Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accura...Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.展开更多
On a compact Riemann surface with finite punctures P_(1),…P_(k),we define toric curves as multivalued,totallyunramified holomorphic maps to P^(n)with monodromy in a maximal torus of PSU(n+1).Toric solutions to SU(n+1...On a compact Riemann surface with finite punctures P_(1),…P_(k),we define toric curves as multivalued,totallyunramified holomorphic maps to P^(n)with monodromy in a maximal torus of PSU(n+1).Toric solutions to SU(n+1)Todasystems on X\{P_(1);…;P_(k)}are recognized by the associated toric curves in.We introduce character n-ensembles as-tuples of meromorphic one-forms with simple poles and purely imaginary periods,generating toric curves on minus finitelymany points.On X,we establish a correspondence between character-ensembles and toric solutions to the SU(n+1)system with finitely many cone singularities.Our approach not only broadens seminal solutions with two conesingularities on the Riemann sphere,as classified by Jost-Wang(Int.Math.Res.Not.,2002,(6):277-290)andLin-Wei-Ye(Invent.Math.,2012,190(1):169-207),but also advances beyond the limits of Lin-Yang-Zhong’s existencetheorems(J.Differential Geom.,2020,114(2):337-391)by introducing a new solution class.展开更多
Accurately predicting battery degradation is crucial for battery system management.However,due to the complexities of aging mechanisms and limitations of historical data,comprehensively indicating battery degradation ...Accurately predicting battery degradation is crucial for battery system management.However,due to the complexities of aging mechanisms and limitations of historical data,comprehensively indicating battery degradation solely through maximum capacity loss assessment is challenging.While machine learning offers promising solutions,it often overlooks domain knowledge,resulting in reduced accu racy,increased computational burden and decreased interpretability.Here,this study proposes a method to predict the voltage-capacity(V-Q) curve during battery degradation with limited historical data.This process is achieved through two physically interpretable components:a lightweight interpretable physical model and a physics-informed neural network.These components incorporate domain knowledge into machine learning to improve V-Q curve prediction performance and enhance interpretability.Extensive validation was conducted on 52 batteries of different types under different testing conditions.The proposed method can accurately predict future V-Q.curves for hundreds of cycles using only one-present-cycle V-Q curve,with root mean square error and mean absolute error basically less than 0.035 Ah and R^(2) basically less than 98.5%.This means that incremental capacity curves can be extracted from the predicted results for a more comprehensive and accurate battery degradation analysis.Furthermore,the method can flexibly adjust prediction length and density to cater to the practical needs of long-cycle prediction and data generation.This study provides a viable method for rapid degradation prediction and is expected to be generalized to in-vehicle implementations.展开更多
This research extends the literature on the environmental Phillips curve(EPC)and environmental Kuznets curve(EKC)by focusing on the 38 member economies of the Organization for Economic Co-operation and Development(OEC...This research extends the literature on the environmental Phillips curve(EPC)and environmental Kuznets curve(EKC)by focusing on the 38 member economies of the Organization for Economic Co-operation and Development(OECD).Using panel data from 2000 to 2021,the study employs several econometric techniques,including fixed effects,feasible generalized least squares,two-stage least squares,and the generalized method of moments.Our primary findings reveal that unemployment has a significant negative impact on CO_(2)emissions,thereby supporting the validity of the EPC hypothesis within OECD countries.This suggests a trade-off between unemployment and reductions in CO_(2)emissions.Similarly,the results validate the EKC hypothesis,with further analysis indicating that the EKC exhibits an N-shaped curve-an important contribution to the literature on environmental dynamics in advanced economies.Additionally,the results show that both trade openness and renewable energy usage have significantly improved environmental quality in OECD economies.Finally,extensive causality testing identifies both one-way and two-way causal relationships among the key variables examined.These findings have important policy implications for the management of environmental quality and macroeconomic variables in the OECD context.展开更多
With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement c...With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement curve of uplift piles is crucial for evaluating their uplift bearing characteristics,which facilitates the risk evaluation,design,and construction of large infrastructural supports.In this study,a load-displacement curve model based on piezocone penetration test(CPTU)data is proposed via the load transfer method.Experimental tests are conducted to analyze the uplift bearing characteristics and establish a correlation between the proposed model and CPTU data.The results of the proposed load-displacement curve are compared with the results from numerical simulations and those calculated by previous methods.The results show that the proposed curves appropriately evaluated the uplift bearing characteristics and improved the accuracy in comparison with previous methods.展开更多
This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and a...This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and analyzing the shortterm cost curve graphs,it presents the internal relationship between costs and output.Then,it focuses on researching its application strategies in multiple aspects such as enterprise production decisions,market pricing,and industry competition analysis.展开更多
