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Naked-eye observation of water-forming reaction on palladium etalon:transduction of gas-matter reaction into light-matter interaction

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PhotoniX 2023年第1期4卷 349-363页

作者： Jongsu Lee Eui-Sang Yu Taehyun Kim In Soo Kim Seok Chung Seung Jae Kwak Won Bo Lee Yusin Pak Yong-Sang Ryu Sensor System Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Electrical and Computer Engineering Seoul National UniversitySeoul 08826Republic of Korea Smart Materials Research Section Electronics and Telecommunications Research InstituteDaejeon 34129Republic of Korea Department of Micro/Nano Systems Korea University02841 SeoulRepublic of Korea Nanophotonics Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea School of Mechanical Engineering Korea UniversitySeoul 02841Republic of Korea School of Chemical and Biological Engineering Seoul National UniversitySeoul 08826Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02481Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02481Republic of Korea

Palladium is the most prominent material in both scientific and industrial research on gas storage,purification,detection,and catalysis due to its unique properties as a catalyst and hydrogen *** the dynamic optical phenomena of palladium reacting with hydrogen,transduction of the gas-matter reaction into light-matter interaction is attempted to visualize the dynamic surface chemistry and reaction *** simple geometry of the metal-dielectric-metal structure,Fabry-Perot etalon,is employed for a colorimetric reactor,to display the catalytic reaction of the exposed gas via water-film/bubble formation at the dielectric/palladium *** adsorption/desorption behavior and catalytic reaction of hydrogen and oxygen on the palladium surface display highly repeatable and dramatic color changes based on two distinct water formation trends:the foggy effect by water bubbles and the whiteout effect by water film *** and experiments demonstrate the robustness of the proposed Fabry-Perot etalon as an excellent platform for monitoring the opto-physical phenomena driven by heterogeneous catalysis.

关键词： Palladium Hydrogen Oxygen Fabry-Perot resonators Colorimetric sensors Water-forming reaction

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Protocol for the fabrication of self-standing (nano)cellulose-based 3D scaffolds for tissue engineering

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STAR Protocols 2025年第1期6卷 103583页

作者： Mohan, Tamilselvan Bračič, Matej Bračič, Doris Lackner, Florian Nagaraj, Chandran Štiglic, Andreja Dobaj Kargl, Rupert Kleinschek, Karin Stana Graz University of Technology Institute for Chemistry and Technology of Biobased System (IBioSys) Stremayrgasse 9 Graz 8010 Austria University of Maribor Faculty of Mechanical Engineering Laboratory for Characterization and Processing of Polymers Smetanova Ulica 17 Maribor 2000 Slovenia Department of Internal Medicine Division of Pulmonology Medical University of Graz Graz Austria Institute of Automation Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Members of the European Polysaccharide Network of Excellence (EPNOE) Austria

Three-dimensional (3D) and porous scaffolds made from nanocellulosic materials hold significant potential in tissue engineering (TE). Here, we present a protocol for fabricating self-standing (nano)cellulose-based 3D scaffolds designed for in vitro testing of cells from skin and cartilage tissues. We describe steps for preparation of nanocellulose ink, scaffold formation using 3D printing, and freeze-drying. We then detail post-processing procedures to enhance mechanical properties, stability, and biocompatibility. This protocol offers researchers a framework for developing versatile and sustainable biomaterials for regenerative medicine. For complete details on the use and execution of this protocol, please refer to Mohan et al.¹ and Štiglic et al.² © 2025 The Authors

关键词： Biotechnology and bioengineering Chemistry Material sciences Tissue engineering

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Continuous Prediction of Leg Kinematics during Walking using Inertial Sensors, Smart Glasses, and Embedded Computing

Continuous Prediction of Leg Kinematics during Walking using...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Oleksii Tsepa Roman Burakov Brokoslaw Laschowski Alex Mihailidis Department of Computer Science AGE-WELL Networks of Centres of Excellence program University of Toronto and the Vector Institute for Artificial Intelligence Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto ON Canada Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mechanical and Industrial Engineering University of Toronto Toronto ON Canada Toronto Rehabilitation Institute University Health Network Toronto ON Canada Institute of Biomedical Engineering University of Toronto Toronto ON Canada

Unlike traditional hierarchical controllers for robotic leg prostheses and exoskeletons, continuous systems could allow persons with mobility impairments to walk more naturally in real-world environments without requiring high-level switching between locomotion modes. To support these next-generation controllers, we developed a new system called KIFNet (Kinematics and Image Fusing Network) that uses lightweight and efficient deep learning models to continuously predict the leg kinematics during walking. We tested different sensor fusion methods to combine kinematics data from inertial sensors and computer vision data from smart glasses and found that adaptive instance normalization achieved the lowest RMSE predictions for knee and ankle joint kinematics. We also deployed our model on an embedded device. Without inference optimization, our model was 20 times faster than the previous state-of-the-art and achieved 20% higher prediction accuracies, and during some locomotor activities like stair descent, decreased RMSE up to 300%. With inference optimization, our best model achieved 125 FPS on an NVIDIA Jetson Nano. These results demonstrate the potential to build fast and accurate deep learning models for continuous prediction of leg kinematics during walking based on sensor fusion and embedded computing, therein providing a foundation for real-time continuous controllers for robotic leg prostheses and exoskeletons.

