A Constrained Local Neighborhood Approach for Efficient Markov Blanket Discovery in Undirected Independent Graphs

When learning the structure of a Bayesian network,the search space expands significantly as the network size and the number of nodes increase,leading to a noticeable decrease in algorithm efficiency.Traditional constraint-based methods typically rely on the results of conditional independence tests.However,excessive reliance on these test results can lead to a series of problems,including increased computational complexity and inaccurate results,especially when dealing with large-scale networks where performance bottlenecks are particularly evident.To overcome these challenges,we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected independence graphs.This method uses the Markov blanket discovery algorithm to refine the constraints in the initial search space,sets an appropriate constraint radius,thereby reducing the initial computational cost of the algorithm and effectively narrowing the initial solution range.Specifically,the method first determines the local neighborhood space to limit the search range,thereby reducing the number of possible graph structures that need to be considered.This process not only improves the accuracy of the search space constraints but also significantly reduces the number of conditional independence tests.By performing conditional independence tests within the local neighborhood of each node,the method avoids comprehensive tests across the entire network,greatly reducing computational complexity.At the same time,the setting of the constraint radius further improves computational efficiency while ensuring accuracy.Compared to other algorithms,this method can quickly and efficiently construct undirected independence graphs while maintaining high accuracy.Experimental simulation results show that,this method has significant advantages in obtaining the structure of undirected independence graphs,not only maintaining an accuracy of over 96%but also reducing the number of conditional independence tests by at least 50%.This significant performance improvement is due to the effective constraint on the search space and the fine control of computational costs.

Investigation on the Relationship between Population Density and Satellite Image Features——a Deep Learning Based Approach

Timely and accurate population statistic data plays an important role in many fields.To illustrate the demographic characteristics,population density is a crucial factor in evaluating population data.With a dynamic regional migration in population,it is a challenging job to evaluate population density without a census-based survey.We present the approach to classify satellite images in different magnitudes in population density and execute the comparative experiment to discuss the factors that influence the identification to the images with the deep learning approach.In this paper,we use satellite imagery and community population density data.With convolutional neural networks,we evaluated the performance of CNN on population estimation with satellite images,found the features that are important in population estimation,and then perform the sensitive analysis.

Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

To obtain the optimal Bayesian network(BN)structure,researchers often use the hybrid learning algorithm that combines the constraint-based(CB)method and the score-and-search(SS)method.This hybrid method has the problemthat the search efficiency could be improved due to the ample search space.The search process quickly falls into the local optimal solution,unable to obtain the global optimal.Based on this,the Particle SwarmOptimization(PSO)algorithm based on the search space constraint process is proposed.In the first stage,the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of the initial particles.In the second stage,the update mechanism is redefined,so that each step of the update process is consistent with the current structure which forms a one-to-one correspondence.At the same time,the“self-awakened”mechanism is added to prevent precocious particles frombeing part of the best.After the fitness value of the particle converges prematurely,the activation operation makes the particles jump out of the local optimal values to prevent the algorithmfromconverging too quickly into the local optimum.Finally,the standard network dataset was compared with other algorithms.The experimental results showed that the algorithmcould find the optimal solution at a small number of iterations and a more accurate network structure to verify the algorithm’s effectiveness.

Event-triggered and consensus-based attitude tracking alignment for discrete-time multiple spacecraft system exploiting interference

This paper presents a discrete-time attitude control strategy with equi-global practical stabilizability for aligning the attitude of multiple spacecraft to a predesigned configuration according to a time-variant reference.By utilizing the interference of the wireless channel,the communication scheme designed in this paper can save communication resources,amount of computation,and energy proportionally to the number of spacecraft.The exact discrete-time model and approximate discrete-time model of the consensus-based spacecraft tracking system are given.Then the framework for the design of an event-triggered control scheme for the exact discrete-time system via its approximate models is developed,which avoids the periodic actuation,and Zeno behavior is proved to be excluded.Furthermore,the control scheme can handle the presence of the unknown fading channel.Finally,simulation results are presented to demonstrate the effectiveness of the control strategy.

