Predicting hepatocellular carcinoma: A new non-invasive model based on shear wave elastography

BACKGROUND Integrating conventional ultrasound features with 2D shear wave elastography(2D-SWE)can potentially enhance preoperative hepatocellular carcinoma(HCC)predictions.AIM To develop a 2D-SWE-based predictive model for preoperative identification of HCC.METHODS A retrospective analysis of 884 patients who underwent liver resection and pathology evaluation from February 2021 to August 2023 was conducted at the Oriental Hepatobiliary Surgery Hospital.The patients were divided into the modeling group(n=720)and the control group(n=164).The study included conventional ultrasound,2D-SWE,and preoperative laboratory tests.Multiple logistic regression was used to identify independent predictive factors for RESULTS In the modeling group analysis,maximal elasticity(Emax)of tumors and their peripheries,platelet count,cirrhosis,and blood flow were independent risk indicators for malignancies.These factors yielded an area under the curve of 0.77(95%confidence interval:0.73-0.81)with 84%sensitivity and 61%specificity.The model demonstrated good calibration in both the construction and validation cohorts,as shown by the calibration graph and Hosmer-Lemeshow test(P=0.683 and P=0.658,respectively).Additionally,the mean elasticity(Emean)of the tumor periphery was identified as a risk factor for microvascular invasion(MVI)in malignant liver tumors(P=0.003).Patients receiving antiviral treatment differed significantly in platelet count(P=0.002),Emax of tumors(P=0.033),Emean of tumors(P=0.042),Emax at tumor periphery(P<0.001),and Emean at tumor periphery(P=0.003).CONCLUSION 2D-SWE’s hardness value serves as a valuable marker for enhancing the preoperative diagnosis of malignant liver lesions,correlating significantly with MVI and antiviral treatment efficacy.

Maximum initial primary wave model for low-Froude-number reservoir landslides based on wave theory

The impulse waves induced by large-reservoir landslides can be characterized by a low Froude number.However,systematic research on predictive models specifically targeting the initial primary wave is lacking.Taking the Shuipingzi 1#landslide that occurred in the Baihetan Reservoir area of the Jinsha River in China as an engineering example,this study established a large-scale physical model(with dimensions of 30 m×29 m×3.5 m at a scale of 1:150)and conducted scaled experiments on 3D landslide-induced impulse waves.During the process in which a sliding mass displaced and compressed a body of water to generate waves,the maximum initial wave amplitude was found to be positively correlated with the sliding velocity and the volume of the landslide.With the increase in the water depth,the wave amplitude initially increased and then decreased.The duration of pressure exertion by the sliding mass at its maximum velocity directly correlated with an elevated wave amplitude.Based on the theories of low-amplitude waves and energy conservation,while considering the energy conversion efficiency,a predictive model for the initial wave amplitude was derived.This model could fit and validate the functions of wavelength and wave velocity.The accuracy of the initial wave amplitude was verified using physical experiment data,with a prediction accuracy for the maximum initial wave amplitude reaching 90%.The conversion efficiency(η)directly determined the accuracy of the estimation formula.Under clear conditions for landslide-induced impulse wave generation,estimating the value ofηthrough analogy cases was feasible.This study has derived the landslide-induced impulse waves amplitude prediction formula from the standpoints of wave theory and energy conservation,with greater consideration given to the intrinsic characteristics in the formation process of landslide-induced impulse waves,thereby enhancing the applicability and extensibility of the formula.This can facilitate the development of empirical estimation methods for landslide-induced impulse waves toward universality.

Validation and Coupling of the SWAN Wave Prediction Model by WRF for the Persian Gulf

Generation of waves is affected by forces that exerted constantly in the oceans. The most obvious reason for the appearance of surface-waves is a process of interaction between atmosphere and sea surface that results in wind generation. Wave predictions are usually issued for a maximum of a few days for using in different fields such as shipping, fishing, oil industry, tourism, and to increase the safety of seafarers and beach habitants, maintaining economic assets and optimal utilization of natural resources. In this study, SWAN model has been run for this research over the Oman sea and the Persian Gulf. For implementation of SWAN, another dynamic model with prediction ability of 99-hours also has been used. In this example, wind field is obtained from the outputs of the WRF model converted to the required format for SWAN model. The computational network of SWAN model has been set to spatial grid points of 6 minutes with 1-hour temporal scale. Standard validation ways, including experimental verification, Multiplicative Bias, Mean Error and Root Mean Square Error are used in this study by comparing together for evaluation of accuracy of the model outputs. The results show that the prediction of wave heights by the model for 9 to 24-hour prediction could be the most accurate.

