This paper draws together empirical work that has been produced by the authors in two different autistic spaces: the Swedish magazine Empowerment produced by and aimed at adults with autism, and English-speaking autistic communities online. While the two points of data collection are quite different, there are important points of commonality that enable us to explore central issues concerning autistic and neurotypical space and the meanings assigned to these in different contexts. The paper aims to introduce the notion of social geographies of autism, based on talks among adults with autism and a social movement to promote autistic identities, giving examples from our previous work that has spanned both online and off-line spaces. Key issues discussed in the paper include a focus on autistic political platforms and the carving out of both social and political spaces for people with autism. In doing so, neuro-separate and neuro-shared spaces must be negotiated. 相似文献
Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.
In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems. 相似文献