Effects of contemporary cryo-compression on post-training performance in elite academy footballers

2022 ◽  
Author(s):  
Jill Alexander ◽  
Jane Keegan ◽  
Antony Reedy ◽  
David Rhodes
Keyword(s):  
Training Performance

Predictive Validity of a Selection Procedure for Air Traffic Controller Trainees

2013 ◽  
Vol 3 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Yvonne Pecena ◽  
Doris Keye ◽  
Kristin Conzelmann ◽  
Dietrich Grasshoff ◽  
Peter Maschke ◽  
...  
Keyword(s):  
Predictive Validity ◽  
Selection Procedure ◽  
Air Traffic ◽  
Performance Criteria ◽  
Training Phase ◽  
Air Traffic Controller ◽  
Training Performance ◽  
Navigation Service ◽  
German Aerospace ◽  
Training Success

The job of an air traffic controller (ATCO) is very specific and demanding. The assessment of potential suitable candidates requires a customized and efficient selection procedure. The German Aerospace Center DLR conducts a highly selective, multiple-stage selection procedure for ab initio ATCO applicants for the German Air Navigation Service Provider DFS. Successful applicants start their training with a training phase at the DFS Academy and then continue with a unit training phase in live traffic. ATCO validity studies are scarcely reported in the international scientific literature and have mainly been conducted in a military context with only small and male samples. This validation study encompasses the data from 430 DFS ATCO trainees, starting with candidate selection and extending to the completion of their training. Validity analyses involved the prediction of training success and several training performance criteria derived from initial training. The final training success rate of about 79% was highly satisfactory and higher than that of other countries. The findings demonstrated that all stages of the selection procedure showed predictive validity toward training performance. Among the best predictors were scores measuring attention and multitasking ability, and ratings on general motivation from the interview.

Personality, Attitudes, and Pilot Training Performance: Preliminary Analysis

1988 ◽  
Author(s):  
Frederick M. Siem ◽  
Thomas R. Carretta ◽  
Theresa A. Mercatante
Keyword(s):  
Preliminary Analysis ◽  
Pilot Training ◽  
Training Performance

scholarly journals Implementation support for contingency management: preferences of opioid treatment program leaders and staff

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Kelli Scott ◽  
Shelly Jarman ◽  
Samantha Moul ◽  
Cara M. Murphy ◽  
Kimberly Yap ◽  
...  
Keyword(s):  
Performance Feedback ◽  
Contingency Management ◽  
Scale Up ◽  
Team Approach ◽  
Evidence Based ◽  
Implementation Support ◽  
Training Performance ◽  
Opioid Treatment ◽  
Treatment Providers ◽  
External Facilitation

Abstract Background Contingency management (CM), a behavioral intervention that provides incentives for achieving treatment goals, is an evidence-based adjunct to medication to treat opioid use disorder. Unfortunately, many front-line treatment providers do not utilize CM, likely due to contextual barriers that limit effective training and ongoing support for evidence-based practices. This study applied user-informed approaches to adapt a multi-level implementation strategy called the Science to Service Laboratory (SSL) to support CM implementation. Methods Leaders and treatment providers working in community-based opioid treatment programs (OTPs; N = 43) completed qualitative interviews inquiring about their preferences for training and support implementation strategies (didactic training, performance feedback, and external facilitation). Our team coded interviews using a reflexive team approach to identify common a priori and emergent themes. Results Leaders and providers expressed a preference for brief training that included case examples and research data, along with experiential learning strategies. They reported a desire for performance feedback from internal supervisors, patients, and clinical experts. Providers and leaders had mixed feelings about audio-recording sessions but were open to the use of rating sheets to evaluate CM performance. Finally, participants desired both on-call and regularly scheduled external facilitation to support their continued use of CM. Conclusions This study provides an exemplar of a user-informed approach to adapt the SSL implementation support strategies for CM scale-up in community OTPs. Study findings highlight the need for user-informed approaches to training, performance feedback, and facilitation to support sustained CM use in this setting.

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

2021 ◽  
pp. 1-12
Author(s):  
Omid Izadi Ghafarokhi ◽  
Mazda Moattari ◽  
Ahmad Forouzantabar
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Wide Area ◽  
Load Modeling ◽  
Wide Area Monitoring ◽  
Training Performance ◽  
Spatial Features ◽  
Temporal Features ◽  
Frequency Domains ◽  
Area Monitoring

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.

scholarly journals Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM

2021 ◽  
Vol 12 (2) ◽  
pp. 57
Author(s):  
Yongping Cai ◽  
Yuefeng Cen ◽  
Gang Cen ◽  
Xiaomin Yao ◽  
Cheng Zhao ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Simple Structure ◽  
High Accuracy ◽  
Stator Winding ◽  
High Power Density ◽  
Temperature Prediction ◽  
Permanent Magnet Synchronous Motors ◽  
Training Performance ◽  
Synchronous Motors ◽  
Better Than

Permanent Magnet Synchronous Motors (PMSMs) are widely used in electric vehicles due to their simple structure, small size, and high power-density. The research on the temperature monitoring of the PMSMs, which is one of the critical technologies to ensure the operation of PMSMs, has been the focus. A Pseudo-Siamese Nested LSTM (PSNLSTM) model is proposed to predict the temperature of the PMSMs. It takes the features closely related to the temperature of PMSMs as input and realizes the temperature prediction of stator yoke, stator tooth, and stator winding. An optimization algorithm of learning rate combined with gradual warmup and decay is proposed to accelerate the convergence during the training and improve the training performance of the model. Experimental results reveal the proposed method and Nested LSTM (NLSTM) achieves high accuracy by comparing with other intelligent prediction methods. Moreover, the proposed method is slightly better than NLSTM in temperature prediction of PMSMS.

Sign in / Sign up

Export Citation Format

Share Document