Article
Engineering, Environmental
Ali Jozaghi, Haojing Shen, Dong-Jun Seo
Summary: Accurate spatial estimation of extremes is crucial in environmental research and risk assessment. This paper introduces adaptive conditional bias-penalized kriging, which objectively prescribes weights to improve estimation of extremes without compromising performance.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Environmental Sciences
Zedong Yang, Zhongke Bai, Zhiheng Qin
Summary: This study utilized data from 2005 and 2015 to develop a new method for assessing soil pollution using spatial interpolation analysis and point combination. Findings revealed significant changes in soil pollution over the decade, with a correlation observed between these changes and the intensity of human activities.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Mechanics
Ozgur Tumuklu, Kyle M. Hanquist
Summary: This study investigates the spatial-temporal characteristics of laminar hypersonic flows at Mach 7.10 with different Reynolds numbers. The validity of the continuum assumption is tested by comparing previous kinetic and current continuum approaches. The impact of velocity slip and temperature jumps on flow and surface parameters is investigated. It is found that the flow field depends on spanwise effects and is fully 3D even at low pressure. High-fidelity numerical schlieren videos show strong spanwise oscillations in 3D configurations.
Article
Computer Science, Interdisciplinary Applications
Xingnian Jiang, Xinqing Wang, Yue Liu, Emmanuel John M. Carranza, Shuyun Xie, Xiang Wan
Summary: Multi-scale geochemical data are crucial for mineral exploration and environmental assessment. However, these data are often incomplete or sparse due to various factors. In this study, a conditional generative adversarial network (GAN) was developed to simulate downscaled geochemical data by learning nonlinear relationships between fine and coarse soil geochemical datasets. The proposed model was tested in Qianjiang City, Hubei Province, China and showed efficient extrapolation capabilities. The results were evaluated through exploratory data analysis, error analysis, variogram analysis, and uncertainty analysis.
COMPUTERS & GEOSCIENCES
(2023)
Article
Engineering, Civil
Guofeng Zhang, Guanghui Tian, Daxin Cai, Rui Bai, Jinhe Tong
Summary: The study proposes a method for merging radar and rain gauge data to improve rainfall estimation accuracy, showing that this method is more accurate and has certain universality compared to traditional methods.
JOURNAL OF HYDROLOGY
(2021)
Article
Economics
Guillaume Allaire Pouliot
Summary: This study presents methodology for regression analysis of misaligned data, where the independent and dependent variables do not coincide geographically. Two complementary methods are developed and investigated to avoid the need for covariance estimation or specification. A detailed reanalysis of Maccini and Yang (2009) reveals significant quantitative differences but largely sustains qualitative conclusions.
JOURNAL OF ECONOMETRICS
(2023)
Article
Engineering, Environmental
Nanda R. Aryal, Owen D. Jones
Summary: In this study, stochastic spatial-temporal models are fitted to high-resolution rainfall radar data using Approximate Bayesian Computation (ABC). The models are constructed from cluster point-processes, and the Simulated Method of Moments (SMM) is introduced to initialize the ABC fit. The use of ABC is crucial for fitting models of this complexity.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Statistics & Probability
Maggie D. Bailey, Soutir Bandyopadhyay, Douglas Nychka
Summary: This article introduces a new approximate conditional simulation method for generating ensembles of random fields. The method is based on circulant embedding and extends the algorithm to irregularly spaced data points. The methods have been shown to be accurate for practical inference and significantly speed up computation.
Article
Chemistry, Multidisciplinary
Xin Wang, Hao Sun, Young-Ki Kim, Daniel B. Wright, Michael Tsuei, Nathan C. Gianneschi, Nicholas L. Abbott
Summary: This study presents a chemically triggered polymerization method based on a liquid crystal printhead, which allows for spatial and temporal control. By designing different geometries of the liquid crystal printhead, various polymerization reactions can be achieved: bulk solution polymerization, synthesis of thin surface-confined polymeric coatings, polymerization-induced self-assembly of block copolymers to form different nanostructures, and 3D printing of polymeric structures based on local solution conditions. The approach utilizes amphiphiles, multivalent ions, and biomolecules as stimuli.
