To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the conc...To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.展开更多
Despite the geographical distance between China and Africa,the rich customs and lifestyles of the African people present an interesting area of study for international professionals.It is this facet of Africa that fas...Despite the geographical distance between China and Africa,the rich customs and lifestyles of the African people present an interesting area of study for international professionals.It is this facet of Africa that fascinated us most during our research journey across the continent.展开更多
Urban green spaces have positive effects on both physical and mental wellbeing.However,few studies have focused on the trends and thresholds of the effects of different influences on restorative benefits when viewing ...Urban green spaces have positive effects on both physical and mental wellbeing.However,few studies have focused on the trends and thresholds of the effects of different influences on restorative benefits when viewing scenes differfeaturing plant communities.We measured subjective evaluations and objective physiological data from 44 participants who viewed images of plant communities in the yellow to green hue range to compare differences in restorative benefits among plant communities at different visual distances,as well as quantifying the influencing factors involved.The following results were found:(1)Coniferous and multi-layered plant communities were found to provide greater restorative benefits,and the restorative benefits grew with increasing visual distance.(2)Shape and color characteristics were significantly correlated with restorative benefits,but the relationship is not simply linear.(3)The restorative benefits were found to be greatest when crown proportion was 61.23%,trunk proportion ranged from 4.11%to 13.70%,and the value of color index value ranged from 25.44%to 35.56%;the restorative benefits gradually increased when sky proportion exceeded 12.95%-13.19%,the fractal dimension exceeded 1.62-1.67,and hue index exceeded 91.64°-95.67°;additionally,the restorative benefits decreased when the saturation index increased.This study provides a scientific basis for the construction and improvement of plant landscapes in urban green spaces.展开更多
Urbanization destroys wildlife habitats,fragmenting them into small patches with poor connectivity,leading to population declines in species sensitive to such chan ges.Escape is the most common anti-predator strategy ...Urbanization destroys wildlife habitats,fragmenting them into small patches with poor connectivity,leading to population declines in species sensitive to such chan ges.Escape is the most common anti-predator strategy adopted by birds,refuges in habitats reduce or eliminate predation risk.Therefore,creating habitats with suitable refuges for birds has significant implications for their conservation.However,there have been few studies on refuge selection in birds.This study examined the Eurasian Tree Sparrow(Passer montanus)and Oriental Magpie(Pica serica)in urban and rural areas of Chengde City,northern China by measuring their alert distance(AD),flight initiation distance(FID),an d distance fled(DF)and analyzed their refuge selection characteristics after escaping.The FID/AD ratio was employed to assess the behavioral differences of birds in the risk trade-off.The results showed that the FID and FID/AD of both species were lower in urban areas than in rural areas and were negatively correlated with immediate human density.Sparrow FID was significantly affected by group size and landing substrate type.The FID of sparrows was positively correlated with the group size.The sparrows that fled to bushes escaped earlier.In urban and rural areas,sparrows exhibited significantly lower FID,DF,and FID/AD than magpies.The species adopted different refuge selection strategies,with magpies preferentially selecting trees with greater vertical height and sparrows selecting both trees and bushes.Further analysis indicated that the horizontal and vertical distances fled of both species were lower when fleeing to bushes.Urban planning and conservation areas construction should incorporate the ecological needs of local bird species to rationally configure their habitat structure,thereby optimizing the effect of avian conservation.展开更多
In order to address the issue of overly conservative offline reinforcement learning(RL) methods that limit the generalization of policy in the out-of-distribution(OOD) region,this article designs a surrogate target fo...In order to address the issue of overly conservative offline reinforcement learning(RL) methods that limit the generalization of policy in the out-of-distribution(OOD) region,this article designs a surrogate target for OOD value function based on dataset distance and proposes a novel generalized Q-learning mechanism with distance regularization(GQDR).In theory,we not only prove the convergence of GQDR,but also ensure that the difference between the Q-value learned by GQDR and its true value is bounded.Furthermore,an offline generalized actor-critic method with distance regularization(OGACDR) is proposed by combining GQDR with actor-critic learning framework.Two implementations of OGACDR,OGACDR-EXP and OGACDRSQR,are introduced according to exponential(EXP) and opensquare(SQR) distance weight functions,and it has been theoretically proved that OGACDR provides a safe policy improvement.Experimental results on Gym-MuJoCo continuous control tasks show that OGACDR can not only alleviate the overestimation and overconservatism of Q-value function,but also outperform conservative offline RL baselines.展开更多
