The effect of the "tendency to report injuries" on minor accident statistics: A follow up study

Thesis submitted to Univ. of Tennessee, Knoxville. Statistical methods were used in an attempt to determine the effect of the tendency to report accidents on the systemic variance in minor injury data over a 6-yr period. The population surveyed included 245 journeymen of 9 skilled craft groups continuously employed during the period. Results indicate that injury and non- injury visits to the dispensary were relatively stable during the 6-yr period and that there was a significant positive relation between non-occupational and injury visits during the period of study. (C.H.

Speed scaling to manage temperature

We consider speed scaling algorithms to minimize device temperature subject to the constraint that every task finishes by its deadline. We assume that the device cools according to Fouriers law. We show that the optimal offline algorithm proposed in [18] for minimizing total energy (that we call YDS) is an O(1)-approximation with respect to temperature. Tangentially, we observe that the energy optimality of YDS is an elegant consequence of the well known KKT optimality conditions. Two online algorithms, AVR and Optimal Available, were proposed in [18] in the context of energy management. It was shown that these algorithms were O(1)-competitive with respect to energy in [18] and [2]. Here we show these algorithms are not O(1)-competitive with respect to temperature. This demonstratively illustrates the observation from practice that power management techniques that are effective for managing energy may not be effective for managing temperature. We show that the most intuitive temperature management algorithm, running at such a speed so that the temperature is constant, is surprisingly not O(1)-competitive with respect to temperature. In contrast, we show that the online algorithm BKP, proposed in [2], is O(1)-competitive with respect to temperature. This is the first O(1)-competitiveness analysis with respect to temperature for an online algorithm

The Link Between Temporal Attention and Emotion: A Playground for Psychology, Neuroscience, and Plausible Artificial Neural Networks

In this paper, we will address the endeavors of three disciplines, Psychology, Neuroscience, and Artificial Neural Network (ANN) modeling, in explaining how the mind perceives and attends information. More precisely, we will shed some light on the efforts to understand the allocation of attentional resources to the processing of emotional stimuli. This review aims at informing the three disciplines about converging points of their research and to provide a starting point for discussion