检索结果-内蒙古大学图书馆

Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

引用

Frontiers of environmental Science & engineering 2022年第5期16卷 115-125页

作者： Bingdi Liu Lin Zhang Jason H.Knouft Fangqiong Ling Department of Energy Environmental and Chemical EngineeringWashington University in St.LouisSt.LouisMO 63130USA Department of Biology Saint Louis UniversitySt.LouisMO 63103USA National Great Rivers Research and Education Center East AltonMO 62035USA Department of Computer Science and Engineering Washington University in St.LouisSt.LouisMO 63130USA Division of Biological and Biomedical Sciences Washington University in St.LouisSt.LouisMO 63110USA Division of Computational and Data Science Washington University in St.LouisSt.LouisMO 63130USA

Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution *** variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling ***,we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri,USA and examined the microbial community composition with 16S rRNA gene amplicon *** microbiomes across the transect exhibited extremely high similarity,with Pearson’s correlation coefficients above 0.9 for all pairwise community composition ***,despite the high similarity,PERMANOVA revealed significant spatial differences between near-shore and nearcenter communities(p=0.001).Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity(R^(2)=0.89).Despite being primarily explained by neutral processes,LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center,indicating an additional role of environmental ***,the local variations within a river transect can have profound impacts on the documentation of alpha ***-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level,yet four,six and seven samples were able to capture 80%of the phylum-level,family-level,and genus-level diversity,*** study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms,demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.

关键词： Microbiome Freshwater Taxon accumulation curve Community assembly

来源：评论

学校读者我要写书评

暂无评论

Ultrathin-shell epitaxial Ag@Au core-shell nanowires for high-performance and chemically-stable electronic, optical, and mechanical devices

引用

Nano Research 2021年第11期14卷 4294-4303页

作者： Yangzhi Zhu Sanggon Kim Xuezhi Ma Peter Byrley Ning Yu Qiushi Liu Xiaoming Sun Da Xu Sangshan Peng Martin C.Hartel Shiming Zhang Vadim Jucaud Mehmet R.Dokmeci Ali Khademhosseini Ruoxue Yan Department of Chemical and Environmental Engineering Bourns College of EngineeringUniversity of California-RiversideRiversideCA92521USA Terasaki Institute for Biomedical Innovation Los AngelesCA90064USA Department of Electrical and Computer Engineering University of California-RiversideRiversideCA92521USA State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical TechnologyBeijing100029China State Key Laboratory of Chemo/Biosensing and Chemometrics College of Chemistry and Chemical EngineeringHunan UniversityChangsha410082China Department of Bioengineering University of California-Los AngelesLos AngelesCA90095USA Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Material Science and Engineering Program University of California-RiversideRiversideCA92521USA

Silver nanowires (AgNWs) hold great promise for applications in wearable electronics, flexible solar cells, chemical and biological sensors, photonic/plasmonic circuits, and scanning probe microscopy (SPM) due to their unique plasmonic, mechanical, and electronic properties. However, the lifetime, reliability, and operating conditions of AgNW-based devices are significantly restricted by their poor chemical stability, limiting their commercial potentials. Therefore, it is crucial to create a reliable oxidation barrier on AgNWs that provides long-term chemical stability to various optical, electrical, and mechanical devices while maintaining their high performance. Here we report a room-temperature solution-phase approach to grow an ultra-thin, epitaxial gold coating on AgNWs to effectively shield the Ag surface from environmental oxidation. The Ag@Au core-shell nanowires (Ag@Au NWs) remain stable in air for over six months, under elevated temperature and humidity (80 °C and 100% humidity) for twelve weeks, in physiological buffer solutions for three weeks, and can survive overnight treatment of an oxidative solution (2% H2O2). The Ag@Au core-shell NWs demonstrated comparable performance as pristine AgNWs in various electronic, optical, and mechanical devices, such as transparent mesh electrodes, surface-enhanced Raman spectroscopy (SERS) substrates, plasmonic waveguides, plasmonic nanofocusing probes, and high-aspect-ratio, high-resolution atomic force microscopy (AFM) probes. These Au@Ag core-shell NWs offer a universal solution towards chemically-stable AgNW-based devices without compromising material property or device performance.

