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第四届全国（海洋）环境微塑料污染与管控学术研讨会

作者： Liuqingqing Liu Yan Li Xianxiang Luo Hao Zheng Institute of Coastal Environmental Pollution Control College of Environmental Science and Engineering Sanya Oceanographic Institution Ministry of Education Key Laboratory of Marine Environment and Ecology Ocean University of China Marine Ecology and Environmental Science Laboratory Pilot National Laboratory for Marine Science and Technology

Increasing studies reported that nanoplastics(NPs) posed toxic effects on ***, the impact of NPs on the population response of zooplankton in marine ecosystems remains poorly understood. Therefore, we investigated the effects of three functionalized polystyrene(PS) NPs(amino-modified PS(PS-NH2), carboxylated PS(PS-COOH), and unmodified PS) on the population growth of rotifers(Brachionus plicatilis) and explored the regulatory mechanisms underlying the transgenerational reproductive strategy through a four-generation exposure experiment at environmentally relevant concentrations. Our results showed that PS-NH2 induced the strongest inhibitory effect on the population growth of rotifers among the three NPs, due to the strongest suspension stability and positive charge. PS-NH2multigenerational exposure decreased the fecundity of rotifers via decreasing the offspring and shortening the reproductive period, which were the important reasons for the inhibited population growth. Notably, an irreversible transgenerational "hormesis" was observed in the reproductive strategy of rotifers exposed to PS-NH2, resulting in a shift from r-selection to K-selection. The reduction of ingestion capacity and digestive function induced by bioaccumulated NPs was responsible for the transgenerational alteration of reproductive strategy as the energy regulation ***, the disturbed energy homeostasis was accompanied by an increase in apoptosis in the digestive system of NP-stressed rotifers, which triggered by oxidant-mediated mitochondrial depolarization and Ca2+ homeostasis imbalance, thus providing the cytotoxicity mechanism for reducing energy intake. Furthermore, the genetic regulation was identified as a key determinant in inducing transgenerational alterations in reproductive strategy, highlighting the irreversible transgenerational risks of NPs. This work expanded our current understanding of the regulation mechanisms responsible for the transgenerationa

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujun Zhang Yanqu Chen Jiakai Wang Jin Hu Renshuai Tao Xianglong Liu State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression.

关键词： Accuracy Target recognition Perturbation methods Noise Robustness Acoustics Speech processing Digital audio players Automatic speech recognition Resilience

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Performance optimization of microbial electrolysis cell (MEC) for palm oil mill effluent (POME) wastewater treatment and sustainable Bio-H2 production using response surface methodology (RSM)

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INTERnational JOURNAL OF HYDROGEN ENERGY 2022年第34期47卷 15464-15479页

作者： Chandrasekhar, Abudukeremu Kadier ab Junying Wang ab K. Wang, Junying Chandrasekhar, K. Abdeshahian, Peyman Islam, M. Amirul Ghanbari, Farshid Bajpai, Mukul Katoch, Surjit Singh Bhagawati, Prashant Basavaraj Li, Hui Kalil, Mohd Sahaid Hamid, Aidil Abdul Abu Hasan, Hassimi Ma, Peng-Cheng [a]Laboratory of Environmental Science and Technology The Xinjiang Technical Institute of Physics and Chemistry Key Laboratory of Functional Materials and Devices for Special Environments Chinese Academy of Sciences Urumqi 830011 China [b]Center of Material and Opto-electronic Research University of Chinese Academy of Sciences Beijing 100049 China [c]School of Civil and Environmental Engineering Yonsei University Seoul 03722 Republic of Korea [d]Department of Biotechnology Engineering School of Lorena University of São Paulo São Paulo 12602-810 Brazil [e]Laboratory for Quantum Semiconductors and Photon-based BioNanotechnology Department of Electrical and Computer Engineering Faculty of Engineering Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada [f]Research Center for Environmental Contaminants (RCEC) Abadan University of Medical Sciences Abadan Iran [g]Environmental Engineering Lab Civil Engineering Department National Institute of Technology Hamirpur Hamirpur Himachal Pradesh 177005 India [h]Faculty of Engineering Department of Civil Engineering Annasaheb Dange College of Engineering and Technology Ashta Maharashtra 416301 India [i]Department of Chemical and Process Engineering Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia [j]School of Biosciences and Biotechnology Faculty of Science and Technology National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia [k]Research Centre for Sustainable Process Technology Faculty of Engineering and Built Environment National University of Malaysia (UKM) UKM Bangi 43600 Selangor Malaysia

