The Kinetics of the Work Capacity Above Critical Power

The critical power (CP) model includes two constants: the CP and the W′ [P = W′ / t) + CP]. The W′ is the finite work capacity available above CP. Power output above CP results in depletion of the W′; complete depletion of the W′ results in exhaustion. It is possible to model the charge and discharge of the W′ during intermittent exercise using a novel integrating model (the W′BAL model), and to generate a function describing a curvilinear relationship between time constants of reconstitution of the W′ in terms of the difference between recovery power and CP (DCP) (r2 = 0.77). The depletion of the W′ as predicted by the W′BAL model during intermittent exercise is linearly related to the rise in V ̇O_2 above exercise baseline (r2 = 0.82 – 0.96). During intermittent exercise, the W′BAL model is generally robust with respect to the length of work and recovery interval, yielding a mean under-prediction of the W′BAL of only -1.6 ±1.1 kJ. The amount of W′ remaining after a period of intermittent exercise correlates with the difference between the subject’s V ̇O_2 at that time (V ̇O_2START) and V ̇O_2PEAK (DVO2) (r = 0.79, p < 0.01). Moreover, the W′BAL model also performs well in the field, permitting accurate estimation of the point at which an athlete becomes exhausted during hard training or competition (mean W′BAL at exhaustion = 0.5 ± 1.3 kJ (95% CI = 0 – 0.9 kJ). The W′BAL model meets the mathematical criteria of an excellent diagnostic test for exhaustion (area under ROC curve = 0.91). 31P magnetic resonance spectroscopy during single leg extensor exercise revealed a correlation between the recovery of the W′BAL model and recovery of creatine phosphate ([PCr]) after a bout of exhaustive single leg extensor exercise (r = 0.99, p < 0.01). The W′BAL model also accurately predicted recovery of the W′ in this setting (r = 0.97, p < 0.05). However, a complete understanding of the relationship between the depletion and recovery of [PCr] and the depletion and recovery of the W′ remains elusive. Muscle carnosine content is curvilinearly related to the rate of W′BAL recovery, with higher muscle carnosine associated with faster recovery, with implications for muscle buffering capacity and calcium handling. The W′BAL model may be recast in the form of a differential equation, permitting definition of the time constant of recovery of the W′BAL in terms of the subject’s known W′ and the DCP. This permits the scaling of the model to different muscle groups or exercise modalities. Moreover, modifications to this mathematical form may help explain some of the variability noted in the model in earlier studies, suggesting novel avenues of research. However, the present formulation of the W′BAL model is mathematically robust and represents an important addition to the scientific armamentarium, which may aid the understanding the physiology of human performance

Prediction of Critical Power and W′ in Hypoxia: Application to Work-Balance Modelling

Purpose: Develop a prediction equation for critical power (CP) and work above CP (W′) in hypoxia for use in the work-balance ([Formula: see text]) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W′ at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W′ at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W′ were used to compute W′ during HIIT using differential ([Formula: see text]) and integral ([Formula: see text]) forms of the [Formula: see text] model. Results: CP decreased at altitude (P < 0.001) as described by 3rd order polynomial function (R(2) = 0.99). W′ decreased at 4,250 m only (P < 0.001). A double-linear function characterized the effect of altitude on W′ (R(2) = 0.99). There was no significant effect of parameter input (actual vs. predicted CP and W′) on modelled [Formula: see text] at 2,250 m (P = 0.24). [Formula: see text] returned higher values than [Formula: see text] throughout HIIT (P < 0.001). During HIIT, [Formula: see text] was not different to 0 kJ at completion, at 250 m (0.7 ± 2.0 kJ; P = 0.33) and 2,250 m (−1.3 ± 3.5 kJ; P = 0.30). However, [Formula: see text] was lower than 0 kJ at 250 m (−0.9 ± 1.3 kJ; P = 0.058) and 2,250 m (−2.8 ± 2.8 kJ; P = 0.02). Conclusion: The altitude prediction equations for CP and W′ developed in this study are suitable for use with the [Formula: see text] model in acute hypoxia. This enables the application of [Formula: see text] modelling to training prescription and competition analysis at altitude

Global Research Alliance N2O chamber methodology guidelines: considerations for automated flux measurement

Nitrous oxide (N2O) emissions are highly episodic in response to nitrogen additions and changes in soil moisture. Automated gas sampling provides the necessary high temporal frequency to capture these emission events in real time, ensuring the development of accurate N2O inventories and effective mitigation strategies to reduce global warming. This paper outlines the design and operational considerations of automated chamber systems including chamber design and deployment, frequency of gas sampling, and options in terms of the analysis of gas samples. The basic hardware and software requirements for automated chambers are described, including the major challenges and obstacles in their implementation and operation in a wide range of environments. Detailed descriptions are provided of automated systems that have been deployed to assess the impacts of agronomy on the emissions of N2O and other significant greenhouse gases. This information will assist researchers across the world in the successful deployment and operation of automated N2O chamber systems

Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe's terrestrial ecosystems : a review

Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.Peer reviewe

Triathlon

Triathlon consists of swimming, cycling, and running. Due to the large volume of training required, athlete injury may be the result of mechanical or physiological insult. Overuse injuries are common, as are traumatic injuries. However, athletes may also suffer physiological injury as a result of overwhelming the homeostatic mechanisms of the body. Many injuries can be avoided through appropriate planning in both the short and long term, termed periodization

The W\u27 balance model: Mathematical and methodological considerations

Since its publication in 2012, the W\u27 balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model\u27s intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W\u27 balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W\u27 during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W\u27 balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise