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Overview of the Measures and Techniques Used to Protect Traffic Lines against Shifting Sands in China

Journal of Resources and Ecology 2021年第1期12卷 124-135页

作者： MA Ning GUO Qun LI Yu LI Shenggong Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China National Ecosystem Science Data Center Beijing 100101China Sino-Danish College University of Chinese Academy of SciencesBeijing 100190China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China Key Lab for Resources Use and Environmental Remediation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Shifting sands are one of the main contributors to desertification in China. This paper briefly reviews the measures and techniques which are used to protect traffic lines by stabilizing and fixing sands in the desert and desertification-affected arid and semi-arid areas in north China. We introduce the types and features of these measures and techniques, including mechanical, chemical, and biological measures, and outline how they have been applied in different areas and in different traffic lines over the past six decades, from 1950 s to 2010 s, taking the Baotou-Lanzhou railway, the Qinghai-Tibet railway, and the Tarim Desert highway as examples Mechanical measures such as erecting sand-retaining wind walls and placing straw checkerboards have proved to be very efficient for stabilizing shifting sands and protecting traffic lines that pass through the desert areas. Chemical measures are not widely used in the current sand fixing systems because of their high cost and potential pollution risks. Biological measures are preferred because they exhibit much better sand fixation performance and longer duration than the former two types of measures despite their relatively high cost. A combination of different measures is usually adopted in some areas to attain better sand-fixing effects. Stabilizing sand dune surfaces with mechanical measures or irrigation from underground water or river if available helps early recruitment of some drought-tolerant plants(xerophytes). We also point out the restrictions for existing sand-fixing measures and techniques and future research orientation. This review has implications for addressing eco-environmental issues associated with infrastructure construction that is part of the Belt and Road Initiative in desert and desertification-affected arid and semi-arid areas in the Mongolian Plateau.

关键词： traffic lines desertification shifting sands wind erosion sand fixing system

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Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors

arXiv

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arXiv 2020年

作者： Cho, Taewon Chung, Julianne Jiang, Jiahua Department of Mathematics Virginia Tech BlacksburgVA United States Department of Mathematics Computational Modeling and Data Analytics Division Academy of Integrated Science Virginia Tech BlacksburgVA United States Department of Mathematics Virginia Tech BlacksburgVA United States

When solving ill-posed inverse problems, a good choice of the prior is critical for the computation of a reasonable solution. A common approach is to include a Gaussian prior, which is defined by a mean vector and a symmetric and positive definite covariance matrix, and to use iterative projection methods to solve the corresponding regularized problem. However, a main challenge for many of these iterative methods is that the prior covariance matrix must be known and fixed (up to a constant) before starting the solution process. In this paper, we develop hybrid projection methods for inverse problems with mixed Gaussian priors where the prior covariance matrix is a convex combination of matrices and the mixing parameter and the regularization parameter do not need to be known in advance. Such scenarios may arise when data is used to generate a sample prior covariance matrix (e.g., in data assimilation) or when different priors are needed to capture different qualities of the solution. The proposed hybrid methods are based on a mixed Golub-Kahan process, which is an extension of the generalized Golub-Kahan bidiagonalization, and a distinctive feature of the proposed approach is that both the regularization parameter and the weighting parameter for the covariance matrix can be estimated automatically during the iterative process. Furthermore, for problems where training data are available, various data-driven covariance matrices (including those based on learned covariance kernels) can be easily incorporated. Numerical examples from tomographic reconstruction demonstrate the potential for these methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Differential equations

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Calibrated multi-probabilistic prediction as a defense against adversarial attacks 31st

Calibrated multi-probabilistic prediction as a defense again...

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31st Benelux Conference on Artificial Intelligence and the 28th Belgian Dutch Conference on Machine Learning, BNAIC/BENELEARN 2019

作者： Peck, Jonathan Goossens, Bart Saeys, Yvan Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium Data Mining and Modeling for Biomedicine VIB Inflammation Research Center Ghent9052 Belgium Department of Telecommunications and Information Processing IMEC Ghent University Ghent9000 Belgium

