The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aer...The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles(QUAVs).This scheme mainly consists of predefinedtime estimators and fixed-time tracking controllers,with a hybrid Laplacian matrix describing the communication among these QUAVs.At the first layer,we devise predefined time estimators for leading and following QUAVs,enabling accurate estimation of desired information.In the second layer,we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults.Fixedtime translational tracking controllers are then proposed,and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers.We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly.The paper includes a demonstration of the control system stability through mathematical proof,as well as the presentation of simulation results and comparative simulations.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator ...Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator and Bayesian model averaging(BMA)to optimize the predictions of RCfrom sophisticated theoretical models.The CBP estimator treats the residual between the theoretical and experimental values of RCas a continuous variable and derives its posterior probability density function(PDF)from Bayesian theory.The BMA method assigns weights to models based on their predictive performance for benchmark nuclei,thereby accounting for the unique strengths of each model.In global optimization,the CBP estimator improved the predictive accuracy of the three theoretical models by approximately 60%.The extrapolation analyses consistently achieved an improvement rate of approximately 45%,demonstrating the robustness of the CBP estimator.Furthermore,the combination of the CBP and BMA methods reduces the standard deviation to below 0.02 fm,effectively reproducing the pronounced shell effects on RCof the Ca and Sr isotope chains.The studies in this paper propose an efficient method to accurately describe RCof unknown nuclei,with potential applications in research on other nuclear properties.展开更多
BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs ...BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs and association with cardiometabolic risk factors(CMRFs).AIM To clarify the relationship among CK18F,NITs,and CMRF.METHODS We included 125 patients who were assessed for SLD and had CK18F measured in cross-sectional study.The fibrosis-4 index(FIB-4),steatosis-associated fibrosis estimator(SAFE)score,liver stiffness(LS),controlled attenuation parameter,and FibroScan-aspartate aminotransferase(FAST)score were compared with CK18F as existing NITs.RESULTS CK18F was associated with aspartate aminotransferase,alanine aminotransferase,and triglyceride(TG).FAST and SAFE score were associated with high CK18F(>260 U/L),but not FIB-4 or LS.The cut-off values for TG and high-density lipoprotein(HDL)cholesterol used to determine high CK18F using receiver operating characteristics analysis were 126 mg/dL and 56 mg/dL respectively.High TG(>126 mg/dL)and low HDL(<56 mg/dL)were associated with high CK18F.The risk of high CK18F was higher when high TG and low HDL were combined than when each was present alone.CMRF was higher in the high CK18F group,but was not associated with CK18F levels.However,when the TG and HDL criteria for CMRF were replaced by TG>126 mg/mL and HDL<56 mg/dL,modified CMRF(mCMRF)was associated with CK18F levels,with a higher risk of high CK18F than CMRF.CONCLUSION CK18F is a new NIT associated with SAFE score and FAST.High TG,low HDL,and mCMRF are associated with high CK18F.展开更多
In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in res...In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in resources,continuous emergence of new deposits,and strong impetus injected into the industry by technological innovation and policy support.The global rare earth resource supply pattern was further optimized (Table 1).1.Fruitful results in resource growth and new deposit discoveriesBrazil emerged as a core region for resource growth.The Colossus rare earth deposit saw a 150%increase in resources and announced its first reserve estimate.The Caldeira rare earth deposit’s resource estimate grew by 50%.The combined ore resources in the Caladão rare earth deposit’s Zones A and B reached 5.72×10~8 tonnes,with a total rare earth oxide(TREO) grade of 0.1506%,concurrently hosting 2.29×10~4tonnes of gallium metal resources.展开更多
Rare earth(RE)Y-type zeolite was synthesized in situ by acidic co-hydrolysis route and hydrothermal method.The key process parameters were optimized based on the RE utilization rate.The effect of inducing a rotating p...Rare earth(RE)Y-type zeolite was synthesized in situ by acidic co-hydrolysis route and hydrothermal method.The key process parameters were optimized based on the RE utilization rate.The effect of inducing a rotating packed bed(RPB)in premixing and crystallization on crystallinity and RE utilization rate was further investigated.The results indicate that lanthanide(La)cations are successfully introduced into the sodalite cage of Y-type zeolite.The optimized conditions are that the molar ratio of Si/La is 150,premixing for 5 h,crystallization at 90℃ for 18 h,and calcination at 550℃ for 3.5 h.At this stage,the RE utilization rate reaches 74.5%.Compared with the conventional stirred tank reactor(STR),RPB can effectively shorten the premixing time and crystallization time by 4.3 h and 6 h,improve the crystallinity by 23%and RE utilization rate by 7.5%.The RE utilization rate is more than 80%by RPB,surpassing the effectiveness of using the one-exchange one-calcination process in the traditional liquid ion exchange process.It is expected to provide a reference for the in-situ efficient and green synthesis of RE zeolite.展开更多
