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Tuning the Size and Stiffness of Inflatable Particles

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arXiv 2025年

作者： Pashine, Nidhi Wang, Dong Baines, Robert Goyal, Medha Shattuck, Mark D. O’Hern, Corey S. Kramer-Bottiglio, Rebecca Department of Mechanical Engineering Yale University New HavenCT06520 United States Department of Physics BioInspired Institute Syracuse University SyracuseNY13244 United States Department of Physics Benjamin Levich Institute City College of New York New York CityNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t → 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications. © 2025, CC BY.

关键词： Stiffness

AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

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arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

Pressure-driven homogenization of lithium disilicate glasses

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arXiv 2023年

作者： Bakhouch, Yasser Buchner, Silvio Silveira, Rafael Abel Resende, Leonardo Pereira, Altair Soria Hasnaoui, Abdellatif Atila, Achraf LS2ME Faculté Polydisciplinaire Khouribga Sultan Moulay Slimane University of Beni Mellal B.P 145 Khouribga25000 Morocco Graduate Program in Materials Science Federal University of Rio Grande do Sul Porto Alegre Brazil Department of Material Science and Engineering Saarland University Saarbrücken66123 Germany

Lithium disilicate glasses and glass-ceramics are good potential candidates for biomedical applications and solid-state batteries, and serve as models of nucleation and crystal growth. Moreover, these glasses exhibit a phase separation that influences their nucleation and crystallization behavior. The atomistic mechanisms of the phase separation and their pressure dependence are unclear so far. Here, we used molecular dynamics simulations supported by experiments to assess the spatial heterogeneity of lithium disilicate glasses prepared under pressure. We show that the glass heterogeneity decreases with increasing the cooling pressure and almost disappears at pressures around 30 GPa. The origin of the heterogeneity is due to the attraction between Li cations to form clustering channels, which decreases with pressure. Through our results, we hope to provide valuable insights and guidance for making glass-ceramics with controlled crystallization. Copyright © 2023, The Authors. All rights reserved.

关键词： Phase separation

AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection

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arXiv 2023年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan The Department of Computer Science National Chengchi University Taipei11605 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Forged content shared widely on social media platforms is a major social problem that requires increased regulation and poses new challenges to the research community. The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilizes visual modality or audio modality. While there are some methods in the literature that exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multi-modal datasets of deepfake videos involving acoustic and visual manipulations. Moreover, these existing methods are mostly based on CNN and suffer from low detection accuracy. Inspired by the recent success of Transformer in various fields, to address the challenges posed by deepfake technology, in this paper, we propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation to achieve effective video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multi-modal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that our best model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. © 2023, CC BY.

关键词： Statistical tests

Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm

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Impact of Image Enhancement for Osteoporosis Detection Based...

Computer System, Information Technology, and Electrical engineering (COSITE), International Conference on

作者： Riza Dianil Iman Roslidar Roslidar Kahlil Muchtar Amalia Amalia Mohammad Syaryadhi Alfatirta Mufti Novi Maulina Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an Fl-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance.

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Classification of Hotel Customer Based on Support Vector Machine

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Classification of Hotel Customer Based on Support Vector Mac...

International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Choirul Huda Yaya Heryadi Lukas Wayan Suparta Antoni Wibowo Computer Science Department Doctor of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Engineering Research Group (CERG) Atma Jaya University Jakarta Indonesia Computer Science Department Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665499705

The tourism industry has contributed positively for regional and global economic income in recent years. Although this sector faces a hard challenge in COVΓD-19 pandemic, it has been predicted that it will rebound and will continue in growing as in previous periods. Tourist experience should become important to be improved regarding the service level for visitors such as the improvement of hotel customer accommodation. The aim of this study is classifying the hotel customer based on Support Vector Machine (SVM). This classification conducted through classification of satisfaction level and the related factors. The result of the study presented a good accuracy and precision of SVM in conducting customer hotel classification at 93% accuracy level with error level of prediction result for about 0.27 based on Root Mean Squared Error (RSME) function. The features of customer satisfaction at this study are proposed to be important preferences for improvement of customer services in hospitality.

关键词： Support vector machines Economics Mechatronics Pandemics Tourism industry Europe Machine learning

Outcoupling Hyperbolic Modes from Aligned Carbon Nanotube Films

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Outcoupling Hyperbolic Modes from Aligned Carbon Nanotube Fi...

CLEO: QELS_Fundamental science, QELS 2022

作者： Jerome, Bryant Prasad, Ciril S. Doumani, Jacques Dewey, Oliver S. Baydin, Andrey Pasquali, Matteo Kono, Junichiro Gao, Weilu Alabastri, Alessandro Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Chemistry Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering University of Utah Salt Lake CityUT84112 United States

Aligned carbon nanotube films make an excellent hyperbolic material platform in the infrared. Here, we experimentally demonstrate the presence of high-k modes in aligned carbon nanotube films and outcouple them to fre... 详细信息

ISBN: (纸本)9781557528209

关键词： Carbon films

3D printed bioabsorbable composite scaffolds of poly (lactic acid)-tricalcium phosphate-ceria with osteogenic property for bone regeneration

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Biomaterials and Biosystems 2024年 13卷 100086页

