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14th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2023

作者： Hashimoto, Shigehiro Kishimoto, Kazuya Biomedical Engineering Department of Mechanical Engineering Kogakuin University Tokyo1638677 Japan

ISBN: (纸本)9781950492718

The effect of force field on elongation of a cell has been studied in vitro. The stripe-pattern ridges (0.7 μm height, 3 μm width, and 3 μm interval) was made on the surface of the scaffold plate. Variation was made on the angle between the longitudinal direction of the ridge and the direction of the force field by centrifuge: 0 degrees, 45 degrees, and 90 degrees. Myoblast (C2C12: mouse myoblast cell line) was used in the experiment. After the centrifugation of 50 G or 100 G for 5 hours, the behavior of each cell (migration velocity, rate of area change, and length including pseudopodia) has been tracked by the microscopic time-lapse two-dimensional projected images for 200 minutes. Every cell made elongation along the stripe-pattern ridges. Experimental results show relatively low activity of cell elongation in the 0 degrees region. The results would be applied to accelerate the formation of engineered tissues. © 2023 Proceedings IMCIC - International Multi-Conference on Complexity, Informatics and Cybernetics.

关键词： Cell culture

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Biodegradable WE43 Mg alloy/hydroxyapatite interpenetrating phase composites with reduced hydrogen evolution

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Bioactive Materials 2024年第12期42卷 519-530页

作者： Lenka Drotarova Karel Slamecka Tomas Balint Michaela Remesova Radovan Hudak Jozef Zivcak Marek Schnitzer Ladislav Celko Edgar B.Montufar Central European Institute of Technology Brno University of TechnologyPurkynova 123Brno61200Czech Republic Faculty of Mechanical Engineering Brno University of TechnologyTechnicka 2Brno 61669Czech Republic Department of Biomedical Engineering and Measurement Faculty of Mechanical EngineeringTechnical University of KosiceLetna 9Kosice04200Slovakia

Biodegradable magnesium implants offer a solution for bone repair without the need for implant ***,concerns persist regarding peri-implant gas accumulation,which has limited their widespread clinical ***,there is a need to minimise the mass of magnesium to reduce the total volume of gas generated around the *** porosity is a direct approach to reducing the mass of the implants,but it also decreases the strength and degradation *** study demonstrates that the infiltration of a calcium phosphate cement into an additively manufactured WE43 Mg alloy scaffold with 75% porosity,followed by hydrothermal treatment,yields biodegradable magnesium/hydroxyapatite interpenetrating phase composites that generate an order of magnitude less hydrogen gas during degradation than WE43 *** enhanced degradation resistance results from magnesium passivation,allowing osteoblast proliferation in indirect contact with ***,the composites exhibit a compressive strength 1.8 times greater than that of the scaffolds,falling within the upper range of the compressive strength of cancellous *** results emphasise the potential of the new biodegradable interpenetrating phase composites for the fabrication of temporary osteosynthesis *** cement hardening and magnesium passivation during hydrothermal processing is crucial for achieving both high compressive strength and low degradation rate.

关键词： Magnesium Biodegradable metal Calcium phosphate cement Hydroxyapatite Composite

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DrugEL: Ensemble Learning Model for Identification of Druggable Proteins

DrugEL: Ensemble Learning Model for Identification of Drugga...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Wu, Fang-Xiang University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada University of Saskatchewan Department of Electrical and Computer Engineering Saskatoon Canada University of Saskatchewan Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the development of targeted therapies for diseases like cancer and metabolic disorders. Identifying druggable proteins involves various methods, including computational prediction models, mass spectrometry (MS), and biochemical assays, but achieving high accuracy remains a challenge. This study proposes DrugEL, an ensemble learning model that uses Bayesian inference to integrate predictions from multiple algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), LightGBM (LGBM), and Decision Tree (DT) with seven feature extraction methods (LSA, AAC, PAAC, GAAC, NMBroto, AAIndex, and KNN). The results show that DrugEL outperforms existing models in terms of accuracy (0.9758), MCC (0.9515), AUC (0.9758), sensitivity (0.9742) and specificity (0.9774), particularly excelling with the LSA method. © 2024 IEEE.

