Lottery Numbers and Ordered Statistics

The lottery has long captivated the imagination of players worldwide, offering the tantalizing possibility of life-changing wins. While winning the lottery is largely a matter of chance, as lottery drawings are typically random and unpredictable. Some people use the lottery terminal randomly generates numbers for them, some players choose numbers that hold personal significance to them, such as birthdays, anniversaries, or other important dates, some enthusiasts have turned to statistical analysis as a means to analyze past winning numbers identify patterns or frequencies. In this paper, we use order statistics to estimate the probability of specific order of numbers or number combinations being drawn in future drawings.

基于GMS数值模拟的某矿床涌水量预测研究

某矿床在开采过程中遇到一定程度的涌水。为解决涌水问题,并为防治水方案提供依据,以该矿床为研究对象,通过分析矿床水文地质条件、含水层类型及特征、地下水补给径流和排泄条件、地质构造特征、水文地质试验资料和矿床开采设计资料等,对矿床水文地质条件进行了概化,对研究区水文地质单元进行了划定,采用GMS软件建立了地下水流数值模型,预测得到了-200 m以下中段的矿坑涌水量。

Evaluation of Serum Anti-Müllerian Hormone (AMH) Values for 28,016 Bulgarian Women: Prognostic Statistical Model of Age Specific AMH Declining

The present study aims to establish a relationship between serum AMH levels and age in a large group of women living in Bulgaria, as well as to establish reference age-specific AMH levels in women that would serve as an initial estimate of ovarian age. A total of 28,016 women on the territory of the Republic of Bulgaria were tested for serum AMH levels with a median age of 37.0 years (interquartile range 32.0 to 41.0). For women aged 20 - 29 years, the Bulgarian population has relatively high median levels of AMH, similar to women of Asian origin. For women aged 30 - 34 years, our results are comparable to those of women living in Western Europe. For women aged 35 - 39 years, our results are comparable to those of women living in the territory of India and Kenya. For women aged 40 - 44 years, our results were lower than those for women from the Western European and Chinese populations, close to the Indian and higher than Korean and Kenya populations, respectively. Our results for women of Bulgarian origin are also comparable to US Latina women at age 30, 35 and 40 ages. On the base on constructed a statistical model to predicting the decline in AMH levels at different ages, we found non-linear structure of AMH decline for the low AMH 3.5) the dependence of the decline of AMH on age was confirmed as linear. In conclusion, we evaluated the serum level of AMH in Bulgarian women and established age-specific AMH percentile reference values based on a large representative sample. We have developed a prognostic statistical model that can facilitate the application of AMH in clinical practice and the prediction of reproductive capacity and population health.

Developing three-dimensional groundwater flow modeling for the Erbil Basin using Groundwater Modeling System (GMS)

This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Iraq,is a vital water resource area facing increasing water demands and environ-mental challenges.The three-dimensional nature of the groundwater flow system is crucial for accurately understanding and managing water resources in the basin.The modeling process involved data collection,geological and hydrogeological characterization,conceptual model development,and numerical simulation using GMS software MODFLOW 2000 package.Various parameters such as hydraulic conductivity,recharge rates,and boundary conditions were integrated into the model to represent the complex hydrogeo-logical conditions of the basin.Model calibration was performed by comparing simulated groundwater levels with observed data from monitoring wells across the basin,using the automatic calibration method of automated Parameter Estimation(PEST).Pilot points were applied to adjust the hydraulic conductivity in the model area spatially.Sensitivity analysis was conducted to assess the influence of key parameters on model predictions and to identify areas of uncertainty.The developed three-dimensional groundwater flow model provides valuable insights into the dynamics of groundwater flow,recharge-discharge mechanisms,and potential impacts of future scenarios such as climate change and water resource management strategies.It serves as a useful tool for decision-makers,water resource managers,and researchers to evaluate differ-ent management scenarios and formulate sustainable groundwater management policies for the Erbil Basin.In conclusion,this study demonstrates the effectiveness of using GMS for developing three-dimensional groundwater flow models in complex hydrogeological settings like the Erbil Basin,contributing to improved understanding and management of groundwater resources in the region.

