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On the computation of ISO amplitude and frequency indices using a discrete wavelet transform model and application to the study of the annual cycle of ISO activity in Central Africa

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Bulletin of Atmospheric Science and Technology 2021年第1-4期2卷 1-18页

作者： Sandjon, Alain Tchakoutio Takougang, Armand Nzeukou Ibouraïma, Yabi Vondou, Derbetini Appolinaire Tchotchou, Angennes Lucie Djiotang Department of Computer Science including Basic sciences Higher Technical Teacher’s Training College Kumba University of Buea P.O box 249 Buea Road Kumba Cameroon Laboratory of environmental modelling and atmospheric Physics University of Yaoundé I Yaoundé Cameroon Laboratory of industrial Systems and environmental engineering Fotso Victor university institute of Technology University of Dschang Bandjoun Cameroon Pierre PAGNEY Laboratory: Climate Water Ecosystems and Development (LACEEDE) Department of Geography and regional planning (DGAT) Université Abomey-Calavi Abomey-Calavi Benin

The role of the intraseasonal oscillations (ISOs) in the tropical climate system has been widely investigated by researchers around the world. However for the better representation of ISOs in climate models, one of the greatest challenges remains the definition of suitable ISO-related metrics. As attempt to the definition of these ISO-related metrics, we proposed in this paper an algorithm for the computation of ISO intensity (ISOI) and ISO periods (ISOP) indices using a discrete wavelet transform (DWT) model. Then we used these indices to investigate the annual cycle of ISO activity in Central Africa (CA). The plots of spatial distribution of ISOI and ISOP show that both quantities exhibit strong month-to-month variations throughout the year. In fact, the high values of ISOI are observed during December–May and lower values during June–November, while the ISOP values are high December–February and June–August and lower values during March–May and September–December. We also accessed the large-scale dynamics associated with the ISO activity and found that over the land, the incursions of the moisture flux from Atlantic and Indian basins are of great contribution in the annual variations of ISOI. Finally, we studied the impact of ISO on rainfall and showed that the influence of ISO on rainfall strongly varies throughout the year, with maximum impact during June and minimum impact during November. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Central Africa Discrete wavelet transform Intraseasonal oscillations

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Comparison of Time Series Clustering Algorithms for Machine State Detection

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Procedia CIRP 2020年 93卷 1352-1357页

作者： Martin Hennig Manfred Grafinger Detlef Gerhard Stefan Dumss Patrick Rosenberger TU Wien Institute of Engineering Design and Product Development – Research Area Mechanical Engineering Informatics and Virtual Product Development Getreidemarkt 9/307 1060 Vienna Austria Ruhr-Universität Bochum Institute of Product and Service Engineering - Chair for Digital Engineering Systems Universitätsstraße 150 44801 Bochum Germany

New developments in domains like mathematics and statistical learning and availability of easy-to-use, often freely accessible software tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. One of the most exciting developments is in the area of machine learning. Time series clustering could be utilized in machine state detection which can be used in predictive maintenance or online optimization. This paper presents a comparison of freely available time series clustering algorithms, by applying several combinations of different algorithms to a database of public benchmark technical data.

关键词： Machine Learning Industry 4.0 Time Series Clustering Predictive Maintenance Unsupervised Learning Internet of Things

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Predicting climate change: A comparative analysis of time series models for CO 2 concentrations and temperature anomalies

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Environmental Modelling & Software 2025年 192卷

作者： Zahra Rezaei Sara Safi Samghabadi Mohammad Amin Amini Dingjing Shi Yaser Mike Banad School of Electrical and Computer Engineering University of Oklahoma Norman OK 73019 USA School of Electrical and Computer Engineering Azad University Karaj Alborz Iran Department of Computer Engineering Azad University Jasb Markazi Iran Department of Psychology Georgia Institute of Technology Atlanta GA 30332 USA Institute for Resilient Environmental and Energy Systems University of Oklahoma Norman OK 73019 USA Intelligent Neuromorphic and Quantum Understanding for Research and Engineering (INQUIRE) Laboratory University of Oklahoma Norman OK 73019 USA

