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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wenzhao Li Shahryar Fazli Surendra Maharjan Hesham El-Askary Earth Systems Science and Data Solutions Lab Schmid College of Science and Technology Chapman University Orange CA USA Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Department of Environmental Sciences Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study evaluates the potential application of hyperspectral Earth Surface Mineral Dust Source Investigation (EMIT) remote sensing for monitoring harmful algal blooms (HABs) and water quality in Clear Lake, California. The research focuses on correlating the chlorophyll-a (Chl-a) concentrations with EMIT spectral signatures, using waterbody-wide statistical analysis of Chl-a and EMIT data sampling at various lake locations. Results demonstrate distinct spectral signatures associated with varying Chl-a levels, highlighting the potential of hyperspectral imaging in differentiating algae levels and assessing water quality variables. It also indicates the EMIT’s utility in filling data gaps and offering high-resolution monitoring. This study underscores the need for further research in hyperspectral imaging for aquatic ecosystems, especially under challenging atmospheric conditions, enhancing our understanding of water quality dynamics.

关键词： Satellites Statistical analysis Algae Water quality Lakes Filling Minerals

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Quasienergy resonance in a dynamic Wannier-Stark ladder

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Physical Review B 2013年第20期87卷 205305-205305页

作者： Yuya Nemoto Ken-ichi Hino Nobuya Maeshima Doctoral Program in Frontier Science Graduate School of Pure and Applied Sciences University of Tsukuba Tsukuba Ibaraki 305-8573 Japan Division of Materials Science Faculty of Pure and Applied Sciences University of Tsukuba Tsukuba 305-8573 Japan Center for Computational Sciences University of Tsukuba Tsukuba 305-8577 Japan

We examine a continuum effect of a dynamic Wannier-Stark ladder (DWSL) driven by a cw laser—with Fac and ω as amplitude and frequency, respectively—by means of an excess density of states (DOS), ρ(ex)(E), closely related to the more familiar DOS and proportional to the lifetime of a resonance state. It is mathematically shown that ρ(ex)(E) is governed by three different physical mechanisms: the single-channel resonance mechanism, the multichannel nonresonance mechanism, and the multichannel resonance mechanism. The last mechanism becomes more important with the increase in Fac. The effect of the interchannel interaction is maximized when the ratio of a Bloch frequency to ω, represented as η, equals unity. In the actual calculations based on the R-matrix Floquet theory, it is revealed that, in a large-Fac region, ρ(ex)(E) for η=1 shows a complicated spectral structure composed of a couple of newly growing peaks, in contrast to ρ(ex)(E) for η=3 which just shows a monotonic change of a single spectral peak. It is speculated that the pronounced feature of the former spectra is attributed to the Fano-like multichannel resonance mechanism, whereas the feature of the latter case is attributed to the multichannel nonresonance mechanism.

关键词： WANNIER-stark effect DENSITY of states RESONANCE R-matrices FLOQUET theory

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Bridging Accuracy and Efficiency: The Role of PINNs in Immune Response Simulations

Bridging Accuracy and Efficiency: The Role of PINNs in Immun...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Thiago E. Fernandes Rodrigo W. Dos Santos Marcelo Lobosco Graduate Program on Computational Modelling Federal University of Juiz de Fora Juiz de Fora Brazil Department of Computer Science Institute of Exact Sciences Federal University of Juiz de Fora Juiz de Fora Brazil

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system’s solutions, offering substantial computational efficiency for this application.

关键词： Training Pathogens Accuracy computational modeling Artificial neural networks Myocardium Mathematical models Numerical models computational efficiency Biological neural networks

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Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

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Procedia Computer science 2019年 157卷 31-37页

作者： Puspita Anggraini Kaban Robert Kurniawan Rezzy Eko Caraka Bens Pardamean Budi Yuniarto Sukim Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia College of Informatics Chaoyang University of Technology Taichung City 41349 Taiwan (R.O.C.) Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.

关键词： biclustering disaster mitigation social vulnerability SoVI spatial pattern

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Deep learning inter-atomic potential for thermal and phonon behaviour of silicon carbide with quantum accuracy

arXiv

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arXiv 2021年

作者： Fu, Baoqin Sun, Yandong Zhang, Linfeng Wang, Han Xu, Ben Key Laboratory of Radiation Physics and Technology of the Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China Graduate School China Academy of Engineering Physics Beijing100193 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China

Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It’s applications are strongly dependent on its thermal conductivity, which is highly sensitive to microstructures. Molecular dynamics (MD) simulation is the most used methods to address thermal transportation mechanisms in devices or microstructures of nano-meters. However, the implementation of MD is limited in SiC because of lacking accurate inter-atomic potentials. In this work, using the Deep Potential (DP) methodology, we developed two inter-atomic potentials (DP-*Corresponding author. IAPs) for SiC based on two adaptively generated datasets within the density functional approximations at the local density and the generalized gradient levels. These two DP-IAPs manifest their speed with quantum accuracy in lattice dynamics simulations as well as scattering rate analysis of phonon transportation. Combining with molecular dynamics simulations, the thermal transport and mechanical properties were systematically investigated. The presented methodology and the inter-atomic potentials pave the way for a systematic approach to model heat transport in SiC related devices using multiscale modelling. Copyright © 2021, The Authors. All rights reserved.

