ACUTE EFFECTS OF FOREARM KINESIO TAPING ON MUSCLE STRENGTH AND FATIGUE IN HEALTHY TENNIS PLAYERS

The aim of this study was to explore the acute effects of Kinesio taping (KT) applied over the wrist extensors and flexors on muscle strength and endurance. Fourteen participants completed 50 consecutive maximal concentric wrist extension and flexion repetitions at 60 °/s and 210 °/s in KT, placebo taping, and no taping conditions. There was no significant KT effect on the strength output (peak moment and peak / average power). KT reduced work fatigue and induced an increased regression of torque compared to no taping at 60 °/s. These findings provide preliminary evidences suggesting that KT may not be able to modulate strength production in healthy athletes immediately, but would have a significant positive effect on muscle fatigue resistance during repeated concentric muscle actions

Optimal Decentralized Load Frequency Control for Power System: A Mean-Field Team Approach

RÉSUMÉ Le problème de réglage fréquence-puissance (RFP) dans les réseaux électriques connaît un regain récent d’intérêt vu la pénétration de plus en plus importante dans ces réseaux de sources d’énergie renouvelable solaire ou éolienne, c’est à dire avec caractéristiques d’intermittence. En effet, la fréquence est un signal dont le comportement est sensible à tout déséquilibre entre génération et demande d’électricité, et son maintien dans un voisinage serré de sa valeur nominale (60 Hz en Amérique du Nord), est essentiel pour la stabilité du réseau. Le RFP vise à contrôler la puissance de sortie des générateurs en réponse aux changements de fréquence (dans le cas d’une zone unique) ainsi qu’à ceux des échanges d’énergie par rapport à leur valeur programmée dans les lignes de raccordement (dans le cas de zones multiples). Les techniques actuelles de RFP présentent un mélange de caractéristiques de centralisation et de décentralisation. Dans ce mémoire, nous souhaitons revisiter les algorithmes de RFP à la lumière des derniers développements de la théorie des équipes et jeux à champ moyens (mean field teams and mean field games), en exploitant le fait que le signal de fréquence global utilisé pour coordonner les générateurs est en réalité une moyenne pondérée des fréquences locales à un grand nombre de générateurs. Nous explorerons ainsi dans ce mémoire des approches de commande intégrale-proportionnelle avec structure de coût quadratique pour le RFP. Le problème de commande est formulé comme un problème d’équipe linéaire quadratique selon la structure d’information champ-moyen partagé, c’est-à-dire que chaque générateur observe son propre état (incluant 3 variables internes supposées mesurables) ainsi que le champ moyen consistant en une moyenne des états de tous les générateurs. La commande décentralisée correspond à la solution du problème d’équipe à champ moyen. Cette dernière est obtenue en résolvant 2 équations de Riccati dans le cas d’une zone isolée, l’une associée au générateur local et l’autre associée au champ moyen (ces équations deviennent des équations de Riccati couplées dans le cas d’un problème à 2 zones). Le mémoire est composé de deux parties dédiées respectivement à l’analyse de la commande pour une zone isolée, et celle associée à deux zones interconnectées. Dans la première partie, nous introduisons la théorie de l’équipe dans le contrôle RFP à zone unique. Il s’agit de problèmes de décision multi-agents dans lesquels tous les agents (générateurs individuels) partagent un coût commun [Mahajan et al., 2012]. L’approche de commande actuelle utilise une contrainte de taux de génération unique [Tan, 2010] pour contrôler le comportement de tous les agents/machines et la répartition de la charge se fait en se référant à la taille de la machine, ce qui est simple mais apriori un peu trop grossier.----------ABSTRACT Load frequency control or LFC is a fundamental mechanism for maintaining the stability of electric power systems. It aims at controlling the power output of generators in response to either changes in frequency (in a single area case) or in response to both changes in frequency and tie-line power interchange (in multi- area cases). Indeed frequency is a ubiquitous signal in power systems and its excursions away from its nominal value are indicative of imbalances between generation and load. Interest in LFC has come back to the fore in view of the challenges raised by increasing levels of penetration of renewable intermittent sources (Wind and solar energy). This situation creates frequent and important mismatches between system generation and system load, and thus create the need for more effective LFC schemes. The current set up is based on estimating a single integral control based power mismatch variable and redistributing a share of the correspondingly needed generation increase or decrease among units according to their power rating [Tan, 2010]. While this has proved to be a robust and algorithmically simple scheme, it is a rather rough approach, as it tends to ignore the particular current state of each generator when provided with a new set point. In order to allow more flexible and less aggressive control to each individual generator, normally only represented as a single aggregate unit, novel decentralized linear quadratic-proportional integral control methods for load frequency control respectively based on so-called mean field team theory (for single and two area systems) and mean field games ( for two area systems) are discussed in this thesis. The control problem is formulated as a linear quadratic (LQ) team problem under meanfield sharing (MFS) information structure, i.e., each generator observes its own state (3 state variables) and the mean field, that is in this context the average state of all generators if they are all identical, or the vector of class specific mean states in a non homogeneous multi-class situation. Also, following a team solution scheme developed in [Arabneydi and Mahajan, 2016], a separate mean field control term is a feedback on the vector of mean class specific individual states. The overall result is a decentralized control policy with coordination by the mean field term. The optimal solution is obtained by solving 2 Riccati equations, one for the local generator and another one associated with the mean field (this becomes instead a system of coupled Riccati equations in the subsequent mean field game game solution of the 2 area problem), for the full observation model

