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Spatiotemporal Analysis of Meteorological Drought Using Standardized Precipitation Index (SPI) in Gabiley Region, Somaliland

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Journal of Geoscience and Environment Protection 2023年第5期11卷 47-59页

作者： Abdisamad Abib Omar Jie-Lun Chiang Bashir Hassan Daud Department of Tropical Agriculture and International Cooperation National Pingtung University Science and Technology Taiwan Department of Soil and Water Conservation National Pingtung University Science and Technology Taiwan College of Engineering International Master Program on Natural Hazards Mitigation and Management National Cheng Kung University Taiwan

Drought is a common natural disaster worldwide, with varying durations, severity levels, and spatial extents. This study aimed to model the spatiotemporal variation of meteorological drought events in the Gabiley region of Somaliland. The study utilized primary data collected from the meteorological station in Gabiley and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data to develop the standardized precipitation index (SPI) at a 3-month timescale. The results of the study revealed that the study area was characterized by drought and received inadequate precipitation, resulting in catastrophic droughts that negatively impacted the socioeconomic situation of the community. Mild-to-severe meteorological drought events occurred every two to three years, with the most severe droughts occurring in 1998, 2002, 2009, 2015, and 2017. Specifically, the year 2015 experienced the most severe meteorological drought in the region during the studied period. The predominant type of drought was a mild year in the study area. The SPI was found to potentially identify meteorological drought, making it a useful tool for policymakers as they develop drought adaptation and mitigation policies. This study provides valuable information that can benefit local authorities and policymakers in creating drought mitigation and adaptation strategies in the Gabiley region.

关键词： Meteorological Drought SPI CHRIPS Rainfall Gabiley

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A Physics-Informed Deep Learning Description of Knudsen Layer Reactivity Reduction

arXiv

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arXiv 2024年

作者： McDevitt, Christopher J. Tang, Xian-Zhu Nuclear Engineering Program Department of Materials Science and Engineering University of Florida GainesvilleFL32611 United States Theoretical Division Los Alamos National Laboratory Los Alamos NM87545 United States

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov-Fokker-Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, predictions from the Vlasov-Fokker-Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov-Fokker-Planck PINN and results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile demonstrating that the Vlasov-Fokker-Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions. © 2024, CC BY.

关键词： Deep learning

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An Efficient Surrogate Model of Secondary Electron Formation and Evolution

arXiv

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arXiv 2024年

作者： McDevitt, Christopher J. Arnaud, Jonathan Tang, Xian-Zhu Nuclear Engineering Program Department of Materials Science and Engineering University of Florida GainesvilleFL32611 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

This work extends the adjoint-deep learning framework for runaway electron (RE) evolution developed in Ref. [1] to account for large-angle collisions. By incorporating large-angle collisions the framework allows the avalanche of REs to be captured, an essential component to RE dynamics. This extension is accomplished by using a Rosenbluth-Putvinski approximation to estimate the distribution of secondary electrons generated by large-angle collisions. By evolving both the primary and multiple generations of secondary electrons, the present formulation is able to capture both the detailed temporal evolution of a RE population beginning from an arbitrary initial momentum space distribution, along with providing approximations to the saturated growth and decay rates of the RE population. Predictions of the adjoint-deep learning framework are verified against a traditional RE solver, with good agreement present across a broad range of parameters. © 2024, CC BY.

关键词： Electrons

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A Physics-Constrained Deep Learning Treatment of Runaway Electron Dynamics

arXiv

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arXiv 2024年

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem is solved, its solution can be used to project the RE density forward in time for an arbitrary initial momentum space distribution of REs. Furthermore, by employing a PINN, a parametric solution to the adjoint problem can be learned. Thus, once trained, this adjoint-deep learning framework is able to efficiently project the RE density forward in time across various plasma conditions while still including a fully kinetic description of RE dynamics. As an example application, the temporal evolution of the density of primary electrons is studied, with particular emphasis on evaluating the decay of a RE population when below threshold. Predictions from the adjoint-deep learning framework are found to be in good agreement with a traditional relativistic electron Fokker-Planck solver, for several distinct initial conditions, and across an array of physics parameters. Once trained the PINN thus provides a means of generating RE density time histories with exceptionally low online execution time. © 2024, CC BY.