关键词： Contrastive Learning

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Birefringence enhancement in terahertz photonic crystal fiber via liquid-filled holes

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Applied Optics 2025年第9期64卷 2186-2194页

作者： Pizzaia, João Paulo Lebarck Bastida, Gabriel Catabriga Segatto, Marcelo Eduardo Vieira Coelho, Elizandra Pereira Roque Leal-Junior, Arnaldo Gomes Castellani, Carlos Eduardo Schmidt LABTEL Electric Engineering Department Universidade Federal do Espírito Santo Vitoria Brazil LabSensores Mechanical Engineering Department Universidade Federal do Espírito Santo Vitoria Brazil Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava70800 Czech Republic

This study investigates the enhancement of birefringence in terahertz (THz) porous-core photonic crystal fibers (PC-PCFs) through strategic filling of their cladding air holes with various liquid configurations. Terahertz PCFs are crucial for applications requiring precise control over polarization states and low losses, such as in telecommunications and sensing. By employing Ansys Lumerical finite difference method simulations, three distinct liquid-filling strategies (Cases A, B, and C) were analyzed. Each case focused on filling different sections of the PCF's structure: outer ring;outer ring limited to outermost holes;and inner ring, respectively. The simulations evaluated the influence of these configurations on the PCF's refractive indices, birefringence, and attenuation within the THz frequency range. The results demonstrate that filling specific air holes with water can effectively increase the PCF's birefringence, offering tailored polarization characteristics for THz applications. For instance, Case C, in which the inner ring holes are filled, showed a substantial increase in birefringence from 0.0274 to 0.0447, highlighting its potential for polarization-sensitive devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Birefringence

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Advancements in hybrid energy storage systems for enhancing renewable energy-to-grid integration

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Sustainable Energy Research 2024年第1期11卷 1-23页

作者： Adeyinka, Adekanmi Miracle Esan, Oladapo Christopher Ijaola, Ahmed Olanrewaju Farayibi, Peter Kayode Department of Mechanical Engineering Auburn University AuburnAL36832 United States Department of Mechanical Aerospace and Biomedical Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Mechanical Engineering Wichita State University WichitaKS67270 United States Seagate Technology Springtown Industrial Estate Londonderry United Kingdom

The global energy sector is currently undergoing a transformative shift mainly driven by the ongoing and increasing demand for clean, sustainable, and reliable energy solutions. However, integrating renewable energy sources (RES), such as wind, solar, and hydropower, introduces major challenges due to the intermittent and variable nature of RES, affecting grid stability and reliability. Hybrid energy storage systems (HESS), which combine multiple energy storage devices (ESDs), present a promising solution by leveraging the complementary strengths of each technology involved. This comprehensive review examines recent advancements in grid-connected HESS, focusing on their components, design considerations, control strategies, and applications. It provides a detailed analysis of technological progress in various ESDs and the critical role of power conversion, control, energy management, and cooling systems in optimizing HESS performance. Highlighting case studies of some notable and successful HESS implementations across the globe, we illustrate practical applications and identify the benefits and challenges encountered. By addressing these challenges, HESS can significantly enhance the efficiency and reliability of RES, supporting the shift towards a sustainable and resilient energy infrastructure. The paper concludes by identifying future research directions, highlighting the development of intelligent control systems, sustainable materials, and efficient recycling processes to ensure the widespread adoption and long-term viability of HESS. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Control systems

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Optimizing Biometric Recognition with PPG Signals: A Comparative Study of Deep Learning Models and 2D Conversion Techniques

Optimizing Biometric Recognition with PPG Signals: A Compara...

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International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Cherry, Ali Nasser, Aya Ali, Mohamad Abou Salameh, Wassim Ballout, Hadi Hajj-Hassan, Mohamad Department of Biomedical Engineering Lebanese International University Bekaa Lebanon Department of Biomedical Engineering The International University of Beirut Beirut Lebanon Department of Mechanical Engineering The International University of Beirut Beirut Lebanon University of Genoa Genoa Italy

ISBN: (纸本)9783031840999

The integration of liveness detection mechanisms in biometric systems is crucial for countering spoofing attacks, enhancing security, and optimizing lifestyle applications. This study explores the utilization of Photoplethysmography (PPG) signals for this purpose, leveraging their inherent liveness properties and cost-effectiveness. In the preprocessing stage, PPG signals of an online dataset is transformed into three different conversion methods, such as PPG signal representation, spectrograms, and Gram matrices. Five diverse models, ResNet50V2, DenseNet201, EfficientNetV2_B0, ConvNextBase, and an EfficientNetV2_B0 model with LSTM integration were training on the online dataset to be able to choose the best conversion technique into two dimensional format with the best model for biometric recognition. Remarkably high accuracies were achieved across multiple models on the online dataset, with the standout performer being the EfficientNetV2_B0 model integrated with LSTM, employing Gram matrix as a conversion technique which achieve 99% for the testing accuracy. This holistic approach has not only deepened our understanding of PPG signal processing but also highlighted the potential of deep learning models in future advancements in biometric identification and beyond. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photoplethysmography

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Photoplethysmography-Based Biometric System: Evaluating Deep Learning Techniques for Enhanced Security

Photoplethysmography-Based Biometric System: Evaluating Deep...

