Effects of Plyometric and Resistance Training on Muscle Strength and Neuromuscular Function in Young Adolescent Soccer Players

This study examined the effect of 8-weeks of resistance (RT) and plyometric (PLYO) training on maximal strength, power and jump performance compared with no added training (CON), in young male soccer players. Forty-one 11-13 year-old soccer players were divided into three groups (RT, PLYO, CON). All participants completed 5 isometric knee extensions at 90° and 5 isokinetic knee extensions at 240°/s pre- and post-training. Peak torque (PT), peak rate of torque development (pRTD), electromechanical-day (EMD), rate of muscle activation (Q30), muscle cross-sectional area (mCSA) and jump performance were examined. Both RT and PLYO resulted in significant (p < 0.05) increases in PT, pRTD and jump performance. RT resulted in significantly greater increases in both isometric and isokinetic PT, while PLYO resulted in significantly greater increases in isometric pRTD and jump performance compared with CON (p < 0.05). Q30 increased to a greater extent in PLYO (20%) compared with RT (5%) and CON (-5%) (p = 0.1). In conclusion, 8-weeks of RT and PLYO resulted in significant improvements in muscle strength and jump performance. RT appears to be more effective at eliciting increases in maximal strength while PLYO appears to enhance explosive strength, mediated by possible increases in the rate of muscle activation.

Properties and Algorithms of the KCube Interconnection Networks

The KCube interconnection network was first introduced in 2010 in order to exploit the good characteristics of two well-known interconnection networks, the hypercube and the Kautz graph. KCube links up multiple processors in a communication network with high density for a fixed degree. Since the KCube network is newly proposed, much study is required to demonstrate its potential properties and algorithms that can be designed to solve parallel computation problems. In this thesis we introduce a new methodology to construct the KCube graph. Also, with regard to this new approach, we will prove its Hamiltonicity in the general KC(m; k). Moreover, we will find its connectivity followed by an optimal broadcasting scheme in which a source node containing a message is to communicate it with all other processors. In addition to KCube networks, we have studied a version of the routing problem in the traditional hypercube, investigating this problem: whether there exists a shortest path in a Qn between two nodes 0n and 1n, when the network is experiencing failed components. We first conditionally discuss this problem when there is a constraint on the number of faulty nodes, and subsequently introduce an algorithm to tackle the problem without restrictions on the number of nodes.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.