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AI-enabled Lorentz microscopy for quantitative imaging of nanoscale magnetic spin textures

npj Computational Materials 2024年第1期10卷 2102-2111页

作者： Arthur.R.C.McCray Tao Zhou Saugat Kandel Amanda Petford-Long Mathew J.Cherukara Charudatta Phatak Materials Science Division Argonne National LaboratoryLemontILUSA Applied Physics Program Northwestern UniversityEvanstonILUSA Center for Nanoscale Materials Argonne National LaboratoryLemontILUSA Advanced Photon Source Argonne National LaboratoryLemontILUSA Department of Materials Science and Engineering Northwestern UniversityEvanstonILUSA

The manipulation and control of nanoscale magnetic spin textures are of rising interest as they are potential foundational units in next-generation computing *** this requires a quantitative understanding of the spin texture behavior under external stimuli using in situ *** transmission electron microscopy(LTEM)enables real-space imaging of spin textures at the nanoscale,but quantitative characterization of in situ data is extremely ***,we present an AI-enabled phase-retrieval method based on integrating a generative deep image prior with an image formation forwardmodel for *** approach uses a single out-of-focus image for phase retrieval and achieves significantly higher accuracy and robustness to noise compared to existing ***,our method is capable of isolating sample heterogeneities from magnetic contrast,as shown by application to simulated and experimental *** approach allows quantitative phase reconstruction of in situ data and can also enable near real-time quantitative magnetic imaging.

关键词： textures magnetic Lorentz

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Terahertz phonon polariton concentrator based on perovskite oxides

Terahertz phonon polariton concentrator based on perovskite ...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Xu, Rui Lin, Tong Luo, Jiaming Chen, Xiaotong Blackert, Elizabeth R. Moon, Alyssa R. JeBailey, Khalil M. Zhu, Hanyu Department of Materials Science and Nano Engineering Rice University Houston United States Applied Physics Graduate Program Rice University Houston United States Program Rice University Houston United States

ISBN: (纸本)9781957171258

We report the experimental demonstration of surface phonon-polaritonic devices in a broad spectral range of 7-13 THz based on SrTiO3. We designed planar ultrafast concentrators that amplifies transient electric field by 6 times and measured an average field of 0.5 GV/m in an optically resolvable area in the far field. © 2023 The Author(s)

关键词： Perovskite

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Conversion of twisted light to twisted excitons using carbon nanotubes

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npj Computational Materials 2022年第1期8卷 392-402页

作者： Xiaoning Zang Nirpendra Singh Mark T.Lusk Udo Schwingenschlögl Applied Physics Program Physical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal23955-6900Saudi Arabia Department of Physics Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates Department of Physics Colorado School of MinesGoldenCO80401USA

Carbon nanotubes are explored as a means of coherently converting the orbital angular momentum of light to an excitonic form that is more amenable to quantum information *** analytical analysis,based on dynamical conductivity,is used to show that orbital angular momentum is conserved,modulo N,for a carbon nanotube illuminated by radially polarized,twisted *** result is numerically demonstrated using real-time time-dependent density functional theory which captures the absorption of twisted light and the subsequent transfer of twisted *** results suggest that carbon nanotubes are promising candidates for constructing optoelectronic circuits in which quantum information is more readily processed while manifested in excitonic form.

关键词： twisted carbon light

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Correlations in interacting electron liquids: Many-body statistics and hyperuniformity

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Physical Review B 2024年第10期110卷 104201页

作者： Haina Wang Rhine Samajdar Salvatore Torquato Department of Chemistry Department of Physics Princeton Center for Theoretical Science Princeton Materials Institute Program in Applied and Computational Mathematics

