HSPM:A Better Model to Effectively Preventing Open-Source Projects from Dying

With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS project network,namely an Open-Source Software ECOsystem(OSSECO).Unfortunately,not all OS projects in the open-source ecosystem can be healthy and stable in the long term,and more projects will go from active to inactive and gradually die.In a tightly connected ecosystem,the death of one project can potentially cause the collapse of the entire ecosystem network.How can we effectively prevent such situations from happening?In this paper,we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model.Then,we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects.Finally,we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model(HSPM)to predict the OS project survival state.The experimental results show that HSPM significantly improved compared to the traditional prediction model.Our work can substantially assist OS project managers in maintaining their projects’health.It can also provide an essential reference for developers when choosing the right open-source project for their production activities.

OPTIONAL AND PREDICTABLE PROJECTIONS OF SET-VALUED MEASURABLE PROCESSES

In this paper,the optional and predictable projections of set-valued measurable processes are studied.The existence and uniqueness of optional and predictable projections of set-valued measurable processes are proved under proper circumstances.

Verification of Subseasonal-to-Seasonal Forecasts for Major Stratospheric Sudden Warmings in Northern Winter from 1998/99 to 2012/13

This study reports verification results of hindcast data of four systems in the subseasonal-to-seasonal(S2S)prediction project for major stratospheric sudden warmings(MSSWs)in northern winter from 1998/99 to 2012/13.This report deals with average features across all MSSWs,and possible differences between two MSSW types(vortex displacement and split types).Results for the average features show that stratospheric forecast verifications,when further averaged among the four systems,are judged to be successful for lead times around 10 d or shorter.All systems are skillful for lead times around 5 d,whereas the results vary among the systems for longer lead times.A comparison between the MSSW types overall suggests larger forecast errors or lower skill for MSSWs of the vortex split type,although the differences do not have strong statistical significance for almost all cases.This limitation is likely to at least partly reflect the small sample size of the MSSWs available.

Multiple mobile-obstacle avoidance algorithm for redundant manipulator

In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacles and a manipulator was obtained according to the states of obstacles and transformed to escape velocity of the corresponding link of the manipulator.The escape velocity was introduced to the gradient projection method to obtain the joint velocity of the manipulator so as to complete the obstacle avoidance trajectory planning.A7-DOF manipulator was used in the simulation,and the results verified the effectiveness of the algorithm.

The development of U.S.soil erosion prediction and modeling

Soil erosion prediction technology began over 70 years ago when Austin Zingg published a relationship between soil erosion(by water)and land slope and length,followed shortly by a relationship by Dwight Smith that expanded this equation to include conservation practices.But,it was nearly 20 years before this work's expansion resulted in the Universal Soil Loss Equation(USLE),perhaps the foremost achievement in soil erosion prediction in the last century.The USLE has increased in application and complexity,and its usefulness and limitations have led to the development of additional technologies and new science in soil erosion research and prediction.Main among these new technologies is the Water Erosion Prediction Project(WEPP)model,which has helped to overcome many of the shortcomings of the USLE,and increased the scale over which erosion by water can be predicted.Areas of application of erosion prediction include almost all land types:urban,rural,cropland,forests,rangeland,and construction sites.Specialty applications of WEPP include prediction of radioactive material movement with soils at a superfund cleanup site,and near real-time daily estimation of soil erosion for the entire state of Iowa.