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Dissemination Characteristics and Exposure Risk Assessment of Antibiotic Resistance Genes via Aerosols from Wastewater Treatment Processes

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Water (Switzerland) 2025年第9期17卷 1305-1305页

作者： Ding, Diangang Sun, Jianbin Chi, Mingjia Liu, Lan Ren, Zening Liu, Jianwei Beijing Engineering Research Center of Sustainable Urban Sewage System Construction and Risk Control Beijing University of Civil Engineering and Architecture Beijing100044 China School of Environment and Energy Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences Nanjing210008 China

Wastewater treatment plants (WWTPs) have been confirmed as reservoirs of antibiotic resistance genes (ARGs). This study systematically investigated the distribution patterns of ARGs across different treatment units in municipal WWTPs, along with the environmental drivers, dissemination characteristics, and exposure risks of aerosol-borne ARGs in aerated tank environments. The results revealed a high compositional similarity in aerosol-borne ARGs across the sampling sites, with multidrug ARGs predominating at an average relative abundance of 52%, followed sequentially by tetracycline (11%), MLS (10%), and glycopeptide resistance genes (7%). The diffusion of aerosol-borne ARGs is significantly influenced by environmental factors including temperature, relative humidity, wind speed, and total suspended particulate (TSP) concentration, with temperature being the most dominant factor affecting the dispersion of ARGs. The atmospheric dispersion model demonstrates that aerosol-borne ARGs decay with increasing downwind distance, showing potential for transport from aeration tanks to locations exceeding 1500 m along the prevailing wind direction. Both within wastewater treatment units and downwind areas, adult males had higher respiratory exposure doses but lower skin contact doses compared to females, with respiratory doses exceeding skin contact by 3–4 orders of magnitude. This study highlights the potential health risks posed by aerosol-borne ARG transmission from WWTP operations. © 2025 by the authors.

关键词： Risk assessment

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SurvGraph: A hybrid-graph attention network for survival prediction using whole slide pathological images in gastric cancer

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Neural Networks 2025年 189卷 107607页

作者： Zhao, Yuanshen Li, Longsong Yu, Xi Han, Ke Duan, Jingxian Liang, Dong Chai, Ningli Li, Zhi-Cheng Institute of Biomedical and Health Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Gastroenterology The First Medical Center of Chinese PLA General Hospital Beijing China Department of Gastroenterology Longgang District Central Hospital of Shenzhen Shenzhen China The Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences State Key Laboratory of Biomedical Imaging Science and System Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong China

Whole slide pathological images have shown significant potential for patient prognostication. Graph representation learning provides a robust framework for in-depth analysis of whole-slide images to construct predictive models. In this study, we introduce SurvGraph, an innovative graph-based deep learning network designed for gastric cancer survival prediction using whole slide pathological images. SurvGraph employs a hybrid graph construction approach that integrates multiple feature types, including color, texture, and deep learning features extracted from the pathological images to build node representations. SurvGraph utilizes a multi-head attention graph network, which performs survival prediction based on the graph structure. We evaluate the SurvGraph model on a large dataset of 708 gastric cancer patients from three independent cohorts for overall survival prediction. To assess the impact of various feature sets, we examine their performance when used individually and in combination. With five-fold cross-validation, our results demonstrate that the SurvGraph model achieves an average concordance index (C-index) of 0.706 with a standard deviation (SD) of 0.019. The proposed SurvGraph model has also attained a C-index of 0.708 (SD = 0.040) in the external testing set. In addition to baseline comparisons, we conducted a comprehensive benchmarking study comparing SurvGraph against established graph neural network architectures and multiple instance learning-based deep learning frameworks. The results indicate that the SurvGraph model outperforms the compared prediction models, suggesting its potential as a valuable tool for enhancing gastric cancer prognosis estimation. © 2025

关键词： Deep learning

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FDformer: A Fuzzy Dynamic Transformer-Based Network for Efficient Industrial Time Series Prediction

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IEEE Transactions on Fuzzy systems 2025年

作者： Ren, Lei Zhao, Tuo Wang, Haiteng The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Hangzhou International Innovation Institute Beihang University Hangzhou311115 China The State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

Industrial time series prediction is highly important for the predictive maintenance of Industrial Internet of Things (IIoT) devices. Deep learning methods have demonstrated state-of-the-art performance in the field of time series prediction. However, time series data from complex industrial scenarios often contain substantial uncertainty. This makes it difficult for deterministic deep learning models to achieve accurate predictions. Moreover, existing static methods often fail to meet the real-time requirements of industrial environments. To address the challenges, this study introduces fuzzy learning into deep learning models to overcome the drawbacks of fixed model representations. Therefore, we propose a fuzzy dynamic transformer (FDformer) that can adaptively adjust network depth according to the complexity of individual samples. Subsequently, we design a fuzzy feature extraction mechanism to capture feature information within the fuzzy membership degree, enabling the feature-level fusion of the fuzzy representation with the dynamic depth representation. Finally, we propose a training method for dynamically allocating loss weights, emphasizing the contribution of various samples to different exits, thereby improving the performance of time-series dynamic networks. Experiments on multiple datasets indicate that FDformer achieves minimal computational costs and excellent prediction accuracy across multiple datasets, outperforming state-of-the-art algorithms. © 1993-2012 IEEE. All rights reserved.

