检索结果-内蒙古大学图书馆

First exploration of Pr6O11 nanoparticle integration in borotellurite glasses: Synthesis, characterization, and performance for enhanced mechanical strength and radiation shielding

引用

Ceramics International 2025年第13期51卷 18191-18202页

作者： Kilic, G. Ilik, E. Kavaz, E. Durmus, Hasan Güler, Ömer Birdogan, Selcuk ALMisned, Ghada Tekin, H.O. Eskisehir Osmangazi University Faculty of Science Department of Physics Eskisehir26040 Turkey Ataturk University Faculity of Science Department of Physics Erzurum25240 Turkey Vocational School of Health Services Izmir University of Economics Izmir Turkey Munzur University Rare Earth Elements Application and Research Center Tunceli62000 Turkey Istanbul34956 Turkey Department of Physics College of Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Medical Diagnostic Imaging College of Health Sciences University of Sharjah Sharjah27272 United Arab Emirates Istinye University Faculty of Engineering and Natural Sciences Computer Engineering Department Istanbul34396 Turkey

This study investigates the incorporation of Pr6O11 nanoparticles into lithium borotellurite glass matrices to enhance their mechanical and radiation shielding properties. Glass compositions, synthesized with varying Pr6O11 concentrations from 0 to 8 mol%, exhibited increasing densities from 4.00783 g cm−3 to 4.94440 g cm−3 and reduced molar volumes, confirming nanoparticle-induced densification. X-ray diffraction analysis revealed amorphous structures with shifts in the hollow band indicating compact network rearrangements. Scanning electron microscopy and energy-dispersive X-ray analyses confirmed homogeneous Pr distribution up to 6 mol%, with clustering observed in 8 mol% samples. Vickers’ microhardness values progressively increased, highlighting enhanced mechanical strength due to reduced non-bridging oxygen ions and network cross-linking. Gamma-ray shielding experiments demonstrated superior performance of the 8 mol% sample (Pr8), with the highest mass attenuation coefficients, effective atomic number, and reduced half-value layer. Neutron attenuation assessments further confirmed improved shielding capabilities, with Pr8 achieving the highest effective removal cross-section. In conclusion, Pr6O11-doped lithium borotellurite glasses demonstrate significant potential for advanced radiation shielding applications. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： X ray diffraction analysis

来源：评论

学校读者我要写书评

暂无评论

OBoctNet: Enhancing Ophthalmic Biomarker Detection Through Active Learning and Explainable ai in Radiological Analysis

引用

Cognitive Computation 2025年第3期17卷 1-18页

作者： Acharja, Samya Hasan, Md. Zahid Chamok, Farjana Haque Fahim, Kayes Uddin Shuva, Taslima Ferdaus Bulbul, Abdullah Al-Mamun Khan, Risala Tasin Rahman, Md. Tanvir Kaiser, M. Shamim Mahmud, Mufti Moni, Mohammad Ali Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Savar Dhaka1341 Bangladesh School of Health and Rehabilitation Sciences The University of Queensland St Lucia BrisbaneQLD4072 Australia Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail1902 Bangladesh Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Bio Systems and Machines King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia AI and Cyber Futures Institute Charles Sturt University BathurstNSW2795 Australia Rural Health Research Institute Charles Sturt University OrangeNSW2800 Australia School of Information Technology Washington University of Science and Technology AlexandriaVA22314 United States

Optical coherence tomography (OCT) scanning is crucial for the diagnosis of widespread ophthalmic diseases. Traditionally, experts manually identify diseases and biomarkers from OCT scans. Recently, modern medical imaging practices have increasingly utilized deep learning techniques to speed up and improve diagnostic accuracy in ophthalmology. However, obtaining accurately labeled datasets is a significant challenge in medical imaging due to the expertise required for precise annotation by trained professionals. This article presents a novel method, named OBoctNet, with a new two-stage training strategy to improve the identification of ophthalmic biomarkers using OCT scans from the OLIVES dataset, which contains only 12% labeled data. This approach leverages a robust methodology that effectively uses labeled and unlabeled data to enhance biomarker identification accuracy, achieving a cumulative performance increase of 23% across 50% of the biomarkers when compared to the previous studies. To better identify biomarkers, the OBoctNet employs an active learning strategy that uses unlabeled data and dynamically ensembles models based on their performance within each experimental setup. Additionally, the usage of Gradient weighted Class Activation Mapping (GradCAM) helps identify regions of interest associated with relevant biomarkers, enhancing interpretability and transparency for potential clinical adoption. © The Author(s) 2025.

