Rapid urbanization has caused significant changes along the urban-rural gradient,leading to a variety of landscapes that are mainly shaped by human activities.This dynamic interplay also influences the distribution an...Rapid urbanization has caused significant changes along the urban-rural gradient,leading to a variety of landscapes that are mainly shaped by human activities.This dynamic interplay also influences the distribution and characteristics of trees outside forests(TOF).Understanding the pattern of these trees will support informed decision-making in urban planning,in conservation strategies,and altogether in sustainable land management practices in the urban context.In this study,we employed a deep learning-based object detection model and high resolution satellite imagery to identify 1.3 million trees with bounding boxes within a 250 km^(2)research transect spanning the urban-rural gradient of Bengaluru,a megacity in Southern India.Additionally,we developed an allometric equation to estimate diameter at breast height(DBH)from the tree crown diameter(CD)derived from the detected bounding boxes.Our study focused on analyzing variations in tree density and tree size along this gradient.The findings revealed distinct patterns:the urban domain displayed larger tree crown diameters(mean:8.87 m)and DBH(mean:43.78 cm)but having relatively low tree density(32 trees per hectare).Furthermore,with increasing distance from the city center,tree density increased,while the mean tree crown diameter and mean tree basal area decreased,showing clear differences of tree density and size between the urban and rural domains in Bengaluru.This study offers an efficient methodology that helps generating instructive insights into the dynamics of TOF along the urban-rural gradient.This may inform urban planning and management strategies for enhancing green infrastructure and biodiversity conservation in rapidly urbanizing cities like Bengaluru.展开更多
Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herb...Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herbaceous plants,with notable applications in species such as Arabidopsis(Yin et al.,2024),soybean(Zhang et al.,2024),rice(Zhang et al.,2020),and Chinese cabbage(Li et al.,2021).However,its application in fruit trees is limited.This is primarily due to their long growth cycles and lack of rapid,efficient,and stable transgenic systems,which severely hinders foundational research involving plant genetic transformation(Mei et al.,2024).Furthermore,for subtropical fruit trees,the presence of recalcitrant seeds adds an extra layer of difficulty to genetic transformation(Umarani et al.,2015),as most methods rely on seed germination as a basis for transformation.展开更多
Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating t...Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating the adoption of advanced,automated approaches for improved forest conservation and management.This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery.A dataset of 3157 images was collected and divided into training(2528),validation(495),and testing(134)sets.To enhance model robustness and generalization,data augmentation was applied to the training part of the dataset.Various YOLO-based models,including YOLOv8,YOLOv9,YOLOv10,YOLOv11,and YOLOv12,were evaluated using different hyperparameters and optimization techniques,such as stochastic gradient descent(SGD)and auto-optimization.These models were assessed in terms of detection accuracy and the number of detected trees.The highest-performing model,YOLOv12m,achieved a mean average precision(mAP@50)of 0.908,mAP@50:95 of 0.581,recall of 0.851,precision of 0.852,and an F1-score of 0.847.The results demonstrate that YOLO-based object detection offers a highly efficient,scalable,and accurate solution for individual tree detection in satellite imagery,facilitating improved forest inventory,monitoring,and ecosystem management.This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.展开更多
The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations...The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations traversing shallow waters,where ice-berg keels may reach the seabed,potentially damaging subsea structures.Consequently,costly and time-intensive iceberg manage-ment operations,such as towing and rerouting,are undertaken to safeguard subsea and offshore infrastructure.This study,therefore,explores the application of extra tree regression(ETR)as a robust solution for estimating iceberg draft,particularly in the preliminary phases of decision-making for iceberg management projects.Nine ETR models were developed using parameters influencing iceberg draft.Subsequent analyses identified the most effective models and significant input variables.Uncertainty analysis revealed that the superior ETR model tended to overestimate iceberg drafts;however,it achieved the highest precision,correlation,and simplicity in estimation.Comparison with decision tree regression,random forest regression,and empirical methods confirmed the superior perfor-mance of ETR in predicting iceberg drafts.展开更多
