A longitudinal study of body change strategies of fitness center attendees

This study evaluated predictors of strategies to change weight and muscles among men and women who attend fitness centers. A questionnaire was completed by 107 men (mean age = 39.17, SD = 13.14), and 151 women (mean age = 35.31, SD = 11.38) who regularly attended fitness centers at two points in time, one year apart. The only unique predictor of body change strategies over time for men was body dissatisfaction predicting drive for thinness; for women, body dissatisfaction predicted strategies to lose weight, drive for thinness, use of food supplements to lose weight and levels of bulimia. Media messages also predicted drive for thinness and bulimia among women. These findings would suggest that attendance body dissatisfaction is an important factor predicting other normative and health risk behaviors among fitness center attendees, particularly women at a fitness center over a 12 month period was not generally associated with adverse health risk behaviors.

Community center-based resistance training for the maintenance of glycemic control in adults with type 2 diabetes

OBJECTIVE--The purpose of this study Was to determine whether beneficial effects on glycemic control of an initial laboratory-supervised resistance training program could be sustained through a community center-based maintenance program.

RESEARCH DESIGN AND METHODS--We studied 57 overweight (BMI [greater than or equal to] 27 kg/[m.sup.2]) sedentary men and women aged 40-80 years with established (>6 months) type 2 diabetes. Initially, all participants attended a twice-weekly 2-month supervised resistance training program conducted in the exercise laboratory. Thereafter, participants undertook a resistance training maintenance program (2 times/week) for 12 months and were randomly assigned to carry this out either in a community fitness and recreation center (center) or in their domestic environment (home). Glycemic control ([HbA.sub.1c] [A1C]) was assessed at 0, 2, and 14 months.

RESULTS--Pooling data from the two groups for the 2-month supervised resistance training program showed that compared with baseline, mean A1C fell by -0.4% [95% CI -0.6 to -0.2]. Within-group comparisons showed that A1C remained lower than baseline values at 14 months in the center group (-0.4% [-0.7 to -0.03]) but not in the home group (-0.1% [-0.4 to 0.3]). However, no between-group differences were observed at each time point. Changes in A1C during the maintenance period were positively associated with exercise adherence in the center group only.

CONCLUSIONS--Center-based but not home-based resistance training was associated with the maintenance of modestly improved glycemic control from baseline, which was proportional to program adherence. Our findings emphasize the need to develop and test behavioral methods to promote healthy lifestyles including increased physical activity in adults with type 2 diabetes.

Energy-efficient management of data center resources for cloud computing : a vision, architectural elements, and open challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.

Fitting soft center-near design multifocal lenses

The recent launch of the Air Optix Aqua Multifocal lens (Ciba Vision) means that there are three silicone hydrogel (SiHy) multifocal lenses available. The benefits of SiHy materials have been well documented (Dumbleton, 2006), but until recently they weren't available in multifocal designs. With an aging population, it's likely that a large number of contact lens wearers are approaching presbyopia, which will result in a dramatic increase in demand for multifocal lenses.

Using social network analysis to identify key child care center staff for obesity prevention interventions: a pilot study

Introduction. Interest has grown in how systems thinking could be used in obesity prevention. Relationships between key actors, represented by social networks, are an important focus for considering intervention in systems. Method. Two long day care centers were selected in which previous obesity prevention programs had been implemented. Measures showed ways in which physical activity and dietary policy are conversations and actions transacted through social networks (interrelationships) within centers, via an eight item closed-ended social network questionnaire. Questionnaire data were collected from (17/20; response rate 85%) long day care center staff. Social network density and centrality statistics were calculated, using UCINET social network software, to examine the role of networks in obesity prevention. Results. “Degree” (influence) and “betweeness” (gatekeeper) centrality measures of staff inter-relationships about physical activity, dietary, and policy information identified key players in each center. Network density was similar and high on some relationship networks in both centers but markedly different in others, suggesting that the network tool identified unique center social dynamics. These differences could potentially be the focus of future team capacity building. Conclusion. Social network analysis is a feasible and useful method to identify existing obesity prevention networks and key personnel in long day care centers.

Type-aware task placement in geo-distributed data centers with low OPEX using data center resizing

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the type-aware task placement problem over geo-distributed data centers. With the consideration of the diversity of user requests and server clusters in modern data centers, we formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an efficient solution is designed for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the greedy algorithm and much approaches the optimal results. © 2014 IEEE.

It’s not all about language ability: motivational cultural intelligence matters in call center performance

his study focuses on the role of motivational cultural intelligence (CQ) in call center performance. Call centers mainly rely on verbal communication with language ability playing a significant role in delivery of tasks. This study argues that motivational CQ, or the interest and efficacy when interacting with individuals from culturally diverse backgrounds, plays a significant role in call center performance. This study was conducted in the Philippines, one of the top destinations for offshore services like call centers. Studies were conducted at two time points to determine the relationship between language ability, motivational CQ, and task performance. At Time 1, the language ability of 125 call center agent applicants was determined and assessed. At Time 2 which was conducted six months later, performance data were obtained and the level of the motivational CQ of the respondents measured. Results show that language ability is positively and significantly related to task performance. However, when motivational CQ was included, the relationship between language ability and task performance became non-significant, which conveys the full mediating role of motivational CQ in that relationship.

K-Center: an approach on the multi-source identification of information diffusion

The global diffusion of epidemics, computer viruses, and rumors causes great damage to our society. It is critical to identify the diffusion sources and timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this paper, based on the knowledge of infected nodes and their connections, we propose a novel method to identify multiple diffusion sources, which can address three main issues in this area: 1) how many sources are there? 2) where did the diffusion emerge? and 3) when did the diffusion break out? We first derive an optimization formulation for multi-source identification problem. This is based on altering the original network into a new network concerning two key elements: 1) propagation probability and 2) the number of hops between nodes. Experiments demonstrate that the altered network can accurately reflect the complex diffusion processes with multiple sources. Second, we derive a fast method to optimize the formulation. It has been proved that the proposed method is convergent and the computational complexity is O(mn log α) , where α = α (m,n) is the slowly growing inverse-Ackermann function, n is the number of infected nodes, and m is the number of edges connecting them. Finally, we introduce an efficient algorithm to estimate the spreading time and the number of diffusion sources. To evaluate the proposed method, we compare the proposed method with many competing methods in various real-world network topologies. Our method shows significant advantages in the estimation of multiple sources and the prediction of spreading time.