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Chemical characteristics and sources apportionment of volatile organic compounds in the primary urban area of Shijiazhuang,North China Plain

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Journal of Environmental Sciences 2025年第3期149卷 465-475页

作者： Xiao Zhang Junfeng Wang Jiangwei Zhao Junliang He Yali Lei Kai Meng RuiWei Xue Zhang Miaomiao Zhang Shuangying Ni Eleonora Aruffo College of geographical sciences and Environment Shijiazhuang UniversityShijiazhuang 050035China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210044China Hebei Province Ecology Environmental Monitoring Center Shijiazhuang 050037China Key Lab of Geographic Information Science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China Hebei Provincial Institute of Meteorological Sciences Shijiazhuang 050021China Key Laboratory of Meteorology and Ecological Environment of Hebei Province Shijiazhuang 050021China Hebei Provincial Academy of Ecological Environmental Science Shijiazhuang 050037China Department of Advanced Technologies in Medicine&Dentistry University“G.d’Annunzio”of Chieti-PescaraChieti 66100Italy Center for Advanced Studies and Technology-CAST Chieti 66100Italy

VOCs(Volatile organic compounds)exert a vital role in ozone and secondary organic aerosol production,necessitating investigations into their concentration,chemical characteristics,and source apportionment for the effective implementation of measures aimed at preventing and controlling atmospheric *** to October 2020,onlinemonitoringwas conducted in the main urban area of Shijiazhuang to collect data on VOCs and analyze their concentrations and ***,the PMF(positive matrix factorization)method was utilized to identify the VOCs *** indicated that the TVOCs(total VOCs)concentration was(96.7±63.4μg/m^3),with alkanes exhibiting the highest concentration of(36.1±26.4μg/m^3),followed by OVOCs(16.4±14.4μg/m^3).The key active components were alkenes and aromatics,among which xylene,propylene,toluene,propionaldehyde,acetaldehyde,ethylene,and styrene played crucial roles as reactive *** sources derived from PMF analysis encompassed vehicle emissions,solvent and coating sources,combustion sources,industrial emissions sources,as well as plant sources,the contribution of which were 37.80%,27.93%,16.57%,15.24%,and 2.46%,***,reducing vehicular exhaust emissions and encouraging neighboring industries to adopt low-volatile organic solvents and coatings should be prioritized to mitigate VOCs levels.

关键词： Volatile organic compounds Pollution characteristics Ozone formation potential OH radical loss rate Source apportionment

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Physics-informed temporal convolutional network with auto-regressive residual for significant wave height prediction

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Ocean engineering 2025年 329卷

作者： Li, Jiansheng Qin, Pan Wang, Aina Yu, Xiangjun School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Information Liaoning University Shenyang110036 China Department of Military Oceanography and Hydrography Dalian Naval Academy Dalian116018 China

Significant wave height (SWH) serves as a critical metric for assessing marine conditions. While current neural network models have demonstrated considerable success in SWH prediction, their efficacy is frequently limited by the need for more data and the lack of interpretability. Addressing these challenges, we propose a physics-informed temporal convolutional network with auto-regressive residual (PITCN-AR) for SWH prediction, which effectively integrates spatiotemporal dependency information into the training process. First, the PITCN-AR framework utilizes temporal convolutional networks (TCN) as the backbone for the physics-informed neural network. This design adeptively captures the spatiotemporal dependencies of wave height data. To enhance training efficiency and introduce additional dependencies, we integrate an auto-regressive model into the residual block of TCN. Furthermore, TCN is embedded within a hybrid data-physics loss function, and a mini-batch learning strategy is implemented to optimize the performance of PITCN-AR. Finally, the efficacy and practicality of PITCN-AR are rigorously validated using both synthetic and real-world data. Comparative analysis reveals that our model outperforms several state-of-the-art methods, achieving a lower root mean square error and a higher coefficient of determination, thereby underscoring its better predictive accuracy and reliability. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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Performance-Aware control of Modular Batteries For Fast Frequency Response

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IEEE Transactions on Sustainable Energy 2025年

作者： He, Yutong Ruan, Guangchun Zhong, Haiwang Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Massachusetts Institute of Technology Lab for Information and Decision Systems 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. © 2010-2012 IEEE.