A tree's basal area(BA)and wood volume scale exponentially with tree diameter in species-specifc patterns.Recent observed increases in tree growth suggest these allometric relationships are shifting in response to...A tree's basal area(BA)and wood volume scale exponentially with tree diameter in species-specifc patterns.Recent observed increases in tree growth suggest these allometric relationships are shifting in response to climate change,rising CO_(2) levels,and/or changes in forest management.We analyzed 9,214 cores from nine conifer and 11 broadleaf species grown in managed mixed-species stands in the upper Midwest to quantify how well diameter(diameter at breast height(DBH))serves to predict BA growth and above-ground wood and carbon(C).These samples include many large trees.We ft mixed models to predict BA growth and above-ground biomass/C from diameter,tree height,and the BA of nearby trees while controlling for site effects.Models account for 55%–83%of the variance in log(recent growth),improving predictions over earlier models.Growth-diameter scaling exponents covary with certain leaf and stem(but not wood)functional traits,reflecting growth strategies.LogBA increment scales linearly with log(diameter)as trees grow bigger in 16/20 species and growth actually accelerates in Quercus rubra L.Three other species plateau in growth.Growth only decelerates in red pine,Pinus resinosa Ait.Growth in whole-tree,above-ground biomass,and C accelerate even more strongly with diameter(mean exponent:2.08 vs.1.30 for BA growth).Sustained BA growth and accelerating wood/C growth contradict the common assumption that tree growth declines in bigger trees.Yield tables and silvicultural guidelines should be updated to reflect these current relationships.Such revisions will favor delaying harvests in many managed stands to increase wood production and enhance ecosystem values including C fxation and storage.Further research may resolve the relative roles of thinning,climatic conditions,nitrogen inputs,and rising CO2 levels on changing patterns of tree growth.展开更多
This paper examines the application of the Verkle tree—an efficient data structure that leverages commitments and a novel proof technique in cryptographic solutions.Unlike traditional Merkle trees,the Verkle tree sig...This paper examines the application of the Verkle tree—an efficient data structure that leverages commitments and a novel proof technique in cryptographic solutions.Unlike traditional Merkle trees,the Verkle tree significantly reduces signature size by utilizing polynomial and vector commitments.Compact proofs also accelerate the verification process,reducing computational overhead,which makes Verkle trees particularly useful.The study proposes a new approach based on a non-positional polynomial notation(NPN)employing the Chinese Remainder Theorem(CRT).CRT enables efficient data representation and verification by decomposing data into smaller,indepen-dent components,simplifying computations,reducing overhead,and enhancing scalability.This technique facilitates parallel data processing,which is especially advantageous in cryptographic applications such as commitment and proof construction in Verkle trees,as well as in systems with constrained computational resources.Theoretical foundations of the approach,its advantages,and practical implementation aspects are explored,including resistance to potential attacks,application domains,and a comparative analysis with existing methods based on well-known parameters and characteristics.An analysis of potential attacks and vulnerabilities,including greatest common divisor(GCD)attacks,approximate multiple attacks(LLL lattice-based),brute-force search for irreducible polynomials,and the estimation of their total number,indicates that no vulnerabilities have been identified in the proposed method thus far.Furthermore,the study demonstrates that integrating CRT with Verkle trees ensures high scalability,making this approach promising for blockchain systems and other distributed systems requiring compact and efficient proofs.展开更多
Primary challenges with a forest inventory program are surveyors with various levels of experience and the turnover of inexperienced surveyors.Few studies have looked at the consistency of an instrument’s results amo...Primary challenges with a forest inventory program are surveyors with various levels of experience and the turnover of inexperienced surveyors.Few studies have looked at the consistency of an instrument’s results among inexperienced surveyors.Most studies have assessed whether instruments were significantly different.These tests do not indicate whether instruments were statistically equivalent,i.e.,that choosing either one would be acceptable under a certain level of tolerance.This study evaluated the consistency and statistical equivalence among instruments for measuring diameter at breast height(DBH)and for total tree height(HT)among inexperienced surveyors.The study was conducted as a randomized experiment with students from an introductory tree measurement course,using four types of DBH and HT instruments,and with different tree attributes.For DBH,the results show that D-tape was the most consistent across tree attributes and teams of inexperienced surveyors and was only statistically interchangeable with Caliper with a tolerance≥3 cm.For HT,Ultrasound was the most consistent but only statistically interchangeable with Laser with a tolerance≥8 m.A single type of instrument for measuring DBH and for HT is recommended,especially when field crews may be a mixture of experienced and inexperienced surveyors.Our study provides initial recommendations on the choice of instruments when either purchasing new ones or replacing old ones in forest inventories.展开更多
Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban citi...Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.展开更多
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making co...The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.展开更多
A previous paper showed that the real numbers between 0 and 1 could be represented by an infinite tree structure, called the ‘infinity tree’, which contains only a countably infinite number of nodes and arcs. This p...A previous paper showed that the real numbers between 0 and 1 could be represented by an infinite tree structure, called the ‘infinity tree’, which contains only a countably infinite number of nodes and arcs. This paper discusses how a finite-state Turing machine could, in a countably infinite number of state transitions, write all the infinite paths in the infinity tree to a countably infinite tape. Hence it is argued that the real numbers in the interval [0, 1] are countably infinite in a non-Cantorian theory of infinity based on Turing machines using countably infinite space and time. In this theory, Cantor’s Continuum Hypothesis can also be proved. And in this theory, it follows that the power set of the natural numbers P(ℕ) is countably infinite, which contradicts the claim of Cantor’s Theorem for the natural numbers. However, this paper does not claim there is an error in Cantor’s arguments that [0, 1] is uncountably infinite. Rather, this paper considers the situation as a paradox, resulting from different choices about how to represent and count the continuum of real numbers.展开更多
The Chinese tree shrew has gained prominence as a model organism due to its phylogenetic proximity to primates,offering distinct advantages over traditional rodent models in biomedical research.However,the neuroanatom...The Chinese tree shrew has gained prominence as a model organism due to its phylogenetic proximity to primates,offering distinct advantages over traditional rodent models in biomedical research.However,the neuroanatomy of this species remains insufficiently defined,limiting its utility in neurophysiological and neuropathological studies.In this study,immunofluorescence microscopy was employed to comprehensively map the distribution of three calciumbinding proteins,parvalbumin,calbindin D-28k,and calretinin,across the tree shrew cerebrum.Serial brain sections in sagittal,coronal,and horizontal planes from 12 individuals generated a dataset of 3638 cellular-resolution images.This dataset,accessible via Science Data Bank(https://doi.org/10.57760/sciencedb.23471),provides detailed region-and laminar-selective distributions of calcium-binding proteins valuable for the cyto-and chemoarchitectural characterization of the tree shrew cerebrum.This resource will not only advance our understanding of brain organization and facilitate basic and translational neuroscience research in tree shrews but also enhance comparative and evolutionary analyses across species.展开更多
基金financial support provided by the German Research Foundation,DFG,through grant number KL894/23-2 and NO 1444/1-2 as part of the Research Unit FOR2432/2the China Scholarship Council(CSC)that supports the first author with a Ph D scholarshipsupport provided by Indian partners at the Institute of Wood Science and Technology(IWST),Bengaluru。
文摘Rapid urbanization has caused significant changes along the urban-rural gradient,leading to a variety of landscapes that are mainly shaped by human activities.This dynamic interplay also influences the distribution and characteristics of trees outside forests(TOF).Understanding the pattern of these trees will support informed decision-making in urban planning,in conservation strategies,and altogether in sustainable land management practices in the urban context.In this study,we employed a deep learning-based object detection model and high resolution satellite imagery to identify 1.3 million trees with bounding boxes within a 250 km^(2)research transect spanning the urban-rural gradient of Bengaluru,a megacity in Southern India.Additionally,we developed an allometric equation to estimate diameter at breast height(DBH)from the tree crown diameter(CD)derived from the detected bounding boxes.Our study focused on analyzing variations in tree density and tree size along this gradient.The findings revealed distinct patterns:the urban domain displayed larger tree crown diameters(mean:8.87 m)and DBH(mean:43.78 cm)but having relatively low tree density(32 trees per hectare).Furthermore,with increasing distance from the city center,tree density increased,while the mean tree crown diameter and mean tree basal area decreased,showing clear differences of tree density and size between the urban and rural domains in Bengaluru.This study offers an efficient methodology that helps generating instructive insights into the dynamics of TOF along the urban-rural gradient.This may inform urban planning and management strategies for enhancing green infrastructure and biodiversity conservation in rapidly urbanizing cities like Bengaluru.
