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Identification of fishing ground hotspot of traditional purse seine fisher at northern waters of Aceh - A community-based data collection approach 1

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Identification of fishing ground hotspot of traditional purs...

1st International and National Symposium on Aquatic Environment and Fisheries, INSAEF 2020

作者： Haridhi, H.A. Rizal, S. Nanda, M. Muhammad Wilson, C.R. Department of Marine Sciences Faculty of Marine and Fisheries Syiah Kuala University Banda Aceh Indonesia Marine and Fisheries Research Center Syiah Kuala University Banda Aceh Indonesia Doctoral Program of Mathematics and Applied Science Graduate School Syiah Kuala University Banda Aceh Indonesia Asian Development Bank Mandaluyong Philippines

Fishing grounds are among the most important key of information kept by the fishermen. Understanding the fishing ground locations are considered important as an evaluation of the fishing effort being reached by the fishermen as well as understanding the pressure being received to the fish stock at the surrounding water. The recent study has shown that a community-based data collection approach was found successful in recording and analyzing the fishing ground locations and its properties. One of the few projects implementing this concept is the Community-Based Bathymetric Survey (CBBS), which was realized by the Body of Rehabilitation and Reconstruction (BRR) Nanggroe Aceh Darussalam (NAD) - Nias with funding from the Asian Development Bank (ADB). As many as 151 GPS-transducer recording devices were installed to the fishermen boats along the coast of Aceh, with 45 devices were installed to the traditional purse seine fisher at the northern water of Aceh. These devices were configured to automatically record locations each time the boat moved 30 m. Other important information, such as water depth, time, speed, and sea surface temperature (SST) were recorded as well. This study aims to examine the fishing ground and its fishing hotspot of the traditional purse seine fisher in the northern waters of Aceh. The data collection was performed between June 2007 and May 2009 and gathered as many as 922 data sets consist of 6,170,648 data points, from the 45 purse seine boats. Trough the data, we are able to identify the typical fishing activities of purse seiner and as many as 1,619 data points were being detected. The results indicate that there are five main regions of the fishing ground hotspot in the northern waters of Aceh, i.e. 1. Western offshore of Lhok Nga, 2. Northern offshore of Ulee Lheue, 3. Western offshore of Aceh Islands, 4. Eastern offshore of Aceh Island, and 5. Northern offshore of Krueng Raya. Among the fishing ground hotspot analyzed, the Western offshor

关键词： Location

A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

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Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

Global existence and uniqueness of solutions for one-dimensional reaction-interface systems

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arXiv 2021年

作者： Chen, Yan-Yu Ninomiya, Hirokazu Wu, Chang-Hong Department of Mathematics National Taiwan University Taipei10617 Taiwan School of Interdisciplinary Mathematical Sciences Meiji University 4-21-1 Nakano Nakano-ku Tokyo164-8525 Japan Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu300 Taiwan

In this paper, we provide a mathematical framework in studying the wave propagation with the annihilation phenomenon in excitable media. We deal with the existence and uniqueness of solutions to a one-dimensional free boundary problem (called a reaction-interface system) arising from the singular limit of a FitzHugh-Nagumo type reaction-diffusion system. Because of the presence of the annihilation, interfaces may intersect each other. We introduce the notion of weak solutions to study the continuation of solutions beyond the annihilation time. Under suitable conditions, we show that the free boundary problem is well-posed. Copyright © 2021, The Authors. All rights reserved.

关键词： Boundary value problems

Laboratory modeling of moist convection using a reactive fluid

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Physical Review Fluids 2025年第5期10卷 053505-053505页

作者： Valentin Dorel Daniel Lecoanet Michael Le Bars CNRS Aix Marseille Université Centrale Marseille IRPHE Marseille France Department of Engineering Sciences and Applied Mathematics and Center for Interdisciplinary Exploration and Research in Astrophysics Northwestern University Evanston Illinois USA

We present a laboratory experiment to model convection in the atmosphere driven by latent heat of condensation (“moist convection”). The system consists of a tank filled with an aqueous solution containing a color indicator that renders the fluid yellow and transparent when acidic and blue and opaque when basic. A sodium lamp positioned above the tank serves as a heat source. Initially, the solution is entirely acidic and stably stratified in temperature. A basic layer is progressively generated at the tank's bottom through water electrolysis, forming an initially stable, blue region. This layer is internally heated by absorbing the heat from the sodium lamp, modeling the latent heat of condensation in the atmosphere. Over time, instabilities develop at the interface between the yellow and blue layers. We investigate the variation of the instability wavelength and complement our observations with a linear stability analysis to elucidate the underlying mechanisms and the process of wavelength selection. Direct numerical simulations are then employed to explore the first steps of the nonlinear regime in connection with experimental observations.

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Experimental Study on Assistive System Using Statistical Signal Processing Technology for Visually Impaired People 10th

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Experimental Study on Assistive System Using Statistical Sig...

10th International Conference on Health Informatics and Medical Systems, HIMS 2024 and 10th International Conference on Biomedical Engineering, BIOENG 2024, held as part of the World Congress in Computer Science, Computer Engineering and applied Computing, CSCE 2024

作者： Shimizu, Shunji Hori, Masaya Inoue, Hiroaki Miwakeichi, Fumikazu Hirai, Nobuhide Kikuchi, Senichiro Kato, Satoshi Department of Applied Information Engineering Suwa University of Science Chino Japan Department of Engineering and Management Suwa University of Science Chino Japan Shimizu Research Laboratory Suwa University of Science Chino Japan Department of Interdisciplinary Statistical Mathematics The Institute of Statistical Mathematics Tachikawa Japan Health Administration Center Tokyo Medical and Dental University Bunkyo Japan Department of Rehabilitation Sciences Gunma University Maebashi Japan Oyama Fujimidai Hospital Shimotsuke Japan

ISBN: (纸本)9783031859076

In this study, we will investigate a new image signal processing method in order to develop a highly useful walking support device for visually impaired people. Visually impaired people walk independently using assistive devices. Currently, there are many support devices that use object detection and object recognition using deep learning, but there are still no highly useful devices. The smallest unit constituting an image signal is a pixel. Adjacent pixels are correlated with each other. The positional relationship of pixels expresses the features of objects, and scenery in the image. Autoregressive (AR) model is a regression analysis method used to analyze time-series signals. AR models are one of the basic methods for signal processing for speech recognition and control. Furthermore, in image signal processing, it can be used as a signal processing method when data has some kind of order, such as texture feature extraction, contour processing, and video processing. In this experiment, we converted the pixel array of the image signal into a one-dimensional time-series signal and process the signal. We tried to identify objects or regions within images. The method proposed in this study may be used to extract "Point of Interest" (POI) and "Point of Reference" (POR), which are important information for visually impaired people. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image texture

Towards Model Reduction for Power System Transients with Physics-Informed PDE

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arXiv 2021年

作者： Pagnier, Laurent Fritzsch, Julian Jacquod, Philippe Chertkov, Michael Program in Applied Mathematics University of Arizona Tucson United States School of Applied Sciences of Western Switzerland HES-SO Sion Switzerland Department of Quantum Matter Physics University of Geneva Geneva Switzerland

This manuscript reports the first step towards building a robust and efficient model reduction methodology to capture transient dynamics in a transmission level electric power system. Such dynamics is normally modeled on seconds-to-tens-of-seconds time scales by the so-called swing equations, which are ordinary differential equations defined on a spatially discrete model of the power grid. Following Seymlyen (1974) and Thorpe, Seyler, and Phadke (1999), we suggest to map the swing equations onto a linear, inhomogeneous Partial Differential Equation (PDE) of parabolic type in two space and one time dimensions with time-independent coefficients and properly defined boundary conditions. We illustrate our method on the synchronous transmission grid of continental Europe. We show that, when properly coarse-grained, i.e., with the PDE coefficients and source terms extracted from a spatial convolution procedure of the respective discrete coefficients in the swing equations, the resulting PDE reproduces faithfully and efficiently the original swing dynamics. We finally discuss future extensions of this work, where the presented PDE-based modeling will initialize a physics-informed machine learning approach for real-time modeling, n − 1 feasibility assessment and transient stability analysis of power systems. © 2021, CC BY.