关键词： Predictive maintenance

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Sensitive components of temperature-induced track deformation on cable-stayed bridge impacting dynamic response of high-speed train based on deep learning

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Zhang, Qian Cai, Xiaopei Wang, Jijun Liu, Weibin Zhong, Yilin Zhang, Moyan Wang, Ji Railway Engineering Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China State Key Laboratory of High-speed Railway Track System Beijing100081 China School of Civil Engineering Beijing Jiaotong University Beijing100044 China Beijing Glory PKPM Technology Co. Ltd. Beijing100013 China

To explore the impact of different components of track deformation caused by temperature variations of cable-stayed bridges (CSBs) and slab tracks on the dynamic response of high-speed trains (HSTs), this paper established an analytical framework based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and deep learning. Initially, a finite element model for the slab track-CSB and a multi-body dynamics model for HST-CSB were established to calculate the temperature-induced deformation of the slab tracks on the CSB and the corresponding dynamic response of the HST. Subsequently, the ICEEMDAN and Convolutional Neural Networks (CNN)-Bi-Long Short-term Memory (BiLSTM)-Convolutional Block Attention Module (CBAM), were fused to obtain the integrated ICEEMDAN-CNN-BiLSTM-CBAM (ICBC) model. The ICEEMDAN can effectively decompose the signal of track deformation by frequency, while BiLSTM had superior performance in capturing the spatial features of input data. The model could establish the mapping relationship between track deformation components and vehicle dynamic responses. The main conclusions obtained are as follows: When both the CSB and slab track experienced a simultaneous temperature rise, it resulted in an intricate combination of short-wave track deformations, long-wave bridge deformations, and localized deformations induced by girder ends. At the mid-span location, since the span length is as large as 450m, the wavelength of the deformation induced by temperature variations has also increased. By extracting the weight vectors of the output neural network model, the contribution of each Intrinsic Mode Functions to the vehicle dynamic response was obtained. The dynamic response of HSTs was most sensitive to the long-wave and large-amplitude components in the temperature deformation of slab track on CSB. This study could contribute to the understanding of the spatial deformation components of slab tracks on CSB and provide scien

关键词： Cable stayed bridges

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Superior mechanical properties for an additively manufactured crack-free Ni-based superalloy with an inherited metastable microstructure after heat treatment

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Journal of Materials Research and Technology 2025年 36卷 8108-8121页

作者： Dongyu Wei Lu Shi Wenzhe Zhou Yusheng Tian Beiya Wang Rui Wang Jian He Decheng Kong Guoliang Zhu Baode Sun Shanghai Key Laboratory of Advanced High-temperature Materials and Precision Forming and State Key Laboratory of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Aerospace System Engineering Institute Shanghai 201108 China China Aerosp Sci & Technology Corp Beijing Inst Space Long March Vehicle Beijing 100076 China

Localized non-equilibrium and rapid solidification usually generate a distinctive metastable microstructure in metals and alloys produced through the laser powder bed fusion. Therefore, it is crucial to modulate the printed microstructure for the final mechanical properties. Several heat treatment procedures were developed to control the cellular structure and carbides of the LPBF (Laser powder bed fusion) ZGH4142 alloy in this work. The relationship between heat treatment, microstructure, and mechanical properties was thoroughly investigated. It shows that traditional standard heat treatment is not the optimal approach for ZGH4142 alloy, and the specimens exhibit moderate high-temperature strength but reduced ductility after high-temperature heat treatment, owing to the presence of oversized carbides. However, directly aged ZGH4142 alloy (900 °C) demonstrates a superior combination of high-temperature ultimate tensile strength (139.7 MPa) and elongation (11.5 %) at 1100 °C. The preserved metastable cellular microstructures hinder dislocation transfer between adjacent cells and limit dislocation slipping within cells for the directly aged counterparts, and the fine carbides strengthen the grain boundary and prevent premature instability therein. The research findings provide valuable insights for exploring heat treatment processes to maintain the inherited metastable microstructure formed during the printing process for other AM Ni-based superalloys.

关键词： Inherited metastable microstructure Mechanical behavior Laser powder bed fusion Ni-based superalloy

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One-stone-two-birds: over 26% efficiency in perovskite solar cells via synergistic crystallization & interface regulation

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Energy and Environmental Science 2025年

作者： Jiao, Boxin Tan, Liguo Ye, Yiran Ren, Ningyu Li, Minghao Li, Hang Li, Xiaoyi Yi, Chenyi State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Center of Materials Science and Optoelectronics Engineering College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Carbon Neutrality Tsinghua University Beijing100084 China