ADVANCED MATERIALS
(2022)
Article
Engineering, Mechanical
Yasong Sun, Ruihuai Bai, Jing Ma
Summary: This study focuses on designing and optimizing an indirect liquid cooling system for cylindrical lithium-ion batteries, using COMSOL Multiphysics simulation software to analyze and optimize the cooling systems. By adopting the Kriging method for approximate modeling and non-dominated sorting genetic algorithm (NSGA-II) for optimization, the research demonstrates improved performance and temperature uniformity in the battery cooling system.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2022)
Article
Energy & Fuels
Hao Fu, Peng Li, Xiaopeng Fu, Jinyue Yan, Zhiying Wang, Kun Wang, Jianzhong Wu, Chengshan Wang
Summary: This paper presents a compact real-time simulator for large-scale wind farms based on field programmable gate array (FPGA). A spatial-temporal parallel design method is proposed to address the demand for huge computing resources associated with detailed modeling. The wind farm is decoupled into subsystems and the electrical system and control system of each subsystem are solved in parallel. The simulation incorporates module-level and superscalar pipeline techniques to improve hardware resource utilization. Case studies demonstrate the accuracy and effectiveness of the proposed design.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2023)
Article
Engineering, Civil
Sadegh Vanda, Mohammad Reza Nikoo, Parnian Hashempour Bakhtiari, Malik Al-Wardy, Jan Franklin Adamowski, Jiri Simunek, Amir H. Gandomi
Summary: A risk-based simulation-optimization model was developed to minimize unsatisfied water demand, risk of water quality standard violations, and reservoir recovery time in the case of sudden MTBE pollution. Operational rules were found to depend on thermal conditions and MTBE intrusion properties, with consideration of often-neglected pollution scenarios. A graph model for conflict resolution was established based on environmental conditions and local rules to reach compromise operational rules.
JOURNAL OF HYDROLOGY
(2022)
Article
Geosciences, Multidisciplinary
Steinar Love Ellefmo, Thomas Kuhn
Summary: Minerals and metals play a crucial role in society, and there is great potential in mining mineral resources from the deep ocean floor. This study utilized images and expert knowledge to estimate nodules abundance, showcasing the importance of utilizing data effectively for better informed estimates. Future improvements will focus on enhancing the estimation of minimum and maximum values at image locations.
NATURAL RESOURCES RESEARCH
(2021)
Article
Geochemistry & Geophysics
Shengchao Chen, Ting Shu, Huan Zhao, Guo Zhong, Xunlai Chen
Summary: This article proposes a novel radar echo extrapolation algorithm called TempEE, which tackles the challenges in radar echo extrapolation by avoiding cumulative error, incorporating multilevel temporal-spatial attention mechanism, and using a parallel encoder. Extensive experiments on a real-world dataset have demonstrated the effectiveness and indispensability of TempEE.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Chemistry, Multidisciplinary
Xiaoya An, Ziming Wang, Ding Wang, Song Liu, Cheng Jin, Xinpeng Xu, Jianjun Cao
Summary: Trajectory clustering algorithms are widely used in traffic flow analysis, logistics and transportation management, and crime analysis for mining the movement trends and behavioral patterns of objects. However, existing algorithms have limitations in utilizing the temporal attributes of trajectory data, resulting in low clustering accuracy and long clustering time for spatial-temporal trajectory data. In this paper, a parallel DBSCAN algorithm called STRP-DBSCAN is proposed, which adopts spatial-temporal random partitioning to reduce the clustering time and improve the execution efficiency. Furthermore, the PER-SAC algorithm is presented for autotuning the optimal parameters of DBSCAN using deep reinforcement learning, achieving higher clustering accuracy and stability.
APPLIED SCIENCES-BASEL
(2023)
Article
Statistics & Probability
Joseph Guinness, Montserrat Fuentes
JOURNAL OF MULTIVARIATE ANALYSIS
(2016)
Article
Statistics & Probability
Joseph Guinness, Dorit Hammerling
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2018)
Article
Computer Science, Theory & Methods
Joseph Guinness
Summary: In this study, a single-pass algorithm was proposed for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, providing an efficient means for applying the Fisher scoring algorithm for maximizing the loglikelihood. The advantages of the optimization techniques were demonstrated in numerical examples and an application to Argo ocean temperature data, showing that the new methods can find maximum likelihood estimates faster and more reliably, especially when the covariance function has many parameters. This enables practitioners to fit nonstationary models to large spatial and spatial-temporal datasets.
STATISTICS AND COMPUTING
(2021)
Article
Statistics & Probability
Amanda Muyskens, Joseph Guinness, Montserrat Fuentes
Summary: This study introduces computational methods for estimating a flexible nonstationary spatial model effectively, especially when the field size is too large. By using a stochastic approximation to the score equations, the study provides tools for evaluating the approximate score efficiently. The proposed methods were tested through simulations to predict average daily temperature.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2022)
Article
Statistics & Probability
Alfredo Farjat, Brian J. Reich, Joseph Guinness, Ross Whetten, Steven McKeand, Fikret Isik
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2017)