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Dual template using P123-gelatin for synthesized large mesoporous silica for enhanced adsorption of dyes

South African Journal of Chemical engineering 2023年 43卷 312-326页

作者： Safitri, Widiya Nur Habiddin, Habiddin Ulfa, Maria Trisunaryanti, Wega Bahruji, Hasliza Holilah, Holilah Rohmah, Alya Awinatul Sholeha, Novia Amalia Jalil, Aishah Abdul Santoso, Eko Prasetyoko, Didik Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember East Java Keputih Sukolilo Surabaya 60111 Indonesia Chemistry Education Study Program Faculty of Teacher Training and Education Universitas Sebelas Maret Jl. Ir. Sutami 36A Surakarta 57126 Indonesia Department of Chemistry Faculty of Science Universitas Gadjah Mada Sekip Utara Sleman Indonesia Centre for Advanced Material and Energy Sciences Universiti Brunei Darussalam Bandar Seri Begawan BE 1410 Brunei Darussalam Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Department of Food Science and Technology Faculty of Agriculture Halu Oleo University Indonesia College of Vocational Studies Bogor Agricultural University (IPB University) Jalan Kumbang No. 14 Bogor 16151 Indonesia Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia 81310 UTM Johor Skudai Johor Bahru Malaysia

Mesoporous Silica with a large mesopore channel was synthesized using the sol-gel method for dye adsorption. A dual template consisting of P123 and gelatin at different ratios was employed in the sol-gel synthesis to enhance surface area and mesopore diameter. Gelatin is a green pore directing agent that strengthens P123 thermal stability during the calcination of silicate gel. Optimization of P123:gelatin ratio to 1:0.02 increased the surface area of mesoporous silica to 561.9 m²/g and expanded the pore diameter to 10 nm. The large pore diameter and surface area enhanced methylene blue adsorption capacity to 363.63 mg/g. The adsorption of methylene blue on mesoporous silica follows the pseudo second-order kinetic and the Langmuir model. Regeneration studies using thermal and chemical treatments exhibited the potential of mesoporous silica as an adsorbent for multiple adsorption-desorption of dyes. © 2022 The Author(s)

关键词： Dual template Dye Gelatin Mesoporous silica Pluronic P123

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Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

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IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： Optimization algorithms

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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Tipping Prediction of a Class of Large-Scale Radial-Ring Neural Networks

SSRN

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SSRN 2024年

作者： Lu, Yunxiang Xiao, Min Wu, Xiaoqun Karimi, Hamid Reza Xie, Xiangpeng Cao, Jinde Zheng, Wei Xing College of Automation College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China College of Computer Science and Software Engineering Shen Zhen University Shen Zhen518060 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China School of Mathematics Southeast University Nanjing210096 China School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

Understanding the emergence of collective dynamics in large-scale neural networks remains a challenging endeavor. This paper aims to address this gap by utilizing dynamic systems theory, particularly emphasizing tipping mechanisms. First of all, we introduce a novel $(n+mn)$-scale radial-ring neural network and utilize the topological approach of Coates' flow graph to determine the characteristic equation of the linearized network. Secondly, by deriving stability conditions and predicting the tipping point using the algebraic approach based on the integral element idea, we identify significant factors such as synaptic transmission delay, self-feedback coefficient, and network topology. Finally, we validate the effectiveness of the methodology in predicting the tipping point through numerical simulations. The simulations provide a thorough portrayal of the dynamics exhibited by large-scale neural networks, while also integrating robustness tests. This research contributes to a deeper understanding of the mechanisms underlying collective dynamics in large-scale neural networks, offering valuable insights for both theoretical frameworks and practical applications. © 2024, The Authors. All rights reserved.

关键词： Flow graphs

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Event detection and evolution in multi-lingual social streams

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Frontiers of Computer science 2020年第5期14卷 213-227页

作者： Yaopeng Liu Hao Peng Jianxin Li Yangqiu Song Xiong Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Department of Computer Science and Engineering HKUSTHong Kong99907China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China

Real-life events are emerging and evolving in social and news *** methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual *** this end,we propose a multi-lingual event mining model,namely MLEM,to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English,Chinese,French,German,Russian and ***,we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec *** propose an 8-tuple to describe event for correlation analysis and evolution graph *** evaluate the MLEM model using a massive human-generated dataset containing real world *** results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.

