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Computational fluid dynamics analysis of hybrid foam-mat freeze drying process

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AIP Conference Proceedings 2024年第1期2991卷

作者： Retno Wulandari Evi Susanti Melisa Surya Andini Bagus Anggoro Dwicahyo Arvin Dwi Nanda Mechanical Engineering Department Faculty of Engineering Universitas Negeri Malang Semarang Street No. 5 Malang 65145 Jawa Timur Indonesia Biotechnology Program Department of Applied Science Faculty of Mathematics and Natural Science Universitas Negeri Malang Semarang Street No. 5 Malang 65145 Jawa Timur Indonesia

To produce halal, safe, and affordable food collagen are factors that need to be resolved in food processing industry. Collagen was made from milkfish scales and B. megaterium TR-10. Freeze drying method has been around for a long time and usually use in food processing, including collagen production. Freeze drying techniques need in this separation of protein and water as a solvent due addition of acetone and NaCl so that the protein has not to denaturation due to processing above 50ᵒC. Computational method to simulate heat and mass transfer freeze drying in the collagen formation at -60ᵒC with a pressure less than 10 Pa to obtain optimal efficiency. When compared to conventional dryers, hybrid freeze dryers excel in several key areas: drying efficiency, energy efficiency, moisture removal procedure, product quality, and nutrient or bioactive ingredient preservation. The simulation using a foam mat with variation 1mm, 3mm, and 5mm. Changing product into a foam to expand the surface area and mass transfer increase thus drying process take a short time. This simulation begins with designing use Ansys R21 and the next process is numerical calculations to analyze heat and mass transfer in freeze drying process to obtain optimal efficiency result. The optimal value is obtained because effectiveness heat and mass transfer process when the collagen converted into foam causing surface largen with thinnest layers. Drying time with hybrid technique is between 1/4 to 1/7 times faster compared non-foam drying.

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Image and Data Mining in Reticular Chemistry Using GPT-4V

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arXiv 2023年

作者： Zheng, Zhiling He, Zhiguo Khattab, Omar Rampal, Nakul Zaharia, Matei A. Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet CA94720 United States Department of Computer Science Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States KACST−UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

The integration of artificial intelligence into scientific research has reached a new pinnacle with GPT-4V, a large language model featuring enhanced vision capabilities, accessible through ChatGPT or an API. This study demonstrates the remarkable ability of GPT-4V to navigate and mine complex data for metal-organic frameworks especially from graphical sources. Our approach involved an automated process of converting 346 scholarly articles into 6240 images, which represents a benchmark dataset in this task, followed by deploying GPT-4V to categorize and analyze these images using natural language prompts. This methodology enabled GPT-4V to accurately identify and interpret key plots integral to MOF characterization, such as nitrogen isotherms, PXRD patterns, and TGA curves, among others, with accuracy and recall above 93%. The model's proficiency in extracting critical information from these plots not only underscores its capability in data mining but also highlights its potential in aiding the creation of comprehensive digital databases for reticular chemistry. In addition, the extracted nitrogen isotherm data from those selected literature allowed for a comparison between theoretical and experimental porosity values for over 200 compounds, highlighting certain discrepancies and underscoring the importance of integrating computational and experimental data. This work highlights the potential of AI in accelerating scientific discovery and innovation, bridging the gap between computational tools and experimental research, and paving the way for more efficient, inclusive, and comprehensive scientific inquiry. Copyright © 2023, The Authors. All rights reserved.

