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Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

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NeuroImage 2025年 314卷 121256-121256页

作者： Qin, Jianmei Wu, Haoting Wu, Chenqing Guo, Tao Zhou, Cheng Duanmu, Xiaojie Tan, Sijia Wen, Jiaqi Zheng, Qianshi Yuan, Weijin Zhu, Zihao Chen, Jingwen Wu, Jingjing He, Chenyu Ma, Yiran Liu, Chunlei Xu, Xiaojun Guan, Xiaojun Zhang, Minming Department of Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Joint Laboratory of Clinical Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China State Key Laboratory of Computer-aided Design & Computer Graphics Zhejiang University College of Computer Science and technology Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University College of Control Science and Engineering Hangzhou China Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Helen Wills Neuroscience Institute University of California Berkeley CA United States

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), *** consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87–0.90, FUNC2T1: 0.67–0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinso

关键词： Parkinson's disease Quantitative susceptibility mapping Resting-state functional connectivity Subcortical nuclei

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Parallel Governance for Decentralized Autonomous Organizations enabled by Blockchain and Smart Contracts

Parallel Governance for Decentralized Autonomous Organizatio...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wen Wen Ding XiaoLong Liang JiaChen Hou Ge Wang Yong Yuan Junqing Li Fei-Yue Wang Institute of Systems Engineering Macau University of Science and Technology Macau China School of Mathematics Renmin University of China Beijing China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Decentralized autonomous organizations(DAOs) enabled by blockchain and smart contracts is regarded as an effective tool to solve corporate governance problems. It can minimize the contract risks, principal-agent dilemmas, and other issues in corporate governance. However, DAOs also face a variety of governance issues, such as internal governance issues and external governance issues caused by the technical attributes of blockchain and smart contracts. DAO is a typical complex system with the characteristics of both social and engineering complexity. It is difficult for existing governance theories to solve governance problems in DAOs. The parallel intelligence theory based on the artificial systems + computational experiments + parallel execution (ACP) method is a suitable research and practical approach to solve management and control problems for complex systems. Therefore, we propose a parallel governance framework for DAO based on the parallel intelligence theory and discuss its key research issues. Finally, we construct a parallel governance system for GnosisDAO, and conduct computational experiments on its voting mechanism. The results show the defects of GnosisDAO governance mechanism and we thus propose further research directions. We consider that parallel governance represents the future trend of DAO research and practice. This paper is aimed at stimulating further effort and providing helpful guidance and reference for future research works.

关键词： Computational modeling Digital twin Conferences Smart contracts Tools Systems modeling Market research

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ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection

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Knowledge-Based Systems 2025年 321卷

作者： AbdulAzeem, Yousry Magdy Balaha, Hossam Bamaqa, Amna Badawy, Mahmoud Elhosseini, Mostafa A. Zewail City of Science and Technology October Gardens 6th of October City Giza12578 Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

Disease prediction with the help of computers has achieved significant progress in this area;however, it still requires a more accurate identification of each data feature. In the past few years, ML-based medical diagnoses have become increasingly dependent on the datasets rather than the algorithms. Consequently, the more features in a dataset, the more difficult the model training process will be. A feature selection (FS) strategy can be used to eliminate unwanted features before the training in order to improve the model performance. Due to the great increase in the number of features, FS causes a complex combinatorial problem, which is NP-hard. The primary goal of this research is to create a novel framework for improving the Manta-ray Foraging Optimizer (MRFO) algorithm's performance. By the method of exploration–exploitation toggle adjustments, this process becomes the most efficient one by using the Expected State–action-reward-state–action (ESARSA) reinforcement learning strategy. The study employed 12 datasets as well as two classifiers (K-Nearest-Neighbor and Decision-Tree), both with and without hyperparameter adjustment. The study compared the performances of three feature selection methods: no feature selection, the original MRFO algorithm, and the proposed ESARSAMRFO-FS algorithm. The results revealed that ESARSA-MRFO-FS is the most effective of the three algorithms with classifiers and optimization scenarios. Besides, experiments comparing MRFO and ESARSA-MRFO performance on 44 benchmarks are presented in the article. In five functions, ESARSA-MRFO outperforms MRFO. These results confirm that the proposed method is efficient for feature selection in medical diagnosis because of its accuracy and low processing costs. © 2025 Elsevier B.V.

