Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Genesis, metallogenic model, and prospecting prediction of the Nibao gold deposit in the Guizhou Province, China

Southwestern Guizhou province is one of China’s most important distribution areas of Carlin-type gold deposits. The Nibao deposit is a typical gold deposit in southwestern Guizhou. To elucidate the genesis of the Nibao gold deposit, establish a metallogenic model, and guide prospecting prediction, we systematically collected previously reported geological, geochemical, and dating data and discussed the genesis of the Nibao gold deposit,based on which we proposed the metallogenic model.Earlier works show that the Nibao anticline, F1 fault, and its hanging wall dragged anticline(Erlongqiangbao anticline) were formed before or simultaneously with gold mineralization, while F2, F3, and F4 faults postdate gold mineralization. Regional geophysical data showed extensive low resistivity anomaly areas near the SBT(the product of tectonic slippage and hydrothermal alteration)between the P2/P3 and the strata of the Longtan Formation in the SSE direction of Nibao anticline in the lower plate of F1 and hanging wall dragged anticline(Erlongqiangbao anticline), and the anomaly areas are distributed within the influence range of anticlines. Simultaneously, soil and structural geochemistry show that F1, Nibao anticline,Erlongqiangbao anticline, and their transition areas all show good metallogenic elements(Au, As, and S) assemblage anomalies, with good metallogenic space and prospecting possibilities. There are five main hypotheses about the source of ore-forming fluids and Au in the Nibao gold deposit:(1) related to the Emeishan mantle plume activity;(2) source from the Emeishan basalt;(3) metamorphic fluid mineralization;(4) basin fluid mineralization;(5) related to deep concealed magmatic rocks;of these, the mainstream understanding is the fifth speculation. It is acknowledged that the ore-forming fluids are hydrothermal fluids with medium–low temperature, high pressure, medium–low salinity, low density, low oxygen fugacity, weak acidity, weak reduction, and rich in CO_(2)and CH_(4). The fluid pressure is 2–96.54 MPa, corresponding to depths of 0.23–3.64 km. The dating results show that the metallogenic age is ~141 Ma, the extensional tectonic environment related to the westward subduction of the Pacific Plate. Based on the above explanation, the genetic model related to deep concealed magmatic rocks of the Nibao gold deposit is established, and favorable prospecting areas are outlined;this is of great significance for regional mineral exploration and studying the genesis of gold deposits.

An integrated ore prospecting model for the Nyasirori gold deposit in Tanzania

The Nyasirori gold deposit,located in the middle-western end of the Musoma-Mara Archean greenstone belt in Tanzania,is a tectonic altered rock type gold deposit controlled by shear tectonic zone.This work conducted high-precision ground magnetic measurements to delineate fault structures and favorable prospecting targets,utilized induced polarization(IP)intermediate gradient to roughly determine the distribution and extension of the tectonic altered zone and gold ore(mineralized)bodies,and further carried out IP sounding and magnetotelluric sounding to locate the tectonic altered zone and gold ore(mineralized)bodies.The anomalous gradient belt of the combination of positive and negative micromagnetic measurements reflects the detail of shallow surface tectonic alteration zone and gold mineralization body.Micromagnetic profile anomalies indicate the spatial location and occurrence of concealed tectonic alteration zone and gold(mineralized)ore bodies.Soil geochemical measurements indicate that the ore-forming element Au correlates well with As and Sb,and As and Sb anomalies have a good indication to gold orebodies.Based on the multi-source geological-geophysical-geochemical information of the Nyasirori gold deposit,this work established an integrated prospecting model and proposed a set of geophysical and geochemical methods for optimizing prospecting targets.

Classification,Minerogenic Models and Prospecting of Realgar/Orpiment Deposits in China

China's realgar/orpiment deposits may be classified into three types, the stratabound, hot-water sedimentary and hydrothermal, according to their mineralizing processes, geological occurrences, tectonic and geochemical features. The three types may be further distinguished into seven subtypes, namely, the Xiaguan, Shuiluo, Jiepaiyu, Songpan, Shixia, Wangzhuang and Ninghshan ones. On this basis three minerogenic models are established, and based on studies of their geochemistry and minerogenic mechanisms the prerequisites for prospecting for these types of deposits are given in the paper.

