Swimming Performance Post Blood Flow Restriction Training in Collegiate Swimmers

PURPOSE: To determine if blood flow restriction (BFR) training improved performance and physiological factors in collegiate swimmers. METHODS: Participants (n=10) separated into 2 groups (control [CON] & experimental [OCC]), completed 9 supervised trainings within 3 weeks. Pre- and post-testing included: VO2max, Wingate, swim time trials (TT), strength, and DEXA. Training was identical except OCC underwent bilateral thigh BFR [blood pressure (BP) cuffs inflated 70-90% of systolic BP]. Training: treadmill walking 20 minutes (5x3-minutes at 3 mph, 5% grade, 1-minute rest), followed by bodyweight strength training (squats, lunges & step-ups). Pain levels (scale: 1-10) were taken after the second set of lunges, cuff inflated (PainA), and after all lunges, cuff deflated (PainB). Paired t-tests determined significant change within groups, independent t-tests determined significance between groups, ReANOVA determined significance of pain levels. RESULTS: Both groups increased 1 RM leg press CON: 18.0 ± 8.155 (kg) (p=0.008) and OCC: 15.200 ± 5.805 (p=0.004); 1 RM chest press (kg) increased significantly in OCC (p=0.031). Mean peak power (W/kg) increased 1.530 ± 2.389 (p=0.225) CON and 3.772 ± 3.088 OCC (p=0.052). Pain levels were significantly different between days (p=0.012), and between PainA vs PainB (p=0.008). No significant change in swimming TT, VO2max, total work, fatigue index, or body fat occurred. CONCLUSION: This BFR training program did not improve swimming performance but indicated adaptation to pain may occur

Jamming Model for the Extremal Optimization Heuristic

Extremal Optimization, a recently introduced meta-heuristic for hard optimization problems, is analyzed on a simple model of jamming. The model is motivated first by the problem of finding lowest energy configurations for a disordered spin system on a fixed-valence graph. The numerical results for the spin system exhibit the same phenomena found in all earlier studies of extremal optimization, and our analytical results for the model reproduce many of these features.Comment: 9 pages, RevTex4, 7 ps-figures included, as to appear in J. Phys. A, related papers available at http://www.physics.emory.edu/faculty/boettcher

Continuous extremal optimization for Lennard-Jones Clusters

In this paper, we explore a general-purpose heuristic algorithm for finding high-quality solutions to continuous optimization problems. The method, called continuous extremal optimization(CEO), can be considered as an extension of extremal optimization(EO) and is consisted of two components, one is with responsibility for global searching and the other is with responsibility for local searching. With only one adjustable parameter, the CEO's performance proves competitive with more elaborate stochastic optimization procedures. We demonstrate it on a well known continuous optimization problem: the Lennerd-Jones clusters optimization problem.Comment: 5 pages and 3 figure

Structural and electronic properties of the graphene/Al/Ni(111) intercalation-like system

Decoupling of the graphene layer from the ferromagnetic substrate via intercalation of sp metal has recently been proposed as an effective way to realize single-layer graphene-based spin-filter. Here, the structural and electronic properties of the prototype system, graphene/Al/Ni(111), are investigated via combination of electron diffraction and spectroscopic methods. These studies are accompanied by state-of-the-art electronic structure calculations. The properties of this prospective Al-intercalation-like system and its possible implementations in future graphene-based devices are discussed.Comment: 20 pages, 8 figures, and supplementary materia

Extremal Optimization for Graph Partitioning

Extremal optimization is a new general-purpose method for approximating solutions to hard optimization problems. We study the method in detail by way of the NP-hard graph partitioning problem. We discuss the scaling behavior of extremal optimization, focusing on the convergence of the average run as a function of runtime and system size. The method has a single free parameter, which we determine numerically and justify using a simple argument. Our numerical results demonstrate that on random graphs, extremal optimization maintains consistent accuracy for increasing system sizes, with an approximation error decreasing over runtime roughly as a power law t^(-0.4). On geometrically structured graphs, the scaling of results from the average run suggests that these are far from optimal, with large fluctuations between individual trials. But when only the best runs are considered, results consistent with theoretical arguments are recovered.Comment: 34 pages, RevTex4, 1 table and 20 ps-figures included, related papers available at http://www.physics.emory.edu/faculty/boettcher