ISBN: (纸本)9783030651534

Machine learning (ML) classifiers—in particular deep neural networks—are surprisingly vulnerable to so-called adversarial examples. These are small modifications of natural inputs which drastically alter the output of the model even though no relevant features appear to have been modified. One explanation that has been offered for this phenomenon is the calibration hypothesis, which states that the probabilistic predictions of typical ML models are miscalibrated. As a result, classifiers can often be very confident in completely erroneous predictions. Based on this idea, we propose the MultIVAP algorithm for defending arbitrary ML models against adversarial examples. Our method is inspired by the inductive Venn-ABERS predictor (IVAP) technique from the field of conformal prediction. The IVAP enjoys the theoretical guarantee that its predictions will be perfectly calibrated, thus addressing the problem of miscalibration. Experimental results on five image classification tasks demonstrate empirically that the MultIVAP has a reasonably small computational overhead and provides significantly higher adversarial robustness without sacrificing accuracy on clean data. This increase in robustness is observed both against defense-oblivious attacks as well as a defense-aware white-box attack specifically designed for the MultIVAP. We make our code available at https://***/saeyslab/multivap. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Homogenisation for the monodomain model in the presence of microscopic fibrotic structures

arXiv

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arXiv 2020年

作者： Lawson, Brodie A.J. dos Santos, Rodrigo Weber Turner, Ian W. Bueno-Orovio, Alfonso Burrage, Pamela Burrage, Kevin Centre for Data Science Queensland University of Technology QLD Australia ARC Centre of Excellence for Mathematical and Statistical Frontiers Queensland University of Technology QLD Australia Graduate Program on Computational Modeling Universidade de Juiz de Fora Minas Gerais Brazil Department of Computer Science University of Oxford Oxfordshire United Kingdom

MSC Codes 35B27Computational models in cardiac electrophysiology are notorious for long runtimes, restricting the numbers of nodes and mesh elements in the numerical discretisations used for their solution. This makes it particularly challenging to incorporate structural heterogeneities on small spatial scales, preventing a full understanding of the critical arrhythmogenic effects of conditions such as cardiac fibrosis. In this work, we explore the technique of homogenisation by volume averaging for the inclusion of non-conductive micro-structures into larger-scale cardiac meshes with minor computational overhead. Importantly, our approach is not restricted to periodic patterns, enabling homogenised models to represent, for example, the intricate patterns of collagen deposition present in different types of fibrosis. We first highlight the importance of appropriate boundary condition choice for the closure problems that define the parameters of homogenised models. Then, we demonstrate the technique’s ability to correctly upscale the effects of fibrotic patterns with a spatial resolution of 10 µm into much larger numerical mesh sizes of 100-250 µm. The homogenised models using these coarser meshes correctly predict critical pro-arrhythmic effects of fibrosis, including slowed conduction, source/sink mismatch, and stabilisation of re-entrant activation patterns. As such, this approach to homogenisation represents a significant step towards whole organ simulations that unravel the effects of microscopic cardiac tissue heterogeneities. © 2020, CC BY.

关键词： Heart

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It is time to optimize forest management policy for both carbon sinks and wood harvest in China

引用

National science Review 2025年第4期 18-20页

作者： Xi Li Haicheng Zhang Rong Shang Jingmin Chen Daju Wang Jianhua Zhu Huaguo Huang Simei Lin Baihong Pan Wenping Yuan Shilong Piao Institute of Carbon Neutrality Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University School of Geography and Planning Sun Yat-sen University Key Laboratory for Humid Subtropical Eco-Geographical Processes of the Ministry of Education School of Geographical SciencesFujian Normal University Department of Geography and Planning University of Toronto School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Ecology and Nature Conservation Institute Chinese Academy of Forestry Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry University School of Biological Sciences Center for Earth Observation and Modeling University of Oklahoma

Forestry protection has constituted a fundamental element of China's forest management policy for the past four decades,and has played a critical role in increasing forest area and biomass stock [1,2].Nevertheless, an efficacious forest management policy should balance the dual roles of forests, serving as both carbon sinks and ecosystems [3],