This study presents a novel, integrated modeling framework that combines machine learning (ML) techniques with physics-based approaches to forecast both CO 2 emissions and global temperature anomalies. Unlike prior research that typically addresses these components in isolation, this work concurrently applies and compares five advanced ML models—Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), Facebook Prophet, and a hybrid CNN-LSTM—alongside two physics-based models: a zero-dimensional Energy Balance Model (EBM) and a simplified General Circulation Model (GCM) adapted from NASA's GISS *** monthly global datasets from January 2000 to April 2024, obtained from the National Oceanic and Atmospheric Administration (NOAA) and the Scripps Institution of Oceanography, the models are evaluated based on predictive accuracy (RMSE, MSE, MAE, R 2 ), scalability, and interpretability. Prophet demonstrated the highest accuracy for CO 2 emission forecasting (RMSE = 0.035), while LSTM achieved the best performance in temperature anomaly prediction (RMSE = 0.086). Physics-based models provided interpretable and computationally efficient long-term projections but lacked short-term *** facilitate reproducibility and practical application, we developed ClimateChange-ML, an open-source software package that implements all proposed models, includes trained weights, and provides full documentation and visualization *** novelty of this work lies in its dual-modeling strategy and comprehensive comparative evaluation, highlighting the complementary strengths of data-driven and physically grounded methods. This integrated approach offers a more holistic framework for climate forecasting across multiple temporal scales, providing valuable insights for both scientific understanding and climate policy planning.

关键词： Climate change prediction CO 2 emissions forecasting Global temperature anomalies Greenhouse gas impact Machine learning in climate science Emission-temperature relationship Climate mitigation strategies Time series models in climate

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Lightweight equivariant model for efficient machine learning interatomic potentials

arXiv

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arXiv 2023年

作者： Yang, Ziduo Wang, Xian Li, Yifan Lv, Qiujie Chen, Calvin Yu-Chian Shen, Lei Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Department of Physics National University of Singapore Singapore117551 Singapore -Preferred Program School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China State Key Laboratory of Chemical Oncogenomics School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Shenzhen518055 China Guangdong L-Med Biotechnology Co. Ltd Guangdong Meizhou514699 China Research Institute Chongqing401123 China

In modern computational materials science, deep learning has shown the capability to predict interatomic potentials, thereby supporting and accelerating conventional simulations. However, existing models typically sacrifice either accuracy or efficiency. Moreover, lightweight models are highly demanded for offering simulating systems on a considerably larger scale at reduced computational costs. Here, we introduce a lightweight equivariant interaction graph neural network (LEIGNN) that can enable accurate and efficient interatomic potential and force predictions for molecules and crystals. Rather than relying on higher-order representations, LEIGNN employs a scalar-vector dual representation to encode equivariant features. By learning geometric symmetry information, our model remains lightweight while ensuring prediction accuracy and robustness through the equivariance. Our results show that LEIGNN consistently outperforms the prediction performance of the representative baselines and achieves significant efficiency across diverse datasets, which include catalysts, molecules, and organic isomers. Furthermore, we conduct molecular dynamics (MD) simulations using the LEIGNN force field across solid, liquid, and gas systems. It is found that LEIGNN can achieve the accuracy of ab initio MD across all examined systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Prediction models

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Capture uncertainties in deep neural networks for safe operation of autonomous driving vehicles

arXiv

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arXiv 2021年

作者： Ding, Liuhui Li, Dachuan Liu, Bowen Lan, Wenxing Bai, Bing Hao, Qi Cao, Weipeng Pei, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Sifakis Research Institute for Trustworthy Autonomous Systems Shenzhen518055 China Huawei Technologies Co. Ltd. Shenzhen518129 China

Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively capture the associated aleatoric and epistemic uncertainties of DNNs;(2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle's motion. The proposed approaches are validated via simulated complex scenarios built in CARLA. Experimental results show that the proposed motion planning scheme can cope with uncertainties of DNN-based perception and vehicle motion, and improve the operational safety of autonomous vehicles while still achieving desirable *** Codes 68T40 © 2021, CC BY-NC-ND.

关键词： Deep neural networks

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TMC1 and TMC2 Proteins Are Pore-Forming Subunits of Mechanosensitive Ion Channels

TMC1 and TMC2 Proteins Are Pore-Forming Subunits of Mechanos...

