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Mass spectrometry plays a crucial role in biomedicine by detecting isotopes, contributing significantly to research, diagnostics, and therapy development. This study introduces IsoFusion, a deep learning model designed to address isotope detection in raw mass spectra. Rather than directly applying convolutional layers to all signal and noise peaks, IsoFusion employs a trial-and-error strategy. First, it investigates all potential charge states (trials) and collects signal peaks around expected m/z values for each trial. Then, convolutional layers extract features from each trial, which are fused to identify the correct one. Finally, the reparameterization trick predicts isotope features based on this correct trial. A key advantage of IsoFusion is shared model parameters across all trials, enhancing feature learning for less common charge states using data from prevalent ones. Our results show a significant accuracy improvement for charge state 5, reaching 99.42%, compared to DeepIso’s 43.36%. Moreover, IsoFusion achieves a 97.33% detection accuracy for isotopes, with 2.4% of detected isotopes previously unidentified by four commonly used methods.

The effectiveness of AI-driven drug discovery can be enhanced by pretraining on small molecules. However, the conventional masked language model pretraining techniques are not suitable for molecule pretraining due to the limited vocabulary size and the non-sequential structure of molecules. To overcome these challenges, we propose FragAdd, a strategy that involves adding a chemically implausible molecular fragment to the input molecule. This approach allows for the incorporation of rich local information and the generation of a high-quality graph representation, which is advantageous for tasks like virtual screening. Consequently, we have developed a virtual screening protocol that focuses on identifying estrogen receptor alpha binders on a nucleus receptor. Our results demonstrate a significant improvement in the binding capacity of the retrieved molecules. Additionally, we demonstrate that the FragAdd strategy can be combined with other self-supervised methods to further expedite the drug discovery process.

Global climate action is urgent, with forest carbon stock critical for mitigating climate change, yet vulnerable to its impacts. However, the long-term dynamics of climate-driven forest carbon stock have not fully been expressed. Here, we introduce the Forest Carbon Stock Accumulated by Single Tree growth (FAST) framework and constructed a counterfactual scenario to isolate and quantify the climate-driven changes in forest total carbon stock for 1901–2022. Results show that breakpoints in climate-driven forest carbon stock occurred post-1970 are observed over 62% of the study areas, with Europe experiencing the latest, followed by Asia, and North America the earliest. Furthermore, we observe a prevailing increasing trend in climate-driven forest carbon stock, especially in post-breakpoints period (from 53% to 68%), indicating that climate changes have alleviated constraints on forest carbon storage capacity in most areas. FAST can be utilized for historical, current and future forest carbon stock estimation, providing scientific support for sustainable forest management decisions.

Abstract Agriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China’s carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insufficient to characterize regional heterogeneity. Here, we selected the county-level administrative district as the basic geographical unit and then generated a county-level dataset on the intensity of carbon emissions from crop farming in China during 2000–2019, using random forest regression with multi-source data. This dataset can be used to delineate spatio-temporal changes in carbon emissions from crop farming in China, providing an important basis for decision makers and researchers to design agricultural carbon reduction strategies in China.

Abstract Transition metal oxides (TMOs) exhibit fascinating physicochemical properties, which originate from the diverse coordination structures between the transition metal and oxygen atoms. Accurate determination of such structure-property relationships of TMOs requires to correlate structural and electronic properties by capturing the global parameters with high resolution in energy, real, and momentum spaces, but it is still challenging. Herein, we report the determination of characteristic electronic structures from diverse coordination environments on the prototypical anatase-TiO2(001) with (1 × 4) reconstruction, using high-resolution angle-resolved photoemission spectroscopy and scanning tunneling microscopy/atomic force microscopy, in combination with density functional theory calculation. We unveil that the shifted positions of O 2s and 2p levels and the gap-state Ti 3p levels can sensitively characterize the O and Ti coordination environments in the (1 × 4) reconstructed surface, which show distinguishable features from those in bulk. Our findings provide a paradigm to interrogate the intricate reconstruction-relevant properties in many other TMO surfaces.

