Quantitative Comparative Study of the Performance of Lossless Compression Methods Based on a Text Data Model

Data compression plays a key role in optimizing the use of memory storage space and also reducing latency in data transmission. In this paper, we are interested in lossless compression techniques because their performance is exploited with lossy compression techniques for images and videos generally using a mixed approach. To achieve our intended objective, which is to study the performance of lossless compression methods, we first carried out a literature review, a summary of which enabled us to select the most relevant, namely the following: arithmetic coding, LZW, Tunstall’s algorithm, RLE, BWT, Huffman coding and Shannon-Fano. Secondly, we designed a purposive text dataset with a repeating pattern in order to test the behavior and effectiveness of the selected compression techniques. Thirdly, we designed the compression algorithms and developed the programs (scripts) in Matlab in order to test their performance. Finally, following the tests conducted on relevant data that we constructed according to a deliberate model, the results show that these methods presented in order of performance are very satisfactory:- LZW- Arithmetic coding- Tunstall algorithm- BWT + RLELikewise, it appears that on the one hand, the performance of certain techniques relative to others is strongly linked to the sequencing and/or recurrence of symbols that make up the message, and on the other hand, to the cumulative time of encoding and decoding.

Comparison of R and Excel in the Field of Data Analysis

This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and its powerful data management and analysis tools make it suitable for handling complex data analysis tasks.It is also highly customizable,allowing users to create custom functions and packages to meet their specific needs.Additionally,R language provides high reproducibility,making it easy to replicate and verify research results,and it has excellent collaboration capabilities,enabling multiple users to work on the same project simultaneously.These advantages make R language a more suitable choice for complex data analysis tasks,particularly in scientific research and business applications.The findings of this study will help people understand that R is not just a language that can handle more data than Excel and demonstrate that r is essential to the field of data analysis.At the same time,it will also help users and organizations make informed decisions regarding their data analysis needs and software preferences.

Full-Scale Numerical Simulation of the Local Scour Under Combined Current and Wave Conditions Based on Field Data

The monopile is the most common foundation to support offshore wind turbines.In the marine environment,local scour due to combined currents and waves is a significant issue that must be considered in the design of wind turbine foundations.In this paper,a full-scale numerical model was developed and validated based on field data from Rudong,China.The scour development around monopiles was investigated,and the effects of waves and the Reynolds number Re were analyzed.Several formulas for predicting the scour depth in the literature have been evaluated.It is found that waves can accelerate scour development even if the KC number is small(0.78<KC<1.57).The formula obtained from small-scale model tests may be unsafe or wasteful when it is applied in practical design due to the scale effect.A new equation for predicting the scour depth based on the average pile Reynolds number(Rea)is proposed and validated with field data.The equilibrium scour depth predicted using the proposed equation is evaluated and compared with those from nine equations in the literature.It is demonstrated that the values predicted from the proposed equation and from the S/M(Sheppard/Melville)equation are closer to the field data.

Optimized air-ground data fusion method for mine slope modeling

Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.

Inorganic Crystal Structure Data to be Presented in a Form More Useful for Further Studies

To make inorganic structure data more useful for further studies a five-point list of simple procedures to be followed by authors of crystal structure papers is proposed. 1. A crystal structure should be described with the space group corresponding to its true symmetry. 2. A new structure proposal should be tested, if it is realistic in principle. 3. A structure should be described with a space group in a setting given in the International Tables. 4. For a comparison with other structures the structure data should be standardized with the program STRUCTURE TIDY. 5. 揘ew?structure data should be checked in the databases, Chemical Abstracts or on-line internet resources, if they are really new. The list is supplemented with many explanations, commentaries, examples and references.

On the Combination of “The Textual Research on Historical Documents” and “The Comparative Study of Historical Data” —— and a Discussion on “The Law of Quan-ma and Gui-mei” in Chinese Language Studies

In Chinese language studies, both “The Textual Research on Historical Documents” and “The Comparative Study of Historical Data” are traditional in methodology and they both deserve being treasured, passed on, and further developed. It will certainly do harm to the development of academic research if any of the two methods is given unreasonable priority. The author claims that the best or one of the best methodologies of the historical study of Chinese language is the combination of the two, hence a new interpretation of “The Double-proof Method”. Meanwhile, this essay is also an attempt to put forward “The Law of Quan-ma and Gui-mei” in Chinese language studies, in which the author believes that it is not advisable to either treat Gui-mei as Quan-ma or vice versa in linguistic research. It is crucial for us to respect always the language facts first, which is considered the very soul of linguistics.