关键词： Semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

AugGPT: Leveraging ChatGPT for Text data Augmentation

引用

IEEE Transactions on Big data 2025年第3期11卷 907-918页

作者： Dai, Haixing Liu, Zhengliang Liao, Wenxiong Huang, Xiaoke Cao, Yihan Wu, Zihao Zhao, Lin Xu, Shaochen Zeng, Fang Liu, Wei Liu, Ninghao Li, Sheng Zhu, Dajiang Cai, Hongmin Sun, Lichao Li, Quanzheng Shen, Dinggang Liu, Tianming Li, Xiang University of Georgia School of Computing AthensGA30602 United States South China University of Technology School of Computer Science and Engineering Guangzhou510641 China Lehigh University Department of Computer Science and Engineering BethlehemPA18015 United States Carnegie Mellon University Heinz College of Information Systems and Public Policy PittsburghPA15213 United States Harvard Medical School Department of Radiology Massachusetts General Hospital BostonMA02115 United States Mayo Clinic Department of Radiation Oncology PhoenixAZ85054 United States University of Virginia School of Data Science CharlottesvilleVA22903 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States ShanghaiTech University School of Biomedical Engineering Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks. This challenge is especially prominent in the few-shot learning (FSL) scenario, where the data in the target domain is generally much scarcer and of lowered quality. A natural and widely used strategy to mitigate such challenges is to perform data augmentation to better capture data invariance and increase the sample size. However, current text data augmentation methods either can’t ensure the correct labeling of the generated data (lacking faithfulness), or can’t ensure sufficient diversity in the generated data (lacking compactness), or both. Inspired by the recent success of large language models (LLM), especially the development of ChatGPT, we propose a text data augmentation approach based on ChatGPT (named "AugGPT"). AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. The augmented samples can then be used in downstream model training. Experiment results on multiple few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach over state-of-the-art text data augmentation methods in terms of testing accuracy and distribution of the augmented samples. © 2015 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Decentralized and Network-Aware UAV Service Deployment for Dependency-Driven applications

Decentralized and Network-Aware UAV Service Deployment for D...

引用

IEEE Conference on Wireless Communications and Networking

作者： Talip Tolga Sarı Christian Quadri Gökhan Seçinti Angelo Trotta Department of Computer Science and Engineering University of Bologna Italy Department of Computer Engineering Istanbul Technical University Turkey Department of Computer Science Università degli Studi di Milano Italy BTS - Digital Twin Application and Research Center Istanbul Technical University Turkey

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Unmanned Aerial Vehicle (UAV) swarms enable the rapid deployment of IoT services in dynamic and challenging environments. While these swarms offer flexibility and close proximity to sensing and actuation points, efficiently deploying interdependent services at scale remains a core challenge. Traditional centralized methods struggle to handle the complexity of large UAV networks, leading to increased latency and limited reliability. In this paper, we propose a decentralized approach to service deployment in UAV swarms. Our method relies on local information at each node, allowing UAVs to make their own assignment decisions. Over time, these decisions are iteratively refined as nodes exchange status updates and adapt to network changes. This process avoids the bottlenecks of centralized coordination and enables more responsive resource allocation. Simulation results show that our approach supports the successful deployment of a high number of tasks while maintaining low latency. These findings indicate that decentralized methods with local resource knowledge, improves both scalability and responsiveness in UAV-based IoT systems.

关键词： Simulation Scalability Autonomous aerial vehicles Robustness Sensors Internet of Things Resource management Vehicle dynamics Optimization Testing

来源：评论

学校读者我要写书评

暂无评论

Predicting CRISPR Cas9 Off-Target Activities With a Two-Stage Deep Learning Framework

IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 956-962页

作者： Tianshan Zhang Bei Jiang Guang Yang Zhijie Bao Jiajie Peng AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shanxi China The National Engineering Laboratory for In tegrated Aerospace Ground Ocean Big Data Application Technology Shanxi China Tianjin second people's hospital Tianjin China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China

The CRISPR-Cas9 system, found across bacteria and archaea, enables efficient genome engineering in eukaryotic cells. There is a challenge that Cas9 guide RNA may cause off-target activities. Although numbers of methods have been proposed to predict off-target activities in guide RNA designing, this procedure involves numerous potential off-target sites, which causes label imbalance problem. To address this problem, we developed a deep learning framework, named CAF-Net (Cas9 Augmentation and Finetune Network), for predicting off-target activities of CRISPR-Cas9. First, we pretrain an embedding model to extract features from target and guide sequence pairs. Subsequently, data augmentation is applied to these features. And finally, the model is finetuned with synthetic samples. Evaluation results demonstrate its performance on published datasets.

