Abstract The detection of surface electromyography (sEMG) signals on the skin has attracted increasing attention because of its ability to monitor muscle conditions in a noninvasive manner and thus possesses great application potential to assess athletic status and training efficiency in real time or to evaluate postoperative muscle rehabilitation conveniently. Here, a flexible wireless sEMG monitoring system that consists of a stretchable sEMG epidermal patch and a flexible printed circuit board to provide real-time evaluation of muscle strength and fatigue is reported. The epidermal patch is designed to have good stretchability and permeability and optimized to ensure a low contact impedance with the skin and minimized background noise for sEMG signal acquisition with high fidelity. Six commonly used time-domain and two frequency-domain features extracted from sEMG signals are systematically analyzed, and a strategy for feature selection and pattern identification is proposed that eventually enables the real-time assessment of muscle strength and fatigue by using an integrated system in a wearable form.
Tight and shale reservoirs are forming important components of the global hydrocarbon landscape, which impede the free thermal movement of fluid molecules, with numerous nanoscale pores. The confined hydrocarbons in the nanopores cannot be industrially produced from conventional exploration and development methods, with deviated fluid phase behavior under nano-confinement effects. Most commonly important fluid phase behavior in nanopores has been simulated and compared with the bulk cases previously, including phase coexistence, critical properties, and density distribution of confined fluids. This paper focuses on the deviated fluid phase behavior under nano-confinement effects by Monte Carlo modeling. The Monte Carlo simulation is still limited to modeling the macroscopic pore-related behavior like capillarity and complex fluid and solid materials. Moreover, the Monte Carlo simulation is usually scale-restricted and the pore-size range where the nano-confinement effect fails to work needs to be quantitatively determined. Overall, for the tight and shale fluid phase behavior, a functional Monte Carlo model, coupled with the long-range correction and configuration bias techniques, is suggested to include both the multi-component fluids and skeleton.
The sensitivity of fluvial sediment load to climate change and predictions of future sediment load in cold basins remain poorly investigated, although changes in river sediment transport have important geomorphological, ecological, and societal implications. Here, we adapt a sediment elasticity approach to examine the sensitivity of fluvial suspended sediment load to changes in air temperature and precipitation in the headwater of the Yangtze River (HYR) on the inner Tibetan Plateau. Results show that every 1 °C increase in air temperature can increase the suspended sediment load by 14–27 % by intensifying thermally-driven glacial and permafrost erosional processes, and every 10 % increase in precipitation can increase the suspended sediment load by 16–24 % through enhancing pluvial-driven erosional processes. We predict an increase of 60–85 % in the suspended sediment loads in HYR by 2050 relative to the present-day period under the Representative Concentration Pathway 4.5, as both air temperature and precipitation are projected to increase. Our analysis highlights that smaller upland rivers appear to respond to modern climate change more rapidly and intensively than larger downstream rivers due to the larger glacier and permafrost coverages, poorer vegetation, as well as steeper fluvial relief, and higher sediment connectivity. This study provides a framework and a data-driven sediment elasticity approach to predict climate change and cryosphere degradation-driven changes in future fluvial suspended sediment load in cold basins, highlights the importance of the spatial scale effects in modulating fluvial responses, and has implications for assessing the impacts of climate change on channel morphology and aquatic ecosystems.
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of mainstream forecasting research and activities. Combining multiple forecasts produced for a target time series is now widely used to improve accuracy through the integration of information gleaned from different sources, thereby avoiding the need to identify a single “best” forecast. Combination schemes have evolved from simple combination methods without estimation to sophisticated techniques involving time-varying weights, nonlinear combinations, correlations among components, and cross-learning. They include combining point forecasts and combining probabilistic forecasts. This paper provides an up-to-date review of the extensive literature on forecast combinations and a reference to available open-source software implementations. We discuss the potential and limitations of various methods and highlight how these ideas have developed over time. Some crucial issues concerning the utility of forecast combinations are also surveyed. Finally, we conclude with current research gaps and potential insights for future research.
High-performance stretchable strain sensors are highly desirable for various scenarios, such as health monitoring and human-robot interfaces. Here, we propose a universal strain engineering strategy that introduces an inhomogeneous spatial distribution of stress and promotes crack propagation behavior leading to a critical state between network and channel morphologies, achieving stretchable strain sensors with high sensitivity, a wide working range and good linearity. Approaches for introducing soft-rigid interfaces, enlarging elastic modulus mismatches and matching dimensions have been employed to execute the strategy for network-crack strain sensors with collapsed nanocone cluster structures as representatives. The strain sensors can be tuned to realize a gauge factor of 690.95 in a linear working range of 0–40% (R2 = 0.993) or a gauge factor of 113.70 in a larger linear working range of 0–120% (R2 = 0.999). Intraocular pressure monitoring and dynamic facial asymmetry assessment have been demonstrated based on these sensors to show their great application potential.
