检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Hsieh, Ming-Tsuen Wang, Chun-Hua Chang, Jin-Yu Chung, Wen-Yu Huang, Yu-Chen Lee, Chon-Lin Department of Marine Environment and Engineering National Sun Yat-sen University Kaohsiung Taiwan College of Medicine Chang Gung Memorial University Taiwan Department of Thoracic Medicine Chang Gung Memorial Hospital Taipei Taiwan Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Public Health Kaohsiung Medical University Kaohsiung Taiwan National Sun Yat-sen University Kaohsiung Taiwan Center of Excellence for the Oceans National Taiwan Ocean University Keelung20224 Taiwan

Megacity residents may suffer adverse health effects, because polycyclic aromatic hydrocarbons associated with particulate matter (PM2.5-PAHs) are carcinogenic and the abundance of emission sources. In this study, we investigated the spatiotemporal distribution of PM2.5-PAHs in Taipei areas and estimated the potential sources using Principal Component Analysis-Absolute Principal Component Score (PCA-APCS) to evaluate the lifetime excess cancer risk (ECR). The mean concentration of PM2.5-PAHs was 1.11 ± 0.79 ng m-3, and the corresponding benzo[a]pyrene equivalent (BaPeq) was 0.1 ng m-3. The ECR was estimated to be 8.7 × 10-6, which was higher than the U.S. Environmental Protection Agency acceptable level, indicating a potential risk in Taipei. Among the potential sources, gasoline and diesel vehicle exhaust (traffic emissions) was the major contributor of PAHs (48%), followed by fossil fuel combustion (34%), biomass combustion (13%), and evaporated/unburned oil source (5%). In terms of BaPeq contribution, traffic emissions were the highest at 59%, and the highest ECR contribution from traffic emissions in densely populated urban centers was 3.09 x 10-5. To achieve the Sustainable Development Goal (SDG) of good health and well-being, priority should be given to controlling emissions from gasoline and diesel vehicles. This study contributes to a better understanding of the analysis of potential sources and provides information for policy decisions. © 2024, The Authors. All rights reserved.

关键词： Principal component analysis

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Expedited Block Transmission in Blockchain Network by using Clusters

Expedited Block Transmission in Blockchain Network by using ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoqi Hua Jian Chen Peiyun Zhang Zhangjie Fu Haibin Zhu Kezhong Lu Jigang Ren School of Software Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics of Ministry of Education and the School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Mathematics Nipissing University North Bay ON Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio.

关键词： Throughput Blockchains Peer-to-peer computing Delays Synchronization Cybernetics

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Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein

arXiv

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arXiv 2025年

作者： Guo, Xiaotong Yang, Deqian Wang, Dan Zhao, Haochen Li, Yuan Sui, Zhilin Zhou, Tao Zhang, Lijun Meng, Yanda State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Thoracic Surgery National Clinical Research Center for Cancer Cancer Hospital Shenzhen Hospital China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China R&D Center Guangxi Huayi Artificial Intelligence Medical Technology Co. Ltd. China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Exeter Exeter United Kingdom Department of Cardiovascular & Metabolic Medicine University of Liverpool Liverpool United Kingdom Guangzhou Jiayi Software Technology Co. Ltd. China

Accurate segmentation of pulmonary structures is crucial in clinical diagnosis, disease study, and treatment planning. Significant progress has been made in deep learning-based segmentation techniques, but most require much labeled data for training. Consequently, developing precise segmentation methods that demand fewer labeled datasets is paramount in medical image analysis. The emergence of pre-trained vision-language foundation models, such as CLIP, recently opened the door for universal computer vision tasks. Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data. However, exploring these models for pulmonary artery-vein segmentation is still limited. This paper proposes a novel framework called Language-guided self-adaptive Cross-Attention Fusion Framework. Our method adopts pre-trained CLIP as a strong feature extractor for generating the segmentation of 3D CT scans, while adaptively aggregating the cross-modality of text and image representations. We propose a s pecially designed adapter module to fine-tune pre-trained CLIP with a self-adaptive learning strategy to effectively fuse the two modalities of embeddings. We extensively validate our method on a local dataset, which is the largest pulmonary artery-vein CT dataset to date and consists of 718 labeled data in total. The experiments show that our method outperformed other state-of-the-art methods by a large margin. Our data and code will be made publicly available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Multi-Orbit Spectrum Sensing for Uplink NOMA System Towards Next-Generation IoT Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Tianheng Xu, Yinjun Wu, Jingyi Zhang, Haijun Zhou, Ting Hu, Honglin Leung, Victor C.M Chinese Academy of Sciences Shanghai Advanced Research Institute Shanghai 201210 China Shanghai Frontier lnnovation Research Institute Shanghai 201100 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing 100049 China University of Science and Technology Beijing Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing 100083 China Shanghai University School of Microelectronics Shanghai 201210 China Shenzhen University College of Computer Science and Software Engineering Shenzhen 518060 China The University of British Columbia Department of Electrical and Computer Engineering Vancouver V6T1Z4 BC Canada

