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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Doumani, Jacques Lou, Minhan Dewey, Oliver Hong, Nina Fan, Jichao Baydin, Andrey Pasquali, Matteo Yomogida, Yohei Yanagi, Kazuhiro Saito, Riichiro Kono, Junichiro Gao, Weilu Department of Electrical and Computer Engineering Rice University HoustonTX United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Carbon Hub Rice University HoustonTX United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States J.A. Woollam Co. LincolnNE United States The Smalley-Curl Institute Rice University HoustonTX United States Department of Chemistry Rice University HoustonTX United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University HoustonTX United States

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Dichroism

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Molecular-Functionalized Metal-Organic Frameworks Enabling Tribocatalytic Organic Decomposition

SSRN

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SSRN 2023年

作者： Zhang, Yihe Kang, Tian Han, Xin Yang, Weifeng Gong, Wei Li, Kerui Guo, Yinben School of Materials Science and Engineering Shanghai University of Engineering Science Shanghai201620 China State Key Laboratory of Chemical Engineering School of Chemical Engineering East China University of Science and Technology Shanghai200237 China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University Shanghai201620 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore

Metal-organic frameworks (MOFs) have been used in triboelectric areas for either improving the outputs or expanding some novel applications. However, research on the effects of the grafted groups of MOFs molecular to triboelectricity remains scarce. Here, we modified the MIL-101(Cr) with three groups (pyridine, amino, nitro) to obtain a series of X-MOFs (X=-PY, -NH2 and -NO2) and clarified their position in triboelectric series. The great electron-donating ability of pyridine granted PY-MOF positive tribopolarity and effectively improved the power density by 9.6 times. Moreover, PY-MOF was proved as significant tribocatalytic material with degradation rate of 97% in 3 h via degradation of methylene blue (MB) dye in aqueous environment. This work shows that the modification of MOF molecular with functional groups can modulate the triboelectric properties of MOFs for largely improving the output performance and show great application prospects in the field of tribo-catalysis. © 2023, The Authors. All rights reserved.

关键词： Nanogenerators

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Dynamic Fire and Smoke Detection Module with Enhanced Feature Integration and Attention Mechanisms

SSRN

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SSRN 2024年

作者： Amjad, Ammar Tsung, Chang Hsien Tai, Li-Chia Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Computer Science and Information Engineering Chang Gung Universiry Taoyuan333 Taiwan Bachelor Program in Artificial Intelligence Chang Gung Universiry Taoyuan333 Taiwan Department of Physical Medicine and Rehabilitation at Linkou Taoyuan333 Taiwan

Essential components of early fire warning systems include effective fire and smoke detection mechanisms. Despite object detection-based technologies demonstrating promising results, the unique characteristics of fire and smoke, the complexity of fire environments, the scarcity of labeled datasets, and the presence of noise in images necessitate further improvements. We have developed a YOLOv7-tiny optimized architecture called the Dynamic Fire and Smoke Detection Module (DFDM) to address these challenges. Our approach includes an asymptotic feature pyramid network (AFPN) to bridge semantic gaps, thus enhancing fire detection capabilities in complex environments. A cross-level dual attention mechanism also refines critical smoke and fire detection features. To enhance performance while reducing model complexity, we introduced a novel partial convolution module designed to filter out extraneous information effectively. Experimental results demonstrate that DFDM provides robust fire and smoke detection capabilities, giving it a competitive advantage over more complex systems. Extensive visualization studies confirm DFDM's effectiveness in object recognition across varied settings. © 2024, The Authors. All rights reserved.

关键词： Smoke

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Revealing flat bands and hybridization gaps in a twisted bilayer graphene device with microARPES

arXiv

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arXiv 2024年

作者： Jiang, Zhihao Hsieh, Kimberly Jones, Alfred J.H. Majchrzak, Paulina Sahoo, Chakradhar Watanabe, Kenji Taniguchi, Takashi Miwa, Jill A. Chen, Yong P. Ulstrup, Soren Department of Physics and Astronomy Interdisciplinary Nanoscience Center Aarhus University Aarhus C8000 Denmark Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Physics and Astronomy School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States