As an important strategic rare-earth resource,bastnaesite has long been a global research focus.The carbochlorination process,as an efficient and low-cost extraction method,can be applied to treat bastnaesite,achievin...As an important strategic rare-earth resource,bastnaesite has long been a global research focus.The carbochlorination process,as an efficient and low-cost extraction method,can be applied to treat bastnaesite,achieving ideal rare-earth extraction results in just one-step reaction.By using inexpensive chlorine gas as the chlorinating agent,it avoids lengthy procedural steps and the generation of acid-base waste liquids.Based on this,we propose a novel carbochlorination process for bastnaesite involving a fluorine-fixing agent.Thermodynamic data for the carbochlorination process of bastnaesite were calculated using the group contribution method.Thermodynamic feasibility was verified through Gibbs free energy.The effects of different chlorination times,fluorine-fixing agent dosages,chlorine flow rates,and chlorination temperatures on the carbochlorination process of bastnaesite were investigated.Experimental studies showed that under optimal chlorination conditions,a temperature of 800℃,a duration of 60 min,a fluorine-fixing agent dosage of 10%,and a chlorine flow rate of 10 L·min^(−1),the chlorination rates of rare-earth elements,Ca,Ba,and Fe in bastnaesite reached 96%,99%,98%,and 99%,respectively.The reaction mechanism was explored and analyzed based on characterization results such as mineral phase composition,micromorphology and thermogravimetry of water-washed residues under different chlorination conditions.Additionally,kinetic experiments were conducted at varying reaction temperatures and chlorine flow rates,revealing that the carbon-chlorination process is primarily controlled by chemical reactions.展开更多
The orthogonal time frequency space(OTFS)modulation is a novel modulation scheme that can effectively cope with the high Doppler expansion caused by high mobility.Since it modulates data on delay-Doppler(DD)domain and...The orthogonal time frequency space(OTFS)modulation is a novel modulation scheme that can effectively cope with the high Doppler expansion caused by high mobility.Since it modulates data on delay-Doppler(DD)domain and makes full use of the sparse characteristics of DD domain,it has been widely studied to design efficient channel estimation and signal detection schemes.In this paper,we design a novel superimposed pilot pattern with transition band,which replaces the traditional embedded pilot(EP)guard zero-symbols,and perform a two-stage channel estimation.In the first stage,we fully utilize the dispersion characteristics of OTFS signal in DD domain,and use threshold decision to make coarse channel estimation.In the second stage,we use the results of the coarse estimation for iterative signal detection and accurate channel estimation.During the second stage,we make full use of the sparsity of the channel in DD domain,remodel the received signal into the form of sparse channel vector multiplied by channel coefficient matrix,and introduce Doppler index segmentation factor(DISF)to subdivide the Doppler index to solve the problem of fractional Doppler.Simulations reveal that,the scheme proposed in this paper has higher spectral efficiency compared with traditional EP scheme and lower peak-to-average power ratio(PAPR)compared with traditional superimposed pilot scheme.展开更多
In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant chal...In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.展开更多
Ln@MOFs by anchoring rare metal ions(Ln) into metal-organic frameworks(MOFs) are proved to have great potential in the field of luminescent molecular thermometer.Nevertheless,the current research indicated that the po...Ln@MOFs by anchoring rare metal ions(Ln) into metal-organic frameworks(MOFs) are proved to have great potential in the field of luminescent molecular thermometer.Nevertheless,the current research indicated that the poor structural stability and low sensitivity hindered their application scope.In this work,a new MOF Zn-450 luminescent thermometer with multiple emission fluorescence characteristics was synthesized by the combination of 3,3,5,5-biphenyl tetracarboxylic acid(H_(4)L) and Zn^(2+) ion under solvothermal conditions.Interestingly,a high relative sensitivity of 1.43 % K^(-1) was found within 80-300 K based on Zn-450.Subsequently,two high-sensitivity luminescent Ln@MOFs(Ln = Eu and Tb) were further fabricated by doping rare earth ions into Zn-450 based on the post-synthesis strategy.Among them,the Eu@Zn-450 demonstrates various luminous behaviors while achieving an increased relative sensitivity of 1.63 % K^(-1).In addition,the continuously visible red,pink,and purple luminescent emissions at the same temperature range were observed,suggesting that the Eu@Zn-450 could be utilized as a luminescent colorimetric molecular thermometer.Importantly,this work can present new possibilities for the development of rare earth-doped luminescence and its temperature sensing properties.展开更多
