High detail stationary optimization models for gas networks: validation and results

Due to strict regulatory rules in combination with complex nonlinear physics, major gas network operators in Germany and Europe face hard planning problems that call for optimization. In part 1 of this paper we have developed a suitable model hierarchy for that purpose. Here we consider the more practical aspects of modeling. We validate individual model components against a trusted simulation tool, give a structural overview of the model hierarchy, and use its large variety of approximations to devise robust and efficient solution techniques. An extensive computational study demonstrates the suitability of our models and techniques for previously unsolvable problems in gas network planning.     

The impact of the number of parallel warehouses on total inventory

In the strategic design of a distribution system, the right number of stock points for the various products is an important question. In the past decade, a strong trend in the consumer goods industry led to centralizing the inventory in a single echelon consisting of a few parallel warehouses or even a single distribution center for a Europe-wide distribution system. Centralizing inventory is justified by the reduction in total stock which mostly overcompensates the increasing transportation cost. The effect of centralization is usually described by the “Square Root Law”, stating that the total stock increases with the square root of the number of stock points. However, in the usual case where the warehouses are replenished in full truck loads and where a given fill rate has to be satisfied, the Square Root Law is not valid. This paper explores that case. It establishes functional relationships between the demand to be served by a warehouse and the necessary safety and cycle stock for various demand settings and control policies, using an approximation of the normal loss function and its inverse. As a consequence, the impact of the number of parallel warehouses on the total stock can be derived. The results can be used as tools in network design models.     

Retrieval during learning facilitates subsequent memory encoding.

In multiple-list learning, retrieval during learning has been suggested to improve recall of the single lists by enhancing list discrimination and, at test, reducing interference. Using electrophysiological, oscillatory measures of brain activity, we examined to what extent retrieval during learning facilitates list encoding. Subjects studied 5 lists of items in anticipation of a final cumulative recall test and did either a retrieval or a no-retrieval task between study of the lists. Retrieval was from episodic memory (recall of the previous list), semantic memory (generation of exemplars from an unrelated category), or short-term memory (2-back task). Behaviorally, all 3 forms of retrieval enhanced recall of both previously and subsequently studied lists. Physiologically, the results showed an increase of alpha power (8–14 Hz) from List 1 to List 5 encoding when no retrieval activities were interpolated but no such increase when any of the 3 retrieval activities occurred. Brain–behavior correlations showed that alpha-power dynamics from List 1 to List 5 encoding predicted subsequent recall performance. The results suggest that, without intermittent retrieval, encoding becomes ineffective across lists. In contrast, with intermittent retrieval, there is a reset of the encoding process for each single list that makes encoding of later lists as effective as encoding of early lists. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Alliance in individual psychotherapy.

This article reports on a research synthesis of the relation between alliance and the outcomes of individual psychotherapy. Included were over 200 research reports based on 190 independent data sources, covering more than 14,000 treatments. Research involving 5 or more adult participants receiving genuine (as opposed to analogue) treatments, where the author(s) referred to one of the independent variables as “alliance,” “therapeutic alliance,” “helping alliance,” or “working alliance” were the inclusion criteria. All analyses were done using the assumptions of a random model. The overall aggregate relation between the alliance and treatment outcome (adjusted for sample size and non independence of outcome measures) was r = .275 (k = 190); the 95% confidence interval for this value was .25–.30. The statistical probability associated with the aggregated relation between alliance and outcome is p