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Polyzwitterionic cross-linked double network hydrogel electrolyte enabling high-stable Zn anode

Nano Research 2024年第6期17卷 5278-5287页

作者： Mengyu Shi Junlong Zhang Guochuan Tang Ben Wang Sen Wang Xiaoxian Ren Guojie Li Weihua Chen Chuntai Liu Changyu Shen State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450002China College of Chemistry and Green Catalysis Center Zhengzhou UniversityZhengzhou 450001China Research Institute of Interdisciplinary Science and School of Materials Science and Engineering Dongguan University of TechnologyDongguan 523808China

Zn metal anode suffers from dendrite issues and passive byproducts,which severely plagues the practical application of aqueous Zn metal ***,a polyzwitterionic cross-linked double network hydrogel electrolyte composed of physical crosslinking(hyaluronic acid)and chemical crosslinking(synthetic zwitterionic monomer copolymerized with acrylamide)is introduced to overcome these *** the one hand,highly hydrophilic physical network provides an energy dissipation channel to buffer stress and builds a H_(2)O-poor interface to avoid side *** the other hand,the charged groups(sulfonic and imidazolyl)in chemical crosslinking structure build anion/cation transport channels to boost ions’kinetics migration and regulate the typical solvent structure[Zn(H_(2)O)_(6)]^(2+)to R-SO_(3)^(−)[Zn(H_(2)O)_(4)]^(2+),with uniform electric field distribution and significant resistance to dendrites and parasitic *** on the above functions,the symmetric zinc cell exhibits superior cycle stability for more than 420 h at a high current density of 5 mA·cm^(−2),and Zn||MnO_(2)full cell has a reversible specific capacity of 150 mAh·g^(−1)after 1000 cycles at 2 C with this hydrogel ***,the pouch cell delivers impressive flexibility and cyclability for energy-storage applications.

关键词： Zn anode dendrites parasitic reactions polyzwitterion double network hydrogel electrolyte

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms

Pedestrian detection and gender recognition utilizing YOLO a...

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作者： Zixuan Zhong Software and Systems engineering Lappeenranta University of Technology

As crowd-based activities continue to surge in locales such as markets and restaurants,the significance of understanding pedestrian flow is increasingly *** recent years,advancements in dynamic pedestrian detection,facilitated by the YOLO(You Only Look Once)algorithm,have seen widespread application in areas like crowd management and occupancy *** YOLO algorithm has demonstrated high accuracy and efficiency in real-time object tracking and ***,for specific use cases,data derived solely from monitoring pedestrian flows may prove *** study presents YOLO-Gender,a system leveraging YOLO and Convolutional Neural Network(CNN) for pedestrian tracking and gender *** objective is to enhance the richness of data extracted from surveillance camera footage,thus rendering it more valuable for societal *** YOLO suite of algorithms,hailed for their superior performance and rapid iteration speed,is among the most extensively utilized tools in the *** proposed system is predicated on YOLO v8,the most advanced iteration of the YOLO algorithm,released in 2023,which boasts its highest accuracy to date.

关键词： YOLO v8 pedestrian tracking gender classification Convolutional Neural Network transfer learning

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MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

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Computers, Materials & Continua 2025年第2期82卷 2969-2984页

作者： Ruoxin Li Shaoxiong Wu Fengping Deng Zhongli Tian Hua Cai Xiang Li Xu Xu Qi Liu NARI-TECH Nanjing Control System Co. Ltd.Nanjing211106China School of Software Nanjing University of Information Science&TechnologyNanjing210044China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044China

Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.

关键词： Electrical load forecasting cloud computing smart grid weather attributes energy consumption time-series analysis

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Cross-Modal Relation and Sketch Prototype Learning for Zero-Shot Sketch-Based Image Retrieval 6

Cross-Modal Relation and Sketch Prototype Learning for Zero-...