关键词： Battery management systems

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Removal of high concentrations of zinc, cadmium, and nickel heavy metals by Bacillus and Comamonas through microbially induced carbonate precipitation

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Biodegradation 2025年第3期36卷 1-18页

作者： Rajasekar, Adharsh Zhao, Cailin Wu, Suowei Murava, Raphinos Tackmore Norgbey, Eyram Omoregie, Armstrong Ighodalo Moy, Charles K. S. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC) Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CIC-AEET) Nanjing University of Information Science & Technology Nanjing 210044 China School of Geography and Environmental Science University of Reading Reading RG67BE United Kingdom Centre for Borneo Regionalism and Conservation University of Technology Sarawak No. 1 Jalan University Sarawak Sibu 96000 Malaysia Department of Civil Engineering Xi’an Jiaotong-Liverpool University Jiangsu Suzhou 215123 China

Heavy metal pollution in urban freshwater, driven by anthropogenic activities, poses significant risks to aquatic ecosystems and human health due to its toxicity and persistence. Recently, urease-producing bacteria have gained attention for their ability to remove heavy metals through microbial-induced carbonate precipitation (MICP). In this study, eight urease-producing bacteria were exposed to individual solutions of zinc (Zn²⁺), cadmium (Cd²⁺), and nickel (Ni²⁺) at concentrations ranging from 0 to 6 mM to assess their resistance. Three strains—Bacillus subtilis HMZC1 (B2), Bacillus sp. HMZCSW (B6), and Comamonas sp. HMZC (B11)—survived at 4 mM and 6 mM, while most others could not tolerate 4 mM. Their urea-degrading ability was tested at different pH levels, identifying an optimal pH of 7 for MICP. Heavy metal carbonate precipitation experiments at 4 mM and 6 mM revealed that all three strains achieved > 93% removal of Zn²⁺, Ni²⁺, and Cd²⁺ within 72 h. Comamonas sp. HMZC exhibited the highest efficiency, achieving > 95% removal of certain heavy metals at 6 mM. Statistical analysis using one-way ANOVA revealed significant differences (p < 0.05) in heavy metal removal efficiencies among the strains for certain treatment conditions (Cd²⁺ and Zn²⁺ at 4 mM), although not all comparisons reached statistical significance. Scanning Electron Microscopy and X-ray Diffraction confirmed the morphology and composition of the precipitated heavy metal carbonates. Our findings demonstrate that urease-producing bacteria can effectively immobilize multiple heavy metals, highlighting the MICP process as a practical and sustainable biological approach for ecological restoration and wastewater treatment. © The Author(s) 2025.

关键词： Applied microbiology Heavy metal carbonates Heavy metals MICP Ureolytic bacteria

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A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Rehman, Khalil Ur Jianqiang, Li Yasin, Anaa Bilal, Anas Basheer, Shakila Ullah, Inam Jabbar, Muhammad Kashif Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Beijing University of Technology College of Software Engineering Beijing100124 China Hainan Normal University College of Information Science and Technology Hainan Haikou571158 China Princess Nourah Bint Abdulrahman University Department of Information Systems College of Computer and Information Science Riyadh11671 Saudi Arabia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide. © 2013 IEEE.

关键词： Deep neural networks

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Switching Multiplicative Watermark Design Against Covert Attacks

arXiv

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arXiv 2025年

作者： Gallo, Alexander J. Anand, Sribalaji C. Teixeira, Andre M.H. Ferrari, Riccardo M.G. Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden Department of Information Technology Uppsala University PO Box 337 UppsalaSE-75105 Sweden Delft Center for Systems and Control Mechanical Engineering TU Delft Delft Netherlands

Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber-physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output gain of the closed loop system, including some supposed attack dynamics. A worst-case scenario of a matched covert attack is assumed, presuming that an attacker with full knowledge of the closed-loop system injects a stealthy attack of bounded energy. Our algorithm, given watermark filter parameters at some time instant, provides optimal next-step parameters. Analysis of the algorithm is given, demonstrating its features, and demonstrating that through initialization of certain parameters outside of the algorithm, the parameters of the multiplicative watermarking can be randomized. Simulation shows how, by adopting our method for parameter design, the attacker’s impact on performance diminishes. © 2025, CC BY-NC-ND.