3D Geology Modeling from 2D Prospecting Line Profile Map

Using prospecting line profile map in combination with drilling and other information for 3D reconstruction of geological model is an important method of 3D geological modeling.?This paper?discusses the theory and implementation method of 2D prospecting line map into 3D prospecting line map and then into 3D model. The authors propose that it needs twice upgrading dimension to reconstruction 3D geology model from prospecting line profile map. The first upgrading dimension is to convert profile from 2D into 3D profile,?i.e.?the 2D points in the 2D profile map upgrading dimensional transformation to 3D points in a 3D profile. The second upgrading dimension is that transform 0D point 1D curve and 2D polygon feature into 1D curve, 2D surface and 3D solid feature. The paper reexamines contents and forms in prospecting line map from the two different viewpoints of geology and geographic information science. The process of 3D geology modeling from 2D prospecting map is summarized as follows. Firstly, profile is divided into several sections by beginning, end and drill point of the prospecting line. Next, a 3D folded upright profile frame is built by 2D folded prospecting line on the plan map. Then, 2D points of features on 2D profile are converted into 3D points on 3D profile section by section. And then, adding switch control points for the long line crossover two segments. Lastly, 1D curve features are upgraded to 2D surface.

Geological Characteristics and Regional Prospecting Model of Wulanchongji Gold Orebody, Alxa Youqi, Inner Mongolia, China

Five gold deposits (mineralization) were found in the study area by means of geological mapping, soil geochemical survey and trough exploration engineering. The ore-bearing lithology is mainly metamorphic feldspar sandstone of the Upper Carboniferous Benbatu Formation, and the gold (mineralization) body is controlled by both structural factors and stratigraphic factors of the Upper Carboniferous Benbatu Formation. The genetic type is preliminary concluded to be volcanic hydrothermal type, and the metallogenic age is late Variscan. In this paper, by studying the geological characteristics and metallogenic geological conditions of the gold orebody in the area, a regional prospecting model has been established, which is of great significance to better guide the prospecting work of similar gold deposits in the area and the region.

Manganese potential mapping in western Guangxi-southeastern Yunnan(China) via spatial analysis and modal-adaptive prospectivity modeling

While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geological factors, the complexity of geological process and the asymmetry of geo-information. In this work, the manganese potential mapping for further exploration targeting is implemented via spatial analysis and modal-adaptive prospectivity modeling. On the basis of targeting criteria developed by the mineral system approach, the spatial analysis is leveraged to extract the predictor variables to identify features of the geological process. Specifically, a metallogenic field analysis approach is proposed to extract metallogenic information that quantifies the regional impacts of the synsedimentary faults and sedimentary basins. In the integration of the extracted predictor variables, a modal-adaptive prospectivity model is built, which allows to adapt different data availability and geological process. The resulting prospective areas of high potential not only correspond to the areas of known manganese deposits but also provide a number of favorable targets in the region for future mineral exploration.

Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling

Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidenc

Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a datadriven machine learning approach for tungsten mineralisation.The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets.The data-driven Random ForestTM algorithm is employed to model tungsten mineralisation in SW England using a range of geological,geochemical and geophysical evidence layers which include a depth to granite evidence layer.Two models are presented,one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step.The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model.The typically subjective approach is guided using the Receiver Operating Characteristics(ROC)curve tool where transformed data are compared to known training samples.The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model.The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets.Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known.The Confidence Metric,derived from model variance,is employed to further evaluate the models.The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation.The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables.Finally,legacy mining data,from drilling reports and mine descriptions,is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits.Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 m.In summary,the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling,while the newly derived Confidence Metric generates reliable mineral exploration targets.