The high-pressure mercury intrusion (HPMI) experiment is widely used to assess the pore architecture oftight sandstone reservoirs. However, the conventional analysis of the high- pressure mercury intrusionhas always f...The high-pressure mercury intrusion (HPMI) experiment is widely used to assess the pore architecture oftight sandstone reservoirs. However, the conventional analysis of the high- pressure mercury intrusionhas always focused on the mercury injection curves themselves, neglecting the important geologicalinformation conveyed by the mercury ejection curves. This paper quantitatively describes the fractalcharacteristics of ejection curves by using four fractal models, i.e.,. Menger model, Thermodynamicmodel, Sierpinski model, and multi- fractal model. In comparison with mercury injection curves, weexplore the fractal significance of mercury ejection curves and define the applicability of different fractalmodels in characterizing pore architectures. Investigated tight sandstone samples can be divided intofour types (Types A, B, C and D) based on porosity, permeability, and mercury removal efficiency. Type Dsamples are unique in that they have higher permeability (>0.6 mD) but lower mercury removal effi-ciency (<35%). Fractal studies of the mercury injection curve show that it mainly reflects the pore throatcharacteristics, while the mercury ejection curve serves to reveal the pore features, and porosity andpermeability correlate well with the fractal dimension of the injection curve, while mercury removalefficiency correlates only with the Ds' value of the ejection curve. The studies on the mercury ejectioncurves also reveal that the small pores and micropores of the Type C and Type D samples are moredeveloped, with varying pore architecture. The fractal dimension Ds' value of Type D samples is greaterthan that of Type C samples, and the dissolution of Type D samples is more intense than that of Type Csamples, which further indicates that the Type D samples are smaller in pore size, rougher in surface, andwith greater difficulty for the hydrocarbon to enter, resulting in their reservoir capacity probably lessthan that of Type C samples. In this regard, the important information characterized by the mercuryejection curve should be considered in evaluating the tight sandstone reservoirs. Finally, the Menger andThermodynamic models prove to be more suitable for describing the total pore architecture, while theSierpinski model is better for characterizing the variability of the interconnected pores.展开更多
Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causin...Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic storms.Consequently,it is very important to accurately predict the time period of solar flares.This paper proposes a flare prediction model,based on physical images of active solar regions.We employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 hours.The reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 hours.The True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and 0.5556.Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.展开更多
Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated opt...Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.展开更多
In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization al...In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.展开更多
Unsaturated soil mechanics is crucial in understanding ground conditions and constructing geotechnical structures,particularly amidst the challenges posed by global climate change.Nevertheless,acquiring accurate soil ...Unsaturated soil mechanics is crucial in understanding ground conditions and constructing geotechnical structures,particularly amidst the challenges posed by global climate change.Nevertheless,acquiring accurate soil suction values remains challenging due to limitations in existing methodologies,such as susceptibility to cavitation,high costs,and time-intensive procedures.Hence,this study employs a high-suction polymer sensor(HSPS)to evaluate the polymer's performance in determining soil suction.Subsequently,the polymers were used to measure unsaturated soil properties,especially soil-water characteristics curves(SWCC),based on osmotic principles.Five polymer samples classified as superabsorbent polymers(SAP)were synthesized with varying degrees of crosslinking,and their properties were assessed through swelling test and Fourier-transform infrared spectroscopy(FTIR).The soil sample from Turan,located within Nazarbayev University,was analyzed using a bimodal equation to determine the best fit.Results revealed that the swelling value and structural integrity of the polymer significantly affect soil suction capacity,with the findings being deemed temperature-independent,thereby obviating the need for calibration.Two potential factors hindering suction increase were identified:cavitation within the polymer or a reduction in the osmotic gradient due to polymer transformation into hydrogel formation.Overall,the novel polymer shows promise as an alternate material for SWCC measurement considering its simple method and being more sustainable compared to the other polymers,although further investigation is required to enhance the suction potential.展开更多
The unreasonable application of nitrogen fertilizer poses a threat to agricultural productivity and the environment protection in Northeast China.Therefore,accurately assessing crop nitrogen requirements and optimizin...The unreasonable application of nitrogen fertilizer poses a threat to agricultural productivity and the environment protection in Northeast China.Therefore,accurately assessing crop nitrogen requirements and optimizing fertilization are crucial for sustainable agricultural production.A three-year field experiment was conducted to evaluate the effects of planting density on the critical nitrogen concentration dilution curve(CNDC)for spring maize under drip irrigation and fertilization integration,incorporating two planting densities:D1(60,000 plants ha^(-1))and D2(90,000 plants ha^(-1))and six nitrogen levels:no nitrogen(N0),90(N90),180(N180),270(N270),360(N360),and 450(N450)kg ha^(-1).A Bayesian hierarchical model was used to develop CNDC models based on dry matter(DM)and leaf area index(LAI).The results revealed that the critical nitrogen concentration exhibited a power function relationship with both DM and LAI,while planting density had no significant impact on the CNDC parameters.Based on these findings,we propose unified CNDC equations for maize under drip irrigation and fertilization integration:Nc=4.505DM-0.384(based on DM)and Nc=3.793LAI-0.327(based on LAI).Additionally,the nitrogen nutrition index(NNI),derived from the CNDC,increased with higher nitrogen application rates.The nitrogen nutrition index(NNI)approached 1 with a nitrogen application rate of 180 kg ha^(-1)under the D1 planting density,while it reached 1 at 270 kg ha^(-1)under the D2 planting density.The relationship between NNI and relative yield(RY)followed a“linear+plateau”model,with maximum RY observed when the NNI approached 1.Thus,under the condition of drip irrigation and fertilization integration in Northeast China’s spring maize production,the optimal nitrogen application rates for achieving the highest yields were 180 kg ha^(-1)at a planting density of 60,000 plants ha^(-1),and 270 kg ha^(-1)at a density of 90,000 plants ha^(-1).The CNDC and NNI models developed in this study are valuable tools for diagnosing nitrogen nutrition and guiding precise fertilization practices in maize production under integrated drip irrigation and fertilization systems in Northeast China.展开更多
