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Synergistic effect of bonding heterogeneity and phonon localization in introducing excellent thermoelectric properties in layered heteroanionic NdZnSbO material

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Journal of Energy Chemistry 2025年第1期100卷 259-272页

作者： Da Wan Shuwei Tang Shulin Bai Xiaodong Li Peng Ai Wanrong Guo Tuo Zheng College of Materials science and Engineering Liaoning Technical University faculty of Chemistry Northeast Normal University

Layered rare-earth metal oxides, harnessing the dual properties of oxides and two-dimensional layered materials, exhibit remarkable thermal stability and quantum confinement effects. Therefore, this work adopts the first-principles calculation combined with the Boltzmann transport theory to predict the thermoelectric properties of NdZnSbO compound. The coexistence of weak interlayer van der Waals interactions, robust intralayer ionic bonding, and partial covalent bonding leads to remarkable bonding heterogeneity, which engenders pronounced phonon scattering and imposes constraints on thermal transport along the out-of-plane direction. The weakened chemical bonds induced by the antibonding states, together with the rattling-like behavior of the Zn atom, culminate in the profound anharmonicity in the layered NdZnSbO compound. The weakening bond and heavy element contribute to the softness of phonon modes, which significantly diminishes the phonon group velocity. The redistribution-dominated four-phonon scattering process spans a large optical gap, which effectively reduces the lattice thermal conductivity. The NdZnSbO compound exhibits direct semiconductor characteristic with a bandgap of 0.73 e V by adopting the Heyd-Scuseria-Ernzerhof(HSE06) functional in combination with spin–orbit coupling(SOC) effect. The multi-valley feature of NdZnSbO compound augur favorably for band degeneracy, thus amplifying the power factor. Consequently, an optimal figure-of-merit(ZT) of 3.40 at 900 K is achieved for the n-type NdZnSbO compound. The present study delves deeply insights into the origins for the low thermal conductivity of NdZnSbO compound and proposes an optimization scheme to enhance overall thermoelectric performance.

关键词： Bond strength (chemical)

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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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Computer Modeling in Engineering & sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana Department of Civil Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Computer Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

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Electrochemical conversion of methane to bridge the gap in the artificial carbon cycle

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Journal of Energy Chemistry 2025年第1期100卷 286-308页

作者： Yuhao Peng Yuefeng Song Ihar Razanau Juanxiu Xiao Wei Xiao Di Hu Guoxiong Wang College of Chemistry and Molecular sciences Hubei Key Laboratory of Electrochemical Power Sources Wuhan University State Key Laboratory of Catalysis Dalian National Laboratory for Clean Energy iChEM (Collaborative Innovation Center of Chemistry for Energy Materials) Dalian Institute of Chemical Physics Chinese Academy of Sciences Laboratory of Physical-Chemical Technologies SSPA Scientific and Practical Materials Research Centre of NAS of Belarus State Key Laboratory of marine Resources Utilization in South China Sea Collaborative Innovation Center of Marine Science and Technology School of Marine Science and Engineering Hainan University Department of Chemical and Environmental Engineering University of Nottingham Ningbo China

Methane, an abundant one-carbon（C1） resource, is extensively used in the industrial production of vital fuels and value-added chemicals. However, current industrial methane conversion technologies are energy-and carbon-intensive, mainly due to the high activation energy required to break the inert C–H bond, low selectivity, and problematic side reactions, including CO2emissions and coke deposition. Electrochemical conversion of methane（ECM） using intermittent renewable energy offers an attractive solution, due to its modular reactor design and operational flexibility across a broad spectrum of temperatures and pressures. This review emphasizes conversion pathways of methane in various reaction systems, highlighting the significance and advantages of ECM in facilitating a sustainable artificial carbon cycle. This work provides a comprehensive overview of conventional methane activation mechanisms and delineates the complete pathways of methane conversion in electrolysis contexts. Based on surface/interface chemistry, this work systematically analyzes proposed reaction pathways and corresponding strategies to enhance ECM efficiency towards various target products, including syngas, hydrocarbons, oxygenates, and advanced carbon materials. The discussion also encompasses opportunities and challenges for the ECM process, including insights into ECM pathways, rational electrocatalyst design, establishment of benchmarking protocols, electrolyte engineering, enhancement of CH4conversion rates, and minimization of CO2emission.

