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An enzyme-free interdigitated electrode for ketamine detection

Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A 2023年第5期46卷 441-452页

作者： Phan, Thien Luan Van Hieu, Nguyen Lin, Jing-Jenn Wu, Hsing-Ju Chu, Pei-Yi Hsiao, Chin-Sung Yao, Fiona Yan-Dong Lin, Yung-Kai Ching, Congo Tak Shing Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung Taiwan Viet Nam Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Taiwan Research Assistant Center Show Chwan Memorial Hospital Taiwan Department of Medical Research Chang Bing Show Chwan Memorial Hospital Taiwan Department of Pathology Show Chwan Memorial Hospital Taiwan School of Medicine College of Medicine Fu Jen Catholic University Taiwan Computer Science and Information Engineering Asia University Taiwan Division of Science Engineering and Health Studies College of Professional and Continuing Education The Hong Kong Polytechnic University Hong Kong Department of Food Science National Taiwan Ocean University Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan

Ketamine is an anesthetic, which when used wrongly would affect judgment and behavior, increasing risks to individual such as unwanted or risky sexual behavior. This study reports the development of an admittance- and capacitance-spectroscopy-based sensor to detect ketamine in solution, such as whiskey, a popular alcoholic drink. The result confirms the proposed method is capable of detecting the presence of ketamine in whiskey, in concentration as low as 6.25 ng/μL, by using admittance and capacitance measured at 6.75 MHz, as well as quantifying the measured admittance (sensitivity: 300 μΩ−1/Log (ng/μL);limit of detection (LOD): 0.16 ng/μL) or capacitance data (sensitivity: 2 nF/Log (ng/μL);LOD: 0.73 ng/μL) at 18 kHz and 7.69 MHz, respectively. © 2023 The Chinese Institute of Engineers.

关键词： Capacitance

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Automated Recognition of Structures in Scanning Electron Microscopy Images Using Specialized Algorithms in MATLAB and Python: An Overview 16

Automated Recognition of Structures in Scanning Electron Mic...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Marascu, Valentina Mihailescu, Marius Iulian Nita, Stefania Loredana Rogobete, Marius 'Spiru Haret' University Scientific Research Center in Mathematics and Informatics Faculty of Engineering and Computer Science Bucharest Romania National Institute for Laser Plasma and Radiation Physics Low Temperature Plasma Department Bucharest Romania 'Ferdinand i' Military Technical Academy Department of Computers and Cybersecurity Bucharest Romania Telematics Harman International Bucharest Romania

ISBN: (纸本)9798350371154

Applied physics serves as the bridge connecting theoretical principles with their practical implementations in everyday situations. Informatics plays a crucial role in this subject because of its comprehensive and intricate capacity to simulate and forecast physical phenomena. This research highlights the indispensability of computer-based algorithms for analyzing the Scanning Electron Images of the obtained materials, particularly materials synthesized by laboratory plasma. Herein, the conducted research highlights the advantages of image processing via Matlab and Python algorithms, in order to analyze the synthesized structures. This approach can offer perspectives on physical processes by employing image processing techniques. The approaches and future directions were identified. © 2024 IEEE.

关键词： Python

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

Parallel Approximations for High-Dimensional Multivariate No...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao 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 Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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Capsule: An Out-of-Core Training Mechanism for Colossal GNNs

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Proceedings of the ACM on Management of Data 2025年第1期3卷 1-30页

作者： Yongan Xiang Zezhong Ding Rui Guo Shangyou Wang Xike Xie S. Kevin Zhou School of Computer Science and Technology University of Science and Technology of China (USTC) Hefei Anhui China & Data Darkness Lab Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China School of Artificial Intelligence and Data Science USTC Hefei Anhui China & Data Darkness Lab Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China School of Biomedical Engineering USTC Suzhou Jiangsu China & Data Darkness Lab Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China School of Biomedical Engineering USTC Suzhou Jiangsu China & MIRACLE Center Suzhou Institute for Advanced Research USTC Suzhou Jiangsu China

