Snowmelt is a critical component of the global water cycle and a vital freshwater source for both ecosystems and human societies. Yet the global partitioning of snowmelt into runoff and evaporation remains poorly quantified. Here, using a process-based hydrological model (FLEX-Global) forced by meteorological data from 1980 to 2014 and validated against observed streamflow and snow water equivalent, we present a comprehensive assessment of global snowmelt partitioning. The model results are independently supported by two additional approaches: an empirical partitioning equation and inverse estimations from three global hydrological models. We show that 53%–71% of snowmelt runs off globally (excluding Antarctica and Greenland), while 29%–47% contributes to evaporation. Snowmelt partitioning exhibits distinct latitudinal and climatic patterns: contributions of snowmelt to both runoff and evaporation increase with latitude. In cold–humid high-latitude regions, more than 60% of snowmelt becomes runoff, whereas in mid-latitude arid regions, 63%–91% is released from the terrestrial ecosystems as evaporation. Elevation further modulates snow hydrology in mid-latitude mountains, where snowmelt generates 58%–74% of total runoff and 51%–66% of total evaporation—significantly higher than contributions at lower elevations. The traditional definition of snowmelt runoff (snowfall/total runoff) estimates that snowfall accounts for 38% of total runoff, whereas our snowmelt-partitioning approach (snowmelt runoff/total runoff) yields a much lower contribution of 11%–18%. Our results underscore snowmelt's dual role in sustaining freshwater availability and supporting vegetation water demand, redefining its importance in the global hydrological cycle and associated ecosystem services.

Through the vast adoption and application of emerging technologies, the intelligence and autonomy of smart mobility can be substantially elevated to address more diversified demands and supplies. Along with this trend, a systematic collaboration among three essential elements of smart mobility services, namely devices, data and functions, is being studied to comprehensively break down the intrinsic barriers that existed in current solutions, to support the integration of connectable devices, the fusion of heterogeneous data, the composability of reusable functions, and the flexibility in their cooperations. To enable such a collaboration, this paper proposes a federated platform, called Future Mobility Sensing Advisor (FMSA), which can 1) manage the three elements through standardized interfaces separately and uniformly; 2) create a fully connected knowledge graph to orchestrate the three elements efficiently and effectively; 3) support the client-server interaction in centralized and federated modes to handle service requests and edge resources with various availability and accessibilities jointly and adaptively; and 4) accommodate various mobility services to foster harmonious and sustainable mobility tenderly and invisibly. Moreover, the efficiency and effectiveness of the platform are also tested through a performance evaluation, and a pilot supported at the Great Boston Area, respectively. As a result, it shows that FMSA can 1) achieve high performance by using the two interaction modes selectively, and 2) renovate smart mobility towards sustainability through personalized services that can measure user preferences and system objectives mutually.

The present work demonstrates how drug-loaded mesoporous silica nanoparticles (MSNPs) can be prepared by a sequential flash nanoprecipitation (FNP) technique. A sequential FNP technique is developed relying on a combination of two multi-inlet vortex mixers (MIVM), by which a continuous process that involves the formation of micelle-based templates followed by an in situ formation of MSNPs is achieved. Moreover, a widely used biological nematicide, abamectin (Abm), is added during the formation of micelles, ultimately leading to Abm-loaded MSNPs with high encapsulation efficiency. The obtained Abm-loaded MSNPs show excellent stability and inhibition activity against the livability of Meloidogyne incognita. Importantly, the parameters of the resulting MSNPs, such as silica shell thickness and inner cavity size of MSNPs, can be easily controlled by tuning the compositions of the reactant streams. We believe that such a simple approach towards direct preparation of drug-loaded MSNPs would find promising up-scale applications in various fields, such as drug delivery, bioimaging, and formulation technology.

Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based stated preferences (SP) surveys leveraging state-of-the-art smartphone-based survey platforms and their revealed preferences sensing capabilities. A random experimental design generates context-aware SP profiles using user specific socioeconomic characteristics and past travel data along with relevant web data for scenario generation. The generated choice tasks are automatically validated to reduce the number of dominant or inferior alternatives in real-time, then validated using Monte-Carlo simulations offline. In this paper we focus our attention on mode choice and design an experiment that considers a wide range of possible existing mode alternatives along with a new alternative on-demand mobility service that does not exist in real life. This experiment is then used to collect SP data or a sample of 224 respondents in the Greater Boston Area. A discrete mode choice model is estimated to illustrate the benefit of the proposed method in capturing current context-specific preferences in response to the new scenario.

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a <q>special report in 2018 on the impacts of global warming of 1.5 °C  above pre-industrial levels and related global greenhouse gas emission pathways</q>. In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5 °C , including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).