灰色系统理论对城市道路交通噪声的建模与预测

以豫西三门峡市1996～2000年道路交通噪声监测数据为依据，运用灰色系统理论建立了GM(1，1)预测模型，经用四种不同方法对模型精度进行检验，均满足要求，用此模型预测三门峡市未来几年道路交通噪声呈下降趋势。     

江苏省卫生人力资源配置的公平性及预测研究

目的 评价江苏省卫生人力资源配置的公平性,预测各类卫生人员的数量,为江苏省卫生事业的发展提供理论依据。方法 运用泰尔指数与集聚度评价资源配置的公平性,运用灰色GM (1, 1) 模型、多项式回归模型、二次指数平滑法以及组合预测模型对数据进行预测。结果 2015—2019年江苏省泰尔指数总体呈上升趋势,卫生人力资源配置差异性拉大,注册护士的资源配置差异最大。从地区来看,卫生人力资源集聚度由大到小分别为苏南＞苏中＞苏北。卫生人员灰色GM (1, 1) 模型预测效果最好,其他三类人员均是组合预测模型预测效果最好。结论 江苏省应合理配置卫生人力资源,加大卫生人员的培养力度,加强卫生人才队伍建设。     

四种预测心脏体积方法准确性和精确度的评价

为探讨以胸廓径线、体表面积、胸围、胸部身体的厚度预测心脏体积方法的可靠性和精确度 ,用X线平片进行心脏径线的测量 ,用逐步回归法得出以胸廓径线、体表面积、胸围、胸部身体的厚度预测个体心脏体积的回归方程 ,并比较它们的准确性和精确度。结果表明 :四种方法都可以用来预测个体心脏体积 ,但后两种方法精确度较前两种方法高。认为利用胸围、胸部身体的厚度预测心脏体积的方法较为简便、经济、又不失其可靠性和精确度 ,在大规模普查中筛选心脏疾患的对象有一定的实用价值     

超声测量胎儿LL/FSTT预测胎儿体重

目的：探讨应用B型超声测量胎儿肝脏长度与胎儿股骨皮下组织厚度的比值（LL/FSTT）预测胎儿出生体重的临床价值。方法：用B型超声对400例胎儿双顶径、头围、腹围、股骨长度、肝脏长度与股骨皮下组织的厚度进行测量并分析与新生儿出生体重的关系。结果；肝脏长度与股骨皮下组织厚度的比值和胎儿体重密切相关（r=0.901）。对正常胎儿体重、巨大儿、胎儿宫内发育迟缓（IUGR）诊断的敏感性分别是88％、89％、86％，特异性分别是90％、92％、88％。结论：用胎儿肝脏长度与股骨皮下软组织厚度的比值预测胎儿体重的方法简单、准确，为临床掌握分娩时机和分娩方式提供可靠参考数据。     

肾综合征出血热发病率的小波预测模型

目的 建立季节性水平变化趋势时间序列小波预测模型，提高肾综合征出血热(HWRS)发病率的预测步长及精度。方法 对原始序列进行多层小波分解，分解后的各层分别用自回归滑动平均(ARIMA)模型进行预测，将各层的预测值合并作为原序列的最终预测值。结果 小波预测模型4步预测精度为82.45％，而ARIMA建模的4步预测精度为67.97％。结论 用小波预测模型对水平变化趋势的HWRS作短、中期预测是有效、可行的。     

A Comparison of the Accuracy of Clinician Prediction of Survival Versus the Palliative Prognostic Index

Context

Survival predictions for advanced cancer patients impact many aspects of care, but the accuracy of clinician prediction of survival (CPS) is low. Prognostic tools such as the Palliative Prognostic Index (PPI) have been proposed to improve accuracy of predictions. However, it is not known if PPI is better than CPS at discriminating survival.

The future in clinical genetics: affective forecasting biases in patient and clinician decision making

When clinicians facilitate and patients make decisions about predictive genetic testing, they often base their choices on the predicted emotional consequences of positive and negative test results. Research from psychology and decision making suggests that such predictions may often be biased. Work on affective forecasting—predicting one's future emotional states—shows that people tend to overestimate the impact of (especially negative) emotional events on their well‐being; a phenomenon termed the impact bias. In this article, we review the causes and consequences of the impact bias in medical decision making, with a focus on applying such findings to predictive testing in clinical genetics. We also recommend strategies for reducing the impact bias and consider the ethical and practical implications of doing so.     

Monitoring air pollution: use of early warning systems for public health

Research confirming the detrimental impact poor ambient air quality and episodes of abnormally high pollutants has on public health, plus differential susceptibility, calls for improved understanding of this complex topic among all walks of society. The public and particularly, vulnerable groups, should be aware of their quality of air, enabling action to be taken in the event of increased pollution. Policy makers must have a sound awareness of current air quality and future trends, to identify issues, guide policies and monitor their effectiveness. These attitudes are dependent upon air pollution monitoring, forecasting and reporting, serving all interested parties. Apart from the underlying national regulatory obligation a country has in reporting air quality information, data output serves several purposes. This review focuses on provision of real-time data and advanced warnings of potentially health-damaging events, in the form of national air quality indices and proactive alert services. Some of the challenges associated with designing these systems include technical issues associated with the complexity of air pollution and its science. These include inability to provide precise exposure concentrations or guidance on long-term/cumulative exposures or effects from pollutant combinations. Other issues relate to the degree to which people are aware and positively respond to these services. Looking to the future, mobile devices such as cellular phones, equipped with sensing applications have potential to provide dynamic, temporally and spatially precise exposure measures for the mass population. The ultimate aim should be to empower people to modify behaviour-for example, when to increase medication, the route/mode of transport taken to school or work or the appropriate time to pursue outdoor activities-in a way that protects their health as well as the quality of the air they breathe.     

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction ($≈ \mathcal{O}(10^{−6}))$ of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.