Automatic Variable Selection for High-Dimensional Linear Models with Longitudinal Data

High-dimensional longitudinal data arise frequently in biomedical and genomic research. It is important to select relevant covariates when the dimension of the parameters diverges as the sample size increases. We consider the problem of variable selection in high-dimensional linear models with longitudinal data. A new variable selection procedure is proposed using the smooth-threshold generalized estimating equation and quadratic inference functions (SGEE-QIF) to incorporate correlation information. The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE-QIF. The proposed procedure avoids the convex optimization problem and is flexible and easy to implement. We establish the asymptotic properties in a high-dimensional framework where the number of covariates increases as the number of cluster increases. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure.

A Survey of Sediment Fineness and Moisture Content in the Soyang Lake Floodplain Using GPS Data

Soyang Lake is the largest lake in Republic of Korea bordering Chuncheon,Yanggu,and Inje in Gangwon Province.It is widely used as an environmental resource for hydropower,flood control,and water supply.Therefore,we conducted a survey of the floodplain of Soyang Lake to analyze the sediments in the area.We used global positioning system(GPS)data and aerial photography to monitor sediment deposits in the Soyang Lake floodplain.Data from three GPS units were compared to determine the accuracy of sampling location measurement.Sediment samples were collected at three sites:two in the eastern region of the floodplain and one in the western region.A total of eight samples were collected:Three samples were collected at 10 cm intervals to a depth of 30 cm from each site of the eastern sampling point,and two samples were collected at depths of 10 and 30 cm at the western sampling point.Samples were collected and analyzed for vertical and horizontal trends in particle size and moisture content.The sizes of the sediment samples ranged from coarse to very coarse sediments with a negative slope,which indicate eastward movement from the breach.The probability of a breach was indicated by the high water content at the eastern side of the floodplain,with the eastern sites showing a higher probability than the western sites.The results of this study indicate that analyses of grain fineness,moisture content,sediment deposits,and sediment removal rates can be used to understand and predict the direction of breach movement and sediment distribution in Soyang Lake.

Analysis of the Diffractive Deep Inelastic Scattering Data with Running Coupling and Gluon Number Fluctuations

We study the effects of running coupling and gluon number fluctuations in the latest diffractive deep inelastic scattering data. It is found that the description of the data is improved once the running coupling and gluon number fluctuations are included with x2/d.o.f. = 0.867, x2/d.o.f. = 0.923 and x2/d.o.f. = 0.878 for three different groups of experimental data. The values of diffusive coefficient subtracted from the fit are smaller than the ones obtained by considering only the gluon number fluctuations in our previous studies. The smaller values of the diffusive coefficient are in agreement with the theoretical predictions, where the gluon number fluctuations are suppressed by the running coupling which leads to smaller values of the diffusive coefficient.

Fraction of Missing Information (γ) at Different Missing Data Fractions in the 2012 NAMCS Physician Workflow Mail Survey

In his 1987 classic book on multiple imputation (MI), Rubin used the fraction of missing information, γ, to define the relative efficiency (RE) of MI as RE = (1 + γ/m)?1/2, where m is the number of imputations, leading to the conclusion that a small m (≤5) would be sufficient for MI. However, evidence has been accumulating that many more imputations are needed. Why would the apparently sufficient m deduced from the RE be actually too small? The answer may lie with γ. In this research, γ was determined at the fractions of missing data (δ) of 4%, 10%, 20%, and 29% using the 2012 Physician Workflow Mail Survey of the National Ambulatory Medical Care Survey (NAMCS). The γ values were strikingly small, ranging in the order of 10?6 to 0.01. As δ increased, γ usually increased but sometimes decreased. How the data were analysed had the dominating effects on γ, overshadowing the effect of δ. The results suggest that it is impossible to predict γ using δ and that it may not be appropriate to use the γ-based RE to determine sufficient m.