Effect Evaluation of CBL Combined with Rain Classroom Teaching Method in Medical Statistics

Objective: To explore the application effect of CBL combined with rain classroom teaching method in medical statistics courses. Methods: The undergraduate students of medical imaging technology in 2019 and 2020 in a university were selected as the research objects. A cluster sampling method was used to select 79 undergraduate students from 2019 in the control group and 75 undergraduate students from 2020 in the experimental group. Traditional teaching method and CBL combined with rain classroom teaching method was used in the control group and experimental group respectively. The final examination scores of the two groups were compared. In experimental group, the correlation between the average score in the rain classroom and the final examination score was tested, and the teaching effect was evaluated. Results: The average score of final examination in experimental group and control group was 79.13 ± 10.32 points and 71.54 ± 14.752 points, respectively, which had a statistically significant difference (Z = 2.586, P = 0.012);the final examination scores of the students in the experimental group were positively correlated with the average scores of the rain classroom (r = 0.372, P = 0.001), and the proportion of satisfaction in the experimental group was 94.7%. Conclusion: The CBL combined with rain classroom teaching method can improve the teaching effectiveness of medical statistics courses.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

基于GMS的地面沉降模型的研究和实践应用

以南沙地面沉降区域为研究对象,运用GMS软件模拟分析压缩土层、建筑荷载、地下水位等要素对地面沉降的影响程度,采用试错法,建立有效的地面沉降模型,并结合现状及历史数据进行预测和推演,模拟多种工况下南沙区地下水水位变化和地面沉降的变化量,结果表明:南沙区地面沉降较为严重的区域主要集中在龙穴岛北部,其建筑荷载造成的沉降量最大可达到149 mm。该模型的构建及运用为南沙区在地面沉降管理决策方面起到良好的辅助作用,具有应用性和实践意义。

Comparative Analysis of Statistical Thickness Models for the Determination of the External Specific Surface and the Surface of the Micropores of Materials: The Case of a Clay Concrete Stabilized Using Sugar Cane Molasses

In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and surface of micropores) of a clay concrete without molasses and clay concretes stabilized with 8%, 12% and 16% molasses. The results obtained show that Hasley’s model can be used to obtain the external surfaces. However, it does not allow the surface of the micropores to be obtained, and is not suitable for the case of simple clay concrete (without molasses) and for clay concretes stabilized with molasses. The Carbon Black, Jaroniec and Harkins and Jura models can be used for clay concrete and stabilized clay concrete. However, the Carbon Black model is the most relevant for clay concrete and the Harkins and Jura model is for molasses-stabilized clay concrete. These last two models augur well for future research.

基于GMS的矿井涌水量预测

矿井涌水是矿山安全开采中难以解决又必须面对的问题。GMS(groundwater modeling system)具有强大的前、后处理功能,实现了地质模型、水文地质参数等的可视化,提供的自检功能为调试模型提供了极大方便。以宁夏某矿为例,应用GMS对其水文地质条件进行概化,建立水文地质结构模型和水文地质概化模型,通过MODFLOW模块对其涌水量进行预测。结果表明:三维地质模型具有很好地可视性,研究区地层能够更加直观的得以展示,模拟所得水位与实测水位拟合效果在置信区间内,模拟区首采面的涌水量的预测值为1450 m~3/h。为矿山开采、工程建设中涌水量预测的研究提供了更为直观、更为便捷的方法。

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.

Statistical Approach to Basketball Players’Skill Level

In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely important. In this article, a complex non-linear process is considered by taking into account the average points per game of each player, playing time, shooting percentage, and others. This physics-informed statistics is to construct a multiple linear regression model with physics-informed neural networks. Based on the official data provided by the American Basketball League, and combined with specific methods of R program analysis, the regression model affecting the player’s average points per game is verified, and the key factors affecting the player’s average points per game are finally elucidated. The paper provides a novel window for coaches to make meaningful in-game adjustments to team members.