作者： Harb, Samarah V. Kolanthai, Elayaraja Pugazhendhi, Abinaya S. Beatrice, Cesar A.G. Pinto, Leonardo A. Neal, Craig J. Backes, Eduardo H. Nunes, Ana C.C. Selistre-de-Araújo, Heloisa S. Costa, Lidiane C. Coathup, Melanie J. Seal, Sudipta Pessan, Luiz A. Department of Materials Engineering (DEMa) Federal University of Sao Carlos (UFSCar) SP São Carlos Brazil Advanced Materials Processing and Analysis Center Department of Materials Science and Engineering University of Central Florida Orlando FL United States Biionix Cluster College of Medicine University of Central Florida Orlando FL United States Graduate Program in Materials Science and Engineering Department of Materials Engineering (DEMa) Federal University of São Carlos (UFSCar) SP São Carlos Brazil Department of Physiological Sciences Federal University of São Carlos (UFSCar) SP São Carlos Brazil

The fabrication of customized implants by additive manufacturing has allowed continued development of the personalized medicine field. Herein, a 3D-printed bioabsorbable poly (lactic acid) (PLA)- β-tricalcium phosphate (TCP) (10 wt %) composite has been modified with CeO₂ nanoparticles (CeNPs) (1, 5 and 10 wt %) for bone repair. The filaments were prepared by melt extrusion and used to print porous scaffolds. The nanocomposite scaffolds possessed precise structure with fine print resolution, a homogenous distribution of TCP and CeNP components, and mechanical properties appropriate for bone tissue engineering applications. Cell proliferation assays using osteoblast cultures confirmed the cytocompatibility of the composites. In addition, the presence of CeNPs enhanced the proliferation and differentiation of mesenchymal stem cells;thereby, increasing alkaline phosphatase (ALP) activity, calcium deposition and bone-related gene expression. Results from this study have shown that the 3D printed PLA-TCP-10%CeO₂ composite scaffold could be used as an alternative polymeric implant for bone tissue engineering applications: avoiding additional/revision surgeries and accelerating the regenerative process. © 2023

关键词： Additive manufacturing Biomaterial Bone tissue engineering Ceria nanoparticle Polymer-matrix composites

Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics

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Quantitative Biology 2020年第3期8卷 228-237页

作者： Naoki Matsuda Ken-ichi Hironaka Masashi Fujii Takumi Wada Katsuyuki Kunida Haruki Inoue Miki Eto Daisuke Hoshino Yasuro Furuichi Yasuko Manabe Nobuharu LFujii Hiroyuki Noji Hiromi Imamura Shinya Kuroda Department of Biological Sciences Graduate School of ScienceUniversity of TokyoTokyo 113-0033Japan Molecular Genetics Research Laboratory Graduate School of ScienceUniversity of TokyoTokyo 113-0033Japan Department of Mathematical and Life Sciences Graduate School of Integrated Sciences for LifeHiroshima UniversityHiroshima 739-8526Japan Laboratory of Computati onal Biology Graduate School of Biological SciencesNara Institute of Science and Tech no logyNara 630-0192Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier SciencesUniversity of TokyoTokyo 113-0033Japan Department of Engineering Science Graduate School of Informatics and EngineeringUniversity of Electro-CommunicationsTokyo 182-8585Japan Department of Health Promotion Sciences Graduate School of Human Health SciencesTokyo Metropolitan UniversityTokyo 192-0397Japan Department of Applied Chemistry Graduate School of EngineeringUniversity of TokyoTokyo 113-8656Japan Department of Functional Biology Graduate School of BiostudiesKyoto UniversityKyoto 606-8501Japan CREST Japan Science and Technology CorporationTokyo 113-0033Japan

Backgrounds ATP is the major energy source for myotube contraction,and is quickly produced to compensate ATP consumption and to maintain sufficient ATP *** is consumed mainly in cytoplasm and produced in mitochondria during myotube *** understand the mechanism of ATP homeostasis during myotube contraction,it is essential to monitor mitochondrial ATP at single-cell level,and examine how ATP is produced and consumed in ***:We established C2C12 cell line stably expressing fluorescent probe of mitochondrial ATP,and induced differentiation into myotubes・We gave electric pulse stimulation to the differentiated myotubes,and measured mitochondrial *** constructed mathematical model of mitochondrial ATP at single-cell level,and analyzed kinetic parameters of ATP production and ***:We performed hierarchical clustering analysis of time course of mitochondrial ATP,which resulted in two *** 1 showed strong transient increase,whereas cluster 2 showed weak transient *** modeling at single-cell level revealed that the ATP production rate of cluster 1 was larger than that of cluster 2,and that both regulatory pathways of ATP production and consumption of cluster 1 were faster than those of cluster *** 1 showed larger mitochondrial mass than cluster 2,suggesting that cluster 1 shows the similar property of slow muscle fibers,and cluster 2 shows the similar property of fast muscle *** Cluster 1 showed the stronger mitochondrial ATP increase by larger ATP production rate,but not smaller *** 1 might reflect the larger oxidative capacity of slow muscle fiber.

关键词： mitocondrial ATP production ATP consumption single-cell analysis mathematical modeling