There has been a welcome recent shift towards taking account of the views of those who have traditionally been seen as lacking competence, including those with learning disabilities. Innovative methods have been devised to help people express their views and research demonstrates that people with learning disabilities can be taught this skill. However, none of this work has involved people with profound and multiple learning disabilities and serious doubts have been raised about the extent to which it is possible to ascertain the views of this group. Those operating at a preintentional level may not express, or have, views in the usually understood sense. Methods which attempt to ascertain the views of this group are highly inferential and it is often only possible to infer immediate preferences. It is important that the limitations of such methods are acknowledged. A case study is used to demonstrate that, in relation to major life decisions, taking account of a wide range of assessment information may give a clearer picture of the preferences of someone with profound and multiple learning disabilities than subjective interpretations of their behaviour or proxies. This should be combined with a focus on teaching the person so that they acquire as much control over their own lives as possible.

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Abstract. The effect of weather elements on the incidence of different types of crime has been the focus of a number of research studies. However, the detailed geographical dimension of this relationship has been largely ignored. The aim of this paper is to broaden the research on weather and crime to consider the effect of weather parameters on the spatial arrangement of crime within an urban area of the UK. A novel combination of techniques that are capable of both evaluating statistically and visualising geographically the effect of weather variables on the incidence of one type of crime, namely calls for police service for disorder or disturbances, is presented. These techniques are examined in relation to the theories that have traditionally been put forward to explain such trends. We conclude that, in our study area, both temperature and humidity exert significant effects on the spatial patterning of incidents of disorder or disturbances. Rainfall, wind speed, and wind direction were found not to have a significant effect for this type of call for service. More research is needed to see how transferable these findings are to other geographical areas with different climatic regimes.

BackgroundCervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.MethodScene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.ResultsThe evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.ConclusionsThe major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.