应用免疫策略的多传感器决策融合设计

使用基于改进型人工免疫策略的优化算法,在统计相关的观测条件下,对固定融合规则的基于奈曼-皮尔逊准则的分布式多传感器决策融合系统进行优化设计。算法首先以筛选算子进行预搜索缩小范围,然后使用人工免疫策略方法进行全局搜索,计算过程无须使用目标函数的导数信息。对分布式多传感器决策融合系统的优化设计结果表明,优化算法在收敛性和精度上均优于传统梯度算法,并在此基础上对不同信号期望值下的最优融合规则进行了讨论。     

基于免疫原理的控制器参数整定方法

基于免疫原理,建立了一个基于免疫机制进行控制器参数整定的数学模型。给出了参数整定问题中抗原、抗体和亲和力的定义,对基于免疫原理的控制器参数整定过程进行了详细的描述。最后以PID的参数整定为例,控制一个高阶系统,大延迟系统和非最小相位系统。并与Z-N,MOMI等算法的整定结果进行了对比实验,结果表明该方法对控制器的参数整定是有效的。     

Putting practice at the heart of social work education: can practice skills be reliably graded by different markers in child and family social work contexts?

The Frontline programme is a social work qualification route, in England, which began in 2014. Students are based in statutory child and family contexts supported by an academic staff member and practice educator. The assessment strategy on the programme includes seven graded observations of students engaging in social work, marked by both staff. This paper investigates reliability of grading of direct practice between different markers on the programme. It reports findings for 30 recordings of direct observations of practice that were graded during the first cohort. These included observations graded by an academic and by a practice educator. Each was independently then graded by an academic, blind to the original score or who marked it. An acceptable level of reliability was found between the independent grader and the first mark (r = .621). In general the level of agreement was higher between the independent grader (a social work academic) and academics. In comparison, practice educators tended to give higher grades to students. Nonetheless, overall the reliability of marking suggests it is possible to agree on marks for students, which points to the potential for grading of practice to be more widely used in social work education.     

Improving the biomarker diagnostic capacity via functional transformations

The use of the area under the receiver-operating characteristic, ROC, curve (AUC) as an index of diagnostic accuracy is overwhelming in fields such as biomedical science and machine learning. It seems that a larger AUC value has become synonymous with a better performance. The functional transformation of the marker values has been proposed in the specialized literature as a procedure for increasing the AUC and therefore the diagnostic accuracy. However, the classification process is based on some regions (classification subsets) which support the decision made; one subject is classified as positive if its marker is within this region and classified as negative otherwise. In this paper we study the capacity of improving the classification performance of univariate biomarkers via functional transformations and the impact of this transformation on the final classification regions based on a real-world dataset. Particularly, we consider the problem of determining the gender of a subject based on the Mode frequency of his/her voice. The shape of the cumulative distribution function of this characteristic in both the male and the female groups makes the resulting classification problem useful for illustrating the differences between having useful diagnostic rules and obtaining an optimal AUC value. Our point is that improving the AUC by means of a functional transformation can produce classification regions with no practical interpretability. We propose to improve the classification accuracy by making the selection of the classification subsets more flexible while preserving their interpretability. Besides, we provide different graphical approximations which allow us a better understanding of the classification problem.     

The relationship between circulating testosterone and inflammatory cytokines in men

Testosterone is the predominant gonadal androgen in men. Low testosterone levels are found to be associated with an increased in metabolic risk and systematic inflammation. Since adipose tissue is a source of inflammatory cytokines, testosterone may regulate inflammation by acting on adipose tissue. This review aimed to explore the role of testosterone in inflammation and its mechanism of action. Both animal studies and human studies showed that (1) testosterone deficiency was associated with an increase in pro-inflammatory cytokines; (2) testosterone substitution reduced pro-inflammatory cytokines. The suppression of inflammation by testosterone were observed in patients with coronary artery disease, prostate cancer and diabetes mellitus through the increase in anti-inflammatory cytokines (IL-10) and the decrease in pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α). Despite these, some studies also reported a non-significant relationship. In conclusion, testosterone may possess anti-inflammatory properties but its magnitude is debatable. More evidence is needed to validate the use of testosterone as a marker and in the management of chronic inflammatory diseases.     

