基于Relief-F算法的心血管介入患者术后死亡风险预测

针对心血管介入患者全周期病理数据普遍存在缺失、不连续、非结构化等问题,建立了心血管介入专病数据库,并采用基于Relief-F算法的预测方法,对心血管介入患者术后死亡风险进行预测。首先参照HL7、CDISC等国际心血管疾病统一标准对各数据源进行标准化处理,建立研究数据集,并对数据进行清洗和预处理;其次采用Relief-F算法对特征进行选择,最终保留30个特征变量;再次选择逻辑回归、支持向量机、随机森林等3种机器学习方法进行建模分析,并采用10折交叉验证方法对分类器进行训练;最后引入准确率等模型评价指标来评估各算法在数据集上的分类预测效果。实验结果表明:随机森林的分类效果在该研究数据集上的表现最佳,准确率达到81.97%,精确率为86.90%,召回率为82.14%,F1值为0.8441。该研究提出的方法能够客观反映患者术后死亡风险,为心血管介入患者术后死亡风险预测提供了一种有效的解决方案。

Electric-controlled pressure relief valve for enhanced safety in liquid-cooled lithium-ion battery packs

The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.

Stress relief and crystal face transition process contribute to the stability of zinc anodes

Zn electrodes are suffering the dendrite growth owing to the enrichment of local space charge, distinct exposed face and residual stress. In this work, we investigated the crystal face properties and stress state of Zn foil through static energy calculations, dynamic crystal growth analysis and finite element simulation of stress states. Then thermal driven is deployed to modify the exposure face and residual stress of Zn foil, aiming for a dendrite-free electrode. The calculation of surface energies and simulation of crystal growth models for different crystal faces indicate that the(0 0 1) face can maintain stability during deposition. Inspired by this mechanism, the(1 0 1) exposed commercial Zn foil is modified by thermal processing. Firstly, the exposure level of the(0 0 1) face increases, though only the peak corresponding to the(0 0 2) crystal face is observed, due to the extinction effect of the densely packed plane(0 0 1) face.Further, the surface morphology becomes smooth and the stress is released with the progresses time.These stress relief and crystal face transition process strengthen the uniformity of ion distribution, and increase the interface stability during the crystal growth, which reduce the defect sites in the deposition.As a result, the Zn electrode exhibits tiny voltage hysteresis and outstanding cycle stability, which reveals improved electrochemical performance. Additionally, Li and Na can also be improved in exposed crystal faces and release strain energy through similar methods to enhance cycling stability.

Impacts of radiation therapy on quality of life and pain relief in patients with bone metastases

Bone metastases(BM)are a common complication in advanced cancer patients,significantly contributing to morbidity and mortality due to their ability to cause pain,fractures,and spinal cord compression.Radiation therapy(RT)is vital in managing these complications by targeting metastatic lesions to ease pain,improve mobility,and reduce the risk of skeletal-related events such as fractures.Evidence supports the effectiveness of RT in pain relief,showing its ability to provide significant palliation and lessen the need for opioid painkillers,thereby enhancing the overall quality of life(QoL)for patients with BM.However,optimizing RT outcomes involves considerations such as the choice of radiation technique,dose fractionation schedules,and the integration of supportive care measures to mitigate treatment-related side effects like fatigue and skin reactions.These factors highlight the importance of personalized treatment planning tailored to individual patient needs and tumor characteristics.This mini-review aims to provide comprehensive insights into the multifaceted impacts of RT on pain management and QoL enhancement in BM patients,with implications for refining clinical practices and advancing patient care through the synthesis of findings from various studies.

不平衡数据集上的Relief特征选择算法

Relief算法为系列特征选择方法,包括最早提出的Relief算法和后来拓展的ReliefF算法,核心思想是对分类贡献大的特征赋予较大的权值;特点是算法简单,运行效率高,因此有着广泛的应用。但直接将Relief算法应用于有干扰的数据集或不平衡数据集,效果并不理想。基于Relief算法,提出一种干扰数据特征选择算法,称为阈值-Relief算法,有效消除了干扰数据对分类结果的影响。结合K-means算法,提出两种不平衡数据集特征选择算法,分别称为K-means-ReliefF算法和K-means-Relief抽样算法,有效弥补了Relief算法在不平衡数据集上表现出的不足。实验证明了本文算法的有效性。

Stress Relief Measurements in Young Adults and Adolescents—A Comparative Pilot Study with Different Relaxation Programs