Disordered hyperuniform many-body systems are exotic states of matter with novel optical, transport, and mechanical properties. These systems are characterized by an anomalous suppression of large-scale density fluctuations compared to ordinary liquids. The structure factor of disordered hyperuniform systems often obeys the scaling relation S(k)∼Bkα with B,α>0 in the limit k→0. Ground states of d-dimensional free fermionic gases, which are fundamental models for many metals and semiconductors, are key examples of quantum disordered hyperuniform states with important connections to random matrix theory. However, the effects of electron-electron interactions as well as the polarization of the electron liquid on hyperuniformity have not been explored thus far. In this paper, we systematically address these questions by deriving the analytical small-k behaviors (and, associated, α and B) of the total and spin-resolved structure factors of quasi-one-dimensional, two-dimensional, and three-dimensional electron liquids for varying polarizations and interaction parameters. We validate that these equilibrium disordered ground states are hyperuniform, as dictated by the fluctuation-compressibility relation. Interestingly, free fermions, partially polarized interacting fermions, and fully polarized interacting fermions are characterized by different values of the small-k scaling exponent α and coefficient B. In particular, partially polarized fermionic liquids exhibit a unique form of multihyperuniformity, in which the net configuration exhibits a stronger form of hyperuniformity (i.e., larger α) than each individual spin component. The detailed theoretical analysis of such small-k behaviors enables the construction of corresponding equilibrium classical systems under effective one- and two-body interactions that mimic the pair statistics of quantum electron liquids. Our paper thus reveals that highly unusual hyperuniform and multihyperuniform states can be achieved in simple

关键词： Electronic structure Quantum many-body systems Two-dimensional electron gas

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Direct current generation by dielectric loss in ferroelectrics

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Physical Review B 2024年第4期110卷 045129-045129页

作者： Takahiro Morimoto Naoto Nagaosa Department of Applied Physics (CEMS) Wako Saitama 351-0198 Japan Fundamental Quantum Science Program TRIP Headquarters

We study direct current (DC) generation induced by microwave irradiation to ferroelectric materials. The DC generation originates from microwave absorption called dielectric loss due to the delay of a dielectric response. Such current generation can be formulated as the low-frequency limit of the phonon shift current which arises from an increase of electric polarization accompanying photoexcitation of phonons due to the electron-phonon coupling. To study the DC generation by the dielectric loss, we apply the diagrammatic treatment of nonlinear optical responses to photoexcitations of phonons and derive the general formula for phonon shift current. We then study the DC generation in the low-frequency region and find that the current scales as ∝ω2 for the linearly polarized light and time-reversal symmetric systems. We estimate the order of magnitude of the DC generation by dielectric loss, indicating its feasibility for experimental detection in the GHz region.

关键词： Phonons Photovoltaic effect Ferroelectrics Diagrammatic methods

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physics-informed neural networks for modeling water flows in a river channel

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1001-1015页

作者： Nazari, Luis Fernando Camponogara, Eduardo Seman, Laio Oriel Department of Mathematics Federal Institute of Education Science and Technology Catarinense Rio do Sul89160-202 Brazil Department of Automation and Systems Engineering Federal University of Santa Catarina Florianopolis88040-900 Brazil Program in Applied Computer Science Universidade Do Vale Do Itajai Itajai88302-901 Brazil

The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for

关键词： Floods

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The Influence of Workplace Environment on Employee Turnover at Delmon Poultry Company: Key Insights and Findings

The Influence of Workplace Environment on Employee Turnover ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Alaghbari, Mohammed Abdulrazzaq Ateeq, Ali Milhem, Marwan Garad, Askar Ahmed Yusuf, Ammar Yusuf College of Administrative Sciences Department of Business Administration Applied Science University Bahrain Bahrain College of Administrative and Financial Science Administrative Science Department Gulf University Sanad26489 Bahrain Universitas Muhammadyiah Yogyakarta Magister Program Yogyakarta Indonesia College of Arts and Science Department of Computer Science Applied Science University Bahrain Bahrain

ISBN: (纸本)9798350369106

The present study examines how organizational work environments affect employee turnover at Delmon Poultry Company. The researcher used a structured questionnaire and a quantitative approach to collect data from 340 employees. The results show that the organizational work environment has a greater impact on employee attrition than the social environment, accounting for 75.5% of the turnover variance. Although the social environment is substantial, it only accounts for 29.2% of the variance. The research establishes that attrition reduction necessitates the enhancement of organizational factors, including policy impartiality and management practices. In order to offer a more comprehensive understanding of attrition dynamics, future research should investigate these relationships across various industries and over time. The study contributes to the literature by emphasizing the significance of a supportive organizational environment for employee retention and providing practical recommendations for human resource management to improve employee retention strategies. © 2024 IEEE.