关键词： epitaxial growth core-shell nanowire plasmonic waveguides atomic force microscopy(AFM)probe transparent electrode wearable electronics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Spectral Efficiency in IoT Networks using Deep Deterministic Policy Gradient and Opportunistic NOMA

Enhancing Spectral Efficiency in IoT Networks using Deep Det...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Neha Mazhar Syed Asad Ullah Haejoon Jung Qurrat-Ul-Ain Nadeem Syed Ali Hassan School of Electrical Engineering & Computer Science (SEECS) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Electronic Engineering Kyung Hee University (KHU) Yongin South Korea Engineering Division New York University (NYU) Abu Dhabi Abu Dhabi United Arab Emirates NYU Tandon School of Engineering Brooklyn United States of America

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Amidst the ongoing debate about limited spectral availability, there remains a persistent demand for the development of spectrally efficient self-sustainable network (SSN) models. This paper addresses this challenge by optimizing spectral efficiency (SE) in uplink transmissions for an energy harvesting (EH)-enabled secondary user (SU) that operates opportunistically among multiple primary users (PUs) in an Internet-of-things (IoT) network. The PUs are assumed to employ a rotational time division multiple access (TDMA) scheme for transmissions, where the signals are divided into time slots for each PU to transmit data in a cyclic manner, while the SU uses an opportunistic non-orthogonal multiple access (NOMA) technique to transmit data without interfering with the PU transmissions, such that, at any given time slot, a PU and a SU share the same frequency band simultaneously. The SE of the system is maximized jointly by employing convex optimization and a deep reinforcement learning (DRL) model, specifically the deep deterministic policy gradient (DDPG) algorithm. Simulations demonstrate that the proposed approach significantly improves the SE of the considered IoT network, highlighting its potential for efficient spectrum management in IoT networks. We present a comprehensive SE analysis of the system, which further underscores the robustness and adaptability of our approach in optimizing SE under diverse operational conditions.

关键词： NOMA Time division multiple access Adaptation models Vehicular and wireless technologies Time-frequency analysis Spectral efficiency Benchmark testing Approximation algorithms Convex functions Uplink

来源：评论

学校读者我要写书评

暂无评论

Blending of Resolution-Jumping and Beamforming for Enhanced Tomographic Reconstruction

Blending of Resolution-Jumping and Beamforming for Enhanced ...

引用

International Conference on Intelligent Systems and Networks, ICISN 2024

作者： Theu, Luong Thi Huy, Tran Quang Phung, Viet Bac T. Nguyen, Tien-Anh Duc-Tan, Tran Hoa Binh University My Dinh Nam Tu Liem Hanoi100000 Viet Nam Faculty of Physics Hanoi Pedagogical University 2 Hanoi100000 Viet Nam Center for Environmental Intelligence and College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam Department of Physics Le Quy Don Technical University 236 Hoang Quoc Viet Street Bac Tu Liem District Hanoi100000 Viet Nam Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi100000 Viet Nam

ISBN: (纸本)9789819755035

Due to its widespread use, safety, low cost, capacity to provide real-time images and ongoing image quality improvements, ultrasound imaging is frequently chosen over other medical imaging modalities for diagnostic purposes. The inverse acoustic scattering issue was linearized to form the basis of ultrasonic tomography (UT). This makes it possible to detect small fluctuations in a reference medium. This work suggests employing resolution-jumping and beamforming techniques for tomographic ultrasound imaging. The beamformed technique employs multiple probe transmitting elements concurrently to generate a narrow beam for noise reduction, while also employing resolution-jumping to enhance the reconstruction’s quality and speed. Numerical simulation results demonstrate a substantial improvement in image recovery quality, along with a considerable reduction in total imaging runtime when the proposed method is applied. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： beamforming DBIM inverse scattering Resolution-jumping ultrasonic tomography

来源：评论

学校读者我要写书评

暂无评论

Automatic classification of 10 blood cell subtypes using transfer learning via pre-trained convolutional neural networks

引用

Informatics in Medicine Unlocked 2024年 49卷

作者： Asghar, Rabia Kumar, Sanjay Hynds, Paul Environmental Sustainability and Health Institute Technological University (TU) Dublin Ireland Department of Electronic and Computer Engineering University of Limerick Ireland Spatiotemporal Environmental Epidemiology Research (STEER) Group Technological University (TU) Dublin Ireland

