Perspectives on studies on soil carbon stocks and the carbon sequestration potential of China

Peer reviewedPostprin

STUDY ON EARTHQUAKE DAMAGE MECHANISM OF AQUEDUCT STRUCTURE BASED ON DIFFERENT BOUNDARY

Numerically simulating an infinite domain foundation is an important method for solving structural dynamics problems. This paper introduces several artificial dynamic boundaries commonly used in the study of structural dynamics, and elaborates the theory and methods of the dynamic infinite element method boundary (IEMB) and viscous–spring artificial boundary (VSAB). The capacity of different boundary effects on seismic waves energy absorption is verified by establishing a layered half-space model. An irrigation aqueduct is taken as a research object. The IEMB, VSAB, and fixed boundary (FB) models are established and the Concrete Damaged Plasticity (CDP) constitutive is introduced, which is aimed at studying the dynamic failure mechanism and the rules of damage development to the aqueduct structure during the seismic duration. The results for the IEMB and VSAB show better energy absorption for the incident waves and a better simulation result for the damping effect of the far field foundation than that of the FB. Comparing the maximum displacement response rules of the three boundaries, it is seen that the maximum displacement response values of the VSAB and dynamic IEMB increased by 6%–48% and 9%–35%, respectively, over the FB. The calculation results of the VSAB are similar to that of the IEMB. The difference between the maximum acceleration response values is 2%–17% whereas the difference between the maximum displacement response values is 0.4%–19%. The IEMB studied in this paper provides a theoretical reference for large–scale building boundary treatment in structural dynamics calculations

Does metal pollution matter with C retention by rice soil?

Date of Acceptance: 17/07/2015 The research work was supported by the China Natural Science Foundation under a grant number of 40830528 and of 40671180. P.S. is a Royal Scoiety-Wolfson Research Merit Award holder and was supported by additional travel funds from a UK BBSRC China Partnership Award. P.S.’s contribution was supported by the UK-China Sustainable Agriculture Innovation Network (SAIN). D.C. was supported by an additional travel and collaboration funding from the China Ministry of Education under a “111” project.Peer reviewedPublisher PD

Pre-Earthquake Ionospheric Perturbation Identification Using CSES Data \u3cem\u3evia\u3c/em\u3e Transfer Learning

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky radius and an input sequence length of 20 consecutive observations during night time. We further explore a transferring learning approach, which initially trains the model with the larger Electro-Magnetic Emissions Transmitted from the Earthquake Regions (DEMETER) dataset, and then tunes the model with the CSES dataset. The transfer-learning performance is substantially higher than that of direct learning, yielding a 12% improvement in the F1 score and a 29% improvement in the MCC value. Moreover, we compare the proposed model SeqNetQuake with other five benchmarking classifiers on an independent test set, which shows that SeqNetQuake demonstrates a 64.2% improvement in MCC and approximately a 24.5% improvement in the F1 score over the second-best convolutional neural network model. SeqNetSquake achieves significant improvement in identifying pre-earthquake ionospheric perturbation and improves the performance of earthquake prediction using the CSES data

Towards Advancing the Earthquake Forecasting by Machine Learning of Satellite Data

Earthquakes have become one of the leading causes of death from natural hazards in the last fifty years. Continuous efforts have been made to understand the physical characteristics of earthquakes and the interaction between the physical hazards and the environments so that appropriate warnings may be generated before earthquakes strike. However, earthquake forecasting is not trivial at all. Reliable forecastings should include the analysis and the signals indicating the coming of a significant quake. Unfortunately, these signals are rarely evident before earthquakes occur, and therefore it is challenging to detect such precursors in seismic analysis. Among the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases

More microbial manipulation and plant defense than soil fertility for biochar in food production: A field experiment of replanted ginseng with different biochars