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ALeRCE light curve classifier: Tidal disruption event expansion pack

arXiv

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arXiv 2025年

作者： Pavez-Herrera, M. Sánchez-Sáez, P. Hernández-García, L. Bauer, F.E. Förster, F. Catelan, M. Arancibia, A. Muñoz Ricci, C. Reyes-Jainaga, I. Bayo, A. Huijse, P. Cabrera-Vives, G. Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Campus San Joaquín Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Nuncio Monseñor Sótero Sanz 100 Providencia Santiago Chile Departamento de Astronomía Universidad de Chile Casilla 36D Santiago Chile European Southern Observatory Karl-Schwarzschild-Strasse 2 Garching bei München85748 Germany Concepción4030000 Chile Instituto de Física y Astronomía Facultad de Ciencias Universidad de Valparaíso Gran Bretana 1111 Playa Ancha Valparaíso Chile Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Campus San Joaquín Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Space Science Institute 4750 Walnut Street Suite 205 BoulderCO80301 United States Faculty of Physical and Mathematical Sciences Universidad de Chile Chile Center for Mathematical Modeling Universidad de Chile Beauchef 851 Santiago8370456 Chile Instituto de Estudios Astrofísicos Facultad de Ingeniería y Ciencias Universidad Diego Portales Av. Ejército Libertador 441 Santiago Chile Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Data Observatory Foundation Santiago Chile Institute of Astronomy KU Leuven Celestijnenlaan 200D LeuvenB-3001 Belgium Department of Computer Science Universidad de Concepción Concepción Chile Data Science Unit Universidad de Concepción Edmundo Larenas 310 Concepción Chile

Context. ALeRCE (Automatic Learning for the Rapid Classification of Events) is currently processing the Zwicky Transient Facility (ZTF) alert stream, in preparation for the Vera C. Rubin Observatory, and classifying objects using a broad taxonomy. The ALeRCE light curve classifier is a balanced random forest (BRF) algorithm with a two-level scheme that uses variability features computed from the ZTF alert stream, and colors obtained from AllWISE and ZTF photometry. Aims. This work develops an updated version of the ALeRCE broker light curve classifier that includes tidal disruption events (TDEs) as a new subclass. For this purpose we incorporated 24 new features, notably including the distance to the nearest source detected in ZTF science images and a parametric model of the power-law decay for transients. We also expanded the labeled set to include 219,792 spectroscopically classified sources, including 60 TDEs. Methods. To effectively integrate TDEs into the ALeRCE’s taxonomy, we identified specific characteristics that set them apart from other transient classes, such as their central position in a galaxy, the typical decay pattern displayed when fully disrupted, and the lack of color variability after disruption. Based on these attributes, we developed features to distinguish TDEs from other transient events. Results. The modified classifier can distinguish between a broad range of classes with a better performance compared to the previous version and it can integate the TDE class achieving 91% recall, also identifying a large number of potential TDE candidates in ZTF alert stream unlabeled data. © 2025, CC BY.

关键词： Taxonomies

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Non-trading days model for Tesco stock market forecasting

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AIP Conference Proceedings 2023年第1期2500卷

作者： Che Normelissa Binti Mohd Zainudin Maria Elena Binti Nor Nur Arina Bazilah Binti Kamisan 1Department of Mathematics & Statistics Faculty of Applied Sciences & Technology Universiti Tun Hussein Onn Malaysia 84600 Muar Johor Malaysia 2Data analytics Sciences and Modeling Focus Group Faculty of Applied Sciences & Technology Universiti Tun Hussein Onn Malaysia 84600 Muar Johor Malaysia 3Department of Science Mathematical Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Johor Malaysia

Stock market prices are only available during the weekdays but not for the weekends and holidays. Due to the issue that leads to a gap in the stock market, the dependency between two consecutive trading days will be probably underestimated and the dependency between two trading days separated by a weekend or holiday will be overestimated. Thus, this issue might affect the forecast accuracy. In this study, the issue had been addressed by applying Autoregressive Moving Average Non-Trading days (ARMA-NT) model on the daily return of stock market price of Tesco forecasting. From the results, ARMA-NT model outperformed the ARMA model in which the data were not divided into trading and non-trading days. This is because the ARMA-NT model has small values of error measurements which are 0.9604 for MSE, 0.7580 for MAE, and 2.8268 for MAPE. Hence, it can be concluded that the daily stock return forecasting can be improved by splitting the data into trading and non-trading days.