The agricultural production space, as where and how much each agricultural product grows, plays a vital role in meeting the increasing and diverse food demands. Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types, using methods such as remote sensing or statistical metrological analysis. In this study, we characterize the agricultural production space (APS) by bipartite network connecting agricultural products and provinces, to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China. The results show that core products are cereal, pork, melon, and pome fruit; meanwhile the milk, grape, and fiber crop show an upward trend in centrality, which is in line with diet structure changes in China over the past decades. The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable. Additionally, identified provincial communities closely resemble China’s agricultural natural zones. Furthermore, the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production. Despite the superior production capabilities of southern provinces, recent years have witnessed a notable decline, warranting special attentions. The findings provide a comprehensive perspective for understanding the complex relationship of agricultural products’ relatedness, production capabilities and production patterns, which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

With increasing concentrations of atmospheric greenhouse gases, the interaction between climate change and agriculture is receiving widespread attention as part of food security and sustainable human development. However, a comprehensive summary of knowledge in the field of climate change and agriculture from a scientometric perspective is still lacking. Here, we selected 25,872 papers related to climate change and agriculture from the Web of Science Core Collection database for the period 1985 to 2023 and used VOSviewer software to reveal the research status and trends. The main results were as follows: (1) the number of papers in this field showed a rapidly increasing trend after 2007, with a clear interdisciplinary characteristic; (2) The United States was the most influential country in this field with 6819 papers and 363,109 citations. China had the second highest number of papers (3722 papers), but the Chinese Academy of Sciences was the most influential institution with the most papers. On an author level, Pete Smith was the most influential; (3) All keywords were divided into four different research topics, such as the impact of climate change on agriculture, climate change mitigation and adaptation in agriculture, and crop growth in response to climate change. Among them, some keywords related to climate change adaptation were the most recent topics of interest in this field. These findings provide effective scientific references for relevant scientists and practitioners to better conduct future theoretical and practical research on climate change and agriculture.

Abstract Larch, a widely distributed tree in boreal Eurasia, is experiencing rapid warming across much of its distribution. A comprehensive assessment of growth on warming is needed to comprehend the potential impact of climate change. Most studies, relying on rigid calendar-based temperature series, have detected monotonic responses at the margins of boreal Eurasia, but not across the region. Here, we developed a method for constructing temporally flexible and physiologically relevant temperature series to reassess growth-temperature relations of larch across boreal Eurasia. Our method appears more effective in assessing the impact of warming on growth than previous methods. Our approach indicates widespread and spatially heterogeneous growth-temperature responses that are driven by local climate. Models quantifying these results project that the negative responses of growth to temperature will spread northward and upward throughout this century. If true, the risks of warming to boreal Eurasia could be more widespread than conveyed from previous works.

The thermos-structural materials with high strength and high temperature water-oxygen corrosion resistance are urgently needed in aero-space fields. SiC whisker reinforced SiC matrix (SiCw/SiC) composites are the ideal candidate materials for their excellent mechanical properties and thermostability. However, the poor resistance to water-oxygen corrosion ability of SiC matrix limits their further application. In this work, a novel SiCw/SiC–Si–Y composites were first prepared by infiltrating silicon-yttrium (Si–Y) alloy into SiCw/C preform via reactive melt infiltration method. The as-obtained composites not only show the excellent mechanical properties (σF = 363 MPa, KⅠC = 9.14 MPa m1/2), and more importantly after water-oxygen corrosion of 20 h at such temperature as high as 1500 °C, the strength and toughness retention of the composites can reach 106.1% and 103.6%, respectively. The SiCw/SiC–Si–Y composites developed in this paper have a great potential as a new high-performance thermos-structural material for aero-space applications.

A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China’s county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods’ accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops.

Food security as a national strategy, closely links to China's economic progress, people's livelihoods and social harmony. The balance between feed grain security and meat security remains a key unresolved issue that significantly impacts China's integral food security goal. In this study, we first apply food security index to demonstrate the efficacy of the self-sufficiency rate as an indicator for evaluating the food security situation, and then evaluate meat security and feed grain security with self-sufficiency rate, as well as explore their production challenges and successful long-term development experiences based on related agricultural data. We find that the burgeoning meat consumption has made feed grains a “rigid demand”, among which pork and soybean are critical parts. Pork consumption accounts for over 65% of total meat consumption in 2020, but its self-sufficient rate is waning. Moreover, the self-sufficiency rate of soybean is as low as 0.15, and a great unit yield gap between China and the United States. However, the current production patterns have already reached their attainable potential in production efficiency due to the declining growth rate of unit yield, which also pose challenges for improving production efficiency and domestic supply of soybean. According to the projection by FAO-OECD, the growth rate of unit yield of soybean will remain stagnant from 2020 to 2030. Furthermore, the percentage of medium-scale and large-scale pig farms is remarkably low, accounting for only 0.578% and 0.013%, respectively. Further analysis indicates that large-scale production in soybean and pig industry offers notable advantages in improving production efficiency and reducing feed consumption. Finally, based on factors such as soybean sowing area, regions with high pig slaughter and scale farms, farm size and the pig regional development planning, we propose the integration of soybean-pig in large-scale production in selected possible provinces, which has the potential to improve the production efficiency of soybean and pig, to strike a balance between feed grain security and meat security. This study has practical significance in optimizing the production space of feed grains and livestock products, to improve production capability and ensure China's food security.