By adopting stochastic density functional theory(SDFT)and mixed stochastic-deterministic density functional theory(MDFT)methods,we perform first-principles calculations to predict the shock Hugoniot curves of boron(pr...By adopting stochastic density functional theory(SDFT)and mixed stochastic-deterministic density functional theory(MDFT)methods,we perform first-principles calculations to predict the shock Hugoniot curves of boron(pressure P=7.9×10^(3)-1.6×10^(6) GPa and temperature T=25-2800 eV),silicon(P=2.6×10^(3)-7.9×10^(5) GPa and T=21.5-1393 eV),and aluminum(P=5.2×10^(3)-9.0×10^(5) GPa and T=25-1393 eV)over wide ranges of pressure and temperature.In particular,we systematically investigate the impact of different cutoff radii in norm-conserving pseudopotentials on the calculated properties at elevated temperatures,such as pressure,ionization energy,and equation of state.By comparing the SDFT and MDFT results with those of other first-principles methods,such as extended first-principles molecular dynamics and path integral Monte Carlo methods,we find that the SDFT and MDFT methods show satisfactory precision,which advances our understanding of first-principles methods when applied to studies of matter at extremely high pressures and temperatures.展开更多
With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair compar...With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.展开更多
In the actual monitoring of deep hole displacement,the identification of slip surfaces is primarily based on abrupt changes observed in the inclinometric curve.In conventional identification methods,inclinometric curv...In the actual monitoring of deep hole displacement,the identification of slip surfaces is primarily based on abrupt changes observed in the inclinometric curve.In conventional identification methods,inclinometric curves exhibiting indications of sliding can be categorized into three types:B-type,D-type,and r-type.The position of the slip surface is typically determined by identifying the depth corresponding to the point of maximum displacement mutation.However,this method is sensitive to the interval of measurement points and the observation scale of the coordinate axes and suffers from unclear sliding surfaces and uncertain values.Based on the variation characteristics of these diagonal curves,we classified the landslide into three components:the sliding body,the sliding interval,and the immobile body.Moreover,three different generalization models were established to analyze the relationships between the curve form and the slip surface location based on different physical indicators such as displacement rate,relative displacement,and acceleration.The results show that the displacement rate curves of an r-type slope exhibit a clustering feature in the sliding interval,and by solving for the depth of discrete points within the step phase,it is possible to determine the location of the slip surface.On the other hand,D-type slopes have inflection points in the relative displacement curve located at the slip surface.The acceleration curves of B-type slopes exhibit clustering characteristics during the sliding interval,while the scattered acceleration data demonstrate wandering characteristics.Consequently,the slip surface location can be revealed by solving the depth corresponding to the maximum acceleration with cubic spline interpolation.The approach proposed in this paper was applied to the monitoring data of a landslide in Yunnan Province,China.The results indicate that our approach can accurately identify the slip surface location and enable computability of its position,thereby enhancing applicability and reliability of the deep-hole displacement monitoring data.展开更多
When carrying out calculations for turbulent flow simulation,one inevitably has to face the choice between accuracy and speed of calculations.In order to simultaneously obtain both a computationally efficient and more...When carrying out calculations for turbulent flow simulation,one inevitably has to face the choice between accuracy and speed of calculations.In order to simultaneously obtain both a computationally efficient and more accurate model,a surrogate model can be built on the basis of some fast special model and knowledge of previous calculations obtained by more accurate base models from various test bases or some results of serial calculations.The objective of this work is to construct a surrogate model which allows to improve the accuracy of turbulent calculations obtained by a special model on unstructured meshes.For this purpose,we use 1D Convolutional Neural Network(CNN)of the encoder-decoder architecture and reduce the problem to a single dimension by applying space-filling curves.Such an approach would have the benefit of being applicable to solutions obtained on unstructured meshes.In this work,a non-local approach is applied where entire flow fields obtained by the special and base models are used as input and ground truth output respectively.Spalart-Allmaras(SA)model and Near-wall Domain Decomposition(NDD)method for SA are taken as the base and special models respectively.The efficiency and accuracy of the obtained surrogate model are demonstrated in a case of supersonic flow over a compression corner with different values for angleαand Reynolds number Re.We conducted an investigation into interpolation and extrapolation by Re and also into interpolation byα.展开更多