基金funded by the Key-Area Research and Development Program of Guangdong Province(Grant No.2022B0202070002)the Guangxi Science and Technology Major Program(Grant No.GuikeAA23023007-2)+1 种基金the Guangdong Province Modern Agricultural Industry Technology System Innovation Team Construction Project(2024CXTD19)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515010303)。
文摘Agrobacterium tumefaciens-mediated transformation has been widely adopted for plant genetic engineering and the study of gene function(Krenek et al.,2015).This method is prevalent in the genetic transformation of herbaceous plants,with notable applications in species such as Arabidopsis(Yin et al.,2024),soybean(Zhang et al.,2024),rice(Zhang et al.,2020),and Chinese cabbage(Li et al.,2021).However,its application in fruit trees is limited.This is primarily due to their long growth cycles and lack of rapid,efficient,and stable transgenic systems,which severely hinders foundational research involving plant genetic transformation(Mei et al.,2024).Furthermore,for subtropical fruit trees,the presence of recalcitrant seeds adds an extra layer of difficulty to genetic transformation(Umarani et al.,2015),as most methods rely on seed germination as a basis for transformation.
基金funding from Horizon Europe Framework Programme(HORIZON),call Teaming for Excellence(HORIZON-WIDERA-2022-ACCESS-01-two-stage)-Creation of the centre of excellence in smart forestry“Forest 4.0”No.101059985funded by the EuropeanUnion under the project FOREST 4.0-“Ekscelencijos centras tvariai miško bioekonomikai vystyti”No.10-042-P-0002.
文摘Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating the adoption of advanced,automated approaches for improved forest conservation and management.This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery.A dataset of 3157 images was collected and divided into training(2528),validation(495),and testing(134)sets.To enhance model robustness and generalization,data augmentation was applied to the training part of the dataset.Various YOLO-based models,including YOLOv8,YOLOv9,YOLOv10,YOLOv11,and YOLOv12,were evaluated using different hyperparameters and optimization techniques,such as stochastic gradient descent(SGD)and auto-optimization.These models were assessed in terms of detection accuracy and the number of detected trees.The highest-performing model,YOLOv12m,achieved a mean average precision(mAP@50)of 0.908,mAP@50:95 of 0.581,recall of 0.851,precision of 0.852,and an F1-score of 0.847.The results demonstrate that YOLO-based object detection offers a highly efficient,scalable,and accurate solution for individual tree detection in satellite imagery,facilitating improved forest inventory,monitoring,and ecosystem management.This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.
文摘The Arctic region is experiencing accelerated sea ice melt and increased iceberg detachment from glaciers due to climate change.These drifting icebergs present a risk and engineering challenge for subsea installations traversing shallow waters,where ice-berg keels may reach the seabed,potentially damaging subsea structures.Consequently,costly and time-intensive iceberg manage-ment operations,such as towing and rerouting,are undertaken to safeguard subsea and offshore infrastructure.This study,therefore,explores the application of extra tree regression(ETR)as a robust solution for estimating iceberg draft,particularly in the preliminary phases of decision-making for iceberg management projects.Nine ETR models were developed using parameters influencing iceberg draft.Subsequent analyses identified the most effective models and significant input variables.Uncertainty analysis revealed that the superior ETR model tended to overestimate iceberg drafts;however,it achieved the highest precision,correlation,and simplicity in estimation.Comparison with decision tree regression,random forest regression,and empirical methods confirmed the superior perfor-mance of ETR in predicting iceberg drafts.
文摘A tree's basal area(BA)and wood volume scale exponentially with tree diameter in species-specifc patterns.Recent observed increases in tree growth suggest these allometric relationships are shifting in response to climate change,rising CO_(2) levels,and/or changes in forest management.We analyzed 9,214 cores from nine conifer and 11 broadleaf species grown in managed mixed-species stands in the upper Midwest to quantify how well diameter(diameter at breast height(DBH))serves to predict BA growth and above-ground wood and carbon(C).These samples include many large trees.We ft mixed models to predict BA growth and above-ground biomass/C from diameter,tree height,and the BA of nearby trees while controlling for site effects.Models account for 55%–83%of the variance in log(recent growth),improving predictions over earlier models.Growth-diameter scaling exponents covary with certain leaf and stem(but not wood)functional traits,reflecting growth strategies.LogBA increment scales linearly with log(diameter)as trees grow bigger in 16/20 species and growth actually accelerates in Quercus rubra L.Three other species plateau in growth.Growth only decelerates in red pine,Pinus resinosa Ait.Growth in whole-tree,above-ground biomass,and C accelerate even more strongly with diameter(mean exponent:2.08 vs.1.30 for BA growth).Sustained BA growth and accelerating wood/C growth contradict the common assumption that tree growth declines in bigger trees.Yield tables and silvicultural guidelines should be updated to reflect these current relationships.Such revisions will favor delaying harvests in many managed stands to increase wood production and enhance ecosystem values including C fxation and storage.Further research may resolve the relative roles of thinning,climatic conditions,nitrogen inputs,and rising CO2 levels on changing patterns of tree growth.
