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Chiral anomaly and Weyl orbit in three-dimensional Dirac semimetal Cd3As2 grown on Si

Nanotechnology 2024年第16期35卷 165002-165002页

作者： Lin, Wei-Chen Tsai, Peng-Ying Zou, Jia-Zhu Lee, Jie-Ying Kuo, Chun-Wei Lee, Hsin-Hsuan Pan, Ching-Yang Yang, Cheng-Hsueh Chen, Sheng-Zong Wang, Jyh-Shyang Jiang, Pei-Hsun Liang, Chi-Te Chuang, Chiashain Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu300 Taiwan Graduate Institute of Applied Physics National Taiwan University Taipei106 Taiwan National Taiwan University Taipei106 Taiwan Department of Electronic Engineering Chung Yuan Christian University Taoyuan320 Taiwan Department of Physics Chung Yuan Christian University Taoyuan320 Taiwan Department of Physics National Taiwan Normal University 106 Taiwan Research Center for Semiconductor Materials and Advanced Optics Chung Yuan Christian University Taoyuan320 Taiwan

Preparing Cd3As2, which is a three-dimensional (3D) Dirac semimetal in certain crystal orientation, on Si is highly desirable as such a sample may well be fully compatible with existing Si CMOS technology. However, there is a dearth of such a study regarding Cd3As2 films grown on Si showing the chiral anomaly. Here, for the first time, we report the novel preparation and fabrication technique of a Cd3As2 (112) film on a Si (111) substrate with a ZnTe (111) buffer layer which explicitly shows the chiral anomaly with a nontrivial Berry’s phase of π. Despite the Hall carrier density ( n 3 D ≈ 9.42 × 10 17 cm−3) of our Cd3As2 film, which is almost beyond the limit for the portents of a 3D Dirac semimetal to emerge, we observe large linear magnetoresistance in a perpendicular magnetic field and negative magnetoresistance in a parallel magnetic field. These results clearly demonstrate the chiral magnetic effect and 3D Dirac semimetallic behavior in our silicon-based Cd3As2 film. Our tailoring growth of Cd3As2 on a conventional substrate such as Si keeps the sample quality, while also achieving a low carrier concentration. © 2024 IOP Publishing Ltd.

关键词： II-VI semiconductors

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Computer Vision and Fuzzy Logic for Sustainable Indonesian Fisheries 3

Computer Vision and Fuzzy Logic for Sustainable Indonesian F...

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3rd International Conference on Eco engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Saputra, Adi Sakinah, Putri Purwoko, Hery Zulardi, Indra Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

Tuna fishing in Indonesia is excessive and if left unchecked, some types of tuna will become extinct within 3-10 years. There is an urgent need to help MMAF for producing more accurate fish catches data. This paper proposed a system that utilizes computer vision, fuzzy logic and Global Positioning System (GPS) for supporting a sustainableIndonesian fishery by recognizing and measuring fish length, determine fish age and where is the catch location. The system can help authority to manage sustainable fisheries management in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method

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engineering Applications of Artificial Intelligence 2024年 133卷

作者： Zhang, Chao Wen, Qifei Li, Deyu Sangaiah, Arun Kumar Lin, Mingwei School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos1102-2801 Lebanon College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

As a critical strategic tool within the framework of the digital economy (DE), artificial intelligence (AI) plays a crucial role in realizing an AI-assisted economy. This novel paradigm has gained widespread acceptance from governments globally, positioning itself as a cornerstone in the practical implementation of the DE. The increasing reliance on digital means for economic transformation, the treatment of data as a critical resource, and the integration of information technologies highlight the synergy between AI and the DE. Against this backdrop, online reviews and SA play a pivotal role, offering rich data streams continually emerging on various platforms. Therefore, this article utilizes a probabilistic linguistic term set (PLTS) to establish a multi-granularity probabilistic linguistic (MG-PL) information system. This approach, through sentiment analysis (SA), enables quantitative analysis of user emotional expressions. The integration of these technological aspects holds the promise of enhancing decision-making processes, facilitating the intellectualization of economic activities, and addressing existing challenges in the application of AI within the DE domain. Initially, SA is conducted on text-based online comment data, forming a probabilistic linguistic incomplete information system. The maximum similarity method is employed to establish a probabilistic linguistic complete information system. Subsequently, an adjustable MG-PL decision-theoretic rough set (DTRS) is developed, followed by the establishment of a three-way group decision model using three-way decisions (3WD) and a directed graph. The model's applicability, stability, and feasibility are demonstrated via a case study centered on the acquisition of new energy vehicles (NEVs). © 2024 Elsevier Ltd

