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Corrigendum to “Zinc/lead bimetallic sulfides as hybrid material for high-performance aqueous asymmetric supercapacitor and photocatalytic degradation of organic dye” [J. Alloy. Compd. 1010 (2025) 177565]

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Journal of Alloys and Compounds 2025年 1010卷

作者： Asmat Ullah Muhammad Ramzan Khawar Naveed Akhtar Shad Sunmin Jang Dongwhi Choi Dongsheng Geng Jabir Shahbaz Ammar M. Tighezza Yasir Javed Awais Ahmad School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 China Department of Mechanical Engineering (Integrated Engineering Program) Kyung Hee University Yongin Gyeonggi 17104 Republic of Korea National Institute for Biotechnology and Genetic Engineering Jhang Road Faisalabad Pakistan Department of Physics University of Agriculture Faisalabad Pakistan Department of Chemistry College of Science King Saud University P. O. Box 2455 Riyadh 11451 Saudi Arabia Department of Chemistry The University of Lahore Lahore Pakistan

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Compressed air energy storage systems (CAES) and assessment of expansion cooling as an ancillary process

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Science Talks 2024年 10卷

作者： Abdul Hai Alami Ahmad Yasin Siren Khuri Ayman Mudallal Mohamad Ayoub Adnan Alashkar Rashid AlRashid Shamma Abdalla Sustainable and Renewable Energy Engineering Department University of Sharjah Sharjah P.O. Box 27272 United Arab Emirates Materials Science and Engineering PhD Program American University of Sharjah Sharjah P.O. Box 26666 United Arab Emirates Mechanical Engineering Department American University of Sharjah Sharjah 26666 United Arab Emirates

This work introduces compressed air energy storage (CAES) systems and their role in mitigating the lag between energy supply and demand. The concept of energy storage relies on storing energy during periods of low demand to supply it during periods of high demand, where CAES stands as a scalable solution with the ability to house ancillary systems in order to harvest useful effects such as expansion cooling. CAES on its own compares well with electrochemical batteries, while bearing a simpler system design and less stringent operational requirements; however, with the integration of thermal management the role of CAES can be extended to produce chilled water that can be used for cooling applications. Not only does expansion cooling broaden the utilization of CAES, it has also enhanced the energetic efficiency of the system by 4%. Future work within the scope of CAES includes the integration of heat pipes and latent heat energy storage, and experimenting with different working gases.

关键词： Compressed air energy storage (CAES) mechanical energy storage Expansion cooling Thermal management

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A Learning-Driven Framework with Spatial Optimization for Surgical Suture Thread Reconstruction and Autonomous Grasping under Multiple Topologies and Environmental Noises

arXiv

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arXiv 2020年

作者： Lu, Bo Chen, Wei Jin, Yue-Ming Zhang, Dandan Dou, Qi Chu, Henry K. Heng, Pheng-Ann Liu, Yun-Hui T stone Robotics Institute Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong T Stone Robotics Institute Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hamlyn Centre for Robotic Surgery Imperial College London United Kingdom Department of Mechanical Engineering Kong Kong Polytechnic University Hong Kong

Surgical knot tying is one of the most fundamental and important procedures in surgery, and a high-quality knot can significantly benefit the postoperative recovery of the patient. However, a longtime operation may easily cause fatigue to surgeons, especially during the tedious wound closure task. In this paper, we present a vision-based method to automate the suture thread grasping, which is a sub-task in surgical knot tying and an intermediate step between the stitching and looping manipulations. To achieve this goal, the acquisition of a suture’s three-dimensional (3D) information is critical. Towards this objective, we adopt a transfer-learning strategy first to fine-tune a pre-trained model by learning the information from large legacy surgical data and images obtained by the on-site equipment. Thus, a robust suture segmentation can be achieved regardless of inherent environment noises. We further leverage a searching strategy with termination policies for a suture’s sequence inference based on the analysis of multiple topologies. Exact results of the pixel-level sequence along a suture can be obtained, and they can be further applied for a 3D shape reconstruction using our optimized shortest path approach. The grasping point considering the suturing criterion can be ultimately acquired. Experiments regarding the suture 2D segmentation and ordering sequence inference under environmental noises were extensively evaluated. Results related to the automated grasping operation were demonstrated by simulations in V-REP and by robot experiments using Universal Robot (UR) together with the da Vinci Research Kit (dVRK) adopting our learning-driven framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Human–machine interaction towards Industry 5.0: Human-centric smart manufacturing

