Three-dimensional Modeling of Ore-forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit,Henan Province,China

Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.

Spatial data mining system for ore-forming prediction

The authors designed the spatial data mining system for ore-forming prediction based on the theory and methods of data mining as well as the technique of spatial database,in combination with the characteristics of geological information data.The system consists of data management,data mining and knowledge discovery,knowledge representation.It can syncretize multi-source geosciences data effectively,such as geology,geochemistry,geophysics,RS.The system digitized geological information data as data layer files which consist of the two numerical values,to store these files in the system database.According to the combination of the characters of geological information,metallogenic prognosis was realized,as an example from some area in Heilongjiang Province.The prospect area of hydrothermal copper deposit was determined.

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.

PREDICTABILITY BETWEEN HUMAN SUBJECTIVE PREFERENCES AND SENSORY FACTORS TOWARDS CLOTHING DURING EXERCISE IN A COlD ENVIRONMENT

Subjects wore T-shirts made from eight fabrics during exercise in a cold environmental condition of 14℃ and 32%RH. Preferences were expressed initially by handling the garments and then again after they had been worn. In the trial, subjective responses to 19 sensation descriptors were recorded. The relationships among the subjective preference votes for different types of clothing and psychological sensory factors were studied by means of canonical correlation analysis.Two highly significant canonical correlations were found, which indicated that the subjective overall preference votes after wearing were very closely related to factors describing tactile and " body-fit" sensations. The subjective preference votes from handling were mainly related to the "body-fit" comfort factor. Canonical correlation redundancy analysis showed that the canonical variables for sensory factors were reasonably good predictors of the canonical variables for subjective preferences, but not vice versa.Squared

Lottery‑like preferences and the MAX effect in the cryptocurrency market

We investigate the significance of extreme positive returns in the cross-sectional pricing of cryptocurrencies.Through portfolio-level analyses and weekly cross-sectional regressions on all cryptocurrencies in our sample period,we provide evidence for a positive and statistically significant relationship between the maximum daily return within the previous month(MAX)and the expected returns on cryptocurrencies.In particular,the univariate portfolio analysis shows that weekly average raw and riskadjusted return differences between portfolios of cryptocurrencies with the highest and lowest MAX deciles are 3.03%and 1.99%,respectively.The results are robust with respect to the differences in size,price,momentum,short-term reversal,liquidity,volatility,skewness,and investor sentiment.

Research on Influencing Factors of Information Dissemination Based on User Preference

The rapid development of the Intemet makes the social network of information dissemination has undergone tremendous changes. Based on the introduction of social network information dissemination mode, this paper analyzes the influencing factors of information dissemination, establishes the user preference model through CP-nets tool, and combines the AHP principle to mine the user＇s preference order, and obtain the user＇s optimal preference feature Portfolio, and finally collect the user in the microblogging platform in the historical behavior data. the use of NetLogo different users of information dissemination decision to predict.

面向产品设计改进的顾客偏好分析与预测

针对顾客产品偏好快速变化对企业分析和预测顾客偏好能力的要求,提出一种面向产品改进的顾客偏好分析与预测方法,首先构建长短期记忆网络模型,预测产品设计迭代期间的情感值和重要度,并计算预测准确度;然后通过基于产品特征情感变化模式的产品设计改进模型判断各个特征的变化模式,明确待改进的产品特征及改进优先级;最后以DJI Mini 2无人机的在线评论为例验证了方法的有效性。

基于工人长短期时空偏好的众包任务分配

近年来,随着移动设备的计算能力和感知能力的提高,基于位置信息的时空众包应运而生,任务分配效果的提升面临许多挑战,其中之一便是如何给工人分配他们真正感兴趣的任务.现有的研究方法只关注工人的时间偏好而忽略了空间因素对偏好的影响,仅关注长期偏好却忽略了短期偏好,同时面临历史数据稀疏导致的预测不准的问题.研究基于长短期时空偏好的任务分配问题,从长期和短期两个角度以及时间和空间两个维度全面考虑工人的偏好,进行时空众包任务分配,提高任务的成功分配率和完成效率.为提升时空偏好预测的准确性,提出分片填充的张量分解算法(SICTD)减小偏好张量的空缺值占比,提出时空约束下的ST-HITS算法,综合考虑工人短期活跃范围,计算短期时空偏好.为了在众包任务分配中最大化任务总收益和工人偏好,设计基于时空偏好的贪心与Kuhn-Munkres(KM)算法,优化任务分配的结果.在真实数据集上的大量实验结果表明,提出的分片填补张量分解算法对时间和空间偏好的RMSE预测误差较基线算法分别下降22.55%和24.17%;在任务分配方面,提出的基于偏好的KM算法表现出色,对比基线算法,在工人总收益和工人完成任务平均偏好值上分别提升40.86%和22.40%.