Phosphorus (P) is an essential nutrient element that is critical for plant growth and ecosystem functionality.The soil P cycle plays multiple roles,such as sustaining plant growth and productivity,regulating nutrient ...Phosphorus (P) is an essential nutrient element that is critical for plant growth and ecosystem functionality.The soil P cycle plays multiple roles,such as sustaining plant growth and productivity,regulating nutrient balance within ecosystems,and enhancing ecosystem adaptability and resilience.This cycle is influenced by factors such as the restoration approach and microbial community dynamics.However,the extent to which the restoration approach alters the P cycle in karst ecosystems and the underlying microbial mechanisms remain poorly understood.The P-cycle multifunctionality index (P-cycle MFI) serves as a comprehensive indicator for evaluating soil P cycle function,and it provides insights into changes in the P cycle between different restoration approaches.To investigate the shifts in soil P-cycle MFI and microbial mechanisms between different restoration approaches,we analyzed soil available P (AP),total P (TP),microbial biomass P (MBP),and the activities of acid phosphatase (ACP) and alkaline phosphatase (ALP).These data were used to calculate the P-cycle MFI by averaging the Z-scores between two restoration approaches(artificial restoration of forest (AF) and natural restoration of forest (NF)) and a control (cropland,CP) at six subtropical karst ecosystem sites in China.We also determined the soil organic carbon (SOC),exchangeable calcium (Ca) and magnesium (Mg),pH,bulk density (BD),microbial biomass C (MBC),and microbial biomass nitrogen (MBN),as well as the community structure,relative abundance,diversity indices,and co-occurrence networks of phoD-harboring bacteria.The results showed that the community structure of phoD-harboring bacteria varied significantly among AF,NF,and CP and across different temperature gradients.These bacteria exhibited increasing complexity and tightness in co-occurrence networks from CP to AF and then to NF,along with the ACP and ALP activities,but not the TP and AP contents.The P-cycle MFI values were significantly higher in NF compared to AF and CP,and the variation was significantly explained by restoration approach,temperature,MBC,MBN,SOC,exchangeable Ca,BD,community structure of phoD-harboring bacteria,and exchangeable Mg.Furthermore,natural restoration had a more substantial impact on the P-cycle MFI than temperature by enhancing SOC,microbial biomass,the complexity and co-occurrence network tightness of the phoD-harboring bacterial community structure,and ACP and ALP activities,but it reduced soil BD.The rare genera of phoD-harboring bacteria significantly influenced the variation of soil P-cycle MFI compared to the dominant genera.This study highlights the importance of rare genera of phoD-harboring bacteria in driving soil P-cycle multifunctionality in karst ecosystems,with natural restoration being more effective than artificial methods for enhancing soil organic matter and microbial community complexity.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters...Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.展开更多
Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative con...Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.展开更多
The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly fa...The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation.展开更多
(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbi...(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.展开更多
AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:...AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.展开更多
Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introdu...Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.展开更多
We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In...We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.展开更多
A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily s...A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.展开更多
基金supported by Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-QN-0733)Guangdong Basic and Applied Basic Research Foundation,China(No.2022A1515110753)+2 种基金China Postdoctoral Science Foundation(No.2022M722583)China Industry-UniversityResearch Innovation Foundation(No.2022IT188)National Key Laboratory of Air-based Information Perception and Fusion and the Aeronautic Science Foundation of China(No.20220001068001)。