关键词： Machine learning

A hybrid semi-Lagrangian cut cell method for advection-diffusion problems with robin boundary conditions in moving domains

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arXiv 2021年

作者： Barrett, Aaron Fogelson, Aaron L. Griffith, Boyce E. Department of Mathematics University of Utah Salt Lake CityUT United States Departments of Mathematics and Bioengineering University of Utah Salt Lake CityUT United States Departments of Mathematics Applied Physical Sciences and Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina Chapel HillNC United States McAllister Heart Institute University of North Carolina Chapel HillNC United States

We present a new discretization approach to advection-diffusion problems with Robin boundary conditions on complex, time-dependent domains. The method is based on second order cut cell finite volume methods introduced by Bochkov et al. [8] to discretize the Laplace operator and Robin boundary condition. To overcome the small cell problem, we use a splitting scheme along with a semi-Lagrangian method to treat advection. We demonstrate second order accuracy in the L1, L2, and L∞norms for both analytic test problems and numerical convergence studies. We also demonstrate the ability of the scheme to convert one chemical species to another across a moving boundary. © 2021, CC BY-SA.

关键词： Advection

Drone-borne calibration pulser for radio observatories detecting ultra-high-energy cosmic rays and neutrinos 38

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Drone-borne calibration pulser for radio observatories detec...

38th International Cosmic Ray Conference, ICRC 2023

作者： Nam, J. Kuo, C.-Y. Shin, B.K. Chen, P. Chen, Y. Hsu, S.-Y. Huang, J.-J. Huang, M.-H.A. Leung, C.H. Liu, T.-C. Wang, S.-H. Wang, M.-Z. Wang, Y.-H. Besson, D. Novikov, A. Hornhuber, C. Young, R. Department of Physics National Taiwan University Taipei Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei Taiwan Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Energy and Resources National United University Miaoli Taiwan Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National PingTung university PingTung900 Taiwan Chung Cheng Institute of Technology National Defense University TaoYuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Physics and Astronomy University of Kansas Lawrence United States Instrumentation Design Lab University of Kansas Lawrence United States Moscow Russia Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei Taiwan

We report on the development of a drone-borne calibration pulser (cal-pulser) for radio observatories detecting ultra-high energy (UHE) cosmic rays and neutrinos. This system allows us to calibrate the radio detector in its field of view regardless of the accessibility of the site. The system is compact and sufficiently lightweight to be attached to a commercial drone. It consists of a solid-state high-power pulse generator, a programmable attenuator, a transmitting antenna, and a differential GPS (D-GPS) module. The drone, with the ∼1.3 kg payload, has a typical flight time of around 25 minutes, corresponding to a maximum flight distance of around 10 km. After developing the first version of the system in 2019, it successfully demonstrated its calibration performance with the TAROGE stations in Taiwan and the TAROGE-M station in Mt. Melbourne, Antarctica. In 2022, the system was upgraded with a dual-band D-GPS for more reliable operation and more accurate determination of the coordinates of the cal-pulser. In addition to the cal-pulser, we investigated aerial photogrammetry using the built-in camera and the D-GPS, which is an efficient method to determine the coordinates of the detector. For objects within 10 m of the drone, an accuracy of a few millimeter was achieved. These drone-borne cal-pulser and the photogrammetry have become essential calibration procedures in the TAROGE-M experiment. We suggest these approaches may find application in many other radio experiments that detect UHE cosmic rays and neutrinos, such as ARIANNA, BEACON, GRAND, as well as IceCube-Gen2. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

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arXiv 2022年

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications. © 2022, CC BY.

关键词： Density functional theory