Controlling the electronic structure of two-dimensional materials using the combination of twist angle and electrostatic doping is an effective means to induce emergent phenomena. In bilayer graphene with an interlayer twist angle near the magic angle, the electronic dispersion is strongly modified by a manifold of hybridizing moirè Dirac cones leading to flat band segments with strong electronic correlations. Numerous technical challenges arising from spatial inhomogeneity of interlayer interactions, twist angle and device functionality have so far limited momentum-resolved electronic structure measurements of these systems to static conditions. Here, we present a detailed characterization of the electronic structure exhibiting miniband dispersions for twisted bilayer graphene, near the magic angle, integrated in a functional device architecture using micro-focused angle-resolved photoemission spectroscopy. The optimum conditions for visualizing the miniband dispersion are determined by exploiting the spatial resolution and photon energy tunability of the light source and applied to extract a hybridization gap size of (0.14 }0.03) eV and flat band segments extending across a moirè mini Brillouin zone. In situ electrostatic gating of the sample enables significant electron-doping, causing the conduction band states to shift below the Fermi energy. Our work emphasizes key challenges in probing the electronic structure of magic angle bilayer graphene devices and outlines conditions for exploring the doping-dependent evolution of the dispersion that underpins the ability to control many-body interactions in the material. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic structure

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Multiplexed color centers in a silicon photonic cavity array

arXiv

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arXiv 2025年

作者： Komza, Lukasz Zhang, Xueyue Song, Hanbin Tang, Yu-Lung Wei, Xin Sipahigil, Alp Department of Physics University of California Berkeley BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Materials Science and Engineering University of California Berkeley BerkeleyCA94720 United States

Entanglement distribution is central to the modular scaling of quantum processors and establishing quantum networks. Color centers with telecom-band transitions and long spin coherence times are suitable candidates for long-distance entanglement distribution. However, high-bandwidth memory-enhanced quantum communication is limited by high-yield, scalable creation of efficient spin-photon interfaces. Here, we develop a silicon photonics platform consisting of arrays of bus-coupled cavities. The coupling to a common bus waveguide enables simultaneous access to individually addressable cavity-enhanced T center arrays. We demonstrate frequency-multiplexed operation of two T centers in separate photonic crystal cavities. In addition, we investigate the cavity enhancement of a T center through hybridized modes formed between physically distant cavities. Our results show that bus-coupled arrays of cavity-enhanced color centers could enable efficient on-chip and long-distance entanglement distribution. © 2025, CC BY.

关键词： Silicon photonics

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Bambang Sulistio Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%.

关键词： Deep learning Sentiment analysis Analytical models Social networking (online) Blogs Decision making Text categorization Bidirectional control Transformers Encoding

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Improved Synaptic Characteristics in Bilayer Memristor by Post-Oxide Deposition Annealing for Pattern Recognition

Improved Synaptic Characteristics in Bilayer Memristor by Po...

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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Kumar, Dayanand Huang, Yi-Rong Pal, Pratibha Saleem, Aftab Singh, Amit Lee, Hoonkyung Wang, Yeong-Her Tseng, Tseung Yuen National Cheng-Kung University Institute of Microelectronics Department of Electrical Engineering Tainan701 Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan National Cheng Kung University Department of Materials Science and Engineering Tainan70101 Taiwan National Yang Ming Chiao Tung University Department of Electrical Engineering and Computer Science 30010 Taiwan Research Center for Applied Sciences Academia Sinica Taipei11529 Taiwan Konkuk University Department of Physics Seoul05029 Korea Republic of

ISBN: (纸本)9781665409230

We present highly stable bilayer HfO2/TiO2 memristive synapse for neuromorphic computing applications. The memristive synapse shows repetitive 220 Long-term potentiation and depression cycles without any breakdown with total number of 220k pulses. The nonlinear values of potentiation and depression are 2.52 and -2.63, respectively with 1000 conductance pulses (500 pulses of potentiation and 500 pulses of depression). The experimental data of potentiation and depression was used to train HNN for pattern recognition of 28×28 pixels comprising 784 synapses. In 16 iterations, the HNN can be successfully trained to identify the input image with a training accuracy of about 97%. Moreover, the device has good retention (104 s) at 120 °C. These excellent synaptic characteristics of annealed device allows it for artificial intelligence systems in near future. © 2022 IEEE.

关键词： Hafnium oxides

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Platform for Evanescent Wave Sensor Fabrication Based on 3D-Printed Fiber Optic Holder

Platform for Evanescent Wave Sensor Fabrication Based on 3D-...

