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Effect of acidification-assisted controlled electric pulse treatment on the pore-fracture structure of bituminous coal

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Powder technology 2025年 464卷

作者： Yan, Fazhi Tian, Yuliang Zhang, Xinghua Liu, Jia Peng, Shoujian Yang, Xuelin Yang, Mengmeng Gao, Changjiong College of Safety and Emergency Management Engineering Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400044 China

controlled electric pulse (CEP) treatment is an emerging technology for enhancing coal seam permeability, which is beneficial for coal fracturing and gas desorption. However, the high breakdown voltage required poses engineering challenges. Acidification treatment not only enhances the electrical conductivity of the coal seam but also improves both pore and fracture structures. In this study, we propose an acidification-assisted controlled electric pulse (ACEP) approach. Scanning electron microscopy and low-temperature N2 adsorption were used to study the evolution of coal pores and fractures after ACEP. The results reveal that CEP breakdown promotes pore development across the full pore size range. Further, acidification simplified the pore structure, leading to pore collapse and closure, and following ACEP treatment, a 5.78 % increase in total pore volume and a 91.6 % increase in macropore volume were observed. Further, the pore structure was significantly optimized, and fissures were elongated and interconnected. Overall, the findings of this study demonstrate the potential of ACEP for enhancing coal seam permeability. © 2025 Elsevier B.V.

关键词： High temperature engineering

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Quantitative microbiome profiling reveals associations between ruminal microbiota shifts and accelerated milk production increase in dairy cows

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Food Research International 2025年 218卷

作者： Zhantao Yang Xianghao Li Yangyi Hao Kai Ren Siyuan Liu Zhijun Cao Yajing Wang Hongjian Yang Shengli Li Wei Wang State Key Laboratory of Animal Nutrition and Feeding Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control College of Animal Science and Technology China Agricultural University Beijing China College of Animal Science and Technology Tarim University Alaer 843300 China College of Animal Science Xinjiang Agricultural University Urumqi Xinjiang 830052 China

Milk is a valuable source of essential nutrients for humans. In dairy cows, the rumen—the largest digestive organ—plays a critical role in supporting high productivity. The postpartum period marks a crucial transition to peak lactation, characterized by a rapid rise in milk and milk solids yield. However, the microbial mechanisms underlying this transition remain poorly understood. Previous studies of periparturient cows' rumen microbiota have primarily relied on relative microbiome profiles (RMP). Due to high false-positive rates of relative abundance (RA), RMP are unsuitable for capturing longitudinal microbial dynamics. In contrast, quantitative microbiome profiles (QMP), which focuses on absolute abundance (AA), offers a more accurate assessment of microbial community changes over time. Using QMP, we identified two distinct enterotypes (Clusters 1 and 2) across all samples, with Cluster 2 increasing progressively during lactation. Subsequently, through differential analysis, correlation analysis, and contribution assessment, we identified three genera— Succinivibrionaceae_UCG-001 , Lachnospira , and Ruminococcus —were strongly associated with the rapid increase in milk yield. Among them, Succinivibrionaceae_UCG-001 showed the most significant contribution to changes in milk yield and energy corrected milk (ECM), with its abundance markedly rising as lactation progressed. Additionally, we observed that the effect of Ruminococcus on ECM may be mediated by changes in ruminal propionate e level. These findings highlight Succinivibrionaceae_UCG-001 as a key taxon driving the early lactation surge in milk production and provide novel insights into the role of the rumen microbiome in optimizing dairy performance.

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Experimental Evaluation on the Microplastics-Release from Breastmilk Bags and Assessment of the Infants-Intake Risk

SSRN

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SSRN 2022年

作者： Liu, Liping Zhang, Xue He, Shanshan Dai, Han Deng, Shihai Jia, Puqi Han, Jie Department of Earth and Environmental Sciences School of Human Settlements and Civil Engineering Xi'an Jiaotong University Xi’an710049 China Key Laboratory for Environmental Pollution Prediction and Control College of Earth and Environmental Sciences Lanzhou University Gansu Province Lanzhou730000 China College of Science and Technology Hebei Agricultural University Huanghua061100 China

The occurrence and transformation of plastic debris in the human food chain have been widely explored. Yet, investigations focusing on infant food packaging remain scarce. It is essential to combat the health risks of plastic debris exposure to infants. This research focused on revealing the ubiquity of white irregular MPs floccules ( © 2022, The Authors. All rights reserved.