关键词： Fokker Planck equation

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Anti-Termite Activity of Azadirachta excelsa Seed Kernel and Its Isolated Compound against Coptotermes curvignathus

Journal of the Korean Wood Science and Technology

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Journal of the Korean Wood science and Technology 2023年第3期51卷 157-172页

作者： Adfa, Morina Wiradimafan, Khafit Pratama, Ricky Febri Sanjaya, Angga Triawan, Deni Agus Yudha S., Salprima Ninomiya, Masayuki Rafi, Mohamad Koketsu, Mamoru Department of Chemistry Faculty of Mathematics and Natural Sciences University of Bengkulu Bengkulu38371 Indonesia Research Center of Sumatera Natural Product and Functional Materials University of Bengkulu Bengkulu38371 Indonesia Master of Chemistry Study Program Faculty of Mathematics and Natural Sciences University of Bengkulu Bengkulu38371 Indonesia Department of Chemistry and Biomolecular Science Faculty of Engineering Gifu University Gifu5011193 Japan Department of Chemistry Faculty of Mathematics and Natural Sciences IPB University Bogor16128 Indonesia Tropical Biopharmaca Research Center-Institute of Research and Community Services IPB University Bogor16128 Indonesia

Azadirachta excelsa, is a plant belonging to the same genus as Indian neem (Azadirachta indica), and its use as a pesticide is reported by few studies. Despite being a different species, it is expected to have the same biopesticide potential as A. indica. Therefore, this study aims to investigate the anti-termite activity of n-hexane and methanol extracts of A. excelsa seed kernel at various concentrations against Coptotermes curvignathus. The methanol extract demonstrated greater termicidal activity than n-hexane at doses test of 2%, 4%, and 8%. It also showed 100% termite mortality on the third day of administering the 8% dose. According to the gas chromatography with mass spectrometry data, the putative main components of the n-hexane extract were hexadecanoic acid, ethyl ester (18.99%), 9,12-octadecadienoic acid (Z,Z)-(16.31%), and 9-octadecenal (16.23%). In contrast, the principal constituents of methanol extract were patchouli alcohol (28.1%), delta-guaiene (15.15%), and alpha-guaiene (11.93%). Furthermore, limonoids profiling of A. excelsa methanol extract was determined using Ultrahigh-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry. The number of limonoids identified tentatively was fifteen, such as 6-deacetylnimbin, nimbolidin C, nimbolide, 6-acetylnimbandiol, 6-deacetyl-nimbinene, salannol, 28-deoxonimbolide, gedunin, nimbandiol, epoxyazadiradione, azadirone, 2',3'-dihydrosalannin, marrangin, nimbocinol, and azadirachtin. They were the same as those reported in the seed and leaves of A. indica, but its largest component in A. excelsa was 6-deacetylnimbin. As a result, the presence of these compounds may be responsible for the anti-termite activity of A. excelsa seed kernel extract. Additionally, column chromatography of methanol extract yielded 6-deacetylnimbin, which was found to be antifeedant and termiticide against C. curvignathus. © 2023 The Korean Society of Wood science & Technology.

关键词： Mass spectrometry

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Robust Object Detection Model for UAV Application

Robust Object Detection Model for UAV Application

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International Automatic Control Conference

作者： Chun Ku Xiu-Zhi Chen Yen-Lin Chen Master Program in Artificial Intelligence Technology National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

Not only common issues on object detection task need to be deal with, for Unmanned Aerial Vehicle (UAV) applications, small object is one of the critical problems that needs to be solved. YOLOv7 is a powerful network architecture that provides high efficiency and accuracy object detection results. This paper adopts YOLOv7 as an object detection model for two different kinds of targets, one is vehicle, and the other is ocean flotsam. By training the model with open datasets and fine-tuning the model with self-collected datasets, we prove through sequences collected from real-world scenarios that YOLOv7 is able to provide robust and accurate object detection results, including vehicles and ocean flotsam, with real-time efficiency. Based on such experimental result, we confirmed that YOLOv7 can be the baseline for object detection model development.

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Nitrogen subsidies to pelagic food webs through profundal methane-oxidising bacteria in oligotrophic fresh water

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Freshwater Biology 2024年第5期69卷 679-691页

作者： dela Paz, Erica Silk P. Okuda, Noboru Ho, Pei-Chi Lin, Fan-Sian Hsieh, Chih-hao Lai, Chao-Chen Fujibayash, Megumu Shiah, Fuh-Kwo Ko, Chia-Ying Institute of Fisheries Science National Taiwan University Taipei Taiwan Microalgal Systematics and Phycology Research Unit Center for Natural Science and Environmental Research De La Salle University Manila Philippines Department of Biology College of Science De La Salle University Manila Philippines Research Center for Humanity and Nature Kyoto Japan Research Center for Inland Seas Graduate School of Science Kobe University Kobe Japan Institute of Oceanography National Taiwan University Taipei Taiwan Research Center for Environmental Changes Academia Sinica Taipei Taiwan Instituted of Ecology and Evolutionary Biology & Master's Program in Biodiversity National Taiwan University Taipei Taiwan Department of Science Education National Taipei University of Education Taipei Taiwan Faculty of Engineering Kyushu University Fukuoka City Japan