Digital Engineering

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Digital engineering 2024年 2卷

作者： Jialu Yang Ying Liu Phillip L. Morgan Department of Mechanical Engineering School of Engineering Cardiff University Cardiff CF24 3AA UK Human Factors Excellence Research Group (HuFEx) and Centre for AI Robotics and Human–Machine Systems (IROHMS) School of Psychology Cardiff University CF10 3AT Cardiff UK

Since the concept of Industry 5.0 was proposed, the emphasis on human–machine interaction (HMI) in industrial scenarios has continued to increase. HMI is part of the factory’s development towards Industry 5.0, mainly because HMI can help realise the human-centric vision. At the same time, to achieve the sustainable and resilient goals proposed by Industry 5.0, green, smart, and more advanced technologies are also considered important driving factors for factories to achieve Industry 5.0. Human-centric smart manufacturing (HCSM) factories that integrate HMI with advanced technologies are expected to become the paradigm of future manufacturing. Therefore, it is necessary to discuss technologies and research directions that may promote the implementation of HCSM in the future. In a smart factory, HMI signals will go through the process of being collected by sensors, processed, transmitted to the data analysis centre and output to complete the interaction. Based on this process, we divide HMI into four parts: sensor and hardware, data processing, transmission mechanism, and interaction and collaboration. Through a systematic literature review process, this article evaluates and summarises the current research and technologies in the HMI field and categorises them into four parts of the HMI process. Since the current usage scenarios of some technologies are relatively limited, the introduction focuses on the possible applications and problems they face. Finally, the opportunities and challenges of HMI for Industry 5.0 and HCSM are revealed and discussed.

关键词： Human–machine interaction Industry 4.0 Industry 5.0 Sensor IoT IIoT Human-centric smart manufacturing Manufacturing informatics Data analytics

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Modeling actuation of ionomer cilia in salt solution under an external electric field

Modeling actuation of ionomer cilia in salt solution under a...

引用

ASME 2019 Dynamic Systems and Control Conference, DSCC 2019

作者： Boldini, Alain Rosen, Maxwell Cha, Youngsu Porfiri, Maurizio Department of Mechanical and Aeronautical Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Mechanical and Aeronautical Engineering Department of Biomedical Engineering New York University Tandon School of Engineering BrooklynNY11201 United States

ISBN: (纸本)9780791859162

A recent experiment by Kim's group from the University of Nevada, Las Vegas has demonstrated the possibility of actuating ionomer cilia in salt solution. When these actuators are placed between two external electrodes, across which a small voltage is applied, they move toward the cathode. This is in stark contrast with the case of ionic polymer metal composites, where these ionomers are plated by metal electrodes and bending occurs towards the anode. Here, we seek to unravel the factors underlying the motion of ionomer cilia in salt solution through a physically-based model of actuation. In our model, electrochemistry is described through the Poisson-Nernst-Planck system in terms of concentrations of cations and anions and voltage, which is solved through the finite element method. Based on computer simulations, we establish that Maxwell stress is the main driving force for the motion of the cilia. © 2019 ASME.

关键词： Electrodes

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Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis

arXiv

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arXiv 2024年

作者： Ketabi, Sara Wagner, Matthias W. Hawkins, Cynthia Tabori, Uri Ertl-Wagner, Birgit Betina Khalvati, Farzad Neurosciences and Mental Health Research Program The Hospital for Sick Children Toronto Canada Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Institute of Medical Science University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Diagnostic and Interventional Radiology The Hospital for Sick Children Toronto Canada Department of Diagnostic and Interventional Neuroradiology University Hospital Augsburg Augsburg Germany Division of Hematology and Oncology The Hospital for Sick Children Toronto Canada Paediatric Laboratory Medicine Division of Pathology The Hospital for Sick Children Toronto Canada Department of Medical Imaging University of Toronto Toronto Canada

Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact that the features contributing to a model's prediction are unclear to radiologists and hence, clinically irrelevant, i.e., lack of explainability. As the invaluable sources of radiologists' knowledge and expertise, radiology reports can be integrated with MRI in a contrastive learning (CL) framework, enabling learning from image-report associations, to improve CNN explainability. In this work, we train a multimodal CL architecture on 3D brain MRI scans and radiology reports to learn informative MRI representations. Furthermore, we integrate tumor location, salient to several brain tumor analysis tasks, into this framework to improve its generalizability. We then apply the learnt image representations to improve explainability and performance of genetic marker classification of pediatric Low-grade Glioma, the most prevalent brain tumor in children, as a downstream task. Our results indicate a Dice score of 31.1% between the model's attention maps and manual tumor segmentation (as an explainability measure) with test classification performance of 87.7%, significantly outperforming the baselines. These enhancements can build trust in our model among radiologists, facilitating its integration into clinical practices for more efficient tumor diagnosis. Copyright © 2024, The Authors. All rights reserved.