Next-generation Internet of Things (IoT) technology is vital for sixth-Generation (6G) communication systems, driving exponential growth in spectrum resource demand. Spectrum sensing, essential for identifying unused spectrum, and Non-Orthogonal Multiple Access (NOMA), which allows efficient frequency band sharing among users, can significantly improve spectrum efficiency. However, current sensing methods do not fully support NOMA, resulting in suboptimal performance. Motivated by such a circumstance, we propose a feature-based spectrum sensing method for uplink communication in power-domain NOMA IoT scenarios with multi-user interference, aiming to maximize both static and dynamic spectrum efficiency. Firstly, we introduce the notion of orbits to represent non-fully occupied spectral holes. Then we elaborate on the sensing criterion as well as workflow and design the two-stage spectrum sensing framework, which proposes the orbit estimation sensing algorithm in the stage 1 and accurate sensing threshold in the stage 2, to mitigate false alarm fluctuations. The closed-form solution for the estimation threshold and accurate threshold configuration are derived thereafter. Simulation results show that our proposed multi-orbit sensing method has stable performance and achieves average 30% system throughput gains compared to the latest NOMA techniques at 5 dB. © 2002-2012 IEEE.

关键词： feature detection multi-orbit estimation next-generation IoT NOMA Spectrum sensing

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Breamy: An augmented reality mHealth prototype for surgical decision-making in breast cancer

arXiv

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arXiv 2023年

作者： Najafi, Niki Addie, Miranda Meterissian, Sarkis Kersten-Oertel, Marta Applied Perception Lab Department of Computer Science and Software Engineering Concordia University Québec Canada Experimental Surgery Mcgill University Québec Canada Breast Center McGill University Health Centre Québec Canada Department of Surgery Faculty of Medicine McGill University Québec Canada

In 2020, according to WHO, breast cancer affected 2.3 million women worldwide, resulting in 685,000 fatalities. By the end of the year, approximately 7.8 million women worldwide had survived their breast cancer making it the most widespread form of cancer globally. Surgical treatment decisions, including choosing between oncoplastic options, often require quick decision-making within an 8-week time frame. However, many women lack the necessary knowledge and preparation for making such complex informed decisions. Anxiety and unsatisfactory outcomes can result from inadequate decision-making processes, leading to complications and the need for revision surgeries. Shared decision-making and personalized decision aids have shown positive effects on patient satisfaction and treatment outcomes. This paper introduces Breamy, a prototype mobile health (mHealth) application that utilizes augmented reality (AR) technology to assist breast cancer patients in making informed decisions. The app provides 3D visualizations of different oncoplastic procedures, aiming to improve confidence in surgical decision-making, reduce decisional regret, and enhance patient well-being after surgery. To determine the perception of the usefulness of Breamy, we collected data from 166 participants through an online survey. The results suggest that Breamy has the potential to reduce patient's anxiety levels and assist them during the decision-making process. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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Random forest‑based prediction of stroke outcome

arXiv

引用

arXiv 2024年

作者： Fernandez‑Lozano, Carlos Hervella, Pablo Mato‑Abad, Virginia Rodríguez‑Yáñez, Manuel Suárez‑Garaboa, Sonia López‑Dequidt, Iria Estany‑Gestal, Ana Sobrino, Tomás Campos, Francisco Castillo, José Rodríguez‑Yáñez, Santiago Iglesias‑Rey, Ramón Department of Computer Science and Information Technologies Faculty of Computer Science CITIC-Research Center of Information and Communication Technologies Universidade da Coruña A Coruña Spain SERGAS Universidade da Coruña A Coruña Spain Santiago de Compostela Spain Software Engineering Laboratory Department of Computer Science and Information Technologies Faculty of Computer Science University of A Coruña Campus de Elviña A Coruña15071 Spain Hospital Clínico Universitario Rúa Travesa da Choupana s/n Santiago de Compostela15706 Spain Santiago de Compostela Spain

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3‑months after admission. The dataset consisted of patients with ischemic stroke (IS) and non‑traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/ morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3‑months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p‑value © 2024, CC BY.

关键词： Forecasting

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Optimizing the Design of a Silicon-Photonics Interleaver Circuit

Optimizing the Design of a Silicon-Photonics Interleaver Cir...

