Improving the ability to assess potential stroke deficit may aid the selection of patients most likely to benefit from acute stroke therapies. Methods based only on ‘at risk’ volumes or initial neurological conditio...Improving the ability to assess potential stroke deficit may aid the selection of patients most likely to benefit from acute stroke therapies. Methods based only on ‘at risk’ volumes or initial neurological condition do predict eventual outcome but not perfectly. Given the close relationship between anatomy and function in the brain, we propose the use of a modified version of partial least squares (PLS) regression to examine how well stroke outcome covary with infarct location. The modified version of PLS incorporates penalized regression and can handle either binary or ordinal data. This version is known as partial least squares with penalized logistic regression (PLS-PLR) and has been adapted from its original use for high-dimensional microarray data. We have adapted this algorithm for use in imaging data and demonstrate the use of this algorithm in a set of patients with aphasia (high level language disorder) following stroke.展开更多
In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), ob...In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.展开更多
目的:评价影响贝尔面瘫患者预后的临床因素。方法:回顾性分析新疆维吾尔自治区2所医院2010年1月至2024年1月因贝尔面瘫接受治疗的94例患者的临床资料。采用描述性统计学方法对患者的人口统计学特征进行评价,采用卡方检验进行组间比较。...目的:评价影响贝尔面瘫患者预后的临床因素。方法:回顾性分析新疆维吾尔自治区2所医院2010年1月至2024年1月因贝尔面瘫接受治疗的94例患者的临床资料。采用描述性统计学方法对患者的人口统计学特征进行评价,采用卡方检验进行组间比较。通过多因素logistic回归分析模型确定影响患者预后的独立危险因素。拟合受试者工作特征(receiver operating characteristic,ROC)曲线并计算曲线下面积(area under curve,AUC)评价模型效能。结果:纳入患者的年龄为28~77岁,平均年龄(49.30±6.96)岁,其中女性占51.1%。单因素分析显示,烟(P=0.016)、高血压(P=0.007)、糖尿病(P=0.005)及较高的House-Brackmann(H-B)分级与患者部分恢复相关(P<0.001)。多因素logistic回归分析模型结果显示,吸烟(OR=8.295,P=0.010)、高血压(OR=9.667,P=0.008)及较高的H-B分级(OR=9.094,P=0.032)是贝尔面瘫患者部分恢复的独立危险因素。三者对患者预后(面瘫部分恢复)有一定预测价值(吸烟AUC为0.67,高血压为0.68,高H-B分级为0.68)。是否接受物理治疗和不同的药物干预与患者面瘫的恢复无统计学相关性(P>0.05)。结论:吸烟、高血压和较高的H-B分级是影响贝尔面瘫患者预后的独立危险因素,基于这些因素建立的预测模型具有一定的区分能力,可为临床优化诊疗方案提供参考。展开更多
现有电力负荷预测方法面临诸多挑战,尤其是在考虑气象因素对负荷波动的影响时,传统方法往往忽视气象特征与负荷之间复杂的非线性关系,导致预测精度不足。对此文中提出一种基于气象相似日修正(meteorological similar day correction,MS...现有电力负荷预测方法面临诸多挑战,尤其是在考虑气象因素对负荷波动的影响时,传统方法往往忽视气象特征与负荷之间复杂的非线性关系,导致预测精度不足。对此文中提出一种基于气象相似日修正(meteorological similar day correction,MSDC)和改进鹦鹉优化(improved parrot optimizer,IPO)线性分解(decomposition-based linear,DLinear)的日前电力负荷预测模型。首先运用Logistic映射、自适应变异策略、螺旋波动搜索IPO对DLinear超参数进行优化,然后由DLinear提取数据的周期性和趋势性特征,最后通过比对气象特征欧氏距离修正负荷预测值,形成基于IPO-DLinear-MSDC的日前电力负荷预测模型。采用2024年6月至10月湖南株洲地区总电力负荷数据集进行仿真分析,IPO-DLinear-MSDC模型的输出平均绝对百分比误差(mean absolute percentage error,MAPE)、决定系数R2分别为4.67%、0.833,相较于IPO-DLinear与PO-DLinear模型,MAPE分别下降了0.83个百分点、1.43个百分点,R2分别提升了0.074、0.125。展开更多
文摘Improving the ability to assess potential stroke deficit may aid the selection of patients most likely to benefit from acute stroke therapies. Methods based only on ‘at risk’ volumes or initial neurological condition do predict eventual outcome but not perfectly. Given the close relationship between anatomy and function in the brain, we propose the use of a modified version of partial least squares (PLS) regression to examine how well stroke outcome covary with infarct location. The modified version of PLS incorporates penalized regression and can handle either binary or ordinal data. This version is known as partial least squares with penalized logistic regression (PLS-PLR) and has been adapted from its original use for high-dimensional microarray data. We have adapted this algorithm for use in imaging data and demonstrate the use of this algorithm in a set of patients with aphasia (high level language disorder) following stroke.
文摘In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.
文摘目的:评价影响贝尔面瘫患者预后的临床因素。方法:回顾性分析新疆维吾尔自治区2所医院2010年1月至2024年1月因贝尔面瘫接受治疗的94例患者的临床资料。采用描述性统计学方法对患者的人口统计学特征进行评价,采用卡方检验进行组间比较。通过多因素logistic回归分析模型确定影响患者预后的独立危险因素。拟合受试者工作特征(receiver operating characteristic,ROC)曲线并计算曲线下面积(area under curve,AUC)评价模型效能。结果:纳入患者的年龄为28~77岁,平均年龄(49.30±6.96)岁,其中女性占51.1%。单因素分析显示,烟(P=0.016)、高血压(P=0.007)、糖尿病(P=0.005)及较高的House-Brackmann(H-B)分级与患者部分恢复相关(P<0.001)。多因素logistic回归分析模型结果显示,吸烟(OR=8.295,P=0.010)、高血压(OR=9.667,P=0.008)及较高的H-B分级(OR=9.094,P=0.032)是贝尔面瘫患者部分恢复的独立危险因素。三者对患者预后(面瘫部分恢复)有一定预测价值(吸烟AUC为0.67,高血压为0.68,高H-B分级为0.68)。是否接受物理治疗和不同的药物干预与患者面瘫的恢复无统计学相关性(P>0.05)。结论:吸烟、高血压和较高的H-B分级是影响贝尔面瘫患者预后的独立危险因素,基于这些因素建立的预测模型具有一定的区分能力,可为临床优化诊疗方案提供参考。
文摘现有电力负荷预测方法面临诸多挑战,尤其是在考虑气象因素对负荷波动的影响时,传统方法往往忽视气象特征与负荷之间复杂的非线性关系,导致预测精度不足。对此文中提出一种基于气象相似日修正(meteorological similar day correction,MSDC)和改进鹦鹉优化(improved parrot optimizer,IPO)线性分解(decomposition-based linear,DLinear)的日前电力负荷预测模型。首先运用Logistic映射、自适应变异策略、螺旋波动搜索IPO对DLinear超参数进行优化,然后由DLinear提取数据的周期性和趋势性特征,最后通过比对气象特征欧氏距离修正负荷预测值,形成基于IPO-DLinear-MSDC的日前电力负荷预测模型。采用2024年6月至10月湖南株洲地区总电力负荷数据集进行仿真分析,IPO-DLinear-MSDC模型的输出平均绝对百分比误差(mean absolute percentage error,MAPE)、决定系数R2分别为4.67%、0.833,相较于IPO-DLinear与PO-DLinear模型,MAPE分别下降了0.83个百分点、1.43个百分点,R2分别提升了0.074、0.125。