Cutting-edge platforms of graph neural networks (GNNs), such as DGL and PyG, harness the parallel processing power of GPUs to extract structural information from graph data, achieving state-of-the-art (SOTA) performance in fields such as recommendation systems, knowledge graphs, and bioinformatics. Despite the computational advantages provided by GPUs, these GNN platforms struggle with scalability challenges due to the colossal graphical structures processed and the limited memory capacities of GPUs. In response, this work introduces Capsule, a new out-of-core mechanism for large-scale GNN training. Unlike existing out-of-core GNN systems, which use main or secondary memory as operative memory and use CPU kernels during non-backpropagation computation, Capsule uses GPU memory and GPU kernels. By substantially leveraging the parallelization capabilities of GPUs, Capsule significantly enhances GNN training efficiency. In addition, Capsule can be smoothly integrated to mainstream open-source GNN frameworks, DGL and PyG, in a play-and-plug manner. Through a prototype implementation and comprehensive experiments on real datasets, we demonstrate that Capsule can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of the main memory, compared to SOTA out-of-core GNN systems.

关键词： gpus graph data management graph neural networks

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On the Design of Digital Event-Based Controllers for a Class of Nonlinear Systems: an Application to Unmanned Aerial Vehicles

On the Design of Digital Event-Based Controllers for a Class...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Mario Di Ferdinando Alessandro Modesti Nicola Epicoco Stefano Di Gennaro Pierdomenico Pepe Department of Information Engineering Computer Science and Mathematics Center of Excellence for Research DEWS University of L’Aquila L’Aquila Italy Department of Engineering LUM University Casamassima BA Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In the last decade, control systems for Unmanned Aerial Vehicles (UAVs) have been an important topic in research due to their increasing usage in many practical fields, and have been extensively studied by resorting to classical continuous-time nonlinear design. Although this approach can simultaneously incorporate different control requirements in the design, the computed control laws are approximated and applied within digital environments in absence of formal performance guarantees. In this work, we present a methodology for the design of dynamic quantized sampled-data event-based stabilizers for a class of time-varying nonlinear systems at the aims of applying the proposed strategy to the autonomous navigation problem of UAVs. Starting from classical nonlinear design achieving tracking of prescribed trajectories in continuous time (emulation approach), we prove the existence of a suitably fast sampling and of an accurate quantization of the input/output channels ensuring the practical stability of the error dynamics by means of quantized sampled-data event-based controllers. Simulations performed in an non-ideal setting confirm the potential of the approach.

关键词： Asymptotic stability Quantization (signal) Accuracy Design methodology Autonomous aerial vehicles Stability analysis Trajectory Nonlinear dynamical systems Vehicle dynamics Time-varying systems

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Characterization of Systematic Bias in ALOS-2 Multilooked Interferograms

Characterization of Systematic Bias in ALOS-2 Multilooked In...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ryu Sugimoto Yu Morishita Masanobu Shimada Ryo Natsuaki Chiaki Tsutsumi Ryosuke Nakamura Toru Kouyama Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Division of Architectural Civil and Environmental Engineering School of Science and Engineering Tokyo Denki University Japan Department of Electrical Engineering and Information Systems The University of Tokyo Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Interferometric SAR time series analysis using multilooked interferograms has measured ground deformation with high accuracy over large areas, but is known to be contaminated by a systematic bias when using only short-term interferograms. This bias, also referred to as a "fading signal", has been investigated using the abundant time series data from Sentinel-1 C-band SAR, and soil moisture and biomass changes have been suggested as two possible causes for the observed bias. Several correction methods have been proposed to mitigate the phase bias, assuming a periodic observation strategy. The phase bias is supposed to increase with the wavelength, but the bias in ALOS-2 L-band SAR has not been investigated in detail because the data distribution policy restricts its users. In addition, these correction methods are not suitable for the infrequently observed data such as ALOS-2. In this paper, we investigated the systematic bias in ALOS-2 multilooked interferograms with respect to various interferometric pairs. We also corrected the phase bias using the noise-filtering technique. The bias in ALOS-2 multilooked interferograms showed >3 mm/year with an average temporal baseline of 144 days, which is equivalent to that of Sentinel-1 with the temporal baseline of 78 days. That is, the bias effect toward the temporal baseline was larger in ALOS-2 than in Sentinel-1. The noise-filtering technique could mitigate the bias with less pairs regardless of land-use.

