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Hydrogen peroxide-enhanced magnetic resonance imaging: A novel approach for diagnosing anorectal-fistula

World Journal of Radiology 2025年第3期17卷 5-8页

作者： Riya Karmakar Devansh Gupta Arvind Mukundan Hsiang-Chen Wang Department of Mechanical Engineering National Chung Cheng UniversityChiayi 62102Taiwan Department of Computer Science&Engineering Thapar Institute of Engineering&TechnologyPatiala 147001PunjabIndia Department of Mechanical Engineering Center for Innovative Research on Aging SocietyNational Chung Cheng UniversityChiayi 621Taiwan

In this editorial,a commentary on the article by Chang et al has been provided,the course of treatment of anorectal fistulas,especially complex and recurring ones,require accurate diagnostic procedures for determining ideal surgical *** ways of imaging sometimes fall short,offering insufficient insights in aggravated *** this editorial,a novel application of hydrogen peroxide-enhanced magnetic resonance imaging(HP-MRI)that promises significant improvements in the imaging of anorectal *** is based on a retrospective investigation of 60 patients,contrasts the new HP-MRI with conventional diagnostic techniques such as physical examination,trans-perineal ultrasonography and poor spatial resolution *** findings demonstrate HP-MRI's incredible diagnostic performance,with sensitivity and specificity rates of 96.08%and 90.91%,respectively,and unparalleled interobserver agreement(Kappa values ranging from 0.80 to 0.89).It has been a significant advancement for assessment of anorectal fistulas providing a better roadmap for surgical planning,lowering recurrence rates as well as reduced personal and financial burden on patients by reducing the need for repeated treatment and extended hospital *** remaining funds can be utilized for treatment of other medical *** HP-MRI provides us a healthier&more efficient society by improvising patients well-being&optimized healthcare infrastructure.

关键词： Anorectal fistulas Magnetic resonance imaging Hydrogen peroxide Diagnostic imaging Fistula tract visualization Diagnostic accuracy Minimally invasive imaging Gadolinium contrast agent Retrospective analysis Perianal fistulas

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A New Model Based on Graph Neural Network Feedback for Crystal Property Prediction

A New Model Based on Graph Neural Network Feedback for Cryst...

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Big Data & Artificial Intelligence & Software engineering (ICBASE), International Conference on

作者： Jian Zhu Yongshen Ren Huaijuan Zang Shu Zhan School of Computer and Information Engineering Hefei University of Technology Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Faculty of Metallurgical and Energy Engineering Kunming University of Science and Technology Kunming China National Engineering Research Center of Vacuum Metallurgy Kunming University of Science and Technology Kunming China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

The prediction of crystal properties is crucial in crystal design. Currently, most methods employ graph neural networks to model crystal structures and have achieved satisfactory prediction accuracy. However, accurately simulating the complex relationships between atoms and molecules in crystal structures remains challenging. The prediction performance of crystal graph networks also depends on the effective extraction of crystal features, typically requiring a close correlation between the properties to be predicted and the extracted features. In this study, a model retraining method is utilized, where the loss function in the retraining phase is constructed using feedback from a pre-trained crystal graph network model to adjust model parameters and enhance the accuracy of predicting crystal properties. This method enhances the predictive performance of multiple crystal graph neural networks and accelerates the development of high-performance crystals without the need to add various features or incorporate additional neural network layers. The research findings demonstrate the significant effectiveness of the proposed method in enhancing the prediction performance of four models: CGCNN, OGCNN, GATGNN, and ALIGNN. Optimization metrics support this claim.

