Surface polaritons,as surface electromagnetic waves propagating along the surface of a medium,have played a crucial role in enhancing photonic spin Hall effect(PSHE)and developing highly sensitive refractive index(RI)...Surface polaritons,as surface electromagnetic waves propagating along the surface of a medium,have played a crucial role in enhancing photonic spin Hall effect(PSHE)and developing highly sensitive refractive index(RI)sensors.Among them,the traditional surface plasmon polariton(SPP)based on noble metals limits its application beyond the near-infrared(IR)regime due to the large negative permittivity and optical losses.In this contribution,we theoretically proposed a highly sensitive PSHE sensor with the structure of Ge prism-SiC-Si:InAs-sensing medium,by taking advantage of the hybrid surface plasmon phonon polariton(SPPhP)in mid-IR regime.Here,heavily Si-doped InAs(Si:InAs)and SiC excite the SPP and surface phonon polariton(SPhP),and the hybrid SPPhP is realized in this system.More importantly,the designed PSHE sensor based on this SPPhP mechanism achieves the multi-stage RI measurements from 1.00025-1.00225 to 1.70025-1.70225,and the maximal intensity sensitivity and angle sensitivity can be up to 9.4×10^(4)μm/RIU and245°/RIU,respectively.These findings provide a new pathway for the enhancement of PSHE in mid-IR regime,and offer new opportunities to develop highly sensitive RI sensors in multi-scenario applications,such as harmful gas monitoring and biosensing.展开更多
Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.展开更多
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
Although previous researchers have attempted to decipher ore genesis and mineralization in the Erdaokan Ag-Pb-Zn deposit,some uncertainties regarding the mineralization process and evolution of both ore-forming fluids...Although previous researchers have attempted to decipher ore genesis and mineralization in the Erdaokan Ag-Pb-Zn deposit,some uncertainties regarding the mineralization process and evolution of both ore-forming fluids and magnetite types still need to be addressed.In this study,we obtained new EPMA,LA-ICP-MS,and in situ Fe isotope data from magnetite from the Erdaokan deposit,in order to better understand the mineralization mechanism and evolution of both magnetite and the ore-forming fluids.Our results identified seven types of magnetite at Erdaokan:disseminated magnetite(Mag1),coarse-grained magnetite(Mag2a),radial magnetite(Mag2b),fragmented fine-grained magnetite(Mag2c),vermicular gel magnetite(Mag3a1 and Mag3a2),colloidal magnetite(Mag3b)and dark gray magnetite(Mag4).All of the magnetite types were hydrothermal in origin and generally low in Ti(<400 ppm)and Ni(<800 ppm),while being enriched in light Fe isotopes(δ^(56)Fe ranging from−1.54‰to−0.06‰).However,they exhibit different geochemical signatures and are thus classified into high-manganese magnetite(Mag1,MnO>5 wt%),low-silicon magnetite(Mag2a-c,SiO_(2)<1 wt%),high-silicon magnetite(Mag3a-b,SiO_(2)from 1 to 7 wt%)and high-silicon-manganese magnetite(Mag4,SiO_(2)>1 wt%,MnO>0.2 wt%),each being formed within distinct hydrothermal environments.Based on mineralogy,elemental geochemistry,Fe isotopes,temperature trends,TMg-mag and(Ti+V)vs.(Al+Mn)diagrams,we propose that the Erdaokan Ag-Pb-Zn deposit underwent multi-stage mineralization,which can be broken down into four stages and nine sub-stages.Mag1,Mag2a-c,Mag3a-b and Mag4 were formed during the first sub-stage of each of the four stages,respectively.Additionally,fluid mixing,cooling and depressurization boiling were identified as the main mechanisms for mineral precipitation.The enrichment of Ag was significantly enhanced by the superposition of multi-stage ore-forming hydrothermal fluids in the Erdaokan Ag-Pb-Zn deposit.展开更多
In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese compa...In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese company Unitree Robotics’G1 robot,embodying the innovation that has propelled China forward as the world’s second largest economy.展开更多
This paper presents a new criterion for determining the unloading points quantitatively and consistently in a multi-stage triaxial test.The radial strain gradient(RSG)is first introduced as an arc tangent function of ...This paper presents a new criterion for determining the unloading points quantitatively and consistently in a multi-stage triaxial test.The radial strain gradient(RSG)is first introduced as an arc tangent function of the rate of change of radial strain to time.RSG is observed to correlate closely with the stress state of a compressed sample,and reaches a horizontal asymptote as approaching failure.For a given rock type,RSG value at peak stress is almost the same,irrespective of the porosity and permeability.These findings lead to the development of RSG criterion:Unloading points can be precisely determined at the time when RSG reaches a pre-determined value that is a little smaller than or equal to the RSG at peak stress.The RSG criterion is validated against other criteria and the single-stage triaxial test on various types of rocks.Failure envelopes from the RSG criterion match well with those from single-stage tests.A practical procedure is recommended to use the RSG criterion:an unconfined compression or single-stage test is first conducted to determine the RSG at peak stress for one sample,the unloading point is then selected to be a value close to the RSG at peak stress,and the multi-stage test is finally performed on another sample using the pre-selected RSG unloading criterion.Generally,the RSG criterion is applicable for any type of rocks,especially brittle rocks,where other criteria are not suitable.Further,it can be practically implemented on the most available rock mechanical testing instruments.展开更多
A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and metho...A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.展开更多