Structure analysis of shale and prediction of shear wave velocity based on petrophysical model and neural network

Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.

Power Maximization of A Point Absorber Wave Energy Converter Using Improved Model Predictive Control

This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves’ behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method’s efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.

Comparison of Different Added Power in Waves Prediction Methods

In order to predict the speed loss in the actual sea states more precisely, delivered power shall be measured more accurately as an input. Therefore, based on a 50,000 DWT tanker, various results obtained from different prediction methods were compared by a series of model tests performed in calm water and in waves. It is shown that speed loss deprived from RTIM （resistance and thrust identity method） method in regular waves test could satisfy the engineering requirements most.

Model-Predictive Control Strategy for an Array of Wave-Energy Converters

To facilitate the commercialization of wave energy in an array or farm environment, effective control strategies for improving energy extraction efficiency of the system are important. In this paper, we develop and apply model-predictive control(MPC) to a heaving point-absorber array, where the optimization problem is cast into a convex quadratic programming(QP)formulation,which can be efficiently solved by a standard QP solver. We introduced a term for penalizing large slew rates in the cost function to ensure the convexity of this function. Constraints on both range of the states and the input capacity can be accommodated. The convex formulation reduces the computational hurdles imposed on conventional nonlinear MPC. For illustration of the control principles,a point-absorber approximation is adopted to simplify the representation of the hydrodynamic coefficients among the array by exploiting the small devices to wavelength assumption. The energycapturing capabilities of a two-cylinder array in regular and irregular waves are investigated. The performance of the MPC for this two-WEC array is compared to that for a single WEC, and the behavior of the individual devices in head or beam wave configuration is explained. Also shown is the reactive power required by the power takeoff system to achieve the performance.

Evaluation of numerical wave model for typhoon wave simulation in South China Sea

The simulating waves nearshore(SWAN) model has typically been designed for wave simulations in near-shore regions. In this study, the model's applicability to the simulation of typhoon waves in the South China Sea(SCS) was evaluated. A blended wind field, consisting of an interior domain based on Fujita's model and an exterior domain based on Takahashi's model, was used as the driving wind field. The waves driven by Typhoon Kai-tak over the SCS that occurred in 2012 were selected for the numerical simulation research. Sensitivity analyses of time step, grid resolution, and angle resolution were performed in order to obtain optimal model settings. Through sensitivity analyses, it can be found that the time step has a large influence on the results, while grid resolution and angle resolution have a little effect on the results.

PREDICTION OF OCEANIC DATA

The Kalman filter is used to predict the velocity of littoral current, the wave direction, the sea depth and the wave steepness. In this paper the Kazumasa model has been modified to deal with two cases: 1) For the positions a bit far from the shore, the interaction between the velocity of littoral current as well as the wave direction and the sea depth as well as the wave steepness must be considered. 2) For the positions very close to the shore, three new parameters describing the asymmetry wave are introduced to deal with wave breaking. The results from the modified model are compared with observed data, and the comparison indicates that the modified model is better and capable of giving more accurate results.

Comparative Experimental and Numerical Study of Wave Loads on A Monopile Structure Using Different Turbulence Models

This study numerically and experimentally investigates the effects of wave loads on a monopile-type offshore wind turbine placed on a 1:25 slope at different water depths as well as the effect of choosing different turbulence models on the efficiency of the numerical model.The numerical model adopts a two-phase flow by solving Unsteady Reynolds-Averaged Navier−Stokes(URANS)equations using the Volume Of Fluid(VOF)method and three differentk-ωturbulence models.Typical environmental conditions from the East China Sea are studied.The wave run-up and the wave loads applied on the monopile are investigated and compared with relevant experimental data as well as with mathematical predictions based on relevant theories.The numerical model is well validated against the experimental data at model scale.The use of different turbulence models results in different predictions on the wave height but less differences on the wave period.The baseline k-ωturbulence model and Shear-Stress Transport(SST)k-ωturbulence model exhibit better performance on the prediction of hydrodynamic load,at a model-scale water depth of 0.42 m,while the laminar model provides better results for large water depths.The SST turbulence model performs better in predicting wave run-up for water depth 0.42 m,while the laminar model and standard k-ωmodel perform better at water depth 0.52 m and 0.62 m,respectively.