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2022生物物理大会

作者： Yanyan Jia Yimeng Zhao Tsukasa Kusakizako Yao Wang Chengfang Pan Yuwei Zhang Osamu Nureki Motoyuki Hattori Zhiqiang Yan State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Department of Neurosurgery at Huashan Hospital Human Phenome Institute Ministry of Education Key Laboratory of Contemporary Anthropology Collaborative Innovation Center of Genetics and Development Institute of Brain Science Department of Physiology and Biophysics School of Life Sciences Fudan University State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and Development Multiscale Research Institute for Complex Systems Department of Physiology and Biophysics School of Life Sciences Fudan University Department of Biological Sciences Graduate School of Science The University of Tokyo

Transmembrane channel-like (TMC) 1 and 2 are required for the mechanotransduction of mouse inner ear hair cells and localize to the site of mechanotransduction in mouse hair cell stereocilia. However, it remains unclear whether TMC1 and TMC2 are indeed ion channels and whether they can sense mechanical force directly. Here we express TMC1 from the green sea turtle(CmTMC1) and TMC2 from the budgerigar (MuTMC2) in insect cells,

关键词： TMC1 and TMC2 Proteins Are Pore-Forming Subunits of Mechanosensitive Ion Channels

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Determinants of the Embodied Co2 Transfers Through Electricity Trade within China

SSRN

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SSRN 2023年

作者： Peng, Xu Chen, Hong Long, Ruyin Zhong, Honglin Sun, Qingqing Ma, Wanqi Zhang, Chao Zhao, Dandan Yang, Guangfei Hong, Jingke Duan, Cuncun Wang, Saige Qi, Xinxian Wei, Pengbang Zhang, Pengfei Chen, Jindao School of Business Jiangnan University Wuxi214122 China Institute of Blue and Green Development Weihai Institute of Interdisciplinary Research Shandong University Weihai264209 China School of Economics and Management China University of Mining and Technology China School of Economics and Management Tongji University Shanghai200092 China Water & Development Research Group Department of Built Environment Aalto University PO Box 15200 Espoo00076 Finland Institute of Systems Engineering Dalian University of Technology Dalian116024 China School of Management Science and Real Estate Chongqing University Chongqing400045 China Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology College of Water Sciences Beijing Normal University Beijing100875 China School of Environment Beijing Normal University Beijing100875 China School of Geography and Ocean Science Nanjing University Nanjing210023 China School of Management Zhengzhou University Zhengzhou450001 China School of Civil Engineering & Engineering Management Guangzhou Maritime University Guangzhou510725 China

There is unequal spatial distribution of resource endowment, population density, industrial structure, and economic development with diverse differences in labor, energy, and capital productivities in China. However, previous studies paid little attention to the determinants of CO2 transfers embodied in electricity trade. In this study, we use both the absolute and comparative advantage theories to reveal the determinants of embodied CO2 transfers through electricity trade within China. Results show that electricity sector has higher labor productivity but lower asset efficiency and energy productivity than that of mining and manufacturing sectors in China. The large-scale electricity trade alleviates the shortage of electricity supply in developed regions by outsourcing electricity to the less-developed regions, changes the CO2 flow embodied in power grid, and reduces the unequal spatial distribution of coal and natural gas reserves with the differences in labor productivity, asset efficiency, and energy productivity across China. Econometric shows that coal reserve contributes to an increase of embodied CO2 emission, while natural gas reduces the embodied CO2 emission. The regional differences in the opportunity cost of labor productivity of non-electricity sector is the dominant factor in determining the embodied CO2 transfers through electricity trade within China, but asset efficiency and energy productivity could play a limited role. Our finding provides the details about the power grid expansion planning with considering climate change mitigation in the future. © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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Cyber-Physical System for Industrial Automation Using Quantum Deep Learning

Cyber-Physical System for Industrial Automation Using Quantu...

