Time trends and gender disparities of Chinese cataract burden and their predictions

AIM:To evaluate the trends and changes in the number and rates of disability-adjusted life years(DALYs)and prevalence of cataract in China between 1990 and 2019,and to predict the trends of cataract burden from 2020 to 2030.METHODS:The Global Burden of Diseases(GBD)database was employed to collect the data on DALYs and the prevalence of cataract in China,which was distinguished by age and sex during the past three decades from 1990 to 2019,and then changes in the number and rates of cataract from 2020 to 2030 were predicted.All data were analyzed by the R program(version 4.2.2)and GraphPad Prism 9.0 statistics software.RESULTS:The number of DALYs of cataract increased from 449322.84 in 1990 to 1087987.61 in 2019,number of cataract cases increased from 5607600.94 in 1990 to 18142568.96 in 2019.The age-standardized DALY rates(ASDR)generally increased slightly[estimated annual percentage change(EAPC=0.1;95%CI:-0.24 to 0.45)],age-standardized prevalence rates(ASPR)also increased(EAPC=0.88;95%CI:0.6 to 1.15).Cataract burden increased with age and female gender.Among the causes of cataract,air pollution was the most important,followed by smoking,high fasting plasma glucose,and high body mass index(BMI).The burden of cataract is predicted to grow persistently from 2020 to 2030,the number of DALYs and prevalence for cataract will rise to 2336431 and 43698620 respectively by 2030,the ASDR is predicted to be 85/100000 and ASPR will be 1586/100000 in 2030,females will still be at greater risk of suffering from cataract than males.CONCLUSION:The burden of cataract in China kept rising from 1990 to 2019.Increasing age and female gender are risk factors for cataract.Air pollution,smoking,high fasting plasma glucose,and high BMI are associated with cataract.The burden of cataract in China will gradually increase from 2020 to 2030,the elderly women in particular need attention.Our results may be of help for providing reference strategies to reduce cataract burden in the near future.

ANALYSIS OF CRITERIA PREDICTING THE TENDENCY OF PILOT INDUCED OSCILLATION FOR LINEAR NON-LINEAR SYSTEM

Some problems encountered in applying Smith's technique to predict the PIO tendency for non-linear pilot-vehicle loop, are thoroughly analyzed. Subsequently, modified PIO predictable criteria are developed, in addition, to make also a certain improvement on Smith's PIO definition and PIO types. These modified criteria are applied to predict PIO tendency of various different configurations on the variable stability aircraft NT-33 in case of supposed non-linearity, and predicted results are compared with the flight tests and analytical results in the case of linear hypothesis given in Ref. (4)

The Rhythm of Earthquake Activity and an Analysis on the Earthquake Tendency of the World, the Chinese Continent and North China

Based on the Morlet complex wavelet transformation,the authors put forward a kind of new method for distinguishing periods of seismic activity and quietude and a new physical thought on the time-dependent wavelet accumulation energy spectrum with periods, the time-frequency distribution of wavelet vibration period spectrum and period-specific wavelet vibration spectrum. By applying the above methods to a time series which is composed of earthquake accumulation energy per year for the world, the Chinese continent and North China, respectively, we obtained some new information about the rhythm of shallow earthquake activity. Considering the historic earthquakes and the rhythm characteristics of current strong earthquake activity, the earthquake tendency in the next years is discussed.

Seismic pattern treatment method through calculation of seismic density at grid nodes

Analysis of seismic data and seismicity characteristics in China, we gave a method to deal with seismic patterns by calculating density at grid nodes. Number of earthquakes and epicenter distribution are considered comprehensively in this method. Effect of datum accuracy is stressed on parameter confirmation. Seismic patterns from this method are stable and can reflect seismic characteristics reliably. These seismic patterns are the base of quantitative analysis of seismicity. It can be applied in seismic tendency analysis and medium-long term earthquake prediction, earthquake countermeasure and risk mitigation.

汽轮机热力性能智能评估与劣化分析系统研究进展

随着火力发电行业的不断发展,汽轮机作为核心设备之一,其高效可靠地运行对于电厂的稳定供电至关重要。故障预测与诊断技术能够提前评估机组实际运行过程中性能劣化甚至故障的原因,是保证大型汽轮机组安全、稳定运行的重要技术之一。从经验知识、数学模型和数据驱动3个方面介绍了故障预测与诊断技术的国内外研究现状。针对汽轮发电机组热力性能评估和劣化趋势分析问题,总结了智能预测诊断技术未来的发展趋势,为火电汽轮机组高效灵活安全运行提供参考。