关键词： Measurement Accuracy Correlation Crystals Predictive models Feature extraction Graph neural networks Optimization Material properties Software engineering

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IoT-Aided Intelligent Irrigation System Using Edge Computing 2

IoT-Aided Intelligent Irrigation System Using Edge Computing

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Ayoade, Idowu Adetona Nwakanma, Cosmas Ifeanyi Alimi, Sheriff Akinde, Olusola Kunle Adebisi, Oluwadare Oladeji, Dolapo Michael Ajayi, Dolapo Boluwatife Kayode, Michael Ife First Technical University Department of Mechatronics Engineering Ibadan Nigeria Kumoh National Institute of Technology ICT Convergence Research Center Gumi Korea Republic of Babcock University Ilishan Department of Computer Science and Artificial Intelligence Remo Nigeria First Technical University Department of Electrical and Electronics Engineering Ibadan Nigeria

ISBN: (纸本)9798350358834

Farmers in developing nations are faced with limited technology for smart and economical ways of irrigation. This is worsened by the high cost of imports since most of these nations are import-dependent. Increasing agricultural yield to meet the growing population in most of these nations has led their governments to pursue cost-effective ways of managing water and other resources required for optimum agricultural yield. In this study, the Internet of Things (IoT) and edge computing are proposed to provide an intelligent irrigation system to bridge the irrigation gap. This research was carried out using a greenhouse with a controlled environment i.e., the ambient temperature, air humidity, ultraviolet radiations (UV), soil temperature, and soil moisture content were all monitored and measured in real-Time within the controlled environment. These data were collected using field sensors processed using IoT-enabled microcontrollers (Arduino kits). The data were processed via Amazon Web Services (A WS) for ontological capabilities of the system consisting of computational capabilities of IoT server and Edge Server. Internet-enabled mobile devices were used to monitor and adopt preferential yield rates for cultivation. Also, an irrigation sequencing algorithm was developed, which uses predictive machine learning K-Mean Clustering Algorithm to predict a more accurate soil moisture content with minimum standard error and a lower mean square error of 0.11 to the predicted soil content compared to when only SVR with mean square error of 0.12. © 2023 IEEE.

关键词： Internet of things

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WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs 25

WebCode2M: A Real-World Dataset for Code Generation from Web...

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34th ACM Web Conference, WWW 2025

作者： Gui, Yi Li, Zhen Wan, Yao Shi, Yemin Zhang, Hongyu Su, Yi Chen, Bohua Chen, Dongping Wu, Siyuan Zhou, Xing Jiang, Wenbin Jin, Hai Zhang, Xiangliang Huazhong University of Science and Technology Wuhan China Peking University Beijing China Chongqing University Chongqing China Hubei University of Automotive Technology Shiyan China Rabbitpre AI Shenzhen China University of Notre Dame Notre Dame United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology China

ISBN: (纸本)9798400712746

Automatically generating webpage code from webpage designs can significantly reduce the workload of front-end developers, and recent Multimodal Large Language Models (MLLMs) have shown promising potential in this area. However, our investigation reveals that most existing MLLMs are constrained by the absence of high-quality, large-scale, real-world datasets, resulting in inadequate performance in automated webpage code generation. To fill this gap, this paper introduces WebCode2M, a new dataset comprising 2.56 million instances, each containing a design image along with the corresponding webpage code and layout details. Sourced from real-world web resources, WebCode2M offers a rich and valuable dataset for webpage code generation across a variety of applications. The dataset quality is ensured by a scoring model that filters out instances with aesthetic deficiencies or other incomplete elements. To validate the effectiveness of WebCode2M, we introduce a baseline model based on the Vision Transformer (ViT), named WebCoder, and establish a benchmark for fair comparison. Additionally, we introduce a new metric, TreeBLEU, to measure the structural hierarchy recall. The benchmarking results demonstrate that our dataset significantly improves the ability of MLLMs to generate code from webpage designs, confirming its effectiveness and usability for future applications in front-end design tools. Finally, we highlight several practical challenges introduced by our dataset, calling for further research. © 2025 Copyright held by the owner/author(s).