Prospectivity modeling of porphyry copper deposits: recognition of efficient mono-and multi-element geochemical signatures in the Varzaghan district, NW Iran

The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

Geochemical Anomalies Identified by Multifractal Modeling: Implications for Mineral Exploration in the Ziyoutun Cu-Au District, Jilin Province, China

The Ziyoutun Cu-Au district is located in the Jizhong–Yanbian Metallogenic Belt and possesses excellent prospects. However, the thick regolith and complex tectonic settings present challenges in terms of detecting and decomposition of weak geochemical anomalies. To address this challenge, we initially conducted a comprehensive analysis of 1:10,000-scale soil geochemical data. This analysis included multivariate statistical techniques, such as correlation analysis, R-mode cluster analysis, Q–Q plots and factor analysis. Subsequently, we decomposed the geochemical anomalies, identifying weak anomalies using spectrum-area modeling and local singularity analysis. The results indicate that the assemblage of Au-Cu-Bi-As-Sb represents the mineralization at Ziyoutun. In comparison to conventional methods, spectrumarea modeling and local singularity analysis outperform in terms of identification of anomalies. Ultimately, we considered four specific target areas(AP01, AP02, AP03 and AP04) for future exploration, based on geochemical anomalies and favorable geological factors. Within AP01 and AP02, the geochemical anomalies suggest potential mineralization at depth, whereas in AP03 and AP04 the surface anomalies require additional geological investigation. Consequently, we recommend conducting drilling, following more extensive surface fieldwork, at the first two targets and verifying surface anomalies in the last two targets. We anticipate these findings will significantly enhance future exploration in Ziyoutun.

Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

基于PROSPECT+SAIL模型的遥感叶面积指数反演

以PROSPECT+SAIL模型为基础,从物理机理角度反演植被叶面积指数(LAI)。首先,通过FLAASH模型进行大气校正,使得图像像元值表达植被冠层反射率;然后,根据LOPEX 93数据库和JHU光谱数据库选择植物生化参数和光谱数据,以PROSPECT模型模拟出的植物叶片反射率和透射率作为SAIL模型的输入参数,得到植被冠层反射率,将结果与遥感影像的植被冠层反射率对应,回归出植被LAI;最后,以地面实测数据对遥感反演数据进行验证,并分析了误差的可能来源。

基于PROSPECT模型的植物叶片干物质估测建模研究

为了快速、准确估测植物叶片干物质含量,为作物长势及健康状况监控提供数据支撑,利用光谱分析技术探讨了干物质含量敏感光谱波段提取方法及其估测建模方法。试验数据由叶片辐射传输模型PROSPECT在干物质含量(0.001~0.02)g·cm^(-2)范围内进行模拟,随机产生1000条400~2500nm的光谱曲线,其中600条光谱曲线用于建模研究、400条光谱曲线作为模型验证数据,同时应用叶片光学特性数据库LOPEX93中325条叶片光谱-干物质含量数据进行进一步验证。首先应用试验数据进行局部敏感性分析,初步得到叶片干物质敏感波段范围,再运用改进Sobol算法进行全局敏感性分析,提取了干物质含量敏感的光谱波段范围,在此敏感波段范围运用波段组合算法计算归一化植被指数NDVI与叶片干物质含量相关系数,优选了4组相关性大的波段组合建立归一化干物质指数NDMI_((1644,1719))、NDMI_((1871,2294))、NDMI_((2150,2271))、NDMI_((1496,2282))用于干物质含量估测建模。结果表明:NDMI_((1644,1719))和NDMI_((1871,2294))模型中三次多项式形式(cubic)效果最佳、NDMI_((1496,2282))模型中幂指数形式(power)效果最佳,三者中NDMI_((1871,2294))的三次多项式模型最优,决定系数R^2为0.837,对叶片干物质含量具有较好的估测能力。

