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.
Most ambitious climate change mitigation pathways indicate multifold bioenergy expansion to support the energy transition, which may trigger increased biomass imports from major bioenergy-consuming regions. However, the potential global land-use change and sustainability trade-offs alongside the bioenergy trade remain poorly understood. Here, we apply the Global Biosphere Management Model (GLOBIOM) to investigate and compare the effects of different increasing bioenergy import strategies in line with the 1.5℃-compatible bioenergy demand in China, which is projected to represent 30% of global bioenergy consumption by the middle of the century. The results show that sourcing additional bioenergy from different world regions could pose heterogeneous impacts on the local and global land systems, with implications on food security, greenhouse gas emissions, and water and fertilizer demand. In the worst cases under strict trade settings, relying on biomass import may induce up to 25% of unmanaged forests converted to managed ones in the supplying regions, while in an open trade environment, increasing bioenergy imports would drastically change the trade flows of staple agricultural or forestry products, which would further bring secondary land-use changes in other world regions. Nevertheless, an economically optimized biomass import portfolio for China has the potential to reduce global overall sustainability trade-offs with food security and emission abatement. However, these benefits vary with indicator and time and are conditional on stricter land-use regulations. Our findings thus shed new light on the design of bioenergy trade strategies and the associated land-use regulations in individual countries in the era of deep decarbonization.
Domestic and industrial wastewater treatment plants (WWTPs) are facing formidable challenges in effectively eliminating emerging pollutants and conventional nutrients. In microbiome engineering, two approaches have been developed: a top-down method focusing on domesticating seed microbiomes into engineered ones, and a bottom-up strategy that synthesizes engineered microbiomes from microbial isolates. However, these approaches face substantial hurdles that limit their real-world applicability in wastewater treatment engineering. Addressing this gap, we propose the creation of a Global WWTP Microbiome-based Integrative Information Platform, inspired by the untapped microbiome and engineering data from WWTPs and advancements in artificial intelligence (AI). This open platform integrates microbiome and engineering information globally and utilizes AI-driven tools for identifying seed microbiomes for new plants, providing technical upgrades for existing facilities, and deploying microbiomes for accidental pollution remediation. Beyond its practical applications, this platform has significant scientific and social value, supporting multidisciplinary research, documenting microbial evolution, advancing Wastewater-Based Epidemiology, and enhancing global resource sharing. Overall, the platform is expected to enhance WWTPs’ performance in pollution control, safeguarding a harmonious and healthy future for human society and the natural environment.
The benefits of developing the world’s hydropower potential are intensely debated when considering the need to avoid or minimize environmental impacts. However, estimates of global unused profitable hydropower potential with strict environmental constraints have rarely been reported. In this study we performed a global assessment of the unused profitable hydropower potential by developing a unified framework that identifies a subset of hydropower station locations with reduced environmental impacts on the network of 2.89 million rivers worldwide. We found that the global unused profitable hydropower potential is 5.27 PWh yr−1, two-thirds of which is distributed across the Himalayas. Africa’s unused profitable hydropower is 0.60 PWh yr−1, four times larger than its developed hydropower. By contrast, Europe’s hydropower potential is extremely exploited. The estimates, derived from a consistent and transparent framework, are useful for formulating national hydropower development strategies.