文摘The paper presents a two-layer,disturbance-resistant,and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles(QUAVs).This scheme mainly consists of predefinedtime estimators and fixed-time tracking controllers,with a hybrid Laplacian matrix describing the communication among these QUAVs.At the first layer,we devise predefined time estimators for leading and following QUAVs,enabling accurate estimation of desired information.In the second layer,we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults.Fixedtime translational tracking controllers are then proposed,and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers.We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly.The paper includes a demonstration of the control system stability through mathematical proof,as well as the presentation of simulation results and comparative simulations.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金supported by the National Natural Science Foundation of China(Nos.12475135,12035011,and 12475119)the Shandong Provincial Natural Science Foundation,China(No.ZR2020MA096)the Fundamental Research Funds for the Central Universities(No.22CX03017A)。
文摘Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator and Bayesian model averaging(BMA)to optimize the predictions of RCfrom sophisticated theoretical models.The CBP estimator treats the residual between the theoretical and experimental values of RCas a continuous variable and derives its posterior probability density function(PDF)from Bayesian theory.The BMA method assigns weights to models based on their predictive performance for benchmark nuclei,thereby accounting for the unique strengths of each model.In global optimization,the CBP estimator improved the predictive accuracy of the three theoretical models by approximately 60%.The extrapolation analyses consistently achieved an improvement rate of approximately 45%,demonstrating the robustness of the CBP estimator.Furthermore,the combination of the CBP and BMA methods reduces the standard deviation to below 0.02 fm,effectively reproducing the pronounced shell effects on RCof the Ca and Sr isotope chains.The studies in this paper propose an efficient method to accurately describe RCof unknown nuclei,with potential applications in research on other nuclear properties.
文摘BACKGROUND Serum cytokeratin 18 fragment(CK18F)has been developed as a new noninvasive test(NIT)for risk assessment of steatotic liver disease(SLD);however,there are few reports on its relationship with existing NITs and association with cardiometabolic risk factors(CMRFs).AIM To clarify the relationship among CK18F,NITs,and CMRF.METHODS We included 125 patients who were assessed for SLD and had CK18F measured in cross-sectional study.The fibrosis-4 index(FIB-4),steatosis-associated fibrosis estimator(SAFE)score,liver stiffness(LS),controlled attenuation parameter,and FibroScan-aspartate aminotransferase(FAST)score were compared with CK18F as existing NITs.RESULTS CK18F was associated with aspartate aminotransferase,alanine aminotransferase,and triglyceride(TG).FAST and SAFE score were associated with high CK18F(>260 U/L),but not FIB-4 or LS.The cut-off values for TG and high-density lipoprotein(HDL)cholesterol used to determine high CK18F using receiver operating characteristics analysis were 126 mg/dL and 56 mg/dL respectively.High TG(>126 mg/dL)and low HDL(<56 mg/dL)were associated with high CK18F.The risk of high CK18F was higher when high TG and low HDL were combined than when each was present alone.CMRF was higher in the high CK18F group,but was not associated with CK18F levels.However,when the TG and HDL criteria for CMRF were replaced by TG>126 mg/mL and HDL<56 mg/dL,modified CMRF(mCMRF)was associated with CK18F levels,with a higher risk of high CK18F than CMRF.CONCLUSION CK18F is a new NIT associated with SAFE score and FAST.High TG,low HDL,and mCMRF are associated with high CK18F.
文摘In 2025,the global rare earth exploration and development sector achieved breakthroughs across multiple fronts.Projects advanced intensively across the Americas,Oceania,Africa,and Europe,with significant growth in resources,continuous emergence of new deposits,and strong impetus injected into the industry by technological innovation and policy support.The global rare earth resource supply pattern was further optimized (Table 1).1.Fruitful results in resource growth and new deposit discoveriesBrazil emerged as a core region for resource growth.The Colossus rare earth deposit saw a 150%increase in resources and announced its first reserve estimate.The Caldeira rare earth deposit’s resource estimate grew by 50%.The combined ore resources in the Caladão rare earth deposit’s Zones A and B reached 5.72×10~8 tonnes,with a total rare earth oxide(TREO) grade of 0.1506%,concurrently hosting 2.29×10~4tonnes of gallium metal resources.
基金supported by the NationalKey Research and Development Program of China(2023YFA1507701)National Natural Science Foundation of China(U22B6011,22288102)“Announcement and Challenge”Science and Technology Project of Xinjiang Uygur Autonomous Region(XJKJTJBGS-2023).