基于PROSPECT和4-scale模型的光化学植被指数尺度转换

【目的】光化学植被指数(PRI)对于准确估计植被光能利用率(LUE)有着重要的作用。但在不同的尺度(叶片、冠层、景观尺度)上,PRI与LUE二者之间的关系及其影响因素不同。传感器获得的光谱为像元及冠层光谱,叶片尺度的PRILUE关系模型无法直接用于冠层尺度的数据,因此需要对冠层尺度的PRI指数进行尺度转换。【方法】首先通过叶片尺度的PROSPECT模型,模拟不同生化参数下叶片的反射率与透射率,进而计算叶片尺度PRI指数与简单比值PRI指数(记为SR-PRI)。其次,将获得的叶片尺度反射率、透射率作为参数输入到4-scale模型中,获取不同叶面积指数(LAI)下冠层尺度的反射率,计算得出冠层尺度的PRI、SR-PRI。建立不同LAI下PRI、SR-PRI的冠层−叶片尺度转换函数,并对不同尺度上影响PRI、SR-PRI的因子进行敏感性分析。【结果】PRI、SR-PRI在进行冠层与叶片尺度转化过程中,都表现出很明显的线性关系,并且拟合效果(R2)呈现出随LAI的增大而增大的趋势。对比相同LAI水平下的PRI、SR-PRI的拟合结果发现,SR-PRI的拟合效果普遍要优于PRI。【结论】4-scale模型用来进行PRI与SR-PRI在冠层、叶片间的尺度转换是可行的,通过建立不同LAI下的尺度转换函数,可以实现将冠层尺度的PRI、SR-PRI转化到叶片尺度。

基于PROSPECT+SAIL模型反演叶面积指数的较强适用性植被指数的筛选

基于PROSPECT+SAIL植被辐射传输模型,通过控制不同的植被生化变量、地表参数和土壤光谱参数建立光谱数据集,定量地分析了归一化植被指数(NDVI)、比值植被指数(SR)、土壤调节植被指数(SAVI)等10种常用的植被指数(VIs)对叶面积指数(LAI)的响应.利用敏感性函数定量地筛选出具有较强适用性的转换型土壤调节植被指数(TSAVI).在此基础上,分别建立了TSAVI及常用植被指数NDVI反演LAI的模型.以张掖市南部地区的TM影像为数据源,进行了LAI的反演,并利用黑河生态水文遥感试验获得的中游LAI数据集对模型进行精度评价.结果表明:TSAVI–LAI模型最佳拟合关系为指数形式,其反演结果与LAI实测值的偏差最小(0.200),R2最大(0.686),RMSE最小(0.397).TSAVI可以作为较强适用性植被指数来进行LAI的反演.

脑血管病急性期血尿酸/血肌酐比值与脑血管事件复发及死亡的关系:一项前瞻性队列研究

背景脑卒中在世界各地有较高的死亡率和复发率。血尿酸(SUA)是嘌呤代谢的产物,已被认为是心脑血管病的危险因素。血尿酸/血肌酐比值(SUA/Scr)是代表肾功能标准化的SUA,目前有关SUA/Scr在急性脑血管病中的作用仍有争议。目的探讨脑血管病急性期SUA/Scr与脑血管事件复发和死亡的关系。方法本研究为前瞻性队列研究,选取2006年9月—2019年9月天津市环湖医院连续收治的首次发生脑血管事件的13313例患者为研究队列,并对患者进行随访,随访截至2020年9月。随访方式为门诊及电话相结合。随访主要终点事件为全因死亡,次要终点事件为脑血管事件复发、心血管事件复发、其他血管事件发生(如下肢动静脉栓塞)。采用Cox比例风险回归模型探究SUA/Scr与脑血管事件复发与死亡的关系。结果根据脑血管病急性期SUA/Scr四分位数,将患者分为Q1组(SUA/Scr≤3.16,n=3520)、Q2组(3.16<SUA/Scr≤3.94,n=3280)、Q3组(3.94<SUA/Scr≤4.92,n=3270)、Q4组(SUA/Scr>4.92,n=3243)。截至随访结束,774例(5.8%)患者死亡,2064例(15.5%)患者复发脑血管事件。脑血管病急性期SUA/Scr位于Q1~Q4的患者中,男性复发脑血管病的例数依次为302、375、408、337例,女性依次为99、125、169、249例;男性复发脑梗死的例数依次为261、314、345、283例,女性依次为90、101、142、205例;男性复发大动脉粥样硬化型脑梗死的例数依次为154、191、214、183例,女性依次为58、52、45、31例;男性全因死亡的例数依次为165、128、131、88例,女性依次为57、63、62、80例;男性因脑梗死死亡的例数依次为93、72、70、46例,女性依次为31、33、36、44例;男性因大动脉粥样硬化型脑梗死死亡的例数依次为58、52、45、31例,女性依次为17、18、27、24例。调整多项混杂因素后,SUA/Scr位于Q4相较于Q1是男性急性脑梗死复发的影响因素(HR=0.690,95%CI=0.500~0.953,P=0.026);SUA/Scr位于Q4相较于Q1是男性脑梗死亚组患者大动脉粥样硬化型脑梗死复发的影响因素(HR=0.740,95%CI=0.578~0.947,P=0.017)。SUA/Scr位于Q4相较于Q1是男性全因死亡、因脑梗死死亡的影响因素(HR=0.575,95%CI=0.368~0.901,P=0.003;HR=0.610,95%CI=0.353~0.814,P=0.011)。SUA/Scr位于Q3、Q4相较于Q1是男性出院后死亡的影响因素(HR=0.656,95%CI=0.476~0.904,P=0.010;HR=0.582,95%CI=0.409~0.829,P=0.001)。SUA/Scr位于Q4相较于Q1是男性脑梗死亚组患者因大动脉粥样硬化型脑梗死死亡的影响因素(HR=0.580,95%CI=0.386~0.873,P=0.007)。结论一定范围内,脑血管病急性期SUA/Scr升高对男性患者脑血管事件复发及死亡有一定的保护作用,低SUA/Scr与男性大动脉粥样硬化型脑梗死的死亡和复发风险升高有关,但与小动脉闭塞型脑梗死和心源性卒中复发和死亡无关。在女性患者中没有观察到SUA/Scr与脑血管事件复发及死亡的关系。

