss the implication of our findings to the current knowledge on the morphological changes induced by homeostatic plasticity.

机载前视合成孔径雷达Chirp Scaling成像算法研究

针对目前机载侧视合成孔径雷达(SAR)无法对飞行路线正前方进行高分辨率成像的问题,该文研究了一种新型机载前视SAR,分析了前视SAR的工作原理和方位分辨率提高的可行性。基于空间几何模型和回波信号形式,推导并给出了适用于机载前视SAR成像的chirp scaling算法,给出了各相位补偿因子表达式及算法实现步骤。模拟了前视SAR点目标回波数据,并利用该方法对分布于场景中心及边缘的点目标阵进行了成像仿真,分析了成像效果,成像质量指标与理论值基本吻合,证实了算法的有效性。

合成孔径声呐Chirp Scaling成像算法

在合成孔径声呐领域中经典的成像处理方法是距离-多普勒算法。距离-多普勒算法的主要缺点是在进行方位处理时需要进行距离二次脉压和在进行距离徙动校正时需要进行插值处理,这样将导致运算量迅速增加,并且降低了成像精度。而ChirpScaling算法是在二维频域中精确实现距离门徙动校正和二次距离压缩,避免了插值运算,用该算法处理了仿真点目标的回波数据,结果表明该算法既能够实现更加精确的成像又能够避免插值运算而提高运算效能。

基于星上实时信号处理机的Chirp Scaling算法实现方法

基于改进的ChirpScaling算法 ,提出了一种适用于星上实时信号处理机的高效成像处理实现方法 ,其核心思想也可应用于其它高分辨率星载SAR的精确成像算法 .首先介绍了实时信号处理机的结构 ,分析了数据处理粒度及其并行流水处理结构 .在此基础上 ,针对单个粒度的成像处理重新设计了算法流程 ,提出了一种高效的实现方法 .利用实时信号处理机 ,对仿真的回波数据进行了成像处理实验 ,结果表明 :在 41s内能够完成成像处理 ,成像处理速度和图像质量满足系统设计要求 ,从而验证了该方法的有效性 .

基于距离向Scaling原理的聚束SAR极坐标格式成像算法

极坐标格式成像算法(PFA)的实现存在两个问题:去调频后存在残余视频相位(RVP);极坐标格式转化成直角坐标格式的插值处理运算量大,且插值精度会影响聚焦效果。针对这两个问题,该文提出了一种基于Scaling原理的距离向重采样方法,而方位向则采用Chirp-Z变换,在完成去RVP的同时完全避免了插值。该算法与传统PFA相比,仅仅使用FFT及信号复乘,更利于在硬件上实现,极大节省了计算成本,而且所得图像质量也有一定提高。另外与已存在的距离向CZT方法相比该文流程更为简单,包含更少的FFT及信号复乘。仿真实验验证了该文算法的有效性。

Chirp-Scaling算法中的方位傅立叶变换

基于正侧视合成孔径雷达对点目标的回波冲激响应模型 ,运用三步法在原始回波信号直接对应的时域和频域中详细地分析和推导了Chirp -Scaling算法中的方位向傅立叶变换 ,给出了泰勒级数的展开技巧并找出了算法中的重要误差源。最后 。

基于Chirp Scaling成像算法的高分辨聚束式合成孔径声呐

基于两步处理算法和ChirpScaling算法,提出一种适用于条带式成像算法的通用高分辨聚束式合成孔径声呐(SAS)模型。该模型结合了谱分析(SPECAN)算法和ChirpScaling算法的优点,算法首先采用deramp和升采样处理技术实现方位的粗聚焦,消除了聚束式SAS特有的方位频谱混迭现象,然后应用ChirpScaling原理实现距离的精确聚焦,并补偿deramp处理引起的方位相位误差,实现方位精聚焦。基于该通用模型,给出了实现的步骤,整个算法无需任何插值操作,只需复乘和FFT即可完成。该算法适用于宽测绘带高分辨率聚束式SAS的精确而高效成像处理。最后,通过计算机仿真,验证了该通用模型的有效性。