With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used ...With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used to improve efficiency.This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand.New customers appear in the delivery process at any time and are periodically optimized according to time slices.Then,we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem.The classical ant colony algorithm uses spatial distance to select nodes,while TS-ACO considers the impact of both temporal and spatial distance on node selection.Meanwhile,we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows(MDVRPTW)dataset to evaluate the performance of our system.According to the experimental results,TS-ACO,which considers spatial and temporal distance,is more effective than the classical ACO,which only considers spatial distance.展开更多
In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean func...In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean function with the classical kernel estimator,the proposed test statistics are built upon a modified minimum distance between a nonparametric fit and a parametric estimator under the null hypothesis for the variance function.Asymptotic properties of the estimator of the parameters in the variance function are discussed,and the large sample distribution of the test statistics under the null hypothesis is established,as well as the consistency and the power under some local alternative hypotheses.Extensive numerical studies demonstrate that the proposed test procedures have satisfactory finite sample performance.Finally,two real data examples further showcase the effectiveness of the proposed test in real applications.展开更多
At present, enhancing the digital literacy of village cadres faces practical constraints such as governance capability gaps induced by the second-level digital divide, the absence of a systematic cultivation system, i...At present, enhancing the digital literacy of village cadres faces practical constraints such as governance capability gaps induced by the second-level digital divide, the absence of a systematic cultivation system, incomplete last-kilometer implementation for translating skills into practice, and inadequate incentive mechanisms. As a novel educational form deeply integrating digital information technology with teaching and learning, distance open education provides multi-directional empowerment for enhancing village cadres digital literacy. This is achieved through open learning methods, the application of modern digital technologies, tailored learning programs, and the establishment of "overpass bridges" for translating learning outcomes into practice. Based on this, it is essential to further streamline the pathways for educating and cultivating village cadres, vigorously promote the digitalization of teaching, practice, and support services, develop systematic and localized digital education resources, and actively explore and establish a complementary credit bank system for their digital literacy cultivation.展开更多
Developing advanced cathode modification strategies to address the inherent high charge density of Al^(3+) is essential for achieving high-energy-density and long-cycle-life rechargeable aluminum batteries(RABs).Herei...Developing advanced cathode modification strategies to address the inherent high charge density of Al^(3+) is essential for achieving high-energy-density and long-cycle-life rechargeable aluminum batteries(RABs).Herein,we engineer tetraethylammonium(TEA)cation intercalation as a dual-function strategy that concurrently enables interlayer distance enlargement and electrostatic shielding effects,resolving Al^(3+) polarization-induced sluggish kinetics and cathode degradation in RABs.TEA intercalation triggers exceptional V2O5 interlayer expansion from 4.37 to 13.10Å,while the modulated charge distribution generates an electrostatic shielding effect that significantly weakens the Coulombic interactions between Al^(3+) and V2O5 frameworks.This dual mechanism collectively enhances ion diffusion kinetics and suppresses lattice stress accumulation.Ex situ X-ray diffraction and transmission electron microscopy analyses confirm that the“molecular pillar effect”of TEA enables minimal and highly reversible structural deformation of the cathode(<2.0%volume change after 200 cycles),demonstrating zero-strain aluminum-storage behavior.The optimized cathode delivers a high reversible capacity of 258 mAh g^(−1) at 0.5 A g^(−1),maintains 99%capacity retention at 5.0 A g^(−1),and exhibits an ultralow capacity decay rate of 0.01%per cycle over 6000 cycles.This work opens new pathways for designing stable high-performance RAB cathodes through synergistic modulation of electronic and lattice structures.展开更多
Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backg...Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backgrounds,small target sizes,and high annotation costs,which limit its practical application and widespread adoption.To address these issues,a semi-supervised spore detection framework is proposed for use under complex background conditions.Firstly,a difficulty perception scoring function is designed to quantify the detection difficulty of each image region.For regions with higher difficulty scores,a masking strategy is applied,while the remaining regions are adversarial augmentation is applied to encourage the model to learn fromchallenging areasmore effectively.Secondly,a Gaussian Mixture Model is employed to dynamically adjust the allocation threshold for pseudo-labels,thereby reducing the influence of unreliable supervision signals and enhancing the stability of semi-supervised learning.Finally,the Wasserstein distance is introduced for object localization refinement,offering a more robust positioning approach.Experimental results demonstrate that the proposed framework achieves 88.9% mAP50 and 60.7% mAP50-95,surpassing the baseline method by 4.2% and 4.6%,respectively,using only 10% of labeled data.In comparison with other state-of-the-art semi-supervised detection models,the proposed method exhibits superior detection accuracy and robustness.In conclusion,the framework not only offers an efficient and reliable solution for plant pathogen spore detection but also provides strong algorithmic support for real-time spore detection and early disease warning systems,with significant engineering application potential.展开更多