Juvenile survival is a key life-history influence on population dynamics and adaptive evolution.We analyzed the effects of individual chara-cteristics,early environment,and maternal investment on juvenile survival in ...Juvenile survival is a key life-history influence on population dynamics and adaptive evolution.We analyzed the effects of individual chara-cteristics,early environment,and maternal investment on juvenile survival in a large solitary hibernating rodent-yellow ground squirrel Spermophilus fulvus using Cox mixed-effects models.Only 48%of weaned pups survived to dispersal and 17%survived to hibernation.Early life expectancy was primarily determined by individual characteristics and,to a lesser extent,by the early environment.The strongest and pos-itive predictor of juvenile survival was body mass which crucially affected mortality immediately after weaning.Males suffered higher mortality than females after the onset of dispersal;however,the overall difference between sexes was partly masked by high rates of mortality in the first days after emergence in both sexes.Later emerged juveniles had lower life expectancy than the earliest pups.The overall effect of local juvenile density was positive.Prolonged lactation did not enhance juvenile survival:Pups nursed longer survived shorter than the young nursed for a shorter period.Our findings support the hypothesis that females of S.fulvus cannot effectively regulate maternal expenditures to mitigate the effects of unfavorable conditions on their offspring.The strategy to deal with seasonal time constraints on life history in female S.fulvus suggests an early termination of maternal care at the cost of juvenile quality and survival.This female reproductive strategy corresponds to a"fast-solitary"life of folivorous desert-dwelling S.fulvus and other solitary ground squirrels with prolonged hibernation.展开更多
Global inland surface water bodies such as lakes and reservoirs,important components of the hydrosphere and ecosphere,are increasingly affected by climate change.Generating bathymetric volume-areaheight (BVAH) curves ...Global inland surface water bodies such as lakes and reservoirs,important components of the hydrosphere and ecosphere,are increasingly affected by climate change.Generating bathymetric volume-areaheight (BVAH) curves for global inland surface water bodies can enhance our understanding of their topography and climate impacts.However,accurately quantifying the topographic patterns of these water bodies remains challenging due to the difficulties in collecting comprehensive bathymetric data.Therefore,we collected and processed over 2000 bathymetric maps of global water bodies from over 50 different data sources and then developed the BVAH model.Finally,the BVAH hydrological curves of 16671 global inland surface water bodies (larger than 10 km~2) were generated.The results include but are not limited to (1) For most targeted water bodies,area (A) and volume (V) exhibit significant power function relationships with surface heights (H),with optimal power values quantified as 1.42 for A and 2.42 for V.(2) The BVAH model outperforms GLOBathy in estimating area and volume changes,achieving higher correlation coefficients (CC) of approximately 0.962 for the area and 0.991 for volume,and demonstrating lower percentages of root mean squared errors (PRMSE) around 10.9% for the area and 4.8% for volume.(3) In the case study of the Xizang Plateau and various large global reservoirs,the BVAH curve database can capture dynamic volume changes.As a unified simulation of the bathymetric topographical patterns,our bathymetric dataset and corresponding BVAH curve database have great potential to contribute to effective water resource management and ecological conservation efforts worldwide.展开更多
AASHTO’s guideline for geometric design of highways and similar guidelines require that roadside areas on the inside of horizontal curves be cleared of high objects to provide stopping sight distance. The guidelines ...AASHTO’s guideline for geometric design of highways and similar guidelines require that roadside areas on the inside of horizontal curves be cleared of high objects to provide stopping sight distance. The guidelines have analytical models for determining the extent of clearance, known as the horizontal sightline offset or clearance offset, for simple curves. Researchers in the past have developed analytical models for clearance offsets for spiraled and reverse curves. Very few researchers developed analytical models for available sight distances for compound curves. Still missing are models for horizontal sightline offsets and locations of the offsets for compound curves. The objective of this paper is to present development of analytical models and charts for determining horizontal sightline offsets and their locations for compound curves. The paper considers curves whose component arcs are individually shorter than stopping sight distance. The resulting models and the charts have been verified with accurate values determined using graphical methods. The models and the charts will find application in geometric design of highway compound curves.展开更多
In this paper,we present a class of novel Bernstein-like basis functions,which is an extension of classical Bernstein basis functions.The properties of this group of bases are analyzed and the Bézier-like curve w...In this paper,we present a class of novel Bernstein-like basis functions,which is an extension of classical Bernstein basis functions.The properties of this group of bases are analyzed and the Bézier-like curve with two shape parameters h1,h2is defined.The basis functions and Bézier-like curves have properties similar to cubic Bernstein basis functions and cubic Bézier curves,respectively.Furthermore,we construct Bézier-like curves with energy constraints and consider the C1and G1Hermite interpolation with minimal energy.Finally,some representative examples show the applicability and effectiveness of the proposed method.展开更多
文摘Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.
基金supported by the National Natural Science Foundation of China(11931009,12271495,11971450,and 12071449)Anhui Initiative in Quantum Information Technologies(AHY150200)the Project of Stable Support for Youth Team in Basic Research Field,Chinese Academy of Sciences(YSBR-001).