关键词： RNA Bioinformatics Feature extraction DNA Genomics Convolutional neural networks Computational modeling Transformers Predictive models CRISPR

来源：评论

学校读者我要写书评

暂无评论

Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

引用

IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

AdvMap: Crafting Adversarial Maps to Counter ai aimbot in First-Person Shooter Games

引用

IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

ai-based automatic aiming cheats (a.k.a., ai aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since ai aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter ai aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various ai aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding

引用

IEEE Transactions on Mobile Computing 2025年

作者： Li, Yajun Zhao, Jumin Li, Dengao Wu, Hejun Xu, Shuang Bai, Ruiqin Taiyuan University of Technology College of Electronic Information and Optical Engineering Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan China Sun Yat-sen University Department of Computer Science Guangzhou China

Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strengthdifferences (SSDs) among tags;or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called SalixLeaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, SalixLeaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art. © 2002-2012 IEEE.

关键词： Backscattering

来源：评论

学校读者我要写书评

暂无评论

SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity

SELF-Clustering Graph Transformer Approach to Model Resting ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Bishal Thapaliya Esra Akbas Ram Sapkota Bhaskar Ray Vince Calhoun Jingyu Liu Department of Computer Science Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the sub clusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures.

关键词： Biometrics Correlation Cognitive processes Biological system modeling Organizations Predictive models Functional magnetic resonance imaging Transformers Brain modeling Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

Transfer learning‑based attenuation correction in ^99mTc-TRODAT-1 SPECT for Parkinson’s disease using realistic simulation and clinical data

引用

EJNMMI Physics 2025年第1期12卷 1-14页

作者： Huang, Wenbo Jiang, Han Du, Yu Wang, Haiyan Sun, Hao Hung, Guang-Uei Mok, Greta S. P. Biomedical Imaging Laboratory (BIG) Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau SAR Taipa Macao PET-CT Center Fujian Medical University Union Hospital Fuzhou China Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau SAR Taipa Macao Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering Southern Medical University Guangzhou China Department of Nuclear Medicine Chang Bing Show Chwan Memorial Hospital Changhua County Taiwan

Purpose: Dopamine transporter (DAT) SPECT is an effective tool for early Parkinson’s disease (PD) detection and heavily hampered by attenuation. Attenuation correction (AC) is the most important correction among other corrections. Transfer learning (TL) with fine-tuning (FT) a pre-trained model has shown potential in enhancing deep learning (DL)-based AC methods. In this study, we investigate leveraging realistic Monte Carlo (MC) simulation data to create a pre-trained model for TL-based AC (TLAC) to improve AC performance for DAT SPECT. Methods: A total number of 200 digital brain phantoms with realistic ^99mTc-TRODAT-1 distribution was used to generate realistic noisy SPECT projections using MC SIMIND program and an analytical projector. One hundred real clinical ^99mTc-TRODAT-1 brain SPECT data were also retrospectively analyzed. All projections were reconstructed with and without CT-based attenuation correction (CTAC/NAC). A 3D conditional generative adversarial network (cGAN) was pre-trained using 200 pairs of simulated NAC and CTAC SPECT data. Subsequently, 8, 24, and 80 pairs of clinical NAC and CTAC DAT SPECT data were employed to fine-tune the pre-trained U-Net generator of cGAN (TLAC-MC). Comparisons were made against without FT (DLAC-MC), training on purely limited clinical data (DLAC-CLI), clinical data with data augmentation (DLAC-AUG), mixed MC and clinical data (DLAC-MIX), TL using analytical simulation data (TLAC-ANA), and Chang’s AC (ChangAC). All datasets used for DL-based methods were split to 7/8 for training and 1/8 for validation, and a 1-/2-/5-fold cross-validation were applied to test all 100 clinical datasets, depending on the numbers of clinical data used in the training model. Results: With 8 available clinical datasets, TLAC-MC achieved the best result in Normalized Mean Squared Error (NMSE) and Structural Similarity Index Measure (SSIM) (TLAC-MC;NMSE = 0.0143 ± 0.0082/SSIM = 0.9355 ± 0.0203), followed by DLAC-AUG, DLA

关键词： 99mTc-TRODAT-1 Attenuation correction Brain SPECT Monte carlo simulation Parkinson’s disease Transfer learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：