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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

Journal of Energy Chemistry 2025年第2期101卷 524-534页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Department of Electrical and Electronic Engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic University Photonics Research Institute The Hong Kong Polytechnic University School of Electrical Computer and Telecommunications Engineering University of Wollongong

Accurate prediction of the remaining useful life(RUL) is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning

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3D morphology of crab(Macrophthalmus japonicus) burrows from the Pearl River Delta front,China:The physicochemical factors,with implications for the rock record

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Journal of Palaeogeography 2025年第1期14卷 203-220页

作者： Yuan-Yuan Wang Ya-Bin Zhang Song-Lin Gou Guo-Cheng Zhang Institute of Resource and Environment Henan Polytechnic University Henan International Joint Laboratory of Biogenic Traces and Sedimentary Minerals Henan Polytechnic University

Neoichnological characterization of modern depositional settings addresses the response of benthic animals to ecological conditions and their fluctuations, usually on a relatively short time scale. In this way, analogue models for interpretation of sedimentary strata in the geological record can be *** japonicus(De Haan, 1835), a crab, is commonly found in the Pearl River Delta front of southern China. The burrows of M. japonicus are vertical or inclined, I-, U-, Y-, J-and L-shaped tubes, and these are imaged in 7.5-cm-diameter sediment cores. The cores were studied by X-ray radiography, computed tomography, and VG Studio MAX reconstruction to obtain three-dimensional images for elaborate morphological study. This crab's burrows are analogous to the trace fossil Psilonichnus. Several environmental parameters, i.e., sediment grain size and its total organic carbon(TOC) content, and turbidity and salinity of water, have been measured. The results indicated that M. japonicus prefers to live in mesohaline water with turbidity levels of 20-170 NTU, where bioturbation is more abundant. It prefers to occur in mud and silty sand substrates, with TOC content decreasing as particle size increases. The sedimentary environment, grain size and ichnofacies of analogical trace fossils Psilonichnus are discussed which supports the interpretation that Psilonichnus is a good indicator of delta front environments.

关键词： Neoichnology Macrophthalmus japonicus Pearl River Delta front Physicochemical factor

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Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

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Computers, Materials & Continua 2025年第1期82卷 843-862页

作者： Feifei Yu Yongxian Huang Guoyan Chen Xiaoqing Yang Canyi Du Yongkang Gong School of Mechatronic Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China School of Automotive and Transportation Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China

To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire *** the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different *** collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,*** collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two *** results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,*** underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other ***,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline *** analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire *** delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)*** findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant ***,in the SENET model,data points of various types were more widely and evenly dispersed,demonstra

关键词： Channel attention SENET model engine misfire fault fault detection

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K-Corruption Intermittent Attacks for Violating the Codiagnosability

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 159-172页

作者： Ruotian Liu Yihui Hu Agostino Marcello Mangini Maria Pia Fanti IEEE the Department of Electrical and Information Engineering Polytechnic University of Bari the School of Automation Xi'an University of Posts and Telecommunications

In this work, we address the codiagnosability analysis problem of a networked discrete event system under malicious attacks. The considered system is modeled by a labeled Petri net and is monitored by a series of sites, in which each site possesses its own set of sensors, without requiring communication among sites or to any coordinators. A net is said to be codiagnosable with respect to a fault if at least one site could deduce the occurrence of this fault within finite steps. In this context, we focus on a type of malicious attack that is called stealthy intermittent replacement attack. The stealthiness demands that the corrupted observations should be consistent with the system's normal behavior, while the intermittent replacement setting entails that the replaced transition labels must be recovered within a bounded of consecutive corrupted observations(called as K-corruption intermittent attack). Particularly, there exists a coordination between attackers that are separately effected on different sites, which holds the same corrupted observation for each common transition under attacks. From an attacker viewpoint, this work aims to design Kcorruption intermittent attacks for violating the codiagnosability of systems. For this purpose, we propose an attack automaton to analyze K-corruption intermittent attack for each site, and build a new structure called complete attack graph that is used to analyze all the potential attacked paths. Finally, an algorithm is inferred to obtain the K-corruption intermittent attacks, and examples are given to show the proposed attack strategy.

关键词： Codiagnosability decentralized structure discrete event system intermittent attack Petri net

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A Multi-Condition Sequential Network Ensemble for Industrial Energy Storage Prediction Considering the Condition Switching Characteristics

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 369-380页

作者： Tianyu Wang Fan Zhou Yangjie Wu Jun Zhao Wei Wang IEEE the School of Control Science and Engineering Dalian University of Technology the School of Information Science and Engineering Dalian Polytechnic University

As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.