Understanding gas adsorptions in porous media is of critical importance to numerous academic research and industrial applications. Here, adsorptions of methane and carbon dioxide, which are primary compositions of natural gas and carbon capture and storage (CCS) processes, on different minerals are specifically investigated and their effects on reservoir productions and carbon storage processes are evaluated. First, an improved simplified local-density (SLD) model is developed to calculate the gas adsorptions by considering the complex porous composition and confinement effects induced phenomena. Then, the improved SLD model is embedded into a self-developed field simulation program to analyse the production processes of a selected large-scale gas reservoir. The proposed improved adsorption model is validated to be accurate for various multiscale minerals at different temperature and pressure conditions. By using the coupling with reservoir numerical simulation, the adsorption model is successfully up-scaled to practical field scale which is efficient and effective for predicting gas productions and analysing relevant influential factors, such as temperature, pressure and reservoir physical properties. The proposed theoretical approach provides strong technical support for future natural gas productions and CCS projects with its capability of calculating gas adsorptions on different minerals and practical gas field productions.
Semivolatile organic compounds (SVOCs) represent an important class of indoor pollutants. The partitioning of SVOCs between airborne particles and the adjacent air influences human exposure and uptake. Presently, little direct experimental evidence exists about the influence of indoor particle pollution on the gas–particle phase partitioning of indoor SVOCs. In this study, we present time-resolved gas- and particle-phase distribution data for indoor SVOCs in a normally occupied residence using semivolatile thermal desorption aerosol gas chromatography. Although SVOCs in indoor air are found mostly in the gas phase, we show that indoor particles from cooking, candle use, and outdoor particle infiltration strongly affect the gas–particle phase distribution of specific indoor SVOCs. From gas- and particle-phase measurements of SVOCs spanning a range of chemical functionalities (alkanes, alcohols, alkanoic acids, and phthalates) and volatilities (vapor pressures from 10–13 to 10–4 atm), we find that the chemical composition of the airborne particles influences the partitioning of individual SVOC species. During candle burning, the enhanced partitioning of gas-phase SVOCs to indoor particles not only affects the particle composition but also enhances surface off-gassing, thereby increasing the total airborne concentration of specific SVOCs, including diethylhexyl phthalate.
Abstract Geographic range size of endemic species is the most important indicator of species' vulnerability to extinction and conservation prioritization, yet variation in range size among species and across space has been relatively understudied. We investigated the variations and geographic patterns of the range size of 9898 angiosperm species endemic to China and compared the effects of historical and contemporary climate and species' functional traits associated with dispersal ability (including growth form, fruit type, and sexual system) on range size variations. Our results revealed that narrow-ranged endemic species are clustered in Southwest China where angiosperm species' richness peaks. Winter temperature had the strongest and negative effect on the range size of narrow-ranged endemic species across space and species, while climate seasonality had the strongest and positive effect on the range size of wide-ranged endemic species. Both historical and contemporary climate have also influenced species range size indirectly via their effects on species' functional traits associated with dispersal ability. Range size of all endemic species, narrow-ranged and wide-ranged, showed little phylogenetic signal, suggesting that phylogenetic conservatism plays a minor role in range size variations. Our results show that the range size of angiosperm species endemic to China is driven by both extrinsic spatiotemporal environmental factors and intrinsic species' traits that allow species to cope with environmental change.
Floral symmetry plays an important role in plant-pollinator interactions and may have remarkable impacts on angiosperm diversification. However, spatiotemporal patterns in floral symmetry and drivers of these patterns remain unknown. Here, using newly compiled floral symmetry (actinomorphy versus zygomorphy) data of 279,877 angiosperm species and their distributions and phylogenies, we estimated global geographic patterns and macroevolutionary dynamics of floral symmetry. We found that frequency of actinomorphic species increased with latitude, while that of zygomorphic species decreased. Solar radiation, present-day temperature, and Quaternary temperature change correlated with geographic variation in floral symmetry frequency. Evolutionary transitions from actinomorphy to zygomorphy dominated floral symmetry evolution, although the transition rate decreased with decreasing paleotemperature throughout the Cenozoic. Notably, we found that zygomorphy may not favor diversification of angiosperms as previously observed in some clades. Our study demonstrates the influence of (paleo)climate on spatiotemporal patterns in floral symmetry and challenges previous views about role of flower symmetry in angiosperm diversification. (Paleo)climate profoundly influenced spatiotemporal patterns of angiosperm floral symmetry.