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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of

Xiaoming Yuan,Jiahui Chen,Ning Zhang,Qiang Ye,Changle Li,Chunsheng Zhu,Xuemin Sherman Shen,

《工程（英文）》 doi: 10.1016/j.eng.2023.04.015

摘要： High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles (IoVs). However, it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment. In order to protect data privacy and improve data learning efficiency in knowledge sharing, we propose an asynchronous federated broad learning (FBL) framework that integrates broad learning (BL) into federated learning (FL). In FBL, we design a broad fully connected model (BFCM) as a local model for training client data. To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients, we construct a joint resource allocation and reconfigurable intelligent surface (RIS) configuration optimization framework for FBL. The problem is decoupled into two convex subproblems. Aiming to improve the resource scheduling efficiency in FBL, a double Davidon–Fletcher–Powell (DDFP) algorithm is presented to solve the time slot allocation and RIS configuration problem. Based on the results of resource scheduling, we design a reward-allocation algorithm based on federated incentive learning (FIL) in FBL to compensate clients for their costs. The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency, accuracy, and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing     Internet of Vehicles     Federated learning     Broad learning     Reconfigurable intelligent surfaces     Resource allocation    

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Rapid and long-effective removal of broad-spectrum pollutants from aqueous system by ZVI/oxidants

Sana Ullah, Xuejun Guo, Xiaoyan Luo, Xiangyuan Zhang, Siwen Leng, Na Ma, Palwasha Faiz

《环境科学与工程前沿（英文）》 2020年 第14卷 第5期 doi: 10.1007/s11783-020-1268-3

摘要： Abstract • The coupling of oxidants with ZVI overcome the impedance of ZVI passive layer. • ZVI/oxidants system achieved fast and long-effective removal of contaminants. • Multiple mechanisms are involved in contaminants removal by ZVI/oxidant system. • ZVI/Oxidants did not change the reducing property of ORP in the fixed-bed system. Zero-valent iron (ZVI) technology has recently gained significant interest in the efficient sequestration of a wide variety of contaminants. However, surface passivation of ZVI because of its intrinsic passive layer would lead to the inferior reactivity of ZVI and its lower efficacy in contaminant removal. Therefore, to activate the ZVI surface cheaply, continuously, and efficiently is an important challenge that ZVI technology must overcome before its wide-scale application. To date, several physical and chemical approaches have been extensively applied to increase the reactivity of the ZVI surface toward the elimination of broad-spectrum pollutants. Nevertheless, these techniques have several limitations such as low efficacy, narrow working pH, eco-toxicity, and high installation cost. The objective of this mini-review paper is to identify the critical role of oxygen in determining the reactivity of ZVI toward contaminant removal. Subsequently, the effect of three typical oxidants (H2O2, KMnO4, and NaClO) on broad-spectrum contaminants removal by ZVI has been documented and discussed. The reaction mechanism and sequestration efficacies of the ZVI/oxidant system were evaluated and reviewed. The technical basis of the ZVI/oxidant approach is based on the half-reaction of the cathodic reduction of the oxidants. The oxidants commonly used in the water treatment industry, i.e., NaClO, O3, and H2O2, can be served as an ideal coupling electron receptor. With the combination of these oxidants, the surface corrosion of ZVI can be continuously driven. The ZVI/oxidants technology has been compared with other conventional technologies and conclusions have been drawn.

关键词： Zero-Valent Iron (ZVI)     Oxidants     Heavy Metals (HMs)     Metalloids     Nitrate     Phosphate    

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从H1N1血凝素序列提取的沙门氏菌传递的COBRA-HA1抗原对甲型流感亚型产生广谱保护作用 Article

