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Sensing property of Ga2O3-based extended-gate field-effect transistors for a living cell viability sensor

Sensors and Actuators A: Physical 2023年 349卷

作者： Chou, Hsin-Yu Chiang, Jung-Lung Yu, Chang-Tze Ricky Chen, Jo-Mei Maureen Wuu, Dong-Sing Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Chemistry National Chi-Nan University Nantou54561 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

In this study, gallium oxide (Ga2O3) nanorods were deposited onto an indium tin oxide (ITO) glass substrate to develop a real-time living cell viability sensor. Ga2O3 nanorods had characteristics of cell population sensing based on cellular metabolism to detect cell viability. The pH sensing characteristics of Ga2O3-based extended-gate field-effect transistors (EGFETs) with and without the annealing process were measured from pH 1–11. Three different sensor heads, ITO/glass, GaOOH/ITO/glass, and Ga2O3/ITO/glass, were produced as EGFETs and immersed into a pH standard buffer and high-glucose Dulbecco's modified Eagle's medium (DMEM) to investigate hydrogen ion sensing. Results showed that the Ga2O3/ITO/glass probe had the best hydrogen ion sensing sensitivity and stability in different sample buffers. In contrast, GaOOH/ITO/glass had instability and lower sensitivity due to interference with other components in the medium. The drift characteristic of sensors was analyzed to detect long-term stability. The Ga2O3/ITO/glass probe showed lower drift properties, which can potentially monitor the DMEM situation in real-time for a long-term period. During in vitro detection, the Ga2O3/ITO/glass probe revealed a 70 μA/cells change in IDS-VDS analysis and a 10 μV/cells change in IDS-VGS analysis, which was superior linearly dependent within 10,000 cells. The biocompatibility of the Ga2O3/ITO/glass probe was also detected by 3-(4,5-dimethylthiazol-2-yl)− 2,5-diphenyltetrazolium bromide assay and showed no cytotoxicity. Overall, the Ga2O3/ITO/glass EGFET sensor has potential applications as a living cell viability device in microenvironmental studies in the future. © 2022 Elsevier B.V.

关键词： Nanorods

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Experimental implementation of an efficient test of quantumness

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Physical Review A 2024年第1期109卷 012610-012610页

作者： Laura Lewis Daiwei Zhu Alexandru Gheorghiu Crystal Noel Or Katz Bahaa Harraz Qingfeng Wang Andrew Risinger Lei Feng Debopriyo Biswas Laird Egan Thomas Vidick Marko Cetina Christopher Monroe Institute for Quantum Information and Matter and Department of Computing and Mathematical Sciences California Institute of Technology Pasadena California 91125 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA IonQ Inc. College Park Maryland 20740 USA Departments of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Institute for Theoretical Studies ETH Zürich Zürich CH 8001 Switzerland Duke Quantum Center and Department of Physics Duke University Durham North Carolina 27708 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Chemical Physics Program and Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA

A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits nonclassical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efficient verification or noninteractive challenges with inefficient (exponential time) verification. In this paper, we execute an efficient noninteractive test of quantumness on an ion-trap quantum computer. Our results significantly exceed the bound for a classical device's success.

关键词： Quantum cryptography Quantum information with trapped ions Quantum protocols Quantum verification

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Parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics

Parallel Approximations of the Tukey g-and-h Likelihoods and...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Sagnik Mondal Sameh Abdulah Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires $\mathcal{O}(n^{2})$ storage and $\mathcal{O}(n^{3})$ operations, which can be a huge task considering that the number of geographical locations, $n$ , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey ${g-}$ and $-h$ (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagon

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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A transient stability enhancement method for Grid-Forming converters enabling operation across the four-quadrant range of Power Angle

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Abrantes-Ferreira, Armando J. G. Oliveira, Alexandre C. Lima, Antonio M. N. Wang, Xiongfei Universidade Federal de Campina Grande Graduate Program in Electrical Engineering Paraiba Campina Grande58429-900 Brazil KTH Royal Institute of Technology Division of Electric Power and Energy Systems Stockholm10044 Sweden Aalborg University Department of Energy Aalborg9220 Denmark

Grid-forming (GFM) converters are a promising solution for integrating converter-based resources into power grids and can be broadly categorized into (i) Synchronous Machine (SM)-inspired methods, which emulate SM control at different abstraction levels, and (ii) Virtual Oscillator Control (VOC) methods, based on oscillator network synchronization principles. Both behave as voltage sources, making them prone to transient angle instability under severe disturbances, despite differences in their phase synchronization dynamics. Existing transient stability enhancement methods for GFM converters restrict operation of power angle δ within the range [-90, 90°], limiting converter utilization and hence reducing its ability to support grid during contingencies. This paper analyzes the large-signal synchronization stability of two GFM methods—dispatchable VOC (dVOC) and Virtual Synchronous Generator (VSG)—and proposes a mode-adaptive power-angle control strategy to enhance their transient stability in ultra-weak grids, i.e., with a Short Circuit Ratio (SCR) ≈ 1 across varying X/R ratios. Assuming constant grid voltage and known line X/R ratio, the strategy estimates δ using only local measurements while maintaining a constant voltage amplitude at converter’s output. By regulating active power to control δ, it enables GFM converters to operate with SCR lower than unity across the four-quadrant range of power angle during high-impedance faults. Experimental validation confirms enhanced converter power controllability for both dVOC and VSG systems. Controller Hardware-in-the-Loop (CHIL) results demonstrate the proposed solution’s superiority over existing methods in terms of transient stability and voltage support, while exhibiting robustness against line X/R ratio measurement errors. Therefore, the proposed solution has shown improved voltage support and better utilization of the full controllability of converter output power for both dVOC and VSG-based systems. © 2013 IEEE.