Microbial electrolysis cells (MECs) are a new bio-electrochemical method for converting organic matter to hydrogen gas (H-2). Palm oil mill effluent (POME) is hazardous wastewater that is mostly formed during the crude oil extraction process in the palm oil industry. In the present study, POME was used in the MEC system for hydrogen generation as a feasible treatment technology. To enhance biohydrogen generation from POME in the MEC, an empirical model was generated using response surface methodology (RSM). A central composite design (CCD) was utilized to perform twenty experimental runs of MEC given three important variables, namely incubation temperature, initial pH, and influent dilution rate. Experimental results from CCD showed that an average value of 1.16 m(3) H-2/m(3) d for maximum hydrogen production rate (HPR) was produced. A second-order polynomial model was adjusted to the experimental results from CCD. The regression model showed that the quadratic term of all variables tested had a highly significant effect (P < 0.01) on maximum HPR as a defined response. The analysis of the empirical model revealed that the optimal conditions for maximum HPR were incubation temperature, initial pH, and influent dilution rate of 30.23 ?, 6.63, and 50.71%, respectively. Generated regression model predicted a maximum HPR of 1.1659 m(3) H-2/m(3) d could be generated under optimum conditions. Confirmation experimentation was conducted in the optimal conditions determined. Experimental results of the validation test showed that a maximum HPR of 1.1747 m(3) H-2/m(3) d was produced. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Microbial electrolysis cell (MEC) Hydrogen production Palm oil mill effluent (POME) Wastewater Optimization Response surface methodology (RSM)

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A Dent Defect Identification and Quantization Algorithm of Buried Pipeline Via the IMU Data

A Dent Defect Identification and Quantization Algorithm of B...

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Hongning Zhang Tong Shi Qingquan Duan Xiaoben Liu Chenxi Song Rui Li Kuang Fu National Engineering Research Center for Pipeline Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of Petroleum-Beijing Beijing China Safety Environment & Technology Supervision Research Institute Southwest Oil and Gas Field Company Petro China Chengdu China PipeChina Institute of Science and Technology Tianjin Tianjin China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

Oil and gas pipelines cross various complex terrains. Gular internal inspections are key for their safe and efficient function. Recently, the Inertial Measurement Unit (IMU) has been widely applied in pipeline inspections for its low cost, high precision, and suitability for long distances. However, current research by scholars internationally and domestically primarily focuses on pipeline strain identification using inertial navigation. There is a lack of intelligent identification and quantitative analysis for the size of dents in long-distance oil and gas pipelines based on inertial navigation data. Addressing this gap, this paper introduces a quantitative dent identification method for pipelines using a CNN-LSTM-Attention hybrid neural network. Initially, IMU inspection data undergo preprocessing and dimensionality reduction via the Principal Component Analysis (PCA) algorithm. A deep learning model comprising a CNN-LSTM-Attention neural network is constructed, capable of autonomously learning and identifying pipeline dent characteristics, optimizing the learning rate, and generating non-linear output, thereby realizing quantitative dent detection. The findings indicate that the proposed deep learning model possesses robust recognition and quantification capabilities, suitable for IMU data from pipeline internal inspections. The presented technical method effectively identifies and quantifies pipeline dent dimensions. Comparisons of convergence speeds at various learning rates revealed faster convergence at 0.001, with an accuracy of 92.4%. This allows for high-precision quantitative identification of pipeline dents, providing theoretical and technical support for the safety assessment of pipeline operations.

关键词： Deep learning Analytical models Quantization (signal) Accuracy Oils Pipelines Inertial navigation

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Grafting Glycidyl Methacrylate Onto the Surface of M40j Carbon Fiber by Acid Activation and Ionizing Radiation at Different Doses

SSRN

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SSRN 2024年

作者： Zhou, Xuan Zhou, Haifeng Xu, Shiai Liu, Weihua Tang, Zhongfeng Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China School of Materials Science and Engineering East China University of Science and Technology Shanghai200237 China National Key Laboratory of Materials Behavior and Evaluation Technology in Space Environment Harbin Institute of Technology Harbin150001 China

Functionalization of T800 carbon fiber (TCF) with high carbon content and strong surface inertness is a huge challenge. In our previous work, glycidyl methacrylate (GMA) was grafted onto the surface of M40J through acid-activated synergistic ionizing radiation. However, grafting process was not optimized and need to be improved. In this study, the influence of absorbed dose on the grafting of GMA onto M40J fibers was investigated. The grafting degree of M40J was able to effectively controlled by adjusting the dose. The grafting degree of M40J reaches a maximum value of 17.9%. After grafting, a great number of ellipsoidal polymers appeared on the surface of M40J. This result effectively reduces the waste of grafting monomers and energy, ultimately achieving better modification results at lower absorbed doses. © 2024, The Authors. All rights reserved.

