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Journal of Rock Mechanics and Geotechnical Engineering 2024年第3期16卷 727-740页

作者： Chengkai Fan Na Zhang Bei Jiang wei victor liu Department of Civil and Environmental Engineering University of AlbertaEdmontonAlbertaT6G 2E3Canada Department of Mathematical and Statistical Sciences University of AlbertaEdmontonAlbertaT6G 2G1Canada

Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.***.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.

关键词： Oil sands production Open-pit mining Deep learning Principal component analysis(PCA) Artificial neural network Mining engineering

Comparison of antimony and arsenic behaviour at the river-lake junction in the middle of the Yangtze River Basin

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Journal of Environmental Sciences 2024年第2期136卷 189-200页

作者： Huiji liu wei Zeng Ziyang Lai Mengchang He Chunye Lin wei Ouyang Xitao liu State Key Laboratory of Water Environment Simulation School of EnvironmentBeijing Normal UniversityBeijing 100875China Advanced Interdisciplinary Institute of Environment and Ecology Beijing Normal UniversityZhuhai 519087China

As typical metalloid toxic elements widely distributed in environmental media,the geochemical behaviour of antimony(Sb)and arsenic(As)affects environmental safety.We selected the surface waters and sediments at the river-lake junction of Dongting Lake as the research objects,analysed the concentration and chemical partitioning of Sb and As,assessed its contamination and ecological risk levels,and discussed its sources and potential influencing factors.The concentrations of dissolved Sb and As in surface waters were low(<5.46μg/L),and the concentrations of Sb and As in surface sediments were 2.49-22.65mg/kg and 11.10-136.34 mg/kg,respectively.Antimony and As in sediments were mainly enriched in the fraction of residues,but the proportion of As in bioavailability was significantly higher than that of Sb.Although the contamination level of Sb was higher than that of As,the risk assessment code(RAC)showed that the ecological risk level of As was higher than that of Sb.Rainwater erosion and mining activities(in the midstream of Zijiang River)were the main contaminated sources of Sb,while As was affect mainly by rainwater erosion.The contamination and ecological risk of Sb in the inlet of the Zijiang River should receive considerable attention,while those of As in the inlet of the Xiangjiang River should also be seriously considered.This study highlights the need for multi-index-based assessments of contamination and ecological risk and the importance of further studies on the environmental behaviour of metalloids in specific hydrological conditions,such as river-lake junctions.

关键词： Contamination Ecological risks Antimony(Sb) Arsenic(As) River-lake junction Dongting Lake

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Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment

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Building Simulation 2024年第3期17卷 399-414页

作者： Giovanni Calzolari wei liu Division of Sustainable Buildings Department of Civil and Architectural EngineeringKTH Royal Institute of TechnologyBrinnelvägen 23Stockholm11428Sweden

This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled framework between computational fluid dynamics(CFD)simulations and deep learning models.The coupling approach is showcased by the development of a data-driven turbulence model.The new turbulence model is built using a deep learning neural network,whose mapping structure is based on a zero-equation turbulence model for built environment simulations,and is coupled with the CFD software OpenFOAM to create a hybrid framework.The neural network is a standard shallow multi-layer perceptron.The number of hidden layers and nodes per layer was optimized using Bayesan optimization algorithm.The framework is trained on an indoor environment case study,as well as tested on an indoor office simulation and an outdoor building array simulation.Results show that the deep learning based turbulence model is more robust and faster than traditional two-equation Reynolds average Navier-Stokes(RANS)turbulence models,while maintaining a similar level of accuracy.The model also outperforms the standard algebraic zero-equation model due to its superior ability to generalize to various flow scenarios.Despite some challenges,namely the mapping constraint,the limited training dataset size and the source of generation of training data,the hybrid framework demonstrates the viability of the coupling technique and serves as a starting point for future development of more reliable and advanced models.

关键词： neural networks computational fluid dynamics(CFD) turbulence model OpenFOAM

Dynamic Simulation of Distribution Networks Using Multi-stage Discontinuous Galerkin Method

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 185-196页

作者： Ruizhi Yu wei Gu weiqi liu Peixin Li wei liu School of Electrical Engineering Southeast UniversityNanjing 210096China. the School of Electrical Engineering Southeast UniversityNanjing 210096China School of Automation Nanjing University of Science and TechnologyNanjing 210094China

