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Operando FTIR study on water additive in lithium-sulfur batteries to mitigate shuttle effect

Journal of Energy Chemistry 2024年第11期98卷 702-713页

作者： Érick A.Santos Martim C.Policano Manuel J.Pinzón Isabela Galantini Vanessa A.Goncalves Francisco C.B.Maia Lucyano J.A.Macedo Gustavo Doubek Renato G.Freitas Hudson Zanin Advanced Energy Storage Division Center for Innovation on New EnergiesSchool of Electrical and Computer EngineeringUniversity of CampinasAv Albert Einstein 400CampinasSP 13083-852Brazil Catalytic Processes and Materials Group Department of Chemical EngineeringFaculty of Science and TechnologyMESA+Institute for NanotechnologyUniversity of TwentePO Box 2177500 AE EnschedeThe Netherlands Centre for Cooperative Research on Alternative Energies(CIC EnergiGUNE) Basque Research and Technology Alliance(BRTA)Vitoria-Gasteiz 01510Spain Institute of Physics&Institute of Chemistry Laboratory of Computational MaterialsFederal University of Mato GrossoCuiabá78060-900MTBrazil Brazilian Synchrotron Light Laboratory Brazilian Center for Research in Energy and MaterialsCampinas 13083-970SPBrazil Advanced Energy Storage Division LABCenter for Innovation on New EnergiesSchool of Chemical EngineeringUniversity of CampinasAv Albert Einstein 500Campinas 13083-852SPBrazil

Additives in the electrolytes of Li-S batteries aim to increase overall capacity,improve Li ion conductivity,enhance cyclability,and mitigate the shuttle effect,which is one of the major issues of this ***,the use of water as an additive in the commonly used electrolyte,1.0 M LiTFSI/1.0%(w/w) LiNO_(3) and a 1:1 mixture of 1,3-dioxolane(DOL) and 1,2-dimethoxyethane(DME) was *** used Co_(2)Mn_(0.5)Al_(0.5)O_(4)(CMA) as an electrocatalyst anchored on an activated carbon(AC) electrode with added sulfur via a melt-diffusion *** structural analysis of CMA via Rietveld refinement showed interatomic spaces that can promote ionic conductivity,facilitating Li^(+) ion *** tests determined 1600 ppm as the optimal water concentration,significantly reducing the shuttle ***-mortem XPS analysis focused on the lithium metal anode revealed the formation of Li_(2)O layers in dry samples and LiOH in wet *** capacity was observed in wet samples,which can be attributed to the superior ionic conductivity of LiOH at the electrode/electrolyte interface,surpassing that of Li_(2)O by 12 ***,Operando FTIR experiments provided real-time insights into electrolyte degradation and SEI formation,elucidating the activity mechanisms of water and Li_(2)CO_(3) over the *** work presents results that could aid future advancements in Li-S battery technology,offering possibilities to mitigate its challenges with inexpensive and scalable additives.

关键词： Diffusion Li-S Ionic conductivity Operando FTIR

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Enhancing 5G and 6G networks through a dynamic dual-stage machine learning heuristic framework for selecting UEs as UE-VBSs

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Ad Hoc Networks 2025年 177卷

作者： Ioannou, Iacovos Rani, S.V. Jansi Nagaradjane, Prabagarane Christophorou, Christophoros Vassiliou, Vasos Pitsillides, Andreas Department of CSE Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Computer Science University of Cyprus Cyprus CYENS Research Center Cyprus Wireless Technology Lab Department of ECE Sri Sivasubramaniya Nadar College of Engineering Chennai India Department of Electrical & Electronic Engineering Science University of Johannesburg South Africa Cyprus Larnaka Cyprus

Adapting mobile networks to the diverse and evolving demands of 5G and forthcoming 6G technologies requires flexible, efficient, and dynamic strategies—especially in ultra-dense environments and infrastructure-limited areas. This paper proposes a robust two-stage Machine Learning (ML) heuristic framework to dynamically select a group of User Equipment (UEs) to act as Virtual Base Stations (UE-VBSs) for network augmentation. In the first stage, Self-Organizing Maps (SOM) are employed to cluster UEs based on their spatial characteristics while preserving topological relationships, achieving a silhouette score of 0.64—a 30% improvement over conventional methods such as K-Means (0.46) and Mean-Shift (0.43). In the second stage, a Random Forest classifier enhanced via the Synthetic Minority Over-sampling Technique (SMOTE) attains an average accuracy of 97% and an F1-Score of 0.88 in identifying eligible devices to become UE-VBSs, outperforming recent frameworks that typically report accuracies ranging between 85% and 92%. Comparative evaluation results demonstrate that our two-stage ML heuristic framework not only improves clustering accuracy and UE-VBS classification but also consistently outperforms state-of-the-art clustering methods in terms of network sum rate, power consumption, and scalability. Specifically, across all device densities (i.e., 200, 400, 600, 800, and 1000 UEs), our approach achieves the highest sum rate—peaking at nearly 1.8 billion bps (or 1.8 Gbps) at 1000 UEs—thus surpassing methods such as Affinity Propagation and Grid-based Clustering. Furthermore, by intelligently selecting UE-VBSs, the framework significantly reduces power consumption by effectively minimizing redundant transmissions and interference, making it an energy-efficient solution for large-scale 5G networks. Although the complexity of SOM clustering and Random Forest classification introduces higher computational overhead, the resulting improvements in throughput, energy efficiency