文摘Rare earth(RE)Y-type zeolite was synthesized in situ by acidic co-hydrolysis route and hydrothermal method.The key process parameters were optimized based on the RE utilization rate.The effect of inducing a rotating packed bed(RPB)in premixing and crystallization on crystallinity and RE utilization rate was further investigated.The results indicate that lanthanide(La)cations are successfully introduced into the sodalite cage of Y-type zeolite.The optimized conditions are that the molar ratio of Si/La is 150,premixing for 5 h,crystallization at 90℃ for 18 h,and calcination at 550℃ for 3.5 h.At this stage,the RE utilization rate reaches 74.5%.Compared with the conventional stirred tank reactor(STR),RPB can effectively shorten the premixing time and crystallization time by 4.3 h and 6 h,improve the crystallinity by 23%and RE utilization rate by 7.5%.The RE utilization rate is more than 80%by RPB,surpassing the effectiveness of using the one-exchange one-calcination process in the traditional liquid ion exchange process.It is expected to provide a reference for the in-situ efficient and green synthesis of RE zeolite.
基金supported by the National Natural Science Foundation of China(ZX20230386)the 2023 Special Project for High-Industrial Base Reconstruction Quality Development of the Manufacturing Industry(2023ZY01019-11)the sixth batch of top talent support funds(QNBJ-2022-04).
文摘As an important strategic rare-earth resource,bastnaesite has long been a global research focus.The carbochlorination process,as an efficient and low-cost extraction method,can be applied to treat bastnaesite,achieving ideal rare-earth extraction results in just one-step reaction.By using inexpensive chlorine gas as the chlorinating agent,it avoids lengthy procedural steps and the generation of acid-base waste liquids.Based on this,we propose a novel carbochlorination process for bastnaesite involving a fluorine-fixing agent.Thermodynamic data for the carbochlorination process of bastnaesite were calculated using the group contribution method.Thermodynamic feasibility was verified through Gibbs free energy.The effects of different chlorination times,fluorine-fixing agent dosages,chlorine flow rates,and chlorination temperatures on the carbochlorination process of bastnaesite were investigated.Experimental studies showed that under optimal chlorination conditions,a temperature of 800℃,a duration of 60 min,a fluorine-fixing agent dosage of 10%,and a chlorine flow rate of 10 L·min^(−1),the chlorination rates of rare-earth elements,Ca,Ba,and Fe in bastnaesite reached 96%,99%,98%,and 99%,respectively.The reaction mechanism was explored and analyzed based on characterization results such as mineral phase composition,micromorphology and thermogravimetry of water-washed residues under different chlorination conditions.Additionally,kinetic experiments were conducted at varying reaction temperatures and chlorine flow rates,revealing that the carbon-chlorination process is primarily controlled by chemical reactions.
基金supported by National Natural Science Foundation(NNSF)of China under Grant 62001351the Foundation of National Key Laboratory of Electromagnetic Environment(6142403220202)the Stability Support Fund for Basic Military Industrial Research Institutes(A240104130).
文摘The orthogonal time frequency space(OTFS)modulation is a novel modulation scheme that can effectively cope with the high Doppler expansion caused by high mobility.Since it modulates data on delay-Doppler(DD)domain and makes full use of the sparse characteristics of DD domain,it has been widely studied to design efficient channel estimation and signal detection schemes.In this paper,we design a novel superimposed pilot pattern with transition band,which replaces the traditional embedded pilot(EP)guard zero-symbols,and perform a two-stage channel estimation.In the first stage,we fully utilize the dispersion characteristics of OTFS signal in DD domain,and use threshold decision to make coarse channel estimation.In the second stage,we use the results of the coarse estimation for iterative signal detection and accurate channel estimation.During the second stage,we make full use of the sparsity of the channel in DD domain,remodel the received signal into the form of sparse channel vector multiplied by channel coefficient matrix,and introduce Doppler index segmentation factor(DISF)to subdivide the Doppler index to solve the problem of fractional Doppler.Simulations reveal that,the scheme proposed in this paper has higher spectral efficiency compared with traditional EP scheme and lower peak-to-average power ratio(PAPR)compared with traditional superimposed pilot scheme.
