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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

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IOP Conference Series: Earth and Environmental science 2021年第1期674卷

作者： H A Haridhi S Rizal M Nanda Muhammad C R Wilson 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 Phillippines

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

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The bulk van der Waals layered magnet CrSBr is a quasi-1D material

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arXiv 2022年

作者： Klein, Julian Pingault, Benjamin Florian, Matthias Heißenbüttel, Marie-Christin Steinhoff, Alexander Song, Zhigang Torres, Kierstin Dirnberger, Florian Curtis, Jonathan B. Weile, Mads Penn, Aubrey Deilmann, Thorsten Dana, Rami Bushati, Rezlind Quan, Jiamin Luxa, Jan Sofer, Zdenek Alù, Andrea Menon, Vinod M. Wurstbauer, Ursula Rohlfing, Michael Narang, Prineha Lončar, Marko Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Electrical and Computer Engineering Department of Physics University of Michigan Ann ArborMI48109 United States Institut für Festkörpertheorie Westfälische Wilhelms-Universität Münster Münster48149 Germany Institut für Theoretische Physik Universität Bremen P.O. Box 330 440 Bremen28334 Germany Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Department of Physics City College of New York New YorkNY10031 United States College of Letters and Science UCLA Los AngelesCA90095 United States Department of Physics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark MIT.nano Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Institute of Physics Center for Nanotechnology University of Münster Münster48149 Germany

Correlated quantum phenomena in one-dimensional (1D) systems that exhibit competing electronic and magnetic order are of strong interest for studying fundamental interactions and excitations, such as Tomonaga-Luttinger liquids and topological orders and defects with properties completely different from the quasiparticles expected in their higher-dimensional counterparts. However, clean 1D electronic systems are difficult to realize experimentally, particularly magnetically ordered systems. Here, we show that the van der Waals layered magnetic semiconductor CrSBr behaves like a quasi-1D material embedded in a magnetically ordered environment. The strong 1D electronic character originates from the Cr-S chains and the combination of weak interlayer hybridization and anisotropy in effective mass and dielectric screening with an effective electron mass ratio of meX/meY ∼ 50. This extreme anisotropy experimentally manifests in strong electron-phonon and exciton-phonon interactions, a Peierls-like structural instability and a Fano resonance from a van Hove singularity of similar strength of metallic carbon nanotubes. Moreover, due to the reduced dimensionality and interlayer coupling, CrSBr hosts spectrally narrow (1 meV) excitons of high binding energy and oscillator strength that inherit the 1D character. Overall, CrSBr is best understood as a stack of weakly hybridized monolayers and appears to be an experimentally attractive candidate for the study of exotic exciton and 1D correlated many-body physics in the presence of magnetic order. Copyright © 2022, The Authors. All rights reserved.

关键词： Excitons

Retraction notice to "Hydrological and climate impacts on river characteristics of Pahang River Basin, Malaysia" [ Heliyon 9 2023 e21573]

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Heliyon 2025年第1期11卷 e41351页

作者： Mohd Khairul Amri Kamarudin Mohd Ekhwan Toriman Noorjima Abd Wahab Mohd Armi Abu Samah Khairul Nizam Abdul Maulud Firdaus Mohamad Hamzah Ahmad Shakir Mohd Saudi Sunardi Sunardi Faculty of Applied Social Science University Sultan Zainal Abidin Gong Badak Campus 21300 Terengganu Malaysia. East Coast Environmental Research Institute (ESERI) University Sultan Zainal Abidin Gong Badak Campus Nerus 21300 Kuala Malaysia. Faculty of Social Sciences and Humanities National University of Malaysia 43600 Bangi Selangor Malaysia. Kulliyyah of Science International Islamic University Malaysia 25200 Kuantan Pahang Malaysia. Earth Observation Center Institute of Climate Change National University of Malaysia 43600 Bangi Selangor Malaysia. Department of Mathematics Defense Base Center National Defence University of Malaysia Sungai Besi Camp 57000 Kuala Lumpur Malaysia. Department of Environmental Health Institute of Medical Science Technology Universiti Kuala Lumpur Kajang Selangor Malaysia. Graduate Program on Environmental Studies Postgraduate School Universitas Padjadjaran Jl. Dipati Ukur No. 35 Bandung 40132 Indonesia.

[This retracts the article DOI: 10.1016/***.2023.e21573.].

