Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—esp...Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.展开更多
In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,...In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,and artificial weak negative anomalies form around the positive anomalies in the horizontal direction,resulting in a reduction in the overall resolution.To fully utilize the model weighting function,this study constructs a combined model weighting function.First,a new depth weighting function is constructed by adding a regulator into the conventional depth weighting function to overcome the skin eff ect and inhibit the divergence at the deep area of the inversion results.A horizontal weighting function is then constructed by extracting information from the observation data;this function can suppress the formation of artificial weak anomalies and improve the horizontal resolution of the inversion results.Finally,these two functions are coupled to obtain the combined model weighting function,which can replace the conventional depth weighting function in 3D inversion.It improves the vertical and horizontal resolution of the inversion results without increasing the algorithm complexity and calculation amount,is easy to operate,and adapts to any 3D inversion method.Two model experiments are designed to verify the effectiveness,practicability,and anti-noise of the combined model weighting function.Then the function is applied to the 3D inversion of the measured aeromagnetic data in the Jinchuan area in China.The obtained inversion results are in good agreement with the known geological data.展开更多
Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evalua...Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evaluated through a multidimensional framework encompassing market dynamics,macroeconomic factors,financial stability,governance,supply chains,and production safety.By integrating the Analytic Hierarchy Process(AHP)with entropy weighting,a hybrid weighting model that mitigates the limitations of singular methods is established.The analysis of this study identifies financial risk(weight:0.347)and production safety(weight:0.298)as dominant risk drivers.These quantitative insights offer a basis for resource prioritization and targeted risk mitigation strategies in chemical enterprises.展开更多
Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extracti...Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.展开更多
In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heati...In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heating time,microwave power,microwave heating time on the center temperature,moisture content,the chroma(C*),the total color difference(ΔE*),shape fidelity,hardness,and the total anthocyanin content of 3D printed raspberry preserves were analyzed by response surface method(RSM).The results showed that under combining with the two methods,infrared heating improved the fidelity and quality degradation of printed products,while microwave heating enhanced the efficiency of infrared heating.Infrared-microwave combination cooking could maintain relatively stable color appearance and shape of 3D printed raspberry preserves.The AHP–CRITIC hybrid weighting method combined with the response surface test to determine the comprehensive weights of the evaluation indicators optimized the process parameters,and the optimal process parameters were obtained:infrared heating temperature of 190℃,infrared heating time of 10 min and 30 s,microwave power of 300 W,and microwave heating time of 2 min and 6 s.The 3D printed raspberry cooking methods obtained under the optimal conditions seldom had color variation,porous structure,uniform texture,and high shape fidelity,which retained the characteristics of personalized manufacturing by 3D printing.This study could provide a reference for the postprocessing and quality control of 3D cooking methods.展开更多
Purpose–To systematically characterize and objectively evaluate basic railway safety management capability,creating a closed-loop management approach which allows continuous improvement and optimization.Design/method...Purpose–To systematically characterize and objectively evaluate basic railway safety management capability,creating a closed-loop management approach which allows continuous improvement and optimization.Design/methodology/approach–A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards,railway safety rules and regulations and existing safety data from railway transport enterprises is presented.The system comprises a guideline layer including safety committee formation,work safety responsibility,safety management organization and safety rules and regulations as its components,along with an index layer consisting of 12 quantifiable indexes.Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.Findings–The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.Originality/value–Construction of an evaluation index system that is quantifiable,generalizable and accessible,accurately reflects the main aspects of railway transportation enterprises’basic safety management capability and provides interoperability across various railway transportation enterprises.The application of the game theoretic combination weighting method to derive composite weights which combine experts’subjective evaluations with the objectivity of data.展开更多