基金funded by the Ministry of Science and Higher Education of Kazakhstan and carried out within the framework of the project AP23488112“Development and study of a quantum-resistant digital signature scheme based on a Verkle tree”at the Institute of Information and Computational Technologies.
文摘This paper examines the application of the Verkle tree—an efficient data structure that leverages commitments and a novel proof technique in cryptographic solutions.Unlike traditional Merkle trees,the Verkle tree significantly reduces signature size by utilizing polynomial and vector commitments.Compact proofs also accelerate the verification process,reducing computational overhead,which makes Verkle trees particularly useful.The study proposes a new approach based on a non-positional polynomial notation(NPN)employing the Chinese Remainder Theorem(CRT).CRT enables efficient data representation and verification by decomposing data into smaller,indepen-dent components,simplifying computations,reducing overhead,and enhancing scalability.This technique facilitates parallel data processing,which is especially advantageous in cryptographic applications such as commitment and proof construction in Verkle trees,as well as in systems with constrained computational resources.Theoretical foundations of the approach,its advantages,and practical implementation aspects are explored,including resistance to potential attacks,application domains,and a comparative analysis with existing methods based on well-known parameters and characteristics.An analysis of potential attacks and vulnerabilities,including greatest common divisor(GCD)attacks,approximate multiple attacks(LLL lattice-based),brute-force search for irreducible polynomials,and the estimation of their total number,indicates that no vulnerabilities have been identified in the proposed method thus far.Furthermore,the study demonstrates that integrating CRT with Verkle trees ensures high scalability,making this approach promising for blockchain systems and other distributed systems requiring compact and efficient proofs.
文摘Primary challenges with a forest inventory program are surveyors with various levels of experience and the turnover of inexperienced surveyors.Few studies have looked at the consistency of an instrument’s results among inexperienced surveyors.Most studies have assessed whether instruments were significantly different.These tests do not indicate whether instruments were statistically equivalent,i.e.,that choosing either one would be acceptable under a certain level of tolerance.This study evaluated the consistency and statistical equivalence among instruments for measuring diameter at breast height(DBH)and for total tree height(HT)among inexperienced surveyors.The study was conducted as a randomized experiment with students from an introductory tree measurement course,using four types of DBH and HT instruments,and with different tree attributes.For DBH,the results show that D-tape was the most consistent across tree attributes and teams of inexperienced surveyors and was only statistically interchangeable with Caliper with a tolerance≥3 cm.For HT,Ultrasound was the most consistent but only statistically interchangeable with Laser with a tolerance≥8 m.A single type of instrument for measuring DBH and for HT is recommended,especially when field crews may be a mixture of experienced and inexperienced surveyors.Our study provides initial recommendations on the choice of instruments when either purchasing new ones or replacing old ones in forest inventories.
基金supports this paper.Project Nos.NSTC-112-2221-E-324-003 MY3,NSTC-111-2622-E-324-002 and NSTC-112-2221-E-324-011-MY2.