基于OD的高速公路收费站出入口流量预测方法

目前,高速公路收费站拥堵问题成为影响高速公路通行效率的重要因素,为收费站管理人员提供精准高效的拥堵流量预警具有重要的现实意义。文章基于GC-LSTM模型进行改进,提出了一种称为GC-LSTM-OD的高速公路出入口多步流量预测模型。该模型能够有效预测流量演变趋势,同时具备适应非常态情况的能力。为验证模型性能,文章使用了沿江高速太仓-常州段的现实流量数据,实验结果表明,相较于其他模型,GC-LSTM-OD在多步流量预测方面表现出更加优异的性能。

新疆农业碳排放时空特征及驱动因素研究

【目的】研究新疆农业碳排放的时空特征及驱动因素,为该地区推动农业农村绿色发展、实现“双碳”目标提供参考。【方法】以种植业、畜牧业、农业能源终端消费的19类碳源为原始数据,对2007—2021年新疆农业碳排放量和碳排放强度进行测算;以种植业和畜牧业的15类碳源为原始数据,对2010、2015、2020年新疆14个地州(市)碳排放量和碳排放强度进行测算;运用LMDI(对数平均除以指数法)模型对其驱动因素进行分析;运用灰色预测模型对新疆2022—2030年农业碳排放的发展进行趋势预测。【结果】2007—2021年新疆农业碳排放量总体呈现“平稳—上升—下降—上升”的趋势,农业碳排放强度总体呈现“下降—上升—下降”的趋势;2010、2015、2020年伊犁哈萨克自治州碳排放总量最高、克拉玛依碳排放总量最低、克孜勒苏柯尔克孜自治州碳排放强度最高,2010年巴音郭楞蒙古自治州碳排放强度最低,2015、2020年吐鲁番碳排放强度最低;农业碳排放驱动因素影响大小为:农业经济水平效应>农业结构效应>农业人口规模效应>农业生产效率;2022—2030年,预测新疆的农业碳排放量呈现增长趋势、强度呈现下降趋势。【结论】2007—2021年新疆农业碳排放总量整体呈现上升趋势,农业碳排放强度整体呈现下降趋势;农业经济水平效应对农业碳排放的增加有促进作用,农业结构效应、农业人口规模效应、农业生产效率对农业碳排放的增加有抑制作用;预计在2022—2030年,新疆农业碳排放量将逐年增加、农业碳排放强度将逐年下降,新疆农业减排固碳潜力巨大。

基于数据挖掘的高校学生考研成绩预测分析

随着硕士研究生考试初试难度越来越大,影响考研结果的因素众多,以安徽建筑大学建筑类设计专业高校学生为研究对象,以本科毕业生的在校成绩和考研初试成绩作为样本数据,通过Logistic回归分类算法、SVM支持向量机算法、KNN算法三种实验建模测试对比,寻找对应变化规律,提高考研初试成绩变量之间的关联性,从而得出预测结果,从平均预测误差看,Logistic回归分类算法的预测方法具有较高的适应力和稳定性,准确性更适合初试无高数科目的建筑类设计专业高校学生。得出要特别注重加强对专业课、课程设计的学习,同时对政治和英语注意学习态度的结论。为帮助其预测学业发展趋势、制定职业生涯规划上提供数据支撑。

融合SARIMA与BiLSTM的水利设施形变预测

水利设施形变预测可以有效地判断水利设施的运行状态。水利设施安全监测数据是时间序列数据,既有趋势性又有季节性。为了获得更准确的预测结果,文中提出一种基于季节自回归差分移动平均(SARIMA)模型和双向长短时记忆(BiLSTM)网络的预测模型,以解决无法充分挖掘数据中正向与反向的关联进行预测的问题。该模型采用SARIMA模型预测变形数据中的线性分量,采用BiLSTM模型预测变形数据中的非线性分量,使得模型能够更好地提取历史数据中的非线性关系以及正向与反向关系从而提高预测准确度。结合某水电站4#引水涵洞监测数据,使用SARIMA-BiLSTM模型对裂缝计开合度时间序列进行了预测,并与反向传播神经网络模型、SARIMA模型和SARIMA-LSTM模型的预测结果进行对比,比对结果证明所提方法有效地提高了预测精度。

