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Liquid crystal-based optical aptasensor for the sensitive and selective detection of Gram-negative bacteria

science China Chemistry 2022年第10期65卷 2023-2030页

作者： Mengjun Liu Minmin Zhang Jiamei Chen Ruizhi Yang Zhaofan Huang Zhenping Liu Na Li Lingling Shui Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices School of Information and Optoelectronic Science and EngineeringSouth China Normal UniversityGuangzhou 510006China Joint Laboratory of Optofluidic Technology and Systems National Center for International Research on Green OptoelectronicsSouth China Academy of Advanced OptoelectronicsSouth China Normal UniversityGuangzhou 510006China

The sensitive and differential detection of Gram-negative bacteria is essential in food processing,environmental monitoring,and the daily chemical ***,we propose and validate a liquid crystal(LC)-based aptasensor for the ultrasensitive detection of Escherichia coli(***),a model of Gram-negative *** nematic liquid crystal of 4-cyano-4’-pentylbiphenyl(5 CB) molecules can be orderly or disorderly arranged at the LC-aqueous interface via different stimuli,causing changes in optical texture due to *** schlieren texture is observed when a mixture solution of aptamer and hexadecyl trimethyl ammonium bromide(CTAB) is dripped onto the segmented LC films on a copper *** specific binding of aptamers with target bacteria biomarkers liberates the CTAB molecules,which then self-assemble at the LC-aqueous interface to induce the vertical alignment of *** optical transition from bright to dark is therefore achieved via the LC molecular orientation and serves as an *** the prominent affinity and specificity of the aptamer,the established sensitive and selective *** assay shows an ultralow detection limit of 27 cfu/*** prepared aptasensor can also be applied for the sensitive and selective determination of *** in fruit juice,soft drink,and cosmetic products,and shows great promise for the on-site detection of Gram-negative bacteria with high sensitivity and specificity for environmental monitoring,food safety assessment,and household chemical inspection.

关键词： liquid crystal molecular arrangement optical sensor aptamer Gram-negative bacteria

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Question Answering-based Socio-economic Indicator Extraction 7

Question Answering-based Socio-economic Indicator Extraction

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7th International Conference on Big Data and information Analytics, BigDIA 2021

作者： Xu, Chi Pan, Yan Guo, Lixiang Zhang, Xin Wang, Yinsen National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9781665424660

Socio-economic indicators are important statistical data used to monitor and evaluate the development of economy and society. They are of great value to policy makers of government, organizations and enterprises. Previous methods to measure socio-economic conditions are through data collecting or large-scale surveys by manpower. Over recent years, with the quick development of Internet, a huge amount of online text becomes available and contains clues of socio-economic indicators. As a result, rule-based methods and traditional machine learning methods are emerging to extract indicators more effectively but they require too much prior domain knowledge and feature engineering. Recently, deep learning approaches are dominating in Natural Language Processing (NLP) and formulating NLP tasks as machine reading comprehension (MRC) problems has been proven to be effective. Therefore, we propose a RoBERTa-based model that treats indicator extraction as a question answering (QA) task. By answering questions customized for indicator elements which are the components of the indicator, it transforms extracting indicator elements to identifying and extracting answer spans from the given contexts. Experiments show that it outperforms other deep learning baselines. © 2021 IEEE.

关键词： Extraction

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Design, fabrication, and characterization of hemispherical glass optics for MEMS mirror packaging applications

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Optics Express 2025年第6期33卷 13343-13357页

作者： Xie, Ke Su, Yongquan Huang, Hao Liu, Yichen Wang, Yanlong Weng, Zhichao Chen, Hao Wang, Lihao Wu, Zhenyu School of Microelectronics Shanghai University Shanghai200444 China Shanghai Industrial µTechnology Research Institute Shanghai201800 China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China School of Intergrated Circuit Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China MExpert Technologies Co. Ltd Shanghai201899 China

This paper presents an improved chemical foaming process (CFP) for fabricating uniform hemispherical glass arrays on 8-inch glass wafers for MEMS mirror optical packaging. Firstly, analytical and simulated models are constructed to predict the parameters of hemispherical glass. The fabrication processes of hemispherical glasses and stable foaming agents are also detailed. Then, we found that temperature distribution during cooling was the key factor in glass height using statistical analysis. By fixing the mass of the sheet and using a copper board to heat it together, the height uniformity is improved from 20.2% to 16.6%. A method to further improve height uniformity is proposed through simulation and experiment. Finally, the fabricated hemispherical glasses are tested. The thickness difference is less than 2% between CT (computed tomography) and simulation. The outer roughness is 0.622 nm. The compressive strength is larger than 200 MPa, demonstrating that the glass can package the MEMS mirror with a high vacuum level inside and defend it from harsh environments. These glasses can eliminate the light spot and ghost image problems of the flat glass in MEMS mirror encapsulation, proving the great advantages of hemispherical glass. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computerized tomography

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Enhancing Cardiovascular Disease Analytics with Demographic and Behavioral Risk Features through Unsupervised and Ensemble Learning Techniques 12

Enhancing Cardiovascular Disease Analytics with Demographic ...