Background: Chronic stress is a emotional challenge. Constant pressure presents a serious risk of damage to mental and physical health and hence is associated with increased incidence of various diseases. The ability to cope with chronic stress may be a function of psychological resilience including intellectual capacities, but more so of external factors such as life experience and education. Adolescents are more vulnerable to chronic stress than adults. The measures introduced during the COVID-19 pandemic brought up major societal problems. As both children and adolescents lost their life anchors, the prevalence of stress in adolescents increased from 20% to 45%. Chronic psychological stress can impede the normal development of schoolchildren. It may cause anxiety, social withdrawal, interpersonal conflicts and aggression. This applies particularly to those in puberty, with the adolescent already facing unstable social bonds and elevated fear about the future. It is likely that the puberty cohort accounts for the dramatic increase in the prevalence of stress. Since it impacts public health, chronic stress among school-age children is increasingly taking on a socio-political dimension. Non-clinical stress intervention studies can investigate how to achieve stress reduction in school children. Methods: In a small pilot study, we analysed the effects of a training program with four different standard interventions, i.e. mindfulness training, progressive muscle reflection, autogenic training, and sound meditation. We obtained baseline scores of 10 stress-indicators, and re-tested after the interventions were performed. Results: The four applied interventions resulted in a reduction of 8 (out of 10) stress-indicators, such as “feeling stressed” or stress related symptoms (headaches, dizziness, sweating). This positive impact of the interventions significantly reduced “Fears about the future” (p Discussion: School children recognise the positive potential of stress reducing training or interventions. Our results provide evidence in support of integrating the training of relaxation techniques in the school curriculum. We propose that a larger study be undertaken to determine which methods would be most effective.

Palliative care with adequate pain relief challenges the need for euthanasia legislation

Background:In today’s society the ongoing discussion about euthanasia triggers emotionally charged debates surrounding the delicate balance between valuing life and respecting an individual’s autonomy.With the persistence of this debate,there has been the emergence of the concept of the so-called alternative:palliative care.Positioned as a substitute for euthanasia,palliative care aims to alleviate suffering in terminally ill patients without engaging in the ethical dilemmas associated with euthanasia.Methods:This paper explores the facets of palliative care highlighting its core objectives such as providing adequate pain relief as a compassionate alternative to euthanasia.Results:By examining palliative care as a comprehensive approach to end of life support,this study challenges the perceived necessity of euthanasia and advocates,for compassionate and dignified end of life experiences.Conclusion:In conclusion,palliative care emerges as a viable and ethically sound alternative to euthanasia,emphasizing the importance of compassionate end-of-life care and pain management.

Application of STEEP and Interpretive Structural Modeling in the Design Imagery of Taiwan Public Ceramic Relief Murals

Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the main axis of ceramic art in the future.Taiwan public ceramic relief murals(PCRM)are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987.In addition to breaking through the limitations of traditional public ceramic murals,Chu leveraged local culture and sensibility.The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region.This study mainly analyzes and understands the design image of public ceramic murals,taking Taiwan PCRM’s design and creation as the scope,and applies STEEP analysis,that is,the social,technological,economic,ecological,and political-legal environments are analyzed as core factors;eight main important factors in the artistic design image of ceramic murals are evaluated.Then,interpretive structural modeling(ISM)is used to establish five levels,analyze the four main problems in the main core factor area and the four main target results in the affected factor area;and analyze the problem points and target points as well as their causal relationships.It is expected to sort out the relationship between these factors,obtain the hierarchical relationship of each factor,and provide a reference basis and research methods.

基于Relief特征量优化及SOM网络的断路器操作机构状态评估

为提高断路器操作机构状态评估的效率及准确性,文中提出了采用Relief优化方法对分合闸线圈电流特征向量进行优化及SOM网络对线圈电流样本进行分类的高压断路器操作机构状态评估方法。仿真研究结果表明:基于Relief的分合闸线圈电流特征向量优化方法和SOM网络分类方法的操作机构状态评估能够实现操作机构状态的准确评估,同时采用优化后的电流特征向量数据进行状态评估减小了运算量和缩短了运算时间,提高了断路器机构状态评估的效率。

