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.

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.

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.

Correlation of Ground Penetrating Radar Data with Geotechnical Prospect Profiles: Reduto Case Study, Belém-PA, Brazil

The study presented in this manuscript aimed to relate the sedimentary strata imaged by the ground penetrating radar(GPR)method through numerical modeling with the mapping of sedimentary strata acquired through geotechnical surveys.The study aimed to expose how obtaining subsoil information through noninvasive/destructive electromagnetic waves is beneficial,as they are reliable and less costly than drilling holes beyond what is necessary to have a subsurface mapping.In this sense,physical-geological modeling was carried out.The information on the type of sediments,acquired through simple recognition surveys carried out in the city of Belém-PA,helped to create a model of a sedimentary package with its respective intrinsic physical properties.The result shows that the GPR recovered with good vertical and horizontal resolution at the beginning and end of the layers of the sedimentary package studied,proving to be very effective for locating geotechnical sounding points and safely reducing costs.

大语言模型的技术应用前景与风险挑战

针对大语言模型(LLM)技术的快速发展,剖析它的技术应用前景和风险挑战,对通用人工智能(AGI)的发展和治理有重要参考价值。首先,以Multi-BERT(Multilingual Bidirectional Encoder Representations from Transformers)、GPT(Generative Pre-trained Transformer)和ChatGPT(Chat Generative Pre-Trained Transformer)等语言模型为代表,综述LLM的发展脉络、核心技术和评估体系;其次,分析LLM现存的技术局限和安全风险;最后,提出LLM在技术上改进、政策上跟进的建议。分析指出作为发展阶段的LLM,现有模型存在非真实性及偏见性输出、实时自主学习能力欠缺,算力需求庞大,对数据质量和数量依赖性强,语言风格单一;存在数据隐私、信息安全和伦理等方面的安全风险。未来发展可从技术上继续改进,从“大规模”转向“轻量化”、从“单模态”走向“多模态”、从“通用”迈入“垂类”;从政策上实时跟进,实施有针对性的监管措施,规范应用和发展。

农药喷雾系统建模与仿真技术研究综述

农药使用效果与农药喷雾过程密切相关,农药喷雾过程的建模与仿真可弥补因植物生长周期差异、病虫害发生程度不同以及田间作业自然环境不可控等造成的试验研究困难。分析农药施用的抗性、再增猖獗、残留(resistance,resurgence,residue,3R)/效果、效率、生态(efficacy,efficiency,eco-environment,3E)/方法、模型、测试(method,model,measurement,3M),重点综述农药喷雾过程的雾化、输运、沉积等关键环节的模拟仿真研究状况。根据模拟仿真技术发展趋势,提出农药喷雾半实物仿真系统设计的建议,并提出雾化模型及新型喷头雾化机理、群体农药雾滴多相流场模拟、靶标定制与识别模型、综合农药雾滴飘移模型、植物生长模型与农药喷雾系统耦合等建议,以促进原创农药精确喷雾技术研究和高质量植保机械研发。

热液脉型萤石矿床隐伏矿体定位预测综合技术方法

萤石是国家战略性新兴产业发展中不可或缺的关键支撑原材料,被国际社会普遍列为“关键矿产”,随着萤石露头矿的发现殆尽,急需开展隐伏矿体的深部定位预测研究。文章简要论述了断裂构造控制的热液脉型萤石矿床地质特征、分带模式和成矿规律,评述了萤石找矿中常用的勘查技术方法。结合找矿标志和控矿要素分析,总结了隐伏-半隐伏萤石矿体定位预测的找矿模型,提出了“分带型”和“掩埋型”两种类型隐伏萤石矿体及相应的找矿技术方法组合。笔者建立了隐伏萤石矿体定位预测的地质-物探-化探-遥感综合找矿模型及其四步勘查技术流程,这对指导隐伏萤石矿床(体)的找矿勘查具有重要的理论和实践意义。

基于模型驱动和自动演进理论的矿集区找矿预测数据模型应用软件开发

本文旨在构建一个系统而全面支持矿集区找矿预测数据模型应用的软件体系架构。采用模型驱动和自动演进的软件研发理论,服务和支持矿集区找矿预测数据模型应用。研发工作主要集中在3类核心应用软件的研发:数据模型管理类软件、数据模型使用类软件和数据质量控制类软件,推出这些软件将有效地管理数据模型,提高矿集区找矿效率,并确保数据质量可靠性和准确性。提出了一个包含9个层次且完备的支持软件体系架构,全面覆盖数据模型从需求、设计到实际应用和用户层面各个环节。通过构建和研发支持矿集区找矿预测数据模型应用的软件体系架构及一套核心应用软件,成功为矿集区找矿预测提供了有效信息技术支持。这项工作不仅提高了找矿预测理论方法研究层次,还显著提升了软件技术应用水平,并可推广使用,具有重要的找矿指导意义。

基于动态哈夫模型及双边匹配的电动汽车充电引导策略

由于充电站中充电桩数目有限且电动汽车充电耗时长,陆续产生充电需求的各电动汽车用户存在对充电站资源的竞争。这不仅增加了用户排队概率,降低了充电站收益和利用率,而且使得用户在充电站规模、价格、评价等方面的个性化需求得不到充分满足。为此,提出了一种动态哈夫模型与双边匹配方法相结合的电动汽车充电引导策略。首先,对充电站客流、充电订单和充电桩详情等真实数据集进行大数据挖掘,分析公共充电站用户的充电站选择偏好和充电行为特征;然后,基于动态哈夫模型,结合用户充电站选择偏好量化不同区域用户前往不同充电站的概率,并生成充电站推荐列表;最后,将前景理论与双边匹配策略相结合,进行充电引导。算例分析表明,所提策略大幅降低了用户的排队概率,在满足用户个性化充电需求的同时,保障了充电站利益。

石英脉型钨多金属矿床的扇状成矿实例及找矿模型

石英脉型钨矿床是中国数量最多的钨矿床类型,但保有储量消耗迅速,迫切需要创新找矿模型,指导找矿突破。文章结合二十余年的找矿实践,通过详细分析扇状成矿矿床实例,构建了石英脉型钨矿床新的找矿模型。该模型强调赋矿裂隙为岩浆动力成因,在花岗岩体顶部呈扇状分布型式,岩浆期后热液恰在裂隙张开时充填其中而形成扇状成矿系统;提出“就岩找矿”、“就矿找矿”、“就矿找岩”的地质、地球化学和地球物理标志,指导矿床尺度的勘查工程部署。截至目前,该模型已在广东禾尚田钨锡矿床、广西珊瑚钨锡矿床、广西社垌钨钼矿床、江西盘古山钨铋矿床等获得了验证,找矿成效显著。