PDHL-EDAS method for multiple attribute group decision making and its application to 3D printer selection

With the rapid development of 3D printing technology, 3D printers are manufactured based on the principle of 3D printing technology are more and more widely used in the manufacturing industry. Choosing high quality 3D printers for industrial production is of great significance to the economic growth of enterprises. In fact, it is difficult to select the most optimal 3D printers under a single and simple standard. Therefore, this paper establishes the probabilistic double hierarchy linguistic EDAS (PDHL-EDAS) method for the multiple attribute group decision making (MAGDM). Then the CRITIC model is introduced to derive objective weight and the cumulative prospect theory is leaded into obtain the cumulative weight of PDHLTS. In addition, what’s more, the PDHL-EDAS method is built and applied to the choice of high-quality 3D printer. Finally, compared with the available MAGDM methods under PDHLTS, the built method is proved to be scientific and effective. First published online 15 December 202

EFFECTS OF THE EXTERNAL MUSCLE SYSTEM ON THE FUNCTION OF HAMSTRINGS

The purpose of this study is to develop a device which can simulate the function of the hamstrings and increase these muscles' strength. By wearing it during daily training, one can train the hamstrings according to the specific needs of each sport event. Fifteen subjects were involved in the study and the external muscle system was examined by using EMG and kinetics methods. The results showed that there was an increasing trend in jumping performance and muscle co-contraction (pc less than 0.1) during running; There were significant differences (pc less than 0.05): in the first peak force between high loading and none and in the second peak force between low, medium, high loading and none. Thus we believe the system can be used in training sessions. By wearing the system during training, athletes can train muscles when performing movements unique to sport events

ACUTE EFFECTS OF HIGH-DEFINITION TRANSCRANIAL DIRECT CURRENT STIMULATION ON DYNAMIC POSTURAL STABILITY IN A Y-BALANCE TASK

Purpose: The present study aimed to investigate whether anodal transcranial direct current stimulation (a-tDCS) of the primary motor cortex (M1) could affect dynamic postural stability in healthy young adults. Methods: A randomized, crossover, double-blind experimental design was used in this study. Effects of tDCS on dynamic postural stability were assessed baseline and immediately after tDCS. Results: a-tDCS of M1 significantly decreased the COP of medial-lateral displacement on the posteromedial and posterolateral direction, and path length on the posteromedial direction in the Y-balance, while no significant changes in the sham tDCS (s-tDCS) condition. Conclusion: This study provided evidence that a-tDCS enhanced dynamic postural stability in healthy young adult

Efficient Cross-Device Federated Learning Algorithms for Minimax Problems

In many machine learning applications where massive and privacy-sensitive data are generated on numerous mobile or IoT devices, collecting data in a centralized location may be prohibitive. Thus, it is increasingly attractive to estimate parameters over mobile or IoT devices while keeping data localized. Such learning setting is known as cross-device federated learning. In this paper, we propose the first theoretically guaranteed algorithms for general minimax problems in the cross-device federated learning setting. Our algorithms require only a fraction of devices in each round of training, which overcomes the difficulty introduced by the low availability of devices. The communication overhead is further reduced by performing multiple local update steps on clients before communication with the server, and global gradient estimates are leveraged to correct the bias in local update directions introduced by data heterogeneity. By developing analyses based on novel potential functions, we establish theoretical convergence guarantees for our algorithms. Experimental results on AUC maximization, robust adversarial network training, and GAN training tasks demonstrate the efficiency of our algorithms

DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework

Hyperparameter optimization, also known as hyperparameter tuning, is a widely recognized technique for improving model performance. Regrettably, when training private ML models, many practitioners often overlook the privacy risks associated with hyperparameter optimization, which could potentially expose sensitive information about the underlying dataset. Currently, the sole existing approach to allow privacy-preserving hyperparameter optimization is to uniformly and randomly select hyperparameters for a number of runs, subsequently reporting the best-performing hyperparameter. In contrast, in non-private settings, practitioners commonly utilize "adaptive" hyperparameter optimization methods such as Gaussian process-based optimization, which select the next candidate based on information gathered from previous outputs. This substantial contrast between private and non-private hyperparameter optimization underscores a critical concern. In our paper, we introduce DP-HyPO, a pioneering framework for "adaptive" private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization. To accomplish this, we provide a comprehensive differential privacy analysis of our framework. Furthermore, we empirically demonstrate the effectiveness of DP-HyPO on a diverse set of real-world and synthetic datasets