Nano zero-valent iron(nZVI)is a promising phosphate adsorbent for advanced phosphate removal.However,the rapid passivation of nZVI and the low activity of adsorption sites seriously limit its phosphate removal perform...Nano zero-valent iron(nZVI)is a promising phosphate adsorbent for advanced phosphate removal.However,the rapid passivation of nZVI and the low activity of adsorption sites seriously limit its phosphate removal performance,accounting for its inapplicability to meet the emission criteria of 0.1 mg P/L phosphate.In this study,we report that the oxalate modification can inhibit the passivation of nZVI and alter the multi-stage phosphate adsorption mechanism by changing the adsorption sites.As expected,the stronger antipassivation ability of oxalate modified nZVI(OX-nZVI)strongly favored its phosphate adsorption.Interestingly,the oxalate modification endowed the surface Fe(III)sites with the lowest chemisorption energy and the fastest phosphate adsorption ability than the other adsorption sites,by in situ forming a Fe(III)-phosphate-oxalate ternary complex,therefore enabling an advanced phosphate removal process.At an initial phosphate concentration of 1.00 mg P/L,pH of 6.0 and a dosage of 0.3 g/L of adsorbents,OX-nZVI exhibited faster phosphate removal rate(0.11 g/mg/min)and lower residual phosphate level(0.02 mg P/L)than nZVI(0.055 g/mg/min and 0.19 mg P/L).This study sheds light on the importance of site manipulation in the development of high-performance adsorbents,and offers a facile surface modification strategy to prepare superior iron-basedmaterials for advanced phosphate removal.展开更多
Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient ...Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient Boosting(GB)for fault detection in gear systems,applied to the Aalto Gear Fault Dataset,which features a wide range of synthetic and realistic gear failure modes under varied operating conditions.The dataset was preprocessed and analyzed using an ensemble GB classifier,yielding high performance across multiple metrics:accuracy of 96.77%,precision of 95.44%,recall of 97.11%,and an F1-score of 96.22%.To enhance trust in model predictions,the study integrates an explainable AI(XAI)framework using SHAP(SHapley Additive exPlanations)to visualize feature contributions and support diagnostic transparency.A flowchart-based architecture is proposed to guide real-world deployment of interpretable fault detection pipelines.The results demonstrate the feasibility of combining predictive performance with interpretability,offering a robust approach for condition monitoring in safety-critical systems.展开更多
BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is ...BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is invasive,costly and carries risks.Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization.Machine learning(ML)offers a powerful approach to analyze complex clinical data and improve predictive accuracy.This study hypothesized that ML models,utilizing noninvasive ultrasound and serological markers,can accurately predict the risk of EGVs in hepatitis B patients,thereby improving clinical decisionmaking.AIM To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.METHODS We retrospectively collected ultrasound and serological data from 310 eligible cases,randomly dividing them into training(80%)and validation(20%)groups.Eleven ML algorithms were used to build predictive models.The performance of the models was evaluated using the area under the curve and decision curve analysis.The best-performing model was further analyzed using SHapley Additive exPlanation to interpret feature importance.RESULTS Among the 310 patients,124 were identified as high-risk for EGVs.The extreme gradient boosting model demonstrated the best performance,achieving an area under the curve of 0.96 in the validation set.The model also exhibited high sensitivity(78%),specificity(94%),positive predictive value(84%),negative predictive value(88%),F1 score(83%),and overall accuracy(86%).The top four predictive variables were albumin,prothrombin time,portal vein flow velocity and spleen stiffness.A web-based version of the model was developed for clinical use,providing real-time predictions for high-risk patients.CONCLUSION We identified an efficient noninvasive predictive model using extreme gradient boosting for EGVs among hepatitis B patients.The model,presented as a web application,has potential for screening high-risk EGV patients and can aid clinicians in optimizing the use of endoscopy.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.12175107)the Qing Lan Project of Jiangsu Province+2 种基金the Hua Li Talents Program of Nanjing University of PostsTelecommunications,Natural Science Foundation of Nanjing Vocational University of Industry Technology(Grant No.YK22-02-08)the Fund from the Research Center of Industrial Perception and Intelligent Manufacturing Equipment Engineering of Jiangsu Province,China(Grant No.ZK21-05-09)。
文摘Surface polaritons,as surface electromagnetic waves propagating along the surface of a medium,have played a crucial role in enhancing photonic spin Hall effect(PSHE)and developing highly sensitive refractive index(RI)sensors.Among them,the traditional surface plasmon polariton(SPP)based on noble metals limits its application beyond the near-infrared(IR)regime due to the large negative permittivity and optical losses.In this contribution,we theoretically proposed a highly sensitive PSHE sensor with the structure of Ge prism-SiC-Si:InAs-sensing medium,by taking advantage of the hybrid surface plasmon phonon polariton(SPPhP)in mid-IR regime.Here,heavily Si-doped InAs(Si:InAs)and SiC excite the SPP and surface phonon polariton(SPhP),and the hybrid SPPhP is realized in this system.More importantly,the designed PSHE sensor based on this SPPhP mechanism achieves the multi-stage RI measurements from 1.00025-1.00225 to 1.70025-1.70225,and the maximal intensity sensitivity and angle sensitivity can be up to 9.4×10^(4)μm/RIU and245°/RIU,respectively.These findings provide a new pathway for the enhancement of PSHE in mid-IR regime,and offer new opportunities to develop highly sensitive RI sensors in multi-scenario applications,such as harmful gas monitoring and biosensing.