关键词： Employee Turnover Human Resource Management Organizational Environment Workplace Environment

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A Shoe Shopping System based on Convolutional Neural Network Image Recognition 5

A Shoe Shopping System based on Convolutional Neural Network...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Chang, Chin-Chih Wei, Chi-Hung Chen, Chen-Sheng Chen, Jian-An Yeh, Shih-Hsien Hsiao, Sean Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan Chung Hua University Program in Engineering Science Hsinchu Taiwan Chang Gung University Department of Computer Science and Information Engineering Taoyuan Taiwan Chung Hua University Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan

ISBN: (纸本)9798350314694

In this article, we present an innovative approach to enhance the online shoe shopping experience. The convolutional neural network (CNN) image recognition technology was used to enhance shoe classification and recommendations. By training the CNN model on an extensive dataset, unique shoe features and styles were learned. Integrated into a user-friendly online platform, the system offers real-time image recognition, allowing users to snap a photo of a desired shoe for instant identification, including brand, price, and availability details. Moreover, the CNN-based recommendation engine provides personalized suggestions based on style, color, and customer preferences, enriching the shopping experience. Evaluation results confirmed the system's feasibility, and user feedback highlighted its effectiveness in simplifying the shopping process and enhancing satisfaction. This innovative system presents a significant leap in merging AI and e-commerce and shows the potential of image recognition to transform online marketplaces, benefiting consumers, offering valuable insights for retailers, and ultimately reshaping the future of online shoe shopping. © 2023 IEEE.

关键词： convolutional neural network (CNN) deep learning image recognition recommender system

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Monte Carlo study of uncertainty propagation in Euler rotational kinematic equation 17

Monte Carlo study of uncertainty propagation in Euler rotati...

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17th Siam physics Congress: Carbon Neutrality, SPC 2022

作者： Nutsathaporn, C. Chomkokard, S. Wongkokua, W. Ruengittinun, S. Jinuntuya, N. Metrology Program Department of Physics Faculty of Science Kasetsart University Chatuchak Bangkok10900 Thailand Department of Computer Science Faculty of Science Kasetsart University Chatuchak Bangkok10900 Thailand

We perform a Monte Carlo simulation to study the propagation of uncertainty in Euler rotational kinematic equation. The rate gyro data from an IMU sensor are inserted into the Euler rotational kinematic differential equation to calculate the sensor orientation, which is specified by its Euler angles. The uncertainty of sensor orientation is determined by simulating the variation of rate gyro input due to its uncertainty obtained from the calibration process. We find that the uncertainties of the Euler angles grow with time. Their values are proportional to the square root of time elapse. We find also that the uncertainties are linearly proportional to the uncertainties of rate gyro data and the square root of sampling time. These results interestingly resemble the behaviour of the Wiener process, with the step size of random walk depends on the time step. We can use these results as a criterion to choose a suitable IMU due to the characteristic of the integrated rate gyro sensor, especially in the application like the vehicle tracking or posture warning for elderly/disable people, where the precise sensor orientation is needed for a long period of operation. © Published under licence by IOP Publishing Ltd.

关键词： Differential equations

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Local order metrics for many-particle systems across length scales

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Physical Review Research 2024年第3期6卷 033262页

作者： Charles Emmett Maher Salvatore Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics

Formulating order metrics that sensitively quantify the degree of order/disorder in many-particle systems in d-dimensional Euclidean space Rd across length scales is an outstanding challenge in physics, chemistry, and materials science. Since an infinite set of n-particle correlation functions is required to fully characterize a system, one must settle for a reduced set of structural information, in practice. We initiate a program to use the local number variance σN2(R) associated with a spherical sampling window of radius R (which encodes pair correlations) and an integral measure derived from it ΣN(Ri,Rj) that depends on two specified radial distances Ri and Rj. Across the first three space dimensions (d=1,2,3), we find these metrics can sensitively describe and categorize the degree of order/disorder of 41 different models of antihyperuniform, nonhyperuniform, disordered hyperuniform, and ordered hyperuniform many-particle systems at a specified length scale R. Using our local variance metrics, we demonstrate the importance of assessing order/disorder with respect to a specific value of R. These local order metrics could also aid in the inverse design of structures with prescribed length-scale-specific degrees of order/disorder that yield desired physical properties. In future work, it would be fruitful to explore the use of higher-order moments of the number of points within a spherical window of radius R [S. Torquato et al., Phys. Rev. X 11, 021028 (2021)] to devise even more sensitive order metrics.

关键词： Classical statistical mechanics

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