关键词： Health risks

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UV/FeⅡNTA as a novel photoreductive system for the degradation of perfluorooctane sulfonate (PFOS) via a photoinduced intramolecular electron transfer mechanism

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Chemical engineering Journal 2021年 427卷

作者： Sun, Zhuyu Zhang, Chaojie Jiang, Jinchi Wen, Jin Zhou, Qi Hoffmann, Michael R. College of Environmental Science and Engineering Donghua University Shanghai201620 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China State Key Laboratory of Pollution Control and Resources Reuse College of Environmental Science and Engineering Tongji University Shanghai200092 China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials & College of Materials Science and Engineering Donghua University Shanghai201620 China Institute of Theoretical Chemistry Faculty of Chemistry University of Vienna Währinger Str. 17 Vienna1090 Austria Linde-Robinson Laboratories California Institute of Technology PasadenaCA91125 United States

Perfluorooctane sulfonate (PFOS) is a persistent organic pollutant that is toxic and bio-accumulative. Previously, we used hydrated electrons (eaq–) generated by the UV photolysis of nitrilotriacetic acid (NTA) to initiate the photoreductive decomposition of PFOS. However, due to the protonation of NTA and the scavenging effect of H+ on eaq–, this process relies highly on alkaline conditions. Herein, we report on an enhanced UV photoreductive system based on FeⅡNTA, which results in the decomposition of PFOS at pH 8.0 under anoxic conditions. After 10 h of photolysis, the degradation and defluorination efficiencies of PFOS in the UV/FeⅡNTA system were ~ 60% and 29.5%, respectively, with a pseudo first-order degradation rate constant of kobs = 0.081 h−1. Laser flash photolysis results combined with time-dependent density functional theory (TDDFT) calculations indicate that PFOS, Fe(H2O)62+, and NTA form a penta-coordinated metal–ligand complex that undergoes a UV-induced directional electron transfer from FeⅡNTA to PFOS. PFOS decomposes via a mechanism that proceeds through a concerted photoinduced intramolecular charge transfer instead of direct attack by eaq–. Model chelate studies show that the inherent properties of the transition metal ion and the electron-donating capabilities of the complexing ligands determine the efficiency for photoreductive electron transfer. A low apparent activation energy of 4.74 kJ/mol over a broad pH range results in higher electron transfer efficiencies for UV/FeⅡNTA photolysis compared to photolysis initiated by un-complexed NTA. © 2021

关键词： Degradation

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Artificial intelligence-based methods for renewable power system operation

Nature Reviews Electrical Engineering

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Nature Reviews Electrical engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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Observation of non-Hermitian topological disclination states and charge fractionalization

arXiv

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arXiv 2025年

作者： Li, Ruifeng Banerjee, Rimi Mandal, Subhaskar Li, Da Long, Yang Ma, Tianchi Liu, Jianwei Liu, Gui-Geng Chong, Yidong Zhang, Baile Li, Er-Ping College of Information Science & Electronic Engineering Zhejiang Key Laboratory of Intelligent Electromagnetic Control and Advanced Electronic Integration Zhejiang University Hangzhou310027 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Department of Physics Indian Institute of Technology Bombay Mumbai400076 India Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore Singapore Research Center for Industries of The Future Westlake University Hangzhou310030 China Department of Electronic and Information Engineering School of Engineering Westlake University Hangzhou310030 China

There has been significant interest in exploring topological disclination states, which effectively probe the band topology of the host material beyond the conventional bulk-edge correspondence. While most studies in this area have primarily focused on Hermitian systems, recent theoretical work predicts that non-Hermiticity can drive topological phase transitions and host topological disclination states associated with fractional charge. However, no experimental observations have been reported to date. Here, we report the first experimental observation of topological disclination states in electric circuits, induced solely by gain and loss. Through admittance matrix measurements and eigenstate analysis, we confirm their emergence and compute the corresponding fractional charge. Moreover, the disclination mode profile and localization effect can be directly visualized via monochromatic field excitation. Additionally, we demonstrate the emergence of degenerate zero-energy topological disclination states, devoid of fractional charge, in distinct non-Hermitian geometries. Our findings open the possibility of non-Hermiticity-induced fractional charges in two-dimensional non-Hermitian lattices, which may pave the way for advancements in active topological photonic devices. © 2025, CC BY.

关键词： Electric network topology

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Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

SSRN

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SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

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Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

arXiv

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arXiv 2022年

作者： Gao, Jiaqi Huang, Zhizhong Lei, Yiming Shan, Hongming Wang, James Z. Wang, Fei-Yue Zhang, Junping The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China The College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology China Qingdao Academy of Intelligent Industries Qingdao266109 China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://***/bridgeqiqi/DREAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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The pharmacogenomic landscape in the Chinese: An analytics of pharmacogenetic variants in 206,640 individuals

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The Innovation 2025年第2期6卷 28-40,27页