关键词： Sentiment analysis

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Neural Network for Classification of Mns Microinclusions in Steels

SSRN

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SSRN 2023年

作者： de Oliveira Filho, Marcos Fernando Amelio Briquet Caradec, Pierre D. Calsaverini, Rafael Spinelli, José Eduardo Ishikawa, Tomaz Toshimi Graduate Program in Materials Science and Engineering UFSCar SP São Carlos13565-905 Brazil Villares Metals S/A Sumaré SP São Paulo13178-902 Brazil Nubank SP São Paulo05409-000 Brazil Department of Materials Engineering UFSCar SP São Carlos13565-905 Brazil

The influence of MnS inclusions on steel properties is highly noticeable. Manual inclusions classification methods are the most used in laboratories and metallurgical sector industries because of their low cost, while automatic methods have high operating costs, which makes their use more restrict. Neural network models, on the other hand, are extremely advantageous for several applications. The present study is motivated by the use of a neural network model for classifying inclusions in steels. The aim is to achieve the highest possible accuracy in classifying the MnS inclusions severities (0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and 4.5) using optical images captured from steel specimens. The results have showed that the classification of MnS severities was very sensitive to the database number of images. A 98% training accuracy was obtained by increasing the number of images from 3,156 to 4,136, mostly adding images for some severity levels. However, validation and test results were not satisfactory. As such, a severity re-categorization of the database was able to enhance the true positive values, with an error of 8%. In general, the neural network represented speed in decision making, proving to be a potential tool for classifying steel inclusions. © 2023, The Authors. All rights reserved.

关键词： Operating costs

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Effect of hydrocooling on postharvest storage of sorrel (Rumex acetosa L.)

Food Chemistry Advances

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Food Chemistry Advances 2023年 3卷

作者： Gomes, Bianca Almada Ferreira de Barros, Hanna Elísia Araújo Natarelli, Caio Vinicius Lima Zitha, Elídio Zaidine Maurício Carvalho, Elisângela Elena Nunes de Barros Vilas Boas, Eduardo Valério Food Science Department Federal University of Lavras P.O. Box 3037 Minas Gerais Lavras 37200-900 Brazil Graduate Program in Materials Science and Engineering Federal University of São Carlos São Paulo São Carlos 13565-905 Brazil

The objective of this work was to determine the effect of the binomial time/temperature of hydrocooling (10 and 20 min at 0 °C, 10 °C, and 20 °C) on the shelf life of sorrel, a non-conventional vegetable. The experimental design was entirely randomized in a 2 × 3 × 6 factorial scheme with three repetitions. The vegetables were stored at 10±1 °C and 99% relative humidity for 10 days. The effects of time and temperature of hydrocooling on physical and chemical characteristics were observed. The degradation of bioactive compounds was higher in the control samples and samples hydrocooled at 0 °C, resulting in lower antioxidant capacity at 10 days. Hydrocooling at 20 °C for 10 min maintained the color of the vegetables until the tenth day, with a chlorophyll content of 34.54 mg.100 g⁻¹, related to the reduction of the respiration rate and weight loss. Hydrocooled sorrel for 20 min at the same temperature showed a vitamin C content of 63.71 mg.100 g⁻¹ and total phenolics of 249.57 mg of EAG.100 g⁻¹ of fresh matter, by the Fast Blue method. Thus, 20 °C for 10 or 20 min was the most efficient parameter for preserving sorrel samples. © 2023

关键词： Artificial neural network Bioative compounds Postharvest losses Pre-cooling. Shelf life Unconventional vegetable

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Drones Computer Vision using Deep Learning to Support Fishing Management in Indonesia 3

Drones Computer Vision using Deep Learning to Support Fishin...