文摘On a compact Riemann surface with finite punctures P_(1),…P_(k),we define toric curves as multivalued,totallyunramified holomorphic maps to P^(n)with monodromy in a maximal torus of PSU(n+1).Toric solutions to SU(n+1)Todasystems on X\{P_(1);…;P_(k)}are recognized by the associated toric curves in.We introduce character n-ensembles as-tuples of meromorphic one-forms with simple poles and purely imaginary periods,generating toric curves on minus finitelymany points.On X,we establish a correspondence between character-ensembles and toric solutions to the SU(n+1)system with finitely many cone singularities.Our approach not only broadens seminal solutions with two conesingularities on the Riemann sphere,as classified by Jost-Wang(Int.Math.Res.Not.,2002,(6):277-290)andLin-Wei-Ye(Invent.Math.,2012,190(1):169-207),but also advances beyond the limits of Lin-Yang-Zhong’s existencetheorems(J.Differential Geom.,2020,114(2):337-391)by introducing a new solution class.
基金jointly supported by the National Natural Science Foundation of China(Grant No.52277213,52177210,and 52207229)key project of science and technology research program of Chongqing Education Commission of China (Grant No. KJZD-K202201103,KJZD-K202301108)Chongqing Graduate Research Innovation Project (Grant No.CYS240657).
文摘Accurately predicting battery degradation is crucial for battery system management.However,due to the complexities of aging mechanisms and limitations of historical data,comprehensively indicating battery degradation solely through maximum capacity loss assessment is challenging.While machine learning offers promising solutions,it often overlooks domain knowledge,resulting in reduced accu racy,increased computational burden and decreased interpretability.Here,this study proposes a method to predict the voltage-capacity(V-Q) curve during battery degradation with limited historical data.This process is achieved through two physically interpretable components:a lightweight interpretable physical model and a physics-informed neural network.These components incorporate domain knowledge into machine learning to improve V-Q curve prediction performance and enhance interpretability.Extensive validation was conducted on 52 batteries of different types under different testing conditions.The proposed method can accurately predict future V-Q.curves for hundreds of cycles using only one-present-cycle V-Q curve,with root mean square error and mean absolute error basically less than 0.035 Ah and R^(2) basically less than 98.5%.This means that incremental capacity curves can be extracted from the predicted results for a more comprehensive and accurate battery degradation analysis.Furthermore,the method can flexibly adjust prediction length and density to cater to the practical needs of long-cycle prediction and data generation.This study provides a viable method for rapid degradation prediction and is expected to be generalized to in-vehicle implementations.
文摘This research extends the literature on the environmental Phillips curve(EPC)and environmental Kuznets curve(EKC)by focusing on the 38 member economies of the Organization for Economic Co-operation and Development(OECD).Using panel data from 2000 to 2021,the study employs several econometric techniques,including fixed effects,feasible generalized least squares,two-stage least squares,and the generalized method of moments.Our primary findings reveal that unemployment has a significant negative impact on CO_(2)emissions,thereby supporting the validity of the EPC hypothesis within OECD countries.This suggests a trade-off between unemployment and reductions in CO_(2)emissions.Similarly,the results validate the EKC hypothesis,with further analysis indicating that the EKC exhibits an N-shaped curve-an important contribution to the literature on environmental dynamics in advanced economies.Additionally,the results show that both trade openness and renewable energy usage have significantly improved environmental quality in OECD economies.Finally,extensive causality testing identifies both one-way and two-way causal relationships among the key variables examined.These findings have important policy implications for the management of environmental quality and macroeconomic variables in the OECD context.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024M760734)National Science Fund for Distinguished Young Scholars(Grant No.42225206)the National Natural Science Foundation of China(Grant Nos.41877231 and 42072299).
文摘With the increasing construction of port facilities,cross-sea bridges,and offshore engineering projects,uplift piles embedded in marine sedimentary soft soil are becoming increasingly necessary.The load-displacement curve of uplift piles is crucial for evaluating their uplift bearing characteristics,which facilitates the risk evaluation,design,and construction of large infrastructural supports.In this study,a load-displacement curve model based on piezocone penetration test(CPTU)data is proposed via the load transfer method.Experimental tests are conducted to analyze the uplift bearing characteristics and establish a correlation between the proposed model and CPTU data.The results of the proposed load-displacement curve are compared with the results from numerical simulations and those calculated by previous methods.The results show that the proposed curves appropriately evaluated the uplift bearing characteristics and improved the accuracy in comparison with previous methods.
文摘This paper deeply explores the application strategies of short-term cost curves in the field of economics.Firstly,it elaborates on the basic theories and constituent elements of short-term cost curves.By drawing and analyzing the shortterm cost curve graphs,it presents the internal relationship between costs and output.Then,it focuses on researching its application strategies in multiple aspects such as enterprise production decisions,market pricing,and industry competition analysis.
基金The research project was co-funded by the National Natural Science Foundation of China(No.42072172,No.41772120)Shandong Province Natural Science Fund for Distinguished Young Scholars(No.JQ201311)the Graduate Scientific and Technological Innovation Project Financially Supported by Shandong University of Science and Technology(No.SDKDYC190313).