关键词： Methane Electrochemical conversion Reaction mechanism Catalyst design Electrode Electrocatalysis

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Effects of carbon content on the microstructure and tensile properties of a low-density steel

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 391-401页

作者： Yongxuan Shang Mingyu Fan Shuyong Jiang Zhongwu Zhang Key Laboratory of Superlight Materials and Surface Technology Ministry of EducationCollege of Materials Science and Chemical EngineeringHarbin Engineering UniversityHarbin 150001China State Key Laboratory of Metal Material for marine Equipment and Application Iron&Steel Research Institute of Ansteel Group CorporationAnshan 114009China

Carbon can change the phase components of low-density steels and influence the mechanical *** this study,a new method to control the carbon content and avoid the formation ofδ-ferrite by decarburization treatment was *** microstructural changes and mechanical characteristics with carbon content induced by decarburization were systematically ***-Jaoul(C-J)analysis was employed to examine the work hardening characteristics during the tensile *** decarburization by heat treatments,the carbon content within the austenite phase decreased,while Mn and Al were almost unchanged;this made the steel with full austenite transform into the austenite and ferrite dual ***,(Ti,V)C carbides existed in both matrix phase and the mole fraction almost the *** addition,the formation of other carbides *** loss induced a decrease in strength due to the weakening of the carbon solid *** the steel with the single austinite,the deformation mode of austenite was the dislocation planar glide,resulting in the formation of *** the dual-phase steel,the deformation occurred by the dislocation planar glide of austenite first,with the increase in strain,the cross slip of ferrite took place,forming dislocation cells in *** the late stage of deformation,the work hardening of austinite increased rapidly,while that of ferrite increased slightly.

关键词： low-density steels carbon content decarburization strengthening mechanisms work hardening behavior

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Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

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Computer Modeling in Engineering & sciences 2025年第1期142卷 713-732页

作者： Petr Opela Josef Walek Jaromír Kopecek Department of Metallurgical Technologies Faculty of Materials Science and TechnologyVSB–Technical University of OstravaOstrava70800Czech Republic FZU—Institute of Physics of the Czech Academy of sciences Prague18200Czech Republic

In engineering practice,it is often necessary to determine functional relationships between dependent and independent *** relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable ***,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely *** study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic *** ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon *** the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

关键词： Machine learning Gaussian process regression artificial neural networks support vector machine hot deformation behavior

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Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing

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Atmospheric and Oceanic science Letters 2025年第1期18卷 39-45页

作者： Haolan Ren Fei Zheng Tingwei Cao Qiang Wang International Center for Climate and Environment science(ICCES) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina University of Chinese Academy of sciences BeijingChina College of Oceanography Hohai UniversityNanjingChina CAS Key Laboratory of Ocean Circulation and Waves Institute of OceanologyChinese Academy of Sciences and Pilot National Laboratory for Marine Science and Technology(Qingdao)QingdaoChina

Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate *** studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC *** this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its *** a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s *** traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of *** results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter ***,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.

关键词： Recovery of AMOC 4×CO_(2)forcing Key parameter Parameter estimation Data assimilation Machine learning

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Low‑Temperature Oxidation Induced Phase Evolution with Gradient Magnetic Heterointerfaces for Superior Electromagnetic Wave Absorption

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Nano-Micro Letters 2025年第1期17卷 191-204页

作者： Zizhuang He Lingzi Shi Ran Sun Lianfei Ding Mukun He Jiaming Li Hua Guo Tiande Gao Panbo Liu School of Chemistry and Chemical Engineering Northwestern Polytechnical UniversityXi’an 710129People’s Republic of China School of marine science and Technology Northwestern Polytechnical UniversityXi’an 710072People’s Republic of China