Traditional Chinese medicine(TCM)exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets.However,the syndrome-based system of diagnosis and treatment in...Traditional Chinese medicine(TCM)exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets.However,the syndrome-based system of diagnosis and treatment in TCM can easily lead to bias because of varying medication preferences among physicians,which has been a major challenge in the global acceptance and application of TCM.Therefore,a standardized TCM prescription system needs to be explored to promote its clinical application.In this study,we first developed a gradient weighted disease-target-herbal ingredient-herb network to aid TCM formulation.We tested its efficacy against intracerebral hemorrhage(ICH).First,the top 100 ICH targets in the GeneCards database were screened according to their relevance scores.Then,SymMap and Traditional Chinese Medicine Systems Pharmacology(TCMSP)databases were applied to find out the target-related ingredients and ingredient-containing herbs,respectively.The relevance of the resulting ingredients and herbs to ICH was determined by adding the relevance scores of the corresponding targets.The top five ICH therapeutic herbs were combined to form a tailored TCM prescriptions.The absorbed components in the serum were detected.In a mouse model of ICH,the new prescription exerted multifaceted effects,including improved neurological function,as well as attenuated neuronal damage,cell apoptosis,vascular leakage,and neuroinflammation.These effects matched well with the core pathological changes in ICH.The multi-targets-directed gradient-weighting strategy presents a promising avenue for tailoring precise,multipronged,unbiased,and standardized TCM prescriptions for complex diseases.This study provides a paradigm for advanced achievements-driven modern innovation in TCM concepts.展开更多
In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with l...In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.展开更多
Human pose estimation is a challenging task in computer vision.Most algorithms perform well in regular scenes,but lack good performance in occlusion scenarios.Therefore,we propose a multi-type feature fusion network b...Human pose estimation is a challenging task in computer vision.Most algorithms perform well in regular scenes,but lack good performance in occlusion scenarios.Therefore,we propose a multi-type feature fusion network based on importance weighting,which consists of three modules.In the first module,we propose a multi-resolution backbone with two feature enhancement sub-modules,which can extract features from different scales and enhance the feature expression ability.In the second module,we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology.In the third module,we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes,thereby improving the feature extraction ability.We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017(COCO2017),COCO-Wholebody and CrowdPose,achieving the accuracy of 78.9%,67.1%and 77.6%,respectively.Additionally,a series of ablation experiments are designed to show the performance of our work.Finally,we present the visualization of different scenarios to verify the effectiveness of our work.展开更多
In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and...In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantita...Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantitative trait loci(QTL).Here,six favorable TSW QTL alleles from two donor parents were introgress into an elite restorer line,621R,using an integrated strategy combining marker-assisted backcrossing and speed breeding protocols.Through six rounds of backcrossing and convergent crossing followed by two generations of selfing strategies,we developed 13 advanced lines with diverse TSW QTL combinations within 24 months.Field evaluations across three environments revealed that all lines exhibited significantly increased TSW in spring conditions(Minle,Gansu)and winter environments(Wuhan and Jiangling,Hubei)except for two lines which only showed increase in the spring environment.Hybridization assays using these lines as male parents crossed with two male-sterile lines(RG430A and 616A)demonstrated transgressive segregation for TSW:For RG430A-derived hybrids,all crosses significantly outperformed the original control(RG430A×621R)in Wuhan,with 8/13 and 9/13 crosses showing significant TSW increases in Minle and Jiangling,respectively.For 616A-derived hybrids,11/13 and 10/13 crosses exhibited significant TSW enhancement in Minle and Jiangling,compared to 3/13 in Wuhan.Notably,two top-performing hybrids achieved 13.0%and 6.8%higher plot yields,respectively.Our results demonstrate that strategic pyramiding of complementary TSW QTL alleles effectively enhances seed weight in rapeseed,and these improved lines represent valuable genetic resources for developing high-yield hybrids.展开更多
This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,i...This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.展开更多
Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic ...Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Organic electrochemical transistors(OECTs)are promising for next-generation bioelectronics due to their high performance and biocompatibility.Nevertheless,they still face tremendous operational stability challenges du...Organic electrochemical transistors(OECTs)are promising for next-generation bioelectronics due to their high performance and biocompatibility.Nevertheless,they still face tremendous operational stability challenges due to the limited robustness of the organic mixed ionic-electronic conductor(OMIEC)channel.Here,by modulating the molecular weight(MW)of OMiEC,enhanced OECT and relevant circuit operation stabilities are demonstrated,showing more than 3,000,0o0 full cycles(~42 h)with less than 15%current variation in an OECT,and 150,000 cycles(~4 h)with less than 5%voltage variation in an OECT-based inverter,which are among the highest of reported OECT-based electronics.Specifically,p(g2T-T),a typical p-type OMIEC,with varying MW(7-43 kDa),is synthesized,where lower-MW p(g2T-T)(~9 kDa)exhibits superior device performance and cycling stability in OECTs,outperforming those in high-MW counterparts(>30 kDa).It is indicated that low-MW p(g2T-T)maintains higher volumetric capacitance,ordered orientation,and reduced swelling.Therefore,irreversible microstructural degradation is effectively avoided,along with better performance yield.Furthermore,MW regulation enables physiological signal sensing with high tolerance to body fluid environments for 7 days.These findings highlight MW modulation as a versatile approach to suppress excessive swelling,advancing the design of durable OECT-based electronics.展开更多
In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essent...In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.展开更多
Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies...Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.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
基金funded by the National Natural Science Foundation of China(NSFC)under Grant Number 72071209.
文摘Modern battlefields exhibit high dynamism,where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values,leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command,where static weights cannot reflect the operational value decay or surge of key indicators.To address this issue,this study proposes a dynamic adaptive weightingmethod for evaluation indicators based onG1-CRITIC-PIVW.First,theG1(Sequential Relationship Analysis Method)subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking,while the CRITIC(Criteria Importance Through Intercriteria Correlation)objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations,ensuring data-driven objectivity.These two sets of weights are then fused using a deviation coefficient optimization model,minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments.Subsequently,the PIVW(Punishment-Incentive VariableWeight)theory—adapts weights to realtime indicator performance via penalty/incentive rules—is applied for dynamic adjustment.Scenario-specific penalty λ_(1) and incentive λ_(2) thresholds are set based on operational priorities and indicator volatility,penalizing indicators with values below λ_(1) and incentivizing those exceeding λ_(2) to reflect real-time indicator performance.Experimental validation was conducted using an Air Defense and Anti-Missile(ADAM)system effectiveness assessment framework,with data covering 7 indicators across 3 combat scenarios.Results show that compared to static weighting methods,the proposed method reduces MAE(Mean Absolute Error)by 15%-20% and weighted decision error rate by 84.2%,effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios;compared to Entropy-TOPSIS,it lowers MAE by 12% while achieving a weighted Kendall’sτconsistency coefficient of 0.85,ensuring higher alignment with expert judgment.This method enhances the accuracy and scenario adaptability of effectiveness assessment,providing reliable decision support for dynamic battlefield environments.
基金jointly funded by the National Natural Science Foundation of China(No.U2244220,No.42004125)the China Geological Survey Projects(No.DD20240119,No.DD20243245,No.DD20230114,No.DD20243244)the China Postdoctoral Science Foundation(No.2020M670601)。
文摘In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,and artificial weak negative anomalies form around the positive anomalies in the horizontal direction,resulting in a reduction in the overall resolution.To fully utilize the model weighting function,this study constructs a combined model weighting function.First,a new depth weighting function is constructed by adding a regulator into the conventional depth weighting function to overcome the skin eff ect and inhibit the divergence at the deep area of the inversion results.A horizontal weighting function is then constructed by extracting information from the observation data;this function can suppress the formation of artificial weak anomalies and improve the horizontal resolution of the inversion results.Finally,these two functions are coupled to obtain the combined model weighting function,which can replace the conventional depth weighting function in 3D inversion.It improves the vertical and horizontal resolution of the inversion results without increasing the algorithm complexity and calculation amount,is easy to operate,and adapts to any 3D inversion method.Two model experiments are designed to verify the effectiveness,practicability,and anti-noise of the combined model weighting function.Then the function is applied to the 3D inversion of the measured aeromagnetic data in the Jinchuan area in China.The obtained inversion results are in good agreement with the known geological data.