Statistical Inversion Based on Nonlinear Weighted Anisotropic Total Variational Model and Its Application in Electrical Impedance Tomography

Electrical impedance tomography (EIT) aims to reconstruct the conductivity distribution using the boundary measured voltage potential. Traditional regularization based method would suffer from error propagation due to the iteration process. The statistical inverse problem method uses statistical inference to estimate unknown parameters. In this article, we develop a nonlinear weighted anisotropic total variation (NWATV) prior density function based on the recently proposed NWATV regularization method. We calculate the corresponding posterior density function, i.e., the solution of the EIT inverse problem in the statistical sense, via a modified Markov chain Monte Carlo (MCMC) sampling. We do numerical experiment to validate the proposed approach.

Improving Statistical Literacy through Evidence-Based Strategies Among First-Year Education Students in a State University

Statistical literacy is crucial for cultivating well-rounded thinkers.The integration of evidence-based strategies in teaching and learning is pivotal for enhancing students’statistical literacy.This research specifically focuses on the utilization of Share and Model Concepts and Nurturing Metacognition as evidence-based strategies aimed at improving the statistical literacy of learners.The study employed a quasi-experimental design,specifically the nonequivalent control group,wherein students answered pre-test and post-test instruments and researcher-made questionnaires.The study included 50 first-year Bachelor in Secondary Education majors in Mathematics and Science for the academic year 2023-2024.The results of the study revealed a significant difference in the scores of student respondents,indicating that the use of evidence-based strategies helped students enhance their statistical literacy.This signifies a noteworthy increase in their performance,ranging from very low to very high proficiency in understanding statistical concepts,insights into the application of statistical concepts,numeracy,graph skills,interpretation capabilities,and visualization and communication skills.Furthermore,the study showed a significant difference in the post-test scores’performance of the two groups in understanding statistical concepts and visualization and communication skills.However,no significant difference was found in the post-test scores of the two groups concerning insights into the application of statistical concepts,numeracy and graph skills,and interpretation capabilities.Additionally,students acknowledged that the implementation of evidence-based strategies significantly contributed to the improvement of their statistical literacy.

基于GMS的湖口县工业园扩区B区地下水环境影响预测

基于研究区现有的水文地质条件,利用GMS软件构建了研究区水文地质模型和溶质运移模型,并利用此模型对湖口县化工企业的废水调节池在非正常状况下防渗系统发生破裂泄漏,模拟预测了CODcr和氟化物污染物的运移范围。预测结果显示:污染物短时泄露工况下危害风险较小;对于长时泄露工况,在十年以后污染超标范围较大。并根据模拟结果针对该工业园的地下水环境保护现状,提出了地下水污染防控措施与对策。

基于改进GMS算法的无人机室内定位研究

针对无人机室内无全球定位系统(GPS)信号、定位精度较低的问题,提出融合视觉和惯性数据来实现无人机室内定位。前端改进特征匹配算法,针对图像发生旋转运动提出了利用主成分分析法(PCA)计算旋转角度,改变特征点所在网格及其8邻域网格,并根据邻域特征点与匹配特征点欧氏距离设定高斯阈值,提出新的分数统计模型,使正确匹配对的数量增多,提高了特征匹配的快速性和视觉室内定位的准确性。针对图像局部相似产生误匹配问题,提出了利用特征点之间的几何关系确定数据集,通过Pearson相关系数分析数据的相似性,设定阈值剔除置信度较低的特征匹配对,优化视觉估计无人机的位姿信息、后端采用视觉惯性基于滑动窗口的紧耦合优化位姿信息。通过设计室内光线正常与室内光线较暗情况下的无人机悬停实验,分析其飞行日志可知,改进后网格运动统计(GMS)算法的特征匹配速度是原算法的3倍,并在局部相似区域剔除误匹配,特征匹配准确率能达到94%,无人机室内定位精度达到0.02 m,能在复杂军事环境中有更好的应用。