The fabrication of silicon nitride (Si3N4) fiber-reinforced boron nitride (BN) and Si3N4 matrix (Si3N4f/BN/Si3N4) composites with BN interphase through precursor infiltration and pyrolysis (PIP) process is reported. The effects of heating rate during PIP process on mechanical and dielectric properties of composites are analyzed. When the heating rate is 5 °C/min, the overall performance of Si3N4f/BN/Si3N4 composites is optimal, and the flexural strength and fracture toughness attain 138 MPa and 5.7 MPa m1/2, and the dielectric constant and dielectric loss are 3.90 and 0.008, respectively. The composites achieve a synergy combining structural with functional properties. The influence mechanism of the heating rate during PIP process on the Si3N4f/BN/Si3N4 composites disclose the following. First, the heating rate makes an effect on the carbon content in the Si3N4 matrix, and consequently the dielectric properties of the composites are changed. Second, at 8 °C/min, the modulus of Si3N4 matrix is the lowest due to the existence of nanopores. At 2 °C/min, the strong axial tensile stress results in severe cracking of the Si3N4 matrix, so the matrix modulus decrease. Third, the heating rate strongly affects the radial stress state at the interphase, which affects the interfacial shear strength of the composites. The matrix modulus and interfacial shear strength can be optimized at 5 °C/min to improve the mechanical properties of the composites.

The prediction of binding affinities between target proteins and small molecule drugs is essential for speeding up the drug research and design process. To attain precise and effective affinity prediction, computer-aided methods are employed in the drug discovery pipeline. In the last decade, a variety of computational methods has been developed, with deep learning being the most commonly used approach. We have gathered several deep learning methods and classified them into convolutional neural networks (CNNs), graph neural networks (GNNs), and Transformers for analysis and discussion. Initially, we conducted an analysis of the different deep learning methods, focusing on their feature construction and model architecture. We discussed the advantages and disadvantages of each model. Subsequently, we conducted experiments using four deep learning methods on the PDBbind v.2016 core set. We evaluated their prediction capabilities in various affinity intervals and statistically and visually analyzed the samples of correct and incorrect predictions for each model. Through visual analysis, we attempted to combine the strengths of the four models to improve the Root Mean Square Error (RMSE) of predicted affinities by 1.6% (reducing the absolute value to 1.101) and the Pearson Correlation Coefficient (R) by 2.9% (increasing the absolute value to 0.894) compared to the current state-of-the-art method. Lastly, we discussed the challenges faced by current deep learning methods in affinity prediction and proposed potential solutions to address these issues.

Food supply shock is defined as a drastic shortage in food supply, which would likely threaten the achievement of Sustainable Development Goals 2: zero hunger. Traditionally, highly-connected global food supply system was deemed to help overcome shortages easily in response to food supply shock. However, recent studies suggested that overconnected trade networks potentially increase exposure to external shocks and amplify shocks. Here, we develop an empirical–statistical method to quantitatively and meticulously measure the diversity of international food supply chain. Our results show that boosting a country’s food supply chain diversity will increase the resistance of the country to food shocks. The global diversity of food supply chain increased gradually during 1986–2021; correspondingly, the intensity of food shocks decreased, the recovery speed after a shock increased. The food supply chain diversity in high-income countries is significantly higher than that in other countries, although it has improved greatly in the least developed regions, like Africa and Middle East. International emergencies and geopolitical events like the Russia–Ukraine conflict could potentially threaten global food security and impact low-income countries the most. Our study provides a reference for measuring resilience of national food system, thus helping managers or policymakers mitigate the risk of food supply shocks.

