Partial brain fractionated RT was not associated with adverse neuropsychological effects through the first 2 years following therapy.

Proton magnetic resonance spectroscopy (MRS) and neuropsychological testing were conducted on 8 children with attention-deficit/hyperactivity disorder (ADHD-H), with no learning disabilities or comorbidities and 8 controls. Magnetic resonance spectroscopy revealed increased Glutamate/Glutamine in both frontal areas, and increased N-acetyl aspartate and Choline in the right frontal area of the ADHD-H subjects. Neuropsychological testing revealed few within- and between-group differences. Findings related to frontal lobe dysfunction in ADHD-H subjects were noted. N-acetylasparte/creatine (NAA/Creatine) in the right frontal region, and myoinositol/creatine (Myo inositol/Creatine) in the right and left frontal regions appear to be highly associated with the regulation of sensorimotor, language, and memory and learning functioning in children with ADHD-H.

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

No abstract

Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State–Action–Reward–State–Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.