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3rd International Conference on Eco engineering Development, ICEED 2019

作者： Liawatimena, Suryadiputra Atmadja, Wiedjaja Abbas, Bahtiar Saleh Trisetyarso, Agung Wibowo, Antoni Barlian, Erland Hardanto, Lilik Tri Triany, Natassya Afdalena Faisal Sulistiawan, Jaka Yojana, Albert Cahya Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School - Doctor of Research in Management Bina Nusantara University Jakarta11480 Indonesia

It is necessary to study technical factors such as bait used, oceanographic conditions of fishing areas and skipjack tuna trade patterns (Katsuwonus pelamis) as well as other factors in Sulawesi Fisheries. Supporting data to study these technical factors need to be obtained so that it can be improved throughout the year as a skipjack fishing standard with weights taken by the skipjack weights. Therefore, drones and underwater drones with cameras with high-resolution video and images to be fed and processed by computer vision. These drones are operated via Huhate ships which are then used to test efficient technical factors as expected, can be used in determining the size, shape, and colour as well as the analysis of skipjack fish that are in the Fish Aggregating Devices (FADs or Rumpon) in order to manage the management of fisheries managed in Indonesia. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Drones

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Autonomous car using CNN deep learning algorithm 2

Autonomous car using CNN deep learning algorithm

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Sonata, I. Heryadi, Y. Lukas, L. Wibowo, A. Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Jl. Kyai H. Syahdan No.9 Jakarta11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jl Jend. Sudirman No.51 Jakarta12930 Indonesia

Autonomous cars have become an interesting discussion in recent years. Several major automotive industries such as Tesla, GM and Nissan are trying to become pioneers in autonomous car technology. Giant technology companies such as Google Waymo, Baidu and Aptiv also developing autonomous car technology. Several technological approaches are carried out in implementing autonomous cars for recognizing surrounding situations, such as radar, lidar, sonar, GPS, and odometry. An automatic control system is used to control navigation based on the data obtained from these sensors. This paper will discuss the use of CNN deep learning algorithm for recognizing the surrounding environment in creating the automatic navigation required by autonomous cars. The system designed will create and learn the data set that will be taken in advance and the learning outcomes will be implemented in an open simulation system. This simulation shows high accuracy in learning to navigate the autonomous car by observing the surrounding environment. © Published under licence by IOP Publishing Ltd.

关键词： Autonomous vehicles

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X-RIM: Extreme Recurrent Independent Mechanisms for Noise-resistant and Interpretable Stroke Risk Prediction

X-RIM: Extreme Recurrent Independent Mechanisms for Noise-re...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yi-Hsien Hsieh Jung-Chi Hsu Che Lin Lian-Yu Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Internal Medicine Division of Cardiology Fu Jen Catholic University Hospital New Taipei City Taiwan Department of Internal Medicine and Cardio-Vascular Center Division of Cardiology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan Department of Internal Medicine NTU Taipei Taiwan

Stroke is a leading cause of mortality and long-term disability worldwide. An accurate stroke risk prediction is crucial for its early detection and prevention. Using deep learning to exploit patients’ time-series electronic health records (EHRs) has been shown as a promising and efficient solution for such a prediction. Although time-series data could be more informative than a single cross-section in time, real-world time-series EHRs usually have a significantly high missing rate due to irregular patient visits. This could undermine sequential data’s benefits unless a proper deep-learning model design is adopted. Furthermore, deep models have long been challenged for their interpretability, which is especially crucial for medical applications. In this study, we propose an extreme design based on the concept of recurrent independent mechanisms (RIM), termed extreme RIM (X-RIM). With no need for imputation, X-RIM utilizes the information of each input feature’s temporal records through independent recurrent modules. Experiments on real-world data from the National Taiwan University Hospital showed that, in terms of the area under the precision-recall curve (AUPRC), the area under the receiver-operating characteristics curve (AUROC), and Youden Index, X-RIM (AUPRC: 0.210; AUROC: 0.764; Youden: 0.373) outperformed the classic risk score CHA 2 DS 2 -VASc (AUPRC: 0.103; AUROC: 0.650; Youden: 0.223) and other benchmarks in stroke risk prediction. Additional experiments also indicate that individual feature contributions to a prediction could be evaluated intuitively under X-RIM’s independent structure to enhance interpretability.

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