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arXiv 2022年

作者： Li, Huanan Yin, Shixiong He, Huan Xu, Jingjun Alù, Andrea Shapiro, Boris MOE Key Laboratory of Weak-Light Nonlinear Photonics School of Physics Nankai University Tianjin300071 China Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Department of Electrical Engineering City College of The City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Physics Technion-Israel Institute of Technology Haifa32000 Israel

Time metamaterials exhibit a great potential for wave manipulation, drawing increasing attention in recent years. Here, we explore the exotic wave dynamics of an anisotropic photonic time crystal (APTC), formed by an anisotropic medium whose optical properties are uniformly and periodically changed in time. Based on a temporal transfer matrix formalism, we show that a stationary charge embedded in an APTC emits radiation, in contrast to the case of isotropic photonic time crystals, and its distribution in momentum space is controlled by the APTC band structure. Our approach extends the functionalities of time metamaterials, offering new opportunities for radiation generation and control, with implications for both classical and quantum applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Metamaterials

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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An integrated system of mobile application and IoT solution for pond monitoring

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Ferdinan Darmalim Alam Ahmad Hidayat Tjeng Wawan Cenggoro Kartika Purwandari Sutristo Darmalim Bens Pardamean NusaNet Jakarta Indonesia 12710 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The current progress of information and communication technology has been critical to the development of the Internet of Things (IoT) in a aquaculture system. IoT solution in this system utilizes interconnectivity between devices to obtain fish pond monitoring data from sensing devices and then transmit the data to a remote server to be analyzed for decision-making in pond management. In this paper, we propose a smart pond IoT solution that provides a continuous and real-time pond monitoring. The system principally embeds five sensors that measure critical environmental parameters to determine water quality: temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), and salinity. The IoT solution also integrates the development of a mobile application on the android platform that allows remote monitoring capabilities for users to maintain efficient pond management. The application is designed with a friendly-user interface that helps the farmer to monitor sensor measurements, manage fish production cycle, record fish feeding schedule, and also monitor the health status of fish from multiple ponds.

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Terahertz chiral photonic-crystal cavities with broken time-reversal symmetry

arXiv

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arXiv 2024年

作者： Tay, Fuyang Sanders, Stephen Baydin, Andrey Song, Zhigang Welakuh, Davis M. Alabastri, Alessandro Rokaj, Vasil Dag, Ceren B. Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Smalley–Curl Institute Rice University HoustonTX77005 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02139 United States Max Planck Institute for The Structure and Dynamics of Matter Luruper Chaussee 149 Hamburg22761 Germany ITAMP Harvard-Smithsonian Center for Astrophysics CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics Villanova University VillanovaPA19085 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Strong coupling between matter and vacuum electromagnetic fields in a cavity can induce novel quantum phases in thermal equilibrium via symmetry breaking. Particularly, coupling with circularly polarized fields can break time-reversal symmetry, leading to topological modifications in the band structure. Therefore, chiral optical cavities that host chiral vacuum fields are being sought, especially in the terahertz (THz) frequency range, where various large-oscillator-strength resonances exist. Here, we present a novel approach to achieving THz chiral photonic-crystal cavities (PCCs) with high-quality factors (>400) using a magnetoplasma in a lightly doped semiconductor. Numerical simulations of an optimized structure based on InSb in a small perpendicular magnetic field (∼0.2 T) show chiral cavity resonances with near-perfect ellipticity at the surfaces of the central dielectric layer, where one can place atomically thin materials like monolayer graphene. We theoretically estimate an energy gap on the order of 1 meV in graphene when coupled to our proposed chiral cavity, which is potentially measurable in experiments. These THz chiral PCCs offer a promising platform for exploring new phases in cavity-dressed condensed matter with broken time-reversal symmetry. Copyright © 2024, The Authors. All rights reserved.

关键词： Crystal symmetry

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Constructing a Large-scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

TechRxiv

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TechRxiv 2021年

作者： Nagendra, Savinay Kifer, Daniel Mirus, Benjamin Pei, Te Lawson, Kathryn Manjunatha, Srikanth Banagere Li, Weixin Nguyen, Hien Qiu, Tong Tran, Sarah Shen, Chaopeng Computer Science and Engineering Pennsylvania State University University ParkPA United States Civil and Environmental Engineering Pennsylvania State University University ParkPA United States U.S. Geological Survey Landslide Hazards Program GoldenCO United States School of Science Engineering and Technology Pennsylvania State University Harrisburg HarrisburgPA United States Google Inc. Mountain ViewCA United States

Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent small-scale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)? We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). The goal of this framework is to continually update a (landslide) semantic segmentation model with data from new ecoregions without having to revisit data from old ecoregions and without losing performance on them. Using the TSMU framework, we conduct extensive experiments on four

关键词： Landslides

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Multimodal Fusion for Image and Text Classification with Feature Selection and Dimension Reduction

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Journal of Physics: Conference Series 2021年第1期1871卷

作者： Xinran Liu Zhongju Wang Long Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China Shunde Graduate School of University of Science and Technology Beijing Shunde 528300 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083 China

Internet has become an important information platform, and it is very important to accurately understand the multimedia information of the Internet. In this paper, our main task is to do classification based on pictures and texts collected from the Internet, which is a classification problem of multimodal fusion in practice. However, when multimodal data is put together, there may occur the dimension disaster problem. We apply feature selection (FS) and dimension reduction (DR) in feature levels both in later fusion and early fusion to solve this problem. The classification accuracies in different models obtain improvements in different levels respectively. We also discuss the relation between single modals and multimodal in later fusion. In our experiments, images and text can be classified by multimodal models under FS/DR, and of which with the help the multimedia information from the Internet can be analysed better to help enterprises provide better services and products, and then carry out better network marketing and promotion.