留学生Medical Statistics线上课程建设与远程教学的实践与思考

受政治、经济和全球健康等因素影响,远程教育成为高等教育的一个发展趋势。新冠疫情防控期间,受出入境限制,未能返华的留学生一直以远程教学推进学业,成为其间持续进行远程教学最久的群体。本研究总结临床专业本科留学生主干课程Medical Statistics的校级一流线上课程建设与教学实践,并比较了疫情前后,线下教学与远程教学的留学生期末考试成绩,发现远程教学成绩经历波动后逐渐稳定并接近传统线下教学。本文同时对效果影响因素进行了调研和分析,发现“充分学习和使用远程课程中丰富的资源”和“上网是否容易”是留学生学习效果的主要影响因素。提出建设丰富教学资源对促进医学统计学远程学习效果的重要影响,同时提出对策建议,以期进一步提高远程教学水平,服务高校现代化课程体系建设。

基于改进ORB-GMS-SPHP算法的快速图像拼接方法

针对传统图像拼接算法存在拼接速度慢、图像拼接有色差等问题,提出了一种基于ORB-GMS-SPHP算法的大视场多图像拼接方法。该方法首先利用高斯函数构建尺度空间,将高斯尺度空间划分为多个网格,在每个网格内借助FAST算法提取尺度空间特征点,使用BRIEF算法提取描述符并匹配,得到更加均匀分布的特征点;然后使用运动网格统计算法筛选匹配点;最后采用SPHP算法融合图像重叠区域,从而得到完整的拼接图像。将改进的ORB-GMS-SPHP算法与现有的传统特征点匹配算法在特征点匹配精度和特征点匹配速度进行对比与评价,验证了该方法特征点匹配速度快、精度高,并且可以保留更多的正确匹配点的特点。将该拼接方法与传统拼接方法在拼接速度、图像配准均方误差RMSE以及视觉主观判断拼接色差进行对比与评价,验证了该拼接方法具有较快的拼接速度、更高的拼接精度和无明显色差。该方法在2736像素×3648像素图像中,特征点匹配时间降低至6.463 s,图像配准精度RMSE降低至3.87。实验证明该方法特征点匹配速度快、精度高,且拼接精度高、无明显色差。

基于GMS的山西辛安泉域地下含水层结构模型研究

辛安泉域岩溶地区存在较多岩溶管道、人工采煤巷道和复杂的地质构造等,水文地质条件十分复杂。为准确构建辛安泉域三维地表水-地下水数值模型,开展岩溶水资源承载力评价,需要精确建立辛安泉域三维地下含水层结构模型。采用GMS软件基于实际钻孔数据资料、虚拟钻孔数据和水文地质剖面资料精细建立长治市辛安泉域岩溶含水层三维含水层结构模型。结果表明,采用GMS模型,基于实际钻孔数据资料、虚拟钻孔数据和水文地质剖面资料建立的长治市辛安泉域三维含水层结构模型更接近实际,采用这种方法构建含水层结构模型能够为后期精确建立长治市辛安泉域三维地表水-地下水数值模型打下坚实的基础。

The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs

Background:Survival from birth to slaughter is an important economic trait in commercial pig productions.Increasing survival can improve both economic efficiency and animal welfare.The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.Results:We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model,a logit model,and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes(0,1).The results show that in the case of only alive animals having genotype data,unbiased genomic predictions can be achieved when using variances estimated from pedigreebased model.Models using genomic information achieved up to 59.2%higher accuracy of estimated breeding value compared to pedigree-based model,dependent on genotyping scenarios.The scenario of genotyping all individuals,both dead and alive individuals,obtained the highest accuracy.When an equal number of individuals(80%)were genotyped,random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes.The linear model,logit model and probit model achieved similar accuracy.Conclusions:Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes,but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06%to 6.04%.

融合RANSAC与GMS图像特征的匹配算法

图像匹配在物体识别、图像拼接领域一直是尤为重要的一部分。为解决传统图像匹配算法匹配率低的问题,本文提出了一种融合RANSAC与GMS图像特征的匹配算法(RGMS算法)。该算法在ORB的基础上提取图像特征点,利用RANSAC算法对特征点进行随机筛选,最后使用GMS算法对筛选后的特征点进行特征匹配。经采用TUM公开数据测试集与传统的暴力匹配、FLANN和GMS算法进行实证研究,实验结果表明,本文提出的匹配算法具有更好的匹配效果。尤其对于图像纹理较少的情况,本文算法的图像配准率和匹配时间较传统匹配算法具有更明显的优势。