This paper quantitatively discusses the influence of well contact on single-event transient(SET)in sub-20 nm FinFET by two-photon absorption(TPA)pulse laser.Two groups of inverter chains were designed to investigate t...This paper quantitatively discusses the influence of well contact on single-event transient(SET)in sub-20 nm FinFET by two-photon absorption(TPA)pulse laser.Two groups of inverter chains were designed to investigate the impact of well contact distance on the FinFET process.The experimental results show that the SET pulse width has a bimodal symmetric distribution,which is different from that of a bulk planar CMOS device.To investigate the detailed mechanism of the phenomenon,a high-precision FinFET TCAD model was established,in which both Id-Vd and Id-Vg errors were less than 10%compared to the SPICE model provided by the commercial process.TCAD simulation under heavy ion injection showed the mechanism of the abnormal phenomenon,where the well contact plays a major role in charge collection at the near-well contact distance,while the source plays a major role at the far distance.This phenomenon is completely different from that of planar CMOS devices.This indicates that the SET mechanism becomes more complicated during the FinFET process.Therefore,more effective SET hardening methods should be investigated for FinFET.展开更多
Land use conflicts(LUCs)pose a major challenge to urbanization,and their effective regulation is essential for promoting sustainable regional land use.However,the influence of urban development on conflicts has often ...Land use conflicts(LUCs)pose a major challenge to urbanization,and their effective regulation is essential for promoting sustainable regional land use.However,the influence of urban development on conflicts has often been overlooked.This study developed an index system from three dimensions—agricultural production,residential life,and ecological security—and quantified LUCs in China using spatial statistics and a coupling relationship matrix.It further explored the spatial relationships between conflict types and urban built-up areas(UBA)through accessibility analysis,and applied regression analysis to reveal the spatial evolution of conflicts from an urban-scale perspective.The results showed that agricultural-construction conflicts were concentrated in the eastern plains,while agricultural-ecological conflicts prevailed in the mountainous areas in the western region.Spatial distribution of the distance from conflicts to UBA(DCU)exhibited a clear east-west gradient,being closer in the east(less than 20 km)and farther in the west.Between 2000 and 2020,LUCs moved progressively closer to UBA,except in the ecologically fragile western region.For all urban hierarchies except small cities,the average distance was below 10 km;megacities exhibited the shortest DCU,roughly half that of small cities.Moreover,LUCs displayed significant hierarchical scale effects:as urban size increased,distance tended to decrease in a non-linear pattern,with the steepest decline occurring in central China.Land management authorities should work to curb sprawling urban development.Overall,this study provides new insights into the spatial evolution of LUCs and contributes to more sustainable land use management.展开更多
A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However...A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.展开更多
Traditional mining in open pit mines often uses explosives,leading to environmental hazards,with flyrock being a critical issue.In detail,excess flying rock beyond the designated explosion area was identified as the p...Traditional mining in open pit mines often uses explosives,leading to environmental hazards,with flyrock being a critical issue.In detail,excess flying rock beyond the designated explosion area was identified as the primary cause of fatal and non-fatal blasting hazards in open pit mining.Therefore,the accurate and reliable prediction of flyrock becomes crucial for effectively managing and mitigating associated problems.This study used the Light Gradient Boosting Machine(LightGBM)model to predict flyrock in a lead-zinc mine,with promising results.To improve its accuracy,multi-verse optimizer(MVO)and ant lion optimizer(ALO)metaheuristic algorithms were introduced.Results showed MVO-LightGBM outperformed conventional LightGBM.Additionally,decision tree(DT),support vector machine(SVM),and classification and regression tree(CART)models were trained and compared with MVO-LightGBM.The MVO-LightGBM model excelled over DT,SVM,and CART.This study highlights MVO-LightGBM's effectiveness and potential for broader applications.Furthermore,a multiple parametric sensitivity analysis(MPSA)algorithm was employed to specify the sensitivity of parameters.MPSA results indicated that the highest and lowest sensitivities are relevant to blasted rock per hole and spacing with theγ=1752.12 andγ=49.52,respectively.展开更多
Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation f...Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.展开更多
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.展开更多