关键词： Electric power transmission networks

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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International Seminar on Research of Information technology and Intelligent Systems (ISRITI)

作者： Kus Andriadi 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 (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large.

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On the Impact of Knowledge Distillation for Model Interpretability

arXiv

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arXiv 2023年

作者： Han, Hyeongrok Kim, Siwon Choi, Hyun-Soo Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of ZIOVISION Inc. Chuncheon Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Several recent studies have elucidated why knowledge distillation (KD) improves model performance. However, few have researched the other advantages of KD in addition to its improving model performance. In this study, we have attempted to show that KD enhances the interpretability as well as the accuracy of models. We measured the number of concept detectors identified in network dissection for a quantitative comparison of model interpretability. We attributed the improvement in interpretability to the class-similarity information transferred from the teacher to student models. First, we confirmed the transfer of class-similarity information from the teacher to student model via logit distillation. Then, we analyzed how class-similarity information affects model interpretability in terms of its presence or absence and degree of similarity information. We conducted various quantitative and qualitative experiments and examined the results on different datasets, different KD methods, and according to different measures of interpretability. Our research showed that KD models by large models could be used more reliably in various fields. © 2023, CC BY-NC-SA.

关键词： Distillation

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Immersive Modeling Framework for Training Applications

Immersive Modeling Framework for Training Applications

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International Conference on Advanced Learning Technologies (ICALT)

作者： Vinicius Chrisosthemos Teixeira Carlo Smaniotto Mantovani Alexandre Cardoso Carlos Alexandre dos Santos Márcio Sarroglia Pinho Computer Science Graduate Program School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil

This article describes a framework for modeling and executing training in augmented and virtual reality environments. The framework was designed based on characteristics observed in existing training applications for complex tasks, such as the use of tools, control panels and the need for step-by-step instructions. Unlike other frameworks, in the proposed system, it is possible to create the training entirely within the virtual/augmented environment, avoiding constant switching between 2D and 3D environments. The framework allows for the definition of steps in a training program, each of which includes textual instructions, videos, and 3D objects, static or animated, anchored in the real world. To demonstrate the capabilities of the framework, a training program for operating a Universal Testing Machine was created as a case study. Overall, the proposed framework allows for the creation of effective and efficient AR training programs for a variety of tasks and industries.

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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AI-Empowered Data Offloading in MEC-Enabled IoV Networks

arXiv

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arXiv 2022年

作者： Fontes, Afonso de Ribeiro, Igor L. Muhammad, Khan Gandomi, Amir H. Gay, Gregory de Albuquerque, Victor Hugo C. Chalmers The University of Gothenburg Gothenburg Sweden University of Fortaleza CE Fortaleza Brazil Department of Software Sejong University Seoul Korea Republic of Faculty of Engineering & Information Technology University of Technology Sydney Australia Graduate Program on Teleinformatics Engineering Federal University of Ceará Fortaleza CE Fortaleza Brazil Graduate Program on Electrical Engineering Federal University of Ceará CE Fortaleza Brazil

Advancements in smart vehicle design have enabled the creation of Internet of Vehicle (IoV) technologies that can utilize the information provided by various sensors and wireless communication to perform complex functionality. Many of these functionalities rely on high computational power and low latency. Mobile Edge Computing (MEC) technologies have been proposed as a way to meet these requirements, as their proximity and decentralization provide unique benefits for networks like real-time communication, higher throughput, and flexibility. Diverse challenges to the process of offloading data in a MEC-enabled IoV network have emerged, such as offloading reliability in highly mobile environments, security for users within the same network, and energy management to keep users from being disincentivized to participate in the network. This article surveys research studies that use AI as part of the data offloading process, categorized based on four main issues: reliability, security, energy management, and service seller profit. Afterward, this article discusses challenges and future perspectives for IoV technologies. © 2022, CC BY-SA.

关键词： Mobile edge computing

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Artificial-Intelligence-Based Design for Circuit Parameters of Power Converters

arXiv

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arXiv 2023年

作者： Li, Xinze Zhang, Xin Lin, Fanfan Blaabjerg, Frede The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore The College of Electrical Engineering Zhejiang University Hangzhou310027 China Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310058 China ERI@N Interdisciplinary Graduate Program Nanyang Technological University Singapore639798 Singapore The Department of Energy Technology Aalborg University AalborgDK-9220 Denmark

Parameter design is significant in ensuring a satisfactory holistic performance of power converters. Generally, circuit parameter design for power converters consists of two processes: analysis and deduction process and optimization process. The existing approaches for parameter design consist of two types: traditional approach, computer-aided optimization (CAO) approach. In the traditional approaches, heavy human-dependence is required. Even though the emerging CAO approaches automate the optimization process, they still require manual analysis and deduction process. To mitigate human-dependence for the sake of high accuracy and easy implementation, an artificial-intelligence-based design (AI-D) approach is proposed in this paper for the parameter design of power converters. In the proposed AI-D approach, to achieve automation in the analysis and deduction process, simulation tools and batch-normalization neural network (BN-NN) are adopted to build data-driven models for the optimization objectives and design constraints. Besides, to achieve automation in the optimization process, genetic algorithm is used to search for optimal design results. The proposed AI-D approach is validated in the circuit parameter design of the synchronous Buck converter in the 48 V to 12 V accessory-load power supply system in electric vehicle. The design case of an efficiency-optimal synchronous Buck converter with constraints in volume, voltage ripple and current ripple is provided. In the end of this paper, feasibility and accuracy of the proposed AI-D approach have been validated by hardware experiments. © 2023, CC BY-NC-ND.

关键词： Genetic algorithms

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