关键词： Data mining

Navigating the challenges in creating complex data systems: a development philosophy

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arXiv 2022年

作者： Dittmer, Sören Roberts, Michael Gilbey, Julian Biguri, Ander Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Thorpe, Matthew Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Stanczuk, Jan Tang, Jing Babar, Judith Sánchez, Lorena Escudero Teare, Philip Patel, Mishal Wassin, Marcel Holzer, Markus Walton, Nicholas Lió, Pietro Shadbahr, Tolou Sala, Evis Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Department of Radiology University of Cambridge Cambridge United Kingdom Faculty of Mathematics University of Vienna Austria Department of Mathematics University of Manchester Manchester United Kingdom Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow – not biphasically plan and build – DSSs, and one should always employ two types of feedback loops during development: one which tests the code’s correctness and another that evaluates the code’s efficacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Software engineering

Reconstructing the evolution history of networked complex systems

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arXiv 2024年

作者： Wang, Junya Zhang, Yi-Jiao Xu, Cong Li, Jiaze Sun, Jiachen Xie, Jiarong Feng, Ling Zhou, Tianshou Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China Department of Data Analytics and Digitalisation School of Business and Economics Maastricht University Maastricht6200MD Netherlands Tencent Inc Shenzhen518000 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China 138632 Singapore Department of Physics National University of Singapore 117551 Singapore School of Mathematics Sun Yat-sen University Guangzhou510275 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks. © 2024, CC BY-NC-ND.

关键词： Restoration

Improving Transformers with Probabilistic Attention Keys

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arXiv 2021年

作者： Nguyen, Tam Nguyen, Tan M. Le, Dung D. Nguyen, Duy Khuong Tran, Viet-Anh Baraniuk, Richard G. Ho, Nhat Osher, Stanley J. FPT Software AI Center Hanoi Viet Nam Department of Mathematics University of California Los AngelesCA United States College of Engineering and Computer Science VinUniversity Hanoi Viet Nam Deezer Research France Department of Electrical and Computer Engineering Rice University HoustonTX United States Department of Statistics and Data Sciences The University of Texas AustinTX United States

Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and computer vision tasks. It has been observed that for many applications, those attention heads learn redundant embedding, and most of them can be removed without degrading the performance of the model. Inspired by this observation, we propose Transformer with a Mixture of Gaussian Keys (Transformer-MGK), a novel transformer architecture that replaces redundant heads in transformers with a mixture of keys at each head. These mixtures of keys follow a Gaussian mixture model and allow each attention head to focus on different parts of the input sequence efficiently. Compared to its conventional transformer counterpart, Transformer-MGK accelerates training and inference, has fewer parameters, and requires fewer FLOPs to compute while achieving comparable or better accuracy across tasks. Transformer-MGK can also be easily extended to use with linear attention. We empirically demonstrate the advantage of Transformer-MGK in a range of practical applications, including language modeling and tasks that involve very long sequences. On the Wikitext-103 and Long Range Arena benchmark, Transformer-MGKs with 4 heads attain comparable or better performance to the baseline transformers with 8 heads. © 2021, CC BY.

关键词： Benchmarking

Unraveling the impact of sediment size distribution on flow-sediment hysteresis in estuarine environments: insights from size-resolved sediment transport modeling

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Environmental Research Communications 2025年第6期7卷 061011-061011页

作者： Zheng Fang Fanghua Xu Xueyang Zhang Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing 100084 People’s Republic of China Department of Mathematics and Statistics College of Engineering Mathematics and Physical Sciences University of Exeter Devon EX4 4QE United Kingdom Department of Meteorology School of Mathematical Physical and Computational Sciences University of Reading Berkshire RG6 6ET United Kingdom

Flow-sediment hysteresis, a nonlinear time-lagged relationship between sediment transport and hydrodynamic conditions, is particularly important yet difficult to simulate in tidal estuarine environments. This study introduces a size-resolved sediment transport model to simulate flow-sediment hysteresis, with an application in the Hudson River estuary. By incorporating variations in sediment size distribution induced by flocculation, the model allows for a more accurate simulation of flow-sediment hysteresis compared to traditional size-fixed models, achieving accuracy improvements of 4.89% ∼ 12.19% during tidal cycles. Moreover, budget analyses and correlation matrix analyses are conducted. The results indicate that the dynamics of small and large flocs influence hysteresis differentially: flocculation processes dominate the behavior of small flocs, while settling and vertical diffusion processes primarily affect large flocs. These insights offer a deeper understanding of sediment transport in tidal estuaries, providing a robust framework for enhanced predictive modeling which can be adapted to other estuarine environments.