文摘Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evaluated through a multidimensional framework encompassing market dynamics,macroeconomic factors,financial stability,governance,supply chains,and production safety.By integrating the Analytic Hierarchy Process(AHP)with entropy weighting,a hybrid weighting model that mitigates the limitations of singular methods is established.The analysis of this study identifies financial risk(weight:0.347)and production safety(weight:0.298)as dominant risk drivers.These quantitative insights offer a basis for resource prioritization and targeted risk mitigation strategies in chemical enterprises.
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory Southeast University(No.2023D07)the Outstanding Youth Program of Natural Science Foundation of Heilongjiang Province(No.YQ2020F012)the Funda-mental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Indoor visual localization relies heavily on image retrieval to ascertain location information.However,the widespread presence and high consistency of floor patterns across different images of-ten lead to the extraction of numerous repetitive features,thereby reducing the accuracy of image retrieval.This article proposes an indoor visual localization method based on semantic segmentation and adaptive weight fusion to address the issue of ground texture interference with retrieval results.During the positioning process,an indoor semantic segmentation model is established.Semantic segmentation technology is applied to accurately delineate the ground portion of the images.Fea-ture extraction is performed on both the original database and the ground-segmented database.The vector of locally aggregated descriptors(VLAD)algorithm is then used to convert image features into a fixed-length feature representation,which improves the efficiency of image retrieval.Simul-taneously,a method for adaptive weight optimization in similarity calculation is proposed,using a-daptive weights to compute similarity for different regional features,thereby improving the accuracy of image retrieval.The experimental results indicate that this method significantly reduces ground interference and effectively utilizes ground information,thereby improving the accuracy of image retrieval.
基金Supported by the National Natural Science Foundation of China(32072352)。
文摘In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heating time,microwave power,microwave heating time on the center temperature,moisture content,the chroma(C*),the total color difference(ΔE*),shape fidelity,hardness,and the total anthocyanin content of 3D printed raspberry preserves were analyzed by response surface method(RSM).The results showed that under combining with the two methods,infrared heating improved the fidelity and quality degradation of printed products,while microwave heating enhanced the efficiency of infrared heating.Infrared-microwave combination cooking could maintain relatively stable color appearance and shape of 3D printed raspberry preserves.The AHP–CRITIC hybrid weighting method combined with the response surface test to determine the comprehensive weights of the evaluation indicators optimized the process parameters,and the optimal process parameters were obtained:infrared heating temperature of 190℃,infrared heating time of 10 min and 30 s,microwave power of 300 W,and microwave heating time of 2 min and 6 s.The 3D printed raspberry cooking methods obtained under the optimal conditions seldom had color variation,porous structure,uniform texture,and high shape fidelity,which retained the characteristics of personalized manufacturing by 3D printing.This study could provide a reference for the postprocessing and quality control of 3D cooking methods.
基金supported by the China National Railway Group Co.,Ltd.Research and Development Project(P2023T002).
文摘Purpose–To systematically characterize and objectively evaluate basic railway safety management capability,creating a closed-loop management approach which allows continuous improvement and optimization.Design/methodology/approach–A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards,railway safety rules and regulations and existing safety data from railway transport enterprises is presented.The system comprises a guideline layer including safety committee formation,work safety responsibility,safety management organization and safety rules and regulations as its components,along with an index layer consisting of 12 quantifiable indexes.Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.Findings–The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.Originality/value–Construction of an evaluation index system that is quantifiable,generalizable and accessible,accurately reflects the main aspects of railway transportation enterprises’basic safety management capability and provides interoperability across various railway transportation enterprises.The application of the game theoretic combination weighting method to derive composite weights which combine experts’subjective evaluations with the objectivity of data.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82174259 and 82304997)China Postdoctoral Followship Program of CPSF(Grant No.:GZC20233202)+4 种基金China Postdoctoral Science Foundation(Grant No.:2024M753698)the Key Research and Development Program of Hunan Province of China(Grant Nos.:2023SK2021 and 2022SK2015)the Natural Science Foundation of Hunan Province,China(Grant Nos.:2024JJ6632,2022JJ40853,and 2021JJ31117)the Hunan Traditional Chinese Medicine Scientific Research Program,China(Grant Nos.:B2024113,B2024114,and 2021032)the Fundamental Research Funds for the Central Universities of Central South University,China(Grant No.:1053320232786).
