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Quantum Atomic Matter Near Two-Dimensional Materials in Microgravity

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arXiv 2023年

作者： Maestro, Adrian Del Kim, Sang Wook Bigelow, Nicholas P. Thompson, Robert J. Kotov, Valeri N. Department of Physics and Astronomy Min H. Kao Department of Electrical Engineering and Computer Science Institute for Advanced Materials and Manufacturing University of Tennessee KnoxvilleTN37996 United States Department of Physics and Materials Science Program University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy Institute of Optics Center for Coherence and Quantum Optics University of Rochester RochesterNY14627 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

Novel two-dimensional (2D) atomically flat materials, such as graphene and transition-metal dichalcogenides, exhibit unconventional Dirac electronic spectra. We propose to effectively engineer their interactions with cold atoms in microgravity, leading to a synergy between complex electronic and atomic collective quantum phases and phenomena. Dirac materials are susceptible to manipulation and quantum engineering via changes in their electronic properties by application of strain, doping with carriers, adjustment of their dielectric environment, etc. Consequently the interaction of atoms with such materials, namely the van der Waals / Casimir-Polder interaction, can be effectively manipulated, leading to the potential observation of physical effects such as Quantum Reflection off atomically thin materials and confined Bose-Einstein Condensate (BEC) frequency shifts. Copyright © 2023, The Authors. All rights reserved.

关键词： Transition metals

Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles

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arXiv 2022年

作者： Boucharas, Dimitrios G. Androutsos, Christos Gkois, George Tsakanikas, Vassilis D. Pezoulas, Vasileios C. Manousos, Dimitrios Skaramagkas, Vasileios Chatzaki, Chariklia Kontogiannis, Stathis Spandonidis, Christos Pantazis, Alexandros K. Tachos, Nikolaos S. Tsiknakis, Manolis Fotiadis, Dimitrios I. The Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina IoanninaGR-45110 Greece Crete HeraklionGR-70013 Greece Biomedical Informatics and eHealth Laboratory Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Crete HeraklionGR-71004 Greece The Department of Electric and Computer Engineering Hellenic Mediterranean University Crete HeraklionGR-71004 Greece PD Neurotechnology Ltd IoanninaGR-45110 Greece Prisma Electronics SA P.Faliro AthensGR-17564 Greece Crete HeraklionGR-70013 Greece Biomedical Research Institute FORTH IoanninaGR-45110 Greece The Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR-45110 Greece

During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessment tools, such as software platforms and mobile applications, have been proposed by the scientific community and the market that employ sensors to monitor human gait for various purposes ranging from biomechanics to the progression of functional recovery. The framework presented herein offers a valuable digital biomarker for diagnosing and monitoring Parkinson’s disease that can help clinical experts in the decision-making process leading to corrective planning or patient-specific treatment. More accurate and reliable decisions can be provided through a wide variety of integrated Artificial Intelligence algorithms and straightforward visualization techniques, including, but not limited to, heatmaps and bar plots. The framework consists of three core components: the insole pair, the mobile application, and the cloud-based platform. The insole pair deploys 16 plantar pressure sensors, an accelerometer, and a gyroscope to acquire gait data. The mobile application formulates the data for the cloud platform, which orchestrates the component interaction through the web application. Utilizing open communication protocols enables the straightforward replacement of one of the core components with a relative one (e.g., a different model of insoles), transparently from the end user, without affecting the overall architecture, resulting in a framework with the flexibility to adjust its modularity. © 2022, CC BY.

关键词： computer architecture

Electromagnetic-polarization selective composites with quasi-1D van der Waals metallic fillers

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arXiv 2021年

作者： Barani, Zahra Kargar, Fariborz Ghafouri, Yassamin Baraghani, Saba Sudhindra, Sriharsha Mohammadzadeh, Amirmahdi Salguero, Tina T. Balandin, Alexander A. Center Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Department of Chemical and Environmental Engineering University of California RiversideCA92521 United States Department of Chemistry University of Georgia AthensGA30602 United States Material Science and Engineering Program University of California RiversideCA92521 United States

We report on the preparation of flexible polymer composite films with aligned metallic fillers comprised of atomic chain bundles of the quasi-one-dimensional (1D) van der Waals material tantalum triselenide, TaSe3. The material functionality, embedded at the nanoscale level, is achieved by mimicking the design of an electromagnetic aperture grid antenna. The processed composites employ chemically exfoliated TaSe3 nanowires as the grid building blocks incorporated within the thin film. Filler alignment is achieved using the "blade coating" method. Measurements conducted in the X-band frequency range demonstrate that the electromagnetic transmission through such films can be varied significantly by changing the relative orientations of the quasi-1D fillers and the polarization of the electromagnetic wave. We argue that such polarization-sensitive polymer films with quasi-1D fillers are applicable to advanced electromagnetic interference shielding in future communication systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Van der Waals forces

Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

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SSRN

SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 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 Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. 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 data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

Electromagnetic textile absorber applied to 4G and 5G bands

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Scientific reports 2025年第1期15卷 18768页

作者： Paulo H B Carvalho Ewaldo E C Santana Allan K D Barros Filho Paulo F Silva Júnior Keyll C R Martins Talita C Azevedo Waldemir P Martins Mauro A Medeiros Rubens S Gonçalves Lourival M Sousa Filho Gricirene S Correia Gabrielle M Fernandes Talita C Pinheiro Brenda A S Rodrigues Carlos A M Cruz Graduate Program in Electrical Engineering Federal University of Maranhão (UFMA) São Luís MA Brazil. phenrique@ifma.edu.br. Graduate Program in Electrical Engineering Federal University of Maranhão (UFMA) São Luís MA Brazil. Graduate Program in Computer and Systems Engineering Maranhão State University (UEMA) São Luís MA Brazil. Graduate Program in Mechanical Engineering Federal Institute of Maranhão (IFMA) São Luís MA Brazil. Department of Electrical Engineering Federal Institute of Paraíba (IFPB) João Pessoa PB Brazil. Graduate Program in Electrical Engineering Federal University of Amazonas (UFAM) Manaus AM Brazil. Department of Electronics and Computation Federal University of Amazonas (UFAM) Manaus AM Brazil.

Absorber materials are developed to reduce electromagnetic radiation and ensure the compatibility of the operation of electronic equipment in environments subject to interference. This work presents the development of a low-cost textile electromagnetic absorber for 4G and 5G technologies, operating at frequencies of 2.5 GHz and 3.5 GHz. The proposed electromagnetic absorber utilizes a 1 mm-thick Denim substrate, with a graphite composite used in the agricultural and commercial polyvinyl acetate glue industry, with the relationship of 25 wt%. The measurements were carried out in a Vector Network Analyzer, model E5071C Agilent Technologies, with the characterization of the Denim substrate, the glue, and the identification of the best parameters for the construction of the absorber. In the project, three low-cost textile absorbers prototypes were fabricated, with G1 = 0.25 mm, G2 = 0.35 mm, and G3 = 0.5 mm of thickness layers of the composite deposited on a Denim fabric. The results indicate that the absorber prototypes G2 presents great results in the frequency range of 4G and 5G band, with a maximum absorption of 26.6 dB in 3.94 GHz, with a structure 98.83% thinner than the commercial absorber LF-75. The variation in absorption performance may be attributed to the different mechanisms by which the absorbers operate: the commercial absorber LF-75 primarily interacts with the magnetic field, whereas the textile prototype predominantly affects the electric field.

关键词： 4G and 5G bands Electromagnetic Low-cost Textile absorber

Structural, optical, and thermal properties of BN thin films grown on diamond via pulsed laser deposition

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arXiv 2023年

作者： Biswas, Abhijit Alvarez, Gustavo A. Li, Tao Christiansen-Salameh, Joyce Jeong, Eugene Puthirath, Anand B. Iyengar, Sathvik Ajay Li, Chenxi Gray, Tia Zhang, Xiang Pieshkov, Tymofii S. Kannan, Harikishan Elkins, Jacob Vajtai, Robert Birdwell, A. Glen Neupane, Mahesh R. Garratt, Elias J. Pate, Bradford B. Ivanov, Tony G. Zhao, Yuji Tian, Zhiting Ajayan, Pulickel M. Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY14853 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States DEVCOM Army Research Laboratory RF Devices and Circuits AdelphiMD20783 United States Naval Research Laboratory Chemistry Division WashingtonDC20375 United States

Heterostructures based on ultrawide-bandgap (UWBG) semiconductors (bandgap >4.0 eV), such as boron nitride (BN) and diamond, hold significant importance for the development of high-power electronics in the next generation. However, achieving in-situ hetero-epitaxy of BN/diamond or vice versa remains exceptionally challenging due to the complex growth kinetics involved. In this study, we have grown BN thin film on (100) single crystal diamonds using pulsed laser deposition and investigated its structural, magnetic, optical, and thermal properties. The structural analyses confirmed the growth of BN films, which exhibited diamagnetic behavior at room temperature. Notably, the film demonstrated anisotropic refractive index characteristics within the visible-to-near-infrared wavelength range. The room temperature cross-plane thermal conductivity of BN is 1.53 (± 0.77) W/mK, while the thermal conductance of the BN/diamond interface is 20 (± 2) MW/m2K. These findings have significant implications for a range of device applications based on UWBG BN/diamond heterostructures. © 2023, CC BY.

关键词： Diamonds

AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase Researcher Diversity and Advance Health Equity

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AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data...