关键词： Random forests

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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Emo3D: Metric and Benchmarking Dataset for 3D Facial Expression Generation from Emotion Description

arXiv

引用

arXiv 2024年

作者： Dehghani, Mahshid Shafiee, Amirahmad Shafiei, Ali Fallah, Neda Alizadeh, Farahmand Gholinejad, Mohammad Mehdi Behroozi, Hamid Habibi, Jafar Asgari, Ehsaneddin NLP & DH Lab Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Qatar Computing Research In-stitute Doha Qatar

Existing 3D facial emotion modeling have been constrained by limited emotion classes and insufficient datasets. This paper introduces "Emo3D", an extensive "Text-Image-Expression dataset" spanning a wide spectrum of human emotions, each paired with images and 3D blendshapes. Leveraging Large Language Models (LLMs), we generate a diverse array of textual descriptions, facilitating the capture of a broad spectrum of emotional expressions. Using this unique dataset, we conduct a comprehensive evaluation of language-based models’ fine-tuning and vision-language models like Contranstive Language Image Pre-training (CLIP) for 3D facial expression synthesis. We also introduce a new evaluation metric for this task to more directly measure the conveyed emotion. Our new evaluation metric, Emo3D, demonstrates its superiority over Mean Squared Error (MSE) metrics in assessing visual-text alignment and semantic richness in 3D facial expressions associated with human emotions. "Emo3D" has great applications in animation design, virtual reality, and emotional human-computer interaction. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Stochastic Recursive Gradient Descent Optimization-based on Foreground Features of Fisher Vector 14

Stochastic Recursive Gradient Descent Optimization-based on ...

引用

14th International Conference on Digital Image Processing, ICDIP 2022

作者： Kamaleldin, Mohamed Gamal M. Abu-Bakar, Syed A.R. Sheikh, Usman Ullah Electronics and Communications Engineering Department Arab Academy for Science and Technology Cairo11736 Egypt Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81300 Malaysia

ISBN: (纸本)9781510657564

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance, first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51. © 2022 SPIE.

关键词： Support vector machines

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Differential Privacy for Class-based Data: A Practical Gaussian Mechanism

arXiv

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arXiv 2023年

作者： Ramakrishna, Raksha Scaglione, Anna Wu, Tong Ravi, Nikhil Peisert, Sean Division of Network and Systems Engineering EECS KTH Royal Institute of Technology Stockholm Sweden The Department of Electrical and Computer Engineering Cornell Tech New York CityNY10044 United States The Computer Sciences Research and Development Department Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism. We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements. © 2023, CC BY.

关键词： Electric power utilization

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Programming by Example Made Easy

arXiv

引用

arXiv 2023年

作者： Wu, Jiarong Wei, Lili Jiang, Yanyan Cheung, Shing-Chi Ren, Luyao Xu, Chang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Electrical and Computer Engineering McGill University Montreal Canada State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Key Lab of High Confidence Software Technologies Ministry of Education Department of Computer Science and Technology EECS Peking University Beijing China

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee, which leverages an "entity-action" model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee’s effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers. Copyright © 2023, The Authors. All rights reserved.

关键词： Problem oriented languages

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From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities

arXiv

引用

arXiv 2023年

作者： Ishmam, Md Farhan Shovon, Md Sakib Hossain Mridha, M.F. Dey, Nilanjan Department of Computer Science and Engineering Islamic University of Technology Dhaka Bangladesh Advanced Machine Intelligence Research Lab Dhaka Bangladesh Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Computer Science and Engineering Techno International New Town Kolkata India

