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This study explores the application of slouching scores to assess ergonomic posture in augmented reality (AR) environments. Employing Microsoft HoloLens 2 with Xsens motion capture technology, participants engaged in interactive biomechanics tasks, including a practical luggage-lifting exercise. Real-time feedback guided users towards safe posture, emphasizing spinal alignment and reducing physical strain. Slouching scores functioned as quantitative measures of posture quality, establishing a connection between unsafe postures and the requisite postural adjustments. The results illustrate how AR-integrated systems can enhance posture awareness, improve user ergonomics, and promote active learning in both educational and professional settings. 

There is an increasing demand for developing new metrics that can effectively measure the physical demand experienced by users in augmented reality (AR) environments. In this study, we evaluated one of the recent metrics, called “slouching score,” in an AR-based biomechanics lecture. This study aims to uncover the correlation between the AR interaction and the physical demand of users in a different setup compared to the earlier study. The slouching score, which evaluates posture changes that may indicate fatigue during AR interactions, is measured using Xsens motion capture equipment. These calculated scores are compared with responses to physical demand assessments surveyed using NASA-TLX questionnaires. One of the key differences between the current study and earlier ones is that participants had to physically move to access the next AR module in earlier studies. In contrast, this time, participants simply needed to click a virtual arrow button to view the next AR modules, eliminating the need for physical movement. Our preliminary findings show correlations between the slouching score from some modules and the NASA-TLX physical demand ratings. 

The significance of the water-side gas transfer velocity for air–sea CO2 gas exchange (k) and its non-linear dependence on wind speed (U) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear relation linking k to U (hereafter labeled as f(U), where f(.) stands for an arbitrary function of U), the distributional properties of U (treated as a random variable) along with other external factors influencing k, and the time-averaging period used to determine k from U. To address the latter issue, a Taylor series expansion is applied to separate f(U) into a term derived from time-averaging wind speed (labeled as ⟨U⟩, where ⟨.⟩ indicates averaging over a monthly time scale) as currently employed in climate models and additive bias corrections that vary with the statistics of U. The method was explored for nine widely used f(U) parameterizations based on remotely-sensed 6-hourly global wind products at 10 m above the sea-surface. The bias in k of monthly estimates compared to the reference 6-hourly product was shown to be mainly associated with wind variability captured by the standard deviation σσU around ⟨U⟩ or, more preferably, a dimensionless coefficient of variation Iu= σσU/⟨U⟩. The proposed correction outperforms previous methodologies that adjusted k when using ⟨U⟩ only. An unexpected outcome was that upon setting Iu2 = 0.15 to correct biases when using monthly wind speed averages, the new model produced superior results at the global and regional scale compared to prior correction methodologies. Finally, an equation relating Iu2 to the time-averaging interval (spanning from 6 h to a month) is presented to enable other sub-monthly averaging periods to be used. While the focus here is on CO2, the theoretical tactic employed can be applied to other slightly soluble gases. As monthly and climatological wind data are often used in climate models for gas transfer estimates, the proposed approach provides a robust scheme that can be readily implemented in current climate models. 

Abstract The global air‐sea CO₂flux (F) impacts and is impacted by a plethora of climate‐related processes operating at multiple time scales. In bulk mass transfer formulations, F is driven by physico‐ and bio‐chemical factors such as the air‐sea partial pressure difference (∆pCO₂), gas transfer velocity, sea surface temperature, and salinity–all varying at multiple time scales. To de‐convolve the impact of these factors on variability in F at different time scales, time‐resolved estimates of F were computed using a global data set assembled between 1988 and 2015. The F anomalies were defined as temporal deviations from the 28‐year time‐averaged value. Spectral analysis revealed four dominant timescales of variability in F–subseasonal, seasonal, interannual, and decadal with relative amplitude differences varying across regions. A second‐order Taylor series expansion was then conducted along these four timescales to separate drivers across differing regions. The analysis showed that on subseasonal timescales, wind speed variability explains some 66% of the global F anomaly and is the dominant driver. On seasonal, interannual, and decadal timescales, the ∆pCO₂effect controlled by the ∆pCO₂anomaly, explained much of the F anomaly. On decadal timescales, the F anomaly was almost entirely governed by the ∆pCO₂effect with large contributions from high latitudes. The main drivers across timescales also dominate the regional F anomaly, particularly in the mid‐high latitude regions. Finally, the driver of the ∆pCO₂effect was closely connected with the relative strength of atmospheric pCO₂and the nonthermal component of oceanic pCO₂anomaly associated with dissolved inorganic carbon and alkalinity.