基金National Key Laboratory of Unmanned Aerial Vehicle Technology(No.202408)Key Laboratory of Smart Earth(No.KF2023ZD01-05)。
文摘In GNSS-denied environments,signals of opportunity(SOP)offer an efficient and passive solution for navigation and positioning by utilizing ambient signals.Nevertheless,conventional SOP techniques face significant challenges in real-time processing,especially under sub-Nyquist sampling conditions,due to high data acquisition rates and offgrid errors.To address this,this paper proposes the signal reconstruction and kernel sparse encoding(SRKSE)model,a novel general framework for high-precision parameter estimation.By combining compressed sensing with a deep unfolding network,the SRKSE model not only achieves robust signal reconstruction but also effectively reduces quantization errors.Key innovations of SRKSE include dual crossattention mechanisms for enhanced feature extraction,sinc sparse kernel encoding to minimize quantization errors,and a custom loss function for balanced optimization.With these advancements,SRKSE achieves up to a 650-fold improvement in time of arrival(TOA)estimation accuracy while operating at just 1%of the Nyquist sampling rate.The SRKSE surpasses both conventional and deep learning-based techniques in accuracy and efficiency,especially when operating under sub-Nyquist sampling conditions.Simulations and real-world experiments confirm the reliability and potential of SRKSE for real-time applications in IoT and wireless communication.
基金supported by the National Natural Science Foundation of China (No.21801111)the Training Plan for Young Core Teachers in Higher Education of Henan Province (No.2021GGJS131)+1 种基金Natural Science Foundation of Henan Province (No.232300421232)the Heluo Young Talent Lifting Project (No.2023HLTJ02)。
文摘Ln@MOFs by anchoring rare metal ions(Ln) into metal-organic frameworks(MOFs) are proved to have great potential in the field of luminescent molecular thermometer.Nevertheless,the current research indicated that the poor structural stability and low sensitivity hindered their application scope.In this work,a new MOF Zn-450 luminescent thermometer with multiple emission fluorescence characteristics was synthesized by the combination of 3,3,5,5-biphenyl tetracarboxylic acid(H_(4)L) and Zn^(2+) ion under solvothermal conditions.Interestingly,a high relative sensitivity of 1.43 % K^(-1) was found within 80-300 K based on Zn-450.Subsequently,two high-sensitivity luminescent Ln@MOFs(Ln = Eu and Tb) were further fabricated by doping rare earth ions into Zn-450 based on the post-synthesis strategy.Among them,the Eu@Zn-450 demonstrates various luminous behaviors while achieving an increased relative sensitivity of 1.63 % K^(-1).In addition,the continuously visible red,pink,and purple luminescent emissions at the same temperature range were observed,suggesting that the Eu@Zn-450 could be utilized as a luminescent colorimetric molecular thermometer.Importantly,this work can present new possibilities for the development of rare earth-doped luminescence and its temperature sensing properties.
基金supported by the National Key Research and Development Program of China (2022YFF1300705)the Key Research and Development Project of Guangxi,China (Guike AB24010051)+1 种基金the National Natural Science Foundation of China (42261011,32271730 and U20A2011)the Central Public Welfare Research Institutes,Chinese Academy of Geological Sciences (2023020)。
文摘Phosphorus (P) is an essential nutrient element that is critical for plant growth and ecosystem functionality.The soil P cycle plays multiple roles,such as sustaining plant growth and productivity,regulating nutrient balance within ecosystems,and enhancing ecosystem adaptability and resilience.This cycle is influenced by factors such as the restoration approach and microbial community dynamics.However,the extent to which the restoration approach alters the P cycle in karst ecosystems and the underlying microbial mechanisms remain poorly understood.The P-cycle multifunctionality index (P-cycle MFI) serves as a comprehensive indicator for evaluating soil P cycle function,and it provides insights into changes in the P cycle between different restoration approaches.To investigate the shifts in soil P-cycle MFI and microbial mechanisms between different restoration approaches,we analyzed soil available P (AP),total P (TP),microbial biomass P (MBP),and the activities of acid phosphatase (ACP) and alkaline phosphatase (ALP).These data were used to calculate the P-cycle MFI by averaging the Z-scores between two restoration approaches(artificial restoration of forest (AF) and natural restoration of forest (NF)) and a control (cropland,CP) at six subtropical karst ecosystem sites in China.We also determined the soil organic carbon (SOC),exchangeable calcium (Ca) and magnesium (Mg),pH,bulk density (BD),microbial biomass C (MBC),and microbial biomass nitrogen (MBN),as well as the community structure,relative abundance,diversity indices,and co-occurrence networks of phoD-harboring bacteria.The results showed that the community structure of phoD-harboring bacteria varied