关键词： Pediatrics

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Deceleration performance and stopping distance of hybrid composite brake lining pads of basalt and clam shell

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AIP Conference Proceedings 2023年第1期2568卷

作者： I. Ketut Adi Atmika I. Gusti Agung Kade Suriadi I. Nyoman Sutantra I. Dewa Gede Ary Subagia 1)Mechanical Engineering Department of Udayana University Street of Bukit Jimbaran Bali 80361 Indonesia 3)Technology and Material Research Center of Udayana University Street of Bukit Jimbaran Bali 80361 Indonesia 4)Doctoral Program of Engineering Science Engineering Faculty of Udayana University Street of PB Sudirman Bali 80114 Indonesia 2)Mechanical Engineering Department of Institut of Sepuluh Nopember Surabaya 60111 Indonesia

Brake lining is one of the components of the braking system on a vehicle that plays an important role for the safety of the driver. Recently, the development of brake lining materials has attracted the attention of many researchers. At first many brake pads were made of asbestos as the main material, but this brake lining material has begun to be abandoned because it has a negative impact on health and the environment. This paper presents the results of research on the development of a hybrid composite as a brake lining material. This hybrid composite is composed of the main ingredients of basalt and green clam shell waste, as well as a small amount of alumina with phenolic resin as a binder. Hybrid composite brake lining material is made through a sintering process with an emphasis of 2 tons for 30 minutes at a constant temperature of 150°C. Hybrid composite brake pads made 3 variations of weight fraction were tested on a motorcycle to see the performance of deceleration and braking distance. Also tested brake lining with asbestos material as a comparison. The lowest deceleration performance was asbestos brake lining at 6.79 m/s2, while the highest deceleration was HC4 brake lining (35% basalt-15% clam shell) of 11.51 m/s2. While the shortest braking distance is HC4 brake pads (35% basalt-15% shell) of 3.02 meters, while the longest braking distance of asbestos is 15.18 meters. The increase in the percentage of basalt content and the decrease in the percentage of clam shell content indicate a tendency to increase the magnitude of deceleration, and a tendency to decrease the braking distance for each vehicle's initial speed.

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Identifying topologically associating domains using differential kernels

arXiv

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arXiv 2023年

作者： Maisuradze, Luka King, Megan C. Surovtsev, Ivan V. Mochrie, Simon G.J. Shattuck, Mark D. O’Hern, Corey S. Department of Molecular Biophysics and Biochemistry Yale University New HavenCT United States Department of Cell Biology Yale School of Medicine New HavenCT United States Department of Physics Yale University New HavenCT United States Benjamin Levich Institute Physics Department The City College of New York New YorkNY United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT United States

Chromatin is a polymer complex of DNA and proteins that regulates gene expression. The three-dimensional (3D) structure and organization of chromatin controls DNA transcription and replication. High-throughput chromatin conformation capture techniques generate Hi-C maps that can provide insight into the 3D structure of chromatin. Hi-C maps can be represented as a symmetric matrix Aij, where each element represents the average contact probability or number of contacts between chromatin loci i and j. Previous studies have detected topologically associating domains (TADs), or self-interacting regions in Aij within which the contact probability is greater than that outside the region. Many algorithms have been developed to identify TADs within Hi-C maps. However, most TAD identification algorithms are unable to identify nested or overlapping TADs and for a given Hi-C map there is significant variation in the location and number of TADs identified by different methods. We develop a novel method to identify TADs, KerTAD, using a kernel-based technique from computer vision and image processing that is able to accurately identify nested and overlapping TADs. We benchmark this method against state-of-the-art TAD identification methods on both synthetic and experimental data sets. We find that the new method consistently has higher true positive rates (TPR) and lower false discovery rates (FDR) than all tested methods for both synthetic and manually annotated experimental Hi-C maps. The TPR for KerTAD is also largely insensitive to increasing noise and sparsity, in contrast to the other methods. We also find that KerTAD is consistent in the number and size of TADs identified across replicate experimental Hi-C maps for several organisms. Thus, KerTAD will improve automated TAD identification and enable researchers to better correlate changes in TADs to biological phenomena, such as enhancer-promoter interactions and disease states. © 2023, CC BY.