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
基金financially supported by the Heilongjiang Provincial Key R&D Program Project(No.GA21A204)Heilongjiang Provincial Natural Science Foundation of China(No.LH2022D031)the Research Project of Heilongjiang Province Bureau of Geology and Mineral Resources(No.HKY202302).
文摘Although previous researchers have attempted to decipher ore genesis and mineralization in the Erdaokan Ag-Pb-Zn deposit,some uncertainties regarding the mineralization process and evolution of both ore-forming fluids and magnetite types still need to be addressed.In this study,we obtained new EPMA,LA-ICP-MS,and in situ Fe isotope data from magnetite from the Erdaokan deposit,in order to better understand the mineralization mechanism and evolution of both magnetite and the ore-forming fluids.Our results identified seven types of magnetite at Erdaokan:disseminated magnetite(Mag1),coarse-grained magnetite(Mag2a),radial magnetite(Mag2b),fragmented fine-grained magnetite(Mag2c),vermicular gel magnetite(Mag3a1 and Mag3a2),colloidal magnetite(Mag3b)and dark gray magnetite(Mag4).All of the magnetite types were hydrothermal in origin and generally low in Ti(<400 ppm)and Ni(<800 ppm),while being enriched in light Fe isotopes(δ^(56)Fe ranging from−1.54‰to−0.06‰).However,they exhibit different geochemical signatures and are thus classified into high-manganese magnetite(Mag1,MnO>5 wt%),low-silicon magnetite(Mag2a-c,SiO_(2)<1 wt%),high-silicon magnetite(Mag3a-b,SiO_(2)from 1 to 7 wt%)and high-silicon-manganese magnetite(Mag4,SiO_(2)>1 wt%,MnO>0.2 wt%),each being formed within distinct hydrothermal environments.Based on mineralogy,elemental geochemistry,Fe isotopes,temperature trends,TMg-mag and(Ti+V)vs.(Al+Mn)diagrams,we propose that the Erdaokan Ag-Pb-Zn deposit underwent multi-stage mineralization,which can be broken down into four stages and nine sub-stages.Mag1,Mag2a-c,Mag3a-b and Mag4 were formed during the first sub-stage of each of the four stages,respectively.Additionally,fluid mixing,cooling and depressurization boiling were identified as the main mechanisms for mineral precipitation.The enrichment of Ag was significantly enhanced by the superposition of multi-stage ore-forming hydrothermal fluids in the Erdaokan Ag-Pb-Zn deposit.
文摘In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese company Unitree Robotics’G1 robot,embodying the innovation that has propelled China forward as the world’s second largest economy.
文摘This paper presents a new criterion for determining the unloading points quantitatively and consistently in a multi-stage triaxial test.The radial strain gradient(RSG)is first introduced as an arc tangent function of the rate of change of radial strain to time.RSG is observed to correlate closely with the stress state of a compressed sample,and reaches a horizontal asymptote as approaching failure.For a given rock type,RSG value at peak stress is almost the same,irrespective of the porosity and permeability.These findings lead to the development of RSG criterion:Unloading points can be precisely determined at the time when RSG reaches a pre-determined value that is a little smaller than or equal to the RSG at peak stress.The RSG criterion is validated against other criteria and the single-stage triaxial test on various types of rocks.Failure envelopes from the RSG criterion match well with those from single-stage tests.A practical procedure is recommended to use the RSG criterion:an unconfined compression or single-stage test is first conducted to determine the RSG at peak stress for one sample,the unloading point is then selected to be a value close to the RSG at peak stress,and the multi-stage test is finally performed on another sample using the pre-selected RSG unloading criterion.Generally,the RSG criterion is applicable for any type of rocks,especially brittle rocks,where other criteria are not suitable.Further,it can be practically implemented on the most available rock mechanical testing instruments.