基于GF-1遥感影像和relief-mRMR-GASVM模型的小麦白粉病监测

选择合适的建模和特征选择算法对提高作物病害的遥感监测水平有着重要的作用。研究以河北省小麦白粉病为研究对象,基于GF-1/WFV数据共提取了4个波段反射率数据和10个对作物长势和胁迫敏感的植被指数作为初选特征。针对常用的特征提取算法relief算法筛选出的特征存在冗余性的问题,提出了一种relief结合最小冗余最大相关(minimum redundancy maximum relevance,m RMR)的特征降维算法(relief-m RMR)。首先,通过relief算法计算出各特征的权重系数,对特征集进行加权;然后利用m RMR算法选出与类别具有最小冗余性的特征,利用支持向量机(support vector machine,SVM)对河北白粉病进行监测,并用遗传算法(genetic algorithm,GA)优化的SVM(GASVM)建立了白粉病的监测模型(relief-m RMR-GASVM),将监测结果分别与SVM和网格寻优(grid search,GS)算法优化的SVM(GSSVM)的监测结果进行对比分析,同时比较了该方法与Ada Boost、粒子群(Pso)优化的最小二乘支持向量机(least squares support vector machine,Pso-LSSVM)和随机森林(random forest,RF)3种方法的优越性。结果表明,relief-m RMR算法筛选出的特征与GASVM、SVM和GSSVM建立的监测模型精度比传统relief算法筛选特征所建模型的精度分别提高了14.3个百分点、7.2个百分点和7.1个百分点,比传统m RMR算法筛选特征所建模型的精度分别提高了14.3个百分点、14.3个百分点和14.2个百分点。relief-m RMR算法结合GASVM建立的监测模型精度为所有模型中最高,精度为85.7个百分点,分别比SVM和GSSVM所建监测模型精度提高了21.4个百分点和7.2个百分点。此外,GF-1数据结合relief-m RMR-GASVM模型的监测精度分别高出Ada Boost、Pso-LSSVM和RF方法21.4个百分点、14.3个百分点和7.1个百分点。说明GF-1数据结合relief-m RMR-GASVM模型可用于小麦白粉病的遥感监测。

基于ReliefF和PSO混合特征选择的面向对象土地利用分类

针对面向对象土地利用分类存在特征维数过高的问题,提出了一种结合Relief F和粒子群优化算法(particle swarm optimization,PSO)的混合特征选择方法,即首先利用Relief F作为特征预选器滤除相关性小的特征,然后以PSO作为搜索算法,以支持向量机(support vector machine,SVM)的分类精度作为评估函数在剩余特征中选择出最优特征子集。该文以吉林省长春市部分区域为研究区,采用Landsat8遥感影像为数据源,首先对其进行多尺度分割,然后提取影像对象的光谱、纹理、形状和空间关系特征,利用提出的混合特征选择方法选取最优特征子集,最后使用SVM分类器对研究区进行土地利用分类,总体分类精度和Kappa系数分别为85.88%和0.8036,与基于4种其他特征选择方法的土地利用分类结果进行比较,基于Relief F和PSO的混合特征选择方法利用最少的特征获得最高的分类精度,能够有效地用于面向对象土地利用分类。

极大熵Relief特征加权

Relief特征加权的最新研究进展表明其可近似地表述为一个间距最大化优化问题.尽管该类算法广为应用,但仍然存在一些缺陷.为了提高Relief特征加权的适应性和鲁棒性,融合间距最大化和极大熵理论,并由此探讨了新的鲁棒的具有更好适应性的Relief特征加新方法.首先,构造了一个结合极大熵原理的间距最大化目标函数.对于该目标函数,运用优化理论得到一些重要的理论结果.在此基础上,对于两类数据、多类数据和在线数据,提出了一组鲁棒的Relief特征加权算法.利用UCI基准数据集和基因数据集进行了实验验证,结果表明提出的新Relief特征加权算法对噪音和例外点显示出了更好的适应性和鲁棒性.

基于Q-relief的图像特征选择算法

针对特征选择算法——relief在训练个别属性权值时的盲目性缺点,提出了一种基于自适应划分实例集的新算法——Q-relief,该算法改正了原算法属性选择时的盲目性缺点,选择出表达图像信息最优的特征子集来进行模式识别。将该算法应用于列车运行故障动态图像监测系统(TFDS)的故障识别,经实验验证,与其他算法相比,Q-relief算法明显提高了故障图像识别的准确率。

基于Relief相关性特征提取和微分进化支持向量机的短期电价预测

针对电价预测中特征输入量选择的盲目性,本文通过改进传统的Relief算法,提出一种电价预测输入量的自动选取方法,并引入相关性分析来剔除冗余特征。在此基础上,采用支持向量机来建立电价预测模型并应用微分进化算法来优化选择支持向量机的参数以达到提高预测精度的目的。以PJM电力市场的真实电价来进行仿真分析,结果表明本文的特征选取方法能够很好地提取电价的短期趋势特征和周期性特征,而微分进化优化的支持向量机也获得了比常规支持向量机和BP神经网络要好的预测结果,体现了本文方法的优越性。