作者： Lei-Yun Wang Bing Yu Ying Peng Kai Mou Yan Zhan Yi-Min Wang Wei Ji Chun Xu Le-Dong Xiao Yan Chen Hua Wang Zhi-Hua She Peng Dai Gan-Ye Zhao Yang Wang Lu-Lu Yu Miao Yu Ke Liu Jia-Jia Cui Rong Liu Xi Li Yuan-Fei Huang Zhao-Qian Liu Dong-Sheng Ouyang Wei Zhang Qing Li Xing-Liang Xiong Cheng-Xian Guo Jin-Gao Li Qiao-Li Lv Qing-He Xing Hai-Jian Wang Zhi-Ling Li Ji-Chu Wu Long-Jian Huang Jian He Li-Ming Tan Wen-Xu Hong Xue-Chang Wang Chao-Peng Li Qin Lu Long Zhang Xiang-Dong Kong Hong-Hao Zhou Ji-Ye Yin Department of Clinical Pharmacology Xiangya HospitalCentral South UniversityChangsha 410078China Engineering Research Center of Applied Technology of Pharmacogenomics Ministry of EducationChangsha 410078China National Clinical Research Center for Geriatric Disorders Changsha 410008China Institute of Clinical Pharmacology Hunan Key Laboratory of PharmacogeneticsCentral South UniversityChangsha 410078China The Genetics and Prenatal Diagnosis Center The Department of Obstetrics and GynecologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052China Department of Genetic Laboratory Zibo Maternal and Child Health HospitalZibo 255000China The Hunan Children’s Hospital Changsha 410000China National Health Commission Key Laboratory of Birth Defects Research Prevention and TreatmentHunan Provincial Maternal and Child Health Care HospitalChangsha 410000China Department of Pharmacy Hunan Provincial Maternal and Child Health Care HospitalChangsha 410008China Genetalks Co.Ltd. Changsha 410008China Xiangya Medical Laboratory Central South UniversityChangsha 410078China Salus Med Co.Ltd. Shenzhen 518107China Department of Pharmacy Traditional Chinese and Western Medicine Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Department of Pharmacy Wuhan No.1 HospitalWuhan 430022China Department of Pharmacy Xiangya HospitalCentral South UniversityChangsha 410078China Department of General Surgery Xiangya HospitalCentral South UniversityChangsha 410078China Department of Geriatric Surgery Xiangya HospitalCentral South UniversityChangsha 410078China Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples Changsha Duxact Biotech Co.Ltd.Changsha 410000China Center of Clinical Pharmacology The Third Xiangya HospitalCentral South UniversityChangsha 410013China Department of Radiation Oncology Jiangxi Cancer HospitalNanchang 330029China NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinom

Pharmacogenomic landscapes and related databases are important for identifying the biomarkers of drug response and ***,these data are still lacking for the Chinese *** this study,we constructed a pharmacogenomic landscape and an associated database using whole-genome sequencing data generated by non-invasive prenatal testing in 206,640 Chinese *** total,1,577,513 variants(including 331,610 novel variants)were identified among 3,538 pharmacogenes related to 2,086 *** found that the variant spectrum in the Chinese population differed among the seven major *** differences also exist among provinces in *** average numbers of drug enzyme,transporter,and receptor variants were 258,557,and 632,*** correlation analysis indicated that the pharmacogenes affecting multiple drugs had fewer *** the 16 categories of drugs,we found that nervous system,cardiovascular system,and genitourinary system/sex hormone drugs were more likely to be affected by variants of *** of the variants in the enzyme,transporter,and receptor subfamilies showed *** explore the clinical utility of these data,a genetic association study was conducted on 1,019 lung cancer *** novel variants,AKT2 chr19:40770621 C>G and SLC19A1 chr21:46934171 A>C,were identified as novel platinum response ***,a pharmacogenomic database,named the Chinese Pharmacogenomic Knowledge Base(CNPKB:http://***/),was constructed to collect all the *** summary,a pharmacogenomic landscape and database for the Chinese population were constructed in this study,which could support personalized Chinese medicine in the future.

关键词： pharmacogenomics Chinese population personalized medicine pharmacogenomic landscapes associated database whole genome sequencing identifying biomarkers pharmacogenetic variants

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Cuo/Zno Based Photo Microbial Fuel Cell: A More Sensitive Heavy Metal Biosensor

SSRN

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SSRN 2022年

作者： Lu, Yue Hu, Xingxin Tang, Lin Peng, Bo Tang, Jin Zeng, Taotao Zhang, Xunkuo Liu, Qian College of Environmental Science and Engineering Hunan University South Lushan Road Hunan Changsha410082 China Ministry of Education Changsha Hunan 410082 China College of Geographic Science Hunan Normal University Changsha410081 China Hunan Province Key Laboratory of Pollution Control and Resources Reuse Technology University of South China Hunan Hengyang421001 China

The development of biosensors with high sensitivity and detection limit plays an important role in realizing real-time warning of water environment. In this study, a novel self-powered photo-microbial fuel cell ( PMFC ) sensor was developed for the detection of heavy metals in aqueous solution. The improvement of heavy metal detection performance of PMFC was verified in light condition compared to dark condition(25.72% vs. 21.67% of 1 mg L -1 Cd 2+ ). The results show that the inhibition ratio was higher and the linear relationship performed better (R 2 =0.973 of Cd 2+ ) under light condition in the comparison with dark condition (R 2 =0.811 of Cd 2+ ). The power generation performance of PMFC also improved from 51.61 mW/m 2 to 87.49 mW/m 2 . Besides, the mechanism of PMFC sensor detection was proposed by UV−vis diffuse reflectance spectroscopy (DRS) and Mott-Schottky curve. To sum up, this study has taken advantage of the characteristics of PMFC to promote electricity generation in the detection of heavy metals, and which provides a novel method for developing biosensors. © 2022, The Authors. All rights reserved.

关键词： Biosensors

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