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Optical Fiber Sensors (LAWOFS), Latin American Workshop on

作者： Afonso Henrique Henschel Baptista Pedro Bertemes-Filho Aleksander Paterno Raquel Bettega Jose Pedro Mansueto Serbena Nathalia Prediger Ricardo Kamikawachi Department of Electrical Engineering Center for Science and Technology - Santa Catarina State University – CCT-UDESC Joinville Brazil Group of Organic Optoelectronic Devices (GOOD) Federal University of Parana – UFPR Curitiba Brazil Graduate Program in Electrical and Computer Engineering Federal University of Technology - Parana – UTFPR Curitiba Brazil

ISBN: (数字)9786589532026

ISBN: (纸本)9798350362725

This paper describes the low-cost manufacturing process of an evanescent wave fiber sensor platform that allows the etching of the fiber in hydrofluoric acid with the proposed 3D-printed fiber holder in an acid resistant material. The fiber holder is practical for allowing several processing procedures during its use and sensor fabrication, allowing sensor optical and electrical operation. In this work, some aspects of its characterization are presented in the optical domain showing the excitation of surface plasmons in the visible range, and also indication of its electrical operation of the thin-film as electrode.

关键词： Optical fiber polarization Optical fiber sensors Gold Optical polarization Biomedical optical imaging Optical device fabrication Optical films

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Integrated proteomic profiling identifies amino acids selectively cytotoxic to pancreatic cancer cells

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The Innovation 2024年第3期5卷 138-145页

作者： Alfred Akinlalu Zachariah Flaten Komila Rasuleva Md Saimon Mia Aaron Bauer Santhalingam Elamurugan Nega Ejjigu Sudipa Maity Amara Arshad Min Wu Wenjie Xia Jia Fan Ang Guo Sijo Mathew Dali Sun Department of Electrical and Computer Engineering University of Denver2155 E Wesley AvenueDenverCO 80210USA Biomedical Engineering Program North Dakota State University1401 Centennial BoulevardEngineering AdministrationRoom 203FargoND 58102USA Department of Pharmaceutical Sciences School of PharmacyNorth Dakota State University1001 S.1401 Albrecht Boulevard Sudro HallFargoND 58102USA Materials and Nanotechnology Program North Dakota State University1410 North 14th AvenueCIE 201FargoND 58102USA Wenzhou Institute University of Chinese Academy of SciencesWenzhouZhejiang 325000China Department of Aerospace Engineering Iowa State UniversityAmesIA 50011USA Center for Cellular and Molecular Diagnostics Tulane University School of Medicine1430 Tulane AvenueNew OrleansLA 70112USA Department of Biochemistry and Molecular Biology Tulane University School of Medicine1430 Tulane AvenueNew OrleansLA 70112USA Knoebel Institute for Healthy Aging University of Denver2155 E Wesley AvenueDenverCO 80210USA

Pancreatic adenocarcinoma(PDAC)is one of the most deadly cancers,characterized by extremely limited therapeutic options and a poor prognosis,as it is often diagnosed during late disease *** and selective treatments are urgently needed,since current therapies have limited efficacy and significant side *** proteomics analysis of extracellular vesicles,we discovered an imbalanced distribution of amino acids secreted by PDAC tumor *** findings revealed that PDAC cells preferentially excrete proteins with certain preferential amino acids,including isoleucine and histidine,via extracellular *** amino acids are associated with disease progression and can be targeted to elicit selective toxicity to PDAC tumor *** in vitro and in vivo experiments demonstrated that supplementation with these specific amino acids effectively eradicated PDAC ***,we also identified XRN1 as a potential target for these amino *** high selectivity of this treatment method allows for specific targeting of tumor metabolism with very low toxicity to normal ***,we found this treatment approach is easy-to-administer and with sustained tumor-killing ***,our findings reveal that exocytosed amino acids may serve as therapeutic targets for designing treatments of intractable PDAC and potentially offer alternative treatments for other types of cancers.

关键词： finding identif selectivity

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Brain Tumor Detection and Segmentation Using Deep Learning Models

Brain Tumor Detection and Segmentation Using Deep Learning M...

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Artificial Intelligence (WSAI), World Symposium on

作者： Moses Kazeem Abiodun Abidemi Emmanuel Adeniyi Joseph Bamidele Awotunde Halleluyah Aworinde George Tinuoye Agbotiname Lucky Imoize Chun-Ta Li Cheng-Chi Lee Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science Bowen University Iwo Osun State Nigeria Department of Computer Science University of Ilorin Ilorin Kwara State Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Nigeria Bachelor's Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City Taiwan Department of Library and Information Science Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798350374162

ISBN: (纸本)9798350374179

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Computational modeling Brain tumors Brain modeling Distance measurement Classification algorithms Convolutional neural networks

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