检索结果-内蒙古大学图书馆

23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Fusaro, Gioia Yu, Xiang Cui, Fangsen Kang, Jian Institute of High Performance Computing A*Star Singapore School of Architecture Acoustics Group University of Sheffield United Kingdom UCL Institute for Environmental Design and Engineering Bartlett University College London United Kingdom

ISBN: (纸本)9783939296157

The development of windows which can address the issue of high noise levels while maintaining natural ventilation has drawn significant attention recently. However, there are limitations to traditional-Gouble glazing together with duct designs. Previously, window systems have been developed based on the local resonant stopband of acoustic metamaterial (AMM) to achieve dual functions of noise reduction and natural ventilation. In this study, a new effort is made to develop a window system with foldable origami metamaterial, which conceptually proposes a novel approach for acoustic and ventilation design. The proposed device allows air exchange between the interior and exterior domains, and it forms an omni-directional acoustic metacage in the folded state. This paper elaborates the design concept of the proposed device and the important design parameters. The sound reduction performance is investigated using Finite Elements Method (FEM) simulations. The numerical method developed in this work can facilitate the optimisation of origami metamaterial for real window designs. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： FEM Metamaterials Ventilation

来源：评论

学校读者我要写书评

暂无评论

Adsorptive removal of arsenic from drinking water using KOH-modified sewage sludge-derived biochar

Cleaner Water

引用

Cleaner Water 2024年 2卷

作者： Ahsan Shah Arun Arjunan Anusha Thumma Julia Zakharova Timothy Bolarinwa Shreah Devi Maryam Batool School of Engineering Computing and Mathematical Sciences University of Wolverhampton WV1 1LY UK Department of Pharmaceutical Sciences College of Pharmacy Nova Southeastern University Fort Lauderdale FL USA School of Architecture and Built Environment University of Wolverhampton WV1 1LY UK Faculty of Science and Engineering University of Wolverhampton WV1 1LY UK Department of Chemistry University of Sahiwal Sahiwal Pakistan

This paper reveals a green chemistry approach to remove arsenic (As +3 ) from water using potassium hydroxide (KOH) modified sewage sludge-derived biochar (SSDB-KOH). Characterisation of the morphology, particle size and composition of the SSDB-KOH pre- and post-adsorption confirmed porous and heterogenous surface favouring adsorption. At ambient temperature (20 °C), the SSDB-KOH dose of 20 g/l achieved 98 % arsenite removal at nearly neutral solution pH (8). This compared favourably with pristine SSDB, where the performance was limited to 41.6 % removal. The enhanced arsenite adsorption of SSDB-KOH was informed by several mechanisms, including ionic interactions, pore filling and metal-pi interactions. The experimental data fits closely with the Langmuir isotherm and pseudo-second-order kinetic models, revealing monolayer adsorption and the chemical interaction between adsorbents and the adsorbate. The spontaneous and endothermic nature of the process was confirmed by the negative value of ΔG and the positive value of ΔH, respectively. The performance of the biochar was evaluated for four-cycle regeneration. Finally, a cost analysis was performed to demonstrate the economic feasibility of using SSDB-KOH to remove arsenic from water.

关键词： Adsorption KOH-modified biochar Arsenic Batch study Regeneration Drinking water

来源：评论

学校读者我要写书评

暂无评论

A Field Programmable Process-In-Memory architecture Based on RRAM Technology

A Field Programmable Process-In-Memory Architecture Based on...

引用

International Conference on Mechanical, Control and Computer engineering (ICMCCE)

作者： Yuan Liang Longxiang Yin Ning Xu School of Computer Science and Technology Wuhan University of Technology Wuhan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Information Engineering Wuhan University of Technology Wuhan China

Conventional reconfigurable architectures, e.g., Field-Programmable Gate Array (FPGA), are confronted with the inflexibility of the on-chip local memory architecture and the scarce memory resource, which result in the inflexibility to deal with the current memory-thirsty data-intensive applications. Emerging nonvolatile devices, such as resistive random-access memory (RRAM), can operate in a logic-in-memory way, i.e., they can act as both a nonvolatile memory element and a switch, and thus, have the potential to provide higher flexibility and data processing ability. We propose a field-programmable logic-in-memory architecture based on RRAM technology, which is a two-dimensional tile array, where each tile is an RRAM-based crossbar array and can be remolded into three basic modes including logic, interconnect and memory. We further develop a specific adaptive algorithm for placement and routing, which can well exploit the logic-in-memory architecture characteristics. Comparing with traditional FPGAs, our architecture shows 1.9x lower power consumption, 2.8x lower delay, and 5.6x higher performance.