Human blood is primarily composed of plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting oxygen and nutrients to all organs, and stores essential health-related data about the human body. Blood cells are utilized to defend the body against infections and disease. Hence, analysis of blood permits physicians to assess an individual's physiological condition. Blood cells are sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets (thrombocytes) on the basis of their nucleus, shape and cytoplasm. Traditionally, pathologists and hematologists have identified and examined these via microscopy prior to manual classification, with this manual approach being slow and prone to human error. Therefore, it is essential to automate this process. In the current study, transfer learning with a series of pre-trained Convolutional Neural Network (CNN) models—VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2 and DenseNet-201 was applied to the normal peripheral blood cells dataset (PBC). The overall accuracy achieved with individual CNNs ranged from 91.4 % to 94.7 %. Based on these pre-trained architectures, a CNN-based architecture has been developed to automatically classify all ten blood cell types. The proposed transfer learning CNN model was tested on blood cell images from the PBC, Kaggle and LISC datasets. Achieved accuracy was 99.91 %, 99.68 % and 98.79 %, respectively, across these three datasets. The presented CNN architecture outperforms all previous results reported in the scientific/medical literature with a high capacity for framework generalization in future applications of blood cell classification. © 2024 The Authors

关键词： Autoimmune diseases Blood cell subtypes Classification Deep learning Feature extraction Image analyses Machine learning Pre-trained models

来源：评论

学校读者我要写书评

暂无评论

OdCFU-Net for Brain Tumor Segmentation Based on MRI

OdCFU-Net for Brain Tumor Segmentation Based on MRI

引用

IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Shang Wu Sijing Yang Boyue Li Shimin Yin Jianming Zhu Shiyong Li Zhencheng Chen Guilin University of Electronic Technology School of Life and Environmental Science Guilin China School of Computer and Information Security Guilin University of Electronic Science and Technology Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin China Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments Guilin China Guangxi Engineering Technology Research Center of Human-Physiological Information Noninvasive Detection Guilin China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

In the field of medical image analysis, the accurate segmentation of brain tumors plays a crucial role in the diagnosis and treatment of brain-related diseases. This study aims to address key challenges in this domain, with a particular focus on improving feature extraction, information integration, and model generalization capabilities. By concurrently introducing multi-dimensional attention mechanisms in convolutional operations, a method has been developed to dynamically generate convolution kernels in spatial, input channel, and output channel dimensions. This approach optimizes the limitations of traditional static convolutional kernels, maintaining high-performance efficiency while reducing the model’s parameter count. To overcome the deficiencies of traditional convolutional networks in capturing global information, channel and spatial attention mechanisms have been integrated between convolutional layers. This enhances adaptability and global information retrieval capabilities, allowing the model to dynamically adjust the weight of feature maps based on different task requirements. Leveraging the advantages of Vision Transformer in handling global information and addressing its limitations in capturing details and training on small datasets, the algorithm aligns and learns feature representations effectively by incorporating channel attention and normalizing attention weights within its multi-head attention. When applied to the BraTs2021 multi-modal brain tumor image database, the proposed algorithm accurately delineates brain tumor regions, demonstrating excellent performance.

关键词： Training Adaptation models Attention mechanisms Semantic segmentation Virtual environments Transformers Kernel

来源：评论

学校读者我要写书评

暂无评论

CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

CITR: A Coordinate-Invariant Task Representation for Robotic...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

来源：评论

学校读者我要写书评

暂无评论

A Transfer Learning Approach with Modified VGG 16 for Driving Behavior Detection in Intelligent Transportation Systems

A Transfer Learning Approach with Modified VGG 16 for Drivin...