叶片辐射传输模型PROSPECT理论研究

综述植物叶片辐射传输PROSPECT模型的理论,并对其理论来源PLATE模型作了扼要介绍。进一步研究叶片结构、叶片光学吸收参数和叶片反射率、透射率之间的非线性关系,给出如何用单层致密叶片迭代来表示多层非致密叶片的反射率、透射率。讨论了Stokes光学系统理论及其在模型理论中的应用,并用复向量的方法证明其各向同性光学介质中反射率和透射率递推关系式。表明PROS-PECT模型基于良好的物理模型,在满足假设条件下,能够准确模拟叶片的反射率和透射率。

PROSPECT模型的特征波长优化与作物叶绿素含量检测

叶绿素是作物生长诊断的重要参数,对其进行高效检测是农田精细化管理的基础。PROSPECT模型是作物光谱学检测研究的重要工具,可为建立高精度叶绿素诊断模型提供数据集基础。为了建立具有普适性的田间玉米作物叶绿素含量检测模型,使用PROSPECT模型输入叶片结构参数和生化参数模拟叶片400~2500nm波段反射率曲线10650条。在其他参数设置保持不变的情况下,分析光谱反射率曲线对叶绿素含量参数的敏感性,结果显示叶绿素含量仅在400~780nm区间对光谱反射率曲线产生影响。讨论了3种叶绿素检测特征波长筛选策略,分别为:根据敏感性分析结果,选出548~610和694~706nm区域共计76个波长,记为SEN-BAND;基于反向区间偏最小二乘法(Bi-PLS)筛选5个区间共计91个波长,记为BPBAND;基于连续投影算法(SPA),在叶绿素影响区域400~780nm筛选10个特征波长,记为SPA-BAND。进而使用2019年、2020年两年期田间实测玉米叶片光谱反射率曲线和叶绿素含量数据,分别应用上述3种方法选取的特征波长构建玉米叶片叶绿素含量检测模型。结果显示,使用SPA-BAND特征波长构建的模型,在两年期数据中均得到最佳结果。2019年数据模型建模集决定系数(R2c)为0.8156,建模集均方根误差RMSEC为2.9086,验证集决定系数(R2v)为0.7995,验证集均方根误差RMSEV为2.9977。2020年数据模型建模集决定系数(R2c)为0.9492,建模集均方根误差RMSEC为0.9768,验证集决定系数(R2v)为0.9102,验证集均方根误差RMSEV为1.5629。表明,基于PROSPECT模型筛选叶绿素含量特征波长建立的叶绿素诊断模型具有普适性。