Optimal duration of magazine promotions

The planning of promotions and other marketing events frequently requires manufacturers to make decisions about the optimal duration of these activities. Yet manufacturers often lack the support tools for decision making. We assume that customer decisions at the aggregated level follow a state-dependent Markov process. On the basis of the expected economic return associated with dynamic response to stimuli, we determine the ideal length of marketing events using dynamic programming optimization and apply the model to a complex promotion event. Results suggest that this methodology could help managers in the publishing industry to plan the optimal duration of promotion event

A view from the industrial age

Like the constructivist approach to the history of science, the new history of reading has shifted attention from disembodied ideas to the underlying material culture and the localized practices by which it is apprehended. By focusing on the complex embodied processes by which readers make sense of printed objects, historians of reading have provided new insights into the manner in which meaning is both made and contested. In this brief account I argue that these insights are particularly relevant to historians of science, first, because practices of reading, like those of experiment and fieldwork, are constitutive of scientific knowledge, and, second, because attention to the history of reading provides important evidence of the multifaceted and uneven contest for meaning that occurs when science is mobilized in popular culture. The essay concludes by considering some of the surprisingly abundant sources of available evidence from which a history of scientific reading might be constructed for the modern era

OPTIMAL DURATION OF MAGAZINE PROMOTIONS

The planning of promotions and other marketing events frequently requires manufacturers to make decisions about the optimal duration of these activities. Yet manufacturers often lack the support tools for decision making. We assume that customer decisions at the aggregated level follow a state-dependent Markov process. On the basis of the expected economic return associated with dynamic response to stimuli, we determine the ideal length of marketing events using dynamic programming optimization and apply the model to a complex promotion event. Results suggest that this methodology could help managers in the publishing industry to plan the optimal duration of promotion events.

JNCHC Front & Back Matter, Vol. 20, No 2, Fall/Winter 2019

Cover Masthead Contents Call for Papers, Editorial Policy, & Submission Guidelines Dedication -- Art L. Spisak About the Authors About the NCHC Monograph Series Order form Back cove

Adaptive Epidemic Dynamics in Networks: Thresholds and Control

Theoretical modeling of computer virus/worm epidemic dynamics is an important problem that has attracted many studies. However, most existing models are adapted from biological epidemic ones. Although biological epidemic models can certainly be adapted to capture some computer virus spreading scenarios (especially when the so-called homogeneity assumption holds), the problem of computer virus spreading is not well understood because it has many important perspectives that are not necessarily accommodated in the biological epidemic models. In this paper we initiate the study of such a perspective, namely that of adaptive defense against epidemic spreading in arbitrary networks. More specifically, we investigate a non-homogeneous Susceptible-Infectious-Susceptible (SIS) model where the model parameters may vary with respect to time. In particular, we focus on two scenarios we call semi-adaptive defense and fully-adaptive} defense, which accommodate implicit and explicit dependency relationships between the model parameters, respectively. In the semi-adaptive defense scenario, the model's input parameters are given; the defense is semi-adaptive because the adjustment is implicitly dependent upon the outcome of virus spreading. For this scenario, we present a set of sufficient conditions (some are more general or succinct than others) under which the virus spreading will die out; such sufficient conditions are also known as epidemic thresholds in the literature. In the fully-adaptive defense scenario, some input parameters are not known (i.e., the aforementioned sufficient conditions are not applicable) but the defender can observe the outcome of virus spreading. For this scenario, we present adaptive control strategies under which the virus spreading will die out or will be contained to a desired level.Comment: 20 pages, 8 figures. This paper was submitted in March 2009, revised in August 2009, and accepted in December 2009. However, the paper was not officially published until 2014 due to non-technical reason

Special Libraries, February 1960

Volume 51, Issue 2https://scholarworks.sjsu.edu/sla_sl_1960/1001/thumbnail.jp

Special Libraries, April 1962

Volume 53, Issue 4https://scholarworks.sjsu.edu/sla_sl_1962/1003/thumbnail.jp

Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction

Global Navigation Satellite Systems (GNSS) are systems that continuously acquire data and provide position time series. Many monitoring applications are based on GNSS data and their efficiency depends on the capability in the time series analysis to characterize the signal content and/or to predict incoming coordinates. In this work we propose a suitable Network Architecture, based on Long Short Term Memory Recurrent Neural Networks, to solve two main tasks in GNSS time series analysis: denoising and prediction. We carry out an analysis on a synthetic time series, then we inspect two real different case studies and evaluate the results. We develop a non-deep network that removes almost the 50% of scattering from real GNSS time series and achieves a coordinate prediction with 1.1 millimeters of Mean Squared Error

The evaluation of citation distributions.

This paper reviews a number of recent contributions that demonstrate that a blend of welfare economics and statistical analysis is useful in the evaluation of the citations received by scientific papers in the periodical literature. The paper begins by clarifying the role of citation analysis in the evaluation of research. Next, a summary of results about the citation distributions’ basic features at different aggregation levels is offered. These results indicate that citation distributions share the same broad shape, are highly skewed, and are often crowned by a power law. In light of this evidence, a novel methodology for the evaluation of research units is illustrated by comparing the high- and low-citation impact achieved by the U.S., the European Union, and the rest of the world in 22 scientific fields. However, contrary to recent claims, it is shown that mean normalization at the sub-field level does not lead to a universal distribution. Nevertheless, among other topics subject to ongoing research, it appears that this lack of universality does not preclude sensible normalization procedures to compare the citation impact of articles in different scientific fields.