关键词： Nonvolatile memory Computer architecture Switches Routing Delays System-on-chip Reconfigurable architectures

来源：评论

学校读者我要写书评

暂无评论

Evaluation of OpenAI o1: Opportunities and Challenges of AGI

arXiv

引用

arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Performance study on mounting system for displacement transducer in mechanical tests of timber samples using photogrammetry method

Research Square

引用

Research Square 2021年

作者： Mohamed, Ahmed Zhang, Hexin Deng, Yu Lehmann, Martin Sigrist, Christophe Uheida, Kal Zheng, Yu Bahadori-Jahromi, Ali Shahidan, Shahiron Caudoux, Remi School of Engineering and the Built Environment Edinburgh Napier University 10 Colinton Road Scotland EdinburghEH10 5DT United Kingdom Civil Engineering Department Faculty of Engineering Omar Al-Mukhtar University Libya School of Civil Engineering and Architecture Guangxi University of Science and Technology Liuzhou545006 China Institute for Materials and Wood Technology Bern University of Applied Sciences Solothurnstrasse 102 Biel2504 Switzerland School of Environment and Civil Engineering Dongguan University of Technology Dongguan523080 China Department of Civil Engineering and Built Environment School of Computing and Engineering University of West London LondonW5 5RF United Kingdom Faculty of Civil Engineering and Built Environment Universiti Tun Hussein Onn Malaysia Johor Batu Pahat86400 Malaysia Signature Murale 12 rue du Parc Royal Paris75003 France

This paper presents a unique study to reveal the effect of the mounting system of the linear variable differential transformer (LVDT) on the mechanical property tests of timber materials. This effect has been overlooked in the past but proven to be significant enough that will distort the measurements completely if the displacement is relatively small. The reason for causing this error is because the LVDT is not directly measuring targeting points on a sample, instead, the LVDT is measuring the distance between one point on the stop-end (where the tip rested on) and the holder. The wrong common sense hopes that this point on the stop-end and holder reliably following the movement of the sample points. But this study reveals that it is not always the case with the aid from the photogrammetry method. The messages of this paper are simple but alertly useful and, important: 1) the mounting system and the method of installation of the LVDTs have a significant impact on the actual displacement measurement in a mechanical properties test of timber materials. It should be carefully designed and validated before the actual test;2) the displacement distribution is not uniform across the timber sample. This should be taken into account when selecting the mounting points of the LVDTs. Testing standards should consider this effect when recommending displacement transducers to be used in a timber-related test. © 2021, CC BY.

关键词： Timber

来源：评论

学校读者我要写书评

暂无评论

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification 23rd

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classi...

引用

23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

It has been of great interest in the neuroimaging community to model spatiotemporal brain function and disorders based on resting state functional magnetic resonance imaging (rfMRI). A variety of spatiotemporal methods have been proposed for rfMRI so far, including deep learning models such as convolution networks (CNN) and recurrent networks (RNN). However, the dominant models fail to capture the long-distance dependency (LDD) due to their sequential nature, which becomes critical at longer sequence lengths due to memory limit. Inspired by human brains extraordinary ability of long-term memory and attention, the attention mechanism is designed for machine translation to draw global dependencies and achieved state-of-the-art. In this paper, we propose a spatiotemporal attention autoencoder (STAAE) to discover global features that address LDDs in rfMRI. STAAE encodes the information throughout the rfMRI sequence and reveals resting state networks (RSNs) that characterize spatial and temporal properties of the data. Considering that the rfMRI is measured without external tasks, an unsupervised classification framework is developed based on the connectome generated with STAAE. This framework has been evaluated on 281 children with ADHD and 266 normal control children from 4 sites of ADHD200 datasets. The proposed STAAE reveals the global functional interaction in the brain and achieves a state-of-the-art classification accuracy from 59.5% to 77.2% on multiple sites. It is evident that the proposed attention-based model provides a novel approach towards better understanding of human brain. © 2020, Springer Nature Switzerland AG.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE) 23rd

Discovering Functional Brain Networks with 3D Residual Autoe...