关键词： Ensemble learning industrial energy prediction multi-working condition structural deep network transfer learning

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Necroptosis contributes to deoxynivalenolinduced liver injury and inflammation in weaned piglets

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Journal of Animal Science and Biotechnology 2025年第1期16卷 182-192页

作者： Qilong Xu Hanqiu Gong Mohan Zhou Junjie Guo Shaokui Chen Kan Xiao Yulan Liu Hubei Key Laboratory of Animal Nutrition and Feed Science Wuhan Polytechnic UniversityWuhan 430023China

Background The aim of this study was to investigate the role of necroptosis in deoxynivalenol(DON)-induced liver injury and inflammation in weaned *** In Exp.1,12 weaned piglets were divided into 2 groups including pigs fed basal diet and pigs fed diet contaminated with 4 mg/kg DON for 21 *** Exp.2,12 weaned piglets were divided into 2 groups including con-trol piglets and piglets given a gavage of 2 mg/kg body weight(BW)*** Exp.3,24 weaned piglets were used in a 2×2 factorial design and the main factors including necrostatin-1(Nec-1)(DMSO or 0.5 mg/kg BW Nec-1)and DON challenge(saline or 2 mg/kg BW DON gavage).On 21 d in Exp.1,or at 6 h post DON gavage in Exp.2 and 3,pigs were killed for blood samples and liver *** histology,blood biochemical indicators,and liver inflamma-tion and necroptosis signals were *** Dietary or oral gavage with DON caused liver morphological damage in *** DON led to hepato-cyte damage indicated by increased aspartate transaminase(AST)activity and AST/alanine aminotransferase(ALT)ratio,and DON gavage also caused hepatocyte damage and cholestasis indicated by increased AST and alkaline phosphatase(AKP)*** DON caused liver necroptosis indicated by increased protein abundance of total receptor interacting protein kinase 3(t-RIP3)and total mixed lineage kinase domain-like protein(t-MLKL).Moreover,DON gavage increased mRNA expression of interleukin(IL)-6 and IL-1βin *** gavage also induced liver necroptosis demonstrated by increased protein abundance of t-RIP3,phosphorylated-RIP3(p-RIP3),t-MLKL and ***,pretreatment with Nec-1,a specific inhibitor of necroptosis,inhibited liver necroptosis indi-cated by decreased protein expression of t-RIP3,p-RIP3,t-MLKL and ***-1 pretreatment reduced liver morphological damage after DON *** with Nec-1 also attenuated liver damage induced by DON indicated by decreased activities of AST and ***,Nec-

关键词： Deoxynivalenol Liver damage Necroptosis Necrostatin-1 Pigs

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Efficient activation of peroxymonosulfate for catalytic degradation of organic pollutants by simultaneously using low-level cobalt ions and calcium carbonate micro-particles

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Journal of Environmental Sciences 2025年第5期151卷 113-124页

作者： Yu Zhou Xiaobo Wang Nan Wang Shuangshuang Huang Lihua Zhu College of Chemistry and Chemical Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Chemistry and Chemical Engineering Hubei Polytechnic UniversityHuangshi 435003China College of Chemistry and Environmental Engineering Wuhan Polytechnic UniversityWuhan 430023China School of Physics and Technology Center for Electron MicroscopyWuhan UniversityWuhan 430072China

An efficient catalytic system was developed to remove various organic pollutants by simultaneously using low-level cobalt ions,calcium carbonate micro-particles and peroxymonosulfate(PMS).A simple base-induced precipitation was used to successfully loaded Co-centered reactive sites onto the surface of CaCO_(3)*** optimal conditions at 25°C,10 mg/L methylene blue(MB)could be completely degraded within 10 min with 480μg/L Co^(2+),0.4 g/L CaCO_(3)microparticles(or 0.4 g/L Co@CaCO_(3))and 0.1 g/L *** MB degradation followed the pseudo first order kinetics with a rate constant of 0.583 min^(−1),being 8.3,11.5 and 53.0 times that by using Co-OH(0.07 min^(−1)),Co^(2+)(0.044 min^(−1))and CaCO_(3)(0.011 min^(−1))as the catalyst,*** was confirmed that there was a synergistic effect in the catalytic activity between Co species and the CaCO_(3)particles but the major contributor was the highly dispersed Co *** Co^(2+)-containing simulated electroplating wastewater was used as the Co^(2+)source,not only the added MB was also completely degraded within 5 min in this catalytic system,but also the coexisting heavy metal ions were substantially *** presently developed method was applied to simultaneously treat organic wastewater and heavy metals *** present method was also successfully used to efficiently degrade other organic pollutants including bisphenol A,sulfamethoxazole,rhodamine B,tetrabromobisphenol A,ofloxacin and benzoic acid.A catalytic mechanism was proposed for the PMS activation by Co@CaCO_(3).The surface of CaCO_(3)particles favors the adsorption of Co^(2+).More importantly,the surface of CaCO_(3)particles provides plentiful surface-OH and-CO_(3)^(2+),and these surface groups complex with Co^(2+)to producemore catalytically active species such as surface[CoOH]^(−),resulting in rapid Co^(2+)/Co^(3+)cycling and electron *** interactions cause the observed synergistic effect between Co species and Ca

