This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the stre...This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the strength of treated sand.Columns with a diameter of 7.5 cm were constructed using three mixing times(130,190,and 250 s),two execution procedures(normal and zigzag),threeαvalues(300,400,and 500 kg/m^(3)),and three W_(Total)/C ratios(2.5,3.0,and 3.5).For comparison,equivalent laboratory samples were also examined.Results revealed that increasing the mixing time andα,adopting the zigzag execution procedure,and reducing the W_(Total)/C ratio increase E.Outcomes indicated that an increase in E from 0.49-0.70 kJ to 0.70-0.90 kJ,0.90-1.10 kJ,and 1.10-1.40 kJ improves the unconfined compressive strength(UCS)of columns on average by 66%,124%,and 179%,respectively,and the secant modulus by 61%,110%,and 152%.Average strain at maximum stress also rises from 0.68%to 0.75%,0.81%,and 0.84%,respectively.The study identified a threshold in the direct relationship between E and the strength ratio(λ),beyond whichλdid not increase significantly with further increases in E.Additionally,at low and high E levels,DSM samples mainly failed by crushing and cracking modes,respectively.In DSM columns withα=500 kg/m^(3)and W_(Total)/C=2.5,increasing average E from 0.77 kJ to 0.95 kJ,1.08 kJ,and 1.28 kJ resulted in a reduction of coefficients of variation of UCS from 30.4%to 27.8%,24.5%,and 21.1%,respectively.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's per...Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's performance.The multiscale mixing phenomenon in a typical high-pressure turbine rotor flow was studied in this work.The contributions of various scale flows to entropy production and mixing properties were identified.The corresponding physical mechanisms at different scales were explored.It is shown that the large-scale and time-averaged flow contributions to mixing are significant,accounting for approximately 37.1% and 25% of the total.Time-averaged and large-scale flows cause the majority of the fluid deformation of the material surface,while mesoand small-scale flows just generate finer deformations.It raises the area stretch coefficient and the virtual concentration gradient.Thus,mixing is enhanced.Furthermore,time-averaged and large-scale flows account for the majority of the losses in the upstream and downstream regions of the blade tip respectively,accounting for approximately 53.8%and 33.5%of the total.The sheet-like structures—rather than the tip leaking vortex—are the primary source of the loss.High-dissipation regions are produced by the sheet-like structures via the pressure Hessian term and the self-amplification terms.展开更多
Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated...Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated by Brent Crude’s trajectory from pandemic-induced negative pricing to geopolitically driven surges exceeding USD 100 per barrel. This study addresses these complexities through an integrated methodological framework applied to medium-permeability sandstone reservoirs in the Xinjiang oilfield by combining advanced numerical simulations with multivariate regression analysis. The methodology employs Latin Hypercube Sampling (LHS) to stratify geological parameter distributions and constructs heterogeneous reservoir models using Petrel software, rigorously validated through historical production data matching. Production forecasting integrates numerical simulation and Decline Curve Analysis (DCA), while investment estimation utilizes Ordinary Least Squares (OLS) regression to correlate engineering parameters with drilling and completion costs. Economic evaluation incorporates Discounted Cash Flow (DCF) modeling and breakeven analysis, establishing techno-economic boundaries via oil price sensitivity analysis ranging from USD 40 to 90 per barrel. Visualization tools, including 3D heatmaps, delineate nonlinear interactions among engineering, geological, and investment datasets under economic constraints. Key findings demonstrate that for the target reservoirs, as oil prices increase from USD 40 to USD 90 per barrel, the minimum economic thickness threshold decreases from approximately 5.7 m to about 2.5 m, with model prediction errors consistently below 25% across validation datasets. This framework provides scientifically grounded decision support for optimizing capital allocation and offers actionable insights to enhance undeveloped hydrocarbon development planning amid market uncertainty. Ultimately, it supports national energy security through technically robust and economically viable resource exploitation strategies.展开更多
This paper presents the high-order nonlinear spectrum of six-wave mixing(SWM)influenced by ionizing Rydberg atom environment in rubidium thermal vapor.The experimentally measured transmitted SWM signals reveal signifi...This paper presents the high-order nonlinear spectrum of six-wave mixing(SWM)influenced by ionizing Rydberg atom environment in rubidium thermal vapor.The experimentally measured transmitted SWM signals reveal significant spectrum shifts and novel regularities,providing nonlinear spectrum insights into the ionization characteristics of Rydberg atoms.The detailed spectrum variations with increasing ion density are presented,paving the way for multi-wave mixing distribution of plasma and demonstrating SWM’s potential as a tool for measuring the electric field induced by the ionization process.展开更多