Nutrient subsidy of methane-oxidising bacteria (MOB), an alternative basal resource, to plankton communities through pelagic-benthic coupling is expected to alter carbon-to-nitrogen (C/N) ratios and biomass of zooplankton, and affect biological processes in aquatic food webs. Previous studies indicate that most MOB nutrient subsidies to zooplankton are focused largely on C subsidies;nevertheless, considering that N is important to zooplankton and phytoplankton growth, a better understanding of MOB N subsidies to plankton communities is needed. We examined changes in nutrients, phytoplankton mass-specific productivity, MOB reliance, C/N ratios and biomass of zooplankton between summer stratification and winter mixing periods based on in situ investigation in a subtropical deep oligotrophic freshwater ecosystem during 2010–2014. The results revealed that with increasing reliance on MOB during the winter mixing period, zooplankton C/N ratios decreased and had a significantly nonlinearly inverse relationship with zooplankton biomass. The negative relationship between zooplankton biomass and phytoplankton mass-specific productivity was further observed in synchronous time. The profundal MOB enhanced pelagic-profundal coupling and served as an alternative N support for pelagic plankton food webs during the winter and/or less productive seasons, adding to greater understanding of the role of methanotrophs in aquatic food webs. © 2024 John Wiley & Sons Ltd.

关键词： freshwater methane-oxidising bacteria nutrient subsidy phytoplankton zooplankton

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Epilayer on NEAT Grown GaN Schottky Diodes for Radiation Detection

Epilayer on NEAT Grown GaN Schottky Diodes for Radiation Det...

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2023 Transactions of the American nuclear Society Annual Meeting and Technology Expo, ANS 2023

作者： Remy, Jarod Zhang, Yuxuan Talesara, Vishank Zhao, Hongping Lu, Wu Hashimoto, Tadao Cao, Lei R. Nuclear Engineering Program Department of Mechanical and Aerospace Engineering 281 W. Lane Ave. ColumbusOH43210 United States Department of Electrical and Computer Engineering 281 W. Lane Ave ColumbusOH43210 United States Department of Material Science and Engineering The Ohio State University 281 W. Lane Ave ColumbusOH43210 United States SixPoint Materials Inc 37 Industrial Way STE 106 BuelltonCA United States

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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7th International Conference on Computer science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Enhancing Pharmaceutical Wastewater Treatment: Ozone-Assisted Electrooxidation and Precision Optimization Via Response Surface Methodology

SSRN

引用

SSRN 2023年

作者： Phan, Huy N.Q. Leu, Jyh Hoang Nguyen, Vi N.D. Department of Materials Science and Engineering Feng Chia University Taichung40744 Taiwan Master’s Program of Green Energy Science and Technology Feng Chia University Taichung40744 Taiwan Department of Chemical Engineering Feng Chia University Taichung40744 Taiwan

In this study, the treatment of pharmaceutical wastewater was conducted using electrooxidation (EO) and ozone-assisted electrooxidation (EO/O3) with Pt-coated Ti electrodes (Pt/Ti). The removal of chemical oxygen demand (COD), color, and the UV absorption peak at 300 nm (UV300) was investigated in a batch mode reactor. Highly significant quadratic regression models for both processes were revealed through experimental design and optimization using the Box-Behnken Design (BBD) methodology. Current density, initial pH, and electrolysis time were assigned as independent variables for the statistical models. Under optimal conditions of 110.63 A m−2 current density and an initial pH of 4.5 for 477 minutes, stand-alone electrooxidation achieved the removal of 70.5% of COD, 81.25% color reduction, and a 40.01% decrease in UV300. In contrast, ozone-assisted electrooxidation performed even more effectively, with the removal of 84% for COD, 99.43% for color, and 49.63% for UV300. These results were obtained under optimal conditions of 150 A m−2 current density and an initial pH of 8.79 for 480 minutes. In addition, positive results in the kinetic study also gave this study an appeal. It was seen that ozone-assisted electrooxidation is an effective method for the pharmaceutical wastewater treatment. © 2023, The Authors. All rights reserved.

关键词： Ozone

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