A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-consuming for even moderate-scale networks.In this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries.The effectiveness of the proposed method is validated through synthetic and real-world networks.The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

Simulation of stochastic wind field for large complex structures based on modified Fourier spectrum

Simulation for stochastic wind field is very important in analyzing dynamic responses of large complex structures due to strong wind.The typical simulation method is the spectrum representation method (SRM),but the SRM has drawbacks of inferior precision in lower frequency and slow calculating speed.In view of this,the modified Fourier spectrum method (MFSM) is introduced into the simulation of stochastic wind field in this paper.In this method,phase information of wind velocity time history is determined by cross power spectral density (CPSD) between adjacent points,and the wind velocity time history with time and space correlation is generated by iterative modification for CPSD considering auto power spectral density (APSD).Simulation of the wind field for a long-span bridge is undertaken to verify the effectiveness of the MFSM.Simulation results of the SRM and the MFSM are compared.It can be concluded that the MFSM is more accurate and has higher calculation speed than the SRM.

Safety Risk Analysis of Large⁃scale Complex Project based on WBS⁃RBS and Interval Numbers

Safety risks are essential to the success or failure of the large⁃scale complex projects.In order to assess and evaluate the safety risks of the large⁃scale complex projects scientifically,a risk assessment method of work breakdown structure and risk breakdown structure(WBS⁃RBS)is proposed to identify the project risks.In this paper,interval numbers are used to evaluate the risk levels,weights are assigned automatically based on the complexity and risk degree of WBS to distinguish three types of nodes in WBS,and a risk assessment algorithm is designed to assess safety risk at all layers of the project.A case study is conducted to demonstrate how to apply the method.The results show the practicality,robustness and efficiency of our new method,which can be applied to different kinds of large⁃scale complex projects in reality.

3D Printing Hip Prostheses Offer Accurate Reconstruction,Stable Fixation,and Functional Recovery for Revision Total Hip Arthroplasty with Complex Acetabular Bone Defect

Complicated and large acetabular bone defects present the main challenges and difficulty in the revision of total hip arthroplasty(THA).This study aimed to explore the advantages of three-dimensional(3D)printing technology in the reconstruction of such acetabular bone defects.We retrospectively analyzed the prognosis of four severe bone defects around the acetabulum in three patients who were treated using 3D printing technology.Reconstruction of bone defect by conventional methods was difficult in these patients.In this endeavor,we used radiographic methods,related computer software such as Materialise's interactive medical image control system and Siemens NX software,and actual surgical experience to estimate defect volume,prosthesis stability,and installation accuracy,respectively.Moreover,a Harris hip score was obtained to evaluate limb function.It was found that bone defects could be adequately reconstructed using a 3D printing prosthesis,and its stability was reliable.The Harris hip score indicated a very good functional recovery in all three patients.In conclusion,3D printing technology had a good therapeutic effect on both complex and large bone defects in the revision of THA.It was able to achieve good curative effects in patients with large bone defects.

Synergistic effects of tropical cyclones on forest ecosystems:a global synthesis

Tropical cyclones are large-scale strong wind disturbance events that occur frequently in tropical and subtropical coastal regions and often bring catastrophic physical destruction to ecosystems and economic disruption to societies along their paths. Major tropical cyclones can infrequently move into the midaltitudes and inland areas. Ecologically, tropical cyclones have profound impacts on diversity, structure, succession and function of forest ecosystems. The ecological effects are both dramatic and subtle. The dramatic effects can be visible, noticeable and to some extent predictable over the short-term and relatively well documented in the literature. However, the subtle effects are often invisible, complex and at smaller scale relatively unpredictable in the long-term. Many factors, meteorologic, topographic and biologic, simultaneously interact to influence the complexity of patterns of damage and dynamics of recovery. I present a global synthesis on the effects of tropical cyclones on forest ecosystems and the complexity of forest responses, with particular attention on the response to large hurricanes in the neotropics and the temperate North America, and strong typhoons on the subtropical and temperate forests in the East and Southeast Asia. Four major aspects provide on organizational framework for this synthesis：（1） consistent damage patterns,（2） factors that influence response patterns and predict damage risks,（3） complexity of forest responses and recovery, and（4） the long-term effects. This review reveals highly variable and complex effects of tropical cyclones on forest ecosystems. A deep understanding of the synergistic effects of tropical cyclones is essential for effective forest management and biodiversity conservation.

Mapping high resolution National Soil Information Grids of China

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.