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International Conference on System Modeling & Advancement in research Trends (SMART)

作者： Anand Singh Rajawat S.B. Goyal Pradeep Bedi Neagu Bogdan Constantin Maria Simona Raboaca Chaman Verma School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Galgotais University Greater Noida India Department of Power Engineering Gheorghe Asachi Technical University of Iasi Iasi Romania ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Informatics University of Eotvos Lorand Budapest Hungary

ISBN: (纸本)9781665487351

Automation industries are using Industry 4.0 solutions for several reasons. Still, the most important is to make it easier for their employees to get secure information and to work better with people in other departments. The goal of this effort is to improve productivity and efficiency by making it easier to make good decisions at the right time. This will make it possible to make these kinds of improvements. Constraint-based security solutions are becoming increasingly popular as the digital and physical worlds become more connected. Based on the processing speed, the amount of memory that can be used, and the amount of backup power that can be used. The algorithms and security frameworks used in earlier research need many resources. Several researchers have given an overview of the many security problems with the CPS and their studies and recommendations. But constraint-based applications need light security systems that can work with hardware with limited capabilities. We proposed a safety mechanism that can be used with the already-made constraint-based CPS program. To propose Cyber-Physical System for Industrial Automation Using Quantum Deep Learning, We proposed Cyber-Physical System for Industrial Automation that uses Quantum Learning to find attacking patterns.

关键词： Deep learning Productivity Memory management Cyber-physical systems Market research Fourth Industrial Revolution Safety

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The Sensorium competition on predicting large-scale mouse primary visual cortex activity

arXiv

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arXiv 2022年

作者： Willeke, Konstantin F. Fahey, Paul G. Bashiri, Mohammad Pede, Laura Burg, Max F. Blessing, Christoph Cadena, Santiago A. Ding, Zhiwei Lurz, Konstantin-Klemens Ponder, Kayla Muhammad, Taliah Patel, Saumil S. Ecker, Alexander S. Tolias, Andreas S. Sinz, Fabian H. International Max Planck Research School for Intelligent Systems University of Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Institute for Theoretical Physics University of Tübingen Germany Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Electrical and Computer Engineering Rice University Houston United States

The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not only serving as predictive digital twins of sensory cortex for novel hypothesis generation in silico, but also incorporating bio-inspired architectural motifs to progressively bridge the gap between biological and machine vision. The mouse has recently emerged as a popular model system to study visual information processing, but no standardized large-scale benchmark to identify state-of-the-art models of the mouse visual system has been established. To fill this gap, we propose the SENSORIUM benchmark competition. We collected a large-scale dataset from mouse primary visual cortex containing the responses of more than 28,000 neurons across seven mice stimulated with thousands of natural images, together with simultaneous behavioral measurements that include running speed, pupil dilation, and eye movements. The benchmark challenge will rank models based on predictive performance for neuronal responses on a held-out test set, and includes two tracks for model input limited to either stimulus only (SENSORIUM) or stimulus plus behavior (SENSORIUM+). We provide a starting kit to lower the barrier for entry, including tutorials, pre-trained baseline models, and APIs with one line commands for data loading and submission. We would like to see this as a starting point for regular challenges and data releases, and as a standard tool for measuring progress in large-scale neural system identification models of the mouse visual system and beyond. © 2022, CC BY-NC-ND.

关键词： Neural networks

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A framework for managing enterprise architecture debts - Outline and research directions 10

A framework for managing enterprise architecture debts - Out...

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10th International Workshop on Enterprise Modeling and Information systems Architectures, EMISA 2020

作者： Alexander, Peter Hacks, Simon Jung, Jürgen Lichter, Horst Steffens, Ulrike Uludağ, Ömer RWTH Aachen University Research Group Software Construction Aachen Germany KTH Royal Institute of Technology Network and Systems Engineering Stockholm Sweden Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt Germany Hamburg University of Applied Sciences Department of Computer Science Hamburg Germany Technical University of Munich Department of Informatics Munich Germany

Even though enterprise architecture management (EAM) offers a wide range of methods and tools for aligning business with IT, an architect’s work is challenged by reality. The evolution of enterprise architecture (EA) and given constraints (e.g. legacy systems and processes) lead to debts which may complicate and hinder opportunities;however, the management of such debts has not been considered in EAM research. This paper presents a framework for strategically managing EA-debt-related issues and propose open questions as well as future research directions in this field. Copyright © 2020 for this paper by its authors.

关键词： Legacy systems

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