The role of biochar–microbe interaction in plant rhizosphere mediating soilborne disease suppression has been poorly understood for plant health in field conditions. Chinese ginseng ( Panax ginseng C. A. Meyer) is widely cultivated in Alfisols across Northeast China, being often stressed severely by pathogenic diseases. In this study, the topsoil of a continuously cropped ginseng farm was amended at 20 t ha − 1, respectively, with manure biochar (PB), wood biochar (WB), and maize residue biochar (MB) in comparison to conventional manure compost (MC). Post-amendment changes in edaphic properties of bulk topsoil and the rhizosphere, in root growth and quality, and disease incidence were examined with field observations and physicochemical, molecular, and biochemical assays. In the 3 years following the amendment, the increases over MC in root biomass were parallel to the overall fertility improvement, being greater with MB and WB than with PB. Differently, the survival rate of ginseng plants increased insignificantly with PB but significantly with WB (14%) and MB (21%), while ginseng root quality was unchanged with WB but improved with PB (32%) and MB (56%). For the rhizosphere at harvest following 3 years of growing, the total content of phenolic acids from root exudate decreased by 56, 35, and 45% with PB, WB, and MB, respectively, over MC. For the rhizosphere microbiome, total fungal and bacterial abundance both was unchanged under WB but significantly increased under MB (by 200 and 38%), respectively, over MC. At the phyla level, abundances of arbuscular mycorrhizal and Bryobacter as potentially beneficial microbes were elevated while those of Fusarium and Ilyonectria as potentially pathogenic microbes were reduced, with WB and MB over MC. Moreover, rhizosphere fungal network complexity was enhanced insignificantly under PB but significantly under WB moderately and MB greatly, over MC. Overall, maize biochar exerted a great impact rather on rhizosphere microbial community composition and networking of functional groups, particularly fungi, and thus plant defense than on soil fertility and root growth

Pathological Ace2-to-Ace enzyme switch in the stressed heart is transcriptionally controlled by the endothelial Brg1–FoxM1 complex

Genes encoding angiotensin-converting enzymes (Ace and Ace2) are essential for heart function regulation. Cardiac stress enhances Ace, but suppresses Ace2, expression in the heart, leading to a net production of angiotensin II that promotes cardiac hypertrophy and fibrosis. The regulatory mechanism that underlies the Ace2-to-Ace pathological switch, however, is unknown. Here we report that the Brahma-related gene-1 (Brg1) chromatin remodeler and forkhead box M1 (FoxM1) transcription factor cooperate within cardiac (coronary) endothelial cells of pathologically stressed hearts to trigger the Ace2-to-Ace enzyme switch, angiotensin I-to-II conversion, and cardiac hypertrophy. In mice, cardiac stress activates the expression of Brg1 and FoxM1 in endothelial cells. Once activated, Brg1 and FoxM1 form a protein complex on Ace and Ace2 promoters to concurrently activate Ace and repress Ace2, tipping the balance to Ace2 expression with enhanced angiotensin II production, leading to cardiac hypertrophy and fibrosis. Disruption of endothelial Brg1 or FoxM1 or chemical inhibition of FoxM1 abolishes the stress-induced Ace2-to-Ace switch and protects the heart from pathological hypertrophy. In human hypertrophic hearts, BRG1 and FOXM1 expression is also activated in endothelial cells; their expression levels correlate strongly with the ACE/ACE2 ratio, suggesting a conserved mechanism. Our studies demonstrate a molecular interaction of Brg1 and FoxM1 and an endothelial mechanism of modulating Ace/Ace2 ratio for heart failure therapy

WebArena: A Realistic Web Environment for Building Autonomous Agents

With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and tested in simplified synthetic environments, leading to a disconnect with real-world scenarios. In this paper, we build an environment for language-guided agents that is highly realistic and reproducible. Specifically, we focus on agents that perform tasks on the web, and create an environment with fully functional websites from four common domains: e-commerce, social forum discussions, collaborative software development, and content management. Our environment is enriched with tools (e.g., a map) and external knowledge bases (e.g., user manuals) to encourage human-like task-solving. Building upon our environment, we release a set of benchmark tasks focusing on evaluating the functional correctness of task completions. The tasks in our benchmark are diverse, long-horizon, and designed to emulate tasks that humans routinely perform on the internet. We experiment with several baseline agents, integrating recent techniques such as reasoning before acting. The results demonstrate that solving complex tasks is challenging: our best GPT-4-based agent only achieves an end-to-end task success rate of 14.41%, significantly lower than the human performance of 78.24%. These results highlight the need for further development of robust agents, that current state-of-the-art large language models are far from perfect performance in these real-life tasks, and that WebArena can be used to measure such progress.Comment: Our code, data, environment reproduction resources, and video demonstrations are publicly available at https://webarena.dev