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Incorporating self-assembled MXene to improve electrical conductivity of novel ZIF67 derivatives induced by NH4BF4 and NH4HF2 for energy storage

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Journal of Energy Storage 2024年 82卷

作者： Lee, Pin-Yan Chiou, Yu-Ren Cheng, Cheng-Siang Chen, Yu-Chun Kongvarhodom, Chutima Chen, Hung-Ming Yougbaré, Sibidou Husain, Sadang Ho, Kuo-Chuan Kuo, Tsung-Rong Lin, Lu-Yin Department of Chemical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand Gingen technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan /Nanoro 03 B.P 7192 Ouagadougou03 Burkina Faso Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin70124 Indonesia International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

Zeolitic imidazolate framework (ZIF-67) with high surface area and tunable pore size are considered as a promising active material for energy storage, but the low electrical conductivity restricts its performance. Developing ZIF67 derivatives with improved electrical conductivity is necessary to broaden its application in electrochemical fields. In this work, two strategies are applied to improve electrical conductivity and electrochemical performance of ZIF67. Novel structure directing agents of NH4BF4 and NH4HF2 are used to synthesize ZIF67 derivatives with higher theoretical capacitances and favorable layered structures. Self-assembled MXene with different amounts is further incorporated in ZIF67 derivatives to establish composites. The optimal composite electrode shows a high specific capacitance (CF) of 1410.4 F/g (775.6C/g) at 20 mV/s, while the ZIF67 derivative electrode without MXene only shows a CF value of 942.3 F/g (518.3C/g), due to the high surface area and better pore structure as well as the close combination of MXene and the layered ZIF67 derivative for the former case. An energy storage device with the optimal composite and carbon electrodes shows the maximum energy density of 32.40 Wh/kg at 375 W/kg. The Coulombic efficiency of 100% and the CF retention of 77.4% are also achieved after 10,000 cycles. © 2024 Elsevier Ltd

关键词： Energy storage

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology (IAICT)

作者： Rahmad Kurniawan Iwan Iskandar Fitra Lestari Habibi Al Rasyid Harpizon Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Department of Informatics Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Industrial Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia’s top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube’s features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments.

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Unbiased Estimation Using a Class of Diffusion Processes

SSRN

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SSRN 2022年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We study the problem of unbiased estimation of expectations with respect to (w.r.t.) π a given, general probability measure on (Rd,B(Rd)) that is absolutely continuous with respect to a standard Gaussian measure. We focus on simulation associated to a particular class of diffusion processes, sometimes termed the Schrödinger-Föllmer Sampler, which is a simulation technique that approximates the law of a particular diffusion bridge process {Xt}t∈[0,1] on Rd, d ∈ N0. This latter process is constructed such that, starting at X0 = 0, one has X1 ∼ π. Typically, the drift of the diffusion is intractable and, even if it were not, exact sampling of the associated diffusion is not possible. As a result, [10, 15] consider a stochastic Euler-Maruyama scheme that allows the development of biased estimators for expectations w.r.t. π. We show that for this methodology to achieve a mean square error of O(Ε2), for arbitrary Ε > 0, the associated cost is O(Ε−5). We then introduce an alternative approach that provides unbiased estimates of expectations w.r.t. π, that is, it does not suffer from the time discretization bias or the bias related with the approximation of the drift function. We prove that to achieve a mean square error of O(Ε2), the associated cost is, with high probability, O(Ε−2|log(Ε)|2+δ), for any δ > 0. We implement our method on several examples including Bayesian inverse problems. © 2022, The Authors. All rights reserved.

关键词： Diffusion

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Predictive Modeling of Sports-Related Concussions using Clinical Assessment Metrics

Predictive Modeling of Sports-Related Concussions using Clin...

