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Data Assimilation Enhances WRF-Chem Performance in Modeling Volcanic Ash Clouds from Hunga Tonga–Hunga Ha’apai Eruption

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Journal of Meteorological Research 2024年第6期38卷 1122-1140页

作者： Hosni SNOUN Mohammad Mosaed ALAHMADI Amirhossein NIKFAL Abderrazak ARIF William HATHEWAY Meznah A.ALAMRO Alaeddine MIHOUB Moez KRICHEN Numthaja Co. Ltd.Jeddah23345Saudi Arabia Jülich Supercomputing Centre(JSC) Forschungszentrum JülichJülich52428Germany Institut National de la Météorologie Tunis Carthage2035Tunisia Free Researcher AustinTexas78666USA Department of Information Technology College of Computer&Information SciencePrincess Nourah Bint Abdul Rahman UniversityRiyadh11564Saudi Arabia Department of Management Information Systems and Production Management College of Business and EconomicsQassim UniversityBuraidah51452Saudi Arabia Faculty of Computer Science and Information Technology(FCSIT) Al-Baha UniversityAl-Baha65528Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Volcanic eruptions release large amounts of ash clouds and gas aerosols into the atmosphere,which can be simulated by air quality prediction ***,the performance of these models remains unsatisfactory,even though both relevant physics and chemistry are ***,exploring the approaches for improvement such as inclusion of data assimilation is *** this study,we depict the modeling of the volcanic ash dispersion from the Hunga Tonga–Hunga Ha’apai underwater volcano,which erupted in a series of large explosions in late December 2021 and early January *** 15 January 2022,a particularly significant explosion sent a massive ash cloud high into the *** used the inline Weather Research and Forecasting model coupled with chemistry(WRF-Chem)and incorporated meteorological data assimilation within the Flux Adjusting Surface Data Assimilation System(FASDAS).We compared three forecast scenarios:one with only meteorology and no chemistry(OMET),one with gas and aerosol chemistry and no assimilation(NODA),and one with both chemistry and assimilation(FASDAS).We found that FASDAS resulted in lower planetary boundary layer height(PBLH),downward surface shortwave flux,and 2-m temperature by up to 800 m,200 W m^(−2),and 6℃ on the land portion,respectively,while the opposite was observed near the eruption *** validated the model against the observations and the results showed that FASDAS significantly enhanced the model performance in retrieving meteorological ***,the simulations also revealed significant biases in the concentration of volcanic ash around the ash *** from the Copernicus TROPOspheric Monitoring Instrument Sentinel-5 Precursor(TROPOMI-S5P)showed a westward trend of the total SO2 *** work demonstrates the significant contribution of data assimilation to the results of operational air quality predictions during violent volcanic eruption events.

关键词： Hunga Tonga-Hunga Ha’apai volcano WRF-Chem Flux Adjusting Surface Data Assimilation System(FASDAS) data assimilation SO2 TROPOspheric Monitoring Instrument Sentinel-5 Precursor(TROPOMI-S5P)

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StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning

arXiv

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arXiv 2023年

作者： Jing, Peiguang Liu, Xianyi Wang, Ji Wei, Yinwei Nie, Liqiang Su, Yuting School of Electrical and Information Engineering Tianjin University Tianjin China Faculty of Information Technology Monash University Victoria Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Emotion distribution learning has gained increasing attention with the tendency to express emotions through images. As for emotion ambiguity arising from humans’ subjectivity, substantial previous methods generally focused on learning appropriate representations from the holistic or significant part of images. However, they rarely consider establishing connections with the stylistic information although it can lead to a better understanding of images. In this paper, we propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL, which interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. Specifically, we consider exploring the intra- and inter-layer correlations among GRAM-based stylistic representations, and meanwhile exploit an adversary-constrained high-order attention mechanism to capture potential interactions between subtle visual parts. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations to benefit the final emotion distribution learning. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of our proposed StyleEDL compared to state-of-the-art methods. The implementation is released at: https://***/liuxianyi/StyleEDL. © 2023, CC BY.

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Crowd Emergency Evacuation Simulation Time Analysis via Obstacle Optimization Strategy 1

Crowd Emergency Evacuation Simulation Time Analysis via Obst...

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1st International Conference on Material Processing and technology, ICMProTech 2021

作者： Hamizan, S. Roselina, S. Habibollah, H. Yusliza, Y. Lizawati, M.Y. Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Malaysia

The crowd evacuation simulation is essential to provide important results for occupants, especially in the large capacity building compared to the human fire drill exercise. The strategy of evacuation such as the use of obstacles may need to be adapted by many organizations as an aid to help in visualizing and estimating the evacuation time during an emergency. During certain crowd events, they may consider the various setting of the object to ensure smoothness and effective crowd evacuation flow. In this paper, it aims to provide the simulation with 100-1000 agents and testing with obstacle using Anylogic tool and analysis of evacuation time validated using SPSS. The results show that the placement of obstacles near the exit way indeed can reduce the evacuation time and complies with the anti-arching phenomenon during evacuation. © 2021 Institute of Physics Publishing. All rights reserved.