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n...In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R^(2))and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability.展开更多
Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total ...Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total lag and their response mechanisms under drought remain unclear.This study aimed to quantitatively partition the total SPAC water transport time lag through controlled experiments,identify the dominant component driving the drought response,and compare coexisting tree species with contrasting hydraulic strategies:Platycladus orientalis and Quercus variabilis.We conducted potted plant isotope(δ^(2)H)labeling experiments under normal water and drought stress treatments for both species.Using high-frequency isotope sampling and synchronous sap flow monitoring,we quantified the total water transport time lag from the soil surface to canopy branches(T_(iso),based on initial isotope arrival)and the internal plant transport time lag(T_(sap),based on sap flow path integration).An independent laboratory soil mixing experiment determined the baseline soil mixing time lag(T_(mix)),and the lag associated with soil infiltration and root uptake initiation was estimated(T_(soil)=T_(iso)−T_(sap)).The physical mixing of old and new soil water introduced a baseline time lag(T_(mix))of approximately 8-12 h.Under normal water conditions,the internal plant lag(T_(sap):37-40 h)constituted the major part of the total lag(T_(iso):43-46 h),with the estimated soil process lag(T_(soil))being relatively short(3-9 h).Drought stress significantly prolonged the total time lag.Crucially,this increase was primarily driven by a dramatic increase in the internal plant transport time lag(T_(sap)):T_(sap) increased by 77 h(193%)for P.orientalis and 33 h(89%)for Q.variabilis.In contrast,the estimated soil process lag(T_(soil))showed minimal increase(or even decreased)under drought.Consequently,the increase in T_(sap) almost entirely accounted for the prolongation of T_(iso)(T_(iso) increased by 188%for P.orientalis and 63%for Q.variabilis).Furthermore,the shallow-rooted P.orientalis was more sensitive to drought in terms of internal time lag increase compared to the deep-rooted Q.variabilis.Our direct experimental evidence demonstrates that internal plant physiological and hydraulic processes,rather than soil processes,dominantly regulate the response of total SPAC water transport time lag to drought stress.Tree species with different hydraulic strategies exhibit distinct time lag response mechanisms.These findings challenge traditional perspectives potentially overemphasizing soil limitations and highlight the critical importance of understanding internal plant dynamics for accurately predicting the temporal responses of ecosystem water relations.展开更多
The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recogn...BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.展开更多
To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.T...To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.展开更多
In this paper, we investigate the nonparametric regression model based on ρ-mixing errors, which are stochastically dominated by a nonnegative random variable. Weobtain the convergence rate for the weighted estimator...In this paper, we investigate the nonparametric regression model based on ρ-mixing errors, which are stochastically dominated by a nonnegative random variable. Weobtain the convergence rate for the weighted estimator of unknown function g（x） in pth-mean, which yields the convergence rate in probability. Moreover, an example of the nearestneighbor estimator is also illustrated and the convergence rates of estimator are presented.展开更多
The present study introduces a screw-pressing charging method to tackle deficiencies in automation and charge uniformity during the melt-casting of polymer-based energetic materials.To ensure the safety of the experim...The present study introduces a screw-pressing charging method to tackle deficiencies in automation and charge uniformity during the melt-casting of polymer-based energetic materials.To ensure the safety of the experiments,this study used inert materials with similar physical properties to partially substitute for the actual energetic components in the preparation of simulant materials.By thoroughly analyzing slurry physical properties,a simulation framework and an extensive performance evaluation method were developed.Such tools guide the design of the structure and configuration of process parameters.Results demonstrate that employing the Pin element significantly enhances radial mixing within the screw,minimizes temperature variations in the slurry,and improves both efficiency and safety in the mixing process.Further,adjustments such as widening the cone angle of the barrel,modifying the solid content of the slurry,and varying the speed of the screw can optimize the mechanical and thermal coupling in the flow field.These adjustments promote higher-quality slurry and create a safer production environment for the extrusion process.展开更多
Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a r...Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a regression discontinuity design.Methods:We utilized influenza-like illness(ILI)percentage as an indicator of influenza activity,with data from all sentinel hospitals between Week 4,2020,and Week 51,2023.The data is divided into two groups:the COVID-19 epidemic period and the post-epidemic period.Statistical analysis was performed with R software using robust RD design methods to account for potential confounders including seasonality,temperature,and influenza vaccination rates.Results:There was a discernible increase in the ILI percentage during the post-epidemic period.The robustness of the findings was confirmed with various RD design bandwidth selection methods and placebo tests,with certwo bandwidth providing the largest estimated effect size:a 14.6-percentage-point increase in the ILI percentage(β=0.146;95%CI:0.096–0.196).Sensitivity analyses and adjustments for confounders consistently pointed to an increased ILI percentage during the post-epidemic period compared to the epidemic period.Conclusion:The 14.6 percentage-point increase in the ILI percentage in Fujian Province,China,after the end of the COVID-19 pandemic suggests that there may be a need to re-evaluate and possibly enhance public health measures to control influenza transmission.Further research is needed to fully understand the factors contributing to this rise and to assess the ongoing impacts of post-pandemic behavioral changes.展开更多
The mixing process plays a pivotal role in the design,optimization,and scale-up of chemical reactors.For most chemical reactions,achieving uniform and rapid contact between reactants at the molecular level is crucial....The mixing process plays a pivotal role in the design,optimization,and scale-up of chemical reactors.For most chemical reactions,achieving uniform and rapid contact between reactants at the molecular level is crucial.Mixing intensification encompasses innovative methods and tools that address the limitations of inadequate mixing within reactors,enabling efficient reaction scaling and boosting the productivity of industrial processes.This review provides a concise introduction to the fundamentals of multiphase mixing,followed by case studies highlighting the application of mixing intensification in the production of energy-storage materials,advanced optical materials,and nanopesticides.These examples illustrate the significance of theoretical analysis in informing and advancing engineering practices within the chemical industry.We also explore the challenges and opportunities in this field,offering insights based on our current understanding.展开更多
Open channel confluences,where two streams or rivers converge,play a crucial role in hydraulic engineering and river dynamics.These confluences are characterized by complex hydrodynamics influenced by the discharge ra...Open channel confluences,where two streams or rivers converge,play a crucial role in hydraulic engineering and river dynamics.These confluences are characterized by complex hydrodynamics influenced by the discharge ratios of merging water bodies.This study investigated the mixing structure at open channel confluences using three-dimensional numerical modeling.A comprehensive three-dimensional numerical model was developed and validated against a dataset obtained from controlled laboratory experiments.This dataset incorporated three-dimensional time-averaged velocity measurements.The skew-induced and stress-induced equation systems were adopted as the core governing equations,providing a framework for simulating various scenarios.A total of ten different cases were analyzed.The results highlighted the effect of discharge ratios on turbulence,lateral and vertical vorticities,and the distribution of mixing,which intensified with higher magnitudes of discharge ratios.The mixing structure,driven by velocity gradients and vorticity,revealed the significant role of lateral and vertical vorticities in determining hydrodynamic behaviors and mixing distributions at confluences.Specifically,the momentum ratio of incoming flows governed the spatial evolution of mixing processes.This study revealed that the distribution of mixing served as a key indicator for identifying the formation of mid-channel scours.High normalized velocities induced toward the left bank led to the superelevation of the water surface,enhancing the potential for bed material and the formation of significant scour holes beneath the elevated water surface.This novel approach provides a deeper understanding of the mixing patterns at confluences,particularly in scenarios with equilibrated discharge ratios but in different magnitudes.展开更多
This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to d...This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.展开更多
Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence o...Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.展开更多
As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
The implementation of ultrahigh-Ni cathodes in high-energy lithium-ion batteries(LIBs)is constrained by significant structural and interfacial degradation during cycling.In this study,doping-induced surface restructur...The implementation of ultrahigh-Ni cathodes in high-energy lithium-ion batteries(LIBs)is constrained by significant structural and interfacial degradation during cycling.In this study,doping-induced surface restructuring in ultrahigh-nickel cathode materials is rapidly facilitated through an ultrafast Joule heating method.Density functional theory(DFT)calculations,synchrotron X-ray absorption spectroscopy(XAS),and single-particle force test confirmed the establishment of a stable crystal framework and lattice oxygen,which mitigated H2-H3 phase transitions and improved structural reversibility.Additionally,the Sc doping process exhibits a pinning effect on the grain boundaries,as shown by scanning transmission electron microscopy(STEM),enhancing Li~+diffusion kinetics and decreasing mechanical strain during cycling.The in situ development of a cation-mixing layer at grain boundaries also creates a robust cathode/electrolyte interphase,effectively reducing interfacial parasitic reactions and transition metal dissolution,as validated by STEM and time-of-flight secondary ion mass spectrometry(TOF-SIMS).These synergistic modifications reduce particle cracking and surface/interface degradation,leading to enhanced rate capability,structural integrity,and thermal stability.Consequently,the optimized Sc-modified ultrahigh-Ni cathode(Sc-1)exhibits 93.99%capacity retention after 100 cycles at 1 C(25℃)and87.06%capacity retention after 100 cycles at 1 C(50℃),indicating excellent cycling and thermal stability.By presenting a one-step multifunctional modification approach,this research delivers an extensive analysis of the mechanisms governing the structure,microstructure,and interface properties of nickel-rich layered cathode materials(NCMs).These results underscore the potential of ultrahigh-Ni cathodes as viable candidates for advanced lithium-ion batteries(LIBs)in next-generation electric vehicles(EVs).展开更多
文摘This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the strength of treated sand.Columns with a diameter of 7.5 cm were constructed using three mixing times(130,190,and 250 s),two execution procedures(normal and zigzag),threeαvalues(300,400,and 500 kg/m^(3)),and three W_(Total)/C ratios(2.5,3.0,and 3.5).For comparison,equivalent laboratory samples were also examined.Results revealed that increasing the mixing time andα,adopting the zigzag execution procedure,and reducing the W_(Total)/C ratio increase E.Outcomes indicated that an increase in E from 0.49-0.70 kJ to 0.70-0.90 kJ,0.90-1.10 kJ,and 1.10-1.40 kJ improves the unconfined compressive strength(UCS)of columns on average by 66%,124%,and 179%,respectively,and the secant modulus by 61%,110%,and 152%.Average strain at maximum stress also rises from 0.68%to 0.75%,0.81%,and 0.84%,respectively.The study identified a threshold in the direct relationship between E and the strength ratio(λ),beyond whichλdid not increase significantly with further increases in E.Additionally,at low and high E levels,DSM samples mainly failed by crushing and cracking modes,respectively.In DSM columns withα=500 kg/m^(3)and W_(Total)/C=2.5,increasing average E from 0.77 kJ to 0.95 kJ,1.08 kJ,and 1.28 kJ resulted in a reduction of coefficients of variation of UCS from 30.4%to 27.8%,24.5%,and 21.1%,respectively.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金supported by the National Science and Technology Major Project,China(No.J2019-Ⅱ-0012-0032)。
文摘Multiscale mixing of the turbine blade tip leakage and mainstream flows causes considerable aerodynamic loss.Understanding it is crucial to correctly estimating the mixing loss and thus improving the turbine's performance.The multiscale mixing phenomenon in a typical high-pressure turbine rotor flow was studied in this work.The contributions of various scale flows to entropy production and mixing properties were identified.The corresponding physical mechanisms at different scales were explored.It is shown that the large-scale and time-averaged flow contributions to mixing are significant,accounting for approximately 37.1% and 25% of the total.Time-averaged and large-scale flows cause the majority of the fluid deformation of the material surface,while mesoand small-scale flows just generate finer deformations.It raises the area stretch coefficient and the virtual concentration gradient.Thus,mixing is enhanced.Furthermore,time-averaged and large-scale flows account for the majority of the losses in the upstream and downstream regions of the blade tip respectively,accounting for approximately 53.8%and 33.5%of the total.The sheet-like structures—rather than the tip leaking vortex—are the primary source of the loss.High-dissipation regions are produced by the sheet-like structures via the pressure Hessian term and the self-amplification terms.