Wave forecast in the Atlantic Ocean using a double-stage ConvLSTM network

海浪预报对海上运输安全至关重要.本研究提出了一种涵盖物理信息的深度学习模型Double-stage ConvLSTM(D-ConvLSTM)以改进大西洋的海浪预报.将D-ConvLSTM模型与海浪持续性预测和原始ConvLSTM模型的预测技巧进行对比.结果表明,预测误差随着预测时长的增加而增加.D-ConvLSTM模型在预测准确度方面优于前二者,且第三天预测的均方根误差低于0.4 m,距平相关系数约在0.8.此外,当使用IFS预测风替代再分析风时,能够产生相似的预测效果.这表明D-ConvLSTM模型的预测能力能够与ECMWF-WAM模式相当,且更节省计算资源和时间.

基于模型预测的直驱式波浪发电机机侧最优功率控制技术

直驱式波浪发电系统具有结构简单、成本低、效率高等优点,得到了国内外学者的广泛关注。该文以直驱式波浪发电系统为研究对象,分析并建立了直驱式波浪发电装置的水动力数学模型和永磁直线电机的动态数学模型。推导了基于模型预测的功率控制策略用于直驱式波浪发电系统。在功率控制策略中从功率捕获策略和功率跟踪控制策略展开研究以实现将波浪能转换为电能并汇入直流母线。重点围绕功率捕获策略开展基于模型预测控制技术研究,分别从控制策略、预测区间和成本函数三个角度分析了基于模型预测的最优功率捕获策略。建立了仿真模型并进行仿真分析,结果表明,采用基于模型预测的最优功率捕获策略可有效提高不规则波下发电机从波浪发电系统捕获能量。此外,采用以直流母线功率汇入最大为目标的成本函数可以有效地提高发电机输入直流侧能量,具有重要的实用价值。最后通过实验室模拟平台进行了实验测试。

计及弱磁效应的直驱式波浪发电系统变系数模型预测控制策略

为提高波浪发电系统(wave energy converter,WEC)能量捕获效率,并保证其在运行过程中满足系统的各种约束条件,该文以平均捕获功率最优为目标,提出计及弱磁效应的直驱式波浪发电系统变系数模型预测控制(model predictive control,MPC)策略。针对波浪激励力过大导致的变流器输出电压过调制问题,将弱磁控制引入优化算法,在满足发电机弱磁极限的前提下增大发电系统的运行范围,提高WEC的能量捕获效率。此外,基于仿真分析,发现了规则波下MPC目标函数的正则系数与平均捕获功率的对应关系。在此基础上,引入快速傅里叶变换(fast Fourier transformation,FFT)与叠加定理,构建了适用于不规则波输入的直驱式WEC变系数MPC控制策略。仿真结果表明,在不规则波浪下,所提方案能在满足系统约束条件的同时,有效提高能量捕获效率。

OBN地震数据成像处理基本逻辑与关键方法技术

海洋油气勘探逐渐进入深水深层勘探领域,地下地质构造复杂(横向变速剧烈)、目标油藏复杂(由以构造油气藏为主转向构造与地层岩性油气藏并重),同时还可能伴随海底地形及附近岩性的复杂变化,所有因素促使海洋油气地震勘探技术不断变革。提高海洋油气勘探效益的首要问题是发展尽可能满足高精度地震波成像需求的地震数据采集技术及对应的高精度地震波成像技术。当前,无论海上和陆上油气地震勘探,“两宽一高”地震数据采集技术和全波形反演(FWI)/最小二乘逆时偏移(LS_RTM)为代表的地震波成像技术是标志性的领先技术。海上油气地震勘探中,海底节点(OBN)地震数据采集是目前业界公认的、最有可能真正实现“两宽一高”地震数据采集的技术。与拖缆数据采集相比,OBN数据采集具有宽方位照明、数据信噪比高、无检端鬼波、存在实测的(至少一阶自由表面相关)下行波场、四分量观测等优点。尤其是宽方位照明和存在至少一阶自由表面下行波场的特点,使得OBN数据具备了对中深层复杂构造和近海底介质进行高精度成像的能力。着重讨论了高精度地震波成像对地震数据采集的要求,指出OBN数据采集在海洋油气勘探中的必要性;分析了OBN数据采集的地震波场的特点,据此提出OBN数据地震波成像处理的基本逻辑及相应的关键技术;认为海洋油气勘探中地震波成像处理的特殊问题主要由特征反射层引起,海水面、海底面和地下介质中若干强反射层构成了这些特征反射层,提出了模型驱动波动理论特征反射层相关多次波预测与压制的技术路线,并对比了几种代表性的多次波预测的基础理论;指出对应当前的线性化偏移成像算子叠前数据域与叠前成像域是等价的,据此以成像道集后处理为中心,给出期望成像道集的定义,将弱旁瓣、定量的反射系数作为保真高分辨地震波成像的目标,在两个域中尽可能完美实现地下同一反(绕/散)射点、不同炮检距反(绕/散)射子波的同相位叠加,尽可能好地实现保真高分辨带限反射系数的成像;提出最好把带限反射系数成像推进到宽带波阻抗成像的技术路线;结合OBN数据的特点,给出了OBN数据地震波成像处理的基本技术流程,指出各环节的关键方法技术。最后,针对OBN数据四分量观测的特点,指出是实际观测的多波地震波场中的波现象(主要是P_SV波)与地震波传播及模拟理论不匹配导致了当前多波成像结果达不到预期,建议重点研究实际观测的多波地震波场中的波现象与地震波传播及模拟理论不匹配的物理根源,而不是发展更高端的矢量波成像算法。期望本文的思想观点对OBN地震勘探在海洋油气勘探中的进一步应用产生积极的促进作用。