The multimodal task of Visual Question Answering (VQA) encompassing elements of computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers to questions on any visual input. Over time, the scope of VQA has expanded from datasets focusing on an extensive collection of natural images to datasets featuring synthetic images, video, 3D environments, and various other visual inputs. The emergence of large pre-trained networks has shifted the early VQA approaches relying on feature extraction and fusion schemes to vision language pre-training (VLP) techniques. However, there is a lack of comprehensive surveys that encompass both traditional VQA architectures and contemporary VLP-based methods. Furthermore, the VLP challenges in the lens of VQA haven’t been thoroughly explored, leaving room for potential open problems to emerge. Our work presents a survey in the domain of VQA that delves into the intricacies of VQA datasets and methods over the field’s history, introduces a detailed taxonomy to categorize the facets of VQA, and highlights the recent trends, challenges, and scopes for improvement. We further generalize VQA to multimodal question answering, explore tasks related to VQA, and present a set of open problems for future investigation. The work aims to navigate both beginners and experts by shedding light on the potential avenues of research and expanding the boundaries of the field. © 2023, CC BY.

关键词： Question answering

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Exploring Enhancements to the Stacked LSTM Outlier Detection and Correction Method

Exploring Enhancements to the Stacked LSTM Outlier Detection...

引用

International Conference on computer and Applications (ICCA)

作者： Juan Zuluaga Navid Shaghaghi Department of Computer Science and Engineering (CSEN) Ethical Pragmatic and Intelligent Computing (EPIC) Research Lab School of Engineering (SoE) Santa Clara University (SCU) Santa Clara California USA

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Existence of outliers in a dataset render it problematic for Time Series Forecasting. Hence various mathematical and statistical methodologies such as Omission and imputation, Z-Score Normalization, Winsorization, and Robust Regression, have been utilized but often fall short especially when working with datasets containing extensive outlier zones. A novel solution proposed and evaluated by the authors in a previous paper has shown promise especially when utilizing Long Short-Term Memory (LSTM)s, but further enhancements in the form of increasing training data size and variation, ensemble strategies, autoencoders, and smoothing techniques were suggested. This paper reports on the exploration of the effects of these enhancements on said solution.

关键词： Smoothing methods Autoencoders Time series analysis Training data Predictive models Data models Complexity theory Forecasting Long short term memory Anomaly detection

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Strong nonlinear optical processes with extraordinary polarization anisotropy in inversionsymmetry broken two-dimensional PdPSe

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Light(Science & Applications) 2024年第8期13卷 1524-1533页

作者： Song Zhu Ruihuan Duan Xiaodong Xu Fangyuan Sun Wenduo Chen Fakun Wang Siyuan Li Ming Ye Xin Zhou Jinluo Cheng Yao Wu Houkun Liang Junichiro Kono Xingji Li Zheng Liu Qi Jie Wang School of Electrical and Electronic Engineering Nanyang Technological University639798 SingaporeSingapore School of Material Science and Engineering Nanyang Technological University639798 SingaporeSingapore CINTRA CNRS/NTU/THALES UMI 3288Research Techno PlazaNanyang Technological University637371 SingaporeSingapore School of Materials Science and Engineering Harbin Institute of Technology150001 HarbinChina Department of Chemistry National University of Singapore117543 SingaporeSingapore GPL Photonics Lab State Key Laboratory of Applied OpticsChangchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of Sciences130033 ChangchunChina School of Electronics and Information Engineering Sichuan University610064 ChengduSichuanChina School of Physical and Mathematical Sciences Nanyang Technological University637371 SingaporeSingapore Departments of Electrical and Computer Engineering Physics and Astronomyand Materials Science and NanoEngineeringRice UniversityHoustonTXUSA

Nonlinear optical activities,especially second harmonic generation(SHG),are key phenomena in inversion-symmetrybroken two-dimensional(2D)transition metal dichalcogenides(TMDCs).On the other hand,anisotropic nonlinear optical processes are important for unique applications in nano-nonlinear photonic devices with polarization functions,having become one of focused research topics in the field of nonlinear ***,the strong nonlinearity and strong optical anisotropy do not exist simultaneously in common 2D ***,we demonstrate strong secondorder and third-order susceptibilities of 64 pm/V and 6.2×10^(-19)m^(2)/V^(2),respectively,in the even-layer PdPSe,which has not been discovered in other common TMDCs(e.g.,MoS2).Strikingly,it also simultaneously exhibited strong SHG anisotropy with an anisotropic ratio of~45,which is the largest reported among all 2D materials to date,to the best of our *** addition,the SHG anisotropy ratio can be harnessed from 0.12 to 45(375 times)by varying the excitation wavelength due to the dispersion ofχ^(2)*** an illustrative example,we further demonstrate polarized SHG imaging for potential applications in crystal orientation identification and polarization-dependent spatial *** findings in 2D PdPSe are promising for nonlinear nanophotonic and optoelectronic applications.

关键词： materials anisotropy nonlinear

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