关键词： Nearest neighbor search

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Variational Distillation for Multi-View Learning

arXiv

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arXiv 2022年

作者： Tian, Xudong Zhang, Zhizhong Wang, Cong Zhang, Wensheng Qu, Yanyun Ma, Lizhuang Wu, Zongze Xie, Yuan Tao, Dacheng School of Computer Science and Technology East China Normal University Shanghai200062 China The Distributed and Parallel Software Laboratory 2012 Labs Huawei Technologies Hangzhou China Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Information Science and Technology Xiamen University Fujian361005 China School of Computer Science and Techology East China Normal University Shanghai China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China JD Exploer Academy China The University of Sydney Australia

Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions. However, its great success is generally attributed to estimate the multivariate mutual information which is intractable when the network becomes complicated. Moreover, the representation learning tradeoff, i.e., prediction-compression and sufficiency-consistency tradeoff, makes the IB hard to satisfy both requirements simultaneously. In this paper, we design several variational information bottlenecks to exploit two key characteristics (i.e., sufficiency and consistency) for multi-view representation learning. Specifically, we propose a Multi-View Variational Distillation (MV2D) strategy to provide a scalable, flexible and analytical solution to fitting MI by giving arbitrary input of viewpoints but without explicitly estimating it. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels, producing predictive and compact representations naturally. Also, our information-theoretic constraint can effectively neutralize the sensitivity to heterogeneous data by eliminating both task-irrelevant and view-specific information, preventing both tradeoffs in multiple view cases. To verify our theoretically grounded strategies, we apply our approaches to various benchmarks under three different applications. Extensive experiments to quantitatively and qualitatively demonstrate the effectiveness of our approach against state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Remote Online Firmware Upgrade System for Embedded Devices based on ZigBee

Remote Online Firmware Upgrade System for Embedded Devices b...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Pengju Tang Taiguo Qu Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua China

ISBN: (纸本)9781665413015

To reduce the upgrade and maintenance cost of embedded devices effectively, according to the requirement of a remote firmware update of embedded devices, this paper proposed a remote online firmware upgrade technology based on ZigBee. Combined with the requirement of remote online firmware upgrade application of the tower top amplifier, this paper analyzes and designs the system requirements, ZigBee module selection, and design the software, fault analysis, and other aspects. Designed Experiments to test the ZigBee connection success rate under the device startup state and normal operation state, the relationship between transmission firmware size and transmission success rate, system CPU resource consumption, transmission distance, transmission rate, and other performance indicators. The experimental results show that the remote online firmware upgrade technology of embedded devices based on ZigBee can better meet the performance requirements of all aspects of the system. The results show that this scheme has the advantages of simplicity, low cost, and high reliability and has a broad application prospect.

关键词： Performance evaluation Costs Codes Poles and towers Zigbee Maintenance engineering Software

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The Fast Fixed-Time Bipartite Synchronization of Coupled Delayed Neural Networks with Signed Graphs

Research Square

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Research Square 2021年

作者： Yang, Shiju Li, Chuandong Li, Yu Yang, Ting Li, Bo Chongqing Engineering Laboratory for Detection Control and Integrated System School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China College of Electronic and Information Engineering Southwest University Chongqing400715 China Army Logistics Academy of PLA Chongqing401311 China

In this paper, the fixed-time bipartite synchronization problem for coupled delayed neural networks with signed graphs is discussed. Different from traditional neural networks, the interactions between nodes of delayed neural networks can be either collaborative or antagonistic. Furthermore, compared with the initial-condition based finite-time synchronization, the settling time is bounded by a constant within fixed-time regardless of the initial condition. It is worth noting that the fixed-time stable network for bipartite synchronization in this paper achieves more faster convergence than most existing publications. By applying constructing comparison system method, Lyapunov stability theory and inequality techniques, some sufficient criteria for fixed-time bipartite synchronization are obtained. Finally, two numerical examples are granted to display the performance of the obtained results. © 2021, CC BY.

关键词： Synchronization

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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Blockchain, Smart Healthcare and Emerging Technologies (SmartBlock4Health), IEEE International Conference on

作者： Hanan Hussein Al-asadi Maria Goga Haider Abdullah Ali Ramona Cristina Popa Awodele Oludele Faculty of Automatic Control and Computers University POLITEHNICA of Bucharest Bucharest Romania University of Technology-Iraq Baghdad Iraq UTCB Department for Training of Teaching Personnel Technical University of Civil Engineering of Bucharest Bucharest Romania Madenat Alelem University College Baghdad Iraq Faculty of Engineering in Foreign Languages University POLITEHNICA of Bucharest Bucharest Romania computer science department Babcock University Ogun State Nigeria

ISBN: (纸本)9781665460156

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy.

关键词： Smart agriculture Machine learning algorithms Costs Green buildings Soil control systems Agriculture

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Optimal Supervisor Simplification in AMS based on Petri Nets and Genetic Algorithm

Optimal Supervisor Simplification in AMS based on Petri Nets...

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IEEE Conference on Decision and control

作者： Chen Chen Chan Gu Hesuan Hu School of Mechano-Electronic Engineering Xidian University Xi’an Shaanxi P. R. China School of Electrical and Control Engineering Shaanxi University of Science and Technology School of Computer Science & Engineering College of Engineering Nanyang Technological University Singapore State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an Shaanxi P. R. China

ISBN: (纸本)9781665436601

Supervisor simplification is of significant importance in the supervisory control of automated manufacturing systems. In many situations, simplification results are not unique. However, there is little study on the optimal simplification. In the paradigm of discrete event systems, solving the optimal simplification is always faced with the combinational explosion problem. Based on the simplification method proposed by the same authors, our work further investigates the optimal simplified supervisor based on the genetic algorithm (GA). There are two contributions. First, we present a GA in which our simplification method is embedded to derive the basic optimal simplified supervisor when the parameters of the specifications are fixed. Second, a hierarchical GA is proposed to obtain the advanced optimal simplified supervisor when the parameters of the specifications are changeable. This is a multiple-objective optimization problem where both structure simplification and behavior permissiveness are considered. The examples show the effectiveness of our algorithms in solving the optimal supervisor simplification problem.