关键词： Ionizing radiation

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A mechanistic study of ciprofloxacin adsorption by goethite in the presence of silver and titanium dioxide nanoparticles

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Journal of environmental sciences 2022年第8期34卷 46-56页

作者： Jie Tang Yun Wang Qiang Xue Fei Liu Kenneth CCarroll Xiaohua Lu Taogeng Zhou Dengjun Wang MOE Key Laboratory of Groundwater Circulation and Environmental Evolution China University of Geosciences(Beijing)Beijing 100083China Beijing Key Laboratory of Water Resources and Environmental Engineering School of Water Resources and EnvironmentChina University of Geosciences(Beijing)Beijing 100083China Water Science and Management Program New Mexico State UniversityMSC 3QUSA Plant&Environmental Science New Mexico State UniversityLas CrucesNM 88003USA National Institute of Metrology Beijing 100022China Beijing Institute of Technology Beijing 100081China School of Fisheries Aquaculture and Aquatic SciencesAuburn UniversityAuburnAL 36849USA

The adsorption behaviors of ciprofloxacin(CIP),a fluoroquinolone antibiotic,onto goethite(Gt)in the presence of silver and titanium dioxide nanoparticles(AgNPs and TiO_(2)NPs)were *** showed that CIP adsorption kinetics in Gt with or without NPs both followed the pseudo-second-order kinetic *** presence of AgNPs or TiO_(2)NPs inhibited the adsorption of CIP by *** amount of inhibition of CIP sorption due to AgNPs was decreased with an increase of solution pH from 5.0 to *** contrast,in the presence of TiO_(2)NPs,CIP adsorption by Gt was almost unchanged at pHs of 5.0∼6.5 but was decreased with an increase of pH from 6.5 to *** mechanisms of AgNPs and TiO_(2)NPs in inhibiting CIP adsorption by Gt were different,which was attributed to citrate coating of AgNPs resulting in competition with CIP for adsorption sites on Gt,while TiO_(2)NPs could compete with Gt for CIP ***,CIP was adsorbed by Gt or TiO_(2)NPs through a tridentate complex involving the bidentate inner-sphere coordination of the deprotonated carboxylic group and hydrogen bonding through the adjacent carbonyl group on the quinoline *** findings advance our understanding of the environmental behavior and fate of fluoroquinolone antibiotics in the presence of NPs.

关键词： Ciprofloxacin Goethite Silver nanoparticles Titanium dioxide nanoparticles Adsorption

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Characteristics of runoff and sediment yield for two typical erodible soils in southern China

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International Journal of Sediment Research 2022年第5期37卷 653-661页

作者： Xuchao Zhu Yin Liang Lili Qu Longxi Cao Zhiyuan Tian Tong Liu Meng Li State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences College of Advanced Agricultural Sciences University of Chinese Academy of Sciences College of Ecology and Environment Chengdu University of Technology National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China Guangdong Key Laboratory of IntegratedAgro-environmental Pollution Control and Management Institute of Eco-environmental and Soil Sciences Guangdong Academy of Sciences Hydrology and Water Resources Investigation Bureau of Jiangsu Province

Granite red soil(GRS) and Quaternary red clay(QRC) are two typical erodible soils in the red-soil region of southern China. Analytical and comparative studies of the characteristics of runoff and sediment yield for the two soils at various slopes are currently needed. The purpose of the current study was to clarify the characteristics of runoff and sediment yield for GRS and QRC at different slopes and to establish models for estimating sediment yield for the two soils. Forty-eight runoff microplots with four slopes(5°, 15°, 25°, and 35°) and two soils(GRS and QRC) were established and exposed to natural rainfall. Runoff and sediment yield were measured 10 times during the study period. Runoff and sediment yield for the two soils under the various slopes had similar temporal variations, and both increased with prior cumulative erosive rainfall. Runoff for GRS and QRC was moderately temporally variable, with coefficients of variation(CVs) from 46.2% to 60.6%, and sediment yield for QRC was strongly temporally variable, with CVs from 114.8% to 145.8%. Sediment yield for GRS increased with slope, but sediment yield for QRC first increased and then decreased, with a calculated inflection point of 18°, but runoff for both soils decreased with slope. The CVs of both runoff and sediment yield with slope for the two soils ranged from 3.6% to 88.0%, lower than the temporal variabilities, indicating that rainfall may have a larger impact than slope on runoff and sediment yield for QRC and GRS. Under the various slopes, runoff and sediment yield for both soils increased with rainfall and sediment yield increased with runoff, but the proportions of effective rainfall and runoff differed. Pedotransfer-function models based on rainfall, runoff, and slope accurately estimated sediment yield for the two soils, with the model fit coefficient of determination(R2) > 0.81 and the R2for verification >0.79. These results improve the understanding of the laws governing erosion for differe

关键词： Runoff and sediment characteristics Sediment-yield simulation Granite red soil Quaternary red clay Pedotransfer function Red-soil region

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Lightweight Semantic Segmentation Algorithm Based on DeeplabV3 + Network

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science technology and engineering 2024年第24期24卷 10382-10393页

