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Laser-Induced Surface Modification to Improve the Physicochemical Properties of Tantalum for Biomedical Applications

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Journal of Materials engineering and Performance 2025年 1-15页

作者： Mahmoodi, Mahboobeh Sharifpour, Davood Khosroshahi, Mohammad E. Najari, Mahya Parsaeian, Fatemeh Iqbal, Javed Department of Biomedical Engineering Ya. C. Islamic Azad University Yazd Iran Joint Reconstruction Research Center Tehran University of Medical Sciences Tehran Iran Department of Bioengineering University of California Los Angeles USA Department of Materials Engineering Ya. C. Islamic Azad University Yazd Iran Nanobiophotonics & Biomedical Research Laboratory M.I.S. Electronics Inc Richmond Hill Canada Institute for Advanced Non-Destructive & Diagnostic Technologies (IANDIT) University of Toronto Toronto Canada Department of Electrical Engineering and Computer Sciences University of California Berkeley USA Department of Botany Bacha Khan University Charsadda Pakistan

Tantalum (Ta) and its alloys have remarkable potential for medical implants due to their biocompatibility. However, their clinical performance can be compromised by the release of toxic ions in the physiological environment and inadequate wear resistance. To overcome these limitations, this study investigated the surface modification of Ta using a pulsed neodymium-doped yttrium aluminum garnet (Nd:YAG) laser at a wavelength of 1064 nm with fluences of 3.6 and 7.77 mJ/mm2, aiming to enhance its biocompatibility, wear resistance, and corrosion resistance for biomedical applications. Surface morphology and microstructure, surface roughness, hydrophilicity, and wear resistance of the samples after irradiation were evaluated using field emission scanning electron microscope (FE-SEM) and elemental analysis (EDS), atomic force microscope (AFM), contact angle test, and the pin-on-disk method, respectively. The amount of oxygen on the Ta surface decreased with the increase in the number of laser pulses. Due to the formation of the surface melting and the molten pools, the oxygen on the surface penetrated in molten layers and decreased on the surface. Surface modification of Ta by the Nd:YAG laser increased the wettability and the wear resistance of the modified Ta compared to the unmodified Ta as a control sample. The laser-modified Ta at the fluence of 7.77 mJ/mm2 exhibited the minimum coefficient of friction and wear rate compared to the control sample. The microhardness of the laser-modified Ta increased at the higher laser fluence (143.7 HV) compared to the control sample (133 HV). Evaluation of the corrosion behavior of Ta in Hanks’ solution indicated the lowest corrosion current density and the highest corrosion resistance for the laser-modified Ta. The surface roughness of the modified Ta at the fluence of 3.6 mJ/mm2 was two times higher than that of the laser-modified Ta at the fluence of 7.77 mJ/mm2. Bone cell growth was positively affected

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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An Efficient Capsule-based Network for 2D Left Ventricle Segmentation in Echocardiography Images

An Efficient Capsule-based Network for 2D Left Ventricle Seg...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： R. Naghne A. Kazemi H. Moghaddasi M. Rahmani P. Farnia A. Ahmadian J. Alirezaie Image-Guided Surgery Group Research Center of Biomedical Technology and Robotics (RCBTR) Advanced Medical Technologies and Equipment Institute (AMTEI) Tehran University of Medical Sciences Tehran Iran Medical Physics and Biomedical Engineering Department Faculty of Medicine Tehran University of Medical Sciences (TUMS) Department of Electrical and Computer Engineering Toronto Metropolitan University Toronto Ontario Canada

The segmentation of cardiac chambers is essential for the clinical diagnosis and treatment of cardiovascular diseases. It is demonstrated that in cardiac disease, the left ventricle (LV) is extensively involved. Therefore, segmentation of the LV in echocardiographic images is critical for the precise evaluation of factors that influence cardiac function such as LV volume, ejection fraction, and LV mass. Although these measurements could be obtained by manual segmentation of the LV, it would be time-consuming and inaccurate because of the poor quality and low contrast of these images. Convolutional neural networks, commonly referred to as CNNs, have emerged as a highly favored deep learning technique for medical image segmentation. Despite their popularity, the pooling layers in CNNs ignore the spatial information and do not consider the part-whole hierarchy relationships. Furthermore, they require a large training dataset and a large number of parameters. Therefore, Capsule Networks are proposed to address the CNNs limitations. In this study, for the first time, an optimized capsule-based network for object segmentation called SegCaps is proposed to achieve accurate LV segmentation on echocardiography images applied to the CAMUS dataset. The result was compared against the standard 2D-UNet. The modified SegCaps and 2D-UNet achieved an average Dice similarity coefficient (DSC) of 84.48% and 83.28% on LV segmentation, respectively. The capabilities of the CapsNet led to an improvement of 1.44% in DSC with 92.77% fewer parameters than the U-Net. The results indicate that the proposed method leads to accurate and efficient LV *** Relevance— From a clinical point of view, our findings lead to more precise evaluations of critical cardiac parameters, including ejection fraction as well as left ventricle volume at end-diastole and end-systole.