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu 210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu 210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Comprehending the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for grasping the dynamics of the regional water cycle and energy balance. However, the influence of various environmental factors on ET and its components remains unclear. This study addresses this gap by utilizing observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and influencing factors. The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory simulation performance with an average R2 value of 0.75. Our analysis indicates that in alpine meadow, the ratio of transpiration to ET is generally higher (0.55) compared to alpine steppe (0.39). In terms of spatial distribution, both precipitation and leaf area index significantly impact ET and transpiration. Precipitation emerges as the primary driver of interannual spatial variations in ET, while leaf area index plays a pivotal role in regulating proportion of transpiration. Furthermore, our results reveal a transition from water-dominated to energy-dominated in TP grassland ecosystem at an aridity index of 0.73. This finding is significant as it challenges the common application of an aridity index of 0.65 to distinguish arid from humid regions. It suggests that TP grassland, within the 0.65-0.73 range, remain primarily water-driven. These findings enhance our comprehension of the intricate interactions within the water cycle. © 2024, The Authors. All rights reserved.

关键词： Evapotranspiration

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on the Tibetan Plateau

SSRN

引用

SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station School of Ecology Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Study region: The Tibetan Plateau in China, the Third Pole of the *** focus: Accurate descriptions of spatial variation in evapotranspiration (ET) and its components on the Tibetan Plateau (TP) are crucial for understanding the dynamics of the regional water cycle and energy balance. However, how various environmental factors affect ET and its components remains unclear. This study addresses this gap using observational data from 14 sites and employing the Shuttleworth-Wallace-Hu (SWH) model to examine the spatial relationships between ET, its components, and underlying *** hydrological insights for the region: The SWH model parameters were optimized using the Monte Carlo method, achieving a satisfactory model performance with an average R² of 0.75. The ratio of transpiration to ET was generally higher in alpine meadows (0.55) than alpine steppes (0.39). Across the TP, precipitation and the leaf area index together shaped spatial variation in ET and transpiration. Precipitation emerged as the primary driver of interannual variation in ET, while the leaf area index strongly regulated the proportion of ET accounted for by transpiration. Furthermore, above aridity index values of 0.73, grassland ecosystems transitioned from water to energy. This finding is significant as it challenges the common application of an aridity index value of 0.65 to distinguish arid from humid regions. It suggests that TP grasslands, with an AI of 0.65-0.73, remain primarily water-limited. © 2024, The Authors. All rights reserved.

关键词： Biotic

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TARGET SPECIFIC DE NOVO DESIGN OF DRUG CANDIDATE MOLECULES WITH GRAPH TRANSFORMER-BASED GENERATIVE ADVERSARIAL NETWORKS

arXiv

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arXiv 2023年

作者： Ünlü, Atabey Çevrim, Elif Sarıgün, Ahmet Yiğit, Melih Gökay Çelikbilek, Hayriye Bayram, Osman Güvenilir, Heval Ataş Koyaş, Altay Kahraman, Deniz Cansen Olğaç, Abdurrahman Rifaioğlu, Ahmet Banoğlu, Erden Doğan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Turkey Dept. of Chemistry Middle East Technical University Turkey Dept. of Physics Middle East Technical University Turkey Dept. of Computer Engineering Middle East Technical University Turkey Dept. Of Artificial Intelligence Engineering Bahcesehir University Turkey Cancer Systems Biology Lab Graduate School of Informatics Middle East Technical University Turkey Dept. of Pharmaceutical Chemistry Faculty of Pharmacy Gazi University Turkey Laboratory of Molecular Modeling Evias Pharmaceutical R&D Ltd Turkey Dept. of Electrical and Electronics Engineering Iskenderun Technical University Turkey Institute for Computational Biomedicine Heidelberg University Germany

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de novo molecules. However, for them to be useful in real-life drug development pipelines, these models should be able to design drug-like and target-centric molecules. In this study, we propose an end-to-end generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with intended target proteins. The proposed method represents molecules as graphs and processes them via a generative adversarial network comprising graph transformer layers. The system is trained using a large dataset of drug-like compounds and target-specific bioactive molecules to design effective inhibitory molecules against the AKT1 protein, which is critically important in developing treatments for various types of cancer. We conducted molecular docking and dynamics to assess the target-centric generation performance of the model, as well as attention score visualisation to examine model interpretability. Results indicate that our de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands. Using the open-access DrugGEN codebase, it is possible to easily train models for other druggable proteins, given a dataset of experimentally known bioactive molecules. © 2023, CC BY-NC-SA.

关键词： Generative adversarial networks

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