Characterisation of Physiological Performance Measures in Arid and Humid Military Operational Environments

BackgroundMilitary personnel often deploy into hot environments that impose substantial strain on physical and cognitive performance. Hot environments can present as arid or humid and occur in different terrains, requiring different operational approaches. The aim of this study was to characterise the physiological, cognitive and perceptual strain experienced by military personnel during typical operations in arid and humid environments. MethodsNine pack-fit military personnel participated in two heat-stress tests to exhaustion, one in an arid environment (44°C, 21% humidity) and the other in a humid environment (33°C, 78% humidity). Participants walked at 5 km.h-1 while physiological, cognitive and perceptual measures were recorded. Tests were terminated volitionally, or by excessive core temperature or heart rate. Results The operational environments induced similar physiological stress, resulting in no difference in time to exhaustion (p = .155). The humid environment saw a greater elevation in core temperature (+0.3°C, p < .001) and heart rate (+5 b.min-1, p < .001). Skin temperature was greater in the arid environment (+0.4, p < .001) as was sweat evaporation (+0.3 L.h1, p = .045). Baseline performance predictors only provided moderate predictions of performance, whereas changes in perceptual measures provided the best performance predictors during the exercise, specifically perceptions relating to thermal sensation (β = -.65 - -.80) and sleepiness (β = -.79 - -.87). While no differences in cognitive performance were observed, greater cognitive stress was reported by participants over time, regardless of environment (all p < .011). ConclusionsThe humid operational environment elicited a greater thermal strain that may threaten safety, and impair performance, to a greater degree than the arid environment. Perceptual measures of thermal sensation and sleepiness were the best predictors of test termination and could likely be used to monitor thermal tolerance in field settings.

Posture betterment using fuzzy systematic evaluation system in gymnastics for better performance

The future scope of Physical Education (PE) depends on the quality and capacity of the emerging committed professionals to teach across the various fields of activity within the National Curriculum Physical Education (NCPE). The goal of this study is to determine the influence on the selected motor variables of gymnastics integrated into physical training courses. This study utilizes the Fuzzy Systematic Evaluation System (FSES) for identifying the students’ fitness tests and gymnastics skills and for finding significant correlations between fitness test results and gymnastic abilities performance. There was a substantial correlation between flexibility, muscle strength, and stamina in gymnastics and performance. In addition, several fitness tests might serve for several gymnastic skills to indicate as the significant performance predictors. The results of this research show that abdominal strength and flexibility is an essential indicator of the successful gymnastic program contents of the students of the Faculty of Sports and Physical Education. Detecting these cases in the initial stages is the responsibility of every teacher who can participate effectively in collaboration with the therapist to avoid deficits and deficiencies in attitudes by using complex exercises most effectively to influence students’ balance in their bodies.

Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Among pre-retrieval query performance predictors, specificity-based metrics investigate how corpus, query and corpus-query level statistics can be used to predict the performance of the query. In this thesis, we explore how neural embeddings can be utilized to define corpus-independent and semantics-aware specificity metrics. Our metrics are based on the intuition that a term that is closely surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define four groups of metrics: (1) neighborhood-based, (2) graph-based, (3) cluster-based and (4) vector-based metrics. Moreover, we employ learning-to-rank techniques to analyze the importance of individual specificity metrics. To evaluate the proposed metrics, we have curated and publicly share a test collection of term specificity measurements defined based on Wikipedia category hierarchy and DMOZ taxonomy. We report on our extensive experiments on the effectiveness of our metrics through metric comparison, ablation study and comparison against the state-of-the-art baselines. We have shown that our proposed set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings are more effective for performance prediction compared to the state-of-the-art methods. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.

Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Among pre-retrieval query performance predictors, specificity-based metrics investigate how corpus, query and corpus-query level statistics can be used to predict the performance of the query. In this thesis, we explore how neural embeddings can be utilized to define corpus-independent and semantics-aware specificity metrics. Our metrics are based on the intuition that a term that is closely surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define four groups of metrics: (1) neighborhood-based, (2) graph-based, (3) cluster-based and (4) vector-based metrics. Moreover, we employ learning-to-rank techniques to analyze the importance of individual specificity metrics. To evaluate the proposed metrics, we have curated and publicly share a test collection of term specificity measurements defined based on Wikipedia category hierarchy and DMOZ taxonomy. We report on our extensive experiments on the effectiveness of our metrics through metric comparison, ablation study and comparison against the state-of-the-art baselines. We have shown that our proposed set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings are more effective for performance prediction compared to the state-of-the-art methods. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.

Predictors of Athlete’s Performance in Ultra-Endurance Mountain Races

Background: In previous studies, ultra-endurance performance has been associated with training and psychological variables. However, performance under extreme conditions is understudied, mainly due to difficulties in making field measures. Aim: The aim of this study was to analyze the role of training, hydration, nutrition, oral health status, and stress-related psychological factors in athletes’ performance in ultra-endurance mountain events. Methods: We analyzed the variables of race time and training, hydration state, nutrition, oral health status, and stress-related psychological factors in 448 ultra-endurance mountain race finishers divided into three groups according to race length (less than 45 km, 45–90 km, and greater than 90 km), using a questionnaire. Results: Higher performance in ultra-endurance mountain races was associated with better oral health status and higher accumulative altitude covered per week as well as higher positive accumulative change of altitude per week during training. In longer distance races, experience, a larger volume of training, and better hydration/nutrition prior to the competition were associated with better performance. Conclusions: Ultra-endurance mountain athletes competing in longer races (>90 km) have more experience and follow harder training schedules compared with athletes competing in shorter distances. In longer races, a larger fluid intake before the competition was the single best predictor of performance. For races between 45 and 90 km, training intensity and volume were key predictors of performance, and for races below 45 km, oral health status was a key predictor of performance. Psychological factors previously reported as ultra-endurance mountain race performance predictors were inconsistent or failed to predict the performance of athletes in the present research.

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor

© 1997-2012 IEEE. Convolutional neural networks (CNNs) have shown remarkable performance in various real-world applications. Unfortunately, the promising performance of CNNs can be achieved only when their architectures are optimally constructed. The architectures of state-of-the-art CNNs are typically handcrafted with extensive expertise in both CNNs and the investigated data, which consequently hampers the widespread adoption of CNNs for less experienced users. Evolutionary deep learning (EDL) is able to automatically design the best CNN architectures without much expertise. However, the existing EDL algorithms generally evaluate the fitness of a new architecture by training from scratch, resulting in the prohibitive computational cost even operated on high-performance computers. In this paper, an end-to-end offline performance predictor based on the random forest is proposed to accelerate the fitness evaluation in EDL. The proposed performance predictor shows the promising performance in term of the classification accuracy and the consumed computational resources when compared with 18 state-of-the-art peer competitors by integrating into an existing EDL algorithm as a case study. The proposed performance predictor is also compared with the other two representatives of existing performance predictors. The experimental results show the proposed performance predictor not only significantly speeds up the fitness evaluations but also achieves the best prediction among the peer performance predictors.