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National Conference on Radio Science (NSRC)

作者： Hany Mahrous Mostafa Fedawy Mira Abboud Dalia Selim Michael Gad Electronics and Communications Department Faculty of Engineering Arab Academy for Science and Technology and Maritime Transport Cairo Egypt Center of Excellence in Nanotechnology Arab Academy for Science and Technology and Maritime Transport Cairo Egypt Department of Computer Sciences Faculty of Sciences Lebanese University Fanar Lebanon Engineering Physics and Mathematics Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9781665476379

ISBN: (纸本)9781665476386

In this work, the genetic algorithm is applied to optimize the design of a silicon-photonics based interleaver/deinterleaver circuit. The interleaver circuit is used to mix two data streams into one physical channel while the deinterleaver splits them into two different channels. The circuit satisfies the wavelength division multiplexing (WDM) specifications, including a channel 3 dB-bandwidth of ±10 GHz around the maximum transmission, a channel free spectral range of 100 GHz, channel spacing of 50 GHz, crosstalk of −23 dB, and a maximum dispersion of 30 ps/nm. The circuit operates at the telecommunication wavelength of 1550 nm. The genetic algorithm is employed to achieve the best coupling coefficients between the circuit components. The developed algorithm proves very efficient as it provides the design parameters required to achieve all of the WDM requirements in a very short time compared to the previously presented design approach, which relied on excessive numerical calculations besides investigating the circuit transmission in the Z domain.

关键词： Integrated optics Couplings Crosstalk Wavelength division multiplexing Silicon photonics Communications technology Optical ring resonators

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Enhancement of sensitivity near exceptional points in dissipative qubit-resonator systems

arXiv

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arXiv 2025年

作者： Han, Pei-Rong Wu, Fan Huang, Xin-Jie Wu, Huai-Zhi Zou, Chang-Ling Yi, Wei Zhang, Mengzhen Li, Hekang Xu, Kai Zheng, Dongning Fan, Heng Wen, Jianming Yang, Zhen-Biao Zheng, Shi-Biao School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China Fujian Key Laboratory of Quantum Information and Quantum Optics College of Physics and Information Engineering Fuzhou University Fuzhou350108 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Institute of Physics Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering Binghamton University BinghamtonNY13902 United States Department of Physics Kennesaw State University MariettaGA30060 United States

Dissipation usually plays a negative role in quantum metrological technologies, which aim to improve measurement precision by leveraging quantum effects that are vulnerable to environment-induced decoherence. Recently, it has been demonstrated that dissipation can actually be used as a favorable resource for enhancing the susceptibility of signal detection. However, demonstrations of such enhancement for detecting physical quantities in open quantum systems are still lacking. Here we propose and demonstrate a protocol for realizing such non-Hermitian quantum sensors for probing the coupling between a qubit and a resonator subjecting to energy dissipations. The excitation-number conversion associated with the no-jump evolution trajectory enables removal of the noisy outcomes with quantum jumps, implementing the exceptional point (EP), where the Rabi splitting exhibits a divergent behavior in response to a tiny variation of the effective coupling. The sensitivity enhancement near the EP is confirmed by both theoretical calculation and experimental measurement. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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A novel deep learning method for textual sentiment analysis

arXiv

引用

arXiv 2021年

作者： Sadr, Hossein Solimandarabi, Mozhdeh Nazari Pedram, Mir Mohsen Teshnehlab, Mohammad Department of Computer Engineering Rasht Branch Islamic Azad University Rasht Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Industrial Control Center of Excellence Faculty of Electrical and Computer Engineering K. N. Toosi University Tehran Iran

Sentiment analysis is known as one of the most crucial tasks in the field of natural language processing and Convolutional Neural Network (CNN) is one of those prominent models that is commonly used for this aim. Although convolutional neural networks have obtained remarkable results in recent years, they are still confronted with some limitations. Firstly, they consider that all words in a sentence have equal contributions in the sentence meaning representation and are not able to extract informative words. Secondly, they require a large number of training data to obtain considerable results while they have many parameters that must be accurately adjusted. To this end, a convolutional neural network integrated with a hierarchical attention layer is proposed which is able to extract informative words and assign them higher weight. Moreover, the effect of transfer learning that transfers knowledge learned in the source domain to the target domain with the aim of improving the performance is also explored. Based on the empirical results, the proposed model not only has higher classification accuracy and can extract informative words but also applying incremental transfer learning can significantly enhance the classification performance. © 2021, CC BY.

关键词： Sentiment analysis

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Ensuring Privacy and Correlation Awareness in Multi-Dimensional Service Quality Prediction and Recommendation for Iot

SSRN

引用

SSRN 2024年

作者： Zhong, Weiyi Fang, Wei Zhao, Yifan Wang, Sifeng Yan, Chao Jiang, Rong Khan, Maqbool Yang, Xuan Rafique, Wajid Qufu Normal University School of Engineering Rizhao276800 China Qufu Normal University School of Computer Science Rizhao276800 China Weifang University of Science and Technology Weifang262700 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650000 China Software Competence Center Hagenberg Upper Austria 4232 Austria Angeles University Foundation Pampanga2009 Philippines University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada

Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and wise medicine. While several challenges are raised when we fuse the multiple-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named TLTM) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. At last, to validate the effectiveness and efficiency of the TLTM method, we have designed massive experiments on the popular WS-DREAM dataset. The reported experimental results show that our TLTM method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and Time cost. © 2024, The Authors. All rights reserved.

关键词： Quality of service

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