文摘Traditional oilfields face increasing extraction challenges, primarily due to reservoir quality degradation and production decline, which are further exacerbated by volatile international crude oil prices—illustrated by Brent Crude’s trajectory from pandemic-induced negative pricing to geopolitically driven surges exceeding USD 100 per barrel. This study addresses these complexities through an integrated methodological framework applied to medium-permeability sandstone reservoirs in the Xinjiang oilfield by combining advanced numerical simulations with multivariate regression analysis. The methodology employs Latin Hypercube Sampling (LHS) to stratify geological parameter distributions and constructs heterogeneous reservoir models using Petrel software, rigorously validated through historical production data matching. Production forecasting integrates numerical simulation and Decline Curve Analysis (DCA), while investment estimation utilizes Ordinary Least Squares (OLS) regression to correlate engineering parameters with drilling and completion costs. Economic evaluation incorporates Discounted Cash Flow (DCF) modeling and breakeven analysis, establishing techno-economic boundaries via oil price sensitivity analysis ranging from USD 40 to 90 per barrel. Visualization tools, including 3D heatmaps, delineate nonlinear interactions among engineering, geological, and investment datasets under economic constraints. Key findings demonstrate that for the target reservoirs, as oil prices increase from USD 40 to USD 90 per barrel, the minimum economic thickness threshold decreases from approximately 5.7 m to about 2.5 m, with model prediction errors consistently below 25% across validation datasets. This framework provides scientifically grounded decision support for optimizing capital allocation and offers actionable insights to enhance undeveloped hydrocarbon development planning amid market uncertainty. Ultimately, it supports national energy security through technically robust and economically viable resource exploitation strategies.
基金Project supported by the Science and Technology Project of State Grid Corporation of China(Grant No.5700-202355839A-4-3-WL).
文摘This paper presents the high-order nonlinear spectrum of six-wave mixing(SWM)influenced by ionizing Rydberg atom environment in rubidium thermal vapor.The experimentally measured transmitted SWM signals reveal significant spectrum shifts and novel regularities,providing nonlinear spectrum insights into the ionization characteristics of Rydberg atoms.The detailed spectrum variations with increasing ion density are presented,paving the way for multi-wave mixing distribution of plasma and demonstrating SWM’s potential as a tool for measuring the electric field induced by the ionization process.
基金supported by National Natural Science Foundation of China(Grant 62573375)the Natural Science Foundation of Hebei Province(Grant F2024203038)+2 种基金the Science and Technology Research and Development Plan Project of Qinhuangdao City(Grant 202302B048)the Provincial Key Laboratory Performance Subsidy Project(Grant 22567612H)the Shandong Provincial Natural Science Foundation Youth Project(ZR2023QF044)。
文摘In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R^(2))and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability.
基金financial supports from the National Science Foundation of China(42277062,41977149 and 42230714).
文摘Water transport time lag in the Soil-Plant-Atmosphere Continuum(SPAC)significantly impacts ecosystem hydrology and plant water relations,yet the relative contributions of different segments(soil vs.plant)to the total lag and their response mechanisms under drought remain unclear.This study aimed to quantitatively partition the total SPAC water transport time lag through controlled experiments,identify the dominant component driving the drought response,and compare coexisting tree species with contrasting hydraulic strategies:Platycladus orientalis and Quercus variabilis.We conducted potted plant isotope(δ^(2)H)labeling experiments under normal water and drought stress treatments for both species.Using high-frequency isotope sampling and synchronous sap flow monitoring,we quantified the total water transport time lag from the soil surface to canopy branches(T_(iso),based on initial isotope arrival)and the internal plant transport time lag(T_(sap),based on sap flow path integration).An independent laboratory soil mixing experiment determined the baseline soil mixing time lag(T_(mix)),and the lag associated with soil infiltration and root uptake initiation was estimated(T_(soil)=T_(iso)−T_(sap)).The physical mixing of old and new soil water introduced a baseline time lag(T_(mix))of approximately 8-12 h.Under normal water conditions,the internal plant lag(T_(sap):37-40 h)constituted the major part of the total lag(T_(iso):43-46 h),with the estimated soil process lag(T_(soil))being relatively short(3-9 h).Drought stress significantly prolonged the total time lag.Crucially,this increase was primarily driven by a dramatic increase in the internal plant transport time lag(T_(sap)):T_(sap) increased by 77 h(193%)for P.orientalis and 33 h(89%)for Q.variabilis.In contrast,the estimated soil process lag(T_(soil))showed minimal increase(or even decreased)under drought.Consequently,the increase in T_(sap) almost entirely accounted for the prolongation of T_(iso)(T_(iso) increased by 188%for P.orientalis and 63%for Q.variabilis).Furthermore,the shallow-rooted P.orientalis was more sensitive to drought in terms of internal time lag increase compared to the deep-rooted Q.variabilis.Our direct experimental evidence demonstrates that internal plant physiological and hydraulic processes,rather than soil processes,dominantly regulate the response of total SPAC water transport time lag to drought stress.Tree species with different hydraulic strategies exhibit distinct time lag response mechanisms.These findings challenge traditional perspectives potentially overemphasizing soil limitations and highlight the critical importance of understanding internal plant dynamics for accurately predicting the temporal responses of ecosystem water relations.