双浮体直驱波浪发电装置建模分析与基于模型预测控制的能量提取算法研究

基于振荡浮体结构的直驱波浪发电装置具有损耗低、控制灵活等优点,近年来,逐渐成为波浪发电领域的研究热点。双浮体直驱波浪发电装置不需要海上固定平台,能够通过锚系结构在海水中工作,是一种工程可行的直驱波浪发电方案,但其动力学模型更为复杂,相关控制算法缺少实际装置测试。该文通过等效电路的方法对双浮体装置进行建模分析,指出采用张力锚结构可将双浮体装置等效为单浮体装置进行分析,简化控制器的分析与设计,从而建立基于模型预测控制的高效波浪能提取问题。提出一种结合波浪激励力辨识、预测与快速模型预测求解的控制算法,并且设计了双浮体直驱波浪发电装置,在不同波浪条件下进行了相关实验测试。实验结果表明,所提控制算法能够在有效降低运算复杂度的前提下提高装置的波浪能提取效率。

基于EMD-LSTM多变量输入的极端海况预报模型

极端海况是多种外界因素共同作用的结果,传统单变量波浪预报模型无法考虑多变量的影响,因此构建EMD-LSTM多变量输入模型来预报极端海况。以浮标数据作为分析数据集,利用改进的EMD算法消除变量端点效应和时序非平稳性的影响,运用多变量输入模型对其进行预测分析。结果表明:多变量复合模型可对极端海况实现有效提前预警,输入层引入波高、风速和阵风3个关键因子后模型预报性能最佳,比对均方根误差和纳什效率系数可知多变量输入的预报性能较单变量有显著提升。

考虑时间相关性的风速和波浪高度短期预测

风与波浪高度是海洋工程设计的重要参数之一,其准确预测具有重要的工程意义。针对波浪高度复杂多变、难以预测的挑战,本文考虑时间相关性,利用长短时记忆(Long-Short-Term Memory,LSTM)神经网络方法建立风和波浪高度的短期预测模型。首先,对波浪高度和风速等海洋环境原始时间序列进行分析,并选取不同时间间隔对浪高和风速极值进行取值;其次,对风速与波浪高度开展相关性分析,并基于LSTM方法,构建风和波浪高度单步预测模型。在时间间隔为0.5 h时,波浪的预测误差为0.12 m;进而构建风和波浪联合预测模型,预测风速下的波高预测误差仅为0.12m,与实测风速下的波浪预测误差基本一致。