Accurate predictions of the Remaining useful life(RUL)of mechanical equipment are vital for lowering maintenance costs and maintaining equipment reliability and safety.Datadriven RUL prediction methods have made signi...Accurate predictions of the Remaining useful life(RUL)of mechanical equipment are vital for lowering maintenance costs and maintaining equipment reliability and safety.Datadriven RUL prediction methods have made significant progress,but they often assume that the training and testing data have the same distribution,which is often not the case in practical engineering applications.To address this issue,this paper proposes a residual useful life prediction model that combines deep learning and transfer learning.In this model,called transfer convolutional attention mechanism for early-life stage time convolutional network(TCAM-EASTCN),an unsupervised domain adaptation strategy is introduced based on the characterization of subspace distances and orthogonal basis mismatch penalties in the convolutional attention mechanism for early-life stage time convolutional network(CAMEASTCN).This approach minimizes the distribution differences between different domains,enhancing the learning of cross-domain invariant features and effectively reducing the distribution gap between the source and target domains,thereby improving the accuracy of RUL prediction under varying conditions.Experimental results demonstrate that TCAMEASTCN outperforms other models in terms of RUL prediction accuracy and generalization.展开更多
The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces....The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces.The strong convergence result for our method is established under some standard assumptions without any requirement of the knowledge of the Lipschitz constant of the mapping.Several numerical experiments are provided to verify the advantages and efficiency of proposed algorithms.展开更多
K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances.We show that,as a consequence,it also maximizes between-cluster variances.This means that the two measures do not pr...K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances.We show that,as a consequence,it also maximizes between-cluster variances.This means that the two measures do not provide complementary information and that using only one is enough.Based on this property,we propose a new objective function called cluster overlap,which is measured intuitively as the proportion of points shared between the clusters.We adopt the new function within k-means and present an algorithm called overlap k-means.It is an alternative way to design a k-means algorithm.A localized variant is also provided by limiting the overlap calculation to the neighboring points.展开更多
文摘To enhance the accuracy of path planning of unmanned surface vehicles(USVs),the particle swarm optimization algorithm(PSO)is improved based on species migration strategies observed in ecology.By incorporating the concept of particle sight distance,an improved algorithm,called SD-IPSO,is proposed for the real-time autonomous navigation of USVs in marine environments.The algorithm refines the individual behavior pattern of particles in the population,effectively improving both local and global search capabilities while avoiding premature convergence.The effectiveness of the algorithm is validated using standard test functions from CEC-2017 function library,assessing it from multiple dimensions.Sensitivity analysis is conducted on key parameters in the algorithm,including particle sight distance and population size.Results indicate that compared with PSO,SD-IPSO demonstrates significant advantages in optimization accuracy and convergence speed.The application of SD-IPSO in path planning is further investigated through a 14-point traveling salesman problem(TSP)example and navigation autonomous tests of USVs in marine environments.Findings demonstrate that the proposed algorithm exhibits superior optimization capabilities and can effectively address the path planning challenges of USVs.
文摘Despite the geographical distance between China and Africa,the rich customs and lifestyles of the African people present an interesting area of study for international professionals.It is this facet of Africa that fascinated us most during our research journey across the continent.
基金funded by the National Natural Science Foundation of China(32471953)the Educational Department of Liaoning Province Key Research Project(LJ212410153073).
文摘Urban green spaces have positive effects on both physical and mental wellbeing.However,few studies have focused on the trends and thresholds of the effects of different influences on restorative benefits when viewing scenes differfeaturing plant communities.We measured subjective evaluations and objective physiological data from 44 participants who viewed images of plant communities in the yellow to green hue range to compare differences in restorative benefits among plant communities at different visual distances,as well as quantifying the influencing factors involved.The following results were found:(1)Coniferous and multi-layered plant communities were found to provide greater restorative benefits,and the restorative benefits grew with increasing visual distance.(2)Shape and color characteristics were significantly correlated with restorative benefits,but the relationship is not simply linear.(3)The restorative benefits were found to be greatest when crown proportion was 61.23%,trunk proportion ranged from 4.11%to 13.70%,and the value of color index value ranged from 25.44%to 35.56%;the restorative benefits gradually increased when sky proportion exceeded 12.95%-13.19%,the fractal dimension exceeded 1.62-1.67,and hue index exceeded 91.64°-95.67°;additionally,the restorative benefits decreased when the saturation index increased.This study provides a scientific basis for the construction and improvement of plant landscapes in urban green spaces.