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

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MULTI-SCALE FEEDBACK FEATURE REFINEMENT U-NET FOR MEDICAL IMAGE SEGMENTATION

MULTI-SCALE FEEDBACK FEATURE REFINEMENT U-NET FOR MEDICAL IM...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Qin, Xiaofei Xu, Minmin Zheng, Chaoyang He, Changxiang Zhang, Xuedian School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai China Shanghai Key Laboratory of Contemporary Optics System Key Laboratory of Biomedical Optical Technology and Devices Ministry of Education Shanghai China College of Science University of Shanghai for Science and Technology Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781665438643

Designing a novel and efficient architecture is the thrust of medical image segmentation. In this paper, we introduce a novel network named Multi-scale Feedback Feature Refinement U-Net (MFFRU-Net) for medical image segmentation. We design a simple and effective multi-scale feedback structure. Up-sampling and 1 × 1 convolution are used to feedback the feature maps of different scales in the decoder to the encoder, so that multiple high-level and low-level features are fused to obtain more representative features. Specifically, we propose a feature refinement module (FRM) based on the dual attention mechanism in the middle layer of the network. FRM block can enhance the use of spatial and channel information of image features. We evaluate the MFFRU-Net on two datasets. Comprehensive experimental results show that the proposed method is superior to the original U-Net method and other advanced methods. © 2021 IEEE

关键词： Image segmentation

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SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

SSRN

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SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Optimization Method for the Design of Freeform Progressive Addition Lenses Based on Coincident Degree of Weight Distributions for Power and Astigmatism

SSRN

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SSRN 2022年

作者： Xiang, Huazhong Wang, Yaqiong Ding, Qihui Zhan, Xiaodie Zheng, Zexi Chen, Jiabi Wang, Cheng Zhang, Dawei Zhuang, Songlin School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology 516 Jun Gong Road Shanghai200093 China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China Engineering Research Center of Optic Instrument and System Ministry of Education University of Shanghai for Science and Technology Shanghai200093 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

We propose a method for optimizing freeform progressive addition lenses based on the coincident degree of weight distributions for power and astigmatism. The power and astigmatism for freeform PALs were evaluated according to the existing surface shape. The coincident degrees between the calculated and prescribed power and astigmatism were calculated in the multi-viewing-axis condition. Two PALs were designed, simulated, machined, and evaluated using commercial software. The proposed optimization method reduced peripheral astigmatism and improved the optical properties of the PALs. The proposed approach can effectively optimize freeform PALs and advance the design procedure of PALs in optometry. © 2022, The Authors. All rights reserved.

关键词： Optical properties

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A Multilevel Non-interference Vulnerability Analysis Method for Information Leakage Problem

A Multilevel Non-interference Vulnerability Analysis Method ...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Wenjing Zhong Jinjing Zhao Hesuan Hu National Key Laboratory of Science and Technology on Information System Security Beijing China School of Electro-Mechanical Engineering Xidian University Xi&#x0027 an Shaanxi China National Key Laboratory of Science and Technology on Information System Security BeijingChina100101 State Key Laboratory for Manufacturing Systems Engineering School of Electro-Mechanical Engineering Xidian University Xi&#x0027 an Jiaotong University Xi&#x0027 School of Computer Science and Engineering College of Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665418164

Security vulnerabilities caused by information leakage are a critical aspect of the security for information systems. Such vulnerabilities can be identified by non-interference analysis. Strong nondeterministic non-interference (SNNI) and bisimulation SNNI (BSNNI) are the typical properties of non-interference. Existing works related to non-interference analysis are mainly based on the setting with only two security levels. Such setting is coarse-grained compared to the real system. In this paper, we analyze the non-interference with multiple security levels by integer linear programming. Obviously, the non-interference with multiple security levels is more fine-grained but more complex than that with only two security levels. On the perspective of complexity, we provide the general and simplified non-interference analyses in turn for multilevel SNNI and BSNNI which are defined on the concepts of upper sets. The general analysis is derived from the definitions of multilevel SNNI and BSNNI, and solve the identification problem of multilevel SNNI and BSNNI directly. By analyzing and removing the impact of upper sets' coverability, the simplified analysis reduces the redundancy and improves the efficiency in the analysis process.

关键词： Graphics Conferences Redundancy Cyberspace Integer linear programming Data science Mathematics

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Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning

Efficient Visual Perception of Human-Robot Walking Environme...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Dmytro Kuzmenko Oleksii Tsepa Andrew Garrett Kurbis Alex Mihailidis Brokoslaw Laschowski Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada Toronto Rehabilitation Institute Toronto Canada Department of Mechanical and Industrial Engineering Robotics Institute University of Toronto Toronto Canada

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research $\text{will}$ focus on model deployment for onboard real-time inference and control of human-robot walking.

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A Global Asymptotically Stable Nonlinear Robust Bias Observer for MEMS Gyros

TechRxiv

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TechRxiv 2022年

作者： Tang, Ning Chang, Jieling Wang, Lingling Fu, Li Neusypin, Konstantin A. Selezneva, Maria S. Peng, Linping The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Faculty of Computer Science and Control Systems Bauman Moscow State Technical University Moscow105005 Russia Experimental Teaching Demonstration Center of Mechanical and Control Engineering Beihang University Beijing100191 China The School of Mathematics and System Sciences Beihang University Beijing100191 China

In practical applications, commercial-grade Micro-Electro-mechanical system (MEMS) gyros tend to exhibit dynamics sensitivities which are resistant to modeling by linear drift. To address the problem, a new nonlinear robust bias observer (NRBO) is proposed in this paper. Unlike the existing attitude and gyro bias nonlinear observers, we propose a dynamics-sensitive gyro bias decomposition method to tackle unknown nonlinear dynamic models of MEMS gyro bias. We expose the potential advantages of the proposed method: the global asymptotic stability of the NBRO and the robustness to the bias instability of MEMS gyros can be guaranteed through a rational design of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. In addition, the attitude estimation with bias correction is given in the framework of NBRO. The experiment of a cable-parallel robot demonstrate the robustness to the bias instability and the accuracy improvement of MEMS gyros. © 2022, CC BY.

关键词： Gyroscopes

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