作者： Zhang, Xiu-Zai Zhang, Hao Yang, Chang-Jun School of Electronic and Information Engineering Nanjing University of Information Science & Technology Nanjing210044 China Jiangsu Province Atmospheric Environment and Equipment Technology Collaborative Innovation Center Nanjing University of Information Science & Technology Nanjing210044 China China Meteorological Administration China Key Laboratory of Remote Sensing Satellite Radiometric Measurement and Calibration National Satellite Meteorological Center Beijing100081 China

A lightweight semantic segmentation model Faster DeeplabV3 + based on DeeplabV3 + network was improved to address the issues of large parameter count, slow computation speed, and low efficiency in traditional semantic segmentation models. The Faster DeeplabV3 + model used lightweight MobilenetV2 instead of Xception as the backbone feature extraction network, significantly reducing the number of parameters and improving computational speed. Using the combination of deep separable convolution (DSC) and air space pyramid expansion convolution (ASPP), a new depth separable dilated convolution(DSD-Conv) was developed, namely, the depth separable empty space pyramid module (DP-ASPP), which expanded the receptive field while reducing the original convolution parameters and facilitating faster operation. In order to enhance the sensitivity and accuracy of the network in extracting feature information from different dimensions, a dual attention mechanism module was added for processing low-level and high-level feature maps generated in the coding region. Two loss functions, cross entropy and Dice Loss, were integrated to provide more comprehensive and diverse optimization for the model. The improved model was tested on the PASCAL VOC 2012 dataset. The experimental results show that the average intersection to union ratio increases from 76. 57% to 79. 07%, and the segmentation accuracy increases from 91. 2% to 94. 3% . The network parameter count (parameters) of the improved model is reduced by 3. 86 × 106, and the floating-point computational load (GFLOPs) is decreased by 117. 98 G. The proposed algorithm significantly reduces parameter count and improves computational speed, while also optimizing the segmentation effect. © 2024 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Semantic Segmentation

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Atmospheric duct propagation loss prediction based on time convolution network(TCN)

Atmospheric duct propagation loss prediction based on time c...

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作者： Ziliang Wei Jiajing Wu Bo Yin Dongning Jia Jiali Xu School of Information Science and Engineering Ocean University of China National key laboratory of electromagnetic environment Chinese Research Institute of Radiowave Propagation Pilot National Laboratory for Marine Science and Technology

The accurate prediction of propagation loss of radio waves in atmospheric duct environment is a hot issue in radar signal transmission and target detection research. Deep learning has achieved good results in many fields, and there are corresponding applications in the accurate prediction of propagation loss prediction. In this paper, we use the timeseries convolutional network(TCN) model to predict the data of evaporative duct and surface duct over-the-horizon propagation loss time series and compare the prediction effect with the current state-of-the-art time-series prediction network models LSTM and GRU. The RMSE is used as the evaluation index to calculate their accuracy, and the experiments prove that the TCN model has the best prediction effect than the most advanced LSTM and GRU models, both in the evaporative duct and surface duct environments.

关键词： propagation loss time-series prediction TCN LSTM GRU

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Metal ion-promoted fabrication of melanin-like poly(L-DOPA)nanoparticles for photothermal actuation

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science China Chemistry 2020年第9期63卷 1295-1305页

作者： Xianheng Wang Lei Yang Peng Yang Wancai Guo Quan-Ping Zhang Xianhu Liu Yiwen Li College of Polymer Science and Engineering State Key Laboratory of Polymer Materials EngineeringSichuan UniversityChengdu 610065China State Key Laboratory of Environment-Friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China National Engineering Research Center for Advanced Polymer Processing Technology Zhengzhou UniversityZhengzhou 450002China

Melanin-inspired polymers are currently the focus of growing interest for a wide range of applications ranging from energy to biomedical *** researchers have made numerous attempts to prepare and utilize polydopamine nanoparticles(PDA NPs),they have made limited progress in developing and discovering another typical functional mimic of natural melanin,poly(levodopa)(P(L-DOPA))NPs,probably due to the lack of facile synthetic strategies towards satisfactory structural and functional control of melanin-like ***,we reported a one-pot preparation method towards P(L-DOPA)NPs with good yields and controllable size/property in an aqueous solution assisted by various metal ions(i.e.,Ni(II),Mg(II),Ca(II),Fe(III),Mn(II),Co(II),Zn(II)and Cd(II)).Interestingly,the resulting P(L-DOPA)NPs exhibited enhanced light absorption and photothermal behaviors compared with well-established PDA NPs,which can be employed to further fabricate kinds of photothermal composite actuators with promising performances such as folding,switching,and *** study offers a facile and robust way to synthesize new synthetic melanins beyond PDA,and facilitates further functional discovery and evolution of melanin-inspired polymers and composites.

关键词： melanin poly(L-DOPA)nanoparticle metal ion photothermal actuator

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