Abstract The taxonomic structure of microbial community sample is highly habitat-specific, making source tracking possible, allowing identification of the niches where samples originate. However, current methods face challenges when source tracking is scaled up. Here, we introduce a deep learning method based on the Ontology-aware Neural Network approach, ONN4MST, for large-scale source tracking. ONN4MST outperformed other methods with near-optimal accuracy when source tracking among 125,823 samples from 114 niches. ONN4MST also has a broad spectrum of applications. Overall, this study represents the first model-based method for source tracking among sub-million microbial community samples from hundreds of niches, with superior speed, accuracy, and interpretability. ONN4MST is available at https://github.com/HUST-NingKang-Lab/ONN4MST .

Spatial sparsity of the target space has been successfully exploited to provide accurate range-angle images by the methods based on sparse signal reconstruction (SSR) in Multiple-Input Multiple-Output (MIMO) radar imaging applications. The SSR based method discretizes the continuous target space into finite grid points and generates an observation model utilized in image reconstruction. However, inaccuracies in the observation model may cause various degradations and spurious peaks in the reconstructed images. In the process of the image formation, the off-grid problem frequently occurs that the true locations of targets that do not coincide with the computation grid. In this article, we consider the case that the true location of a target has both range and angle-varying two-dimensional (2D) off-grid errors with a noninformative prior. From a variational Bayesian perspective, an iterative algorithm is developed for joint MIMO radar imaging with orthogonal frequency division multiplexing (OFDM) linear frequency modulated (LFM) waveforms and 2D off-grid error estimation of off-grid targets. The targets during multiple probing pulses are modeled as Swerling II case and a unified generalized inverse Gaussian (GIG) prior is adopted for the target reflection coefficient variance at all snapshots. Furthermore, an approach to reducing the computational workload of the signal recovery process is proposed by using singular value decomposition. Experimental results show that the proposed algorithm is insensitive to noise and has improved accuracy in terms of mean squared estimation error under different computation grid interval.

Abstract In recent decades, grain self‐sufficiency has been a key security issue for certain countries, such as Russia, China, India, and France. Climate change may affect agricultural production and possibly influence the grain self‐sufficiency of some countries, threatening their food security. This article provides an updated integrated assessment of the impact of climate change on grain self‐sufficiency at the country level. The study links climatic variables projected by global climate models to economic variables simulated by a global multiregion multisector economic model. Although climate change may considerably reduce land productivity, our simulation results show that climate change in the long term modestly impacts regional grain self‐sufficiency, as the direct effects on yield are largely canceled out by changes in grain production via adaptation measures taken by producers in response to market signals. Behavioral changes of consumers also contribute modestly to cancel out the adverse impact on land productivity. In the RCP8.5 climate change scenario, the adverse impact of climate change on land productivity in 2100 may reach to 25% of the current level, but declines to 5% when changes in production and consumption are considered, thus indicating the role of long‐term adaptation by economic agents.

Understanding the response of vegetation spring phenology to climatic factors in arid regions is crucial for projecting the land-climate interactions of arid ecosystems under climate change. The Xinjiang region, an important part of northern China with complex climatic features, was selected as a case study. We investigated the impact of temperature, precipitation, and solar radiation on the start of the vegetation growing season (SOS) using a long-term series of the normalized difference vegetation index, climate data from 1982 to 2014, and the partial least squares regression (PLS) method. The primary findings were following: (1) the regional scaling of the mean SOS ranged from 95 to 113 day of year (DOY) and showed distinct spatial heterogeneity for different vegetation types. The relative percentage of the area that exhibited advanced SOS (65.80%) was larger than that of the area that exhibited delayed SOS (34.20%), with a regional advancing tendency of 0.19 days per year during the study period. Different vegetation types showed an advancing SOS, which shifted to delaying after 2005. (2) Temperature significantly affected the SOS for various vegetation types. Generally, the warming spring temperatures led to advanced SOS, whereas reduced warming in spring after 2005 reversed the SOS trends. The higher solar radiation occurring from late winter to spring also contributed to the advance in the SOS. Conversely, the increasing precipitation during this period caused a delay in the SOS. These results can provide a useful reference for improving the vegetation phenology models of arid regions.