基金supported by the National Natural Science Foundation of China(Grant No:52271300,52071337)National Key Research and Development Program of China(2022YFC2806501)+1 种基金High-tech Ship Research Projects Sponsored by MIIT(CBG2N21-4-25)Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT14R58).
文摘A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.
基金supported by the National Key Research and Development Program of China(Nos.2022YFA1205602,and 2023YFC3707801)the National Natural Science Foundation of China(Nos.U22A20402,22376073,21936003 and 22306119)China Postdoctoral Science Foundation(No.2023T160419).
文摘Nano zero-valent iron(nZVI)is a promising phosphate adsorbent for advanced phosphate removal.However,the rapid passivation of nZVI and the low activity of adsorption sites seriously limit its phosphate removal performance,accounting for its inapplicability to meet the emission criteria of 0.1 mg P/L phosphate.In this study,we report that the oxalate modification can inhibit the passivation of nZVI and alter the multi-stage phosphate adsorption mechanism by changing the adsorption sites.As expected,the stronger antipassivation ability of oxalate modified nZVI(OX-nZVI)strongly favored its phosphate adsorption.Interestingly,the oxalate modification endowed the surface Fe(III)sites with the lowest chemisorption energy and the fastest phosphate adsorption ability than the other adsorption sites,by in situ forming a Fe(III)-phosphate-oxalate ternary complex,therefore enabling an advanced phosphate removal process.At an initial phosphate concentration of 1.00 mg P/L,pH of 6.0 and a dosage of 0.3 g/L of adsorbents,OX-nZVI exhibited faster phosphate removal rate(0.11 g/mg/min)and lower residual phosphate level(0.02 mg P/L)than nZVI(0.055 g/mg/min and 0.19 mg P/L).This study sheds light on the importance of site manipulation in the development of high-performance adsorbents,and offers a facile surface modification strategy to prepare superior iron-basedmaterials for advanced phosphate removal.
文摘Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient Boosting(GB)for fault detection in gear systems,applied to the Aalto Gear Fault Dataset,which features a wide range of synthetic and realistic gear failure modes under varied operating conditions.The dataset was preprocessed and analyzed using an ensemble GB classifier,yielding high performance across multiple metrics:accuracy of 96.77%,precision of 95.44%,recall of 97.11%,and an F1-score of 96.22%.To enhance trust in model predictions,the study integrates an explainable AI(XAI)framework using SHAP(SHapley Additive exPlanations)to visualize feature contributions and support diagnostic transparency.A flowchart-based architecture is proposed to guide real-world deployment of interpretable fault detection pipelines.The results demonstrate the feasibility of combining predictive performance with interpretability,offering a robust approach for condition monitoring in safety-critical systems.
基金Supported by the Agency Natural Science Foundation of Fujian Province,China,No.2022J011285 and No.2023J011480.
文摘BACKGROUND Severe esophagogastric varices(EGVs)significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage.Endoscopy is the gold standard for EGV detection but it is invasive,costly and carries risks.Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization.Machine learning(ML)offers a powerful approach to analyze complex clinical data and improve predictive accuracy.This study hypothesized that ML models,utilizing noninvasive ultrasound and serological markers,can accurately predict the risk of EGVs in hepatitis B patients,thereby improving clinical decisionmaking.AIM To construct and validate a noninvasive predictive model using ML for EGVs in hepatitis B patients.METHODS We retrospectively collected ultrasound and serological data from 310 eligible cases,randomly dividing them into training(80%)and validation(20%)groups.Eleven ML algorithms were used to build predictive models.The performance of the models was evaluated using the area under the curve and decision curve analysis.The best-performing model was further analyzed using SHapley Additive exPlanation to interpret feature importance.RESULTS Among the 310 patients,124 were identified as high-risk for EGVs.The extreme gradient boosting model demonstrated the best performance,achieving an area under the curve of 0.96 in the validation set.The model also exhibited high sensitivity(78%),specificity(94%),positive predictive value(84%),negative predictive value(88%),F1 score(83%),and overall accuracy(86%).The top four predictive variables were albumin,prothrombin time,portal vein flow velocity and spleen stiffness.A web-based version of the model was developed for clinical use,providing real-time predictions for high-risk patients.CONCLUSION We identified an efficient noninvasive predictive model using extreme gradient boosting for EGVs among hepatitis B patients.The model,presented as a web application,has potential for screening high-risk EGV patients and can aid clinicians in optimizing the use of endoscopy.