关键词： event detection event evolution stream processing multi-lingual anomaly detection

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Leveraging Probe data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Leveraging Probe Data and Machine Learning to Derive and Int...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Jin, Ling Xu, Xiaodan Wang, Yuhan Sadabadi, Kaveh Farokhi Lazar, Alina Don, Duleep Rathgamage Needell, Zachary Spurlock, C Anna Amirgholy, Mahyar Asudegi, Mona Energy Technology Area Berkeley National Laboratory BerkeleyCA94720 United States University of Maryland Center for Advanced Transportation Technology College ParkMD20742 United States Youngstown State University Department of Computer Science and Information YoungstownOH44555 United States Kennesaw State University School of Data Science and Analytics MariettaGA30060 United States Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kennesaw State University Civil and Environmental Engineering Department MariettaGA30060 United States Office of Transportation Policy Studies Federal Highway Administration DC WashingtonDC20590 United States

ISBN: (纸本)9798350324457

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level. © 2023 IEEE.

关键词： Interaction Shapley values machine learning models macroscopic fundamental diagram TreeExplainer United States vehicle probe data

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Pilot test of the mutual assistance system using a wearable device for the elderly in WEB3.0 technology

Pilot test of the mutual assistance system using a wearable ...

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IIAI International Conference on advanced Applied Informatics (IIAIAAI)

作者： Kenji Nakamura Keiichi Yamazaki Yusuke Arano Akiko Yamazaki Hiroshi Koga Naoya Ohta Takuya Mitsuhashi Hideru Obinata Yoshiaki Ohyama Center for Mathematics and Data Science Gunma University Gunma Japan Graduate School of Humanities and Social Sciences Saitama University Saitama Japan Faculty of Media Studies Tokyo University of Technology Tokyo Japan Department of Dermatology School of Medicine Shinshu University Nagano Japan Faculty of Engineering Gunma University Gunma Japan Division of Advanced Science and Technology Japan Advanced Institute of Science and Technology Tokyo Japan Graduate School of Medicine Gunma University Gunma Japan Innovative Medical Research Center Gunma University Hospital Gunma Japan

Using wearable devices and WEB3.0 to make doctor-patient interaction smarter. The aging of the population and COVID-19 have placed a tremendous workload on the medical field. Traditional monitoring has been centralized, with healthcare professionals providing face-to-face care to all patients. This is where blockchain is utilized, where users themselves have data and monitor each other. Mutual monitoring of medical information on the blockchain creates a mutually supportive relationship where users themselves monitor each other. In this demonstration experiment, the system was introduced to six elderly people. This decentralized system in which users themselves monitor each other is called the Web3 type. The Web type is a technology that is expected to break through the current concentration of data in overseas digital industries and leave digital data resources in Japan. This research is unique in that it combines blockchain technology, which is at the core of this technology, with medical information. A mutual assistance system was constructed to visualize and mutually monitor their exercise habits using wearable terminals. As a result, the exercise habits of all participants improved, and unique information was obtained from the questionnaire survey.

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Simultaneous Q-Space Sampling Optimization and Reconstruction for Fast and High-Fidelity Diffusion Magnetic Resonance Imaging

Simultaneous Q-Space Sampling Optimization and Reconstructio...

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IEEE International Symposium on Biomedical Imaging

作者： Jing Yang Jian Cheng Cheng Li Wenxin Fan Juan Zou Ruoyou Wu Qiegen Liu Hairong Zheng Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou China Department of Electronic Information Engineering Nanchang University Nanchang China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise.

关键词： Water In vivo Limiting Magnetic resonance imaging Harmonic analysis Robustness Image reconstruction

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot 6

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

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6th International Conference on Robotics and Automation engineering, ICRAE 2021

作者： Chengbo, Li Xiaoming, Wang Zixi, Huang Guangyao, Pang Xinyu, Han Yaguang, Lin Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an710119 China Shenzhen Middle School Shenzhen China Wuzhou University School of Data Science and Software Engineering Wuzhou China Singapore Polytechnic Advanced Robotics and Intelligent Control Centre Singapore

ISBN: (纸本)9781665406970

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the 'Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm'. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot. © 2021 IEEE.

关键词： Earthquakes

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