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金Supported by High-level Professional Groups in Gangdong Province,No.GSPZYQ2020101Guangdong Province Educational Research Planning Project,No.2024GXJK742。
文摘BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.
基金supported by the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2024ZD0302502 for WZ)the National Natural Science Foundation of China(Grant No.92365210 for WZ)+1 种基金Tsinghua Initiative Scientific Research Program (for WZ)the project of Tsinghua University-Zhuhai Huafa Industrial Share Company Joint Institute for Architecture Optoelectronic Technologies (JIAOT,for YH)。
文摘To fully utilize the resources provided by optical fiber networks,a cross-band quantum light source generating photon pairs,where one photon in a pair is at C band and the other is at O band,is proposed in this work.This source is based on spontaneous four-wave mixing(SFWM)in a piece of shallow-ridge silicon waveguide.Theoretical analysis shows that the waveguide dispersion could be tailored by adjusting the ridge width,enabling broadband photon pair generation by SFWM across C band and O band.The spontaneous Raman scattering(SpRS)in silicon waveguides is also investigated experimentally.It shows that there are two regions in the spectrum of generated photons from SpRS,which could be used to achieve cross-band photon pair generation.A chip of shallow-ridge silicon waveguide samples with different ridge widths has been fabricated,through which cross-band photon pair generation is demonstrated experimentally.The experimental results show that the source can be achieved using dispersion-optimized shallow-ridge silicon waveguides.This cross-band quantum light source provides a way to develop new fiber-based quantum communication functions utilizing both C band and O band and extends applications of quantum networks.
基金Supported by National Natural Science Foundation of China(11426032,11501005)Natural Science Foundation of Anhui Province(1408085QA02,1508085QA01,1508085J06)+5 种基金Provincial Natural Science Research Project of Anhui Colleges(KJ2014A010,KJ2014A020,KJ2015A065)Higher Education Talent Revitalization Project of Anhui Province(2013SQRL005ZD)Quality Engineering Project of Anhui Province(2015jyxm054,2015jyxm057)Students Science Research Training Program of Anhui University(KYXL2014016,KYXL2014013)Applied Teaching Model Curriculum of Anhui University(XJYYKC1401,ZLTS2015052,ZLTS2015053)Doctoral Research Start-up Funds Projects of Anhui University
文摘In this paper, we investigate the nonparametric regression model based on ρ-mixing errors, which are stochastically dominated by a nonnegative random variable. Weobtain the convergence rate for the weighted estimator of unknown function g（x） in pth-mean, which yields the convergence rate in probability. Moreover, an example of the nearestneighbor estimator is also illustrated and the convergence rates of estimator are presented.
