Exercise Induced Fluid Shifts are Distinct to Exercise Mode and Intensity - a Comparison of Blood Flow Restricted and Free Flow Resistance Exercise

Aim: MRI can provide fundamental tools in decoding physiological stressors stimulated by training paradigms. Acute physiological changes induced by three diverse exercise protocols known to elicit similar levels of muscle hypertrophy were evaluated using muscle functional magnetic resonance imaging (mfMRI). Methods: The study was a cross-over study with participants (n=10) performing three acute unilateral knee extensor exercise protocols to failure and a work matched control exercise protocol. Participants were scanned after each exercise protocol; 70% 1 repetition maximum (RM) (FF70); 20% 1RM (FF20); 20% 1RM with blood flow restriction (BFR20); free-flow (FF) control work matched to BFR20 (FF20WM). Post exercise mfMRI scans were used to obtain interleaved measures of muscle R2 (indicator of edema), R2' (indicator of deoxyhemoglobin), muscle cross sectional area (CSA) blood flow and diffusion. Results: Both BFR20 and FF20 exercise resulted in a larger acute decrease in R2, decrease in R2', and expansion of the extracellular compartment with slower rates of recovery. BFR20 caused greater acute increases in muscle CSA than FF20WM and FF70. Only BFR20 caused acute increases in intracellular volume. Post-exercise muscle blood flow was higher after FF70 and FF20 exercise than BFR20. Acute changes in mean diffusivity were similar across all exercise protocols. Conclusion: This study was able to differentiate the acute physiological responses between anabolic exercise protocols. Low-load exercise protocols, known to have relatively higher energy contributions from glycolysis at task failure, elicited a higher mfMRI response. Noninvasive mfMRI represents a promising tool for decoding mechanisms of anabolic adaptation in muscle.

A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

Most of the multiobjective optimization problems in engineering involve the evaluation of expensive objectives and constraint functions, for which an approximate model-based multiobjective optimization algorithm is usually employed, but requires a large amount of function evaluation. Aiming at effectively reducing the computation cost, a novel infilling point criterion EIR2 is proposed, whose basic idea is mapping a point in objective space into a set in expectation improvement space and utilizing the R2 indicator of the set to quantify the fitness of the point being selected as an infilling point. This criterion has an analytic form regardless of the number of objectives and demands lower calculation resources. Combining the Kriging model, optimal Latin hypercube sampling, and particle swarm optimization, an algorithm, EIR2-MOEA, is developed for solving expensive multiobjective optimization problems and applied to three sets of standard test functions of varying difficulty and comparing with two other competitive infill point criteria. Results show that EIR2 has higher resource utilization efficiency, and the resulting nondominated solution set possesses good convergence and diversity. By coupling with the average probability of feasibility, the EIR2 criterion is capable of dealing with expensive constrained multiobjective optimization problems and its efficiency is successfully validated in the optimal design of energy storage flywheel.

A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization

<div> <div> <div> <p>In this paper, a new hypervolume-based evolutionary multi-objective optimization algorithm (EMOA), namely R2HCA-EMOA (R2-based Hypervolume Contribution Approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate the hypervolume contribution. The basic framework of the proposed algorithm is the same as SMS- EMOA. In order to make the algorithm computationally efficient, a utility tensor structure is introduced for the calculation of the R2 indicator variant. Moreover, a normalization mechanism is incorporated into R2HCA-EMOA to enhance the performance of the algorithm. Through experimental studies, R2HCA-EMOA is compared with three hypervolume-based EMOAs and several other state-of-the-art EMOAs on 5-, 10- and 15-objective DTLZ, WFG problems and their minus versions. Our results show that R2HCA-EMOA is more efficient than the other hypervolume-based EMOAs, and is superior to all the compared state-of-the-art EMOAs. </p> </div> </div> </div>

A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization

<div> <div> <div> <p>In this paper, a new hypervolume-based evolutionary multi-objective optimization algorithm (EMOA), namely R2HCA-EMOA (R2-based Hypervolume Contribution Approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate the hypervolume contribution. The basic framework of the proposed algorithm is the same as SMS- EMOA. In order to make the algorithm computationally efficient, a utility tensor structure is introduced for the calculation of the R2 indicator variant. Moreover, a normalization mechanism is incorporated into R2HCA-EMOA to enhance the performance of the algorithm. Through experimental studies, R2HCA-EMOA is compared with three hypervolume-based EMOAs and several other state-of-the-art EMOAs on 5-, 10- and 15-objective DTLZ, WFG problems and their minus versions. Our results show that R2HCA-EMOA is more efficient than the other hypervolume-based EMOAs, and is superior to all the compared state-of-the-art EMOAs. </p> </div> </div> </div>

A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization

<div> <div> <div> <p>In this paper, a new hypervolume-based evolution- ary multi-objective optimization algorithm (EMOA), namely R2HCA-EMOA (R2-based Hypervolume Contribution Approx- imation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate the hypervolume contribution. The basic framework of the proposed algorithm is the same as SMS- EMOA. In order to make the algorithm computationally efficient, a utility tensor structure is introduced for the calculation of the R2 indicator variant. Moreover, a normalization mechanism is incorporated into R2HCA-EMOA to enhance the performance of the algorithm. Through experimental studies, R2HCA-EMOA is compared with three hypervolume-based EMOAs and several other state-of-the-art EMOAs on 5-, 10- and 15-objective DTLZ, WFG problems and their minus versions. Our results show that R2HCA-EMOA is more efficient than the other hypervolume- based EMOAs, and is superior to all the compared state-of-the- art EMOAs. </p> </div> </div> </div>