免疫系统的主组织相容复杂性及其应用

在模拟免疫系统的主组织相容复杂性的基础上,结合模糊逻辑与扩展阴性选择算法提出了一个基于免疫系统主组织相容复杂性的模糊逻辑综合决策算法,并用该算法构建了一个实际的基于网络的入侵检测系统。该算法应用高效的扩展阴性选择算法作出第一次网络流量检测,当网络数据异常特征明显时,能直接检测出入侵。若其不能准确地识别待分析数据,则利用具有检测结果准确优势的规则匹配算法作出二次检测,最后结合两次检测结论用模糊逻辑决策模型做出综合决策。     

基于危险理论的网络风险评估模型

为有效评估网络信息系统的网络风险,提出了一种新的基于危险理论的风险评估模型(DTREM)。在给出网络活动中自体、非自体、免疫细胞的定义,建立由记忆检测器、成熟检测器、未成熟检测器集合构成的入侵检测子模型后,进一步给出了基于危险理论的网络风险定量计算子模型。利用该模型,可以实时定量地计算出主机和网络当前所面临攻击的各类攻击和总体网络风险强度,理论分析和实验结果均表明,该模型为实时网络安全风险评估提供了一种有效的新途径。     

基于免疫系统的非常规突发事件风险识别模型

非常规突发事件的风险识别是应急管理的核心科学问题之一,由于理论研究和实践应用中常常面临“无真可仿”的局面,使得风险识别成为亟待解决的难题.本文综合运用免疫学理论、复杂适应系统涌现理论和计算实验技术,提出了风险识别器抗体向非常规突发事件抗原进化的理论,建立了风险识别的亲和度、抗原清除效果、风险识别器数量和能量等识别标准体系及其模型,采用识别亚非常规突发事件和各类事件组合关系以及它们的涌现来无限接近地认识非常规突发事件的研究思路,通过计算实验和算例分析,验证了研究成果的科学性和可行性.     

组织免疫行为和机制研究

借鉴生物学领域的研究成果,从组织免疫这一新的视角研究企业的适应性问题.提出了组织免疫行为的理论维度,即将组织免疫分为组织非特异性免疫和组织特异性免疫2个维度,并将组织非特异性免疫分解为组织结构、制度规则和组织文化3个要素,将组织特异性免疫分解为组织监视、组织防御和组织记忆3个要素.阐述了组织免疫行为机制模型,对研究结果进行讨论和总结,并指出未来的研究方向.     

Combination of multiple functional markers to improve diagnostic accuracy

Combination of multiple biomarkers to improve diagnostic accuracy is meaningful for practitioners and clinicians, and are attractive to lots of researchers. Nowadays, with development of modern techniques, functional markers such as curves or images, play an important role in diagnosis. There exists rich literature developing combination methods for continuous scalar markers. Unfortunately, only sporadic works have studied how functional markers affect diagnosis in the literature. Moreover, no publication can be found to do combination of multiple functional markers to improve the diagnostic accuracy. It is impossible to apply scalar combination methods to the multiple functional markers directly because of infinite dimensionality of functional markers. In this article, we propose a one-dimension scalar feature motivated by square loss distance, as an alternative of the original functional curve in the sense that, it can retain information to the most extent. The square loss distance is defined as the function of projection scores generated from functional principal component decomposition. Then existing variety of scalar combination methods can be applied to scalar features of functional markers after dimension reduction to improve the diagnostic accuracy. Area under the receiver operating characteristic curve and Youden index are used to assess performances of various methods in numerical studies. We also analyzed the high- or low- hospital admissions due to respiratory diseases between 2010 and 2017 in Hong Kong by combining weather conditions and media information, which are regarded as functional markers. Finally, we provide an R function for convenient application.