We introduce a new theoretical perspective for predicting effective monitoring, which involves a two-stage logic. First, we focus on individual directors, arguing that the likelihood of effective monitoring depends upon a given director possessing multiple qualities. Based on prior research that has not been previously coalesced, we set forth this baseline proposition: A director's likelihood of being an effective monitor in any given domain (say, financial matters) is greatly increased when he or she has all four of the following qualities: independence, expertise in that domain, bandwidth, and motivation. Second, we extend this quadrilateral model -or quad model -to make propositions at the board level. We argue that it is not sufficient for these four qualities to be distributed somewhere among all directors on a given board, as this leaves it likely that there are no directors who can rise to the challenging task of monitoring. We propose that having just one quad-qualified director will be more predictive of board efficacy than will be any customary board descriptors. And we posit that if a board has two or more quad-qualified directors, who can bolster and amplify each other, the company's likelihood of governance failures will be especially reduced. We discuss theoretical and practical implications and lay out a research agenda.

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We consider the problem of identifying the source location of a contaminant via analyzing changes in concentration levels observed by a sensor network in a river system. To address this problem, we propose a framework including two main steps: (i) pre-processing data; and (ii) training and testing a classification model. Specifically, we first obtain a data set presenting concentration levels of a contaminant from a simulation model, and extract numerical characteristics from the data set. Then, random forest models are generated and assessed to identify the source location of a contaminant. By using the numerical characteristics from the prior step as their inputs, the models provide outputs representing the possibility, i.e., a value between 0 and 1, of a spill event at each candidate location. The performance of the framework is tested on a part of the Altamaha river system in the state of Georgia, United States of America.

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