Offering Smoking Cessation Support to Cancer Patients:The Role of Associations Providing Supportive Care in Cancer.Example of the Ligue Contre le Cancer Gironde

Aims:This article aims to explore the interventional and contextual components of smoking cessation support for cancer patients in the context of supportive care in cancer provided by an association,that is viable and effective in the French context,and to describe the partnership research process in which they were developed.Procedure:The intervention was developed from a dataset collected during a viability study for the development of a smoking cessation intervention carried out at the Ligue Contre le Cancer Gironde,a scoping review of evidence-based interventions and two narrative reviews on the determinants and ethical issues of smoking cessation in cancer.Results:The results confirmed a tangible opportunity to develop smoking cessation services within the relevant case because of the obstacles that can be overcome,the facilitators that can be mobilized,and the gaps existing in this field.In addition,they enabled the design of an intervention adapted to the context,guided by a voluntarist,multidisciplinary approach,and focused on patients’well-being.Conclusion:The associations providing supportive care in cancer can initiate and participate in the process of smoking cessation.They can play a key role in mediating between oncology and addictology.

Association between Support and Satisfaction with Life among Older Adults in Ekiti, Nigeria: Findings and Implications

Many developing countries like Nigeria lack policy for the care of the older adults and this creates major challenges for the elderly population. The traditional family institution and community support that used to be safety nest are being adversely affected by westernization. This development might have adverse effect on life satisfaction among the older adults. This hospital based cross-sectional study was designed to determine the association between support and life satisfaction among older adults. A total of 128 subjects participated in the study out of which 28.9% were satisfied with life. Expectation of support was mainly from the family, less from the community and very low from the government. The level of support received from all sources generally fell short of expectations. Marital status and source of livelihood were significantly associated with life satisfaction. There is inadequate social support from the government and support from family and community fell below expectations. Expectations of support were the most strongly correlated with life satisfaction. Support for older adults must be addressed in order to meet their expectations and improve their level of satisfaction with life.

Multi-label learning algorithm with SVM based association

Multi-label learning is an active research area which plays an important role in machine learning. Traditional learning algorithms, however, have to depend on samples with complete labels. The existing learning algorithms with missing labels do not consider the relevance of labels, resulting in label estimation errors of new samples. A new multi-label learning algorithm with support vector machine(SVM) based association(SVMA) is proposed to estimate missing labels by constructing the association between different labels. SVMA will establish a mapping function to minimize the number of samples in the margin while ensuring the margin large enough as well as minimizing the misclassification probability. To evaluate the performance of SVMA in the condition of missing labels, four typical data sets are adopted with the integrity of the labels being handled manually. Simulation results show the superiority of SVMA in dealing with the samples with missing labels compared with other models in image classification.

Regression Analysis of the Number of Association Rules

The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

Mining association rules in incomplete information systems

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

CARM:Context Based Association Rule Mining for Conventional Data

This paper is aimed to develop an algorithm for extracting association rules,called Context-Based Association Rule Mining algorithm(CARM),which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm(CBPNARM).CBPNARM was developed to extract positive and negative association rules from Spatiotemporal(space-time)data only,while the proposed algorithm can be applied to both spatial and non-spatial data.The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations.Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique.The context,in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data.Conditional Probability Increment Ratio(CPIR)is also added in the proposed algorithm that was not used in CBPNARM.The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use.Also,the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM.The rules created by the proposed algorithm are more meaningful,significant,relevant and insightful.The accuracy of the proposed algorithm is compared with the Apriori,PNARM and CBPNARM algorithms.The results demonstrated enhanced accuracy,relevance and timeliness.

NIA2: A fast indirect association mining algorithm

Indirect association is a high level relationship between items and frequent item sets in data. There are many potential applications for indirect associations, such as database marketing, intelligent data analysis, web -log analysis, recommended system, etc. Existing indirect association mining algorithms are mostly based on the notion of post - processing of discovery of frequent item sets. In the mining process, all frequent item sets need to be generated first, and then they are fihered and joined to form indirect associations. We have presented an indirect association mining algorithm （NIA） based on anti -monotonicity of indirect associations whereas k candidate indirect associations can be generated directly from k - 1 candidate indirect associations, without all frequent item sets generated. We also use the frequent itempair support matrix to reduce the time and memory space needed by the algorithm. In this paper, a novel algorithm （NIA2） is introduced based on the generation of indirect association patterns between itempairs through one item mediator sets from frequent itempair support matrix. A notion of mediator set support threshold is also presented. NIA2 mines indirect association patterns directly from the dataset, without generating all frequent item sets. The frequent itempair support matrix and the notion of using tm as the support threshold for mediator sets can significantly reduce the cost of joint operations and the search process compared with existing algorithms. Results of experiments on a real - word web log dataset have proved NIA2 one order of magnitude faster than existing algorithms.