Dry Land Asia is the largest arid and semi-arid region in the northern hemisphere that suffers from land desertification. Over the period 1982–2011, there were both overall improvement and regional degeneration in the vegetation NDVI. We analyze future climate changes in these area using two ensemble-average methods from CMIP5 data. Bayesian Model Averaging shows a better capability to represent the future climate and less uncertainty represented by the 22-model ensemble than does the Simple Model Average. From 2006 to 2100, the average growing season temperature value will increase by 2.9 °C, from 14.4 °C to 17.3 °C under three climate scenarios (RCP 26, RCP 45 and RCP 85). We then conduct multiple regression analysis between climate changes compiled from the Climate Research Unit database and vegetation greenness from the GIMMS NDVI3g dataset. There is a general acceleration in the desertification trend under the RCP 85 scenario in middle and northern part of Middle Asia, northwestern China except Xinjiang and the Mongolian Plateau (except the middle part). The RCP 85 scenario shows a more severe desertification trend than does RCP 26. Desertification in dry land Asia, particularly in the regions highlighted in this study, calls for further investigation into climate change impacts and adaptations.

Heat extremes have a serious impact on humans and on agriculture around the world. As one of the prominent climate change ‘hot spots,’ the Mediterranean area, especially the eastern portion, is expected to be more vulnerable to heat exposure than other regions due to its high population density and urbanization rate. The Paris Agreement includes the goal of ‘holding the increase in the global average temperature to well below 2 °C above preindustrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above preindustrial levels’. It is interesting to study how heat extremes would change in the Mediterranean area in a +1.5 °C and +2 °C global warming world and how they would impact humans and agriculture. Based on high resolution climate scenario data from Coordinated Regional Downscaling Experiment Mediterranean, we calculate several heat extreme indices to study heat extreme changes in the future. We found that in most Mediterranean areas, both daytime and nighttime heat wave intensity and frequency will have a robust increase in the 21st century compared with that of the historical period, with the most prominent areas located in northwest Africa, the Iberian Peninsula, Italy and Middle East. Meanwhile, nighttime heat waves should be garnering more attention because more moderate and extreme events happen in nighttime than in daytime. If the global warming level is limited to 2 °C or even 1.5 °C, the percentage of population exposed to dangerous events (especially one-in-20 year events) and yield loss from maize harvested areas may be much less in the future than the percentage affected during 3 °C warming period. Moreover, limiting global warming to 1.5 °C instead of 2 °C would result in an additional 20%–25% reduction of exposure.

In this study, we develop a simple dimension model to construct the food system in response to the population growth, the land use change and various socio-economic developments. The model simulates the consumption and production of agricultural goods in the individual countries, and maintains the global balance of food through the international trade flows between countries. Although there are some biases in the model results at the country levels, we suggest that such a simple model is feasible for simulating the global food supply system and assessing the uncertainties coming from various environmental and socio-economic factors.

Food consumption puts pressure on natural resources and arable land. In the present study, we examined the dynamics of land requirements for food in South Africa from 1961 to 2007 and investigated the relationships between dietary patterns, yield, cropping intensity, population and the area of required land using the thought experiment method. Strong population growth and the development of agricultural technology (indicated by yield) accounted for more than a 2.5-fold increase in the total land requirements for food from 1961 to 2007. Before the 1990s, the increase in crop yields enabled constant land requirements, whereas, after the 1990s, the combined effect of agricultural technology and population growth, together with a small contribution from dietary changes, led to an increase in the land requirements for food. Our findings confirm that the variation in land requirements for food is a complex, non-linear function of agricultural production techniques, population growth and dietary patterns and show that the complex relationship between dietary pattern changes, and economic development challenges future predictions of land requirements for food in South Africa.