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A Trend Analysis of Test Smells in Python Test Code Over Commit History

A Trend Analysis of Test Smells in Python Test Code Over Com...

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Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Yuki Fushihara Hirohisa Aman Sousuke Amasaki Tomoyuki Yokogawa Minoru Kawahara Department of Computer Science Faculty of Engineering Ehime University Ehime Japan Center for Information Technology Ehime University Ehime Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Okayama Japan

Software testing is an essential activity for developing and maintaining high-quality software. Unit testing with test code (test cases) is a fundamental testing activity, and developers can test their production code whenever they create or modify the code. However, such quality assurance relies on the correctness of the test code. If a test code had a flaw, it would mislead the developers about the hidden faults and prevent early detection of the faults. This paper focuses on "test smells," which may cause test code flaws in Python programs, and analyzes their changing trends over commit history (code changes) toward better Python test code management. Through an empirical data analysis of 100 open-source projects, the paper reports the following findings: (1) a few kinds of test smells constitute the majority of smells detected in the studied projects, and (2) most kinds of smells tend to increase over commits, i.e., many test smells are likely to have remained in test code as technical debt.

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How to manage a multifactor-driven crude oil market more effectively? A revisit based on the multiple criteria perspective

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Resources Policy 2025年 100卷

作者： Yu, Yue Wang, Jianzhou Jiang, He Lu, Haiyan School of Statistics Dongbei University of Finance and Economics Dalian116025 China School of Statistics Jiangxi University of Finance and Economics Nanchang330013 China School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Australia

With the complexity of the international crude oil market deepening, it is of profound significance for information recipients and actors to clarify the operation mechanism of the multi-factor-driven international crude oil market, grasp its crucial drivers, and establish a reasonable and effective analysis and early warning system. This paper reviewed the results of existing studies and proposed a collection of daily frequency proxies from five perspectives based on the principles of plurality, completeness and rationality. What's more, a three-dimensional assessment strategy was also developed based on causality, predictability, and necessity, which complemented and extended existing methodologies and findings on crude oil market drivers and theoretically quantified the economic and statistical properties of various proxies. After that, the most critical agents were extracted by relying on the logic of multi-criteria decision-making, which solved the problem of scattered attention in the analysis of the crude oil market. Finally, based on machine learning and artificial intelligence, a hybrid forecasting model that blended key driving agents with both error accuracy, as well as directional accuracy, was constructed. Taking Brent crude oil, the benchmark for more than two-thirds of the world's crude oil, as an example, the findings verified the importance and necessity of correctly grasping the key drivers and confirmed that this study can provide a more scientifically sound research methodology and theoretical basis for crude oil market analysis and early warning based on limited attention. © 2024 Elsevier Ltd

关键词： Causality Multiple criteria analysis Necessity Predictability Price drivers

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Naïve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset 3

Naïve Bayes Classifier and Particle Swarm Optimization Feat...

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3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019

作者： Talita, A.S. Nataza, O.S. Rustam, Z. Faculty of Computer Science and Information Technology Gunadarma University Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences University of Indonesia Indonesia

The security of a network might be threatened by an intrusion aim to steal classified data or to find weaknesses on the network. In general, network main security systems use a firewall to control and monitor both incoming and outgoing network traffic. Intrusion Detection System can be used to strengthen network security. Several data mining methods have been used to solve Intrusion Detection System (IDS) problem on a network. On this paper we will use Naïve Bayes Classifier along with Particle Swarm Optimization (PSO) as the feature selection method specifically on one of the benchmark dataset on IDS problem, KDD CUP'99. The dataset consists of more than 40 features with more than 400 thousands records. To solve IDS problem on the dataset, it needs a quite expensive cost either on time computation or memory usage hence the use of PSO as the feature selection method. The best classification result was reached when we use 38 features where the accuracy is 99.12%. Particle Swarm Optimization method has several parameters that may affect the classification performance. For future improvement, it is possible to use a parameter optimization method to ensure the best classifier performance. © Published under licence by IOP Publishing Ltd.

关键词： Intrusion detection

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A Data Analysis Method Using Orthogonal Transformation in a Reproducing Kernel Hilbert Space

A Data Analysis Method Using Orthogonal Transformation in a ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lingxiao Qu Yan Pei Jianqiang Li Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima Japan Faculty of Information Technology Beijing University of Technology Beijing China

We propose a data analysis method that combines the objectives of nonlinear principal component analysis and nonlinear discriminant analysis with the kernel method in a reproducing kernel Hilbert space. This method addresses nonlinear data analysis problems in high-dimensional spaces, specifically the reproducing kernel Hilbert space, through the use of the kernel trick. Our proposed method can be considered as a semi-supervised data analysis approach. We evaluate our proposed method using various kernel functions and datasets, both visually and quantitatively. The evaluation results demonstrate that our proposal outperforms kernel principal component analysis and generalized discriminant analysis in terms of classification performance. This indicates the advantages and originality of our proposed method. Furthermore, we analyze and discuss our findings based on the evaluation results, and highlight potential areas for further research and future work related to our proposal.

