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Air-liquid microfluidics-integrated surface-enhanced Raman spectroscopy for selective molecular adsorption and detection to achieve bacterial discrimination

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Biosensors and Bioelectronics 2025年 285卷 117576页

作者： Ku, Chi-Yao Chiang, Yu-Wei Hsu, Huai-Yuan Cheng, Ho-Wen Chen, Ko-Lun Han, Yin-Yi Wang, Juen-Kai Wang, Yuh-Lin Huang, Nien-Tsu Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Molecular Science and Technology Taiwan International Graduate Program Academia Sinica Taipei Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei Taiwan Department of Anesthesiology National Taiwan University Hospital Taipei100229 Taiwan Department of Trauma National Taiwan University Hospital Taipei100229 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei Taiwan Center for Atomic Initiative for New Materials National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Bacterial discrimination is crucial for accurate microbiological diagnosis and timely antibiotic treatment. Surface-enhanced Raman spectroscopy (SERS) is an ideal technique due to its non-invasive, label-free molecular sensing capabilities. By analyzing bacterial supernatants, containing various purine derivatives, SERS can differentiate bacterial species based on their unique spectral distributions. However, the same bacterial species with different antibiotic resistance may secrete similar purine derivatives, differing only slightly in composition. Furthermore, each purine derivative may have a different molecular affinity to the SERS substrate, making it difficult to distinguish the exact molecular ratio. To improve SERS-based bacterial discrimination, we propose an air-liquid microfluidics-integrated SERS system (ALM-SERS) that selectively adsorbs and detects bacterial secretions. By taking features of precise microdroplet manipulation and contact area from microfluidics and features of selective molecular adsorption and fingerprints characterization from the SERS technique, we successfully perform a "sequential molecular adsorption" strategy to address the signal interference of complicated molecular mixtures in existing SERS methods. As a proof of concept, we first evaluate the molecular affinity of purine derivatives and then demonstrate the competitive analyte adsorption using adenine/cytosine and hypoxanthine/uracil sample mixtures. Finally, we tested six bacterial supernatants, including two Gram types and four strains with identical taxonomy but differing antibiotic resistance. Another six clinically isolated bacterial samples with different antibiotic resistance were also applied. Our results showed that a successful bacterial discrimination between similar species was not possible using a single SERS spectrum. In summary, the ALM-SERS system offers a powerful approach for bacterial discrimination, even when spectral differences are subtle. Beyond microb

关键词： Raman spectroscopy

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Topology Rewiring Strategies to construct robust scale-free medical Internet of Things Networks

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Simulation Modelling Practice and Theory 2025年 142卷

作者： Khan, Muhammad Awais Javaid, Nadeem Alrajeh, Nabil Bouk, Safdar Hussain Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan ComSens Lab International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Old Dominion University 5115 Hampton Blvd NorfolkVA23529 United States

The Internet of Things (IoT) network topologies are now most commonly impacted by cyberattacks. The scale-free network topologies have demonstrated great robustness against random attacks by preserving the connectedness of the nodes. The scale-free network topologies’ susceptibility to malicious attacks, however, is a significant worry. It is due to the significance of the scale-free networks in different fields of life like medical, transportation, education, agriculture, etc. Also, high-degree node removal diminishes the network's resiliency and compromises the connection of the majority of nodes. In this study, we offer several rewiring techniques for building scale-free, reliable Medical Internet of Things (MIoT) networks that can withstand malicious attacks. Initially, the scale-free MIoT network's performance optimization is ensured using a heuristic algorithm known as the Great Deluge Algorithm (GDA). Then, four rewiring strategies are formulated. The initial approach is degree dissortativity, which rewires the network if all nodes have high maximum connectivity to other neighbors with a similar degree. For the second strategy, we introduced a degree difference operation based on degree dissortativity to ensure that the edges that are connected possess low dissortativity and degree difference. Meanwhile, the remaining two strategies take into account the node load bound and enhanced GDA to increase network robustness. The performance of the proposed rewiring strategies is validated through simulations. The results prove that the proposed strategies increase network robustness by up to 25% compared to Hill Climbing (HC) and Simulated Annealing (SA). Additionally, the strategies show great success in improving network connectivity and graph density. However, their computational time is higher compared to HC and SA. © 2025 Elsevier B.V.

关键词： Network topology

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A Benchmark and Frequency Compression Method for Infrared Few-Shot Object Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Ruiheng Yang, Biwen Xu, Lixin Huang, Yan Xu, Xiaofeng Zhang, Qi Jiang, Zhizhuo Liu, Yu Beijing Institute of Technology State Key Laboratory of Electromechanical Dynamic Control Beijing100081 China University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia Anhui Polytechnic University School of Computer and Information Wuhan241000 China Technical University of Munich Data Science in Earth Observation Munich85521 Germany Tsinghua University Shenzhen International Graduate School Shenzhen518055 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Infrared few-shot object detection (IFSOD) aims to detect infrared objects with limited labeled examples. Current infrared datasets, however, suffer from limited diversity in object types and classes, hindering robust evaluation of model generalization on novel classes. To systematically assess dataset quality, we propose metrics for class diversity, instance variability, and object density. By integrating three widely used infrared datasets, we construct the first dataset specifically tailored for IFSOD, increasing instance density to 4.8 (a 1.1 improvement) and expanding the number of classes to 18 (a 5-class increase) compared to the source datasets. Furthermore, frequency analysis of spatial features reveals that sparse annotations introduce spectral bias in the frequency domain. Directly transforming spatial features to the frequency domain, however, mixes background noise with object features, causing spectral leakage and impairing the learning of discriminative features for novel classes. To address these issues, we propose the frequency compression few-shot detection (FC-fsd) method, which incorporates a frequency compression (FC) module. The FC module leverages Discrete Cosine Transform (DCT) within localized windows to reduce spectral leakage and enhance feature clarity. With minimal additional computational overhead, FC-fsd significantly outperforms state-of-the-art methods, achieving nAP50 scores of 28.57 (+13.37) and 35.63 (+2.59) in 1-shot and 2-shot settings, respectively. Our dataset is published at https://***/RuihengZhang/IFSOD-dataset. © 2025 IEEE.

关键词： Zero-shot learning

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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international Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Astrophysical expedition: Transit search heuristics for fractional Hammerstein control autoregressive models

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Modern Physics Letters B 2025年第7期39卷

作者： Altaf, Faisal Chang, Ching-Lung Chaudhary, Naveed Ishtiaq Ali Khan, Taimoor Khan, Zeshan Aslam Shu, Chi-Min Raja, Muhammad Asif Zahoor Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Electrical and Computer Engineering International Islamic University Islamabad Capital Territory Islamabad 44000 Pakistan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

This study presents an astrophysics-inspired transit search optimization (TSO) algorithm based on exoplanet search divided into five phases: galaxy phase, star phase, transit phase, neighbor phase and exploitation phase for effective parameter estimation of fractional Hammerstein control autoregressive (Fr-HCAR) systems. Various physical phenomena and real processes can be modeled with Fr-HCAR systems and estimating the Fr-HCAR parameters becomes a vital task. The mean-square error (MSE)-based criterion function is developed, and efficacy of the TSO for Fr-HCAR identification is deeply analyzed for different fractional orders, disturbance levels and degrees of freedom. The TSO remained accurate, convergent, robust and stable for all variations in Fr-HCAR but the accuracy level degrades a little bit for high disturbance and increased degrees of freedom. The reliability and trustworthiness of the TSO for Fr-HCAR identification are endorsed through statistical analyses conducted on sufficient autonomous executions of the scheme. © 2025 World Scientific Publishing Company.

关键词： Astrophysics fractional calculus Hammerstein model nonlinear systems transit search optimization

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Measurement of peripheral nerve magnetostimulation thresholds of a head solenoid coil between 200 Hz and 88.1 kHz

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IEEE Journal of Translational engineering in Health and Medicine 2025年

作者： Barksdale, Alex C. Ferris, Natalie G. Mattingly, Eli Sliwiak, Monika Guerin, Bastien Wald, Lawrence L. Davids, Mathias Klein, Valerie MIT Department of Electrical Engineering and Computer Science CambridgeMA United States Martinos Center for Biomedical Imaging CharlestownMA United States Harvard University Harvard Graduate Program in Biophysics CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology BostonMA United States Harvard Medical School BostonMA United States

Objective: Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC). Methods and procedures: PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds. Results: Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies. Conclusion: Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz. Clinical impact: These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation. © 2013 IEEE.

关键词： Neuroimaging

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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engineering Andreev Bound States for Thermal Sensing in Proximity Josephson Junctions

arXiv

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arXiv 2025年

作者： Jung, Woochan Arnault, Ethan G. Huang, Bevin Park, Jinho Jang, Seong Watanabe, Kenji Taniguchi, Takashi Englund, Dirk Fong, Kin Chung Lee, Gil-Ho Physics Department Pohang University of Science and Technology Pohang37673 Korea Republic of Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Research Center for Functional Materials National Institute for Materials Science Tsukuba Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Department of Physics Northeastern University BostonMA02115 United States Quantum Materials and Sensing Institute BurlingtonMA01803 United States

The thermal response of proximity Josephson junctions (JJs) is governed by the temperature (T)- dependent occupation of Andreev bound states (ABS), making them promising candidates for sensitive thermal detection. In this study, we systematically engineer ABS to enhance the thermal sensitivity of critical current (lc) of proximity JJs, quantified as |dlc/dT/| for threshold readout scheme and |dlc/***−1| for inductive readout scheme. Using a gate-tunable graphene-based JJ platform, we explore the impact of key parameters—including channel length, transparency, carrier density, and superconducting material—on the thermal response. Our results reveal that the proximity-induced superconducting gap plays a crucial role in optimizing thermal sensitivity. Notably, we see a maximum |dlc/***−1| value of 0.6 K−1 at low temperatures with titanium-based graphene JJs. By demonstrating a systematic approach to engineering ABS in proximity JJs, this work establishes a versatile framework for optimizing thermal sensors and advancing the study of ABS-mediated transport. © 2025, CC BY.

关键词： Superconducting materials

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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