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Isotope effects in liquid water via deep potential molecular dynamics

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arXiv 2019年

作者： Ko, Hsin-Yu Zhang, Linfeng Santra, Biswajit Wang, Han Weinan, E. DiStasio, Robert A. Cara, Roberto Department of Chemistry Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry and Chemical Biology Cornell University IthacaNY14853 United States Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g., H or D), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g., isotope effects), and therefore provide yet another challenge for ab initio approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretized path-integral (PI) approach, and machine learning (ML) constitutes a versatile ab initio based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model - a neural-network representation of the ab initio PES - in conjunction with a PI approach based on the generalized Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H2O and D2O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Deterministic guarantees for Burer–Monteiro factorizations of smooth semidefinite programs

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arXiv 2018年

作者： Boumal, Nicolas Voroninski, Vladislav Bandeira, Afonso S. Mathematics Department Program in Applied and Computational Mathematics Princeton University Department of Mathematics Center for Data Science Courant Institute of Mathematical Sciences New York University

We consider semidefinite programs (SDPs) with equality constraints. The variable to be optimized is a positive semidefinite matrix X of size n. Following the Burer–Monteiro approach, we optimize a factor Y of size n× p instead, such that X = YY. This ensures positive semidefiniteness at no cost and can reduce the dimension of the problem if p is small, but results in a non-convex optimization problem with a quadratic cost function and quadratic equality constraints in Y. In this paper, we show that if the set of constraints on Y regularly defines a smooth manifold, then, despite non-convexity, first- and second-order necessary optimality conditions are also sufficient, provided p is large enough. For smaller values of p, we show a similar result holds for almost all (linear) cost functions. Under those conditions, a global optimum Y maps to a global optimum X = YY of the SDP. We deduce old and new consequences for SDP relaxations of the generalized eigenvector problem, the trust-region subproblem and quadratic optimization over several spheres, as well as for the Max-Cut and Orthogonal-Cut SDPs which are common relaxations in stochastic block modeling and synchronization of rotations. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

Contact network changes in ordered and disordered disk packings

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arXiv 2020年

作者： Tuckman, Philip J. VanderWerf, Kyle Yuan, Ye Zhang, Shiyun Zhang, Jerry Shattuck, Mark D. O’Hern, Corey S. Department of Physics Yale University New HavenCT06520 United States Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Physics Yale University Department of Applied New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

We investigate the mechanical response of packings of purely repulsive, frictionless disks to quasistatic deformations. The deformations include simple shear strain at constant packing fraction and at constant pressure, "polydispersity" strain (in which we change the particle size distribution) at constant packing fraction and at constant pressure, and isotropic compression. For each deformation, we show that there are two classes of changes in the interparticle contact networks: jump changes and point changes. Jump changes occur when a contact network becomes mechanically unstable, particles "rearrange", and the potential energy (when the strain is applied at constant packing fraction) or enthalpy (when the strain is applied at constant pressure) and all derivatives are discontinuous. During point changes, a single contact is either added to or removed from the contact network. For repulsive linear spring interactions, second- and higher-order derivatives of the potential energy/enthalpy are discontinuous at a point change, while for Hertzian interactions, third- and higher-order derivatives of the potential energy/enthalpy are discontinuous. We illustrate the importance of point changes by studying the transition from a hexagonal crystal to a disordered crystal induced by applying polydispersity strain. During this transition, the system only undergoes point changes, with no jump changes. We emphasize that one must understand point changes, as well as jump changes, to predict the mechanical properties of jammed packings. Copyright © 2020, The Authors. All rights reserved.

关键词： Potential energy

Blind recognition of k/n rate convolutional encoders from noisy observation

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Journal of Systems Engineering and Electronics 2017年第2期28卷 235-243页

作者： Li Huang Wengu Chen Enhong Chen Hong Chen Computer Science and Technology University of Science and Technology of China Hefei 230026 China Graduate School China Academy of Engineering Physics Beijing 100088 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identification of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefficients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efficient with the use of the block matrix (BM) method. © 2017 Beijing Institute of Aerospace Information.

关键词： Cognitive radio Convolution Convolutional codes Error correction Hadamard matrices Hadamard transforms Linear transformations Mathematical transformations Matrix algebra Signal encoding

Author Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第6期55卷 1078页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Samsung Genome Institute Samsung Medical Center Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Samsung Advanced Institute for Health Sciences and Technology Sungkyunkwan University School of Medicine Seoul South Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Department of Haematology University of Cambridge Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson.org. Quantitative and Comp

Distributed linear regression by averaging

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arXiv 2018年

作者： Dobriban, Edgar Sheng, Yue Wharton Statistics Department University of Pennsylvania United States Graduate Group in Applied Mathematics and Computational Science Department of Mathematics University of Pennsylvania United States

Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communicate short messages. Communication is often the bottleneck. In this paper, we study one-step and iterative weighted parameter averaging in statistical linear models under data parallelism. We do linear regression on each machine, send the results to a central server, and take a weighted average of the parameters. Optionally, we iterate, sending back the weighted average and doing local ridge regressions centered at it. How does this work compared to doing linear regression on the full data? Here we study the performance loss in estimation, test error, and confidence interval length in high dimensions, where the number of parameters is comparable to the training data size. We find the performance loss in one-step weighted averaging, and also give results for iterative averaging. We also find that different problems are affected differently by the distributed framework. Estimation error and confidence interval length increase a lot, while prediction error increases much less. We rely on recent results from random matrix theory, where we develop a new calculus of deterministic equivalents as a tool of broader interest. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear regression