Gradient magnetic heterointerfaces have injected infinite vitality in optimizing impedance matching,adjusting dielectric/magnetic resonance and promoting electromagnetic(EM)wave absorption,but still exist a significant challenging in regulating local phase ***,accordion-shaped Co/Co_(3)O_(4)@N-doped carbon nanosheets(Co/Co_(3)O_(4)@NC)with gradient magnetic heterointerfaces have been fabricated via the cooperative high-temperature carbonization and lowtemperature oxidation *** results indicate that the surface epitaxial growth of crystal Co_(3)O_(4) domains on local Co nanoparticles realizes the adjustment of magnetic-heteroatomic components,which are beneficial for optimizing impedance matching and interfacial ***,gradient magnetic heterointerfaces simultaneously realize magnetic coupling,and long-range magnetic ***,the synthesized Co/Co_(3)O_(4)@NC absorbents display the strong electromagnetic wave attenuation capability of−53.5 dB at a thickness of 3.0 mm with an effective absorption bandwidth of 5.36 GHz,both are superior to those of single magnetic domains embedded in carbon *** design concept provides us an inspiration in optimizing interfacial polarization,regulating magnetic coupling and promoting electromagnetic wave absorption.

关键词： Magnetic heterointerfaces Phase evolution Interfacial polarization Magnetic coupling Electromagnetic wave absorption

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Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic science Letters 2025年第1期18卷 18-23页

作者： Jiarui Cai Bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation Center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern marine science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina China Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

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MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

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Computer Modeling in Engineering & sciences 2025年第1期142卷 279-298页

作者： Diana Abi-Nader Hassan Harb Ali Jaber Ali Mansour Christophe Osswald Nour Mostafa Chamseddine Zaki Computer science Department Faculty of SciencesLebanese UniversityBeirut146404Lebanon College of Engineering and Technology American University of the Middle EastEgaila54200Kuwait Lab-STICC CNRS UMR 6285ENSTA-BretagneBrest29200France

Security and safety remain paramount concerns for both governments and individuals *** today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to ***,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent *** advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying *** paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection *** SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction *** experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy *** these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.

关键词： Firearm and gun detection single shot multi-box detector deep learning one-stage detector MobileNet inception convolutional neural network

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Genes and proteins expressed at different life cycle stages in the model protist Euplotes vannus revealed by both transcriptomic and proteomic approaches

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science China Life sciences 2025年第1期 232-248页

作者： Yaohan Jiang Xiao Chen Chundi Wang Liping Lyu Saleh A.Al-Farraj Naomi A.Stover Feng Gao Key Laboratory of Evolution & marine Biodiversity (Ministry of Education) and Institute of Evolution & marine Biodiversity Ocean University of China marine College Shandong University Zoology Department College of ScienceKing Saud University Department of Biology Bradley University Laboratory for marine Biology and Biotechnology Qingdao Marine Science and Technology Center

Sexual reproduction first appeared in unicellular protists and has continued to be an essential biological process in almost all *** protists, which contain both germline and somatic genomes within a single cell, have evolved a special form of sexual reproduction called conjugation that involves mitosis, meiosis, fertilization, nuclear differentiation, genome rearrangement, and the development of unique cellular structures. The molecular basis and mechanisms of conjugation vary dramatically among ciliates, and many details of the process and its regulation are still largely unknown. In order to better comprehend these processes and mechanisms from an evolutionary perspective, this study provides the first comprehensive overview of the transcriptome and proteome profiles during the entire life cycle of the newly-established marine model ciliate Euplotes vannus. Transcriptome analyses from 14 life cycle stages(three vegetative stages and 11 sexual stages) revealed over 26,000 genes that are specifically expressed at different stages, many of which are related to DNA replication,transcription, translation, mitosis, meiosis, nuclear differentiation, and/or genome rearrangement. Quantitative proteomic analyses identified 338 proteins with homologs associated with conjugation and/or somatic nuclear development in other ciliates, including dicer-like proteins, Hsp90 proteins, RNA polymerase Ⅱ and transcription elongation factors, ribosomal-associated proteins, and ubiquitin-related proteins. Four of these homologs belong to the PIWI family, each with different expression patterns identified and confirmed by RT-qPCR,which may function in small RNA-mediated genome rearrangement. Proteins involved in the nonhomologous end-joining pathway are induced early during meiosis and accumulate in the developing new somatic nucleus, where more than 80% of the germline sequences are eliminated from the somatic genome. A number of new candidate genes and proteins likely t

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