Study and prediction of the 2022 global monkeypox epidemic

The World Health Organization(WHO)declared monkeypox as a public health emergency of international concern(PHEIC)on July 23,2022,their highest level of alert.This raised concerns about the management of the global monkeypox outbreak,as well as the scientific analysis and accurate prediction of the future course of the epidemic.This study used EpiSIX(an analysis and prediction system for epidemics based on a general SEIR model)to analyze the monkeypox epidemic and to forecast the major tendencies based on data from the USA CDC(https://www.cdc.gov)and the WHO(https://www.who.int/health-topics/monkeypox).The global outbreak of monkeypox started in the UK on May 2,2022,which marked the beginning of an epidemic wave.As of October 28,2022,the cumulative number of reported cases worldwide was 77,115,with 36 deaths.EpiSIX simulations predict that the global monkeypox epidemic will enter a low epidemic status on March 1,2023 with the cumulative number of confirmed cases ranging from 85,000 to 124,000,and the total number of deaths ranging from 60 to 87.Our analysis revealed that the basic reproduction number(R0)of monkeypox virus(MPXV)is near to 3.1 and the percentage of asymptomatic individuals is 13.1%–14.5%,both of which are similar to the data for SARS.The vaccination efficiency against susceptibility(VEs)of individuals who have had monkeypox is~79%,and the vaccination efficiency against infectiousness(VEi)of individuals who have had monkeypox is~76%–82%.The mean incubation period for monkeypox is 8 days.In total,94.7%of infected individuals develop symptoms within 20 days and recover within 2 weeks after the confirmation of symptoms.Simulation results using EpiSIX showed that ring vaccination was remarkably effective against monkeypox.Our findings confirmed that a 20-day isolation for close contacts is necessary.

A numerical predicting method on monthly seismic tendency

Considering the deficiency of using vague language in predicting monthly seismic tendency, we propose a numerical predicting method in the paper, which may be more applicable to the society. The method is based on the self-rhythm phenomenon of earthquake activities, which calculates monthly seismic tendency through nonlinear mathematical model. The result of modeling test shows that there exists a kind of seismic cyclic process of every 7 to 8 months in Chinese mainland, and the average error from comparing monthly predicted and observed earthquake magnitudes is below 0.2. Thus the method is more applicable to the society than the experiential prediction.

基于GM(1,1)灰色预测模型的广西民营医院现状及发展趋势分析

目的了解广西民营医院的现状,并通过GM(1,1)灰色预测模型分析广西民营医院在“十四五”期间的发展趋势。方法比较2013—2020年广西民营医院和公立医院的医院数量、床位数、医生数、护士数、诊疗人次数、入院人数。将上述指标作为发展指标,构建GM(1,1)灰色预测模型,分析“十四五”期间广西民营医院的发展趋势。结果2013—2020年,广西民营医院的医院数量、床位数、医生数、护士数、诊疗人次数、入院人数的年均增速均高于公立医院,但在全部医院中的占比仍较低。根据GM(1,1)灰色预测模型,预计在2021—2025年,除公立医院的医院数量外,广西民营医院和公立医院其余指标均呈持续增长的趋势,而且广西民营医院的年均增速普遍高于公立医院。结论广西民营医院的发展态势良好,规模不断扩大,医院数量等多项指标增速较快,但民营医院应将工作重心放在不断提高医疗质量上,进一步强化医疗能力,多渠道拓展医疗业务,开展差异化服务,提高群众的认可度。

融合考生性格的高考志愿推荐算法

随着大数据时代的到来,推荐算法在高考志愿填报领域的应用逐渐普及。为解决考生填报志愿时的迷茫,本高考志愿推荐算法收集了往年高考数据,并实现了高考录取分数线预测模型、高考录取分数排名预测模型,且同时实现了多种职业性格测试。最后,根据考生的高考分数和排名,结合考生的职业性格测试结果以及高校各专业的录取排名预测结果为考生提供高考志愿填报意见。实验结果表明,推荐结果符合考生性格,且能顺利录取。

异质网络中融合多类信息的链路情感倾向预测

信息网络中基于节点间情感关系分析的链路情感倾向预测在商业营销、内容推荐等领域应用广泛,是网络分析的一个研究重点.传统的链路情感倾向预测方法对于数据信息的挖掘不够充分,忽略了对数据深层语义以及节点属性等信息的利用,预测准确度有待提升.针对以上问题,提出了异质网络中融合多种类型信息的链路情感倾向预测模型.模型首先引入预测基值作为特定节点间情感关系的粗略评估,然后结合节点的相似关系以及节点的属性等信息完成预测.其中,在捕获网络中具有相似情感倾向的节点用于预测任务时,提出了一种基于限制路径类型元路径的遍历游走方法.在5个公共数据集上的实验结果验证了所提模型的有效性及对于稀疏矩阵、冷启动问题的处理能力,并揭示了模型各组成部分在预测过程中的作用.