引用

23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

ISBN: (纸本)9783030597276

Functional MRI has attracted increasing attention in cognitive neuroscience and clinical mental health research. Towards understanding how brain give rises to mental phenomena, deep learning has been applied to functional MRI (fMRI) dataset to discover the physiological basis of cognitive process. Considering the unsupervised nature of fMRI due to the complex intrinsic brain activities, an encoder-decoder structure is promising to model hidden structure of latent signal sources. Inspired by the success of deep residual learning, we propose a 68-layer 3D residual autoencoder (3D ResAE) to model deep representations of fMRI in this paper. The proposed model is evaluated on the fMRI data under 3 cognitive tasks in Human Connectome Project (HCP). The experimental results have shown that the temporal representations learned by the encoder matches the task design and the spatial representations can be interpreted to be meaningful functional brain networks (FBNs), which not only include tasks based FBNs, but also intrinsic FBNs. The proposed model also outperforms a 3-layer autoencoder, showing the key factor for the performance improvement is depth. Our work demonstrates the feasibility and success of adopting 2D advanced deep residual networks in computer vision into 3D fMRI volume modeling. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Preface

Green Energy and Technology

引用

Green Energy and Technology 2021年 v页

作者： Seyedzadeh, Saleh Rahimian, Farzad Pour Department of Architecture University of Strathclyde Glasgow United Kingdom School of Computing Engineering and Digital Technologies Teesside University Middlesbrough United Kingdom

来源：评论

学校读者我要写书评

暂无评论

Testing a quantum error-correcting code on various platforms

引用

Science Bulletin 2021年第1期66卷 29-35,M0003页

作者： Qihao Guo Yuan-Yuan Zhao Markus Grassl Xinfang Nie Guo-Yong Xiang Tao Xing Zhang-Qi Yin Bei Zeng Institute for Quantum Computing Baidu ResearchBeijing 100193China Department of Applied Physics Xi’an Jiaotong UniversityXi’an 710049China Center for Quantum Computing Peng Cheng LaboratoryShenzhen 518055China CAS Key Laboratory of Quantum Information University of Science and Technology of ChinaHefei 230026China Max Planck Institute for the Science of Light 91058 ErlangenGermany International Centre for Theory of Quantum Technologies 80-308 GdańskPoland Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for Quantum Technology Research and Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements(MOE) School of PhysicsBeijing Institute of TechnologyBeijing 100081China Department of Physics The Hong Kong University of Science and TechnologyHong KongChina.

Quantum error correction plays an important role in fault-tolerant quantum information *** is usually difficult to experimentally realize quantum error correction,as it requires multiple qubits and quantum gates with high *** we propose a simple quantum error-correcting code for the detected amplitude damping *** code requires only two *** implement the encoding,the channel,and the recovery on an optical platform,the IBM Q System,and a nuclear magnetic resonance *** all of these systems,the error correction advantage appears when the damping rate exceeds some *** compare the features of these quantum information processing systems used and demonstrate the advantage of quantum error correction on current quantum computing platforms.

关键词： Quantum error correction Quantum computation Superconducting circuit NMR system Optical platform

来源：评论

学校读者我要写书评

暂无评论

Pleuropulmonary blastoma in a 3-year-old boy: A case study

引用

AIP Conference Proceedings 2023年第1期2564卷

作者： Mohammed Erkhawan Hameed Rasheed Mansour Youseffi Luca Parisi Muhammad Mahadi Abdul Jamil Saeed Afshin Javid Farideh Javid Faculty of Engineering and Informatics University of Bradford Bradford BD7 1DP United Kingdom Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM) Johore Malaysia School of Architecture Computing and Engineering (ACE) University of East London London E16 2RD United Kingdom School of Applied Sciences Pharmacy University of Huddersfield Huddersfield HD1 3DH United Kingdom

Pleuropulmonary blastoma (PPB) is a very aggressive and rare type of cancer that occurs mainly in children; however, it may also affect adults. PPB originates from the lung or the pleural cavity, but its cause is still unknown. PPB has four different subtypes based on whether it involves cysts and/or cancerous nodules, as well as considering the age at diagnosis and prognosis. Medical imaging plays an important role in the diagnosis of PPB to quantify the extent of metastasis of the tumour, such as in type II and type III, and to titrate the treatment strategy accordingly. In this paper, the case of a deceased 3-year-old boy, who was diagnosed with type II and III PPB, is presented, including an analysis on related medical images involving both ionising (chest X-ray and computed tomography) and non-ionising techniques (ultrasound), as well as the treatments that the child underwent. The patient received surgical resection of the tumour, followed by chemotherapy; however, local tumour relapse occurred, a second resection was not possible, and the relapsed tumour did not respond to chemotherapy leading to the death of the child.

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：