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Fastrealboostbins: An Ensemble Classifier for Fast Predictions Implemented in Python Via *** and ***

SSRN

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SSRN 2023年

作者： Klęsk, Przemyslaw Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Żolnierska 49 Szczecin71-210 Poland

Taking advantage of Numba (a high-performance just-in-time Python compiler), we provide a fast operating implementation of a boosting algorithm in which bins with logit transform values play the role of "weak learners". The software comes as a Python class compliant with scikit-learn library. It allows to choose between CPU and GPU computations for each of the two stages: fit and predict (decision function). The efficiency of implementation has been confirmed on large data sets where the total of array entries (sample size × features count) was of order 1010 at fit stage and 108 at predict stage. In case of GPU-based fit, the main boosting loop is designed as five CUDA kernels responsible for: weights binning, computing logits, computing exponential errors, finding the error minimizer, and examples reweighting. The GPU-based predict is computed by a single CUDA kernel. We apply suitable reduction patterns and mutexes to carry out summations and ‘argmin’ operations. To test the predict stage performance, we compare FastRealBoostBins against state-of-the-art classifiers from *** using large data sets and focusing on response times. In an additional experiment, we make our classifiers operate as object detectors under heavy computational load (over 60k queries per a video frame using ensembles of size 2048). © 2023, The Authors. All rights reserved.

关键词： Python

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Demystify scrambled and encoded images with auto-encoders

Demystify scrambled and encoded images with auto-encoders

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Nguyen, Huu-Thanh Hieu Vu, Quang Huynh, Anh-Dung University of Hong Kong Department of Computer Science City Hong Kong Data Platform ZaloPay Vng Corporation Viet Nam Faculty of Information Technology Van Lang University Viet Nam

ISBN: (纸本)9781665480451

Monitoring systems have become more and more popular. A typical example is a smart home monitoring system which provides peace of mind for people staying far away from their homes. This system can be deployed for different purposes, such as to take care elderly staying at home alone or to provide anti-theft when nobody is at home. In this type of monitoring systems, there is always a problem with protecting the privacy and security of captured images. This problem is often addressed by either scrambling or encoding techniques that add noise in or to distort the images. In the IEEE Big Data 2022 Cup, the challenge is to detect and perform matching between an original image and its scrambled or encoded version. Generally, if we are able to train a machine learning model that would correctly identify the matching, it will be proof that the applied scrambling or encoding technique is not strong enough to protect data and hence it needs to be strengthened further. In this paper, we present an auto-encoder architecture that allows us to demystify scrambled or encoded images as long as the scrambling or encoding techniques are deterministic. Source code is publicly available1. © 2022 IEEE.

关键词： Signal encoding

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Efficient Low Cost Automatic External Defibrillator Circuit 11

Efficient Low Cost Automatic External Defibrillator Circuit

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Sayed, Mohammed S. Elsagheer, Samir A. Hassan, Mohsen A. Egypt-Japan University of Science and Technology Department of Electronics and Communications Engineering New Borg El-Arab City Alexandria21934 Egypt Zagazig University Department of Electronics and Communications Engineering Zagazig Egypt Egypt-Japan University of Science and Technology Computer Science and Information Technology New Borg El-Arab City Alexandria21934 Egypt Aswan Universty Faculty of Engineering Aswan Egypt Egypt-Japan University of Science and Technology Department of Materials Science and Engineering New Borg El-Arab City Alexandria21934 Egypt

ISBN: (纸本)9798350369328

Cardiac arrest happens when the heart stops beating suddenly, hence, blood stops flowing to the brain and other vital organs. The lack of blood flow to the brain and other organs can cause a person to lose consciousness, become disabled or die if not treated immediately. Restoring heart rhythm can happen by delivering an electrical shock to the patient through the chest wall. With the increase of out-of-hospital cardiac arrest (OHCA) the need for Automatic External Defibrillator (AED) increases. AED is a portable device used to restore normal heart rhythm to patients by delivering an electric shock through the chest to the heart when it detects an abnormal rhythm. It is easy-to-use medical device that can be used by normal person. It analyzes the heart's rhythm and, if necessary, deliver an electrical shock, or defibrillation, to help the heart regain its normal rhythm. In this paper we present a low cost and light weight circuit for mobile Automatic External Defibrillator. © 2023 IEEE.

关键词： Electrocardiograms

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