Fatigability and Recovery of Arm Muscles with Advanced Age for Dynamic and Isometric Contractions

This study determined whether age-related mechanisms can increase fatigue of arm muscles during maximal velocity dynamic contractions, as it occurs in the lower limb. We compared elbow flexor fatigue of young (n = 10, 20.8 ± 2.7 years) and old men (n = 16, 73.8 ± 6.1 years) during and in recovery from a dynamic and an isometric postural fatiguing task. Each task was maintained until failure while supporting a load equivalent to 20% of maximal voluntary isometric contraction (MVIC) torque. Transcranial magnetic stimulation (TMS) was used to assess supraspinal fatigue (superimposed twitch, SIT) and muscle relaxation. Time to failure was longer for the old men than for the young men for the isometric task (9.5 ± 3.1 vs. 17.2 ± 7.0 min, P = 0.01) but similar for the dynamic task (6.3 ± 2.4 min vs. 6.0 ± 2.0 min, P = 0.73). Initial peak rate of relaxation was slower for the old men than for the young men, and was associated with a longer time to failure for both tasks (P \u3c 0.05). Low initial power during elbow flexion was associated with the greatest difference (reduction) in time to failure between the isometric task and the dynamic task (r = − 0.54, P = 0.015). SIT declined after both fatigue tasks similarly with age, although the recovery of SIT was associated with MVIC recovery for the old (both sessions) but not for the young men. Biceps brachii and brachioradialis EMG activity (% MVIC) of the old men were greater than that of the young men during the dynamic fatiguing task (P \u3c 0.05), but were similar during the isometric task. Muscular mechanisms and greater relative muscle activity (EMG activity) explain the greater fatigue during the dynamic task for the old men compared with the young men in the elbow flexor muscles. Recovery of MVC torque however relies more on the recovery of supraspinal fatigue among the old men than among the young men

N_pN_n dependence of empirical formula for the lowest excitation energy of the 2^+ states in even-even nuclei

We examine the effects of the additional term of the type $\sim e^{- \lambda' N_pN_n}$ on the recently proposed empirical formula for the lowest excitation energy of the $2^+$ states in even-even nuclei. This study is motivated by the fact that this term carries the favorable dependence of the valence nucleon numbers dictated by the $N_pN_n$ scheme. We show explicitly that there is not any improvement in reproducing $E_x(2_1^+)$ by including the extra $N_pN_n$ term. However, our study also reveals that the excitation energies $E_x(2_1^+)$, when calculated by the $N_pN_n$ term alone (with the mass number $A$ dependent term), are quite comparable to those calculated by the original empirical formula.Comment: 14 pages, 5 figure

EACOF: A Framework for Providing Energy Transparency to enable Energy-Aware Software Development

Making energy consumption data accessible to software developers is an essential step towards energy efficient software engineering. The presence of various different, bespoke and incompatible, methods of instrumentation to obtain energy readings is currently limiting the widespread use of energy data in software development. This paper presents EACOF, a modular Energy-Aware Computing Framework that provides a layer of abstraction between sources of energy data and the applications that exploit them. EACOF replaces platform specific instrumentation through two APIs - one accepts input to the framework while the other provides access to application software. This allows developers to profile their code for energy consumption in an easy and portable manner using simple API calls. We outline the design of our framework and provide details of the API functionality. In a use case, where we investigate the impact of data bit width on the energy consumption of various sorting algorithms, we demonstrate that the data obtained using EACOF provides interesting, sometimes counter-intuitive, insights. All the code is available online under an open source license. http://github.com/eaco