基金financially supported by the Fundamental Research Funds for the Central Universities(Grant No.30923011018)。
文摘The present study introduces a screw-pressing charging method to tackle deficiencies in automation and charge uniformity during the melt-casting of polymer-based energetic materials.To ensure the safety of the experiments,this study used inert materials with similar physical properties to partially substitute for the actual energetic components in the preparation of simulant materials.By thoroughly analyzing slurry physical properties,a simulation framework and an extensive performance evaluation method were developed.Such tools guide the design of the structure and configuration of process parameters.Results demonstrate that employing the Pin element significantly enhances radial mixing within the screw,minimizes temperature variations in the slurry,and improves both efficiency and safety in the mixing process.Further,adjustments such as widening the cone angle of the barrel,modifying the solid content of the slurry,and varying the speed of the screw can optimize the mechanical and thermal coupling in the flow field.These adjustments promote higher-quality slurry and create a safer production environment for the extrusion process.
基金supported by the Youth Scientific Research Project of Fujian Provincial Center for Disease Control and Prevention(2022QN02)the Fujian Provincial Health Youth Scientific Research Project(2023QNA040).
文摘Background:The COVID-1’s impact on influenza activity is of interest to inform future flu prevention and control strategies.Our study aim to examine COVID-19’s effects on influenza in Fujian Province,China,using a regression discontinuity design.Methods:We utilized influenza-like illness(ILI)percentage as an indicator of influenza activity,with data from all sentinel hospitals between Week 4,2020,and Week 51,2023.The data is divided into two groups:the COVID-19 epidemic period and the post-epidemic period.Statistical analysis was performed with R software using robust RD design methods to account for potential confounders including seasonality,temperature,and influenza vaccination rates.Results:There was a discernible increase in the ILI percentage during the post-epidemic period.The robustness of the findings was confirmed with various RD design bandwidth selection methods and placebo tests,with certwo bandwidth providing the largest estimated effect size:a 14.6-percentage-point increase in the ILI percentage(β=0.146;95%CI:0.096–0.196).Sensitivity analyses and adjustments for confounders consistently pointed to an increased ILI percentage during the post-epidemic period compared to the epidemic period.Conclusion:The 14.6 percentage-point increase in the ILI percentage in Fujian Province,China,after the end of the COVID-19 pandemic suggests that there may be a need to re-evaluate and possibly enhance public health measures to control influenza transmission.Further research is needed to fully understand the factors contributing to this rise and to assess the ongoing impacts of post-pandemic behavioral changes.
基金supported by the National Natural Science Foundation of China(22288102,22035007,and 22122815)。
文摘The mixing process plays a pivotal role in the design,optimization,and scale-up of chemical reactors.For most chemical reactions,achieving uniform and rapid contact between reactants at the molecular level is crucial.Mixing intensification encompasses innovative methods and tools that address the limitations of inadequate mixing within reactors,enabling efficient reaction scaling and boosting the productivity of industrial processes.This review provides a concise introduction to the fundamentals of multiphase mixing,followed by case studies highlighting the application of mixing intensification in the production of energy-storage materials,advanced optical materials,and nanopesticides.These examples illustrate the significance of theoretical analysis in informing and advancing engineering practices within the chemical industry.We also explore the challenges and opportunities in this field,offering insights based on our current understanding.
文摘Open channel confluences,where two streams or rivers converge,play a crucial role in hydraulic engineering and river dynamics.These confluences are characterized by complex hydrodynamics influenced by the discharge ratios of merging water bodies.This study investigated the mixing structure at open channel confluences using three-dimensional numerical modeling.A comprehensive three-dimensional numerical model was developed and validated against a dataset obtained from controlled laboratory experiments.This dataset incorporated three-dimensional time-averaged velocity measurements.The skew-induced and stress-induced equation systems were adopted as the core governing equations,providing a framework for simulating various scenarios.A total of ten different cases were analyzed.The results highlighted the effect of discharge ratios on turbulence,lateral and vertical vorticities,and the distribution of mixing,which intensified with higher magnitudes of discharge ratios.The mixing structure,driven by velocity gradients and vorticity,revealed the significant role of lateral and vertical vorticities in determining hydrodynamic behaviors and mixing distributions at confluences.Specifically,the momentum ratio of incoming flows governed the spatial evolution of mixing processes.This study revealed that the distribution of mixing served as a key indicator for identifying the formation of mid-channel scours.High normalized velocities induced toward the left bank led to the superelevation of the water surface,enhancing the potential for bed material and the formation of significant scour holes beneath the elevated water surface.This novel approach provides a deeper understanding of the mixing patterns at confluences,particularly in scenarios with equilibrated discharge ratios but in different magnitudes.