引用

International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Abdullah Hafez Nur Md Simul Hasan Talukder Shihab Adnan Md. Razu Ahmed Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Nuclear Safety Security and Safeguard Division Bangladesh Atomic Energy Regulatory Authority Dhaka Bangladesh Dept. of Computer and Communication Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

The majority of driving errors resulting from poor driving habits, a lack of compliance with the law, and inadequate driving knowledge continue to be a major global problem for public safety. Delivering accurate and timely warnings to drivers for correction while also allowing law enforcement authorities to catch violators is a critical task. To address this problem, the paper examines the complex aspects of human behavior and the different variables that cause distractions on the road, and a highly reliable and robust transfer-learning-based AI-system has been proposed. Initially, a novel dataset with ten types of driving behaviors was developed. Afterward, a fine-tuned modified VGG16-model was applied to classify them. In addition, the model was trained by assessing the efficacy of four optimizers: Adam, SGD, AdaGrad, and RMSProp. The optimized modified VGG16 with the Adam Optimizer proved to be the best, attaining excellent accuracy, recall, F1 score, and precision, all of which were 99.86%. It illustrates the effectiveness of the proposed approach in significantly improving the accuracy of identifying distracted driver- behavior.

关键词： Law enforcement Roads Transfer learning Public security Communications technology Behavioral sciences Reliability

来源：评论

学校读者我要写书评

暂无评论

Coexisting Hyperchaos in a Memristive Neuromorphic Oscillator

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zhang, Xin Li, Chunbiao Lei, Tengfei Iu, Herbert Ho-Ching Kapitaniak, Tomasz Nanjing University of Information Science and Technology School of Electronic and Information Engineering Jiangsu Nanjing210044 China Nanjing University of Information Science and Technology School of Artificial Intelligence Jiangsu Nanjing210044 China Shandong Jinan250200 China University of Western Australia School of Electrical Electronic and Computer Engineering CrawleyWA6009 Australia Lodz University of Technology Division of Dynamics Lodz90-924 Poland

Memristors have been widely integrated into neurons as the bridge for introducing external magnetic induction currents. The complex oscillation induced by the external magnetic stimulation is a hot topic in neuron dynamics. When a memristor is introduced into the Hindmarsh-Rose (HR) neuron to simulate the external magnetic field, a novel memristive neuromorphic hyperchaotic oscillator is constructed. The memristor weight can trigger complex neuronal firing dynamics, including the rare hyperchaotic bursting. Furthermore, when the technology of offset boosting-oriented attractor doubling is employed, a double-scroll hyperchaotic attractor can be generated, which could split into three independent coexisting attractors under some specific offsets. More interesting, two symmetric periodic attractors and two symmetric hyperchaotic attractors can coexist under certain conditions. In this work, a neuron with coexisting hyperchaotic attractors is constructed and exhaustively explored, which provides a good candidate for constituting large-scale brain-like neuromorphic oscillator. A PCB-based hardware circuit produces the oscillations validating the numerical simulations and theoretical analyses. © 1982-2012 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Commercial Indian Bamboo Species Classification on matK DNA Barcode Sequences using Machine Learning Techniques with K-mer

Commercial Indian Bamboo Species Classification on matK DNA ...

引用

computer, electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Ankush D. Sawarkar Deepti D. Shrimankar Lal Singh Anurag Agrahari Sagar Lachure Neeraj Dhanraj Bokde Department of Computer Science and Engineering Visvesvaraya National Institute of Technology (VNIT) Nagpur Maharashtra India Environmental Biotechnology And Genomics Division National Environmental and Engineering Research Institute (NEERI) Nagpur Maharashtra India Center for Quantitative Genetics and Genomics Aarhus University Aarhus Denmark

Bamboo, a grass, belongs to the Poaceae family, with 1642 species from 116 genera worldwide. It has exceptional physical, chemical, and mechanical qualities, which allow it to be employed in over a thousand different ways and contribute to a trade value of USD 2.76 billion. Bamboo is grown using rhizomes, tissue culture, or short branch cuttings without any other checks resulting in incorrect species identification and categorisation. Therefore, the classification or identification of these bamboo use its DNA barcode sequences with a K-mer based method, and machine learning (ML) is the most excellent strategy for resolving issues with the conventional or traditional categorisation of the species. A DNA barcode is a brief genetic signature that helps identify the species to which an organism belongs. It is possible to extract a useful feature from genome sequences using K-mer based approaches, which may then be used to increase comparison accuracy. In this research, we evaluate the classification performance of four supervised ML models on the DNA-barcode sequence of six Indian commercial bamboo species with a different K-mer combination. For this classification, we choose matK barcode region and supervised ML models such as Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). The results analysis of these models on the matK DNA sequence with different K-mers demonstrates that the classification capabilities of the GBM approaches are quite promising, and it has an accuracy of 95.3% on average.

关键词：

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：