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese Automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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Stackable, Storable, Millimeter-Scale Biobatteries Having High Instantaneous Power Outputs

Stackable, Storable, Millimeter-Scale Biobatteries Having Hi...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Maryam Rezaie Anwar Elhadad Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York (SUNY) at Binghamton New York USA Center for Research in Advanced Sensing Technologies & Environmental Sustainability SUNY Binghamton New York USA

ISBN: (纸本)9781665493079

We report a millimeter-sized bacteria-powered biobattery that can generate substantially high-power density (427 µW/cm 2 ) only in 7 minutes after liquid introduction. The biobattery features (i) Bacillus subtilis endospores as a storable, dormant biocatalyst which are primed for quick germination, and (ii) a 3-D graphene hydrogel anode decorated with copper oxide particles for high-power generation. Individual biobattery units are easily stackable in series and three-biobattery pack produces more than enough energy to power a light-emitting diode (LED). This work can release microbial energy technology from its restriction to research settings and translate it to practical real-world applications.

关键词： Heating systems Performance evaluation Microorganisms Hydrogels Graphene Light emitting diodes Copper

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A Buoyant Bio-Solar Cell Array with Long-Lasting High-Power Output: Energy Harvesting from Aquatic Environments

A Buoyant Bio-Solar Cell Array with Long-Lasting High-Power ...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Anwar Elhadad Maryam Rezaie Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York (SUNY) at Binghamton New York USA SUNY Center for Research in Advanced Sensing Technologies & Environmental Sustainability New York Binghamton USA

ISBN: (纸本)9781665493079

Miniaturized bio-solar cells have emerged as a potential sustainable power source for the Internet of Things (IoT) applications deployed in unattended, difficult-to-reach aquatic environments such as oceans, rivers, and lakes. Here, we create a scalable, high-power, long-lasting, and buoyant bio-solar cell stack that integrates significantly improved miniature bio-solar cells in an array. Each cell incorporates a symbiotic microbial consortium consisting of Synechocystis sp. PCC6803 and Bacillus subtilis. While Synechocystis sp. generates electricity through its photosynthetic and respiratory activities, it produces an organic fuel through its photosynthesis, which sustains B. subtilis in the consortium. Each cell was able to generate a sustainable maximum power density of ~70 μWcm −2 .

关键词： Wireless communication Symbiosis Wireless sensor networks Power system measurements Oceans Rivers Internet of Things

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Photonic torons, topological phase transition and tunable spin monopoles

arXiv

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arXiv 2024年

作者： Wu, Haijun Mata-Cervera, Nilo Wang, Haiwen Zhu, Zhihan Qiu, Cheng-Wei Shen, Yijie Wang Da-Heng Center Heilongjiang Key Laboratory of Quantum Control Harbin University of Science and Technology Harbin150080 China Centre for Disruptive Photonic Technologies School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Applied Physics Stanford University StanfordCA94305 United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Creation and control of topological complex excitations play crucial roles in both fundamental physics and modern information science. Torons are a sophisticated class of 3D chiral polar topological structures with both skyrmionic quasiparticle textures and monopole point defects, so far only observed in liquid crystal nonpolar models. Here, we experimentally construct torons with the photonic spin of vector structured light and demonstrate the topological phase transitions among diverse topological states: torons, hopfions, skyrmioniums and monopole pairs. We can also continually tune the toron’s chirality and the helical spin textures of emerging monopole pairs. The birth of photonic torons and tunable monopoles opens a flexible platform for studying nontrivial light-matter interaction and topological informatics. © 2024, CC0.

关键词： Topology

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Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model

Short - Term Prediction of Coke Pushing Current Peak Based o...

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Industrial Cyber-Physical Systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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A Multiple Ant Colony System for the Electric Vehicle Routing Problem with Time Windows

A Multiple Ant Colony System for the Electric Vehicle Routin...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michalis Mavrovouniotis Georgios Ellinas Changhe Li Marios Polycarpou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (纸本)9781665487696

Ant colony optimization (ACO) has been found to be useful on several vehicle routing problem variations. In this work, ACO is applied to the electric vehicle routing problem with time windows (E-VRPTW). The E-VRPTW has a hierarchical multiple objective function, which is to minimize the number of electric vehicles and the total distance traveled. A multiple ACO is applied to E-VRPTW in which two colonies cooperate to minimize the objectives in parallel. A local search is embedded in ACO to improve the quality of the output. The experimental results on a set of benchmark instances show that the multiple ACO is competitive with existing methods.

关键词： Ant colony optimization Vehicle routing Benchmark testing Electric vehicles Routing Search problems Linear programming

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