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arXiv 2021年

作者： Mishra, Sumit Rajendran, Praveen Kumar Har, Dongsoo The Robotics Program Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Division of Future Vehicle Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The CCS Graduate School of Green Transportation Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

In urban cities, with increasing acceptability of shared spaces used by pedestrians and personal mobility devices (PMDs), there is need for pragmatic socially acceptable path planning and navigation management policies. Hence, we propose a socially acceptable global route planner and assess the legibility of the resulting global route. Our approach proposed for choosing global route avoids streets penetrating shared spaces and main routes with high probability of dense usage. The experimental study shows that socially acceptable routes can be effectively found with an average of 10 % increment of route length with optimal hyperparameters. This helps PMDs to reach the goal while taking a socially acceptable and safe route with minimal interaction of different PMDs and pedestrians. When PMDs interact with pedestrians and other types of PMDs in shared spaces, micro-mobility simulations are of prime usage for acceptable and safe navigation policy. Social force models being state of the art for pedestrian simulation are calibrated for capturing random movements of pedestrian behavior. Social force model with calibration can imitate the required behavior of PMDs in a pedestrian mix navigation scheme. Based on calibrated models, simulations on shared space links and gate structures are performed to assist policies related to deciding waiting and stopping time. Also, based on simulated PMDs interaction with pedestrians, location data with finer resolution can be obtained if the resolution of GPS sensor is 0.2 m or less. This will help in formalizing better modelling and hence better micro-mobility policies. Copyright © 2021, The Authors. All rights reserved.

关键词： Navigation

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Verification of Nek4nuc (Nek5000 integrated in NEAMS Workbench) via turbulent pipe flow simulation

Verification of Nek4nuc (Nek5000 integrated in NEAMS Workben...

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2020 Transactions of the American Nuclear Society Winter Meeting, ANS 2020

作者： Fan, Yuqiao Delchini, Marc-Olivier G. Lefebvre, Robert A. Nuclear Engineering Science Laboratory Synthesis Program Oak Ridge Institute for Science and Education Oak RidgeTN37831 United States Reactor and Nuclear Systems Division Oak Ridge National Laboratory Oak RidgeTN37830 United States

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Interaction Between a Low-Temperature Plasma and Graphene: An in situ Raman Thermometry Study

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Physical Review Applied 2021年第2期15卷 024018-024018页

作者： Carla Berrospe-Rodriguez Joseph Schwan Giorgio Nava Fariborz Kargar Alexander A. Balandin Lorenzo Mangolini Department of Mechanical Engineering University of California Riverside 900 University Ave. Riverside California 92521 USA Department of Electrical and Computer Engineering University of California Riverside 900 University Ave. Riverside California 92521 USA Materials Science and Engineering Program University of California Riverside 900 University Ave. Riverside California 92521 USA

The localized release of energy at the surface of plasma-exposed materials plays a crucial role in many plasma-driven processes. Unfortunately, the interaction between low-temperature plasmas and surfaces is both poorly understood and difficult to characterize. Here, we use Raman thermometry to measure the temperature of graphene during plasma exposure. We observe a significant increase in the graphene temperature even for modest input plasma powers. These results suggest that two-dimensional materials are effective surface probes for the quantitative characterization of plasma-induced heating effects and for further investigation of the plasma-surface interaction.

关键词： Chemical Physics & Physical Chemistry Heat transfer High-frequency & RF discharges in plasmas Plasma-solid interactions Temperature Graphene Low-temperature plasma Raman spectroscopy

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Scalable privacy-preserving cancer type prediction with homomorphic encryption

arXiv

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arXiv 2022年

作者： Sarkar, Esha Chielle, Eduardo Gursoy, Gamze Chen, Leo Gerstein, Mark Maniatakos, Michail Tandon School of Engineering New York University Brooklyn United States Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates Program in Computational Biology and Bioinformatics Yale University New Haven United States Department of Computer Science Yale University New Haven United States

Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer/tumor classification from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer/tumor types are identified. At the same time, analysis of high-dimensional data is computationally expensive and is often outsourced to cloud services. Privacy concerns in outsourced ML, especially in the field of genetics, motivate the use of encrypted computation, like Homomorphic Encryption (HE). But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer detection using a real-world dataset consisting of more than 2 million genetic information for several cancer types. Since the data is inherently high-dimensional, we explore smaller ML models for cancer prediction to enable fast inference in the privacy preserving domain. We develop a solution for privacy preserving cancer inference which first leverages the domain knowledge on somatic mutations to efficiently encode genetic mutations and then uses statistical tests for feature selection. Our logistic regression model, built using our novel encoding scheme, achieves 0.98 micro-average area under curve with 13% higher test accuracy than similar studies. We exhaustively test our model’s predictive capabilities by analyzing the genes used by the model. Furthermore, we propose a fast matrix multiplication algorithm that can efficiently handle high-dimensional data. Experimental results show that, even with 40,000 features, our proposed matrix multiplication algorithm can speed up concurrent inference of multiple individuals by approximately 10x and inference of a single individual by approximately 550x, in comparison to standard matrix multiplication. © 2022, CC BY.

