Deregulation in Maine: The Why and How

Maine\u27s retail electricity rates - although (on average) the lowest in New England - were in 1997 (the year Maine passed its restructuring legislation) nearly 40 percent higher than the national average. Industrial customers paid rates 40.4 percent above the U.S. average; commercial 36.9 percent more, and residents (i.e., voters) a whopping 51.3 percent more than residential consumers across the country. Place the risks of investment decisions in the market, where they belong. Technological developments (paralleled by changes in national regulatory policy) have erased the natural monopoly nature of the general portion of the electricity industry. Since regulators are not uniquely qualified to make the best decision on generation investment, why not let the private sector do it? Ratepayers will no longer have to pay for the risk of inefficient decisions. Let investors take the risk - and reap the rewards, if done properly (or if they\u27re lucky)

“Transfer Talk” in Talk about Writing in Progress: Two Propositions about Transfer of Learning

This article tracks the emergence of the concept of “transfer talk”—a concept distinct from transfer of learning—and teases out the implications of transfer talk for theories of transfer of learning. The concept of transfer talk was developed through a systematic examination of 30 writing center transcripts and is defined as “the talk through which individuals make visible their prior learning (in this case, about writing) or try to access the prior learning of someone else.” In addition to including a taxonomy of transfer talk and analysis of which types occur most often in this set of conferences, this article advances two propositions about the nature of transfer of learning: (1) transfer of learning may have an important social, even collaborative, component and (2) although meta-awareness about writing has long been recognized as valuable for transfer of learning, more automatized knowledge may play an important role as well

IN VIVO MEASUREMENT OF RAT SKELETAL MUSCLE OXYGEN CONSUMPTION FOLLOWING BRIEF PERIODS OF ISCHEMIA WITH REPERFUSION AS ASSESSED BY PHOSPHORESCENCE QUENCHING MICROSCOPY

Brief periods of skeletal muscle ischemia (ischemic pre-conditioning) alter cellular metabolism in a way that confers protection over subsequent ischemic episodes. The mechanisms behind this effect have been studied indirectly through assays for the byproducts of ATP synthesis and in vitro studies of cellular signaling cascades and ROS generation. There have been no direct, in vivo assessments of the changes in respiration during reperfusion. We employed phosphorescence quenching microscopy in conjunction with a flow-arrest technique to assess the influences of external, pressure-induced 1- to 10-min focal ischemia on interstitial oxygenation (PISFO2) and the consumption of oxygen (VO2) in spinotrapezius muscles of Sprague-Dawley rats. During reperfusion following an ischemic period VO2 was measured by the rate of PISFO2 decline during brief, serial flow-arrest compressions. Our tests of this intermittent compression technique indicate that 5 s of flow-arrest followed by 15 s of flow restoration allow measurement of VO2 without compromising baseline or reperfusion recovery of PISFO2. There was a steady rise in VO2 during early reperfusion which was correlated with increasing ischemic durations. Treatment with cyanide confirmed that at least some of this increase was due to an upregulation of cytochrome c oxidase activity. Nitric oxide (NO) suppressed VO2 during rest and reperfusion, while L-NAME did not influence respiration under normoxic conditions. L-NAME produced a significant rise in VO2 under hypoxic conditions following 10 minutes of ischemia, indicating a greater role of NO in the regulation of respiration during low PISFO2 conditions. We conclude that physiological levels of NO regulate mitochondrial respiration during hypoxia and confirm that pharmacological elevation of [NO] reduces VO2 in a manner consistent with the ischemic pre-conditioning effect

An Empirical Demonstration of the Existence of Measurement Dependence in the Results of a Meta-Analysis

Objective: Findings from meta-analytic studies that use standardized mean differences (SMDs) may be overly dependent on the original measures that were used to generate SMDs. This may be particularly true when measures have arbitrary metrics or when measures fail to meet measurement equivalence. We test the hypothesis that in such cases, meta-analytic results may vary significantly— statistically and practically—as a function of the measures used to derive SMDs. Methods: We conducted 5 secondary random-effects meta-analyses of SMDs—each under a different measurement scenario—from a published meta-analysis comparing the efficacy of cognitive–behavioral therapy with that of reminiscence therapy for depression in older adults. In each scenario, SMDs were based on scores from measures with arbitrary metrics, some of which failed to meet measurement equivalence. Results: Consistent with the hypothesis, meta-analysis results differed significantly—statistically and practically—between the measurement scenarios under conditions of measurement nonequivalence. Conclusions: Results of meta-analyses involving measures with arbitrary metrics may depend on the measures that the SMDs are based on when measurement equivalence fails to hold. Inferences concerning the relative efficacy of different treatments can be measurement dependent

Mining usage data for adaptive personalisation of smartphone based help-on-demand services

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively