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Fabrication of Ag nanostar and PEI-based SErS substrate for sensitive and rapid detection of SO2: Application for detection of sulfite residues in beer

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Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy 2023年第1期302卷

作者： Leshe Kitaw, Sintayehu Fentahun darge, Haile dagnew Addisu, Kefyalew Thankachan, darieo Wondwosen Ahmed, Yohannis Sheng Chen, Yu Tegenu, Hailemichael Candra, Andy Wu, Tsung-Yun Gou, Yu-Xuan Tsai, Hsieh-Chih Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei 106 Taiwan Advanced Membrane Materials Center National Taiwan University of Science and Technology Taipei 106 Taiwan R&D Center for Membrane Technology Chung Yuan Christian University Taoyuan 320 Chungli Taiwan Department of Material Science and Engineering National Taiwan University of Science and Technology Taipei 106 Taiwan

Because of sulfite's potential toxicity, there is a growing concern about detecting and controlling its concentration in foods, alcoholic beverages, pharmaceuticals, and environmental samples to ensure public health. A branched polyethyleneimine-coated silver nano-star (AgNS@PEI) surface-enhanced raman scattering (SErS) substrate was synthesized in this study for use as a sensitive, simple, rapid, stable, and reproducible non-destructible sulfite detection analytical technique. The seed morphology of the nano-star was created by using hydroxylamine (NH2OH) solution as a primary reducing agent, followed by a slow secondary reduction by trisodium citrate dihydrate (HOC(COONa)(CH2COONa)2 2H2O), resulting in the complete growth of the silver nano-star. For extra stability and selective absorption of sulfur dioxide from the headspace extraction of SO2 from sulfites, the nano-stars were thin coated with branched polyethyleneimine (b-PEI). The results showed that the thin-coated plasmonic substrates selectively absorb sulfur dioxide molecules, allowing sulfites in beer samples to be detected with a detection limit of 0.48 mg/L. Furthermore, the PEI-coated silver nano-star demonstrated increased stability and reproducibility, allowing for longer use of the substrate. recovery experiments with recovery rates ranging from 95 to 112% and relative standard deviations ranging from 1.55 to 8.1% demonstrated that headspace extraction, selective SO2 absorption by the synthesized substrate, and subsequent SErS detections were reliable and valid for practical applications. Finally, this study developed an SO2-sensitive, selective, and robust Si@AgNS@PEI substrate for effective SErS detection and monitoring of sulfite levels in real-world environmental samples. © 2023

关键词： Surface scattering

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Improving drone Classification with Audio-derived Visual Features: A Vision Model Comparison

Improving Drone Classification with Audio-Derived Visual Fea...

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IEEE International Conference on robotic Computing (IrC)

作者： Juann Kim Qian Zhang Eric T Matson Mia Y. Wang Artificial Intelligence R&D Center Korea Military Academy Seoul South Korea Department of Engineering College of Charleston Charleston USA Computer and Information Technology Purdue University West Lafayette USA Department of Computer Science College of Charleston Charleston USA

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The rapid development of unmanned aerial vehicles (UAVs) has intensified the need for advanced classification techniques. This paper presents a novel approach that leverages audio data transformed into visual representations through Mel-Frequency Cepstral Coefficients (MFCCs) for drone classification. Our dataset consists of 28 drone types, each with 100 five-second audio recordings, from which 30 MFCCs are extracted per file. We investigate the effectiveness of this dataset by applying various vision models to the MFCC visualizations. Our results reveal that EfficientNet achieved the highest accuracy at 96.31%, followed by resNet50 at 94.22%, and Vision Transformer at 73.69%. These findings highlight the potential of using audio-derived visual features for robust drone classification and demonstrate the varying performance of different vision models. This study provides a comprehensive examination of the methodology, experimental setup, and results, offering valuable insights into future research directions for enhancing classification accuracy with transformed audio data.

关键词： Visualization Computer vision Accuracy data visualization Transformers data models robustness Mel frequency cepstral coefficient drones residual neural networks

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Federated Graph Neural Networks for dynamic Computation Offloading in Vehicular Networks

Federated Graph Neural Networks for Dynamic Computation Offl...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yanrong Xu Yunlong Lu Yueyue dai Chen Sun Jiajia Liu Wenqi Zhang Hao Wu School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Collaborative Innovation Center of Railway Traffic Safety Beijing China Research Center of 6G Mobile Communications School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wireless Network Research Department (WNRD) Sony R&D Center Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the increasing number of Internet of Things devices and sensors in vehicular network, a huge amount of data is generated. Vehicle Edge Computing (VEC) utilises the computation resources at the edge of the network and can efficiently process this big data through computational offloading techniques. However, due to the neglect of communication network relationships among vehicles, current Vehicle-to-Vehicle (V2V) computation offloading schemes encounter challenges such as high communication latency, substantial communication overhead, and the wastage of computation resources. To address these challenges, we design a computation offloading mechanism based on Federated Graph Neural Network (GNN) for vehicular networks, that is, vehicular FedGNN (V-FedGNN). Firstly, our modeling approach considers features including vehicle speed, location, available resources, and wireless network, which are embedded in the graph structure. Secondly, we design weighted vehicular communication network topology and propose weighted total delay optimization problem. Finally, this paper proposes a prediction model based on Federated Learning (FL) and GNN to minimize the weighted computation offloading delays among vehicle nodes. Experimental results demonstrate that our proposed scheme achieves high offloading prediction accuracy, with an average value of 98.1% and achieves low offloading latency, correspondingly.

关键词： Network topology Federated learning Computational modeling dynamic scheduling Graph neural networks Topology delays Computational efficiency Communication networks Vehicle dynamics

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A deep Learning Framework for Object detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts

A Deep Learning Framework for Object Detection and Tracking:...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object detection and Tracking (OdT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based OdT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training Computer vision Accuracy Annotations Scalability Object detection robustness Convolutional neural networks Object tracking Logistics

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257 W/cm2 power-density laser-driven white-light source for high-speed inspection in semiconductor packaging

SSRN

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SSrN 2024年

作者： Won, Seungjai Kim, Taewon Ji, Wonsoo Jung, Sejin Kim, Seung-Woo Kim, Young-Jin Department of Mechanical Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Daejeon Korea Republic of Solution R&D Center Envision Co. Ltd 98 Gasan digital 2-ro Geumcheon-gu Seoul Korea Republic of

The demand for high-power-density white-light sources has been increased for high-speed inspection of semiconductor and display products. However, focusing the conventional halogen lamps or light-emitting diodes (LEds) remains challenging due to the large emission area at the light sources, which is governed by the étendue limit. Here, we report a high-power-density light illumination of the laser-driven white-light with a small focal spot size of 25 μm. By using a laser diode (Ld) instead of an LEd, the strong optical power can be concentrated on to the phosphor in a small spot size near the diffraction limit, enabling the emission of low-étendue luminescence. The blue Ld pump (λ=445 nm) and broad yellow luminescence (λ=480-750 nm) were focused together on to the sample with a small spot diameter of 25 μm (full width at half maximum, FWHM), producing a record-high power-density of 257 W/cm2. The speckle constants of the blue Ld, yellow luminescent light, and combined white-light after passing through the diffuser plate were carefully evaluated to 0.28, 0.02, and 0.14, respectively. Furthermore, the power scalability was demonstrated by spatially combining the independently generated white-light sources, which did not show any noticeable coherent speckle increase. This tightly-focusable laser-driven white-light will find its strong potential in next-generation high-speed inspection of advanced semiconductor packaging. © 2024, The Authors. All rights reserved.

关键词： Light emitting diodes

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Interplay of Probabilistically Shaped Multilevel Coded Modulation and Fiber Nonlinearity Compensation 27

Interplay of Probabilistically Shaped Multilevel Coded Modul...

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Yoshida, Tsuyoshi Inoue, Takashi Binkai, Masashi Matsuda, Keisuke Koshikawa, Shota Konishi, Yoshiaki Suzuki, Naoki Information Technology R&d Center Mitsubishi Electric Corporation 5-1-1 Ofuna Kamakura Kanagawa247-8501 Japan School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka565-0871 Japan 1-1-1 Umezono Tsukuba Ibaraki305-8568 Japan

Transmission performance and decoding power consumption are investigated in coded modulation (CM) schemes. Probabilistically-shaped multilevel CM shows 21 or 9 ×lower power consumption than uniform bit-interleave... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Optical fiber communication

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Spectrum-effect relationship of immunologic activity of Ganoderma lucidum by UPLC-MS/MS and component knock-out method

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Food Science and Human Wellness 2021年第3期10卷 278-288页

作者： Changqin Li Yiping Cui Jie Lu Lijun Meng Changyang Ma Zhenhua Liu Yan Zhang Wenyi Kang National R&D Center for Edible Fungus Processing Technology Henan UniversityKaifeng 475004China Functional Food Engineering Technology Research Center Henan ProvinceKaifeng475004China Joint International Research Laboratory of Food&Medicine Resource Function Henan ProvinceHenan UniversityKaifeng 475004China Hebei Food Inspection and Research Institute Shijiazhuang 050091China

This study was designed to elucidate the immunoregulation of Ganoderma *** fingerprint and spectrum-effect relationship of *** were established to predict the active compounds and BP neural network model was established to predict the *** the target compounds were identified by high resolution mass *** results indicated that there are both enhanced immunity and immunosuppressive components in *** neural network was trained with the common peak area and immune effi cacy index of *** fi ngerprint as samples,and a combined evaluation system of *** fi ngerprint effi cacy was *** correlation coeffi cient r of BP network model was 0.98643,and the error of pharmacodynamic prediction results was in the ideal *** compounds were identifi ed by high resolution mass *** compounds related to immune activity in *** were determined in this study.

关键词： Ganoderma lucidum rAW 264.7 cell Spectrum-effect relationships Component knock-out BP neural network

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FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features

arXiv

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arXiv 2025年

作者： Hao, Jiacheng Liu, Yiqing Zeng, Siqi He, Yonghong Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate School Shenzhen518055 China Medical Optical Technology R&D Center Research Institute of Tsinghua Pearl River Delta Guangzhou510700 China School of Medicine Nankai University Tianjin300071 China

Breast cancer is one of the most common cancers among women globally, with early diagnosis and precise classification being crucial. With the advancement of deep learning and computer vision, the automatic classification of breast tissue pathological images has emerged as a research focus. Existing methods typically rely on singular cell or tissue features and lack design considerations for morphological characteristics of challenging-to-classify categories, resulting in suboptimal classification performance. To address these problems, we proposes a novel breast cancer tissue classification model that Fused features of Edges, Cells, and Tissues (FECT), employing the resMTUNet and an attention-based aggregator to extract and aggregate these features. Extensive testing on the BrACS dataset demonstrates that our model surpasses current advanced methods in terms of classification accuracy and F1 scores. Moreover, due to its feature fusion that aligns with the diagnostic approach of pathologists, our model exhibits interpretability and holds promise for significant roles in future clinical applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Lung cancer

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Augmenting the Sense of Social Presence in Online Video Games Through the Sharing of Biosignals

SSRN

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SSrN 2023年

作者： Hassan, Modar Kennard, Maxwell Yoshitake, Seiji Ishac, Karlos Takahashi, Shion Kim, SunKyoung Matsui, Takashi Hirokawa, Masakazu Faculty of Systems Information and Engineering University of Tsukuba Ibaraki Japan School of Integrative and Global Majors University of Tsukuba Ibaraki Japan Graduate School of Comprehensive Human Sciences University of Tsukuba Ibaraki Japan Faculty of Engineering and Information Technology University of Technology SydneyNSW Australia Institute of Library Information and Media Science University of Tsukuba Ibaraki Japan Institute of Health and Sport Sciences R&D Center for Sport Innovation R&D Center for Lifestyle Innovation University of Tsukuba Ibaraki Japan

This study outlines the development of a platform that enables real-time biosignal sharing between gaming partners regardless of the game of choice. The platform's ability to augment social presence in an online environment was tested in a pilot study that included 20 participants. The biosignal was collected with the use of a wrist-worn heart rate monitor. Significant improvements were found in the sense of social presence when providing the players with video information or bio-information about their gaming partner. When both the bio and video information were provided simultaneously, the sense of social presence was closest to the baseline condition in which the players were co-located in the same physical environment. Similar trends were not observed in co-presence in the participants of this study. However, grouping the participants' scores by emotional arousal level showed a similar trend in the high-arousal group. © 2023, The Authors. All rights reserved.

关键词： Human computer interaction

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Customizable design ofmultiple-biomolecule delivery platform for enhanced osteogenic responses via‘tailored assembly system’

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Bio-design and Manufacturing 2022年第3期5卷 451-464页

作者： Hyun Lee Min-Kyu Lee Ginam Han Hyoun-Ee Kim Juha Song Yuhyeon Na Chang-Bun Yoon SeKwon Oh Tae-Sik Jang Hyun-do Jung Department of Biomedical-Chemical Engineering Catholic University of KoreaBucheon 14662Republic of Korea Department of Biotechnology The Catholic University of KoreaBucheon 14662Republic of Korea Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Biomedical Implant Convergence Research Center Advanced Institutes of Convergence TechnologySuwon 443-270Republic of Korea School of Chemical and Biomedical Engineering Nanyang Technological University70 Nanyang DriveSingapore 637457Singapore Department of Advanced Materials Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do 15073Republic of Korea Surface R&D Group Korea Institute of Industrial TechnologyIncheon 21999Republic of Korea Department of Materials Science and Engineering Chosun UniversityGwangju 61452Republic of Korea

Porous titanium(Ti)scaffolds have been extensively utilized as bone substitute scaffolds due to their superior biocompatibility and excellent mechanical ***,naturally formed TiO2 on the surface limits fast *** biomolecules have been widely utilized to overcome this issue;however,homogeneous porous Ti scaffolds could not simultaneously deliver multiple biomolecules that have different release *** this study,functionally graded porous Ti scaffolds(FGPTs)with dense inner and porous outer parts were fabricated using a two-body combination and densification *** with growth factor(BMP-2)and antibiotics(TCH)exhibited suitable mechanical properties as bone substituting material and presented good structural *** release of BMP-2 was considerably prolonged,whereas the release of TCH was comparable to that of homogenous porous titanium scaffolds(control group).The osteogenic differentiation obtained using FGPTs was maintained due to the prolonged release of *** antimicrobial properties of these scaffolds were verified using *** in terms of prior release *** addition,various candidates for graded porous Ti scaffolds with altered pore characteristics were presented.

关键词： Functionally graded porous titanium scaffold Tailored assembly system Mechanical tunability Multiplebiomolecule delivery Bone tissue engineering

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