Organic-inorganic hybrid perovskites have emerged as promising photovoltaic materials due to their excellent photoelectronic properties and low-cost fabrication techniques. However, the performance and stability of perovskite solar cells (PSCs) are compromised by the random orientation of optically active phase crystals and bulk/interface defects. In this paper, we developed a multifunctional organic material, parabanic acid, to regulate the crystallization and realize full-range defect passivation integrated passivation of defects in perovskites. Attributed to the synergistic effect of functional groups, perovskite films with concentrated out-of-plane spatial orientation were formed. In addition, the introduction of an interface passivation layer based on parabanic acid achieved integrated passivation of bulk and interface defects in perovskite solar cells. The resulting PSCs achieved a power conversion efficiency of 26.03% (certified 25.51%) with excellent environmental stability. Notably, under standard measurement conditions (ISOS-L-1I), the maximum power point output maintained 96.3% of its initial efficiency over 2000 hours. The synergistic regulation of crystal orientation and integrated passivation of defects provide a new approach to promote the development of perovskite solar cells. © 2025 The Royal Society of Chemistry.

关键词： Organic-inorganic materials

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Data-Driven Learning and Control with Event-Triggered Measurements

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IEEE Transactions on Automatic Control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory

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Journal of systems engineering and Electronics 2018年第2期29卷 321-335页

作者： GENG Chao QU Shiyou XIAO Yingying WANG Mei SHI Guoqiang LIN Tingyu XUE Junjie JIA Zhengxuan Economics and Management School Harbin Institute of Technology at Weihai Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Science and Technology on Space System Simulation Laboratory Beijing Simulation Center

Cloud manufacturing is a specific implementation form of the "Internet + manufacturing" strategy. Why and how to develop cloud manufacturing platform(CMP), however, remains the key concern of both platform operators and users. A microscopic model is proposed to investigate advantages and diffusion forces of CMP through exploration of its diffusion process and mechanism. Specifically, a three-stage basic evolution process of CMP is innovatively proposed. Then, based on this basic process, a more complex CMP evolution model has been established in virtue of complex network theory, with five diffusion forces identified. Thereafter, simulations on CMP diffusion have been conducted. The results indicate that, CMP possesses better resource utilization,user satisfaction, and enterprise utility. Results of simulation on impacts of different diffusion forces show that both the time required for CMP to reach an equilibrium state and the final network size are affected simultaneously by the five diffusion forces. All these analyses indicate that CMP could create an open online cooperation environment and turns out to be an effective implementation of the "Internet + manufacturing" strategy.

关键词： complex network cloud manufacturing innovation diffusion network effect Gale-Shapley algorithm cooperative game theory

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Coupling Effect Between HVDC Grounding Electrode and Natural Gas Pipelines Considering complex Soil Structure Model

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IEEE Transactions on Power Delivery 2025年

作者： Meng, Xiaobo Zhang, Bo Cao, Fangyuan Liao, Yongli Guangzhou University School of Mechanical and Electrical Engineering Guangdong Guangzhou510006 China Tsinghua University State Key Laboratory of Power System Department of Electrical Engineering Beijing100084 China China South Power Grid Electric Power Research Institute Guangzhou510530 China

The HVDC project is the backbone of new energy power transmission in the new power system and the key technology to realize the "double carbon" goal. When the HVDC system uses the earth as the loop path, DC current from the grounding electrode will be injected into the earth, possibly damaging their protective devices, thereby posing a risk to personal safety. The research work on the coupling effect between the HVDC grounding electrode and natural gas pipelines considering complex soil structure model has not been effectively carried out. In the paper, A calculation method of DC current distribution in the complex soil structure model considering the vertical partition and horizontal stratification of soil resistivity is proposed. A simulation model of the influence of the DC ground current on long-distance pipelines under complex geological conditions is established based on the method of moments. The calculation results of the simulation model, which incorporates a complex soil structure, show excellent agreement with the actual test results. The effect of pipeline measures to reduce the impact of the DC current was studied by the simulation model in depth. This provides a theoretical basis for the design and implementation of the protection measures of the pipeline. © 1986-2012 IEEE.

关键词： Natural gas pipelines

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Performance-Aware Control of Modular Batteries For Fast Frequency Response

arXiv

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arXiv 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Tsinghua University Beijing100084 China Lab for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States

Modular batteries can be aggregated to deliver frequency regulation services for power grids. Although utilizing the idle capacity of battery modules is financially attractive, it remains challenging to consider the heterogeneous module-level characteristics such as dynamic operational efficiencies and battery degradation. In addition, real-time decision making within seconds is required to enable fast frequency response. In order to address these issues, this paper proposes a performance-aware scheduling approach for battery modules to deliver fast frequency response (FFR) support. In particular, the conduction loss and switching loss of battery packs as well as converters are captured within a mix-integer quadratic constrained program (MIQCP). The cycle-based aging model identifies the aging cost of battery modules during frequent cycling by introducing the aging subgradient calculation and linearization. Case studies based on real-world battery data show that the proposed scheduling approach can effectively reduce power loss cost by nearly 28%-57% and battery aging cost by 4%-15% compared to conventional methods, which can also enhance the SoC balance. Copyright © 2025, The Authors. All rights reserved.

关键词： Battery management systems

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