文摘The high-pressure mercury intrusion (HPMI) experiment is widely used to assess the pore architecture oftight sandstone reservoirs. However, the conventional analysis of the high- pressure mercury intrusionhas always focused on the mercury injection curves themselves, neglecting the important geologicalinformation conveyed by the mercury ejection curves. This paper quantitatively describes the fractalcharacteristics of ejection curves by using four fractal models, i.e.,. Menger model, Thermodynamicmodel, Sierpinski model, and multi- fractal model. In comparison with mercury injection curves, weexplore the fractal significance of mercury ejection curves and define the applicability of different fractalmodels in characterizing pore architectures. Investigated tight sandstone samples can be divided intofour types (Types A, B, C and D) based on porosity, permeability, and mercury removal efficiency. Type Dsamples are unique in that they have higher permeability (>0.6 mD) but lower mercury removal effi-ciency (<35%). Fractal studies of the mercury injection curve show that it mainly reflects the pore throatcharacteristics, while the mercury ejection curve serves to reveal the pore features, and porosity andpermeability correlate well with the fractal dimension of the injection curve, while mercury removalefficiency correlates only with the Ds' value of the ejection curve. The studies on the mercury ejectioncurves also reveal that the small pores and micropores of the Type C and Type D samples are moredeveloped, with varying pore architecture. The fractal dimension Ds' value of Type D samples is greaterthan that of Type C samples, and the dissolution of Type D samples is more intense than that of Type Csamples, which further indicates that the Type D samples are smaller in pore size, rougher in surface, andwith greater difficulty for the hydrocarbon to enter, resulting in their reservoir capacity probably lessthan that of Type C samples. In this regard, the important information characterized by the mercuryejection curve should be considered in evaluating the tight sandstone reservoirs. Finally, the Menger andThermodynamic models prove to be more suitable for describing the total pore architecture, while theSierpinski model is better for characterizing the variability of the interconnected pores.
基金partially supported by the National Key R&D Program of China (2022YFE0133700)the National Natural Science Foundation of China(12273007)+4 种基金the Guizhou Provincial Excellent Young Science and Technology Talent Program (YQK[2023]006)the National SKA Program of China (2020SKA0110300)the National Natural Science Foundation of China(11963003)the Guizhou Provincial Basic Research Program (Natural Science)(ZK[2022]143)the Cultivation project of Guizhou University ([2020]76).
文摘Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic storms.Consequently,it is very important to accurately predict the time period of solar flares.This paper proposes a flare prediction model,based on physical images of active solar regions.We employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 hours.The reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 hours.The True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and 0.5556.Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.
基金sponsored by the National Natural Science Foundation of China(Grant No.52405443)the Technology Research and Development Plan of China Railway(Grant No.N2023G063)the Fund of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ054).
文摘Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.
基金sponsored by China Geological Survey Project(DD20243193 and DD20230206508).
文摘In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.
基金supported by the research project from the Ministry of Higher Education and Science of the Republic of Kazakhstan(Grant No.AP19675456)Nazarbayev University Collaborative Research Program(CRP)(Grant No.111024CRP2010)Collaborative Research Program(CRP)(Grant No.111024CRP2011).
文摘Unsaturated soil mechanics is crucial in understanding ground conditions and constructing geotechnical structures,particularly amidst the challenges posed by global climate change.Nevertheless,acquiring accurate soil suction values remains challenging due to limitations in existing methodologies,such as susceptibility to cavitation,high costs,and time-intensive procedures.Hence,this study employs a high-suction polymer sensor(HSPS)to evaluate the polymer's performance in determining soil suction.Subsequently,the polymers were used to measure unsaturated soil properties,especially soil-water characteristics curves(SWCC),based on osmotic principles.Five polymer samples classified as superabsorbent polymers(SAP)were synthesized with varying degrees of crosslinking,and their properties were assessed through swelling test and Fourier-transform infrared spectroscopy(FTIR).The soil sample from Turan,located within Nazarbayev University,was analyzed using a bimodal equation to determine the best fit.Results revealed that the swelling value and structural integrity of the polymer significantly affect soil suction capacity,with the findings being deemed temperature-independent,thereby obviating the need for calibration.Two potential factors hindering suction increase were identified:cavitation within the polymer or a reduction in the osmotic gradient due to polymer transformation into hydrogel formation.Overall,the novel polymer shows promise as an alternate material for SWCC measurement considering its simple method and being more sustainable compared to the other polymers,although further investigation is required to enhance the suction potential.
基金supported by the grants from National Key Research and Development Program of China(2023YFD2303300)China Agriculture Research System(CARS-02-15)the Agricultural Science and Technology Innovation Program(CAAS-ZDRW202004).
