Gravity flow test comparisons for mildly thick consistency

Simple measurement tools that provide objective information about the consistency of thickened liquids have received increasing attention given possible application to clinical practice for dysphagia. The International Dysphagia Diet Standardization Initiative (IDDSI) advocates the use of the IDDSI Flow Test for measuring thickness and verifying a prescribed level of consistency. This study compares gravity flow test measurements taken with two 10-ml syringes of starch and gum-thickened samples prepared to a mildly thick consistency using product label information. One of the 10-ml syringes met IDDSI specification and the other 10-ml syringe did not. Thickened samples also were measured using the line spread apparatus. The results showed that two syringes yielded significantly different gravity flow test measurements, often resulting in changes to the classification level of thickness. There was no effect of thickening agent. Line spread measurements did not vary from one another. Study findings indicate the importance of continued testing with the IDDSI Flow Test and education/awareness about the use of the IDDSI reference syringe. An essential aspect about the use of thickened liquids in dysphagia management is that beverage preparations represent their prescribed level of modification. The findings of this study indicate the importance of following syringe specifications when conducting gravity flow testing using the IDDSI methodology. The implication is that the wrong 10-ml syringe could result in errant clinical decisions in determining the accuracy of texture modifications, resulting in care providers who are more likely to over or under thicken modifications to achieve a targeted level of thickness.     

GFP内核加速LAN/SAN协议在SONET中的传输

在电信行业中，SONET网络无处不在，被广泛应用于语音和数据传输。目前的标准协议主要针对传输层，如针对语音和数据的ATM协议以及针对数据传输的HDLC或PPP协议。然而，因为这些协议都不是专门针对通过SONET／SDH网络进行数据传输而开发的，所以都会引起带宽效率的降低。     

Collaborative versus directive interventions in the treatment of eating disorders: Implications for care providers.

Given that individuals with eating disorders, are typically ambivalent about changing their eating patterns, what approach is most helpful in working with this challenging group? This research compared the responses of clients with eating disorders and those of care providers to written clinical vignettes. All participants rated collaborative interventions as more acceptable and more likely to produce positive clinical outcomes than directive interventions. In addition, clients who were least ready for change rated directive interventions as less acceptable and less likely to produce adherence than did clients who were more ready. Despite participants' clear preference for collaborative interventions, directive interventions were rated as equally likely to occur. The implications of participant preferences and reasons that these preferences may not be reflected in actual clinical practice are addressed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Selective harvesting over networks

Active search on graphs focuses on collecting certain labeled nodes (targets) given global knowledge of the network topology and its edge weights (encoding pairwise similarities) under a query budget constraint. However, in most current networks, nodes, network topology, network size, and edge weights are all initially unknown. In this work we introduce selective harvesting, a variant of active search where the next node to be queried must be chosen among the neighbors of the current queried node set; the available training data for deciding which node to query is restricted to the subgraph induced by the queried set (and their node attributes) and their neighbors (without any node or edge attributes). Therefore, selective harvesting is a sequential decision problem, where we must decide which node to query at each step. A classifier trained in this scenario can suffer from what we call a tunnel vision effect: without any recourse to independent sampling, the urge to only query promising nodes forces classifiers to gather increasingly biased training data, which we show significantly hurts the performance of active search methods and standard classifiers. We demonstrate that it is possible to collect a much larger set of targets by using multiple classifiers, not by combining their predictions as a weighted ensemble, but switching between classifiers used at each step, as a way to ease the tunnel vision effect. We discover that switching classifiers collects more targets by (a) diversifying the training data and (b) broadening the choices of nodes that can be queried in the future. This highlights an exploration, exploitation, and diversification trade-off in our problem that goes beyond the exploration and exploitation duality found in classic sequential decision problems. Based on these observations we propose D\(^3\)TS, a method based on multi-armed bandits for non-stationary stochastic processes that enforces classifier diversity, which outperforms all competing methods on five real network datasets in our evaluation and exhibits comparable performance on the other two.     

Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks

Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks.

     

Weighing the Evidence of Common Beliefs in Obesity Research

Obesity is a topic on which many views are strongly held in the absence of scientific evidence to support those views, and some views are strongly held despite evidence to contradict those views. We refer to the former as “presumptions” and the latter as “myths.” Here, we present nine myths and 10 presumptions surrounding the effects of rapid weight loss; setting realistic goals in weight loss therapy; stage of change or readiness to lose weight; physical education classes; breastfeeding; daily self-weighing; genetic contribution to obesity; the “Freshman 15”; food deserts; regularly eating (versus skipping) breakfast; eating close to bedtime; eating more fruits and vegetables; weight cycling (i.e., yo-yo dieting); snacking; built environment; reducing screen time in childhood obesity; portion size; participation in family mealtime; and drinking water as a means of weight loss. For each of these, we describe the belief and present evidence that the belief is widely held or stated, reasons to support the conjecture that the belief might be true, evidence to directly support or refute the belief, and findings from randomized controlled trials, if available. We conclude with a discussion of the implications of these determinations, conjecture on why so many myths and presumptions exist, and suggestions for limiting the spread of these and other unsubstantiated beliefs about the obesity domain.     

What about the kids? Parenting and child adjustment in the context of couple therapy.

Little research examines parenting and children's adjustment when couples engage in therapy. We examined how couples with and without children improve with couple therapy and whether they also report improvements in parenting and child adjustment. With up to twenty six sessions of couple therapy, 134 couples, 68 of whom had children, showed improved marital satisfaction during treatment, which was ultimately maintained over the 2-year follow-up, regardless of whether they had children. Couples married relatively longer, both with and without children, evidenced greater improvement. Couples with children reported less conflict over child rearing and better child adjustment during treatment, but only improvements in the former were maintained. Conflict over child rearing mediated the relationship between marital distress and child adjustment over therapy and the 2-year follow-up. These preliminary results suggest that couples in therapy may decrease their conflict over child rearing during treatment and they may be able to maintain these gains for at least two years following treatment; moreover, over the course of treatment, this decreased conflict is tied to improved child adjustment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A sweep-line algorithm for spatial clustering

This paper presents an agglomerative hierarchical clustering algorithm for spatial data. It discovers clusters of arbitrary shapes which may be nested. The algorithm uses a sweeping approach consisting of three phases: sorting is done during the preprocessing phase, determination of clusters is performed during the sweeping phase, and clusters are adjusted during the post processing phase. The properties of the algorithm are demonstrated by examples. The algorithm is also adapted to the streaming algorithm for clustering large spatial datasets.