关键词： Fading channels Systematics Soil measurements Time series analysis Soil moisture Sentinel-1 Geoscience and remote sensing

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Brain organoids in motor cortex involved in motor output 24

Brain organoids in motor cortex involved in motor output

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9th International Conference on Biomedical Signal and Image Processing, ICBIP 2024

作者： Fan, Xiaoman Xu, Yuchen Xu, Shibo Yang, Yuqi Shi, Lufeng Liu, Yan Zhang, Shaomin Qiushi Academy for Advanced Studies Department of Biomedical Engineering Zhejiang University China Center of Excellence in Biomedical Research on Advanced Integrated-on-Chips Neurotechnologies School of Engineering Westlake University China State Key Laboratory of Reproductive Medicine Institute for Stem Cell and Neural Regeneration Nanjing Medical University China Qiushi Academy for Advanced Studies Department of Computer Science and Technology Zhejiang University China Department of Neurosurgery Sir Run Run Shaw Hospital Medical College Zhejiang University China

ISBN: (纸本)9798400717970

Motor function is essential in daily life while brain damage in motor system such as stroke would lead to long term motor function deficits. Brain organoids, a novel in vitro stem cell culture technology, offer a new idea for motor function recovery. In this study, we pumped out the motor cortex of forelimbs to mimic brain injury in mice and implanted human brain organoids into the cavity combined with microwire electrodes to simultaneously record neural signals from both the organoids and the host animals. Our findings indicate that the brain organoids can be involved in motor output and the involvement can be improved through behavioral training. © 2024 Copyright held by the owner/author(s).

关键词： Stem cells

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Origin of the high propensity for nanoscale deformation twins in CrCoNi medium-entropy alloy

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Journal of Materials science & Technology 2024年第16期183卷 63-71页

作者： Nan-Jun Liu Zhang-Jie Wang Jun Ding Mark Asta Robert O.Ritchie Bin Gan Evan Ma Zhi-Wei Shan Center for Advancing Materials Performance from the Nanoscale(CAMP-Nano) State Key Laboratory for Mechanical Behavior of MaterialsXi’an Jiaotong UniversityXi’an 710049China Center for Alloy Innovation and Design(CAID) State Key Laboratory for Mechanical Behavior of MaterialsXi’an Jiaotong UniversityXi’an 710049China Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCA 94720United States Department of Materials Science&Engineering University of CaliforniaBerkeleyCA 94720United States Beijing Key Laboratory of Advanced High Temperature Materials Central Iron and Steel Research InstituteBeijing 100081China

Single-phase face-centered cubic(fcc)high/medium-entropy alloys(H/MEAs)exhibit a much higher tendency to form nanoscale deformation twins than conventional fcc metals with similar low stacking fault energies(SFEs).This extraordinary propensity for nanotwin formation in H/MEAs cannot therefore be ex-plained by their low SFEs ***,using in situ compression tests of CrCoNi in comparison with Ag nanopillars inside a transmission electron microscope,we found that in the CrCoNi MEA,a high density of nanoscale twins continuously formed with an average thickness of 4.6 *** contrast,for similar experiments on Ag with almost identical SFE,following the nucleation of a few twins,they could further thicken to above one hundred nanometers by twin boundary *** dynamics calculations indicated that in the highly-concentrated CrCoNi solid solution,the magnitude of the energy barriers for nucleating a stacking fault as a twin precursor in the pristine lattice and for the thickening of an existing twin both span a wide range and largely overlap with each ***,twin thickening through successive addition of atomic layers is prone to discontinuation,giving way to the nucleation of new twins at other sites where a lower energy barrier is encountered for partial-dislocation mediated fault formation.

关键词： High-entropy alloys CrCoNi Nanotwins Twin nucleation

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Characterization of 940 nm PCSEL Under Nanosecond Pulsed Operation with Peak Power of Up to 3.12 W 16

Characterization of 940 nm PCSEL Under Nanosecond Pulsed Ope...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Lim, Jun-Yuan Liu, Te-Hua Hong, Yu-Heng Kuo, Hao-Chung Wu, Chao-Hsin Graduate School of Advanced Technology National Taiwan University Taipei106319 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei106319 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taipei106319 Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei106319 Taiwan

This paper examines 940 nm PCSELs for pulsed operation, demonstrating watt-level output at nanosecond pulse widths. The findings underscore the potential of PCSELs for high-performance pulse operations. oels codes: (1... 详细信息

ISBN: (纸本)9798350372076

关键词： Semiconductor lasers

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