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6th International Conference on Computer Science and Artificial Intelligence, CSAI 2022

作者： Song, Yuanping Yu, Yanwei Tang, Hao Guo, Junyuan Wang, Yibo School of Software Engineering University of Science and Technology of China China School of Software Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China

ISBN: (纸本)9781450397773

Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is an innovative cross-modal task that utilizes a sketch to retrieve corresponding images in the zero-shot learning scene. At present, most algorithms treat ZS-SBIR as a typical image classification problem, using image-level features with triplet or cross entropy loss to achieve retrieval, while ignore the correspondence between sketches and images on local features. Therefore, we propose a new Local Feature Contrastive Network (LFCN) for ZS-SBIR from the perspective of contrastive learning. More specifically, a local feature contrastive method is proposed to establish the cross-modal relationships between images and sketches with creatively applying transformers to extract similarity representation from sketch-image pairs to narrow the domain gap. Furthermore, a feature prototype memory bank is designed to learn sketch prototypes to address the in-class diversity problem in sketch domain. A large number of experiments show that our method notably superior to the state-of-the-art algorithms in both TU-Berlin and Sketchy datasets. © 2022 ACM.

关键词： Zero-shot learning

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Dramatizing software Patterns: Focus Group Report 28

Dramatizing Software Patterns: Focus Group Report

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Vranic, Valentino Vranic, Aleksandra Vranic, Branislava Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9798400700408

The focus group on drama patterns we organized at EuroPLoP 2023 attracted 16 participants (apart from the organizers), who together built a short drama play (created by Aleksandra Vranic) based on the motifs from the stories of Alice in Wonderland. We discussed how particular drama patterns metaphorically correspond to some software patterns and elaborated on two examples of such correspondence: the Thoughts Reflecting Environment drama pattern and its correspondence to the Observer design pattern and the Loosely Coupled Situations drama pattern and its correspondence to the Visitor design pattern. Both drama patterns were first played by the participants and then related to the corresponding design pattern. We also played Observer in a technical, nonmetaphoric way. We also made a performance at the banquet, which was accepted with delight. We achieved this with no rehearsals and with the actor for the main role appointed just before the performance, which confirmed once more how powerful patterns are. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： design patterns drama patterns organizational patterns pattern composition

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Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

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Computers, Materials & Continua 2023年第9期76卷 3745-3761页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Zeinel Momynkulov Aray Kassenkhan Saltanat Nuralykyzy Mereilim Iglikova Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmatyKazakhstan Department of Software Engineering Satbayev UniversityAlmatyKazakhstan

Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease *** research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG *** proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)*** considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over *** model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart *** results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five ***,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease ***,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG *** proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and *** study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease *** highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

关键词： Heart disease detection classification CNN LSTM Conv-LSTM

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Discussion Manipulation, Language and Domain Dependent Models: An Overview

Discussion Manipulation, Language and Domain Dependent Model...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Chalas, Filip Stupavsky, Igor Vranic, Valentino Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Slovakia

ISBN: (纸本)9798350347722

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site ***, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset-both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression-accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions. © 2023 IEEE.

关键词： Logistic regression

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Development of Automatic Cartesian Robot Cannabis Seeder using LabVIEW 10

Development of Automatic Cartesian Robot Cannabis Seeder usi...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Kosarat, Rujipan Sangwai, Keattipoom Wongjan, Anan Rajamangala University of Technology Lanna Faculty of Engineering Department of Software Engineering Chiangmai Thailand Rajamangala University of Technology Lanna Faculty of Engineering Department of Electronic Engineering and Automatic Control Systems Chiangmai Thailand

ISBN: (纸本)9798331543273

This article describes the invention of autonomous cannabis seeding equipment to reduce contamination and planting time. The automated cannabis seeder employs an NI myRIO control board to operate a y-axis stepper motor attached to a ball screw to move along the breadth of each row of 14 seed trays. Soil drilling and burial Drill all holes at the same depth. This promotes seed germination and lowers human-seed germ contamination. These 7 soil drill heads and 7 seeders use 3D printers to design and shape the workpiece. An 800-watt air pump smokes the cannabis seeds. Linear electric motors function in two rows in the z-axis. The buffer reverses the drowning process from row 14 to the first row, starting from the 3rd row and continuing through the final series of steps until 14 rows in the y-axis are complete. The hardware controls all program activities in LabVIEW. We tested the automated cannabis seeding machine 100 times on average, sowing cannabis seeds into 98 seed trays and dropping 93 seeds at 94.80% efficiency in 10.38 minutes. A 16 percent faster machine than human work is available. © 2025 IEEE.

关键词： Stepping motors

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