基金supported by DST-FIST(Government of India)(Grant No.SR/FIST/MS-1/2017/13)and Seed Money Project(Grant No.DoRDC/733).
文摘This study numerically examines the heat and mass transfer characteristics of two ternary nanofluids via converging and diverg-ing channels.Furthermore,the study aims to assess two ternary nanofluids combinations to determine which configuration can provide better heat and mass transfer and lower entropy production,while ensuring cost efficiency.This work bridges the gap be-tween academic research and industrial feasibility by incorporating cost analysis,entropy generation,and thermal efficiency.To compare the velocity,temperature,and concentration profiles,we examine two ternary nanofluids,i.e.,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O and TiO_(2)+SiO_(2)+Cu/H_(2)O,while considering the shape of nanoparticles.The velocity slip and Soret/Dufour effects are taken into consideration.Furthermore,regression analysis for Nusselt and Sherwood numbers of the model is carried out.The Runge-Kutta fourth-order method with shooting technique is employed to acquire the numerical solution of the governed system of ordinary differential equations.The flow pattern attributes of ternary nanofluids are meticulously examined and simulated with the fluc-tuation of flow-dominating parameters.Additionally,the influence of these parameters is demonstrated in the flow,temperature,and concentration fields.For variation in Eckert and Dufour numbers,TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher temperature than TiO_(2)+SiO_(2)+Cu/H_(2)O.The results obtained indicate that the ternary nanofluid TiO_(2)+SiO_(2)+Al_(2)O_(3)/H_(2)O has a higher heat transfer rate,lesser entropy generation,greater mass transfer rate,and lower cost than that of TiO_(2)+SiO_(2)+Cu/H_(2)O ternary nanofluid.
文摘Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
基金supported by the National Key R&D Program of China(2022YFB3803501)the National Natural Science Foundation of China(22179008,22209156)+5 种基金support from the Beijing Nova Program(20230484241)support from the China Postdoctoral Science Foundation(2024M754084)the Postdoctoral Fellowship Program of CPSF(GZB20230931)support from beamline BL08U1A of Shanghai Synchrotron Radiation Facility(2024-SSRF-PT-506950)beamline 1W1B of the Beijing Synchrotron Radiation Facility(2021-BEPC-PT-006276)support from Initial Energy Science&Technology Co.,Ltd(IEST)。
文摘The implementation of ultrahigh-Ni cathodes in high-energy lithium-ion batteries(LIBs)is constrained by significant structural and interfacial degradation during cycling.In this study,doping-induced surface restructuring in ultrahigh-nickel cathode materials is rapidly facilitated through an ultrafast Joule heating method.Density functional theory(DFT)calculations,synchrotron X-ray absorption spectroscopy(XAS),and single-particle force test confirmed the establishment of a stable crystal framework and lattice oxygen,which mitigated H2-H3 phase transitions and improved structural reversibility.Additionally,the Sc doping process exhibits a pinning effect on the grain boundaries,as shown by scanning transmission electron microscopy(STEM),enhancing Li~+diffusion kinetics and decreasing mechanical strain during cycling.The in situ development of a cation-mixing layer at grain boundaries also creates a robust cathode/electrolyte interphase,effectively reducing interfacial parasitic reactions and transition metal dissolution,as validated by STEM and time-of-flight secondary ion mass spectrometry(TOF-SIMS).These synergistic modifications reduce particle cracking and surface/interface degradation,leading to enhanced rate capability,structural integrity,and thermal stability.Consequently,the optimized Sc-modified ultrahigh-Ni cathode(Sc-1)exhibits 93.99%capacity retention after 100 cycles at 1 C(25℃)and87.06%capacity retention after 100 cycles at 1 C(50℃),indicating excellent cycling and thermal stability.By presenting a one-step multifunctional modification approach,this research delivers an extensive analysis of the mechanisms governing the structure,microstructure,and interface properties of nickel-rich layered cathode materials(NCMs).These results underscore the potential of ultrahigh-Ni cathodes as viable candidates for advanced lithium-ion batteries(LIBs)in next-generation electric vehicles(EVs).
