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Shotgun crystal structure prediction using machine-learned formation energies

npj Computational Materials 2024年第1期10卷 13-26页

作者： Liu Chang Hiromasa Tamaki Tomoyasu Yokoyama Kensuke Wakasugi Satoshi Yotsuhashi Minoru Kusaba Artem R.Oganov Ryo Yoshida The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo190-8562Japan Technology Division Panasonic Holdings CorporationKadomaOsaka571-8508Japan Skolkovo Institute of Science and Technology Skolkovo Innovation CenterMoscow121205Russia The Graduate Institute for Advanced Studies The Graduate University for Advanced Studies(SOKENDAI)Tachikawa190-8562Japan

Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic ***,this requires repeated first-principles energy calculations,which is often impractical for large crystalline ***,we present significant progress toward solving the crystal structure prediction problem:we performed noniterative,single-shot screening using a large library of virtually created crystal structures with a machine-learning energy *** shotgun method(ShotgunCSP)has two key technical components:transfer learning for accurate energy prediction of pre-relaxed crystalline states,and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal ***-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal *** ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy,reaching 93.3%in benchmark tests with 90 different crystal structures.

关键词： crystal structure crystalline

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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Computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Complex Emotion Estimation Using Analysis-by-Synthesis of Facial Expression Images

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IEEE Access 2025年 13卷 88731-88746页

作者： Shwe Sin Khine, Win Siritanawan, Prarinya Kotani, Kazunori Japan Advanced Institute of Science and Technology School of Information Science Nomi923-1211 Japan Informatics and Interdisciplinary Systems Division Shinshu University Graduate School of Science and Technology Department of Engineering Nagano380-8553 Japan Kanazawa University Faculty of Transdisciplinary Science Institute of Philosophy in Interdisciplinary Science Ishikawa Kanazawa920-1192 Japan

This research proposes an approach for recognizing facial expressions for complex emotion estimation through the utilization of generated facial images. The proposed approach consists of two main parts: facial image generation and facial expression recognition. In the first part, we introduce Conditioned Emotion Generative Adversarial Networks (cEmoGANs) to synthesize images that convey complex facial expressions. Unlike previous methods, our generative model maintains face identity information and expresses various types of complex emotions. This capability encourages the generator to have control over the image generation process, resulting in enhanced image quality, reduced distortion, and increased diversity of generated images. The second part involves the design of a multiple-label classification based on a convolutional neural network trained on the complex facial expression images generated from the first part, which is employed in the recognition of complex facial expressions for emotion estimation. Our model, using images generated by cEmoGANs, demonstrates a notable performance surpassing the capabilities of the previous models in comparative evaluations. © 2013 IEEE.

关键词： Convolutional neural networks

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Indirect Vector Control of Linear Induction Motors Using Space Vector Pulse Width Modulation

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Computers, Materials & Continua 2023年第3期74卷 6263-6287页

作者： Arjmand Khaliq Syed Abdul Rahman Kashif Fahad Ahmad Muhammad Anwar Qaisar Shaheen Rizwan Akhtar Muhammad Arif Shah Abdelzahir Abdelmaboud Electrical Engineering Department University of Engineering and TechnologyLahore54700Pakistan Department of Basic Sciences Deanship of Common First YearJouf UniversitySakakaAljouf72341Saudi Arabia Department of Information Science Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan Sub CampusPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia

Vector control schemes have recently been used to drive linear induction motors(LIM)in high-performance *** trend promotes the development of precise and efficient control schemes for individual *** research aims to present a novel framework for speed and thrust force control of LIM using space vector pulse width modulation(SVPWM)*** framework under consideration is developed in four *** begin,MATLAB Simulink was used to develop a detailed mathematical and electromechanical *** research presents a modified SVPWM inverter control *** tuning the proportional-integral(PI)controller with a transfer function,optimized values for the PI controller are *** the subsystems mentioned above are integrated to create a robust simulation of the LIM’s precise speed and thrust force control *** reference speed values were chosen to evaluate the performance of the respective system,and the developed system’s response was verified using various data *** the low-speed range,a reference value of 10m/s is used,while a reference value of 100 m/s is used for the high-speed *** speed output response indicates that themotor reached reference speed in amatter of seconds,as the delay time is between 8 and 10 *** maximum amplitude of thrust achieved is less than 400N,demonstrating the controller’s capability to control a high-speed LIM with minimal thrust *** to the controlled speed range,the developed system is highly recommended for low-speed and high-speed and heavy-duty traction applications.

关键词： Space vector pulse width modulation linear induction motor proportional-integral controller indirect vector control electromechanical dynamic modeling

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Intelligent radioactive waste process cloud-based system for nuclear power plant decommissioning

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1801-1811页

作者： Chen, Yeh-Cheng Sun, Hung-Min Chen, Ruey-Shun Yeh, Jyh-Haw Fan, Xiaopeng Chou, I-Hsin Department of Computer Science University of California DavisCA United States Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Information Management China university of technology Hsinchu Taiwan Department of Computer Science Boise State University BoiseID United States Shenzhen Institute of Advanced Technology Chinese Academy of Science Shenzhen China Nuclear Instrumentation Division Institute of Nuclear Energy Research Hsinchu Taiwan

