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
The quench sensitivity of 6063 alloy was investigated via constructing time-temperature-property（TTP） curves by interrupted quenching technique and transmission electron microscopy（TEM） analysis.The results show t...The quench sensitivity of 6063 alloy was investigated via constructing time-temperature-property（TTP） curves by interrupted quenching technique and transmission electron microscopy（TEM） analysis.The results show that the quench sensitivity of 6063 alloy is lower than that of 6061 or 6082 alloy,and the critical temperature ranges from 300 to 410℃ with the nose temperature of about 360℃.From TEM analysis,heterogeneous precipitate β-Mg2Si is prior to nucleate on the（AlxFeySiz） dispersoids in the critical temperature range,and grows up most rapidly at the nose temperature of 360℃.The heterogeneous precipitation leads to a low concentration of solute,which consequently reduces the amount of the strengthening phase β＇＇ after aging.In the large-scale industrial production of 6063 alloy,the cooling rate during quenching should be enhanced as high as possible in the quenching sensitive temperature range（410-300℃） to suppress the heterogeneous precipitation to get optimal mechanical properties,and it should be slowed down properly from the solution temperature to 410℃ and below 300℃ to reduce the residual stress.展开更多
[Objective] The aim was to study on the effects of heavy metal stress on the growth curves of Escherichia coli and Bacillus subtili. [Method] Using traditional culture method,Escherichia coli and Bacillus subtili were...[Objective] The aim was to study on the effects of heavy metal stress on the growth curves of Escherichia coli and Bacillus subtili. [Method] Using traditional culture method,Escherichia coli and Bacillus subtili were cultured under heavy metal stress including Cu2＋,Hg2＋,Pb2＋,Cd2＋ and Cr6＋ in different concentrations. Then the growth curves of the bacteria were determined to investigate the effects of exogenous heavy metals on the growth of the two kinds of bacteria. [Result] The proliferation of the two bacteria was inhibited at high concentrations of Hg2＋ and Cd2＋ respectively,and G＋ is more sensitive to them than G-; when the heavy metal concentration was 50 mg/L,the toxicity of the five kinds of heavy metals on the two bacteria was Hg2＋Cd2＋Cu2＋Cr6＋≈Pb2＋. [Conclusion] The research will provide a basis to explore the effects of heavy metal on environment and ecological system.展开更多
A new method for shape modification of non-uniform rational B-splines (NURBS) curves was presented, which was based on constrained optimization by means of altering the corresponding weights of their control points. U...A new method for shape modification of non-uniform rational B-splines (NURBS) curves was presented, which was based on constrained optimization by means of altering the corresponding weights of their control points. Using this method, the original NURBS curve was modified to satisfy the specified geometric constraints, including single point and multi-point constraints. With the introduction of free parameters, the shapes of modified NURBS curves could be further controlled by users without destroying geometric constraints and seem more naturally. Since explicit formulae were derived to compute new weights of the modified curve, the method was simple and easy to program. Practical examples showed that the method was applicable for computer aided design (CAD) system.展开更多
By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using rese...By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using reservoir permeability and a synthesized index. The accuracy of this new method is verified by mercury-injection experiments. Considering the limited quantity of capillary pressure data, a new method is developed to extract the Swanson parameter from the NMR T2 distribution and estimate reservoir permeability. Integrating with NMR total porosity, reservoir capillary pressure curves can be constructed to evaluate reservoir pore structure in the intervals with NMR log data. An in-situ example of evaluating reservoir pore structure using the capillary pressure curves by this new method is presented. The result shows that it accurately detects the change in reservoir pore structure as a function of depth.展开更多
By adopting the method of controlling parameters this paper describes the construction of various kinds of cubic curve segment and curved surface fragment with rational and non rational parameters, and discusses the ...By adopting the method of controlling parameters this paper describes the construction of various kinds of cubic curve segment and curved surface fragment with rational and non rational parameters, and discusses the relationship between controlling parameters, weighted factors and types, kinds and characteristics of curve segments and curved surface fragments. A mathematical method is provided for CAGD with abundant connotations, broad covering region, convenience, flexibility and direct simplicity.展开更多
The /-V-（T） characteristic curves of p-n junctions with the forward voltage as the independent variable, the logarithm of forward current as the dependent variable, and the junction temperature as the parameter, alm...The /-V-（T） characteristic curves of p-n junctions with the forward voltage as the independent variable, the logarithm of forward current as the dependent variable, and the junction temperature as the parameter, almost converge at one point in the first quadrant. The voltage corresponding with the convergence point nearly equals the bandgap of the semiconductor material. This convergence point can be used to obtain the I-V characteristic curve at any temperature.展开更多
Seed germination process has closely relation with material transformation and energy exchange within the seed. Study on its thermal effect is important for understanding the mechanism and the influencing factors of t...Seed germination process has closely relation with material transformation and energy exchange within the seed. Study on its thermal effect is important for understanding the mechanism and the influencing factors of the seed germination. The thermogenetic curves of seed germination ofRobinia pseudoacacia was measured by a new-type conductive microcalorimeter made in Wuhan University. The relationship was analyzed between the germination thermogenetic regulation and seed germination physiology. The thermogentic curves were further analyzed by thermokinetic theory to obtain the dynamic parameters and the thermokinetic model on seed germination ofRobinia pseudoacacia. The relationship of the thermogenetic power (μ w) and the germination time(h) of the germination process of 20 grainsRobinia pseudocacia seeds at 25°C wasP=208.77/[0.1937+0.8063exp(?0.06563t)]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.
