人力资源的预测方法

人力资源规划是必不可少的管理环节．科学的人力资源规划，必须建立在对人员供、需的准确预测的基础上．本文讨论人力资源预测的问题和转移矩阵等方法的使用．     

居民收入和消费预测的灰理论方法

阐述灰色模型GM(1,1)建立及检验的基本理论和方法.并依据2000年—2005年浙江农村居民人均纯收入和人均生活消费数据,利用GM(1,1)模型建模方法,对“十一五”期间浙江农民的纯收入和生活消费进行了预测.     

关于求解常微方程组的离散波形松驰方法的加速收敛

提出一种关于求解常微线性系统的离散波形松驰方法的新的加速收敛技巧。通过对系统矩阵A的分裂，该技巧使迭代矩阵（(zI M)^-1N）具有理想的较小谱半径。在LU分解的基础上给出了一个迭代算法以及用该法与Gauss-Seidel方不垢敛速进行比较的数值例子。     

The Fundamental Experiments of Red Tide Chemical Forecast

Many food algae and red tide algae were cultivated in the f/2 medium, and the nitric oxide (NO) concentration of the medium and the cell density were determined. The experiments on algae were conducted when different concentrations of NO were added into the medium using two methods. The results show that low concentrations of NO were self-produced by marine algae during the exponential growth period and were about nmol/L level. But at the end of the period, i.e., 2 or 3 days before the cell density reaches the maximum, an NO peak appeared, with the NO concentration reaching 10 nmol/L. The NO threshold concentration exists according to the influence of exogenous NO on the marine phytoplankton growth. One type is the threshold concentration that can promote algae growth, and its value is between 10 and 1 nmol level, or even lower. The other type can inhibit the phytoplankton growth, which is about μmol level or higher. The results indicate that red tide algae are far more sensitive to NO than are food algae. The fundamental experimental outcome above may provide a new clue for red tide chemical forecast by inspecting the NO change.     

GM(1,1)改进模型及其应用

根据 GM( 1 ,1 )灰色模型的指数特性 ,通过在区间上求积分给出了关于背景值的一个比较确切的计算公式 ,讨论了由此建立的 GM( 1 ,1 )改进模型的适用范围和预测精度 .结果表明改进模型比原 GM( 1 ,1 )模型适用性要强、模拟和预测精度要高 ,不仅适用于低增长序列、也适用于高增长序列 ,不仅适用于短期预测 ,同样也适用于中、长期预测     

基于物联网的医用耗材智能屋管理系统

针对医用高值耗材种类繁多、管理难度大的问题,为减少人力成本、提高医用高值耗材的智能化管理水平。本文提出了基于物联网的高值医用耗材智能屋的管理系统,介绍了智能屋的整体架构、系统功能、核心业务及流程设计,主要分为结构模块、RFID模块、数据库模块、控制系统模块四大模块,具体有用户管理、出入库管理、综合查询、预警管理四大业务,通过GM(1,1)模型对2022年全年高值医用耗材的月消耗量进行预测分析,并且检验得该预测模型的精度等级为一级,模型预测效果很好,能够为医院采购人员采购提供了一个参考,避免因过度的主观估计而造成的耗材浪费。该系统能够有效地提高医院对于高值医用耗材的管理水平,减少人力、物力和财力的消耗,为广大患者提供更加优质的医疗服务。本文所提的高值医用耗材智能屋管理系统解决了传统的高值医用耗材管理的诸多难题。     

Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms

Any decision-making process that relies on a probabilistic forecast of future events necessarily requires a calibrated forecast. This article proposes new methods for empirically assessing forecast calibration in a multivariate setting where the probabilistic forecast is given by an ensemble of equally probable forecast scenarios. Multivariate properties are mapped to a single dimension through a prerank function and the calibration is subsequently assessed visually through a histogram of the ranks of the observation’s preranks. Average ranking assigns a prerank based on the average univariate rank while band depth ranking employs the concept of functional band depth where the centrality of the observation within the forecast ensemble is assessed. Several simulation examples and a case study of temperature forecast trajectories at Berlin Tegel Airport in Germany demonstrate that both multivariate ranking methods can successfully detect various sources of miscalibration and scale efficiently to high-dimensional settings. Supplemental material in form of computer code is available online.     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis, E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.     

Rapid and label-free classification of pathogens based on light scattering,reduced power spectral features and support vector machine

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis,E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.     

基于SVR和k-近邻群的组合预测在QSAR中的应用

为提高定量构效关系(QSAR)研究的预测精度,发展了一种新的基于支持向量机回归(SVR)非线性筛选分子结构描述符、基于k-近邻群的非线性组合预测方法.首先以均方误差(MSE)最小为原则,以留一法通过多轮末尾淘汰实施分子结构描述符的非线性SVR汰选并给出最优核函数和相应保留描述符;其次基于待测样本与训练样本保留描述符向量的欧氏距离,以不同k-近邻群子模型双重留一法预测值反映样本集的异质性;然后基于MSE最小,以留一法通过多轮末尾淘汰实施近邻群子模型的非线性SVR汰选并给出最优核函数和相应保留子模型;最后基于保留子模型以双重留一法实施组合预测.以取代苯胺和苯酚类化合物对大型溞的QSAR实例验证表明:新方法在所有参比模型中预测精度最高,且能更精细地反映描述符与化合物毒性间的非线性关系,具结构风险最小、非线性、适于小样本,能有效克服过拟合、维数灾和局极小,非线性筛选描述符和子模型,非线性组合预测,自动选择最优核函数及其相应参数,泛化推广能力优异、预测精度高等诸多优点,在QSAR研究中有广泛应用前景.