意图感知的社交网络用户城外移动行为预测

基于社交网络用户生成的时空数据预测用户在城外的移动行为已成为城市协同管理的迫切需求.用户城外出行相对于城内移动而言属于“长尾”事件,导致用户在城外生成的签到数据极度稀疏,现有研究难以利用有限的跨城市签到数据建模用户城外出行偏好,进而准确预测用户在城外的移动行为.为此,本文提出一种意图感知的社交网络用户城外移动偏好建模框架TIEMPO.首先,为缓解数据稀疏性问题,通过随机游走从构建的城外地点网络中采样移动轨迹,利用无监督聚类发现特定数目的用户城外出行意图;其次,引入记忆网络从相似用户在城外的移动轨迹中进一步提炼出行意图;然后,基于迁移学习思想,将用户城内签到与城外出行意图进行交互建模,从而强化用户城外移动偏好表示;最后,融合用户城外移动偏好表示与地点隐含表示对用户访问城外地点的概率进行量化.本文基于多个跨城市签到数据集进行广泛的实验分析,结果表明TIEMPO能有效预测社交网络用户在城外的移动行为,预测准确性指标Acc@10相比基线模型体现出12%~15%的明显优势,排序可靠性指标NDCG@10相比基线模型则超出3%~5%,即使在冷启动预测场景下TIEMPO依然表现最优.

利用混合Plackett-Luce模型的不完整序数偏好预测

聚合不同用户的偏好时,基于序数偏好可以解决不同用户评价准则不一致问题。但用户因为候选项目过多、沟通成本高等原因不能提供完整序数偏好,影响了在线服务信誉度量、群体决策等场景中聚合结果的可靠性和准确性,而现有的预测方法未充分考虑用户群体偏好分布的多样性。针对这一问题,提出一种利用混合Plackett-Luce(PL)模型的不完整序数偏好预测(MixPLPP)方法。首先基于用户现有偏好采样完整拓展排序,其次使用采样的完整排序学习混合PL模型,再次设计基于后验概率最大化的模型选择策略为用户选择模型,最后利用所选模型预测用户完整偏好。在公开数据集Movielens上的实验结果表明,所提方法的预测准确率和Kendall秩相关系数(Kendall CC),相较于向量相似度排序(VSRank)算法提升了5.0%和9.2%;相较于基于确定性的偏好补全(CPC)提升了1.5%和3.5%;相较于BayesMallows-4提升了0.9%和2.2%。实验结果验证了所提方法具有良好的预测能力,在多个数据集上的预测效果都更好。

基于脑电图技术探讨X3D+TIN与GRU融合的女性偏好预测模型

通过解读神经营销背后的机制,基于脑电图采集女性消费者脑电波,探索脑电图的多维特征提取,结合人工智能前沿的视频分类融合算法,提出一种偏好预测的深度融合模型新框架。

中国式经济现代化的影响因素与发展趋势研究

党的二十大报告提出,“要以中国式现代化全面推进中华民族伟大复兴”。中国式经济现代化是中国式现代化的重要组成部分。文章利用国家统计局官方平台发布的数据,在查阅相关文献后,构建了中国式经济现代化的指标体系。首先,基于优劣解距离法(TOPSIS),对指标体系展开评价;其次,运用基于Adaptive-Lasso的变量选择模型提取影响中国式经济现代化的关键因素;最后,使用GM(1,1)灰色预测模型对上述关键因素进行预测。研究表明,当前中国式经济现代化水平较高,且将保持向好态势。期望本文的研究结论与建议可以为中国式经济现代化的高质量发展提供一定的参考。

地下空间坍塌应急救援现场环境安全风险评价及预测

为保障地下空间坍塌应急救援现场救援行动的安全开展和设备的正常运转,提高应急救援安全保障能力,首先通过构建地下空间坍塌应急救援现场环境安全风险评价指标体系,结合层次分析法(AHP)和理想点法(TOPSIS),对现场环境的安全状态进行评价;然后结合自回归移动平均模型(ARMA)和指数平滑模型,对未来一定时间内现场环境的安全状态进行预测;最后将该评价及预测模型应用于某煤矿巷道救援案例,并对救援工作提出建议和改进方案。研究结果可为地下空间应急救援现场的安全防护系统、通风设施配置等提供数据及决策依据。