关键词： Clustering algorithms

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Multi-core synthesis and maximum satisfiability applied to optimal sizing of solar photovoltaic systems

TechRxiv

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TechRxiv 2021年

作者： Trindade, Alessandro Galvão, Edilson Cordeiro, Lucas The Post-graduate Program in Electrical Engineering Manaus Brazil Department of Electricity Federal University of Amazonas Manaus Brazil School of Computer Science The University of Manchester United Kingdom

Annual global energy consumption growth is around 1.3% with forecasts until 2040. Photovoltaic systems became a suitable alternative to nuclear and fossil energy generation. In order to support this technology’s dissemination, we develop and evaluate an automated formal synthesis approach that assists in decision-making for off-grid systems. Our proposed approach, called PVz, is based on a variant of the counterexample-guided inductive synthesis;it has a multi-core feature, which can obtain the optimal sizing of photovoltaic systems focusing on Life Cycle Cost analysis. Given the electrical needs of a home, we seek a set of electrical equipment with the best possible combination of devices that meet the specified requirements. We calculate all costs related to maintenance over 20 years. The results presented are based on seven case studies;some of them are real ones from the Amazon region in Brazil. The same case studies were solved by a commercial optimization tool. Our technique and the commercial tool results were validated with popular simulation software to perform a fair comparison. Furthermore, we analyze some topics such as run-time, optimal solution, and configuration of the resulting systems. We claim that our technique is advantageous compared to the existing approaches in the literature. © 2021, CC BY-NC-SA.

关键词： Decision making

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Predictive Modeling of Sports-Related Concussions using Clinical Assessment Metrics

Predictive Modeling of Sports-Related Concussions using Clin...

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2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020

作者： Subhash, Sujit Obafemi-Ajayi, Tayo Goodman, Dennis Wunsch Ii, Donald Olbricht, Gayla R. Missouri University of Science Technology Mathematics and Statistics Dept RollaMO United States Missouri State University Engineering Program SpringfieldMO United States Missouri University of Science Technology Director of Sports Medicine RollaMO United States

ISBN: (纸本)9781728125473

Concussions represent a growing health concern that are challenging to diagnose and manage. Roughly four million concussions are diagnosed every year in the United States. While research in machine learning applications for concussions has focused on using advanced metrics such neuroimaging techniques and blood biomarkers, these metrics are yet to be implemented at a clinical level due to cost and reliability concerns. Therefore, concussion diagnosis is still reliant on clinical evaluations of symptoms, balance, and neurocognitive status and function. The lack of a universal threshold on these assessments makes the diagnosis process reliant on a physician's interpretation of these assessment scores. This study aims to explore the use of machine learning techniques to aid the concussion diagnosis process. These models could provide an automated means to flag concussed patients even before being seen by a doctor as well as expand the scope of concussion diagnosis to remote locations and areas with limited access to doctors. © 2020 IEEE.

关键词： Diagnosis

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Ambient Volatile Organics Sensor Based on Mesoporous Silica Preconcentrator and a Photoionization Detector: Parametric Study and Simulations

SSRN

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SSRN 2022年

作者： Day, Coco Lee, Tse-Ang Barua, Nirmalay Hutter, Tanya Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom Department of Mechanical Engineering The University of Texas at Austin AustinTX78712 United States Materials Science and Engineering Program The University of Texas at Austin AustinTX78712 United States SensorHut Ltd Cambridge United Kingdom

The adsorption of gas-phase volatile organic compounds into porous materials is the key interaction governing preconcentrator technology, in which adsorbed molecules are concentrated and then desorbed in a sharp pulse for detection. Reliable detection of gas-phase volatile organic compounds by small, portable, inexpensive sensors continues to present a significant challenge;preconcentration is a promising method to address this problem. By adding a preconcentration device to an existing sensor, its sensitivity and limit of detection can potentially be significantly improved. Here, we systematically study the preconcentration of the volatile organics isopropanol and heptane by a mesoporous silica preconcentrator integrated photoionization detector under different parameters and conditions, for the purpose of optimising the performance of the device. The improvement in adsorption gained from cooling the preconcentrator to 5°C during loading is demonstrated, and preconcentration factors of up to 20 are achieved with isopropanol. Heat transfer within the device is shown to be very good, while simulations of the gas flow within the device reveal some areas for potential future improvement. © 2022, The Authors. All rights reserved.