基金funded by the Natural Science Foundation of Hebei Province(C2025201032 to J.W.)High-Level Talents Research Start-Up Project of Hebei University(521100222044 to J.W.)+1 种基金the Huizhi Lead Innovation Space Project in High-tech Zone of Chengde City(HZLC202410 to L.M.)National Undergraduate Innovation and Entrepreneurship Training Program(202510098011 to H.Z.)。
文摘Urbanization destroys wildlife habitats,fragmenting them into small patches with poor connectivity,leading to population declines in species sensitive to such chan ges.Escape is the most common anti-predator strategy adopted by birds,refuges in habitats reduce or eliminate predation risk.Therefore,creating habitats with suitable refuges for birds has significant implications for their conservation.However,there have been few studies on refuge selection in birds.This study examined the Eurasian Tree Sparrow(Passer montanus)and Oriental Magpie(Pica serica)in urban and rural areas of Chengde City,northern China by measuring their alert distance(AD),flight initiation distance(FID),an d distance fled(DF)and analyzed their refuge selection characteristics after escaping.The FID/AD ratio was employed to assess the behavioral differences of birds in the risk trade-off.The results showed that the FID and FID/AD of both species were lower in urban areas than in rural areas and were negatively correlated with immediate human density.Sparrow FID was significantly affected by group size and landing substrate type.The FID of sparrows was positively correlated with the group size.The sparrows that fled to bushes escaped earlier.In urban and rural areas,sparrows exhibited significantly lower FID,DF,and FID/AD than magpies.The species adopted different refuge selection strategies,with magpies preferentially selecting trees with greater vertical height and sparrows selecting both trees and bushes.Further analysis indicated that the horizontal and vertical distances fled of both species were lower when fleeing to bushes.Urban planning and conservation areas construction should incorporate the ecological needs of local bird species to rationally configure their habitat structure,thereby optimizing the effect of avian conservation.
基金supported by the National Natural Science Foundation of China(62373364,62176259)the Key Research and Development Program of Jiangsu Province(BE2022095)。
文摘In order to address the issue of overly conservative offline reinforcement learning(RL) methods that limit the generalization of policy in the out-of-distribution(OOD) region,this article designs a surrogate target for OOD value function based on dataset distance and proposes a novel generalized Q-learning mechanism with distance regularization(GQDR).In theory,we not only prove the convergence of GQDR,but also ensure that the difference between the Q-value learned by GQDR and its true value is bounded.Furthermore,an offline generalized actor-critic method with distance regularization(OGACDR) is proposed by combining GQDR with actor-critic learning framework.Two implementations of OGACDR,OGACDR-EXP and OGACDRSQR,are introduced according to exponential(EXP) and opensquare(SQR) distance weight functions,and it has been theoretically proved that OGACDR provides a safe policy improvement.Experimental results on Gym-MuJoCo continuous control tasks show that OGACDR can not only alleviate the overestimation and overconservatism of Q-value function,but also outperform conservative offline RL baselines.
基金supported by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology.
文摘With the increasing complexity of logistics operations,traditional static vehicle routing models are no longer sufficient.In practice,customer demands often arise dynamically,and multi-depot systems are commonly used to improve efficiency.This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand.New customers appear in the delivery process at any time and are periodically optimized according to time slices.Then,we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem.The classical ant colony algorithm uses spatial distance to select nodes,while TS-ACO considers the impact of both temporal and spatial distance on node selection.Meanwhile,we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows(MDVRPTW)dataset to evaluate the performance of our system.According to the experimental results,TS-ACO,which considers spatial and temporal distance,is more effective than the classical ACO,which only considers spatial distance.
基金supported by the National Natural Science Foundation of China under Grant No.12071267。
文摘In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean function with the classical kernel estimator,the proposed test statistics are built upon a modified minimum distance between a nonparametric fit and a parametric estimator under the null hypothesis for the variance function.Asymptotic properties of the estimator of the parameters in the variance function are discussed,and the large sample distribution of the test statistics under the null hypothesis is established,as well as the consistency and the power under some local alternative hypotheses.Extensive numerical studies demonstrate that the proposed test procedures have satisfactory finite sample performance.Finally,two real data examples further showcase the effectiveness of the proposed test in real applications.