DeepPredict:A Zone Preference Prediction System for Online Lodging Platforms

Online lodging platforms have become more and more popular around the world.To make a booking in these platforms,a user usually needs to select a city first,then browses among all the prospective options.To improve the user experience,understanding the zone preferences of a user's booking behavior will be helpful.In this work,we aim to predict the zone preferences of users when booking accommodations for the next travel.We have two main challenges:(1)The previous works about next information of Points Of Interest(Pals)recommendation are mainly focused on users'historical records in the same city,while in practice,the historical records of a user in the same city would be very sparse.(2)Since each city has its own specific geographical entities,it is hard to extract the structured geographical features of accommodation in different cities.Towards the difficulties,we propose DeepPredict,a zone preference prediction system.To tackle the first challenge,DeepPredict involves users'historical records in all the cities and uses a deep learning based method to process them.For the second challenge,DeepPredict uses HERE places API to get the information of pals nearby,and processes the information with a unified way to get it.Also,the description of each accommodation might include some useful information,thus we use Sent2Vec,a sentence embedding algorithm,to get the embedding of accommodation description.Using a real-world dataset collected from Airbnb,DeepPredict can predict the zone preferences of users'next bookings with a remarkable performance.DeepPredict outperforms the state-of-the-art algorithms by 60%in macro Fl-score.

社会效价对预测群体成员行为的影响:群体规范和个人偏好在其中的作用(英文)

人们预测群体成员行为具有一致性。但行为具有不同的社会效价(积极和消极),本研究探讨了效价是否以及如何影响对群体行为一致性的预测。结果发现,人们预测积极效价的帮助行为在群体中保持一致性的程度高于消极效价的阻碍行为,且群体规范感知而非个人偏好感知在其中起中介作用;群体实体性调节对群体规范和个人偏好的感知,进而改变效价影响群体行为一致性预测的程度。因此,预测行为时人们会灵活考虑群体情境因素的制约作用。

考虑众包工人时空行为偏好的top-k任务推荐模型

如何为面对繁多任务的工人推选出与之偏好密切相关的任务是当前时空众包领域的研究热点,针对已有任务推荐方法大多只关注工人与任务的静态信息匹配,却忽略了工人选择偏好的动态变化,本文提出了考虑众包工人时空行为偏好的top-k任务推荐模型.首先采用时空转移矩阵聚合工人与任务的历史映射信息,然后通过空间注意力机制来增强序列信息的表征能力,再将其纳入到时空循环网络中挖掘工人局部和全局性的移动偏好,进而预测工人的意图动向.最后构建worker-tasks亲和力相关度分数并形成概率个性化任务推荐池,评估工人与他们即将到达区域内任务之间的关联关系,帮助工人快速选择和定位所需任务.实验结果表明,本文所提方法相较于对比模型具有更好的任务推荐效果.

空调控制参数用户设定偏好的预测方法

空调器智能化提升的方向,是使平时需要用户设定的控制参数值自动地调整为用户的偏好值。这就需要空调器能够准确预测出用户偏好的设定参数,在实现上则有赖于对用户历史操作数据的学习,但这又超出了空调器单片机的计算能力。本文提出云端学习及本地计算相结合的空调用户偏好的预测方法,将学习用户历史操作数据等复杂的计算任务放到云端服务器,使得对于控制参数的预测不会超过空调器单片机的计算能力。该方法中,云端服务器完成对用户历史数据的预处理和递归式特征消除后,使用梯度提升框架训练数据并得到学习模型;空调器单片机下载由云端生成的多维矩阵,建立对预测值的插值查询规则后即得到本地预测方法。本文在对上述建立的学习算法和预测方法进行验证时,将上海、重庆和广州3个不同地域的城市的用户夏季使用数据(不含8月)作为训练集,8月的数据作为测试集。验证结果表明:用户实际设定温度与预测值的误差在±0.5℃内的占比平均为84%,最高为88%;用户实际设定风速与预测值的误差在±10%内的占比平均为92%,最高为94%。验证结果表明本文提出的云端学习及本地计算相结合的用户偏好的预测方法能够准确地预测用户偏好。

