The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more ...The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more reliable results. The classification and regression tree （CART） is one of the new modeling techniques which is developed for this purpose. In this study, the classification and regression trees method is explained and tested the power of the financial failure prediction. CART is applied for the data of industry companies which is trade in Istanbul Stock Exchange （ISE） between 1997-2007. As a result of this study, it has been observed that, CART has a high predicting power of financial failure one, two and three years prior to failure, and profitability ratios being the most important ratios in the prediction of failure.展开更多
This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) t...This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm.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
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
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
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
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 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
Mekong River Delta has many home-gardens,here,everybody organizes the tourisms.We observed the real situations and substances,evaluation,a choice at some households in the Mekong River Delta in order to have a purpose...Mekong River Delta has many home-gardens,here,everybody organizes the tourisms.We observed the real situations and substances,evaluation,a choice at some households in the Mekong River Delta in order to have a purpose of search,here,they have the home-gardens;the farmers plant fruit trees at the villages of provinces,that is a place which is influenced by the climate change.We went to the villages such as:Hiep Thanh village,Chau Thanh district,Long An province;Tan Phu village,Tan Phu Dong district,Tien Giang province;Tieu Can village,Tieu Can district,Tra Vinh province to observe the landscape(here 10 households for 1 village),and we took the sample to analyze.We knew the factors such as:drought,deficiency of water,salt water intrusion,flood.These factors influence the trees,assets,diseases,lives of the persons who stay here,and cause many damages.We compare many home-gardens having a climate change with the normal home-gardens.Thus,we propose the reasonable methods in order to fix the consequence and prevent the salt intrusion,flood,important damages…And we present some illustrations.展开更多
Astrocytes are associated with varying brain size between rodents and primates.As a close evolutionary relative of primates,the tree shrew(Tupaia belangeri)provides a valuable comparative model for investigating glial...Astrocytes are associated with varying brain size between rodents and primates.As a close evolutionary relative of primates,the tree shrew(Tupaia belangeri)provides a valuable comparative model for investigating glial architecture.However,the anatomical distribution and morphological characteristics of astrocytes in the tree shrew brain remain poorly characterized.In this study,glial fibrillary acidic protein(GFAP)immunofluorescence was employed to systematically examine the spatial distribution and morphology of astrocytes in the whole brain of tree shrews.Notably,GFAP-immunoreactive(ir)astrocytes were detected throughout the telencephalon,diencephalon,mesencephalon,metencephalon,and myelencephalon.Distinct laminar distribution was evident in regions such as the main olfactory bulb and hippocampus.Semi-quantitative comparisons revealed significant regional differences in astrocyte density between tree shrews and mice,encompassing the main olfactory bulb,accessory olfactory bulb,olfactory tubercle,cortex,hippocampus,cortical amygdaloid nucleus,hypothalamus,thalamus,superior colliculus,interpeduncular nucleus,median raphe nucleus,and parabrachial nucleus.Compared to mice,tree shrews exhibited higher astrocyte density with increased morphological complexity in the posterior hypothalamic nucleus,dorsomedial hypothalamic nucleus,ventromedial hypothalamic nucleus,and periaqueductal gray,but lower density with greater morphological complexity in the hippocampus and substantia nigra.In the paraventricular hypothalamic nucleus and lateral hypothalamic area,GFAP-ir astrocytes displayed comparable densities between tree shrews and mice but exhibited region-specific differences in morphological complexity.This study provides the first brain-wide mapping of GFAP-ir astrocytes in tree shrews,revealing marked interspecies differences in their distribution and morphology,and establishing a neuroanatomical framework for understanding astrocyte involvement in diverse physiological and behavioral functions.展开更多
1 Trees don't create their own heat like mammals do,and they don't have warm shelters or fur coats.So how do they survive the deep freeze of winter?In a way they do hibernate(冬眠)like bears—but in trees this...1 Trees don't create their own heat like mammals do,and they don't have warm shelters or fur coats.So how do they survive the deep freeze of winter?In a way they do hibernate(冬眠)like bears—but in trees this is called dormancy and it's pretty amazing.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
文摘The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more reliable results. The classification and regression tree （CART） is one of the new modeling techniques which is developed for this purpose. In this study, the classification and regression trees method is explained and tested the power of the financial failure prediction. CART is applied for the data of industry companies which is trade in Istanbul Stock Exchange （ISE） between 1997-2007. As a result of this study, it has been observed that, CART has a high predicting power of financial failure one, two and three years prior to failure, and profitability ratios being the most important ratios in the prediction of failure.