基金Supported by Science Research Fund Project of Yunnan Provincial Department of Education-Research on the Pathways for Distance Open Education to Empower the Enhancement of Digital Literacy and Skills of Rural"Leading Geese"in Ethnic Areas(2023J0792)The Ideological and Political Education Reform Project of Yunnan Province in 2022-Exploration and Practice of Integrating Ideological and Political Education into the Training Mode of"Leading Goose"Talents for Rural Revitalization in the Course of Agricultural and Forestry Economics and Management in Open Education.
文摘At present, enhancing the digital literacy of village cadres faces practical constraints such as governance capability gaps induced by the second-level digital divide, the absence of a systematic cultivation system, incomplete last-kilometer implementation for translating skills into practice, and inadequate incentive mechanisms. As a novel educational form deeply integrating digital information technology with teaching and learning, distance open education provides multi-directional empowerment for enhancing village cadres digital literacy. This is achieved through open learning methods, the application of modern digital technologies, tailored learning programs, and the establishment of "overpass bridges" for translating learning outcomes into practice. Based on this, it is essential to further streamline the pathways for educating and cultivating village cadres, vigorously promote the digitalization of teaching, practice, and support services, develop systematic and localized digital education resources, and actively explore and establish a complementary credit bank system for their digital literacy cultivation.
基金supported by the Key R&D Program of Zaozhuang city,China(2024GH12)the Zaozhuang Gathering of Talents Program。
文摘Developing advanced cathode modification strategies to address the inherent high charge density of Al^(3+) is essential for achieving high-energy-density and long-cycle-life rechargeable aluminum batteries(RABs).Herein,we engineer tetraethylammonium(TEA)cation intercalation as a dual-function strategy that concurrently enables interlayer distance enlargement and electrostatic shielding effects,resolving Al^(3+) polarization-induced sluggish kinetics and cathode degradation in RABs.TEA intercalation triggers exceptional V2O5 interlayer expansion from 4.37 to 13.10Å,while the modulated charge distribution generates an electrostatic shielding effect that significantly weakens the Coulombic interactions between Al^(3+) and V2O5 frameworks.This dual mechanism collectively enhances ion diffusion kinetics and suppresses lattice stress accumulation.Ex situ X-ray diffraction and transmission electron microscopy analyses confirm that the“molecular pillar effect”of TEA enables minimal and highly reversible structural deformation of the cathode(<2.0%volume change after 200 cycles),demonstrating zero-strain aluminum-storage behavior.The optimized cathode delivers a high reversible capacity of 258 mAh g^(−1) at 0.5 A g^(−1),maintains 99%capacity retention at 5.0 A g^(−1),and exhibits an ultralow capacity decay rate of 0.01%per cycle over 6000 cycles.This work opens new pathways for designing stable high-performance RAB cathodes through synergistic modulation of electronic and lattice structures.
基金supported by Development of asparagus price database based on agricultural big data(381724).
文摘Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backgrounds,small target sizes,and high annotation costs,which limit its practical application and widespread adoption.To address these issues,a semi-supervised spore detection framework is proposed for use under complex background conditions.Firstly,a difficulty perception scoring function is designed to quantify the detection difficulty of each image region.For regions with higher difficulty scores,a masking strategy is applied,while the remaining regions are adversarial augmentation is applied to encourage the model to learn fromchallenging areasmore effectively.Secondly,a Gaussian Mixture Model is employed to dynamically adjust the allocation threshold for pseudo-labels,thereby reducing the influence of unreliable supervision signals and enhancing the stability of semi-supervised learning.Finally,the Wasserstein distance is introduced for object localization refinement,offering a more robust positioning approach.Experimental results demonstrate that the proposed framework achieves 88.9% mAP50 and 60.7% mAP50-95,surpassing the baseline method by 4.2% and 4.6%,respectively,using only 10% of labeled data.In comparison with other state-of-the-art semi-supervised detection models,the proposed method exhibits superior detection accuracy and robustness.In conclusion,the framework not only offers an efficient and reliable solution for plant pathogen spore detection but also provides strong algorithmic support for real-time spore detection and early disease warning systems,with significant engineering application potential.