IEEE International Conference on Healthcare Informatics (ICHI)

作者： Toufeeq Ahmed Aidan Hoyal Katie Stinson Jay Johnson Zainab Latif Legand L. Burge Alexander Libin Guodong Gordon Gao Nawar Shara Jamboor K. Vishwanatha Department of Biomedical Informatics Vanderbilt University Medical Center Nashville United States Department of Electrical Engineering and Computer Science Howard University Washington D.C. United States MedStar Health Research Institute Georgetown University Washington D.C. United States Carey Business School Johns Hopkins University Baltimore United States Institute for Health Disparities University of North Texas Health Science Center at Fort Worth Fort Worth United States

AIM-AHEAD is an NIH-funded consortium whose goal is to advance health equity and researcher diversity via artificial intelligence and machine learning. In our first year of operations, we launched AIM-AHEAD Connect, a centralized platform for the consortium stakeholders containing mentoring, networking, and professional development resources. This paper describes the resources available in AIM-AHEAD Connect as well as collaborative efforts to provide tools and training for the entire consortium.

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Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images

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Informatics in Medicine Unlocked 2021年 27卷

作者： Mondal, Chayan Hasan, Md. Kamrul Ahmad, Mohiuddin Awal, Md. Abdul Jawad, Md. Tasnim Dutta, Aishwariya Islam, Md. Rabiul Moni, Mohammad Ali Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology Khulna 9203 Bangladesh Department of Biomedical Engineering (BME) Khulna University of Engineering & Technology Khulna 9203 Bangladesh Electronics and Communication Engineering (ECE) Discipline Khulna University Khulna 9208 Bangladesh Department of Electrical and Electronic Engineering (EEE) Bangabandhu Sheikh Mujibur Rahman Science & Technology University Gopalganj 8100 Bangladesh Department of Computer Science Engineering (CSE) Pabna University of Science & Technology Pabna 6600 Bangladesh

Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by the presence of excess immature lymphocytes., Even though automation in ALL prognosis is essential for cancer diagnosis, it remains a challenge due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands that experienced pathologists read cell images carefully, which is arduous, time-consuming, and often hampered by interobserver variation. This article has automated the ALL recognition task by employing deep Convolutional Neural Networks (CNNs). The weighted ensemble of deep CNNs is explored to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates’ corresponding metrics, such as F1-score, area under the curve (AUC), and kappa values. Various data augmentations and pre-processing are incorporated to achieve a better generalization of the network. Our proposed model was trained and evaluated utilizing the C-NMC-2019 ALL dataset. The proposed weighted ensemble model has outputted a weighted F1-score of 89.7%, a balanced accuracy of 88.3%, and an AUC of 0.948 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately exhibit coarse and scatter learned areas in most cases. Since the proposed ensemble yields a better result for the aimed task, it can support clinical decisions to detect ALL patients in an early stage. © 2021 The Authors

关键词： Acute Lymphoblastic Leukemia C-NMC-2019 dataset Deep Convolutional Neural Networks Ensemble image classifiers Transfer learning

A novel near-infrared light responsive 4D printed nanoarchitecture with dynamically and remotely controllable transformation

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Nano Research 2019年第6期12卷 1381-1388页

作者： Haitao Cui Shida Miao Timothy Esworthy Se-jun Lee Xuan Zhou Sung Yun Hann Thomas J.Webster Brent T.Harris Lijie Grace Zhang Department of Mechanical and Aerospace Engineering The George Washington UniversityWashingtonDC 20052USA Department of Chemical Engineering Northeastern UniversityBostonMA 02115USA Department of Neurology and Pathology Georgetown UniversityWashingtonDC 20007USA Departments of Electrical and Computer Engineering The George Washington UniversityWashingtonDC 20052USA Department of Biomedical Engineering The George Washington UniversityWashingtonDC 20052USA Department of Medicine The George Washington UniversityWashingtonDC 20052USA

Four-dimensional (4D) printing is an emerging and highly innovative additive manufacturing process by which to fabricate pre-designed,self-assembly structures with the ability to transform over ***,one of the critical challenges of 4D printing is the lack of advanced 4D printing systems that not only meet all the essential requirements of shape change but also possess smart,dynamic capabilities to spatiotemporally and instantly control the shape-transformation ***,we present a facile 4D printing platform which incorporates nanomaterials into the conventional stimuli-responsive polymer,allowing the 4D printed object to achieve a dynamic and remote controlled,on-time and position shape transformation.A proof-of-concept 4D printed brain model was created using near-infrared light (NIR) responsive nanocomposite to evaluate the capacity for controllable 4D transformation,and the feasibility of photothermal stimulation for modulating neural stem cell *** novel 4D printing strategy can not only be used to create dynamic 3D patterned biological structures that can spatiotemporally control their shapes or behaviors of transformation under a human benign stimulus (NIR),but can also provide a potential method for building complex self-morphing objects for widespread applications.

关键词： near-infrared light responsive four-dimensional (4D) printing dynamically and remotely controllable graphene brain neural stem cell