基于Relief算法的故障图像识别与匹配方法

针对图像识别与匹配问题,提出一种基于Relief算法的新方案。利用k最近邻的Relief算法选择表达图像的最优特征子集,再利用基于最小距离分类器的模板匹配技术,实现故障图像的识别。在特征提取时,改进了对传统的灰度共生矩阵的选取。实验证明,该算法正确识别图像故障与否的概率能到达90%左右,提高了特征选择的有效性,完全满足实际应用中的需要。

基于Relief F和BP神经网络的安全评价指标体系精简化建模

为有效解决某些复杂系统安全评价中涉及的评价指标数目较多、复杂度较高、难以运用传统建立数学解析式的方法对其进行综合评价等问题,提出基于Relief F和BP神经网络的安全评价指标体系(SAI)精简化建模方法。首先,用Relief F算法对SAI进行特征选择,剔除对安全评价主导指标影响较小的辅助指标;然后用BP神经网络对精简后的指标体系进行建模,建立SAI的精简化模型;最后,根据钢板缺陷评价试验数据将这种建模方法与其他方法对比。结果表明,用该方法能够简化安全评价系统模型、降低运算复杂度、提高系统效率。

Poststack reverse-time migration using a non-reflecting recursive algorithm on surface relief

Presently the research based on the accurate seismic imaging methods for surface relief, complex structure, and complicated velocity distributions is of great significance. Reverse-time migration is considered to be one of highly accurate methods. In this paper, we propose a new non-reflecting recursive algorithm for reverse-time migration by introducing the wave impedance function into the acoustic wave equation and the algorithm for the surface relief case is derived from the coordinate transformation principle. Using the exploding reflector principle and the zero-time imaging condition of poststack reverse- time migration, poststack numerical simulation and reverse-time migration with complex conditions can be realized. The results of synthetic and real data calculations show that the method effectively suppresses unwanted internal reflections and also deals with the seismic imaging problems resulting from surface relief. So, we prove that this method has strong adaptability and practicality.

改进Relief-C5.0的恶意域名检测算法

针对目前恶意域名检测算法中分类模型计算复杂度较大、实时性不强以及准确率不高等问题,提出了Rf-C5(Relief-C5.0)恶意域名检测算法模型。提取待测域名的全局URL特征,根据提取的特征按照改进的Relief算法进行权重计算,并依据权重值进行优先级排序;选取权重值排名前20的关键特征作为C5.0分类器的输入端,进行合法域名与恶意域名的分类。实验结果表明,在大样本数据集下,Rf-C5模型与当前主流恶意域名检测算法相比,在提高平均检测速率的基础上,检测准确率提高了1.58~4.91个百分点。

Relief算法在关门车故障自动识别中的应用

针对货车运行故障动态图像检测(TFDS)系统中的图像识别问题,提出一种基于Relief算法的解决方案。该方案采用梯度—共生矩阵的数字特征来描述一幅图像,然后通过Relief算法进行有效的特征选择,根据这些特征,通过模板匹配技术,最终检测出关门车故障。现场运用表明,该方案具有很高的实用性。

Relief-F筛选波段的小麦白粉病早期诊断研究

为了准确监测小麦白粉病染病早期病情,给喷药防治提供技术指导,论文将染病初期的小麦叶片作为研究对象。首先,利用高光谱图像数据,通过图像特征分割出叶片区域和病斑区域,定量计算病情严重度;其次引入Relief-F算法提取染病早期最敏感波段和波段差,计算出白粉病病害指数PMDI(Powdery mildew disease index);并通过分析病情指数DI(Disease index)与11种植被指数(含PMDI指数)的相关性及线性模型,得出PMDI模型有最高的决定系数(R^2=0.839 9)和最低的均方根误差(RMSE=4.522 0),效果优于其他病害植被指数的结果(其中,Normalized Difference Vegetation Index,NDVI的模型决定系数最高,R^2=0.777 1,RMSE=5.336 4);最后,选择PMDI和NDVI植被指数分别构建小麦白粉病染病早期病情严重度的支持向量回归模型。结果表明:经敏感波段筛选构建的PMDI指数的预测结果更好,预测模型的R^2=0.886 3,RMSE=3.553 2,可以实现小麦白粉病早期无损诊断,这为指导作物病害喷药防治提供重要的技术支撑。