关键词： Volatile organic compounds

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The challenges of measuring spin Seebeck noise

arXiv

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arXiv 2023年

作者： Luo, Renjie Zhao, Xuanhan Legvold, Tanner J. Chen, Liyang Liu, Changjiang Hong, Deshun Bhattacharya, Anand Natelson, Douglas Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Program Smalley-Curl Institute Rice University HoustonTX77005 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Just as electronic shot noise in driven conductors results from the granularity of charge and the statistical variation in the arrival times of charge carriers, there are predictions for fundamental noise in magnon currents due to angular momentum being carried by discrete excitations. The inverse spin Hall effect as a transduction mechanism to convert spin current into charge current raises the prospect of experimental investigations of such magnon shot noise. Spin Seebeck effect measurements have demonstrated the electrical detection of thermally driven magnon currents and have been suggested as an avenue for accessing spin current fluctuations. Using spin Seebeck structures made from yttrium iron garnet on gadolinium gallium garnet, we demonstrate the technical challenges inherent in such noise measurements. While there is a small increase in voltage noise in the inverse spin Hall detector at low temperatures associated with adding a magnetic field, the dependence on field orientation implies that this is not due to magnon shot noise. We describe theoretical predictions for the expected magnitude of magnon shot noise, highlighting ambiguities that exist. Further, we show that magnon shot noise detection through the standard inverse spin Hall approach is likely impossible due to geometric factors. Implications for future attempts to measure magnon shot noise are discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Shot noise

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Universal Light-Driven Micro/Nanoscale Rotors

Research Square

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Research Square 2021年

作者： Ding, Hongru Kollipara, Pavana Siddhartha Kotnala, Abhay Chen, Zhihan Zheng, Yuebing Walker Department of Mechanical Engineering University of Texas at Austin AustinTX78712 United States Materials Science & Engineering Program Texas Materials Institute University of Texas at Austin AustinTX78712 United States

The capability of rotating micro/nanoscale particles and structures is important for micro/nanorobotics, three-dimensional particle imaging, and lab-on-a-chip systems. Light-driven rotors are especially attractive due to the fuel-free and remote operation. However, relying on a torque that arises from the momentum exchange with photons, current light-driven rotors require laser beams with designed intensity profile and polarization, or rotors with sophisticated shapes or material birefringence These requirements hinder the light-driven rotation of many highly symmetric or isotropic particles, including biological cells, with simple optics. Herein, we report a universal approach to the out-of-plane rotation of various objects, including spherically symmetric and isotropic particles, using single arbitrary low-power laser beams. Moreover, the driving laser beam is positioned away from the rotors to reduce optical damage from the direct light illumination. The working mechanism of the rotors based on opto-thermo-electrical coupling is elucidated by systematic experiments combined with multiscale simulations. With its general applicability and simple optics, our universal light-driven rotation platform will become an essential component in various scientific research and engineering applications. © 2021, CC BY.

关键词： Laser beams

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Boosting the Photocatalytic Activity of G-C3n4/Zno Heterojunctions Through Optimal Control of Mass Ratio

SSRN

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SSRN 2022年

作者： da Silva, Fausthon Fred da Silva, Rodolfo Bezerra Silva, Thayse R. de Macedo, Daniel Araujo Su, Bo Biomaterials Engineering Bristol Dental School University of Bristol Lower Maudlin Street BristolBS1 2LY United Kingdom PB João Pessoa58051-900 Brazil RN Mossoró59610-090 Brazil Materials Science and Engineering Postgraduate Program UFPB João Pessoa58051-900 Brazil

Carbon nitride (CN, g-C3N4) has emerged as a promising high-activity photocatalyst, despite some disadvantages like fast electron-hole recombination. The formation of CN-based heterojunctions is a viable strategy to overcome unwanted recombination processes. However, the precise control of the experimental parameters during the heterojunction formation is still needed to boost the photocatalytic efficiency. Herein, the influence of the mass ratio on the photocatalytic performance of CN/ZnO heterojunctions was investigated. The samples were obtained at room temperature via impregnation method and ultrasonic treatment. Heterojunctions were characterised by XRD and FT-IR, and interactions between the components were evidenced by the shifts in the diffraction patterns and vibrational spectra. The morphologies were investigated via SEM/TEM, confirming the formation of heterojunction interfaces. A strong influence of the CN/ZnO ratio was found on bandgaps, crystallite sizes. Photocatalytic activity was evaluated using rhodamine B dye in aqueous solution. High photocatalytic performances were obtained for CN/ZnO ≥ 1, and the best ratio was 2. The control of mass ratio changed the formation mechanism from type II heterojunction (CN/ZnO © 2022, The Authors. All rights reserved.

关键词： Carbon nitride

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