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2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020

作者： Subhash, Sujit Obafemi-Ajayi, Tayo Goodman, Dennis Wunsch Ii, Donald Olbricht, Gayla R. Missouri University of Science Technology Mathematics and Statistics Dept RollaMO United States Missouri State University Engineering Program SpringfieldMO United States Missouri University of Science Technology Director of Sports Medicine RollaMO United States

ISBN: (纸本)9781728125473

Concussions represent a growing health concern that are challenging to diagnose and manage. Roughly four million concussions are diagnosed every year in the United States. While research in machine learning applications for concussions has focused on using advanced metrics such neuroimaging techniques and blood biomarkers, these metrics are yet to be implemented at a clinical level due to cost and reliability concerns. Therefore, concussion diagnosis is still reliant on clinical evaluations of symptoms, balance, and neurocognitive status and function. The lack of a universal threshold on these assessments makes the diagnosis process reliant on a physician's interpretation of these assessment scores. This study aims to explore the use of machine learning techniques to aid the concussion diagnosis process. These models could provide an automated means to flag concussed patients even before being seen by a doctor as well as expand the scope of concussion diagnosis to remote locations and areas with limited access to doctors. © 2020 IEEE.

关键词： Diagnosis

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Emergent dimer-model topological order and quasi-particle excitations in liquid crystals: combinatorial vortex lattices

arXiv

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arXiv 2025年

作者： Meng, Cuiling Wu, Jin-Sheng Kos, Žiga Dunkel, Jörn Nisoli, Cristiano Smalyukh, Ivan I. Department of Physics and Chemical Physics Program University of Colorado BoulderCO80309 United States Hiroshima University Hiroshima Higashihiroshima Japan Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia Department of Condensed Matter Physics Jožef Stefan Institute Ljubljana Slovenia Theoretical Division Physics of Condensed Matter and Complex Systems Center For Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

Liquid crystals have proven to provide a versatile experimental and theoretical platform for studying topological objects such as vortices, skyrmions, and hopfions. In parallel, in hard condensed matter physics, the concept of topological phases and topological order has been introduced in the context of spin liquids to investigate emergent phenomena like quantum Hall effects and high-temperature superconductivity. Here, we bridge these two seemingly disparate perspectives on topology in physics. Combining experiments and simulations, we show how topological defects in liquid crystals can be used as versatile building blocks to create complex, highly degenerate topological phases, which we refer to as ‘Combinatorial Vortex Lattices’ (CVLs). CVLs exhibit extensive residual entropy and support locally stable quasi-particle excitations in the form of charge-conserving topological monopoles, which can act as mobile information carriers and be linked via Dirac strings. CLVs can be rewritten and reconfigured on demand, endowed with various symmetries, and modified through laser-induced topological surgery — an essential capability for information storage and retrieval. We demonstrate experimentally the realization, stability, and precise optical manipulation of CVLs, thus opening new avenues for understanding and technologically exploiting higher-hierarchy topology in liquid crystals and other ordered media. Copyright © 2025, The Authors. All rights reserved.

关键词： Laser surgery

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A Smote-Based Churn Prediction System Using Machine Learning Techniques

A Smote-Based Churn Prediction System Using Machine Learning...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Akinyemi Omololu Akinrotimi Roseline Oluwaseun Ogundokun Modinat Abolore Mabayoje Rafiat Ajibade Oyekunle Marion Olubunmi Adebiyi Department of Mathematics Sciences(Computer Science Program) Augustine University Ilara-Epe Epe Nigeria Department of Computer Science Landmark University Omu-Aran Nigeria Department of Multimedia Engineering Kaunas University of Technology Lithuania SDG 4. Quality Education Landmark University Omu Aran Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Information and Communication University of Ilorin Ilorin Nigeria Department of Computer Science Landmark University Omu-Aran Omu Aran Nigeria

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection.