早期主动脉硬化风险筛查模型的构建及验证研究

背景在心血管风险评估领域,主动脉僵硬度被认为是关键的预测指标,颈股脉搏波传导速度(cfPWV)被认为是无创评估主动脉硬化风险的金标准。由于技术难度等挑战,我国cfPWV检测尚未广泛开展。目的本研究旨在开发并验证一种基于心血管危险因素的早期主动脉硬化风险筛查模型,以期替代cfPWV复杂的测量过程,减少对传统测量方法的依赖。方法选取2023年5—11月在安徽医科大学第一附属医院体检中心招募的878名受试者作为研究对象,按照8∶2的比例进行随机抽样分为建模组(n=703)和验证组(n=175)。收集患者一般资料、实验室检查结果及cfPWV。依据cfPWV检查结果和相关指南,将建模组受试者分为无主动脉硬化风险(n=503)和有主动脉硬化风险(n=200)。采用多因素Logistic回归分析并筛选变量,建立列线图评估模型。绘制模型预测主动脉硬化发生风险的受试者工作特征曲线(ROC曲线),以ROC曲线下面积(AUC)、Hosmer-Lemeshow检验评估模型的区分度和校准度,采用Delong检验比较各模型的AUC,采用决策曲线分析(DCA)评估模型临床实用性,并采用Bootstrap法重复采样1000次对模型进行内部验证。结果建模组有主动脉硬化风险者年龄、BMI、收缩压(SBP)、舒张压(DBP)、平均动脉压(MAP)、尿素、空腹血糖(FBG)、低密度脂蛋白胆固醇(LDL-C)、三酰甘油(TG)、总胆固醇(TC)、丙氨酸氨基转移酶(ALT)、天冬氨酸氨基转移酶(AST)、血红蛋白(Hb)、饮酒、血脂异常、糖尿病比例高于无主动脉硬化风险者,肾小球滤过率(GFR)、血小板计数(PLT)低于无主动脉硬化风险者(P<0.05)。多因素Logistic回归分析结果显示年龄(OR=1.112,95%CI=1.082~1.143)、MAP(OR=1.146,95%CI=1.107~1.188)、Hb(OR=1.026,95%CI=1.004~1.049)和FBG(OR=1.353,95%CI=1.076~1.701)是主动脉硬化的独立影响因素(P<0.05)。纳入多因素Logistic回归分析结果差异有统计学意义的指标(年龄、MAP、Hb、FBG)构建预测模型Ⅰ,同时分别纳入吸烟、性别、血脂异常构建模型Ⅱ、模型Ⅲ、模型Ⅳ,绘制模型Ⅰ~模型Ⅳ的ROC曲线,模型Ⅰ~模型Ⅳ的AUC分别为0.941(95%CI=0.923~0.964,P<0.05)、0.941(95%CI=0.922~0.962,P<0.05)、0.941(95%CI=0.922~0.963,P<0.05)、0.939(95%CI=0.919~0.962,P<0.05);Delong检验结果示,模型Ⅰ、模型Ⅱ、模型Ⅲ、模型Ⅳ的AUC比较,差异无统计学意义(P>0.05)。根据多因素Logistic回归分析结果,以年龄、MAP、FBG、Hb为预测因子构建列线图模型,预测模型训练集的AUC为0.941(95%CI=0.920~0.962),灵敏度为0.832,特异度为0.917。验证集的AUC为0.961(95%CI=0.914~1.000),灵敏度为0.872,特异度为0.964。DCA结果显示使用主动脉硬化早期筛查模型可以使受试者在临床中获益。结论本研究基于年龄、MAP、Hb和FBG 4个简易指标,建立了早期主动脉硬化风险筛查模型,提供了便捷、高效的早期血管功能筛查的方法。

基于正交各向异性介质频变AVAZ反演的裂缝含气性预测

依赖频率的AVO反演方法(FDAVO)可以提取与流体有关的异常信息,但由于没有考虑实际储层中各向异性的影响,难以适用于裂缝性油气藏。另外,各向异性通常与频率相关,这一特性有利于利用OVT域叠前地震数据识别有效裂缝。为此,提出基于正交各向异性(OA)介质频率相关的AVAZ(OA-FDAVAZ)反演的裂缝含气性预测方法。首先基于Chapman模型的数值模拟分析弹性参数随频率、入射角等参数的变化特征;然后基于OA介质纵波反射系数近似式推导出OA-FDAVAZ反演的目标方程,构建了新的频散属性Iani;最后利用最小二乘法反演正交各向异性频散属性。模型试算表明:含气裂缝层的正交各向异性频散属性明显强于含水裂缝层,而不含裂缝的各向同性层频散响应微弱,验证了所提频散属性对流体的敏感性。实际OVT域叠前地震数据应用结果表明:正交各向异性频散属性的预测结果与钻探结果吻合较好。与传统方法对比,该方法受背景干扰小,可准确刻画储层中裂缝含气性的空间分布特征。

基于矩匹配模型降阶的直驱式波浪发电系统功率优化

为改善复杂工况下直驱式波浪发电系统功率捕获效果、降低控制器计算负担,基于矩匹配算法改进模型预测控制策略。通过建立系统水动力模型,选取波浪关键频率,利用Sylvester方程计算该频率下系统期望矩,得到匹配幅相特性的降阶模型。根据系统降阶模型,构造并求解可计及电机铜耗的改进二次型成本函数,计算理想电磁力,得到q轴期望电流跟踪值,提升波能捕获能力同时减少系统主要损耗。仿真结果表明:矩匹配降阶算法拟合程度高、动态行为好,所提控制策略波能转换效率提高,系统输出平均功率增加。