关键词： Conferences Petri nets Supervisory control Minimization Genetics Explosions Discrete-event systems

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Interacting Effects of Water and N-P-K Fertilizers on the Resource Use Efficiencies and Fruit Yield of Drip-Fertigated Chinese Wolfberry (Lycium Barbarum L.)

SSRN

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SSRN 2023年

作者： Deng, Zhen Eeswaran, Rasu Yin, Juan Abhiram, Gunaratnam Gong, Siyuan College of Information Engineering Ningxia University Yinchuan750021 China College of Civil and Hydraulic Engineering Ningxia University Yinchuan750021 China Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI48824 United States Department of Agronomy Faculty of Agriculture University of Jaffna Ariviyal Nagar Kilinochchi44000 Sri Lanka Ministry of Education Engineering Research Center for Modern Agricultural Water Resources Efficient Utilization in Dry Areas Yinchuan750021 China Ningxia Water-saving Irrigation and Water Resources Control Engineering Technology Research Center Yinchuan750021 China School of Agriculture and Environment Massey University Palmerston North4442 New Zealand Department of Export Agriculture Uva Wellassa University Badulla90000 Sri Lanka School of Computer Science and Software Engineering Nanjing University of Information Science & Technology Nanjing210032 China

Chinese wolfberry (Lycium barbarum L.) is an important cash crop in the Ningxia region of China. However, low water and fertilizer use efficiencies have limited the growth of the Chinese wolfberry industry. Field experiments were conducted in the central Ningxia of Northwest China during 2018-2019 to investigate the interaction effects of different irrigation and fertilizer levels on agronomic performances of Chinese wolfberry, water use efficiency, fertilizer partial productivity and economic benefit. The optimal range of irrigation and fertilizer inputs for a multi-objective optimization problem was determined using multiple regression, entropy weight method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) coupling comprehensive evaluation method. Three drip irrigation levels were designated based on the percentage of reference crop evapotranspiration (ETo) as low (65% ET0: W1), medium (85% ET0: W2) and high (105% ET0: W3) and three N-P2O5-K2O combined fertilization levels (kg ha-1) were designated as low (135-45-90: F1), medium (180-60-120: F2) and high (225-75-150: F3). The results showed that agronomic performances, water use efficiency (WUE), fertilizer partial productivity (PFP) and economic benefits increased initially and then decreased with increasing irrigation levels under the same fertilization level. Water use efficiency (WUE) was low under high irrigation level and high under low irrigation level. The PFP decreased with increasing fertilization levels and the lowest PFP was observed at high level of fertilizer (F3) application. The above parameters reached the maximum value under the medium irrigation level (85% ET0). Moreover, the multi-objective optimization highlighted that 252-262 mm of irrigation and 185-62-123～200-67-133 kg ha-1 of N-P2O5-K2O fertilization level offer more than 90% of yield, WUE, PFP and economic benefits simultaneously. These results provide a scientific reference for resource optimization under drip-

关键词： Multiobjective optimization

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Semi-automated Construction of Air Pollution Domain Ontology 1

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9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo Zhang, Jiahui Li, Jianqiang Qu, Guangzhi Li, Yong Lang, Jianlei School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China

ISBN: (数字)9789811532504

ISBN: (纸本)9789811532498

Air pollution has become a substantial environmental issue in China, which has seriously affected human health. To clarify the relationship among air pollutants, pollution sources, influencing factors, evaluation indicators and harms, it is essential to build a domain ontology for air pollution. Domain ontologies have been gradually accepted as a method to indicate the relationship between terms. However, there is not a complete ontology in air pollution domain, and building domain ontologies manually is time-consuming and inconvenient. In this paper, a semi-automatic approach is presented to build an ontology in air pollution domain. This paper proposes a method of entity relationship joint extraction based on attention mechanism, which is combined with core concept mining method to extract knowledge, and then concepts, relationships, and relevant instances are organized in hierarchy. Finally, the ontology model is constructed semi-automatically and semantic inference is also carried out. The research shows that this knowledge extraction method can avoid the error accumulation caused by entity recognition and relationship extraction and deepen the inner connection between them, and a large amount of effective knowledge in the field of air pollution can be extracted. In addition, the ontology constructed based on this method can also visually analyze the relationship between various classes and concepts in the field of air pollution, deduce the pollutant propagation path, and provide practical experience for the semi-automatic ontology construction in other fields and data support for further research. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ontology

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