文摘Unlike the detection of marked on-street parking spaces,detecting unmarked spaces poses significant challenges due to the absence of clear physical demarcation and uneven gaps caused by irregular parking.In urban cities with heavy traffic flow,these challenges can result in traffic disruptions,rear-end collisions,sideswipes,and congestion as drivers struggle to make decisions.We propose a real-time detection system for on-street parking spaces using YOLO models and recommend the most suitable space based on KD-tree search.Lightweight versions of YOLOv5,YOLOv7-tiny,and YOLOv8 with different architectures are trained.Among the models,YOLOv5s with SPPF at the backbone achieved an F1-score of 0.89,which was selected for validation using k-fold cross-validation on our dataset.The Low variance and standard deviation recorded across folds indicate the model’s generalizability,reliability,and stability.Inference with KD-tree using predictions from the YOLO models recorded FPS of 37.9 for YOLOv5,67.2 for YOLOv7-tiny,and 67.0 for YOLOv8.The models successfully detect both marked and unmarked empty parking spaces on test data with varying inference speeds and FPS.These models can be efficiently deployed for real-time applications due to their high FPS,inference speed,and lightweight nature.In comparison with other state-of-the-art models,our models outperform them,further demonstrating their effectiveness.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金co-supported by the Foundation of Shanghai Astronautics Science and Technology Innovation,China(No.SAST2022-114)the National Natural Science Foundation of China(No.62303378),the National Natural Science Foundation of China(Nos.124B2031,12202281)the Foundation of China National Key Laboratory of Science and Technology on Test Physics&Numerical Mathematics,China(No.08-YY-2023-R11)。
文摘The problem of collision avoidance for non-cooperative targets has received significant attention from researchers in recent years.Non-cooperative targets exhibit uncertain states and unpredictable behaviors,making collision avoidance significantly more challenging than that for space debris.Much existing research focuses on the continuous thrust model,whereas the impulsive maneuver model is more appropriate for long-duration and long-distance avoidance missions.Additionally,it is important to minimize the impact on the original mission while avoiding noncooperative targets.On the other hand,the existing avoidance algorithms are computationally complex and time-consuming especially with the limited computing capability of the on-board computer,posing challenges for practical engineering applications.To conquer these difficulties,this paper makes the following key contributions:(A)a turn-based(sequential decision-making)limited-area impulsive collision avoidance model considering the time delay of precision orbit determination is established for the first time;(B)a novel Selection Probability Learning Adaptive Search-depth Search Tree(SPL-ASST)algorithm is proposed for non-cooperative target avoidance,which improves the decision-making efficiency by introducing an adaptive-search-depth mechanism and a neural network into the traditional Monte Carlo Tree Search(MCTS).Numerical simulations confirm the effectiveness and efficiency of the proposed method.
文摘A previous paper showed that the real numbers between 0 and 1 could be represented by an infinite tree structure, called the ‘infinity tree’, which contains only a countably infinite number of nodes and arcs. This paper discusses how a finite-state Turing machine could, in a countably infinite number of state transitions, write all the infinite paths in the infinity tree to a countably infinite tape. Hence it is argued that the real numbers in the interval [0, 1] are countably infinite in a non-Cantorian theory of infinity based on Turing machines using countably infinite space and time. In this theory, Cantor’s Continuum Hypothesis can also be proved. And in this theory, it follows that the power set of the natural numbers P(ℕ) is countably infinite, which contradicts the claim of Cantor’s Theorem for the natural numbers. However, this paper does not claim there is an error in Cantor’s arguments that [0, 1] is uncountably infinite. Rather, this paper considers the situation as a paradox, resulting from different choices about how to represent and count the continuum of real numbers.
基金supported by the Science and Technology Innovation(STI)2030-Major Projects(2022ZD0205000 to L.L.)CAS“Light of West China”Program(xbzg-zdsys-202404 to L.L.)+1 种基金Yunnan Revitalization Talent Support Program Yunling Scholar Project(to L.L.)Yunnan Fundamental Research Projects(202305AH340006,202301AS070060 to L.L.,202401AT070206 to X.C.)。
文摘The Chinese tree shrew has gained prominence as a model organism due to its phylogenetic proximity to primates,offering distinct advantages over traditional rodent models in biomedical research.However,the neuroanatomy of this species remains insufficiently defined,limiting its utility in neurophysiological and neuropathological studies.In this study,immunofluorescence microscopy was employed to comprehensively map the distribution of three calciumbinding proteins,parvalbumin,calbindin D-28k,and calretinin,across the tree shrew cerebrum.Serial brain sections in sagittal,coronal,and horizontal planes from 12 individuals generated a dataset of 3638 cellular-resolution images.This dataset,accessible via Science Data Bank(https://doi.org/10.57760/sciencedb.23471),provides detailed region-and laminar-selective distributions of calcium-binding proteins valuable for the cyto-and chemoarchitectural characterization of the tree shrew cerebrum.This resource will not only advance our understanding of brain organization and facilitate basic and translational neuroscience research in tree shrews but also enhance comparative and evolutionary analyses across species.