文摘The unreasonable application of nitrogen fertilizer poses a threat to agricultural productivity and the environment protection in Northeast China.Therefore,accurately assessing crop nitrogen requirements and optimizing fertilization are crucial for sustainable agricultural production.A three-year field experiment was conducted to evaluate the effects of planting density on the critical nitrogen concentration dilution curve(CNDC)for spring maize under drip irrigation and fertilization integration,incorporating two planting densities:D1(60,000 plants ha^(-1))and D2(90,000 plants ha^(-1))and six nitrogen levels:no nitrogen(N0),90(N90),180(N180),270(N270),360(N360),and 450(N450)kg ha^(-1).A Bayesian hierarchical model was used to develop CNDC models based on dry matter(DM)and leaf area index(LAI).The results revealed that the critical nitrogen concentration exhibited a power function relationship with both DM and LAI,while planting density had no significant impact on the CNDC parameters.Based on these findings,we propose unified CNDC equations for maize under drip irrigation and fertilization integration:Nc=4.505DM-0.384(based on DM)and Nc=3.793LAI-0.327(based on LAI).Additionally,the nitrogen nutrition index(NNI),derived from the CNDC,increased with higher nitrogen application rates.The nitrogen nutrition index(NNI)approached 1 with a nitrogen application rate of 180 kg ha^(-1)under the D1 planting density,while it reached 1 at 270 kg ha^(-1)under the D2 planting density.The relationship between NNI and relative yield(RY)followed a“linear+plateau”model,with maximum RY observed when the NNI approached 1.Thus,under the condition of drip irrigation and fertilization integration in Northeast China’s spring maize production,the optimal nitrogen application rates for achieving the highest yields were 180 kg ha^(-1)at a planting density of 60,000 plants ha^(-1),and 270 kg ha^(-1)at a density of 90,000 plants ha^(-1).The CNDC and NNI models developed in this study are valuable tools for diagnosing nitrogen nutrition and guiding precise fertilization practices in maize production under integrated drip irrigation and fertilization systems in Northeast China.
基金supported by the National Key R&D Program of China under Grant No.2025YFB3003603the National Natural Science Foundation of China under Grant Nos.12135002 and 12105209.
文摘By adopting stochastic density functional theory(SDFT)and mixed stochastic-deterministic density functional theory(MDFT)methods,we perform first-principles calculations to predict the shock Hugoniot curves of boron(pressure P=7.9×10^(3)-1.6×10^(6) GPa and temperature T=25-2800 eV),silicon(P=2.6×10^(3)-7.9×10^(5) GPa and T=21.5-1393 eV),and aluminum(P=5.2×10^(3)-9.0×10^(5) GPa and T=25-1393 eV)over wide ranges of pressure and temperature.In particular,we systematically investigate the impact of different cutoff radii in norm-conserving pseudopotentials on the calculated properties at elevated temperatures,such as pressure,ionization energy,and equation of state.By comparing the SDFT and MDFT results with those of other first-principles methods,such as extended first-principles molecular dynamics and path integral Monte Carlo methods,we find that the SDFT and MDFT methods show satisfactory precision,which advances our understanding of first-principles methods when applied to studies of matter at extremely high pressures and temperatures.
基金supported by the National Natural Science Foundation of China (52075420)the National Key Research and Development Program of China (2020YFB1708400)。
文摘With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.
基金supported by the Scientific and Technological Research and Development Programs of China Railway Group Limited(Grant No.2022 Major Special Project-07)Gansu Provincial Technology Innovation Guidance Program-Special Funding for Capacity Building of Enterprise R&D Institutions(Grant No.23CXJA0011)Key R&D and transformation plan of Qinghai Province,China(Special Project for Transformation of Scientific and Technological Achievements No.2022-SF-158).
文摘In the actual monitoring of deep hole displacement,the identification of slip surfaces is primarily based on abrupt changes observed in the inclinometric curve.In conventional identification methods,inclinometric curves exhibiting indications of sliding can be categorized into three types:B-type,D-type,and r-type.The position of the slip surface is typically determined by identifying the depth corresponding to the point of maximum displacement mutation.However,this method is sensitive to the interval of measurement points and the observation scale of the coordinate axes and suffers from unclear sliding surfaces and uncertain values.Based on the variation characteristics of these diagonal curves,we classified the landslide into three components:the sliding body,the sliding interval,and the immobile body.Moreover,three different generalization models were established to analyze the relationships between the curve form and the slip surface location based on different physical indicators such as displacement rate,relative displacement,and acceleration.The results show that the displacement rate curves of an r-type slope exhibit a clustering feature in the sliding interval,and by solving for the depth of discrete points within the step phase,it is possible to determine the location of the slip surface.On the other hand,D-type slopes have inflection points in the relative displacement curve located at the slip surface.The acceleration curves of B-type slopes exhibit clustering characteristics during the sliding interval,while the scattered acceleration data demonstrate wandering characteristics.Consequently,the slip surface location can be revealed by solving the depth corresponding to the maximum acceleration with cubic spline interpolation.The approach proposed in this paper was applied to the monitoring data of a landslide in Yunnan Province,China.The results indicate that our approach can accurately identify the slip surface location and enable computability of its position,thereby enhancing applicability and reliability of the deep-hole displacement monitoring data.