基金supported by Natural Science Foundation of China(Nos.62174180 and 62304258)National Key R&D Program of China(No.2023YFA1609000)。
文摘This paper quantitatively discusses the influence of well contact on single-event transient(SET)in sub-20 nm FinFET by two-photon absorption(TPA)pulse laser.Two groups of inverter chains were designed to investigate the impact of well contact distance on the FinFET process.The experimental results show that the SET pulse width has a bimodal symmetric distribution,which is different from that of a bulk planar CMOS device.To investigate the detailed mechanism of the phenomenon,a high-precision FinFET TCAD model was established,in which both Id-Vd and Id-Vg errors were less than 10%compared to the SPICE model provided by the commercial process.TCAD simulation under heavy ion injection showed the mechanism of the abnormal phenomenon,where the well contact plays a major role in charge collection at the near-well contact distance,while the source plays a major role at the far distance.This phenomenon is completely different from that of planar CMOS devices.This indicates that the SET mechanism becomes more complicated during the FinFET process.Therefore,more effective SET hardening methods should be investigated for FinFET.
基金National Natural Science Foundation of China,No.72474216。
文摘Land use conflicts(LUCs)pose a major challenge to urbanization,and their effective regulation is essential for promoting sustainable regional land use.However,the influence of urban development on conflicts has often been overlooked.This study developed an index system from three dimensions—agricultural production,residential life,and ecological security—and quantified LUCs in China using spatial statistics and a coupling relationship matrix.It further explored the spatial relationships between conflict types and urban built-up areas(UBA)through accessibility analysis,and applied regression analysis to reveal the spatial evolution of conflicts from an urban-scale perspective.The results showed that agricultural-construction conflicts were concentrated in the eastern plains,while agricultural-ecological conflicts prevailed in the mountainous areas in the western region.Spatial distribution of the distance from conflicts to UBA(DCU)exhibited a clear east-west gradient,being closer in the east(less than 20 km)and farther in the west.Between 2000 and 2020,LUCs moved progressively closer to UBA,except in the ecologically fragile western region.For all urban hierarchies except small cities,the average distance was below 10 km;megacities exhibited the shortest DCU,roughly half that of small cities.Moreover,LUCs displayed significant hierarchical scale effects:as urban size increased,distance tended to decrease in a non-linear pattern,with the steepest decline occurring in central China.Land management authorities should work to curb sprawling urban development.Overall,this study provides new insights into the spatial evolution of LUCs and contributes to more sustainable land use management.
基金supported by the National Natural Science Foundation of China(42471336,52379021 and 42201278)the Hebei Province Backbone Talent Program,China(Returnee Platform for Overseas Study)(A20240028)+2 种基金the Hebei Province Statistical Science Research Project,China(2024HZ04)the Hebei Province Graduate Education and Teaching Reform Research Project,China(YJG2024046)the Innovation Ability Training Program for Postgraduate Students of Hebei Provincial Department of Education,China(CXZZSS2025048)。
文摘A comprehensive assessment of grain supply,demand,and ecosystem service flows is essential for identifying grain movement pathways,ensuring regional grain security,and guiding sustainable management strategies.However,current studies primarily focus on short-term grain provision services while neglecting the spatiotemporal variations in grain flows across different scales.This gap limits the identification of dynamic matching relationships and the formulation of optimization strategies for balancing grain flows.This study examined the spatiotemporal evolution of grain supply and demand in the Beijing-Tianjin-Hebei(BTH)region from 1980 to 2020.Using the Enhanced TwoStep Floating Catchment Area method,the grain provision ecosystem service flows were quantified,the changes in supply–demand matching under different grain flow scenarios were analyzed and the optimal distance threshold for grain flows was investigated.The results revealed that grain production follows a spatial distribution pattern characterized by high levels in the southeast and low levels in the northwest.A significant mismatch exists between supply and demand,and it shows a scale effect.Deficit areas are mainly concentrated in the northwest,while surplus areas are mainly located in the central and southern regions.As the spatial scale increases,the ecosystem service supply–demand ratio(SDR)classification becomes more clustered,while it exhibits greater spatial SDR heterogeneity at smaller scales.This study examined two distinct scenarios of grain provision ecosystem service flow dynamics based on 100 and 200 km distance thresholds.The flow increased significantly,from 2.17 to 11.81million tons in the first scenario and from 2.41 to 12.37 million tons in the second scenario over nearly 40 years,forming a spatial movement pattern from the central and southern regions to the surrounding areas.Large flows were mainly concentrated in the interior of urban centers,with significant outflows between cities such as Baoding,Shijiazhuang,Xingtai,and Hengshui.At the county scale,supply–demand matching patterns remained consistent between the grain flows in the two scenarios.Notably,incorporating grain flow dynamics significantly reduced the number of grain-deficit areas compared to scenarios without grain flow.In 2020,grain-deficit counties decreased by28.79 and 37.88%,and cities by 12.50 and 25.0%under the two scenarios,respectively.Furthermore,the distance threshold for achieving optimal supply and demand matching at the county scale was longer than at the city scale in both grain flow scenarios.This study provides valuable insights into the dynamic relationships and heterogeneous patterns of grain matching,and expands the research perspective on grain and ecosystem service flows across various spatiotemporal scales.
