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Self-help and self-censorship: A self-help cultural perspective on organizational silence This paper seeks to explain silence in the workplace through an analytical perspective derived from Judith Butlers work on censorship, and in this way suggest an alternative to explanations in the existing literature on employee silence, which are often tied to the actions and motivations of the individual subject. It is thus argued that self-help books can be seen as indicative of a pervasive culture of self-improvement, which among other things promotes the absence of criticism in the workplace. The empirical point of departure for this argument is the two bestselling self-help books The secret by Rhonda Byrne and The 7 habits of highly effective people by Stephen Covey. Theoretically, the paper applies Butlers notion of ”implicit censorship” where censorship is understood as productive in the sense of being constitutive of language. Hence, in the analysis it is shown how discursive regimes in self-help literature tend to be constructed in such a way, that explicit criticism cannot emerge as a meaningful activity, and is thus implicitly censored.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.