关键词： CaCO3 Low-level cobalt Peroxymonosulfate Oxidative degradation

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Mesh-supported V2O5-WO3/TiO2 nanosheet array catalysts for efficient removal of NOx

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Tungsten 2025年第1期7卷 100-111页

作者： Zhi-Qi Zhang Liu Yang Wen Luo Vladimir An Jing Li Bao-Dan Liu School of Materials Science and Engineering Northeastern University Foshan Graduate School of Innovation Northeastern University Kizhner Research Center School of Advanced Manufacturing Technologies Tomsk Polytechnic University

The design and synthesis of vanadium-based denitration catalysts with low-temperature activity pose significant challenges in the field of toxic gas *** this study,we have developed a novel approach to fabricate highly efficient V2O5-WO3/TiO2monolithic mesh catalysts for NOxreduction at low ***,TiO2nanosheets with strong adhesion and large surface area were grown on Ti mesh using plasma electrolytic oxidation technology and hydrothermal ***,super-small traces of WO3and V2O5active nano-species were subsequently deposited on TiO2nanosheets using the deposition-precipitation *** to traditional ceramic-based V2O5-WO3/TiO2catalysts,our V2O5-WO3/TiO2monolithic mesh catalyst exhibits superior mechanical performance,excellent thermal conductivity,lower pressure drop,higher utilization of active components,and stronger synergistic ***,the V2O5-WO3/TiO2monolithic mesh catalysts exhibited excellent low-temperature catalytic activity with 98% of NOxconversion at 220℃,and remarkable long-term SO2tolerance and water resistance in the selective catalytic reduction（SCR） of NOxwith *** SCR reaction of tiny V2O5and WO3active species supported on TiO2nanosheets directly demonstrated to follow the Eley-Rideal mechanism by in situ infrared spectra.

关键词： Low temperature activity Monolithic mesh catalysts NH3-SCR SO2 tolerance V2O5-WO3/TiO2

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Eigenvalue-Free Interval for Seidel Matrices of Cographs

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Journal of Mathematical Research with Applications 2025年第1期45卷 1-10页

作者： Heming NIU Wei WANG School of Mathematics Physics and Finance Anhui Polytechnic University

Abstract The distribution of Seidel eigenvalues of cographs is investigated in this *** prove that there is no Seidel eigenvalue of nontrivial cographs in the interval (-1,1).We also show the optimality of the interval (-1,1) in the sense that for any■>0 either of the intervals(1,1+■) and (-1-■,-1) contains a Seidel eigenvalue of some cograph of order n when n is sufficiently large.

关键词： cograph Seidel matrix eigenvalue-free interval

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Life cycle dynamic formation temperature response and thermal energy extraction of mine geothermal system considering groundwater flow

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International Journal of Mining Science and Technology 2025年第1期35卷 1-17页

作者： Xibing Li Zhiying Chen Linqi Huang Botao Li Jingyi Yan Peilei Zhang Zhixiang Liu School of Resources and Safety Engineering Central South UniversityChangsha 410083China School of Safety Science and Engineering Henan Polytechnic UniversityJiaozuo 454003China

As mining activities expand deeper,deep high-temperature formations seriously threaten the future safe exploitation,while deep geothermal energy has great potential for *** the formation cooling and geothermal mining in mines to establish a thermos-hydraulic coupling numerical model for fractured *** study investigates the formation heat transfer behaviour,heat recovery performance and thermal economic benefits influenced during the life *** results show that the accumulation of cold energy during the cold storage phase induces a decline in formation *** heat recovery phase is determined by the extent of the initial cold domain,which contracts inward from the edge and decelerates the heat recovery rate *** groundwater velocity increases,the thermal regulation efficiency gradually increases,the production temperature decreases,while the effective radius and thermal power increase first and then *** injected volume and temperature significantly affect,with higher injection temperatures slowing thermal recovery,and the thermal regulation efficiency is more sensitive to changes in formation permeability and thermal *** heat extraction performance is positively correlated with all *** levelized cost of electricity is estimated at 0.1203$/(kW·h)during the cold *** the heat recovery,annual profit is primarily driven by cooling benefits.

关键词： Mine geothermal energy Formation heat transfer behavior Groundwater flow Life cycle assessment Thermal economic analysis

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