减重代谢术后患者食物偏好改变特征及预测因素分析

背景 减重代谢术后患者食物偏好的改变是影响其术后体质量减轻的重要原因,客观评估术后患者食物偏好改变的具体特征,并分析其预测因素尤为重要,基于现有研究结果间较大的差异性,难以指导术后体质量维护。目的 对减重代谢术后患者的食物偏好改变特征及其对减重结局的影响进行调查,分析减重代谢术后食物偏好改变的预测因素。方法 2022年2—8月,采用便利取样法抽取南京医科大学第一附属医院减重随访门诊收治的245例减重代谢术后患者,使用一般资料调查表、减重结局指标调查表、减重代谢术后患者食物偏好改变特征调查问卷对其进行调查。采用独立样本t检验比较减重代谢术后食物偏好改变对减重结局的影响,采用单因素分析及无序多分类Logistic回归分析探究术后食物偏好改变的预测因素。结果 回收有效问卷237份(96.7%),97.0%的减重代谢术后患者食物偏好与术前相比发生改变。减重代谢术后食物偏好改变组患者的术后体质量及体质指数(BMI)下降值、总体体质量丢失率(TWL)与食物偏好不变组患者比较,差异有统计学意义(P<0.05)。无序多分类Logistic回归分析结果显示:术后时间是减重代谢术后患者对蔬菜水果偏好改变的预测因素(P<0.05);婚姻状况、术前合并症是减重代谢术后患者对优质蛋白食物偏好改变的预测因素(P<0.05);长期居住地是对减重代谢术后患者辛辣食物偏好改变的预测因素(P<0.05);性别、长期居住地、手术方式是对减重代谢术后患者咸味零食偏好改变的预测因素(P<0.05);性别、手术方式是对减重代谢术后患者高脂肪肉类偏好改变的预测因素(P<0.05);性别、术后时间是对减重代谢术后患者甜食偏好改变的预测因素(P<0.05);性别、手术方式是对减重代谢术后患者不含酒精的甜味饮料偏好改变的预测因素(P<0.05)。结论 减重代谢术后患者食物偏好改变的发生率高,表现为对高热量食物偏好降低,对健康食物偏好增加的特点。性别、婚姻状况、长期居住地、术前合并症、手术方式及术后时间是减重代谢术后患者不同食物偏好改变特征的预测因素,减重专科医护人员须早期识别目标人群,加强术后饮食教育和指导,改善术后饮食体验,切实保障减重代谢术效果。

基于10种机器学习算法的多指标岩爆倾向性等级评价

为探究在岩爆烈度等级预测中,判别指标数量和类别对模型稳定性的影响,提高岩爆预测精度。综合考虑诱发岩爆的内外因素,选取10个岩爆预测判别指标,通过将判别指标由少到多随机组合,建立1023种指标组合方式;引入10种经典机器学习算法,构建岩爆烈度等级预测模型。为消除异常值,数量级和量纲的影响,采用四分位法寻找并替换异常值、进行归一化等数据预处理方法。利用穷举法,找出10种经典算法下平均精度最高的指标组合。研究表明:指标数量与模型稳定性无线性关系,其中3指标的组合预测效果最好,其综合准确率达到88.89%;算法模型中SVM和RF稳定性最优,综合准确率均达到88.89%;3种新的高稳定性指标组合方式分别是:3指标D、σ_(θ)、W_(e t)组合,5指标D、Is、σ_(max)、σ_(θ)/σ_(c)、W_(e t)组合,7指标D、I_(s)、σ_(θ)、σ_(t)、σ_(c)、W_(e t)、(σ_(c)-σ_(t))/(σ_(c)+σ_(t))组合。用本文优选的判别指标组合和预测模型,对大相岭隧道等进行岩爆倾向性等级评价,预测效果可靠,可为今后岩爆预测提供新的方法。

基于先验信息条件下足球守门员的知觉预测研究

目的:研究守门员基于先验信息下的知觉预测能力,探索当守门员处于射门方向(实际方向)与对手点球偏好(概率方向)一致或不一致的情境下,对表现有效性和加工效率的影响。方法:通过网络平台招募专业足球守门员(专业组)和业余足球守门员(业余组)各16名,共计32名被试。使用E-prime 3.0进行守门员点球方向判断实验,记录被试知觉预测的正确率和反应时。结果:基于先验信息进行知觉预测时,专业组守门员的知觉预测正确率显著高于业余组守门员。不同组别的反应时具有显著性差异,具体表现为在偏好一致(或不一致)的情况下,专业组的反应时都高于业余组。不同偏好条件下的正确率具有显著性差异,其中专业组被试在偏好不同的情况下具有显著性差异,业余组则无显著性差异。结论:与业余守门员相比,专业守门员在基于先验信息进行知觉预测时能更加准确地预判罚点球运动员的射门方向,即在知觉预测上存在专家优势。