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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Sem, Li Xiang Chua, Hui Na Jasser, Muhammed Basheer Wong, Richard T.K. Aldharhani, Ghassan Saleh Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Bandar Sunway Selangor Darul Ehsan47500 Malaysia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia

ISBN: (纸本)9798350391398

Cardiovascular diseases (CVD) are a leading health concern, projected to affect 23.6 million people by 2030. Despite extensive research, challenges remain in conventional diagnostic accuracy and limited healthcare access in low-income communities. Current computing methods for detecting CVD rely on costly medical tests. Subsequently, prior studies have documented many inconsistent insights on the development of CVD due to variations in study design and demographics. This research addresses these gaps by studying the potential of incorporating demographics and behavioral risk factors for CVD detection and experimenting with various modeling and sampling techniques to address imbalanced target distributions and improve detection accuracy. The resampled BRFSS dataset was subjected to regression analysis to capture demographic trends in CVD development, with two cross-referenced datasets for comparison. Association rule mining identified key risk factors linked to CVD. Classifiers, including XGBoost, Random Forest, SVM, Multilayer Perceptron, and Logistic Regression, were used to establish baseline performance, and these classifiers were integrated into CatBoost as a meta-classifier using a stacking ensemble approach. Our findings indicate that demographic factors significantly influence CVD development, but effects vary by region, culture, and lifestyle. Key risk factors such as diabetes, smoking, and stroke were identified as significant contributors to CVD. Our experiment results show that leveraging full features set with fine-tuned ensemble stacking techniques increases model performance and achieves an accuracy of 96%. Our research contributes valuable insights into the consistency of demographic effects and identifies key risk factors for CVD. The proposed ensemble model demonstrates exceptional performance, highlighting its potential as a cost-effective tool for early cardiac detection and improved healthcare accessibility. © 2024 IEEE.

关键词： Contrastive Learning

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Enhanced Hybrid Approach for Real-Time Emotion Recognition Using Canny Edge Detection and Deep Learning on Embedded systems 3

Enhanced Hybrid Approach for Real-Time Emotion Recognition U...

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3rd International Conference on Embedded systems and Artificial Intelligence, ESAI 2024

作者： McHichou, Ismail Ouchker, Rabab Tahiri, Mohamed Amine Amakdouf, Hicham Jamil, Mohammed Ouazzani Qjidaa, Hassan Sidi Mohamed Ben Abdellah-Fez University Laboratory of Electronic Signals and Systems of Information Dhar El Mahrez Faculty of Science Fez Morocco Sidi Mohamed Ben Abdellah-Fez University Engineering Systems and Applications Laboratory National School of Applied Sciences Fez Morocco Fez Morocco

ISBN: (纸本)9798331522056

This paper presents an innovative approach to real-Time emotion recognition, integrating Canny's edge detection and deep learning on an embedded system, namely the Raspberry Pi 3 board. Our method combines the edge detection capabilities of the Canny algorithm with the robust feature extraction and analytical strengths of advanced deep learning techniques. The pre-processing phase involves applying Canny's edge detection to highlight facial contours and important features, which are subsequently processed by a convolutional neural network (CNN) to determine emotional states. This hybrid approach improves the accuracy of emotion recognition, enabling real-Time application on resource-constrained devices. Extensive experiments on reference datasets demonstrate the effectiveness of our method in real time. This work opens up new perspectives for developing portable and efficient emotion recognition systems for applications in healthcare, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an710049 China Air Force Engineering University Air Traffic Control and Navigation College Xi’an710051 China Air Force Engineering University Information and Navigation College Xi’an710043 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Xidian University School of Computer Science and Technology Xi’an710126 China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Speech-Text Based Multi-Modal Training with Bidirectional Attention for Improved Speech Recognition 48

Speech-Text Based Multi-Modal Training with Bidirectional At...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Yuhang Xu, Haihua Huang, Hao Chng, Eng Siong Li, Sheng Bytedance China Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang Provincial Key Laboratory of Multi-Lingual Information Technology Urumqi China Nanyang Technological University School of Computer Science and Engineering Singapore Kyoto Japan

ISBN: (纸本)9781728163277

To let the state-of-the-art end-to-end ASR model enjoy data efficiency, as well as much more unpaired text data by multi-modal training, one needs to address two problems: 1) the synchronicity of feature sampling rates between speech and language (aka text data);2) the homogeneity of the learned representations from two encoders. In this paper we propose to employ a novel bidirectional attention mechanism (BiAM) to jointly learn both ASR encoder (bottom layers) and text encoder with a multi-modal learning method. The BiAM is to facilitate feature sampling rate exchange, realizing the quality of the transformed features for the one kind to be measured in another space, with diversified objective functions. As a result, the speech representations are enriched with more linguistic information, while the representations generated by the text encoder are more similar to corresponding speech ones, and therefore the shared ASR models are more amenable for unpaired text data pretraining. To validate the efficacy of the proposed method, we perform two categories of experiments with or without extra unpaired text data. Experimental results on Librispeech corpus show it can achieve up to 6.15% word error rate reduction (WERR) with only paired data learning, while 9.23% WERR when more unpaired text data is employed1. © 2023 IEEE.

关键词： Speech recognition

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A Frequency-Scanning Stochastic Radiation Source Optimized Design with Q-Factor Characterization Method 16

A Frequency-Scanning Stochastic Radiation Source Optimized D...

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16th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies, UCMMT 2023

作者： Zhang, Shi Lu Guo, Yuan Yue Xue, Fei Fei University of Science and Technology of China Key Laboratory of Electromagnetic Space Information of the Chinese Academy of Sciences Department of Electronic Engineering and Information Science P. O. Box 4 Hefei230027 China

ISBN: (纸本)9798350339406

Frequency-scanning stochastic radiation sources can generate two-dimensional temporal-spatial stochastic radiation fields by utilizing the coupling differences of its units at different frequencies. In microwave staring correlated imaging systems, two-dimensional temporal-spatial stochastic radiation fields with good randomness are required. Therefore, this paper proposes a method for characterizing the quality Q-factor of frequency-scanning stochastic radiating sources. This method can be used as an evaluation standard for the frequency dimension randomness of the two-dimensional stochastic radiation field generated by frequency-scanning stochastic radiating sources, and measures their ability to generate low-correlation stochastic radiation fields. Furthermore, we verify the effectiveness of the proposed method through simulations of three different metamaterial aperture antennas. © 2023 IEEE.

关键词： Metamaterials

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Effects of surface roughness on fatigue behavior of additively manufactured stainless steel 316L 8

Effects of surface roughness on fatigue behavior of additive...

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2022 8th International Forum on Manufacturing technology and engineering Materials, IFEMMT 2022

作者： Zeng, Guowei Ye, Wanrong Liu, Lei Department of Engineering Mechanics Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan430081 China

Surface roughness plays an important role which affects the fatigue performance of additively manufactured metal components. Surface roughness generally contains micro-notches which form stress concentration and significantly deteriorate the fatigue life. Based on nonlinear elastoplasticity finite element (FE) analyses, the roughness model of additively manufactured stainless steel 316L (SS316L) was performed, and the cyclic behavior of surface notch root material under different fatigue stress loads was captured. The stress concentration factor on the selected critical surface position is calculated and the notch root strain analysis are proved by depending on the applied load level. Finally two distinct deformation modes are found: elastic shakedown and ratcheting deformation. © Published under licence by IOP Publishing Ltd.

关键词： Surface roughness

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Capsule Endoscopy Image Deblurring Method Based on Multi-scale Recurrent Network 3

Capsule Endoscopy Image Deblurring Method Based on Multi-sca...

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2023 3rd International Conference on Computer, Remote Sensing and Aerospace, CRSA 2023

作者： Yang, Shiyu Guo, Tai Zhan, Ruoya Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations Harbin University of Science and Technology Heilongjiang Harbin150080 China School of Information Science and Engineering Guilin University and Technology Guangxi Guilin541004 China

During the process of acquiring capsule endoscope images, image motion blur may result from errors made by the operating physician. A multi-scale recurrent attention network is proposed to address the issue of motion blur in collected images. Considering the issue of over-exposure during image acquisition in capsule endoscopy, which can adversely affect network generation results, an image preprocessing module has been incorporated into the network to eliminate highlights caused by over-exposure in input images. To enhance network sampling efficiency, the encoder is equipped with a Convolutional Block Attention Module (CBAM) to extract fuzzy features more effectively. Additionally, depthwise separable convolution is employed to reduce parameter count. Since the structure of images obtained through capsule endoscopy is relatively simple and stable, we have opted for the mean square error as our loss function due to its heightened sensitivity to data errors. The experimental results demonstrate that the proposed network achieves a Peak Signal-to-Noise Ratio (PSNR) of 31.096 and a Structural Similarity Index Measure (SSIM) of 0.925, indicating a significant improvement over DeblurGAN-v2 and Pix2pix. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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