Ram Prasad Aganja, Amal Senevirathne, Chandran Sivasankar, John Hwa Lee

《工程（英文）》 2024年 第32卷 第1期   页码 42-57 doi: 10.1016/j.eng.2023.08.001

摘要：

A universal vaccine is in high demand to address the uncertainties of antigenic drift and the reduced effectiveness of current influenza vaccines. In this study, a strategy called computationally optimized broadly reactive antigen (COBRA) was used to generate a consensus sequence of the hemagglutinin globular head portion (HA1) of influenza virus samples collected from 1918 to 2021 to trace evolutionary changes and incorporate them into the designed constructs. Constructs carrying different HA1 regions were delivered into eukaryotic cells by Salmonella-mediated bactofection using a Semliki Forest virus RdRp-dependent eukaryotic expression system, pJHL204. Recombinant protein expression was confirmed by Western blot and immunofluorescence assays. Mice immunized with the designed constructs produced a humoral response, with a significant increase in immunoglobulin G (IgG) levels, and a cell-mediated immune response, including a 1.5-fold increase in CD4+ and CD8+ T cells. Specifically, constructs #1 and #5 increased the production of interferon-γ (IFN-γ) producing CD4+ and CD8+ T cells, skewing the response toward the T helper type 1 cell (Th1) pathway. Additionally, interleukin-4 (IL-4)-producing T cells were upregulated 4-fold. Protective efficacy was demonstrated, with up to 4-fold higher production of neutralizing antibodies and a hemagglutination inhibition titer > 40 against the selected viral strains. The designed constructs conferred a broadly protective immune response, resulting in a notable reduction in viral titer and minimal inflammation in the lungs of mice challenged with the influenza A/PR8/34, A/Brisbane/59/2007, A/California/07/2009, KBPV VR-92, and NCCP 43021 strains. This discovery revolutionizes influenza vaccine design and delivery; Salmonella-mediated COBRA-HA1 is a highly effective in vivo antigen presentation strategy. This approach can effectively combat seasonal H1N1 influenza strains and potential pandemic outbreaks.

关键词： COBRA     Influenza A     Salmonella     Vaccine     Broad spectral protection    

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High risk factors for pulmonary fungous infection in intensive care units of neurosurgery

ZHU Wenyu, TAN Liping, CHEN Xiangfeng, HUANG Qiang, LAN Qing

《医学前沿（英文）》 2007年 第1卷 第3期   页码 299-303 doi: 10.1007/s11684-007-0057-8

摘要： By analyzing the high risk factors for pulmonary fungous infection in intensive care units of neurosurgery, the strategy of early diagnosis and treatment was explored. According to the domestic diagnostic standard on pulmonary fungous infection, clinical data on 58 patients with the infection in our department were analyzed. One hundred and seventeen strains of fungi were separated from the 58 cases. Candidiasis was the most frequent type, accounting for 92.3% of the cases. Conditions such as the severity of primary diseases, long-time coma, long-term use of broad-spectrum antibiotic, abuse of glucocorticoid, the open airway, and some invasive intubations, may be regarded as high risk factors for pulmonary fungous infection. Fluconazole showed good clinical effects on the treatment of fungous infection. To eliminate these high risk factors, early diagnosis and the use of prophylactic antifungal agents can help reduce the incidence of pulmonary fungous infection.

关键词： seventeen     incidence     glucocorticoid     broad-spectrum antibiotic     Candidiasis    

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MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

《环境科学与工程前沿（英文）》 2023年 第17卷 第6期 doi: 10.1007/s11783-023-1677-1

摘要：

● MSWNet was proposed to classify municipal solid waste.

关键词： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

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Spatial prediction of soil contamination based on machine learning: a review

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1

摘要：

● A review of machine learning (ML) for spatial prediction of soil contamination.

关键词： Soil contamination     Machine learning     Prediction     Spatial distribution    

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Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

《环境科学与工程前沿（英文）》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1738-5

摘要：

● A novel integrated machine learning method to analyze O3 changes is proposed.

关键词： Ozone     Integrated method     Machine learning    

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Machine learning in building energy management: A critical review and future directions

《工程管理前沿（英文）》 2022年 第9卷 第2期   页码 239-256 doi: 10.1007/s42524-021-0181-1

摘要： Over the past two decades, machine learning (ML) has elicited increasing attention in building energy management (BEM) research. However, the boundary of the ML-BEM research has not been clearly defined, and no thorough review of ML applications in BEM during the whole building life-cycle has been published. This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions. An integrated framework of ML-BEM, composed of four layers and a series of driving factors, is proposed. Then, based on the hype cycle model, this paper analyzes the current development status of ML-BEM and tries to predict its future development trend. Finally, five research directions are discussed: (1) the behavioral impact on BEM, (2) the integration management of renewable energy, (3) security concerns of ML-BEM, (4) extension to other building life-cycle phases, and (5) the focus on fault detection and diagnosis. The findings of this study are believed to provide useful references for future research on ML-BEM.