文摘Traditional Chinese medicine(TCM)exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets.However,the syndrome-based system of diagnosis and treatment in TCM can easily lead to bias because of varying medication preferences among physicians,which has been a major challenge in the global acceptance and application of TCM.Therefore,a standardized TCM prescription system needs to be explored to promote its clinical application.In this study,we first developed a gradient weighted disease-target-herbal ingredient-herb network to aid TCM formulation.We tested its efficacy against intracerebral hemorrhage(ICH).First,the top 100 ICH targets in the GeneCards database were screened according to their relevance scores.Then,SymMap and Traditional Chinese Medicine Systems Pharmacology(TCMSP)databases were applied to find out the target-related ingredients and ingredient-containing herbs,respectively.The relevance of the resulting ingredients and herbs to ICH was determined by adding the relevance scores of the corresponding targets.The top five ICH therapeutic herbs were combined to form a tailored TCM prescriptions.The absorbed components in the serum were detected.In a mouse model of ICH,the new prescription exerted multifaceted effects,including improved neurological function,as well as attenuated neuronal damage,cell apoptosis,vascular leakage,and neuroinflammation.These effects matched well with the core pathological changes in ICH.The multi-targets-directed gradient-weighting strategy presents a promising avenue for tailoring precise,multipronged,unbiased,and standardized TCM prescriptions for complex diseases.This study provides a paradigm for advanced achievements-driven modern innovation in TCM concepts.
文摘In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.
基金supported by Ministry of Education Industry-University Cooperation and Collaborative Education Project(China)(220603231024713).
文摘Human pose estimation is a challenging task in computer vision.Most algorithms perform well in regular scenes,but lack good performance in occlusion scenarios.Therefore,we propose a multi-type feature fusion network based on importance weighting,which consists of three modules.In the first module,we propose a multi-resolution backbone with two feature enhancement sub-modules,which can extract features from different scales and enhance the feature expression ability.In the second module,we enhance the expressiveness of keypoint features by suppressing obstacle features and compensating for the unique and shared attributes of keypoints and topology.In the third module,we perform importance weighting on the adjacency matrix to enable it to describe the correlation among nodes,thereby improving the feature extraction ability.We conduct comparative experiments on the keypoint detection datasets of common objects in Context 2017(COCO2017),COCO-Wholebody and CrowdPose,achieving the accuracy of 78.9%,67.1%and 77.6%,respectively.Additionally,a series of ablation experiments are designed to show the performance of our work.Finally,we present the visualization of different scenarios to verify the effectiveness of our work.