关键词： Web Design

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Ethereum smart contract security research:survey and future research opportunities

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Frontiers of computer Science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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Numerical Procedure for Fractional HBV Infection with Impact of Antibody Immune

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computers, Materials & Continua 2023年第2期74卷 2575-2588页

作者： Sakda Noinang Zulqurnain Sabir Muhammad Asif Zahoor Raja Soheil Salahshour Wajaree Weera Thongchai Botmart Department of Mathematics Statistics and Computer Faculty of ScienceUbon Ratchathani UniversityUbon Ratchathani34190Thailand Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliouYunlin64002Taiwan Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbul34494Turkey Department of Mathematics Faculty of ScienceKhon Kaen UniversityKhon Kaen40002Thailand

The current investigations are presented to solve the fractional order HBV differential infection system(FO-HBV-DIS)with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation(LMB)neural networks(NNs),i.e.,*** FO-HBV-DIS with the response of antibody immune is categorized into five dynamics,healthy hepatocytes(H),capsids(D),infected hepatocytes(I),free virus(V)and antibodies(W).The investigations for three different FO variants have been tested numerically to solve the nonlinear *** data magnitudes are implemented 75%for training,10%for certification and 15%for testing to solve the FO-HBV-DIS with the response of antibody *** numerical observations are achieved using the stochastic LMBNNs procedures for soling the FO-HBV-DIS with the response of antibody immune and comparison of the results is presented through the database Adams-Bashforth-Moulton *** authenticate the validity,competence,consistency,capability and exactness of the LMBNNs,the numerical presentations using the mean square error(MSE),error histograms(EHs),state transitions(STs),correlation and regression are accomplished.

关键词： Fractional order HBV differential infection system artificial neural networks nonlinear Levenberg-Marquardt backpropagation Adams-Bashforth-Moulton

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AccelES: Accelerating Top-K SpMV for Embedding Similarity via Low-bit Pruning

AccelES: Accelerating Top-K SpMV for Embedding Similarity vi...

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IEEE Symposium on High-Performance computer Architecture

作者： Jiaqi Zhai Xuanhua Shi Kaiyi Huang Chencheng Ye Weifang Hu Bingsheng He Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computing National University of Singapore Singapore

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

In the realm of recommendation systems, achieving real-time performance in embedding similarity tasks is often hindered by the limitations of traditional Top-K sparse matrix-vector multiplication (SpMV) methods, which suffer from high latency due to inefficient memory access patterns. This paper identifies these critical gaps and introduces AccelES, a novel approach that significantly enhances the efficiency of Top-K SpMV. Our method employs a two-stage calculation scheme: the first stage utilizes a compact, low-bit dataset to quickly identify the most relevant entries, while the second stage performs full-precision calculations solely on this pruned subset, thereby minimizing computational overhead. Furthermore, AccelES incorporates innovative matrix representations, Ultra-CSR and Random-CSR, which optimize memory bandwidth utilization. Experimental results demonstrate that AccelES accelerates performance, surpassing state-of-the-art FPGA, GPU, and CPU solutions by factors of 3.4×, 2.5×, and 153.3×, respectively, under controlled conditions. These advancements not only enhance processing speed but also significantly improve real-time performance in recommendation systems, establishing AccelES as a pivotal contribution to the field of Top-K sparse matrix-vector multiplication.

关键词： Quantization (signal) High dimensional data Memory management Graphics processing units Bandwidth Real-time systems Sparse matrices Recommender systems Field programmable gate arrays Optimization

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Filter-Based Adaptive Model Pruning for Efficient Incremental Learning on Edge Devices

Filter-Based Adaptive Model Pruning for Efficient Incrementa...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jing-Jia Hung Yi-Jung Chen Hsiang-Yun Cheng Hsu Kao Chia-Lin Yang Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Incremental Learning (IL) enhances Machine Learning (ML) models over time with new data, ideal for edge devices at the forefront of data collection. However, executing IL on edges faces challenges due to limited resources. Common methods involve IL followed by model pruning or specialized IL methods for edges. However, the former increases training time due to fine-tuning and compromises accuracy for past classes due to limited retained samples or features. Meanwhile, existing edge-specific IL methods utilize weight pruning, which requires specialized hardware or compilers to speed up and cannot reduce computations on general embedded platforms. In this paper, we propose Filter-based Adaptive Model Pruning (FAMP), the first pruning method designed specifically for IL. FAMP prunes the model before the IL process, allowing fine-tuning to occur concurrently with IL, thereby avoiding extended training time. To maintain high accuracy for both new and past data classes, FAMP adapts the compressed model based on observed data classes and retains filter settings from the previous IL iteration to mitigate forgetting. Across all tests, FAMP achieves the best average accuracy, with only a 2.78% accuracy drop over full ML models with IL. Moreover, unlike the common methods that prolong training time, FAMP takes 35% shorter training time on average than using the full ML models for IL.