AN INCREMENTAL UPDATING ALGORITHM FOR MINING ASSOCIATION RULES

In this letter, on the basis of Frequent Pattern(FP) tree, the support function to update FP-tree is introduced, then an Incremental FP (IFP) algorithm for mining association rules is proposed. IFP algorithm considers not only adding new data into the database but also reducing old data from the database. Furthermore, it can predigest five cases to three cases.The algorithm proposed in this letter can avoid generating lots of candidate items, and it is high efficient.

SNP site-drug association prediction algorithm based on denoising variational auto-encoder

Single nucletide polymorphism(SNP)is an important factor for the study of genetic variation in human families and animal and plant strains.Therefore,it is widely used in the study of population genetics and disease related gene.In pharmacogenomics research,identifying the association between SNP site and drug is the key to clinical precision medication,therefore,a predictive model of SNP site and drug association based on denoising variational auto-encoder(DVAE-SVM)is proposed.Firstly,k-mer algorithm is used to construct the initial SNP site feature vector,meanwhile,MACCS molecular fingerprint is introduced to generate the feature vector of the drug module.Then,we use the DVAE to extract the effective features of the initial feature vector of the SNP site.Finally,the effective feature vector of the SNP site and the feature vector of the drug module are fused input to the support vector machines(SVM)to predict the relationship of SNP site and drug module.The results of five-fold cross-validation experiments indicate that the proposed algorithm performs better than random forest(RF)and logistic regression(LR)classification.Further experiments show that compared with the feature extraction algorithms of principal component analysis(PCA),denoising auto-encoder(DAE)and variational auto-encode(VAE),the proposed algorithm has better prediction results.

Extracting Leading Joint Causes of Death and Mining Associations between Them

The gains in analyzing death from a multiple cause perspective have been recognized for a very long time. Methods that have been adopted have sought to determine additional gains made by treating death as a multiple cause phenomenon as compared to analysis based on a single under-lying cause. This paper shows how association rules mining methodology can be adapted to determine joint morbid causes with strong and interesting associations. Results show that some causes of death that do not appear among the leading causes show strong associations with other causes that would otherwise remain unknown without the use of association rules methodology. Overall, the study found that the leading joint pair of causes of death in South Africa was metabolic disorders and intestinal infectious diseases which accounted for 18.9 deaths per 1000 in 2008, followed by cerebrovascular and hypertensive diseases which accounted for 18.3 deaths per 1000.

Support Vector的几何解释及其在联想记忆中的应用

利用闭凸集上的投影解释 support vector的几何意义 ,利用支持超平面讨论线性分类器的设计问题 .对线性可分情形 ,Support vector由一类数据集合闭凸包在另一类数据集合闭凸包上投影的非零系数向量组成 ,SVM所决定的超平面位于两投影点关于各自数据集合支持超平面的中间 .作为应用 ,文中给出一种设计理想联想记忆前馈神经网络的方法 ,它是 FP算法的一般化 .

Backward Support Computation Method for Positive and Negative Frequent Itemset Mining

Association rules mining is a major data mining field that leads to discovery of associations and correlations among items in today’s big data environment. The conventional association rule mining focuses mainly on positive itemsets generated from frequently occurring itemsets (PFIS). However, there has been a significant study focused on infrequent itemsets with utilization of negative association rules to mine interesting frequent itemsets (NFIS) from transactions. In this work, we propose an efficient backward calculating negative frequent itemset algorithm namely EBC-NFIS for computing backward supports that can extract both positive and negative frequent itemsets synchronously from dataset. EBC-NFIS algorithm is based on popular e-NFIS algorithm that computes supports of negative itemsets from the supports of positive itemsets. The proposed algorithm makes use of previously computed supports from memory to minimize the computation time. In addition, association rules, i.e. positive and negative association rules (PNARs) are generated from discovered frequent itemsets using EBC-NFIS algorithm. The efficiency of the proposed algorithm is verified by several experiments and comparing results with e-NFIS algorithm. The experimental results confirm that the proposed algorithm successfully discovers NFIS and PNARs and runs significantly faster than conventional e-NFIS algorithm.