关键词： Topology

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Nanomaterials in the treatment and diagnosis of rheumatoid arthritis: Advanced approaches

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SLAS Technology 2024年第4期29卷 100146-100146页

作者： Angela, Stefanny Fadhilah, Gianna Hsiao, Wesley Wei-Wen Lin, Hsuan-Yi Ko, Joshua Lu, Steven Che-Wei Lee, Cheng-Chung Chang, Yu-Sheng Lin, Ching-Yu Chang, Huan-Cheng Chiang, Wei-Hung Department of Chemical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Power Mechanical Engineering National Tsing Hua University Hsinchu Taiwan Ph.D. Program for Translational Medicine College of Medical Science and Technology Taipei Medical University Taipei Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Shuang Ho Hospital New Taipei City Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan The Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Chemical Engineering National Taiwan University of Science and Technology Taipei Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Department of Chemistry National Taiwan Normal University Taipei Taiwan Department of Chemical Engineering National Taiwan University of Science and Technology Taipei Taiwan Sustainable Electrochemical Energy Development (SEED) Center National Taiwan University of Science and Technology Taipei Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei Taiwan. Electronic address: weshsiao@mail.ntust.edu.tw

Rheumatoid arthritis (RA), a chronic inflammatory condition that affects persons between the ages of 20 and 40, causes synovium inflammation, cartilage loss, and joint discomfort as some of its symptoms. Diagnostic techniques for RA have traditionally been split into two main categories: imaging and serological tests. However, significant issues are associated with both of these methods. Imaging methods are costly and only helpful in people with obvious symptoms, while serological assays are time-consuming and require specialist knowledge. The drawbacks of these traditional techniques have led to the development of novel diagnostic approaches. The unique properties of nanomaterials make them well-suited as biosensors. Their compact dimensions are frequently cited for their outstanding performance, and their positive impact on the signal-to-noise ratio accounts for their capacity to detect biomarkers at low detection limits, with excellent repeatability and a robust dynamic range. In this review, we discuss the use of nanomaterials in RA theranostics. Scientists have recently synthesized, characterized, and modified nanomaterials and biomarkers commonly used to enhance RA diagnosis and therapy capabilities. We hope to provide scientists with the promising potential that nanomaterials hold for future theranostics and offer suggestions on further improving nanomaterials as biosensors, particularly for detecting autoimmune disorders. Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

关键词： Biomarker Biosensor Diagnosis Nanomaterials Rheumatoid arthritis Therapy

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Efficient sampling of polycyclic aromatic compounds for free energy predictions through active learning

Energy and AI

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Energy and AI 2025年

作者： Mohammed I. Radaideh Matt Raymond Paolo Elvati Jacob C. Saldinger Majdi I. Radaideh Angela Violi Department of Mechanical Engineering University of Michigan Ann Arbor MI 48109-2125 United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109-2125 United States Department of Chemical Engineering University of Michigan Ann Arbor MI 48109-2125 United States Department of Nuclear Engineering and Radiological Sciences University of Michigan Ann Arbor MI 48109-1500 United States Biophysics Program University of Michigan Ann Arbor MI 48109-1500 United States

The physical growth of Polycyclic Aromatic Compounds (PACs) to soot particles plays a significant role in understanding the chemistry of soot formation. Insights into the process can be gained from PACs’ free energy of dimerization landscape. However, because the infeasibly large space of possible PAC dimers cannot be exhaustively simulated, researchers must train machine learning models on a subset of data to impute the rest. To this end, we propose and assess an active learning approach to discovering the optimal PACs for training a machine learning model to predict PACs’ association and dissociation free energies. The comparison between active learning and random sampling showed that active learning has faster loss convergence, requiring fewer training samples to reach the same level of accuracy. The trained model accurately modeled unseen PACs and exhibited robustness against changes in the sampling space used to train the model. More broadly, this work shows how active learning can optimize the design and improve the understanding of more expensive models in specific domains.

关键词： Free energy Polycyclic aromatic compounds (PACs) Active learning Gaussian process regression Representer theorem SHAP

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