significantly among AF,NF,and CP and across different temperature gradients.These bacteria exhibited increasing complexity and tightness in co-occurrence networks from CP to AF and then to NF,along with the ACP and ALP activities,but not the TP and AP contents.The P-cycle MFI values were significantly higher in NF compared to AF and CP,and the variation was significantly explained by restoration approach,temperature,MBC,MBN,SOC,exchangeable Ca,BD,community structure of phoD-harboring bacteria,and exchangeable Mg.Furthermore,natural restoration had a more substantial impact on the P-cycle MFI than temperature by enhancing SOC,microbial biomass,the complexity and co-occurrence network tightness of the phoD-harboring bacterial community structure,and ACP and ALP activities,but it reduced soil BD.The rare genera of phoD-harboring bacteria significantly influenced the variation of soil P-cycle MFI compared to the dominant genera.This study highlights the importance of rare genera of phoD-harboring bacteria in driving soil P-cycle multifunctionality in karst ecosystems,with natural restoration being more effective than artificial methods for enhancing soil organic matter and microbial community complexity.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274348 and 12004335)the National Key Research and Development Program of China(Grant No.2024YFC2813800)。
文摘Presented in this study is a novel method for estimating the depth of single underwater source in shallow water,utilizing vector sensors.The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately.Unlike traditional matched field processing(MFP)and matched mode processing(MMP),the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information.Firstly,the broadband Stokes parameters(BSP)are established using the normal mode theory.Then the nonstationary phase approximation is used to simplify the theoretical derivation,which is necessary when dealing with broadband integrals.Additionally,range terms of the BSP are eliminated by normalization.By analyzing the depth distribution of the normalized broadband Stokes parameters(NBSP),it is found that the NBSP exhibit extreme values at the source depth,which can be used for source depth estimation.So the proposed depth estimation method is based on searching the peaks of the NBSP.Simulations show that this method is effective in relatively simple shallow water environments.Finally,the effect of source range,frequency bandwidth,sound speed profile(SSP),water depth,and signal-to-noise ratio(SNR)are studied.The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than-5 d B and does not need to consider model mismatch issues.Additionally,variations in environmental parameters have minimal impact on estimation accuracy.Compared to MFP,the proposed method requires a higher SNR,but demonstrates superior robustness against fluctuations in environmental parameters.
基金supported by the Fundamental Research Funds for the Central Universities of China(FRF-TP-24-058A)with additional support from the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-02).
文摘Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.
基金supported by the Knowledge Innovation Program of Wuhan-Shuguang Project(Grant No.2023010201020443)the School-Level Scientific Research Project Funding Program of Jianghan University(Grant No.2022XKZX33)the Natural Science Foundation of Hubei Province(Grant No.2024AFB466).
文摘The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation.
基金supported in part by the National Key Research and Development Program of China(2021YFB2900501)in part by the Shaanxi Science and Technology Innovation Team(2023-CX-TD-03)+3 种基金in part by the Science and Technology Program of Shaanxi Province(2021GXLH-Z-038)in part by the Natural Science Foundation of Hunan Province(2023JJ40607 and 2023JJ50045)in part by the Scientific Research Foundation of Hunan Provincial Education Department(23B0713 and 24B0603)in part by the National Natural Science Foundation of China(62401371,62101275,and 62372070).
文摘(Quasi-)closed-form results for the statistical properties of unmanned aerial vehicle(UAV)airto-ground channels are derived for the first time using a novel spatial-vector-based method from a threedimensional(3-D)arbitrary-elevation one-cylinder model.The derived results include a closed-form expression for the space-time correlation function and some quasi-closed-form ones for the space-Doppler power spectrum density,the level crossing rate,and the average fading duration,which are shown to be the generalizations of those previously obtained from the two-dimensional(2-D)one-ring model and the 3-D low-elevation one-cylinder model for terrestrial mobile-to-mobile channels.The close agreements between the theoretical results and the simulations as well as the measurements validate the utility of the derived channel statistics.Based on the derived expressions,the impacts of some parameters on the channel characteristics are investigated in an effective,efficient,and explicable way,which leads to a general guideline on the manual parameter estimation from the measurement description.