Methyl Cholate and Resorcinarene New Carriers for the Recovery of Cr(Ⅲ) Ions by Supported Liquid Membranes (SLM)s

The technique of supported liquid membranes was used to achieve the facilitated transport of Cr(III) ions, using tow amphiphilic carriers, the methyl cholate and resorcinarene. For prepared SLMs, toluene as organic phase and film of polyvinylidene difluoride, as hydrophobic polymer support with 100 μm in thickness and 0.45 μm as the diameter of the pores. The macroscopic parameters (P and J0) on the transport of these ions were determined for different medium temperatures. For these different environments, the prepared SLMs were highly permeable and a clear evolution of these parameters was observed. The parameter J0 depended on the temperature according to the Arrhenius equation. The activation parameters, Ea, ΔH≠ and ΔS≠, for the transition state on the reaction of complex formation (ST) , were determined. To explain these results for this phenomenon, and achieve a better extraction of the substrate, a model based on the substrate complexation by the carrier and the diffusion of the formed complex (ST) was developed. The experimental results verify this model and determine the microscopic parameters (Kass and D*). These studies show that these parameters Kass and D* are specific to facilitated transport of Cr(III) ions by each of the carriers and they are changing significantly with temperature.

Expression of synaptosomal-associated protein-25 in the rat brain after subarachnoid hemorrhage

Synaptosomal-associated protein-25 is an important factor for synaptic functions and cognition. In this study, subarachnoid hemorrhage models with spatial learning disorder were established through a blood injection into the chiasmatic cistern. Immunohistochemical staining and western blot analysis results showed that synaptosomal-associated protein-25 expression in the temporal lobe, hippocampus, and cerebellum significantly lower at days 1 and 3 following subarachnoid hemorrhage. Our findings indicate that synaptosomal-associated protein-25 expression was down-regulated in the rat brain after subarachnoid hemorrhage.

基于碳汇潜力的碳排放空间关联网络结构特征及影响因素——以长江中游城市群为例

考虑碳汇潜力的碳排放空间关联是促进城市群协同减排的重要基础,更是实现碳中和的有力抓手。以长江中游城市群为研究区,对传统引力模型进行修正,构建兼具碳汇潜力的碳排放空间关联网络,采用社会网络分析(SNA)和二次指派程序方法(QAP)探究长江中游城市群碳排放空间关联网络的时空特征与影响因素。结果表明:①研究期间长江中游城市群碳排放网络关联关系数、网络密度和网络关联度呈增长趋势,网络效率和网络等级度不断下降,且网络等级度在2010年后始终为0,表明碳排放空间关联网络日益稠密、网络通达性较强,存在多重叠加的溢出渠道,但等级结构并不森严。基于碳汇潜力的长江中游城市群碳排放空间关联网络从“双核化”向“多极化、多线程”网络形态发展,核心城市的扩散作用不断增强。此外,以抚州、宜昌、鹰潭、上饶和吉安等为代表的高碳生态承载力地区,在碳排放空间关联网络中的受益关联性大于溢出关联性。②2020年,长江中游城市群碳排放空间关联网络形成四大板块,相较于板块内部成员间的碳排放空间关联效应,板块间的碳排放空间关联效应更为明显,即板块间的“碳排放转移”效应较为普遍。净溢出板块主要分布在武汉都市圈、宜荆荆都市圈和长株潭城市群外围城市,处于网络核心圈层位置;净受益板块主要分布在环鄱阳湖城市群东部,“碳排放避难所”效应显著。经纪人板块集中在环鄱阳湖城市群西部,板块间表现出“净溢出板块→经纪人板块→净受益板块”的碳排放传递路径,呈现出明显的“梯度转移”特征。双向溢出板块主要位于长江中游城市群西部,对净溢出和净受益板块都存在碳排放的空间溢出。研究期内,净溢出板块的成员虽然有所变化但总数保持不变,经纪人板块的成员有所增加,而净受益和双向溢出板块的成员均减少,环鄱阳湖城市群的“碳排放避难所”效应有所弱化。③地理邻近关系能够促进城市间碳排放关联关系的建立,而城市间的经济集聚程度、土地利用强度和经济发展水平上的互补性,促使碳排放空间关联网络随着城市间分工与协作的加强而呈现出千里“碳缘”一线牵的特点。