基金funded by the Key Laboratory of Geological Safety of Coastal Urban Underground Space,Ministry of Natural Resources of China(Grant No.BHKF2022Y02)Natural Science Foundation of Guangdong Province,China(Grant No.2024A1515011162)Natural Science Foundation of Shandong Province,China(Grant No.ZR2024QE021).
文摘Traditional mining in open pit mines often uses explosives,leading to environmental hazards,with flyrock being a critical issue.In detail,excess flying rock beyond the designated explosion area was identified as the primary cause of fatal and non-fatal blasting hazards in open pit mining.Therefore,the accurate and reliable prediction of flyrock becomes crucial for effectively managing and mitigating associated problems.This study used the Light Gradient Boosting Machine(LightGBM)model to predict flyrock in a lead-zinc mine,with promising results.To improve its accuracy,multi-verse optimizer(MVO)and ant lion optimizer(ALO)metaheuristic algorithms were introduced.Results showed MVO-LightGBM outperformed conventional LightGBM.Additionally,decision tree(DT),support vector machine(SVM),and classification and regression tree(CART)models were trained and compared with MVO-LightGBM.The MVO-LightGBM model excelled over DT,SVM,and CART.This study highlights MVO-LightGBM's effectiveness and potential for broader applications.Furthermore,a multiple parametric sensitivity analysis(MPSA)algorithm was employed to specify the sensitivity of parameters.MPSA results indicated that the highest and lowest sensitivities are relevant to blasted rock per hole and spacing with theγ=1752.12 andγ=49.52,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.U22A20596)the Shenzhen Science and Technology Program(Grant No.GJHZ20220913142605010)the Jinan Lead Researcher Project(Grant No.202333051).
文摘Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
基金supported in part by the Key Research and Development Program of Shaanxi Province under Grant 2020GY-104in part by the Key Laboratory of Highway Construction Machinery of Shaanxi Province,Key Laboratory of Road Construction Technology and Equipment(Chang'an University),MOE,under Grant 300102250503in part by the Fundamental Research Funds for the Central Universities under Grant CHD 300102250503.
文摘Accurate predictions of the Remaining useful life(RUL)of mechanical equipment are vital for lowering maintenance costs and maintaining equipment reliability and safety.Datadriven RUL prediction methods have made significant progress,but they often assume that the training and testing data have the same distribution,which is often not the case in practical engineering applications.To address this issue,this paper proposes a residual useful life prediction model that combines deep learning and transfer learning.In this model,called transfer convolutional attention mechanism for early-life stage time convolutional network(TCAM-EASTCN),an unsupervised domain adaptation strategy is introduced based on the characterization of subspace distances and orthogonal basis mismatch penalties in the convolutional attention mechanism for early-life stage time convolutional network(CAMEASTCN).This approach minimizes the distribution differences between different domains,enhancing the learning of cross-domain invariant features and effectively reducing the distribution gap between the source and target domains,thereby improving the accuracy of RUL prediction under varying conditions.Experimental results demonstrate that TCAMEASTCN outperforms other models in terms of RUL prediction accuracy and generalization.
基金Supported by NSFC(No.12171062)the Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-JQX0004)+1 种基金the Chongqing Talent Support Program(No.cstc2024ycjh-bgzxm0121)Science and Technology Project of Chongqing Education Committee(No.KJZD-M202300503)。
文摘The purpose of this article is to introduce a new method with a self-adaptive stepsize for approximating a common solution of monotone inclusion problems and variational inequality problems in reflexive Banach spaces.The strong convergence result for our method is established under some standard assumptions without any requirement of the knowledge of the Lipschitz constant of the mapping.Several numerical experiments are provided to verify the advantages and efficiency of proposed algorithms.
文摘K-means uses the sum-of-squared error as the objective function to minimize within-cluster distances.We show that,as a consequence,it also maximizes between-cluster variances.This means that the two measures do not provide complementary information and that using only one is enough.Based on this property,we propose a new objective function called cluster overlap,which is measured intuitively as the proportion of points shared between the clusters.We adopt the new function within k-means and present an algorithm called overlap k-means.It is an alternative way to design a k-means algorithm.A localized variant is also provided by limiting the overlap calculation to the neighboring points.