文摘In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by Biological Breeding-National Science and Technology Major Project(2022ZD04008)National Natural Science Foundation of China(32201854)。
文摘Thousand-seed weight(TSW)is a critical target for genetic improvement in rapeseed(Brassica napus L.).However,phenotypic selection for this trait remains challenging due to its polygenic regulation by multiple quantitative trait loci(QTL).Here,six favorable TSW QTL alleles from two donor parents were introgress into an elite restorer line,621R,using an integrated strategy combining marker-assisted backcrossing and speed breeding protocols.Through six rounds of backcrossing and convergent crossing followed by two generations of selfing strategies,we developed 13 advanced lines with diverse TSW QTL combinations within 24 months.Field evaluations across three environments revealed that all lines exhibited significantly increased TSW in spring conditions(Minle,Gansu)and winter environments(Wuhan and Jiangling,Hubei)except for two lines which only showed increase in the spring environment.Hybridization assays using these lines as male parents crossed with two male-sterile lines(RG430A and 616A)demonstrated transgressive segregation for TSW:For RG430A-derived hybrids,all crosses significantly outperformed the original control(RG430A×621R)in Wuhan,with 8/13 and 9/13 crosses showing significant TSW increases in Minle and Jiangling,respectively.For 616A-derived hybrids,11/13 and 10/13 crosses exhibited significant TSW enhancement in Minle and Jiangling,compared to 3/13 in Wuhan.Notably,two top-performing hybrids achieved 13.0%and 6.8%higher plot yields,respectively.Our results demonstrate that strategic pyramiding of complementary TSW QTL alleles effectively enhances seed weight in rapeseed,and these improved lines represent valuable genetic resources for developing high-yield hybrids.
基金supported by the National Natural Science Foundation of China(62373317)the Tianshan Talent Training Program(2022TSYCCX0013)+3 种基金the Key Project of Natural Science Foundation of Xinjiang(2021D01D10)the Basic Research Foundation for Universities of Xinjiang(XJEDU2023P023)the Xinjiang Key Laboratory of Applied Mathematics(XJDX1401)the Intelligent Control and Optimization Research Platform in Xinjiang University.
文摘This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.
基金financially supported by the National Natural Science Foundation of China(Nos.51909023 and 51775077)the Natural Science Foundation of Liaoning Province(No.2021-MS-140)the Fundamental Research Funds for the Central Universities(No.3132025114)。
文摘Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by the National Key R&D Program of China(2024YFB3211600)the National Natural Science Foundation of China(Nos.62273073,52273316)+4 种基金the National Key R&D Program of China(2023YFC2411800,2022YFE0134800)the Natural Science Foundation of Sichuan(2025ZNSFSC0515)Chengdu Science Technology Bureau(2023-YF06-00028-HZ)and the Fundamental Research Funds for the Central Universities(ZYGX2025TS009,ZYGX2024XJ029,ZYGX2024XJ031)Sci-entific Research Innovation Capability Support Project for Young Faculty(ZYGXQNJSKYCXNLZCXM-M1P).
文摘Organic electrochemical transistors(OECTs)are promising for next-generation bioelectronics due to their high performance and biocompatibility.Nevertheless,they still face tremendous operational stability challenges due to the limited robustness of the organic mixed ionic-electronic conductor(OMIEC)channel.Here,by modulating the molecular weight(MW)of OMiEC,enhanced OECT and relevant circuit operation stabilities are demonstrated,showing more than 3,000,0o0 full cycles(~42 h)with less than 15%current variation in an OECT,and 150,000 cycles(~4 h)with less than 5%voltage variation in an OECT-based inverter,which are among the highest of reported OECT-based electronics.Specifically,p(g2T-T),a typical p-type OMIEC,with varying MW(7-43 kDa),is synthesized,where lower-MW p(g2T-T)(~9 kDa)exhibits superior device performance and cycling stability in OECTs,outperforming those in high-MW counterparts(>30 kDa).It is indicated that low-MW p(g2T-T)maintains higher volumetric capacitance,ordered orientation,and reduced swelling.Therefore,irreversible microstructural degradation is effectively avoided,along with better performance yield.Furthermore,MW regulation enables physiological signal sensing with high tolerance to body fluid environments for 7 days.These findings highlight MW modulation as a versatile approach to suppress excessive swelling,advancing the design of durable OECT-based electronics.
文摘In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.
基金supported by the National Natural Science Foundation of China(Grant No.62106092)the Natural Science Foundation of Fujian Province(Grant Nos.2024J01822,2025J01981)the Natural Science Foundation of Zhangzhou City(Grant No.ZZ2024J28).
文摘Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.
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.