关键词： Silicon carbide

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Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Forecasting Vegetation Health in the MENA Region by Predicti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sachi Perera Wenzhao Li Erik Linstead Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Schmid College of Science and Technology Chapman University Orange CA USA Fowler School of Engineering Chapman University Orange CA USA Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R 2 > 0.95) in the testing and (R 2 > 0.93) in the evaluation process.

关键词： Vegetation mapping MODIS Atmospheric modeling Remote sensing Predictive models Training Support vector machines

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A simulator for underwater unmanned vehicles and cable-like flexible structures

A simulator for underwater unmanned vehicles and cable-like ...

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OCEANS

作者： Marcos Vinicius Scholl Sebastião C. P. Gomes Carlos Rodrigues Rocha Graduate Program in Computational Modeling Federal University of Rio Grande - FURG Rio Grande-RS Brazil Federal Institute of Education Science and Technology of Rio Grande do Sul Rio Grande Campus Rio Grande-RS Brazil

A graphical tool to qualitative analysis of underwater unmanned vehicles and cable-like flexible structures is presented. The software provides a realistic virtual environment where the dynamic behavior of different u... 详细信息

ISBN: (纸本)9781509064298;9780692946909

关键词： Vehicle dynamics Underwater cables Dynamics Mathematical model Software Unmanned vehicles Numerical simulation

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Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts

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Briefings in Bioinformatics 2023年第2期24卷 bbac633-bbac633页

作者： Tu, Yueh-Hua Juan, Hsueh-Fen Huang, Hsuan-Cheng The Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taiwan Department of Life Science Center for Computational and Systems Biology National Taiwan University Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan

Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level. © The Author(s) 2023.

关键词： Gaussian mixture model gene regulatory network single-cell RNA sequencing trajectory analysis

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Continuity Equation of Left Invariant Gauge Field in Quantum Genotype Of Deltacron

SSRN

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SSRN 2022年

作者： Hannongbua, Supot Kanjamapornkul, Kabin Rungrotmongkol, Thanyada Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Biocatalyst and Environmental Biotechnology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

The continuity equation for the invariant gauge field of Deltacron was investigated from the replication and recombination of Delta with Omicron. The new definition of left translation of the invariant gauge field in the quantum genotype of virus and host cell was introduced. The Yang-Mills field in genetic code is an induced curvature in the parallel transport of a unified field between hydrogen bonding between three types of biomolecules. where we defined the invariant gauge field in quantum genotype. This issue allows us to identify the Poincare duality between the nteraction between the behavior field of gene expression in parasitism state and in the curvature of connection in fiber space of Spike protein folding molecule. The Chern Simons current is the anti-self-dual state of mutation between two types of interactive behavior fields in The recombination process of Deltacron was used to plot the Yang-Mills curvature directly from the underlying genetic codes. Further analysis shows that the difference between the tensor correlation with various times of Deltacron the change slope of Omicron BA.1 to the flat curve of the network correlation and closedness centrality of amino acids in receptor binding domain of Spike protein. © 2022, The Authors. All rights reserved.

关键词： Gene expression

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Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning

Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforc...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Junghoon Park Jiook Cha Samuel Yen-Chi Chen Shinjae Yoo Huan-Hsin Tseng Interdisciplinary Program in Artificial Intelligence Seoul National University Department of Psychology Seoul National University Department of Brain and Cognitive Sciences Seoul National University Graduate School of Data Science Seoul National University Wells Fargo Computational Science Initiative Brookhaven National Laboratory

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In the realm of artificial intelligence, deep rein-forcement learning (RL) agents struggle with generalizability and require substantial computational resources, unlike humans who easily adapt and generalize across tasks. To address these challenges, we introduce Quantum Rainbow, a hybrid algorithm that leverages the neural mechanisms of human decision-making and the efficiency of quantum computing. Quantum Rainbow combines variational quantum circuits with the Rainbow Deep Q-Network (DQN) model to create a novel approach in rein-forcement learning that integrates quantum principles into deep learning paradigms. We evaluate our model using behavioral experiments through the Iowa Gambling Task and 4-Armed Bandit Task. Our investigations reveal a significant relationship between the architecture of quantum circuits and the performance of quantum RL agents. Specifically, using causal discovery methods, we demonstrate the critical role of quantum entanglement in enhancing model performance. These findings not only show promising results but also pave the way for future explorations into optimizing quantum circuit architectures for reinforcement learning applications. This study underscores the potential of quantum-enhanced algorithms to achieve “quantum advantage” by addressing fundamental limitations in conventional deep RL methods.

关键词： Analytical models Quantum computing Quantum algorithm Quantum advantage Quantum entanglement computational modeling Reinforcement learning Quantum state Integrated circuit modeling Quantum circuit

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