Fuzzy-TOPSIS Evaluation of Power Product-Service System:A Framework Driven by Big Data

Power product-service system(power PSS),which combines industrial electric products with electric energy services,is an effective solution for power enterprises under the background of the rapid development of power systems.In the life cycle of power PSS,evaluation decision of power PSS alternatives is of great significance for subsequent implementation.To address the power PSS alternative evaluation problem,a power PSS evaluation framework is explored driven by the big data of stakeholder comments.Based on the multi-stakeholder comments of power PSS evaluation decision’s influence factors,the index system is constructed through analyzing and summarizing the co-occurrence matrix and semantic network diagram of high-frequency words.To determine the fuzzy index value of power PSS alternative,the stakeholders’vague opinions expressed by trapezoidal fuzzy number are integrated by group decision method.Fuzzy concept is introduced into the classical Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)method and fuzzy-TOPSIS method is put forward by using the fuzzy index value.The improved TOPSIS is adopted to sequence the power PSS alternatives.The case of power PSS evaluation of six alternatives for a power enterprise shows that the explored framework is effective and can provide a feasible solution for power PSS alternative evaluation.

A Comparative Analysis of the New -3(-n) - 1 Remer Conjecture and a Proof of the 3n + 1 Collatz Conjecture

This scientific paper is a comparative analysis of two mathematical conjectures. The newly proposed -3(-n) - 1 Remer conjecture and how it is related to and a proof of the more well known 3n + 1 Collatz conjecture. An overview of both conjectures and their respective iterative processes will be presented. Showcasing their unique properties and behavior to each other. Through a detailed comparison, we highlight the similarities and differences between these two conjectures and discuss their significance in the field of mathematics. And how they prove each other to be true.

基于fsQCA组态视角的我国医疗资源配置效率提升路径分析

背景当前我国医疗资源配置区域失衡、公平性缺失问题仍然突出。党的“二十大”报告明确提出要“促进优质医疗资源扩容和区域均衡布局”。目的探究提升我国医疗资源配置效率的具体路径,为实现我国医疗资源合理且高效配置、促进基本公共服务均等化提供科学参考。方法于2022年9月—2023年2月开展研究,数据源于《2021中国统计年鉴》《2021中国卫生健康统计年鉴》。将医疗卫生机构数、卫生技术人员数、床位数作为投入指标,以诊疗人次和入院人数为产出指标,通过数据包络分析(DEA)方法对我国2020年31个省份医疗资源配置效率进行测度;以医疗资源配置效率为结果变量,以卫生技术人员占比、每千人口床位数、出院者平均住院日、人均国内生产总值(GDP)、居民可支配收入、财政收入分权和医疗卫生财政预算支出占比为条件变量,运用模糊集定性比较分析(fsQCA)方法从组态视角探究内外部要素对医疗资源配置效率的协同影响机制,剖析高或非高水平医疗资源配置效率的条件组态,明确医疗资源高效率和低效率配置的多重路径。结果2020年我国31个省份医疗资源配置效率整体水平较高,均值为0.852,但省际存在较大差异。组态分析结果可知,医疗资源配置效率的提升是多因素共同作用的结果,共存在3种医疗资源高效率配置的路径。路径1:政府主导型驱动路径,以广西壮族自治区为典型案例。路径2:内外协调型驱动路径,以云南省和甘肃省为代表案例。路径3:均衡型驱动路径,代表案例主要有广东省、福建省和湖北省。非高医疗资源配置效率的路径也存在3条。路径1:政府制约型路径。路径2:经济-政府双重制约型路径,代表案例有黑龙江省和吉林省。路径3:内外制约型路径,典型案例有山西省和西藏自治区。结论内外部各要素及要素间的协同在良性互动过程中共同影响着医疗资源配置效率水平,需优化内外部环境,并对关键资源要素进行有效整合,以形成合力,促进区域医疗资源合理配置。

我国医保基金运行效率提升的组态路径研究——基于DEA模型与模糊集定性比较分析

以我国31个省(自治区、直辖市)为研究样本,基于数据包络分析方法计算各省级医保基金运行效率,并提取区域经济水平、政府支持水平、老龄化水平、城市化水平以及卫健医疗水平等5个指标,运用模糊集定性比较分析方法揭示我国医保基金运行效率差异的条件组态和作用机制。研究发现,我国各地区呈现出两种不同的高医保基金运行效率模式,以及三种低医保基金运行效率组态路径;通过对比高、低组态的关系发现,在一定条件下政府支持水平与城市化水平是影响医保基金运行效率的核心因素。产生高、低医保基金运行效率差异的原因具有非对称性,引起低医保基金运行效率的路径并不是高医保基金运行效率路径的对立面。