The soil chronosequence is a useful method for investigating pedological theories. Soil chemical, physical and mineralogical properties in chronosequences change over time and exhibit systematic and time-dependent trends, which can be used to analyze the rates and directions of pedogenic changes. The potential of soil spectroscopy as an emerging, rapid and cost-effective technique for predicting soil properties has been widely accepted and has motivated the application of spectroscopic techniques to the analysis of soil chronosequence. We present a soil chronosequence derived from 1000-year-old calcareous marine sediments and examine changes in six soil properties over this period. We evaluated the utility of a soil spectroscopic method to detect soil property changes and to predict the pedogenic properties and soil ages of the chronosequence. The results show that some soil pedogenic processes, such as soil organic matter accumulation, CaCO3 leaching and clay migration, can be identified in the millennium chronosequence. Power chronofunctions are formulated for soil organic matter (SOM) and Logarithmic chronofunctions are fitted for clay, CaCO3 and pH. These pedogenic processes are identified in the reflectance intensity and absorption features of soil spectroscopy, and pedogenic properties can be calibrated via soil reflectance spectroscopy. Profile ages can also be predicted via pseudo multi-depth spectra of soil profiles, and soil spectral curves for 0−30 cm generated the best prediction results (RPD = 1.85). We conclude that soil properties, changing due to weathering and soil formation, act as a bridge linking spectroscopy and weathering levels/pedogenic processes. The results imply that applying spectroscopy techniques to chronosequence study and mapping the degree of soil development in certain areas should be possible.

This study evaluates the performances of three global satellite datasets (Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l’ observation de la Terre (SPOT) of the Mongolian Plateau, where in situ observation is insufficient to assess vegetation dynamics on terrestrial systems. We give a comprehensive assessment of the historical changes in vegetation dynamics by using comparative and correlation methods on the three archives using two indices: the growing season’s Normalized Difference Vegetation Index (NDVI) and the Start of the Season Index (SOS). The main findings are: (1) MODIS and SPOT have generally better comparability and consistency in the spatial-temporal trends of NDVI and SOS than AVHRR in this area; (2) all the three archives exhibit better consistency in Inner Mongolia than in Mongolia; (3) integration data analysis of AVHRR (1982–1997) and SPOT (1998–2012) shows that the dynamics of vegetation growth has three distinct phases: enhanced before 1994; a flatter/slightly decreasing trend before 2001; and, then, a rapid recovery between 2001 and 2012 with remarkable spatial heterogeneity, with Inner Mongolia experiencing a significant greening in vegetation NDVI compared with no obvious changes in Mongolia; (4) the temporal average SOS showed no significant “earlier spring” onset during the past 31 years, on the middle and northern Mongolian Plateau.

Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.

Electronic heating of cold crystal lattices in nonlinear multiphoton excitation can transiently alter their physical and chemical properties. In metals where free electron densities are high and the relative fraction of photoexcited hot electrons is low, the effects are small, but in semimetals, where the free electron densities are low and the photoexcited densities can overwhelm them, the intense femtosecond laser excitation can induce profound changes. In semimetal graphite and its derivatives, strong optical absorption, weak screening of the Coulomb potential, and high cohesive energy enable extreme hot electron generation and thermalization to be realized under femtosecond laser excitation. We investigate the nonlinear interactions within a hot electron gas in graphite through multiphoton-induced thermionic emission. Unlike the conventional photoelectric effect, within about 25 fs, the memory of the excitation process, where resonant dipole transitions absorb up to eight quanta of light, is erased to produce statistical Boltzmann electron distributions with temperatures exceeding 5000 K; this ultrafast electronic heating causes thermionic emission to occur from the interlayer band of graphite. The nearly instantaneous thermalization of the photoexcited carriers through Coulomb scattering to extreme electronic temperatures characterized by separate electron and hole chemical potentials can enhance hot electron surface femtochemistry, photovoltaic energy conversion, and incandescence, and drive graphite-to-diamond electronic phase transition.

This paper discussed the analysis of the survey on sustainability of bioenergy conducted in the Philippines, India and China. It acquired general perceptions of the people by asking them (a) specific questions about their level of familiarity with bioenergy; (b) relationship of their work to bioenergy; and (c) their opinion on contribution of various feedstock on the economy and impact of bioenergy production on food security. In addition to these questions, we estimated preference weights of various feedstock based on the conjoint choices on bioenergy’s contribution to social stability, social welfare and ecological balance. The estimates revealed significant trade-offs not only among these three dimensions of sustainability but also the relative importance of energy security, food security and ecosystem capacity to other economic, social and environmental objectives. The types of first generation feedstock that are currently used for biofuel production in the respective countries and those that offer alternative household use are perceived as important to the economy and preferred bioenergy feedstock. Based on the results of the study, the preferred role of bioenergy for sustainable development reflects the social and economic concerns in the respective Asian countries, e.g., energy security in China, food security in India, and ecosystem degradation in the Philippines.