关键词： Measurement Companies Sensitivity and specificity Prediction algorithms Data models Telecommunications Numerical models

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Wave attenuation by a submerged circular porous membrane

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Journal of Ocean engineering and science 2022年

作者： Gayathri, R. Khan, Mohamin B M Behera, Harekrushna Tsai, Chia-Cheng Department of Mathematics SRM Institute of Science and Technology Chengalpattu District Kattankulathur Tamil Nadu 603203 India Bachelor Degree Program in Ocean Engineering and Technology National Taiwan Ocean University Keelung 202301 Taiwan Center of Excellence for Ocean Engineering National Taiwan Ocean University Keelung 202301 Taiwan

Wave action over a submerged circular flexible porous membrane is investigated and the impact of porosity in dampening the far-field wave amplitude is examined in a scattering of incoming waves. The unknown potential for the free surface and the membrane-covered regions are obtained by coupling the respective boundary conditions and Darcy's law for permeable structure. The associated boundary value problem is solved using the method of expansions of the eigenfunctions as Fourier-Bessel series. To understand the efficiency of the present study, wave force excitation on the circular membrane, far-field scattering wave amplitude, energy dissipation, and flow distribution are computed and examined. The study shows that on the leeward side of the membrane the wave amplitude reduces drastically. In addition, the submergence depth has a significant impact on wave energy dissipation. This model will be beneficial in developing the mechanism to minimize wave force on coastal structures for a variety of marine operations. In addition, this structure has minimal environmental effects on different coastline processes and can therefore be used in a variety of coastal/ocean applications. © 2022

关键词： Circular membrane Eigenfunction expansion method Flow distribution Plane wave integration Porous-effect parameter

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Unveiling the frontiers of deep learning: Innovations shaping diverse domains

arXiv

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arXiv 2023年

作者： Ahmed, Shams Forruque Alam, Md. Sakib Bin Kabir, Maliha Afrin, Shaila Rafa, Sabiha Jannat Mehjabin, Aanushka Gandomi, Amir H. School of Mathematical Sciences Sunway University Bandar Sunway Petaling Jaya Selangor Darul Ehsan 47500 Malaysia Department of Mathematics & Physics North South University Dhaka1229 Bangladesh Science and Math Program Asian University for Women Chattogram4000 Bangladesh School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia Óbuda University Budapest1034 Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

Deep learning (DL) allows computer models to learn, visualize, optimize, refine, and predict data. To understand its present state, examining the most recent advancements and applications of deep learning across various domains is essential. However, prior reviews focused on DL applications in only one or two domains. The current review thoroughly investigates the use of DL in four different broad fields due to the plenty of relevant research literature in these domains. This wide range of coverage provides a comprehensive and interconnected understanding of DL’s influence and opportunities, which is lacking in other reviews. The study also discusses DL frameworks and addresses the benefits and challenges of utilizing DL in each field, which is only occasionally available in other reviews. DL frameworks like TensorFlow and PyTorch make it easy to develop innovative DL applications across diverse domains by providing model development and deployment platforms. This helps bridge theoretical progress and practical implementation. Deep learning solves complex problems and advances technology in many fields, demonstrating its revolutionary potential and adaptability. CNN-LSTM models with attention mechanisms can forecast traffic with 99% accuracy. Fungal-diseased mango leaves can be classified with 97.13% accuracy by the multi-layer CNN model. However, deep learning requires rigorous data collection to analyze and process large amounts of data because it is independent of training data. Thus, large-scale medical, research, healthcare, and environmental data compilation are challenging, reducing deep learning effectiveness. Future research should address data volume, privacy, domain complexity, and data quality issues in DL *** Codes 68T07 © 2023, CC BY.

关键词： Natural language processing systems

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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Deep Potentials for Materials science

arXiv

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arXiv 2022年

作者： Wen, Tongqi Zhang, Linfeng Wang, Han Weinan, E. Srolovitz, David J. Department of Mechanical Engineering The University of Hong Kong Hong Kong DP Technology Beijing China AI for Science Institute Beijing China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing China HEDPS CAPT College of Engineering Peking University Beijing China School of Mathematical Sciences Peking University Beijing China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton United States Shenzhen China

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied;i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials. Copyright © 2022, The Authors. All rights reserved.

关键词： Calculations

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