公共数据开放如何促进数字化发展——基于模糊集定性比较分析的研究

随着新一代信息通信技术和计算技术的迅速发展,公共数据在经济、社会、行政、科学研究等领域发挥着越来越重要的作用。为帮助人们理解公共数据开放如何促进数字化发展,文章借助中国内地31个省份的国家统计数据和部分研究者开发的评估数据,利用模糊集定性比较分析方法,研究公共数据开放与其他因素共同推动数字化发展的途径。结果表明:公共数据开放可以与其他因素相结合,共同推动数字化发展。公共数据开放主要通过3条途径促进数字化发展,即研发经费投入型路径、市场规模拉动型路径、数字技术应用型路径。

山西台网地震预警系统和测震台网数据对比分析

收集整理2021年9月至2023年1月山西及邻省所有触发的地震预警事件,针对山西预警JEEW和EEW两套系统产出预警参数和测震数据统一编目数据,从发震时刻、震级、震中位置和震源深度几方面进行对比分析。总体来说,山西地震预警区内地震预警参数基本稳定,可满足山西地震预警条件。

数字政府背景下政府开放数据创新应用提升路径研究--基于31个省域应用数据的模糊集定性比较分析

政府开放数据是典型的创新扩散模型,受到国家政治制度和府际关系的深刻影响,解释政府开放数据创新应用的提升路径需要以府际关系和制度压力规制为逻辑起点,构建开放数据创新扩散模型,应用模糊集定性比较分析对我国31个省域政府开放数据应用创新扩散规律进行总结。研究发现上级压力规制、公众压力、府际竞争和府际学习因素组合是最典型的政府开放数据应用创新扩散模型,对开放数据创新应用和数据价值实现有更好的促进作用。除此之外,对于经济较发达且对上级财政依赖较小的地区可以通过政策压力、府际竞争和府际学习提升数据开放创新活动;而在经济欠发达且对上级财政依赖较大的地区,上级压力对开放数据应用扩散作用更突出。

基于模糊DEMATEL-超效率DEA的可再生能源发电技术综合效益评估方法

“双碳”目标下,各类可再生能源发电技术发展迅速,综合权衡不同可再生能源发电方案的综合效益对可再生能源的优化设计具有重要意义。综合考虑经济效益、环境效益、能源效益和社会效益4个层面,提出了一种基于模糊决策试验和评价实验(decision making trial and evaluation laboratory,DEMATEL)与超效率数据包络分析(data envelopment analysis,DEA)模型的可再生能源发电技术综合效益评估方法。该方法分为投入-产出指标体系构建和综合评估2个阶段。首先,利用三角直觉模糊数处理模糊评价信息,将其与DEMATEL相结合量化各指标之间相互影响关系,基于指标间逻辑分析结果建立投入-产出评估指标体系。然后,基于超效率DEA模型对各可再生能源发电方案进行评估排序,结合投入冗余和产出不足分析结果给出各方案的针对性改善建议,以期为进一步选择和确定可再生能源产业发展战略提供参考。最后以某省10类可再生能源发电单元为研究对象,基于所提研究方法进行综合评估和分析,并与多准则妥协解排序法和熵权法进行对比分析,验证了所提方法的有效性。

基于真实世界数据的观察性因果推断研究新框架(目标试验模拟)及其在中医药领域中的应用展望

“目标试验模拟”(target trial emulation)作为一种新的真实世界研究框架在近年来被正式确立,其可用于指导基于真实世界数据开展观察性因果推断研究,继而进行医疗干预有效性和安全性评价。该框架的核心思想是参照随机对照试验的原则,利用真实世界数据模拟相应的随机对照试验,进而得出干预措施与临床结局间因果关联的结论。其主要实施要点可总结为“3⁃7⁃2”,即声明因果问题、制订模拟方案和模拟目标研究3个实施步骤,纳排标准、治疗策略、干预分配、随访期、结局指标、因果比较和分析计划7个设计要素,对永恒时间偏倚和现用药者偏倚2个关键偏倚的控制。本文通过对该框架的历史与现状、实施要点、经典案例、优势与局限性及其在中医药领域中的应用前景进行概述,以期帮助中医药学者开展中医药真实世界研究,并为构建具有中医药特色的临床评价体系提供新思路。