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The Silk Road Economic Belt and the 21st-Century Maritime Silk Road (B&R) aims at facilitating the twenty-first Century economic development of China. However, climate change, air quality and related feedbacks are affecting the successful development of the environment and societies in the B&R geographical domain. The most urgent risks related to the atmospheric system, to the land system and to hydrospheric and cryospheric processes are changing climate – air quality interactions, air pollution, changing monsoon dynamics, land degradation, and the melting of Tibetan Plateau glaciers. A framework is needed in which a science and technology-based approach has the critical mass and expertise to identify the main steps toward solutions and is capable to implement this roadmap. The Pan-Eurasian Experiment (PEEX) program, initiated in 2012, aims to resolve science, technology and sustainability questions in the Northern Eurasian region. PEEX is now identifying its science agenda for the B&R region. One fundamental element of the PEEX research agenda is the availability of comprehensive ground-based observations together with Earth observation data. PEEX complements the recently launched international scientific program called Digital Belt and Road (DBAR). PEEX has expertise to coordinate the ground-based observations and initiate new flagship stations, while DBAR provides a big data platform on Earth observation from China and countries along the Belt and Road region. The DBAR and PEEX have joint interests and synergy expertise on monitoring on ecological environment, urbanization, cultural heritages, coastal zones, and arctic cold regions supporting the sustainable development of the Belt and Road region. In this paper we identify the research themes of the PEEX related Silk Road agenda relevant to China and give an overview of the methodological requirements and present the infrastructure requirements needed to carry out large scale research program.

High-efficiency upconversion (UC) luminescence has been a hot topic in the development of optical materials. In this study, an extremely strong green UC is successfully achieved in the BaWO4:Yb3+/Er3+ phosphor through engineering the local environment around the luminescent centers, realized by Ca2+ doping. The green UC intensity of Ca2+ doped BaWO4:Yb3+/Er3+ is 6.5 and 2.3 times stronger than that of BaWO4:Yb3+/Er3+ and CaWO4:Yb3+/Er3+, respectively. Eu3+ ions are employed as the structure probe to detect the evolution of the crystal field symmetry with the increase of Ca2+ doping concentration, the results of which match well with the corresponding variation of the UC spectra. Meanwhile, the BaWO4:Yb3+/Er3+/Ca2+ exhibits excellent temperature sensing performance based on the fluorescence intensity ratio (FIR) between the thermally coupled 2H11/2 and 4S3/2 states of Er3+ ions. Its absolute sensitivity and relative sensitivity for optical thermometry can reach 1.21% K−1 and 1.31% K−1, outperforming the majority of the same type optical thermometers. The temperature resolution of the present thermometer remains lower than 0.1 K within the temperature range of 298 K to 573 K. The intense green UC luminescence along with the outstanding thermal properties makes the BaWO4:Yb3+/Er3+ phosphor a promising candidate for optical thermometry. [ABSTRACT FROM AUTHOR]

This paper proposes a macroscopic model to describe the equilibrium distribution of passenger arrivals for the morning commute problem in a congested urban rail transit system. We use a macroscopic train operation sub-model developed by Seo, Wada, and Fukuda to express the interaction between the dynamics of passengers and trains in a simplified manner while maintaining their essential physical relations. The equilibrium conditions of the proposed model are derived and a solution method is provided. The characteristics of the equilibrium are then examined both analytically and numerically. As an application of the proposed model, we analyze a simple time-dependent timetable optimization problem with equilibrium constraints and reveal that a 'capacity increasing paradox', in which a higher dispatch frequency increases the equilibrium cost, exists. Furthermore, insights into the design of the timetable are obtained and its influence on passengers' equilibrium travel costs is evaluated. [ABSTRACT FROM AUTHOR]

Artesunate (ART), an antimalarial drug, possesses the ability to induce autophagy and exhibits a protective effect on diabetes. This study aimed to evaluate the effects of ART on diabetic retinopathy (DR) and to explore the underlying mechanisms. Rats with streptozotocin-induced DR were given intravitreal injection of ART. ART administration inhibited the increase in retinal thickness and prevented blood–retinal barrier in diabetic rats. Further, vascular leukocyte adherence, microglial activation, inflammatory cytokine, and ROS production in the retinas of diabetic rats were also inhibited by ART. Additionally, ART enhanced autophagy in the retinas of diabetic rats as demonstrated by up-regulated Beclin-1 expression and LC3II/I ratio and down-regulated p62. ART also activated AMP-activated protein kinase (AMPK)/sensor class III histone deacetylase sirtuin 1 (SIRT1) pathway. ART, as an autophagy activator, has therapeutic potential in DR treatment. [ABSTRACT FROM AUTHOR]

Cr3+‐activated Gd3Ga5O12 garnet (GGG:Cr3+) near‐infrared (NIR) phosphors have shown promising applications in regulating plant growth. However, the low external quantum efficiency (EQE) results in a wall‐plug efficiency (WPE) of phosphor‐converted light‐emitting diodes (pc)‐LEDs of only ≈15%. Herein, an easy strategy to improve the luminescent properties of the GGG:Cr3+ phosphor is reported. Through optimization of the synthesized technology, the EQE is enhanced to 43.6%. Remarkably, the fabricated pc‐LEDs achieve a WPE as high as 34.3%. These results demonstrate a significant advancement in the development and applications of NIR phosphor materials and NIR pc‐LED devices. [ABSTRACT FROM AUTHOR]

Partitioning evapotranspiration (ET) into vegetation transpiration (T) and soil evaporation (E) is challenging, but it is key to improving the understanding of plant water use and changes in terrestrial ecosystems. Considering that the transpiration of vegetation at night is minimal and can be negligible, we established a machine learning model (i.e., extreme gradient boosting algorithm (XGBoost)) for soil evaporation estimation based on night-time evapotranspiration observation data from eddy covariance towers, remote sensing data, and meteorological reanalysis data. Daytime T was consequently calculated as the difference between the total evapotranspiration and predicted daytime soil evaporation. The soil evaporation estimation model was validated based on the remaining night-time ET data (i.e., model test dataset), the non-growing season ET data of the natural ecosystem, and ET data during the fallow periods of croplands. The validation results showed that XGBoost had a better performance in E estimation, with the average overall accuracy of NSE 0.657, R 0.806, and RMSE 11.344 W/m2. The average annual T/ET of the examined ten ecosystems was 0.50 ± 0.08, with the highest value in deciduous broadleaf forests (0.68 ± 0.11), followed by mixed forests (0.61 ± 0.04), and the lowest in croplands (0.40 ± 0.08). We further examined the impact of the leaf area index (LAI) and vapor pressure deficit (VPD) on the variation in T/ET. Overall, at the interannual scale, LAI contributed 28% to the T/ET variation, while VPD had a small (5%) influence. On a seasonal scale, LAI also exerted a stronger impact (1~90%) on T/ET compared to VPD (1~77%). Our study suggests that the XGBoost machine learning model has good performance in ET partitioning, and this method is mainly data-driven without prior knowledge, which may provide a simple and valuable method in global ET partitioning and T/ET estimation. [ABSTRACT FROM AUTHOR]