基金Supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(No.HR20C0026)the National Research Foundation of Korea(NRF)(No.RS-2023-00247504)the Patient-Centered Clinical Research Coordinating Center,funded by the Ministry of Health&Welfare,Republic of Korea(No.HC19C0276).
文摘AIM:To build a functional generalized estimating equation(GEE)model to detect glaucomatous visual field progression and compare the performance of the proposed method with that of commonly employed algorithms.METHODS:Totally 716 eyes of 716 patients with primary open angle glaucoma(POAG)with at least 5 reliable 24-2 test results and 2y of follow-up were selected.The functional GEE model was used to detect perimetric progression in the training dataset(501 eyes).In the testing dataset(215 eyes),progression was evaluated the functional GEE model,mean deviation(MD)and visual field index(VFI)rates of change,Advanced Glaucoma Intervention Study(AGIS)and Collaborative Initial Glaucoma Treatment Study(CIGTS)scores,and pointwise linear regression(PLR).RESULTS:The proposed method showed the highest proportion of eyes detected as progression(54.4%),followed by the VFI rate(34.4%),PLR(23.3%),and MD rate(21.4%).The CIGTS and AGIS scores had a lower proportion of eyes detected as progression(7.9%and 5.1%,respectively).The time to detection of progression was significantly shorter for the proposed method than that of other algorithms(adjusted P≤0.019).The VFI rate displayed moderate pairwise agreement with the proposed method(k=0.47).CONCLUSION:The functional GEE model shows the highest proportion of eyes detected as perimetric progression and the shortest time to detect perimetric progression in patients with POAG.
基金funded by the Malaysian Ministry of Higher Education through the Fundamental Research Grant Scheme(FRGS/1/2024/ICT02/UCSI/02/1).
文摘Accurate estimation of photovoltaic(PV)parameters is essential for optimizing solar module perfor-mance and enhancing resource efficiency in renewable energy systems.This study presents a process innovation by introducing,for the first time,the Triangulation Topology Aggregation Optimizer(TTAO)integrated with parallel computing to address PV parameter estimation challenges.The effectiveness and robustness of TTAO are rigorously evaluated using two standard benchmark datasets(KC200GT and R.T.C.France solar cells)and a real-world dataset(Poly70W solar module)under single-,double-,and triple-diode configurations.Results show that TTAO consistently achieves superior accuracy by producing the lowest RMSE values and faster convergence compared to state-of-the-art metaheuristic algorithms.In addition,the integration of parallel computing significantly enhances computational efficiency,reducing execution time by up to 85%without compromising accuracy.Validation using real-world data further demonstrates TTAO’s adaptability and practical relevance in renewable energy systems,effectively bridging the gap between theoretical modeling and real-world implementation for PV system monitoring and optimization,contributing to climate mitigation through improved solar energy performance.
文摘We investigated the impact of convexity and isoperimetric deficits on the accuracy of sectional area estimates of tree stems using traditional methods(caliper,tape,formulas based on stem diameter and circumference).In two complementary experiments,the use of photographs to estimate cross-sectional areas was first validated,then the use of a caliper and diameter tape was computer-simulated.The results indicated that the photographic method offers high precision,with mean relative errors below 0.1%,minimal deviation,and no significant bias,and the traditional methods led to substantial and systematic errors,with deviations from circularity and convexity significantly increasing the errors in area estimation.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104190,12104189,12204312)the Natural Science Foundation of Jiangsu Province(Grant No.BK20210874)+2 种基金General project of Natural Science Research in Colleges And Universities of Jiangsu Province(Grant No.20KJB140008)the Jiangxi Provincial Natural Science Foundation(Grant Nos.20224BAB211014 and 20232BAB201042)Key Laboratory of Tian Qin Project(Sun Yat-sen University)。
文摘A scheme is proposed based on a Mach-Zehnder interferometer with high phase sensitivity,utilizing a two-mode squeezed coherent state,generated by four-wave mixing,as input.The phase sensitivity of this scheme easily surpasses the Heisenberg limit when intensity difference detection is applied.Under phase-matching conditions,the quantum Cramér-Rao bound significantly exceeds the Heisenberg limit.Additionally,the scheme exhibits robustness against photon loss.When compared with the modified SU(1,1)interferometer with two coherent state inputs,this approach demonstrates superior measurement sensitivity,evaluated through various detection methods and the quantum Cramér-Rao bound.This work holds potential applications in quantum metrology.