关键词： building energy management     machine learning     integrated framework     knowledge evolution    

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Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 183-197 doi: 10.1007/s11705-021-2073-7

摘要： Flowsheet simulations of chemical processes on an industrial scale require the solution of large systems of nonlinear equations, so that solvability becomes a practical issue. Additional constraints from technical, economic, environmental, and safety considerations may further limit the feasible solution space beyond the convergence requirement. A priori, the design variable domains for which a simulation converges and fulfills the imposed constraints are usually unknown and it can become very time-consuming to distinguish feasible from infeasible design variable choices by simply running the simulation for each choice. To support the exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning models has recently been proposed. However, that approach only considers the exploration of the convergent domain and ignores additional constraints. In this paper, we present an improvement which particularly takes the fulfillment of constraints into account. We successfully apply the proposed algorithm to a toy example in up to 20 dimensions and to an industrially relevant flowsheet simulation.

关键词： machine learning     flowsheet simulations     constraints     exploration    

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Machine learning for fault diagnosis of high-speed train traction systems: A review

《工程管理前沿（英文）》 doi: 10.1007/s42524-023-0256-2

摘要： High-speed trains (HSTs) have the advantages of comfort, efficiency, and convenience and have gradually become the mainstream means of transportation. As the operating scale of HSTs continues to increase, ensuring their safety and reliability has become more imperative. As the core component of HST, the reliability of the traction system has a substantially influence on the train. During the long-term operation of HSTs, the core components of the traction system will inevitably experience different degrees of performance degradation and cause various failures, thus threatening the running safety of the train. Therefore, performing fault monitoring and diagnosis on the traction system of the HST is necessary. In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstrated an excellent performance in traction system fault diagnosis. Machine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensive systematic review is still lacking in this field. This paper primarily aims to review the research and application of machine learning in the field of traction system fault diagnosis and assumes the future development blueprint. First, the structure and function of the HST traction system are briefly introduced. Then, the research and application of machine learning in traction system fault diagnosis are comprehensively and systematically reviewed. Finally, the challenges for accurate fault diagnosis under actual operating conditions are revealed, and the future research trends of machine learning in traction systems are discussed.

关键词： high-speed train     traction systems     machine learning     fault diagnosis    

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卫星移动通信的现状与发展

张乃通,张中兆,初海彬,刘会杰

《中国工程科学》 2002年 第4卷 第10期   页码 11-16

摘要：

介绍了国际上出现的三种星座通信系统，重点分析了几个具有代表性系统的组成、结构、功能及其存在的问题。探讨了卫星移动通信系统的发展趋势，并对发展卫星移动通信做了几点思考。

关键词： 卫星移动通信     星座通信系统     宽带业务    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》   页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

《医学前沿（英文）》 2023年 第17卷 第4期   页码 768-780 doi: 10.1007/s11684-023-0982-1

摘要： Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

关键词： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Automated synthesis of steady-state continuous processes using reinforcement learning

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 288-302 doi: 10.1007/s11705-021-2055-9

摘要： Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

关键词： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcement learning    

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标题 作者 时间 类型 操作

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of

Xiaoming Yuan,Jiahui Chen,Ning Zhang,Qiang Ye,Changle Li,Chunsheng Zhu,Xuemin Sherman Shen,

期刊论文

Rapid and long-effective removal of broad-spectrum pollutants from aqueous system by ZVI/oxidants

Sana Ullah, Xuejun Guo, Xiaoyan Luo, Xiangyuan Zhang, Siwen Leng, Na Ma, Palwasha Faiz

期刊论文

从H1N1血凝素序列提取的沙门氏菌传递的COBRA-HA1抗原对甲型流感亚型产生广谱保护作用

Ram Prasad Aganja, Amal Senevirathne, Chandran Sivasankar, John Hwa Lee

期刊论文

High risk factors for pulmonary fungous infection in intensive care units of neurosurgery

ZHU Wenyu, TAN Liping, CHEN Xiangfeng, HUANG Qiang, LAN Qing

期刊论文

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

期刊论文

Spatial prediction of soil contamination based on machine learning: a review

期刊论文

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

期刊论文

Machine learning in building energy management: A critical review and future directions

期刊论文

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

期刊论文

Machine learning for fault diagnosis of high-speed train traction systems: A review

期刊论文

卫星移动通信的现状与发展

张乃通,张中兆,初海彬,刘会杰

期刊论文

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

期刊论文

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

期刊论文

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Automated synthesis of steady-state continuous processes using reinforcement learning

期刊论文