文摘The quench sensitivity of 6063 alloy was investigated via constructing time-temperature-property（TTP） curves by interrupted quenching technique and transmission electron microscopy（TEM） analysis.The results show that the quench sensitivity of 6063 alloy is lower than that of 6061 or 6082 alloy,and the critical temperature ranges from 300 to 410℃ with the nose temperature of about 360℃.From TEM analysis,heterogeneous precipitate β-Mg2Si is prior to nucleate on the（AlxFeySiz） dispersoids in the critical temperature range,and grows up most rapidly at the nose temperature of 360℃.The heterogeneous precipitation leads to a low concentration of solute,which consequently reduces the amount of the strengthening phase β＇＇ after aging.In the large-scale industrial production of 6063 alloy,the cooling rate during quenching should be enhanced as high as possible in the quenching sensitive temperature range（410-300℃） to suppress the heterogeneous precipitation to get optimal mechanical properties,and it should be slowed down properly from the solution temperature to 410℃ and below 300℃ to reduce the residual stress.
基金Supported by Science Research Fund Project of Yunnan Provincial Education Department "Physiological Toxicity of Heavy Metal Stress on Several Microorganisms" (09Y0382)Yunnan Natural Science Foundation Project "Toxicity Mechanism of Three Aquatic Plants under Heavy Metal Pollution" (2008ZC161M)~~
文摘[Objective] The aim was to study on the effects of heavy metal stress on the growth curves of Escherichia coli and Bacillus subtili. [Method] Using traditional culture method,Escherichia coli and Bacillus subtili were cultured under heavy metal stress including Cu2＋,Hg2＋,Pb2＋,Cd2＋ and Cr6＋ in different concentrations. Then the growth curves of the bacteria were determined to investigate the effects of exogenous heavy metals on the growth of the two kinds of bacteria. [Result] The proliferation of the two bacteria was inhibited at high concentrations of Hg2＋ and Cd2＋ respectively,and G＋ is more sensitive to them than G-; when the heavy metal concentration was 50 mg/L,the toxicity of the five kinds of heavy metals on the two bacteria was Hg2＋Cd2＋Cu2＋Cr6＋≈Pb2＋. [Conclusion] The research will provide a basis to explore the effects of heavy metal on environment and ecological system.
文摘A new method for shape modification of non-uniform rational B-splines (NURBS) curves was presented, which was based on constrained optimization by means of altering the corresponding weights of their control points. Using this method, the original NURBS curve was modified to satisfy the specified geometric constraints, including single point and multi-point constraints. With the introduction of free parameters, the shapes of modified NURBS curves could be further controlled by users without destroying geometric constraints and seem more naturally. Since explicit formulae were derived to compute new weights of the modified curve, the method was simple and easy to program. Practical examples showed that the method was applicable for computer aided design (CAD) system.
文摘By analyzing hundreds of capillary pressure curves, the controlling factors of shape and type of capillary pressure curves are found and a novel method is presented to construct capillary pressure curves by using reservoir permeability and a synthesized index. The accuracy of this new method is verified by mercury-injection experiments. Considering the limited quantity of capillary pressure data, a new method is developed to extract the Swanson parameter from the NMR T2 distribution and estimate reservoir permeability. Integrating with NMR total porosity, reservoir capillary pressure curves can be constructed to evaluate reservoir pore structure in the intervals with NMR log data. An in-situ example of evaluating reservoir pore structure using the capillary pressure curves by this new method is presented. The result shows that it accurately detects the change in reservoir pore structure as a function of depth.
文摘By adopting the method of controlling parameters this paper describes the construction of various kinds of cubic curve segment and curved surface fragment with rational and non rational parameters, and discusses the relationship between controlling parameters, weighted factors and types, kinds and characteristics of curve segments and curved surface fragments. A mathematical method is provided for CAGD with abundant connotations, broad covering region, convenience, flexibility and direct simplicity.
文摘The /-V-（T） characteristic curves of p-n junctions with the forward voltage as the independent variable, the logarithm of forward current as the dependent variable, and the junction temperature as the parameter, almost converge at one point in the first quadrant. The voltage corresponding with the convergence point nearly equals the bandgap of the semiconductor material. This convergence point can be used to obtain the I-V characteristic curve at any temperature.
文摘Seed germination process has closely relation with material transformation and energy exchange within the seed. Study on its thermal effect is important for understanding the mechanism and the influencing factors of the seed germination. The thermogenetic curves of seed germination ofRobinia pseudoacacia was measured by a new-type conductive microcalorimeter made in Wuhan University. The relationship was analyzed between the germination thermogenetic regulation and seed germination physiology. The thermogentic curves were further analyzed by thermokinetic theory to obtain the dynamic parameters and the thermokinetic model on seed germination ofRobinia pseudoacacia. The relationship of the thermogenetic power (μ w) and the germination time(h) of the germination process of 20 grainsRobinia pseudocacia seeds at 25°C wasP=208.77/[0.1937+0.8063exp(?0.06563t)]