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Dynamic network analysis improves protein 3D structural classification

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arXiv 2021年

作者： Newaz, Khalique Piland, Jacob Clark, Patricia L. Emrich, Scott J. Li, Jun Milenković, Tijana Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Chemistry and Biochemistry University of Notre Dame Notre DameIN46556 United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46556 United States Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3D structural features. We recently proposed PSC approaches that model protein 3D structures as protein structure networks (PSNs) and analyze PSN-based protein features, which performed better than or comparable to state-of-the-art sequence or other 3D structure-based approaches in the task of PSC. However, existing PSN-based PSC approaches model the whole 3D structure of a protein as a static PSN. Because folding of a protein is a dynamic process, where some parts of a protein fold before others, modeling the 3D structure of a protein as a dynamic PSN might further help improvethe existing PSC performance. Here, we propose for the first time a way to model 3D structures of proteins as dynamic PSNs, with the hypothesis that this will improve upon the current state-of-the-art PSC approaches that are based on static PSNs (and thus upon the existing state-of-the-art sequence and other 3D structural approaches). Indeed, we confirm this on 71 datasets spanning ∼44,000 protein domains from CATH and SCOPe. © 2021, CC BY.

关键词： Proteins

Towards Demand-Driven Optimization Algorithms in Electromagnetic engineering (Invited Paper)

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Towards Demand-Driven Optimization Algorithms in Electromagn...

IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： Maria Kovaleva David Bulger Karu P. Esselle School of Engineering Macquarie University Sydney Australia Department of Mathematics and Statistics Macquarie University Sydney Australia School of Electrical & Data Engineering University of Technology Sydney Sydney Australia

ISBN: (数字)9781728134482

ISBN: (纸本)9781728134499

With the increasing popularity of optimization algorithms in electromagnetic engineering, it is clear that mixed-variable design problems prevail. This paper shows that the Cross-Entropy (CE) optimization method is intrinsically versatile to handle these and other types of problems. We provide implementation details of two antenna examples optimized by the CE method to demonstrate its elegance and efficiency.

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Integrated data envelopment analysis, multi-criteria decision making, and cluster analysis methods: Trends and perspectives

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Decision Analytics Journal 2023年 8卷

作者： de Oliveira, Maiquiel Schmidt Steffen, Vilmar de Francisco, Antonio Carlos Trojan, Flavio Academic Department of Physics Statistics and Mathematics (DAFEM) Federal University of Technology - Parana (UTFPR) Rua Gelindo João Folador 2000 Francisco Beltrão Paraná 85602-863 Brazil Academic Departments of Engineering (DAENG) Federal University of Technology - Parana (UTFPR) Rua Gelindo João Folador 2000 Francisco Beltrão 85602-863 Paraná Brazil Academic Department of Electronics (DAELE) Federal University of Technology - Parana (UTFPR) Rua Doutor Washington Subtil Chueire 330 Ponta Grossa 84017-220 Paraná Brazil Post-Graduate Program in Production Engineering (PPGEP) Federal University of Technology - Parana (UTFPR) Rua Doutor Washington Subtil Chueire 330 Ponta Grossa 84017-220 Paraná Brazil

Data Envelopment Analysis (DEA), Multi-criteria Decision Analysis (MCDA), and Cluster Analysis (CA) are techniques widely used to help decision-makers determine the solution to problems with multiple and often conflicting criteria. This study presents a comprehensive review of the literature on the DEA, MCDA, and CA models to identify existing and potential applications and future development trends. To this end, the Methodi Ordinatio was applied to determine which publications have the greatest impact, considering articles in three databases: Scopus, ScienceDirect, and Web of Science. We pair the techniques two by two since no results were found to integrate the three techniques. The results point to a portfolio with 490 articles, in which approximately 43.87% of the articles combine DEA and MCDA techniques for solving efficiency and productivity analysis problems. © 2023 The Author(s)

关键词： Cluster analysis Combined decision techniques Data envelopment analysis Literature review Methodi ordinatio Multi-criteria decision making