Dynamic simulation plays a fundamental role in security evaluation of distribution networks(DNs).However,the strong stiffness and non-linearity of distributed generation(DG)models in DNs bring about burdensome computation and noteworthy instability on traditional methods which hampers the rapid response of simulation tool.Thus,a novel L-stable approximate analytical method with high accuracy is proposed to handle these problems.The method referred to as multistage discontinuous Galerkin method(MDGM),first derives approximate analytical solutions(AASs)of state variables which are explicit symbolic expressions concerning system states.Then,in each time window,it substitutes values for symbolic variables and trajectories of state variables are obtained subsequently.This paper applies MDGM to DG models to derive AASs.Local-truncation-error-based variable step size strategy is also developed to further improve simulation efficiency.In addition,this paper establishes detailed MDGM-based dynamic simulation procedure.From case studies on a numerical problem,a modified 33-bus system and a practical large-scale DN,it can be seen that proposed method demonstrates fast and dependable performance compared with the traditional trapezoidal method.

关键词： Approximate analytical method distribution network distributed generation dynamic simulation multi-stage discontinuous Galerkin method variable step size

High-sensitivity formaldehyde gas sensor based on Ce-doped urchin-like SnO_(2) nanowires derived from calcination of Sn-MOFs

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Frontiers of Materials Science 2024年第1期18卷 29-43页

作者： wei Xiao wei Yang Shantang liu Hubei Key Lab of Novel Reactor&Green Chemical Technology School of Chemistry and Environmental EngineeringWuhan Institute of TechnologyWuhan 430073China Hubei Key Laboratory of Wudang Local Chinese Medicine Research Department of ChemistrySchool of Pharmaceutical SciencesHubei University of MedicineShiyan 442000China

Metal-organic frameworks(MOFs)have attracted widespread attention due to their regular structures,multiple material centers,and various ligands.They are always considered as one kind of ideal templates for developing highly sensitive and selective gas sensors.In this study,the advantages of MOFs with the high specific surface area(71.9891 m^(2)·g^(-1))and uniform morphology were fully utilized,and urchin-like SnO_(2) nanowires were obtained by the hydrothermal method followed by the calcination using Sn-MOFs consisting of the ligand of C_(9)H_(6)O_(6)(H_(3)BTC)and Sn/Ce center ions as sacrificial templates.This unique urchin-like nanowire structure facilitated gas diffusion and adsorption,resulting in superior gas sensitivity.A series of Ce-doped SnO_(2) nanowires with different doping ratios were synthesized,and their gas sensing properties towards formaldehyde were studied.The resulted Ce-SnO_(2) was revealed to have high sensitivity(201.2 at 250℃),rapid response(4 s),long-term stability,and good repeatability for formaldehyde sensing,and the gas sensing mechanism of Ce-SnO_(2) exposed to formaldehyde was also systematically discussed.

关键词： Ce-SnO_(2) SnO_(2) gas sensor formaldehyde Sn-MOF

Global Stability to Steady Supersonic Rayleigh Flows in One-Dimensional Duct

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Chinese Annals of Mathematics,Series B 2024年第2期45卷 279-296页

作者： Fenglun wei Jianli liu Department of Mathematics Shanghai University Newtouch Center for Mathematics of Shanghai University

Heat exchange plays an important role in hydrodynamical systems,which is an interesting topic in theory and application.In this paper,the authors consider the global stability of steady supersonic Rayleigh flows for the one-dimensional compressible Euler equations with heat exchange,under the small perturbations of initial and boundary conditions in a finite rectilinear duct.

关键词： Compressible Euler equations Heat exchange Supersonic Rayleigh fow Steady solution Classical solution

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Penetrating canaloplasty in corticosteroid-induced glaucoma:a report of two cases

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International Journal of Ophthalmology(English edition) 2024年第4期17卷 770-772页

作者： Fang liu liu-Shan Zhou wei-wei Zhang Department of Ophthalmology Daqing Longnan Hospitalthe Fifth Affiliated Hospital of Qiqihar Medical CollegeDaqing 163453Heilongjiang ProvinceChina

Dear Editor,We have treated two patients of glucocorticoid-induced glaucoma with penetrating canaloplasty(PCP)since 2021.PCP is a new type internal drainage surgery for glaucoma,which is a bleb-independent filtering surgery combined canaloplasty with trabeculectomy,and the surgery shows a great efficacy of intraocular pressure(IOP)reduction with no surgical complication.PCP surgery started with a fornix-based conjunctival flap and double scleral flap,and Schlemm’s canal was opened,a 10-0 prolene suture was positioned and tensioned within Schlemm’s canal,trabucular meshwork segment was excised and peripheral iridectomy was performed,the scleral flap and the conjunctival flap were closed watertight with 10-0 prolene sutures.