Hybridization chain reaction-based DNA nanomaterials for biosensing,bioimaging and therapeutics

DNA nanomaterials hold great promise in biomedical fields due to its excellent sequence programmability,molecular recognition ability and biocompatibility.Hybridization chain reaction(HCR)is a simple and efficient isothermal enzyme-free amplification strategy of DNA,generating nicked double helices with repeated units.Through the design of HCR hairpins,multiple nanomaterials with desired functions are assembled by DNA,exhibiting great potential in biomedical applications.Herein,the recent progress of HCR-based DNA nanomaterials for biosensing,bioimaging and therapeutics are summarized.Representative works are exemplified to demonstrate how HCR-based DNA nanomaterials are designed and constructed.The challenges and prospects of the development of HCR-based DNA nanomaterials are discussed.We envision that rationally designing HCR-based DNA nanomaterials will facilitate the development of biomedical applications.

骨齿对龟甲骨质层中骨缝结构强度和韧性的影响

龟壳背甲的骨质板由多根独立的肋骨组成,内部的骨缝结构将独立的肋骨组成整体骨质板.基于单轴拉伸、弯曲实验和有限元分析,本文研究了骨缝结构中的骨齿对龟甲骨质板整体强度和韧性的影响.微观形貌观测实验实验表明,龟甲骨质板骨缝结构中的骨齿带有三棱锥和四棱锥的凹槽构型,与已有研究中假设的完美圆锥型结构有着显著差异.通过3D打印技术制备了带有三棱锥和四棱锥凹槽构型的骨缝结构仿生试件并进行了力学测试.结果表明,骨齿的齿峰-齿谷比值是影响骨缝结构整体强韧性的关键因素,而非假设的骨齿侧表面积和投影面积.对于给定形状的骨齿结构而言,齿峰-齿谷比值越大则骨质板的强度和韧性则越大.对龟壳背甲带骨缝的骨质板和相应的仿生试件进行了三点弯曲实验测试,结果同样表明骨齿的齿峰-齿谷比值是整体强韧性能的决定性参数.本文的结果对于缝结构材料的强韧化仿生设计具有指导意义.

Klotho in Osx+-mesenchymal progenitors exerts pro-osteogenic and anti-inflammatory effects during mandibular alveolar bone formation and repair

Maxillofacial bone defects are commonly seen in clinical practice.A clearer understanding of the regulatory network directing maxillofacial bone formation will promote the development of novel therapeutic approaches for bone regeneration.The fibroblast growth factor(FGF)signalling pathway is critical for the development of maxillofacial bone.Klotho,a type I transmembrane protein,is an important components of FGF receptor complexes.Recent studies have reported the presence of Klotho expression in bone.However,the role of Klotho in cranioskeletal development and repair remains unknown.Here,we use a genetic strategy to report that deletion of Klotho in Osx-positive mesenchymal progenitors leads to a significant reduction in osteogenesis under physiological and pathological conditions.Klotho-deficient mensenchymal progenitors also suppress osteoclastogenesis in vitro and in vivo.Under conditions of inflammation and trauma-induced bone loss,we find that Klotho exerts an inhibitory function on inflammation-induced TNFR signaling by attenuating Rankl expression.More importantly,we show for the first time that Klotho is present in human alveolar bone,with a distinct expression pattern under both normal and pathological conditions.In summary,our results identify the mechanism whereby Klotho expressed in Osx+-mensenchymal progenitors controls osteoblast differentiation and osteoclastogenesis during mandibular alveolar bone formation and repair.Klotho-mediated signaling is an important component of alveolar bone remodeling and regeneration.It may also be a target for future therapeutics.

Novel empirical model for predicting residual flexural capacity of corroded steel reinforced concrete beam

In this study,a total of 177 flexural experimental tests of corroded reinforced concrete(CRC)beams were collected from the published literature.The database of flexural capacity of CRC beam was established by using unified and standardized experimental data.Through this database,the effects of various parameters on the flexural capacity of CRC beams were discussed,including beam width,the effective height of beam section,ratio of strength between longitudinal reinforcement and concrete,concrete compressive strength,and longitudinal reinforcement corrosion ratio.The results indicate that the corrosion of longitudinal reinforcement has the greatest effect on the residual flexural capacity of CRC beams,while other parameters have much less effect.In addition,six available empirical models for calculating the residual flexural strength of CRC beams were also collected and compared with each other based on the established database.It indicates that though five of six existing empirical models underestimate the flexural capacity of CRC beams,there is one model overestimating the flexural capacity.Finally,a newly developed empirical model is proposed to provide accurate and effective predictions in a large range of corrosion ratio for safety assessment of flexural failure of CRC beams confirmed by the comparisons.