In pursuit of significant economy growth, most of the developed countries often ignore the importance of radioactive waste management, so the vast majority of nuclear power plants are still based on labor management, which is considered as inefficient way. The nuclear plant has limited operating life, which means it has to be dismantled after designated life limit, and the radioactive waste is also need to be disposed appropriately. In this research, an intelligent nuclear power plant decommissioning mechanism with cloud based was proposed, which include a comprehensive solution for dismantled parts transportation, radioactive waste management platform for radioactive waste storage facilities, and real-time nuclear waste drum status monitoring. Meanwhile, this mechanism also deploys software agents and cloud-based system, RFID as infrastructure to setup integrated information system. The result of this research could improve the current monitoring method for radioactive waste, from locating to storing, with the assistance of cloud-based information system. Furthermore, the solution for dismantled parts transportation may bring more safety for related workers. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： information systems

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Ultrathin BiOCl crystals grown in highly disordered vapor micro-turbulence for deep ultraviolet photodetectors

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Rare Metals 2024年第11期43卷 5921-5931页

作者： Qing Guo Xiu-Jun Wang Lin Wang Xin Ye A-Lei Li Xiao-Hang Pan Yun-Lei Zhong Yong Zhang Li-Xing Kang Key Laboratory of Multifunctional Nanomaterials and Smart Systems Division of Advanced MaterialsSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Key Laboratory of Molecular Optoelectronic Sciences Department of ChemistrySchool of ScienceTianjin UniversityTianjin 300072China School of Electronic and Information Engineering Changshu Institute of TechnologyChangshu 215500China

Crystallization,while a common process in nature,remains one of the most mysterious *** its physical mechanisms is essential for obtaining high-quality ***,crystals grown by thermal evaporation or sublimation nucleate the substrate facing the evaporation ***,a novel vapor micro-turbulence mass transport mechanism in the growth process of ultrathin BiOCl single crystals has been *** this mechanism,the precursor vapor bypasses the solid substrate,forming micro-turbulent vaporizing flows to nucleate on the surface of the substrate facing away from the evaporation *** nucleation kinetics,fast shear flows are known to cause secondary nucleation,increasing nucleation quantity while decreasing the final size of the ***,the nucleation and growth process of BiOCl crystals are controlled by adjusting the micro-turbulence intensity to reduce shear flow energy and dilate phase distribution,resulting in BiOCl crystals with uniform distribution and regular *** structural and morphological characterization confirms the high crystallization quality of the obtained crystals,and the performance of the constructed solar-blind photodetectors is comparable to that of similar *** findings contribute to a deeper understanding of vapor mass transport and crystal growth techniques and may be useful for applications related to metal oxide crystals.

关键词： Ultrathin metal oxides crystals Micro-turbulence Microspacing sublimation Ultraviolet photodetectors Crystallization

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The institute of Electrical Engineers of Japan.

关键词： Capacitance

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Sex Difference in Atrial Fibrillation Recurrence After Catheter Ablation 12

Sex Difference in Atrial Fibrillation Recurrence After Cathe...

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12th International Conference on Awareness science and technology, iCAST 2023

作者： Zhou, Xue Zhu, Xin Li, Tianhui Nakamura, Keijiro Huang, Ming Division of Information Systems University of Aizu Aizu-Wakamatsu Japan Division of Cardiovascular Medicine Toho University Ohashi Medical Center Tokyo Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan

ISBN: (纸本)9798350324693

Atrial fibrillation (AF) i s a c ommon sustained arrhythmia associated with increased risks of cardiovascular and renal diseases and adverse clinical outcomes, including mortality. Catheter ablation is an established method to restore sinus rhythm, but AF recurrence remains high. The impact of sex differences on recurrence after catheter ablation is poorly understood. This study investigated sex differences in AF recurrence using a propensity score matched cohort. A multiple imputation method, "Generates Multivariate Imputations by Chained Equations," addressed missing variables. Univariate Cox proportional hazards regression analysis identified variables significantly associated with AF recurrence (p © 2023 IEEE.

关键词： Regression analysis

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Experimental demonstration of SnO_(2) nanofiber-based memristors and their data-driven modeling for nanoelectronic applications

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Chip 2023年第4期2卷 142-153页

作者： Soumi Saha Madadi Chetan Kodand Reddy Tati Sai Nikhil Kaushik Burugupally Sanghamitra DebRoy Akshay Salimath Venkat Mattela Surya Shankar Dan Parikshit Sahatiya Department of Electrical&Electronics Engineering Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Department of Computer Science and Information Systems Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Nanomagnetic Division Ceremorphic India Pvt LtdIndia Ceremorphic Inc.San JoseCA 95131USA

This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and aluminum(Al)*** yielded a very high ROFF:RON of~104(ION:IOFF of~105)with an excellent activation slope of 10 mV/dec,low set voltage ofVSET~1.14 V and good *** paper physically explained the conduction mechanism in the layered SnO_(2)*** conductive network was composed of nanofibersthat play a vital role in the memristive action,since more conductive paths could facilitate the hopping of electron *** structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claimsreported in this *** machine learning(ML)–assisted,datadriven model of the fabricated memristor was also developedemploying different popular algorithms such as polynomialregression,support vector regression,k nearest neighbors,andartificial neural network(ANN)to model the data of the *** have proposed two types of ANN models(type I andtype II)algorithms,illustrated with a detailed flowchart,to modelthe fabricated *** with standard ML techniques shows that the type II ANN algorithm provides the bestmean absolute percentage error of 0.0175 with a 98%R^(2)*** proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adoptingthe same fabrication recipe,which gave satisfactory ***,the ANN type II model was applied to design and implementsimple AND&OR logic functionalities adopting the fabricatedmemristors with expected,near-ideal characteristics.

关键词： Nanofiber-based memristors Data-driven modeling Artificial neural network(ANN) SnO_(2)

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