基金National Natural Science Foundation of China(No.61163010)
文摘This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm.
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.
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.
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.
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Mekong River Delta has many home-gardens,here,everybody organizes the tourisms.We observed the real situations and substances,evaluation,a choice at some households in the Mekong River Delta in order to have a purpose of search,here,they have the home-gardens;the farmers plant fruit trees at the villages of provinces,that is a place which is influenced by the climate change.We went to the villages such as:Hiep Thanh village,Chau Thanh district,Long An province;Tan Phu village,Tan Phu Dong district,Tien Giang province;Tieu Can village,Tieu Can district,Tra Vinh province to observe the landscape(here 10 households for 1 village),and we took the sample to analyze.We knew the factors such as:drought,deficiency of water,salt water intrusion,flood.These factors influence the trees,assets,diseases,lives of the persons who stay here,and cause many damages.We compare many home-gardens having a climate change with the normal home-gardens.Thus,we propose the reasonable methods in order to fix the consequence and prevent the salt intrusion,flood,important damages…And we present some illustrations.
基金supported by the STI2030-Major Projects(2022ZD0205202)Anhui Provincial Natural Science Foundation(2408085Y043)National Natural Science Foundation of China(82471540,32030046,32200798)。
文摘Astrocytes are associated with varying brain size between rodents and primates.As a close evolutionary relative of primates,the tree shrew(Tupaia belangeri)provides a valuable comparative model for investigating glial architecture.However,the anatomical distribution and morphological characteristics of astrocytes in the tree shrew brain remain poorly characterized.In this study,glial fibrillary acidic protein(GFAP)immunofluorescence was employed to systematically examine the spatial distribution and morphology of astrocytes in the whole brain of tree shrews.Notably,GFAP-immunoreactive(ir)astrocytes were detected throughout the telencephalon,diencephalon,mesencephalon,metencephalon,and myelencephalon.Distinct laminar distribution was evident in regions such as the main olfactory bulb and hippocampus.Semi-quantitative comparisons revealed significant regional differences in astrocyte density between tree shrews and mice,encompassing the main olfactory bulb,accessory olfactory bulb,olfactory tubercle,cortex,hippocampus,cortical amygdaloid nucleus,hypothalamus,thalamus,superior colliculus,interpeduncular nucleus,median raphe nucleus,and parabrachial nucleus.Compared to mice,tree shrews exhibited higher astrocyte density with increased morphological complexity in the posterior hypothalamic nucleus,dorsomedial hypothalamic nucleus,ventromedial hypothalamic nucleus,and periaqueductal gray,but lower density with greater morphological complexity in the hippocampus and substantia nigra.In the paraventricular hypothalamic nucleus and lateral hypothalamic area,GFAP-ir astrocytes displayed comparable densities between tree shrews and mice but exhibited region-specific differences in morphological complexity.This study provides the first brain-wide mapping of GFAP-ir astrocytes in tree shrews,revealing marked interspecies differences in their distribution and morphology,and establishing a neuroanatomical framework for understanding astrocyte involvement in diverse physiological and behavioral functions.
文摘1 Trees don't create their own heat like mammals do,and they don't have warm shelters or fur coats.So how do they survive the deep freeze of winter?In a way they do hibernate(冬眠)like bears—but in trees this is called dormancy and it's pretty amazing.
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.
