Background Vietnam’s network of commune health centers (CHCs) have historically managed acute infectious diseases and implemented national disease-specific vertical programs. Vietnam has undergone an epidemiological transition towards non-communicable diseases (NCDs). Limited data exist on Vietnamese CHC capacity to prevent, diagnose, and treat NCDs. In this paper, we assess NCD service r...

Data Grid is a geographically distributed environment that deals with large-scale data-intensive applications. Effective scheduling in Grid can reduce the amount of data transferred among nodes by submitting a job to a node, where most of the requested data files are available. Data replication is another key optimization technique for reducing access latency and managing large data by storing ...

Data Grid systems require the management of a massive number of data sets to make the data available to the many clients accessing the data. Data is typically replicated in these large scale applications to improve the data availability and job response time. Because the behavior of the thousands of Grid users is very dynamic, it is a challenging problem to determine where and when to make data...

this study examined the effect of urbanization on agricultural production in abia state. specifically, it categorized the land tenancy status of the farmers, analyzed the effect of urbanization on agricultural productivity and identified the constraints to agricultural productivity. multistage random sampling technique was used to select the respondents. primary data collected using structured ...

Many temporal data mining projects use the existing data collected for various purposes, ranging from routinely collected data to process improvement projects and data required for regulatory purposes. In some cases, the set of considered features might be large (a wide data set) and sufficient for extraction of knowledge. In other cases the data set might be narrow and insufficient to extract ...

The detection of outliers from spatio-temporal data is an important task due to the increasing amount of spatio-temporal data available, and the need to understand and interpret it. Due to the limitations of previous data mining techniques, new techniques to detect spatio-temporal outliers need to be developed. In this paper, we propose a spatio-temporal outlier detection algorithm called Outst...