关键词： Networked control systems

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Direct Torque control Strategy of DC-Biased Hybrid Excitation Vernier Machine with Adjustable Zero-Sequence Current

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IEEE Transactions on Industry Applications 2025年

作者： Xu, Liang Zhi, Youhui Qian, Chen Jiang, Tingting Zhao, Wenxiang Niu, Shuangxia Jiangsu University School of Electrical and Information Engineering The Jiangsu Key Laboratory of Drive and Intelligent Control for Electric Vehicle Zhenjiang212013 China Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China Nanjing Institute of Technology School of Electric Power Engineering Nanjing211167 China The Hong Kong Polytechnic University Department of Electrical Engineering Kowloon999077 Hong Kong

This paper proposes a direct torque control strategy for a DC-Biased hybrid excitation vernier machine with adjustable zero-sequence current. The machine structure and drive system are first introduced, and the machine model is built under the stator flux linkage field orientation. Then, the mathematical equations are derived, and the regulation principle of torque and stator flux linkage is clarified. According to model's characteristics, the maximum torque per ampere operation principle for the machine is analyzed. Afterward, a direct torque control strategy based on stator flux linkage adaptive regulation is proposed, where the stator flux linkage can be adaptively regulated under the zero-sequence current reasonable distribution so as to realize maximum torque per ampere operation. In addition, a combined flux linkage observer is designed for accurate estimation of stator flux linkage. Furthermore, since conventional direct torque control strategy has issues with torque and flux ripples, the space vector modulation is utilized. Overall, the experimental results verify the effectiveness of the proposed control strategy. © 2025 IEEE.

关键词： Vector spaces

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Novel Voltage Support Strategies for DGs in Hybrid ACDC Distribution Networks under Faults

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Yuze Guo, Peng Xu, Qianming Shuai, Zhikang Guerrero, Josep M. School of Electrical Engineering and Automation Xiamen University of Technology China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen361012 China Hunan University College of Electrical and Information Engineering Changsha410082 China University of Aalborg Department of Energy Technology Aalborg9220 Denmark

According to the grid codes, the higher the voltage at the point of common coupling (PCC), the longer duration that the voltage-source converters (VSC) can stay connected to the grid. In hybrid ACDC distribution networks (HADNs), owing to the low capacity, VSC-based distributed generator (DG) closest to the fault point may fail to significantly support the voltage at the PCC through current injection. Therefore, other large-capacity converters such as flexible ACDC converters (FACs) should also inject currents to help it. However, in resistive-inductive HADNs, the voltage support effect is related to the current phases of converters, and the fault conditions. In this paper, circumstances under two fault position categories (upstream and downstream fault positions of DG) for double line-to-ground fault and three-phase short-circuit fault are analyzed. The interconnected sequence networks of HADNs under these two fault positions are proposed. Based on the proposed networks, the optimal current phase angles of DG and FAC for maximumly supporting voltages at the PCC of the DG are proposed. Moreover, the line sequence impedance calculation method is proposed. The simulation results verify the proposed strategy. The proposed strategy is suitable for low-capacity DGs in resistive-inductive HADNs to achieve voltage support and fault ride-through. © 2025 IEEE.

关键词： Voltage-source converters

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Off-Policy Temporal Difference Learning for Perturbed Markov Decision Processes: Theoretical Insights and Extensive Simulations

arXiv

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arXiv 2025年

作者： Forootani, Ali Iervolino, Raffaele Tipaldi, Massimo Khosravi, Mohammad Helmholtz Center for Environmental Research-UFZ Permoserstrasse 15 Leipzig04318 Germany Department of Electrical Engineering and Information Technology University of Naples Napoli80125 Italy Department of Electrical and Information Engineering Polytechnic University of Bari Bari70126 Italy Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands

Dynamic Programming suffers from the curse of dimensionality due to large state and action spaces, a challenge further compounded by uncertainties in the environment. To mitigate these issue, we explore an off-policy based Temporal Difference Approximate Dynamic Programming approach that preserves contraction mapping when projecting the problem into a subspace of selected features, accounting for the probability distribution of the perturbed transition probability matrix. We further demonstrate how this Approximate Dynamic Programming approach can be implemented as a particular variant of the Temporal Difference learning algorithm, adapted for handling perturbations. To validate our theoretical findings, we provide a numerical example using a Markov Decision Process corresponding to a resource allocation problem. © 2025, CC BY-NC-ND.

关键词： Contrastive Learning

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Tomography of Quantum States From Structured Measurements via Quantum-Aware Transformer

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IEEE Transactions on Cybernetics 2025年第6期PP卷 PP页

作者： Ma, Hailan Sun, Zhenhong Dong, Daoyi Chen, Chunlin Rabitz, Herschel University of New South Wales School of Engineering and Technology CanberraACT2600 Australia The Australian National University School of Engineering CanberraACT2601 Australia University of Technology Sydney Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology SydneyNSW2007 Australia Nanjing University School of Management and Engineering Department of Control and Systems Engineering Nanjing210093 China Princeton University Department of Chemistry PrincetonNJ08544 United States

Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this article, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise. © 2013 IEEE.

关键词： Quantum computers

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