文摘When carrying out calculations for turbulent flow simulation,one inevitably has to face the choice between accuracy and speed of calculations.In order to simultaneously obtain both a computationally efficient and more accurate model,a surrogate model can be built on the basis of some fast special model and knowledge of previous calculations obtained by more accurate base models from various test bases or some results of serial calculations.The objective of this work is to construct a surrogate model which allows to improve the accuracy of turbulent calculations obtained by a special model on unstructured meshes.For this purpose,we use 1D Convolutional Neural Network(CNN)of the encoder-decoder architecture and reduce the problem to a single dimension by applying space-filling curves.Such an approach would have the benefit of being applicable to solutions obtained on unstructured meshes.In this work,a non-local approach is applied where entire flow fields obtained by the special and base models are used as input and ground truth output respectively.Spalart-Allmaras(SA)model and Near-wall Domain Decomposition(NDD)method for SA are taken as the base and special models respectively.The efficiency and accuracy of the obtained surrogate model are demonstrated in a case of supersonic flow over a compression corner with different values for angleαand Reynolds number Re.We conducted an investigation into interpolation and extrapolation by Re and also into interpolation byα.
基金supported by the Russian Science Foundation,project number 22-24-00610,https://rscf.ru/project/22-24-00610/.
文摘Juvenile survival is a key life-history influence on population dynamics and adaptive evolution.We analyzed the effects of individual chara-cteristics,early environment,and maternal investment on juvenile survival in a large solitary hibernating rodent-yellow ground squirrel Spermophilus fulvus using Cox mixed-effects models.Only 48%of weaned pups survived to dispersal and 17%survived to hibernation.Early life expectancy was primarily determined by individual characteristics and,to a lesser extent,by the early environment.The strongest and pos-itive predictor of juvenile survival was body mass which crucially affected mortality immediately after weaning.Males suffered higher mortality than females after the onset of dispersal;however,the overall difference between sexes was partly masked by high rates of mortality in the first days after emergence in both sexes.Later emerged juveniles had lower life expectancy than the earliest pups.The overall effect of local juvenile density was positive.Prolonged lactation did not enhance juvenile survival:Pups nursed longer survived shorter than the young nursed for a shorter period.Our findings support the hypothesis that females of S.fulvus cannot effectively regulate maternal expenditures to mitigate the effects of unfavorable conditions on their offspring.The strategy to deal with seasonal time constraints on life history in female S.fulvus suggests an early termination of maternal care at the cost of juvenile quality and survival.This female reproductive strategy corresponds to a"fast-solitary"life of folivorous desert-dwelling S.fulvus and other solitary ground squirrels with prolonged hibernation.
基金supported by the National Natural Science Foundation of China (No. 41971377 & No. 41901346)the Fundamental Research Funds for the Central Universities, Peking University。
文摘Global inland surface water bodies such as lakes and reservoirs,important components of the hydrosphere and ecosphere,are increasingly affected by climate change.Generating bathymetric volume-areaheight (BVAH) curves for global inland surface water bodies can enhance our understanding of their topography and climate impacts.However,accurately quantifying the topographic patterns of these water bodies remains challenging due to the difficulties in collecting comprehensive bathymetric data.Therefore,we collected and processed over 2000 bathymetric maps of global water bodies from over 50 different data sources and then developed the BVAH model.Finally,the BVAH hydrological curves of 16671 global inland surface water bodies (larger than 10 km~2) were generated.The results include but are not limited to (1) For most targeted water bodies,area (A) and volume (V) exhibit significant power function relationships with surface heights (H),with optimal power values quantified as 1.42 for A and 2.42 for V.(2) The BVAH model outperforms GLOBathy in estimating area and volume changes,achieving higher correlation coefficients (CC) of approximately 0.962 for the area and 0.991 for volume,and demonstrating lower percentages of root mean squared errors (PRMSE) around 10.9% for the area and 4.8% for volume.(3) In the case study of the Xizang Plateau and various large global reservoirs,the BVAH curve database can capture dynamic volume changes.As a unified simulation of the bathymetric topographical patterns,our bathymetric dataset and corresponding BVAH curve database have great potential to contribute to effective water resource management and ecological conservation efforts worldwide.
文摘AASHTO’s guideline for geometric design of highways and similar guidelines require that roadside areas on the inside of horizontal curves be cleared of high objects to provide stopping sight distance. The guidelines have analytical models for determining the extent of clearance, known as the horizontal sightline offset or clearance offset, for simple curves. Researchers in the past have developed analytical models for clearance offsets for spiraled and reverse curves. Very few researchers developed analytical models for available sight distances for compound curves. Still missing are models for horizontal sightline offsets and locations of the offsets for compound curves. The objective of this paper is to present development of analytical models and charts for determining horizontal sightline offsets and their locations for compound curves. The paper considers curves whose component arcs are individually shorter than stopping sight distance. The resulting models and the charts have been verified with accurate values determined using graphical methods. The models and the charts will find application in geometric design of highway compound curves.
基金Supported by the National Natural Science Foundation of China(Grant No.11801225)University Science Research Project of Jiangsu Province(Grant No.18KJB110005)。
文摘In this paper,we present a class of novel Bernstein-like basis functions,which is an extension of classical Bernstein basis functions.The properties of this group of bases are analyzed and the Bézier-like curve with two shape parameters h1,h2is defined.The basis functions and Bézier-like curves have properties similar to cubic Bernstein basis functions and cubic Bézier curves,respectively.Furthermore,we construct Bézier-like curves with energy constraints and consider the C1and G1Hermite interpolation with minimal energy.Finally,some representative examples show the applicability and effectiveness of the proposed method.
