物流运输调度问题的混沌烟花算法——基于多车型供应链

为了满足供应链物流的不同需求，考虑多种车型、车辆容量、车辆油耗、车辆最大配送距离等约束条件，以最小油耗、最短配送距离为目标，建立多车型供应链物流运输调度模型（Multi-Type Vehicle Routing Problem in Supply Chain，MTVRPSC），并提出一种混沌烟花算法求解该模型。该算法以烟花算法为核心，提出一种编解码策略实现连续空间到MTVRPSC离散空间的映射，重新定义算法的适应度函数、适应度值和适应度的比较方法，并采用混沌初始化策略和混沌搜索策略来增强算法收敛效果。实验结果表明，所提出的算法在求解MTVRPSC时具有较强的寻优能力和稳定性。     

Screening for diabetic retinopathy using computer based image analysis and statistical classification

Diabetic retinopathy is one of the most common causes of blindness in Europe. However, efficient therapies do exist. An accurate and early diagnosis and correct application of treatment can prevent blindness in more than 50% of all cases. Digital imaging is becoming available as a means of screening for diabetic retinopathy. As well as providing a high quality permanent record of the retinal appearance, which can be used for monitoring of progression or response to treatment, and which can be reviewed by an ophthalmologist, digital images have the potential to be processed by automatic analysis systems. We have described the preliminary development of a tool to provide automatic analysis of digital images taken as part of routine monitoring of diabetic retinopathy in our clinic. Various statistical classifiers, a Bayesian, a Mahalanobis, and a KNN classifier were tested. The system was tested on 134 retinal images. The Mahalanobis classifier had the best results: microaneurysms, haemorrhages, exudates, and cotton wool spots were detected with a sensitivity of 69, 83, 99, and 80%, respectively.     

Symbol Grounding or the Emergence of Symbols? Vocabulary Growth in Children and a Connectionist Net

The Symbolic Grounding Problem is viewed as a by-product of the classical cognitivist approach to studying the mind. In contrast, an epigenetic interpretation of connectionist approaches to studying the mind is shown to offer an account of symbolic skills as an emergent, developmental phenomenon. We describe a connectionist model of concept formation and vocabulary growth that auto-associates image representations and their associated labels. The image representations consist of clusters of random dot figures, generated by distorting prototypes. Any given label is associated with a cluster of random dot figures. The network model is tested on its ability to reproduce image representations given input labels alone (comprehension) and to identify labels given input images alone (production). The model implements several well-documented findings in the literature on early semantic development; the occurrence of over- and under-extension errors; a vocabulary spurt; a comprehension/production asymmetry; and a prototype effect. It is shown how these apparently disparate findings can be attributed to the operation of a single underlying mechanism rather than by invoking separate explanations for each phenomenon. The model represents a first step in the direction of providing a formal explanation of the emergence of symbolic behaviour in young children.     

Dynamic updating in DIAS-NIDDM and DIAS causal probabilistic networks

In order to evaluate in humans the safety and immunogenicity of a Pseudomonas aeruginosa vaccine composed of outer membrane proteins (OMPs), CFC-101, we carried out a phase I/IIa clinical trial in healthy male volunteers. Groups of six volunteers were immunized either subcutaneously (s.c.) or intramuscularly (i.m.) with three dosages of the vaccine three times at 7-day intervals. The vaccine was well tolerated by volunteers. Local reactions in the injection sites were generally mild and transient. Significant increases in OMP-specific antibody were observed in both route groups after vaccinations but was higher in the i.m.-immunized group, where vaccination with 0.5 or 1.0 mg doses yielded 100% seroconversion. The specificity of the induced antibodies to P. aeruginosa OMP was demonstrated by western blot analysis and immunoprecipitation assay. An increase in Clq-binding capacity and ability to confer mice protection from lethal challenges with P. aeruginosa indicated the protective efficacy of the elicited antibodies. Based on these data, we concluded that the P. aeruginosa OMP vaccine is safe and effective in humans with an optimal dose of 0.5 and 1.0 mg and that i.m. is the better route than s.c. for this vaccine.     

泰森多边形的离散蝙蝠算法求解多车场车辆路径问题

本文提出一种泰森多边形的离散蝙蝠算法求解多车场车辆路径问题(multi-depot vehicle routing problem,MDVRP).所提出算法以离散蝙蝠算法为核心,融入了一种基于多车场多车辆问题的编解码策略.所提出算法还使用基于泰森多边形的初始化策略加快算法的前期收敛速度,采用基于向量比较机制的适应度函数来控制算法收敛的方向,引入基于近邻策略和优先配送策略的局部搜索算法来提高算法的寻优能力.实验结果表明:在合理的时间耗费内,所提出的算法能有效地求解MDVRP,尤其是带配送距离约束的MDVRP;相对于对比算法,所提出的算法表现出较强的寻优能力和稳定性.     

Strategic planning of the master programme in health informatics at Aalborg University: targeting and updating the programme, to meet explicit customer needs

Education is essentially giving people new skills and qualifications to fulfil certain tasks. In planning and managing educational programmes it is crucial to know what skills and what qualifications are needed to carry out the tasks in question, not to mention the importance of knowing what tasks are relevant to carry out. The programme in health informatics at Aalborg University produces health informatics professionals. The students are developing skills in solving informatics problems in health care organisations. The programme has been running for 3 years now and to maintain the perception of the aim for the programme a number of activities have been launched. In the following, the programme will be presented, the activities to obtain information on how to keep the programme targeted and updated will be described and the changes that are going to be introduced will be outlined.     

Dynamic updating in DIAS-NIDDM and DIAS causal probabilisticnetworks

Diabetes advisory system (DIAS) is a decision support system, which has been developed to provide advice on the amount of insulin injected by subjects with insulin-dependent diabetes mellitus (IDDM). DIAS employs a temporal causal probabilistic network (CPN) to implement a stochastic model of carbohydrate metabolism. The CPN network has recently been extended to provide also advice to subjects with noninsulin-dependent diabetes mellitus (NIDDM). However, due to increased complexity and size of the extended CPN the calculations became unfeasible. The CPN network was, therefore, simplified and a novel approach employed to generate conditional probability tables. The principles of dynamic CPNs were adopted and, in combination with the method of conditioning, learning, and forecasting, were implemented in a time- and memory-efficient way. An evaluation using experimental data was carried out to compare the original and revised DIAS implementations employing data collected by patients with IDDM, and to assess the a posteriori identifiability of model parameters in patients with NIDDM     

Use of the DIAS model to predict unrecognised hypoglycaemia in patients with insulin-dependent diabetes

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism implemented in a causal probabilistic network. It handles data on insulin dose, carbohydrate intake and blood glucose concentration to predict hourly blood glucose concentrations and thus provide an indication of blood glucose values between home blood tests. DIAS was used to predict blood glucose profiles in eight patients with well-controlled insulin-dependent diabetes, who are at increased risk of hypoglycaemia (abnormally low blood glucose levels). DIAS predicted nocturnal hypoglycaemia in six patients and daytime hypoglycaemia in one patient. The occurrence of nocturnal hypoglycaemia was not recognised by the patient or suspected by their doctor but was subsequently confirmed by blood testing in five patients. It is known that unrecognised nocturnal hypoglycaemia is common in patients with well-controlled diabetes. The ability of DIAS to identify such periods of hypoglycaemia with reasonable accuracy illustrates how the advanced technology it employs may provide reliable decision support to clinicians.