基于卷积-长短期记忆神经网络的抽水蓄能机组健康性能趋势预测

为准确掌握抽水蓄能机组的健康性能水平,提出基于卷积-长短期记忆神经网络(CNN-LSTM)的机组健康性能趋势预测方法。首先,为有效地刻画机组的运行特性,构建基于高斯过程回归的机组健康状态模型;然后,设计可量化机组健康性能的指标因子;进一步融合CNN良好的局部特征提取能力和LSTM在时间序列预测方面的优势,提出基于CNN-LSTM的预测模型。对国内某抽水蓄能电站机组监测数据进行的试验结果表明,所提方法可较好地预测机组健康性能的发展趋势。

新诊断2型糖尿病患者酮症倾向的危险因素及预测模型构建

目的探讨新诊断2型糖尿病(type 2 diabetes mellitus,T2DM)患者酮症起病的危险因素,并构建预测模型。方法回顾性分析2017年1月至2022年6月在温州市人民医院内分泌科住院的新诊断T2DM患者205例,根据是否酮症起病分为单纯起病组(125例)、酮症起病组(80例),比较两组患者的临床特征,采用Logistics回归分析酮症发生的危险因素,建立预测模型并采用受试者工作特征(receiver operator characteristic,ROC)曲线评价其对新诊断T2DM患者酮症起病的预测价值。结果与单纯起病组相比,酮症起病组患者男性多见(P=0.009)、年龄更小(P<0.001),收缩压(P=0.003)、高密度脂蛋白(high density lipoprotein,HDL)(P=0.001)、空腹及餐后2小时C肽水平(P<0.001)、促甲状腺激素(thyroid-stimulating hormone,TSH)(P=0.003)、血清游离三碘甲状腺原氨酸(free triiodothyronine,FT3)(P=0.012)更低;起病后下降体重(P<0.001)、糖化血红蛋白(glycosylated hemoglobin,HbA1c)(P<0.001)、稳态模型胰岛素抵抗指数(homeostasis model of assessment for insulin resistance index,HOMA-IR)(P<0.001)更高;多因素Logistics回归结果显示,年龄、HDL、空腹C肽水平、TSH是酮症发生的独立保护因素,HbA1c、HOMA-IR是独立危险因素;根据多因素Logistics回归分析结果建立新诊断T2DM患者酮症预测模型,该模型的曲线下面积(area under the curve,AUC)为0.880[95%CI(0.832,0.928),P<0.001],特异度80.8%、灵敏度86.3%,ROC曲线结果显示,酮症预测模型较各独立指标可更好地预测新诊断T2DM患者酮症的发生。结论年龄、HDL、空腹C肽水平、TSH是新诊断T2DM患者酮症发生的独立保护因素,HbA1c、HOMA-IR是独立危险因素,酮症预测模型对酮症倾向的发生有较好的预测作用。

基于FEEMD与CLSTM的水电机组趋势预测

为及时掌握水电机组的运行状态趋势,本文提出了一种基于快速集成经验模态分解(FEEMD)与卷积-长短记忆神经网络(CLSTM)的水电机组状态趋势预测方法。所提CLSTM预测模型在传统的LSTM单元中引入了卷积操作,可更好地捕捉本征模态分量中的局部特征,且该模型可同时处理时间和空间维度的数据,有效提升预测精度。最后,通过国内某水电机组下导摆度趋势预测实验,验证了该方法可准确预测水电机组的运行状态趋势。

2022年国内苯酚/丙酮生产及市场分析预测

简述了国内苯酚/丙酮生产现状,拟在建装置情况;对国内苯酚/丙酮市场供应、消费结构、市场价格、下游消费情况及进出口情况进行了分析和预测;同时,指出了苯酚丙酮未来的发展趋势,并提出建议。

基于ARMA模型的高考人数预测研究——以H市为例

高考是为高校招生设置的全国性统一考试,是国家级重要考试之一。高考人数的变化既能反映人口发展规律,又能反映某一时期社会政治、经济、文化等方面的特点。以H市2009—2022年高考人数为研究基础,通过时间序列分析算法构建ARMA模型,再利用Matlab软件进行数值计算可预测该市未来五年参加高考的人数。研究结果显示,H市未来五年高考人数可能会持续增加,在2027年左右达到3万人。与此同时,未来几年H市经济、社会、科技等方面的发展会对实际高考人数产生一定影响。

花脑特矿区基于变异系数-突变级数法的岩爆倾向性综合预测

为了准确预测花脑特矿区深部中段岩爆倾向性,选取岩石单轴抗压强度、冲击能量指数、弹性应变能指数、地应力指数和RQD值等五个指标,通过地应力测量、室内岩石力学试验、Sirovision岩体节理裂隙调查,获得岩爆倾向性单指标评价结果,同时引入变异系数-突变级数法建立岩爆多指标评价体系,利用岩爆倾向性单指标评价结果计算突变级数值,并与岩石实际岩爆等级进行比较分析。分析结果表明,样本突变级数值与实际岩爆等级存在正相关关系,可以进行岩爆倾向性预测。研究结果表明:变异系数-印第安人茅舍突变级数值法在白音呼布花脑特矿区具有较好的适用性,可作为矿山后续工程中岩爆倾向性预测依据。