关键词： Training Adaptation models Accuracy Incremental learning Computational modeling Adaptive filters Hardware Data models Software Usability

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Deepfake detection using deep learning methods: A systematic and comprehensive review

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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2024年第2期14卷 e1520-e1520页

作者： Heidari, Arash Jafari Navimipour, Nima Dag, Hasan Unal, Mehmet Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Kadir Has Universitesi Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Management Information Systems Kadir Has Universitesi Istanbul Turkey Department of Computer Engineering Nisantasi Universitesi Istanbul Turkey

Deep Learning (DL) has been effectively utilized in various complicated challenges in healthcare, industry, and academia for various purposes, including thyroid diagnosis, lung nodule recognition, computer vision, large data analytics, and human-level control. Nevertheless, developments in digital technology have been used to produce software that poses a threat to democracy, national security, and confidentiality. Deepfake is one of those DL-powered apps that has lately surfaced. So, deepfake systems can create fake images primarily by replacement of scenes or images, movies, and sounds that humans cannot tell apart from real ones. Various technologies have brought the capacity to change a synthetic speech, image, or video to our fingers. Furthermore, video and image frauds are now so convincing that it is hard to distinguish between false and authentic content with the naked eye. It might result in various issues and ranging from deceiving public opinion to using doctored evidence in a court. For such considerations, it is critical to have technologies that can assist us in discerning reality. This study gives a complete assessment of the literature on deepfake detection strategies using DL-based algorithms. We categorize deepfake detection methods in this work based on their applications, which include video detection, image detection, audio detection, and hybrid multimedia detection. The objective of this paper is to give the reader a better knowledge of (1) how deepfakes are generated and identified, (2) the latest developments and breakthroughs in this realm, (3) weaknesses of existing security methods, and (4) areas requiring more investigation and consideration. The results suggest that the Conventional Neural Networks (CNN) methodology is the most often employed DL method in publications. According to research, the majority of the articles are on the subject of video deepfake detection. The majority of the articles focused on enhancing only one parameter, w

关键词： national security

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Design of Network Environment Advertisable Multipath Transmission Control System for Mobile Device Support

Design of Network Environment Advertisable Multipath Transmi...

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Information engineering (ACIE), IEEE Asia Conference on

作者： Naoya Kitagawa Takehiko Kashiwagi Kohta Ohshima Research and Development Center for Academic Networks National Institute of Informatics Tokyo Japan The University of Electro-Communications Parallel Networks LLC. Tokyo Japan Marine Electromechanical Engineering Division Tokyo University of Marine Science and Technology Tokyo Japan

ISBN: (数字)9798331528409

ISBN: (纸本)9798331528416

Uninterrupted wireless communication on the move is important for remote drone/robot operation, self-driving cars, and maritime autonomous surface ships. This paper describes a functional extension design of our previously developed fieldprogrammable gate array (FPGA)-based multipath transmission control system. The new network environment advertisable method uses the Wi-Fi round-trip time (RTT) and low-power wide-area (LPWA) networks to address the issue of not knowing whether handover is possible until the device is in motion, because Wi-Fi AP scanning is limited to the wireless communication range. We show how to apply the method to the hardware that we previously developed and its practical applicability through performance evaluation of Wi-Fi RTT and LPWA communication.

关键词： Wireless communication Performance evaluation Handover Logic gates Control systems Hardware Mobile handsets Marine vehicles Wireless fidelity Field programmable gate arrays

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