关键词： glaucoma surgery glucocorticoid

The combined effect of bromadiolone and ivermectin (iBr) in controlling both rodents and their fleas

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Integrative Zoology 2024年第1期19卷 156-164页

作者： Ming liu Xinrong WAN wei liu Xingan MA Zhibin ZHANG State Key Laboratory of Integrated Pest Management Institute of ZoologyChinese Academy of SciencesBeijingChina CAS Center for Excellence in Biotic Interactions University of Chinese Academy of SciencesBeijingChina Inner Mongolia Minzu University TongliaoInner MongoliaChina International Society of Zoological Sciences BeijingChina

Rodent pests not only cause severe agricultural loss but also spread zoonotic pathogens to human beings.Antico-agulant rodenticides are widely used to decrease the population densities of rodents but often lead to the spillover of ectoparasites becausefleas and ticks may gather on surviving rodents.Therefore,it is necessary to killfleas and ticks before culling rodents to minimize the risk of pathogen transmission.In this study,we used a mixture of ivermectin(an antiparasitic drug)and bromadiolone(an anticoagulant rodenticide)to control both rodent andflea/tick abundances.We found that in a laboratory test,0.01%ivermectin bait was not lethal for greater long-tailed hamsters after 7 days of treatment,while 0.1%ivermectin bait was lethal for approximately 33%of treated rodents.In afield test,bait containing 0.001%,0.005%,0.01%,and 0.05%ivermectin decreased the number offleas per vole of Brandt’s voles to 0.42,0.22,0.12,and 0.2,respectively,compared with 0.77 in the control group,indicating that 0.01%ivermectin bait performed best in removingfleas.In another laboratory test,bait containing a 0.01%ivermectin and 0.005%bromadiolone mixture caused the death of all voles within 6–14 days after the intake of the bait.In thefield test,the bait containing 0.01%ivermectin and 0.005%bromadiolone reduced the average number offleas per vole to 0.35,which was significantly lower than the 0.77 of the control group.Our results indicate that a 0.01%ivermectin and 0.005%bromadiolone mixture could be used to control both rodents andfleas to minimize the spillover risk of disease transmission when using traditional rodenticides.

关键词： bromadiolone flea iBr ivermectin rodent synergistic control

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

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Defence Technology（防务技术） 2024年第2期32卷 271-284页

作者： Yiwen Zhang wei Zheng Zongqiang liu Qian Xuesen Laboratory of Space Technology China Academy of Space TechnologyBeijing 100094China China Academy of Aerospace Science and Innovation Beijing 100176China State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information EngineeringBeijing 100194China

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

关键词： GNSS-R satellite constellations Sea surface altimetric precision Underwater navigation Multilayer feedforward neural network

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Performance analysis and multi-objective optimization based on a modified irreversible Stirling cycle

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Science China Technological Sciences 2024年第6期67卷 1668-1684页

作者： XU Lei YU MinJie liu ZhiChun liu wei School of Energy and Power Engineering Huazhong University of Science and Technology

The efficiency of the actual Stirling engine is much lower than the ideal Carnot cycle efficiency. To obtain more precise efficiency for Stirling engines, this paper proposes a modified Stirling cycle with a more accurate heat transfer process in Stirling engines based on a thermodynamic function called available potential. The finite-time thermodynamic method is used to analyze the model performance under constant heat source temperature, finite temperature difference heat transfer, and incomplete regenerative processes. A new polytropic process is introduced to model the heat transfer between the working fluid and external heat sources in which only heat above ambient temperature is converted into technical work. The regenerator is divided into numerous smaller heat reservoirs with individual temperature to analyze the incomplete regenerative processes. The expressions of the output power and thermal efficiency are obtained based on the modified irreversible Stirling cycle, and the effects of irreversible losses are analyzed to evaluate the performance of the proposed model. Results indicate that the efficiency of the modified cycle is much lower than that of the ideal Stirling cycle with an isothermal process. With the increase of the average heat transfer temperature difference, there exists an optimum value for the power of the irreversible cycle. The optimum power of the model was obtained for varying thermodynamic parameters by optimizing the average heat transfer temperature difference between the hot and cold sides. To optimize the irreversible model, the multi-objective optimization analysis is carried out based on NSGA-Ⅱ, which results in an optimized output power of 40.87 kW and an optimized thermal efficiency of 44%.

关键词： stirling cycle regenerative processes isopotential processes multi-objective optimization finite time thermodynamics