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Correction : Low carbon futures: assessing the status of decarbonisation efforts at universities within a 2050 perspective

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Energy, Sustainability and Society 2023年第1期13卷 1-2页

作者： Walter Leal Filho Diogo Guedes Vidal Maria Alzira Pimenta Dinis Wim Lambrechts Claudio R. P. Vasconcelos Petra Molthan‑Hill Ismaila Rimi Abubakar Rachel M. Dunk Amanda Lange Salvia Ayyoob Sharifi European School of Sustainability Science and Research Hamburg University of Applied Sciences Ulmenliet 20 21033 Hamburg Germany Department of Natural Sciences Manchester Metropolitan University Chester Street Manchester M11 5GD UK Department of Life Sciences Faculty of Sciences and Technology Center for Functional Ecology‑Science for People and The Planet (CFE) TERRA Associate Laboratory University of Coimbra Calçada Martim de Freitas 3000‑456 Coimbra Portugal UFP Energy Environment and Health Research Unit (FP-ENAS) University Fernando Pessoa (UFP) Praça 9 de Abril 349 4249‑004 Porto Portugal Department of Marketing and Supply Chain Management Faculty of Management Open Universiteit The Netherlands (OUNL) Postbus 2960 6401 DL Heerlen The Netherlands Laboratory of Sustainability Engineering and Consumption Federal University of Paraíba João Pessoa PB Brazil Algoritmi Research Centre School of Engineering University of Minho 4800‑058 Guimarães Portugal Business Sustainability Nottingham Trent University England UK College of Architecture and Planning Imam Abdulrahman Bin Faisal University (Formerly University of Dammam) P.O. Box 1982 Dammam 31441 Saudi Arabia School of Science and the Environment Manchester Metropolitan University Chester Street Manchester M1 5GD UK Graduate Program in Civil and Environment Engineering University of Passo Fundo Campus I‑BR 285 São José Passo Fundo RS 99052‑900 Brazil Graduate School of Humanities and Social Sciences and Network for Education and Research on Peace and Sustainability Hiroshima University Higashi-Hiroshima 739-8530 Japan

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Analysis of backpropagation neural neural network algorithm on student ability based cognitive aspects

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Fahmi Izhari Muhammad Zarlis Sutarman Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Back propagation is one of the supervised learning and multi-layered training program and uses errors during the process of changing the weight value in the backward process as well as the forward propagation. In the method for predicting cognitive abilities backpropagation the first step is to set the input neuron number, the number of neurons that are hidden, and the number of output neurons. The number of neurons used in the program is 6 neurons consisting of cognitive criteria, 6 hidden neuron layers, and 2 neuron outputs. The highest level of accuracy is in the binary sigmoid and bipolar sigmoid activation functions at the 64th epoch with the accuracy of each function of 82.93% +/- 37.63% and 85.37% +/- 35.34%. The smallest root mean squared error value was found in binary sigmoid of 0.266 with a tolerance of +/- 0.258 on the 100th epoch with an accuracy of 80.49% while for the sigmoid bipolar activation function the smallest root mean squared error value was obtained at the epoch 500 of 0.282 with tolerance +/- 0.353.

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Exploration on the Design of Sport Prescription and the Behavior of College Students 23rd

Exploration on the Design of Sport Prescription and the Beha...

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23rd International computer Symposium, ICS 2018

作者： Yu-Chiang, Li Lin, Jun-Yi Wang, Wen-Fong Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Rd. Sec. 3 DouliouYunlin64002 Taiwan Graduate School of Technological and Vocational Education National Yunlin University of Science and Technology 123 University Rd. Sec. 3 DouliouYunlin64002 Taiwan

ISBN: (纸本)9789811391897

The results of this study show the college students’ personal test records, which the average index of college students’ cardiorespiratory strength (3 min) is 29.32, the average index of muscle fitness (1 min sit-ups) 37.71, the average score of softness sitting (frontal bend) 26.53, the average index of body mass index 37.71, and the average index of explosive force (established long jump) 174.63. The goals of exercise is to strengthen muscle fitness, heart and lung, endurance, and weight control. The type/project is ranked first in selecting medium-intensity activities, followed by high-impact activities such as jogging, and finally low-impact activities. Set the exercise target frequency for the selected sports to take the first place in 3 days a week. The exercise intensity is mostly due to the fact that people have diabetes mellitus and are a little tired (or load), but most of them are painful. The duration of exercise is 1 point per 5 min, the average index is 8.81, and most of them are 8 to 10 points each time. Therefore, the most frequent duration of each exercise is 40 to 50 min. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Sports

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Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Lotar Mateus Sinaga Sawaluddin Saib Suwilo Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Naïve Bayes is a prediction method that contains a simple probabilistic that is based on the application of the Bayes theorem (Bayes rule) with the assumption that the dependence is strong. K-Nearest Neighbor (K-NN) is a group of instance-based learning, K-NN is also a lazy learning technique by searching groups of k objects in training data that are closest (similar) to objects on new data or testing data. Classification is a technique in Data mining to form a model from a predetermined data set. Data mining techniques are the choices that can be overcome in solving this problem. The results of the two different classification algorithms result in the discovery of better and more efficient algorithms for future use. It is recommended to use different datasets to analyze comparisons of naïve bayes and K-NN algorithms. the writer formulates the problem so that the research becomes more directed. The formulation of the problem in this study is to find the value of accuracy in the Naïve Bayes and KNN algorithms in classifying data.

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