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International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Cherry, Ali Kourani, Hayat Sbeity, Nadine Ali, Mohamad Abou Salameh, Wassim Dabbous, Ali Hajj-Hassan, Mohamad Department of Biomedical Engineering Lebanese International University Bekaa Lebanon Department of Biomedical Engineering The International University of Beirut Beirut Lebanon Department of Mechanical Engineering The International University of Beirut Beirut Lebanon University of Genoa Genoa Italy

ISBN: (纸本)9783031840999

This study explores the potential of photoplethysmography (PPG) signals as a reliable biometric identification method, emphasizing its significance in addressing security issues through a noninvasive and difficult-to-forge approach. We designed, implemented, and evaluated various models to classify PPG signals, focusing on reliability and accuracy. Using a robust protocol for PPG signal acquisition, signals from 40 individuals were collected. The research explores multiple preprocessing techniques, including data augmentation and normalization to create six distinct datasets. Five deep learning models, including 1D Convolutional Neural Network (1D CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Dense Neural Network (DNN), are implemented and compared for their classification performance. Among these, the 1D CNN model demonstrated superior classification accuracy, achieving 99.94%. This work reinforces the efficacy of PPG signals in biometric recognition and highlights the potential of advanced deep learning techniques in enhancing recognition accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Effect of Ultrasound-Induced Temperature on the Dynamics of the Hodgkin-Huxley Neuron 46

Effect of Ultrasound-Induced Temperature on the Dynamics of ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Badawe, Heba Harouz, Jean Paul Khraiche, Massoud American University of Beirut Biomedical Engineering Department Beirut Lebanon American University of Beirut Mechanical Engineering Department Beirut Lebanon

ISBN: (纸本)9798350371499

Recent studies demonstrate that low intensity, low frequency ultrasound is capable of modulating neural activity in a non-invasive and highly specific manner. We tailored the classical Hodgkin and Huxley (HH) model by incorporating the flexoelectric effect caused by the ultrasound-induced strain gradient across the neural lipid bilayer membrane, along with Pennes bioheat model to account for the thermal energy generated due to extended ultrasound sonication. Our modified model successfully captured the increase in axonal temperature with the increase in ultrasound sonication, reaching the critical "thermal block"after 4 minutes of sonication. The model predicted an increase in the firing rate while suppressing the action potential peak-to-peak voltage. Our study indicates that extended ultrasound sonication generates sufficient heat to generate an inhibitory response. © 2024 IEEE.

关键词： Lipid bilayers

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A Novel Soft Robotic Manipulator Design with Zig-zag Chamber Geometry 10

A Novel Soft Robotic Manipulator Design with Zig-zag Chamber...

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10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Treratanakulchai, Shen Garriga-Casanovas, Arnau Borvorntanajanya, Korn Franco, Enrico Baena, Ferdinando Rodriguezy Mahidol University Biomedical Engineering Department Nakhon Pathom Thailand Imperial College Mechanical Engineering Department London United Kingdom

ISBN: (纸本)9798350394245

The development of soft robots has advanced rapidly in recent years. However, most designs present significant non-linearities and deviate from the desired bending direction in an unpredictable manner. This makes accurate control more difficult. In this paper, we present a novel design of a soft robotic manipulator with zig-zag partition walls, which minimize those deviations. We first outline the design and describe a lost wax manufacturing process for complex geometries. We then report experiments to evaluate the bending characteristics, force, and repeatability of the prototype, and we compare it against an existing design. The results show a significant improvement in bending consistency with minimal deviations. © 2024 IEEE.

关键词： Robotics

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Transfer learning prediction on lithium-ion battery heat release under thermal runaway condition

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Nano Research Energy 2024年第4期3卷 9-12页

作者： Changmin Shi Di Zhu Liwen Zhang Siyuan Song Brian W.Sheldon School of Engineering Brown UniversityProvidenceRI 02912USA Mechanical Engineering North Carolina State UniversityRaleighNC 27606USA Department of Mechanical Aerospace&Biomedical EngineeringUT Space InstituteUniversity of TennesseeKnoxvilleTN 37388USA

Accurately predicting the variability of thermal runaway(TR)behavior in lithium-ion(Li-ion)batteries is critical for designing safe and reliable energy storage ***,traditional calorimetry-based experiments to measure heat release during TR are time-consuming and ***,we highlight an exciting transfer learning approach that leverages mass ejection data and metadata from cells to predict heat output variability during TR *** approach significantly reduces the effort and time to assess thermal risks associated with Li-ion batteries.

关键词： transfer learning machine learning Li-ion battery thermal runway heat release

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