多源视频数据驱动的舰面保障作业三维重建

针对现有公开的航母舰面保障作业视频数据碎片化、相机角度多变、像素质量低下等问题,提出一种多源视频数据驱动的舰面保障作业三维重建方法.该方法首先对视频中对象轨迹方向进行预测,并采用联合外观和方向相似度的跟踪方法实现视频数据的多目标轨迹跟踪;然后基于跟踪结果,依次采用相似度关联、特征轨迹分割、轨迹聚类提取多源数据中的保障作业对象并进行关联与融合;最后对融合得到的轨迹进行逼真三维重建.在MOT20数据集和舰面保障作业公开视频数据上,实验和用户调研数据的t检验结果表明,采用所提方法有99.5%的把握得到可信的舰面保障作业三维重建效果.

数据挖掘技术在管理信息系统中的应用

阐述数据挖掘技术的原理和算法,包括分类、聚类、关联规则挖掘,探讨数据挖掘技术在管理信息系统中的应用。通过数据挖掘技术,企业可以识别风险和异常情况,从而提供预测和决策支持。分析数据挖掘技术在管理信息系统中的挑战和发展方向。

考虑关联波段特性的光谱相似图像分类方法

光谱相似图像分类性能过差会增加光谱信息冗余度,降低地物勘探与军事防御等多种领域的光谱探测效率。为了多元素匀质区分光谱信息与光谱曲线,提出考虑关联波段特性的光谱相似图像分类方法。该方法首先利用光谱匹配消除光谱相似图像白色光源过曝现象。然后提取优化图像的关联波段,并将其作为聚类特征输入支持向量机中。最后根据支持向量机的输出结果,实现光谱相似图像分类。实验结果表明,所提方法分类结果清晰度较高,分类误差或像素块填色错误小,混淆矩阵中同行同列矩形块的分类精度较高。

我国矿产资源综合利用现状评估与发展路径

我国各类矿产资源储量规模和结构不均衡,大型单一富矿不足,共伴生、低品位矿多,这种资源禀赋特点决定了必须走综合利用的发展路线和模式。经过多年努力,我国已形成全球最完整的矿产资源勘查、采选、冶炼、加工和应用产业体系,综合开发利用水平显著提升,油气采收率高位平稳,煤炭平均开采回采率达到70%,铁矿开采回采率和选矿回收率保持在80%左右,有色金属矿种采选指标稳中有升,尾矿废石综合利用步伐加快,缓解了生态环境压力。我国矿产资源供应能力基本保持稳定,为经济社会发展和平稳运行发挥了重要保障作用。近年来,矿产资源供需矛盾依然十分突出,部分矿产增储上产乏力或有下降趋势,产业链上游压力增大,大力提升矿产资源综合利用水平是保障我国资源安全供给的必然选择,也是推进矿业领域生态文明建设和高质量发展的必由之路,更是培育发展矿业新质生产力的重要载体和抓手。本文较为客观地评价了我国矿产资源综合利用基本现状,分析了存在的问题与挑战,认为我国矿产资源综合利用潜力大、前景好,并提出了全面构建矿产资源综合利用五大支撑体系的发展路径。

基于数据挖掘探析广东省名中医洪敏教授治疗失眠用药规律

[目的]探讨广东省名中医洪敏教授治疗失眠的用药规律。[方法]收集整理洪敏教授临床治疗失眠的处方,使用中医传承辅助平台,对处方的用药频次、四气五味归经以及组方规律、潜在新方等进行数据挖掘和分析。[结果]收集筛选出方剂共509条,中药共180味,使用频次前5位的中药依次为炒酸枣仁、法半夏、炙甘草、茯苓、黄芩,高频药物组合有法半夏-炒酸枣仁、炙甘草-法半夏、法半夏-茯苓、炙甘草-炒酸枣仁、茯苓-炒酸枣仁、法半夏-黄芩、陈皮-茯苓等,并得到7个潜在新方组合。[结论]洪敏教授治疗失眠用药以疏肝安神、清热燥湿、健脾化痰为主,临床中注重五运六气思想结合六经辨证理论,并善于灵活运用脏腑补泻理论,多脏并调,注重结合体质、运气、病机,司人、司天、司病,综合调治,使阴阳调和,失眠得愈。