NoSQL databases outperform the traditional RDBMS due to their faster retrieval of large volumes of data, scalability, and high performance. The need for these databases has been increasing  in recent years because data collection is growing tremendously. Structured, unstructured, and semi- structured data storage is allowed in NoSQL, which is not possible in a traditional database. NoSQL needs to compensate with its security feature for its amazing functionalities of faster data access and large data storage. The main concern exists in sensitive information stored in the data. The need to protect this sensitive data is crucial for confidentiality and privacy problems. To understand the severity of preserving sensitive data, recognizing the security issues is important. These security issues, if not resolved, will cause data loss, unauthorized access, database crashes by hackers, and security breaches. This paper investigates the security issues common to the top twenty NoSQL databases of the following types: document, key-value, column, graph, object- oriented, and multi-model. The top twenty NoSQL databases studied were MongoDB, Cassandra, CouchDB, Hypertable, Redis, Riak, Neo4j, Hadoop HBase, Couchbase, MemcacheDB, RavenDB, Voldemort, Perst, HyperGraphDB, NeoDatis, MyOODB, OrientDB, Apache Drill, Amazon, and Neptune. The comparison results show that there are common security issues among the databases. SQL injection security issues were detected in eight databases. The names of the databases were MongoDB, Cassandra, CouchDB, Neo4j, Couchbase, RavenDB, OrientDB, and Apache Drill.

In meta-analysis based on continuous outcome, estimated means and corresponding standard deviations from the selected studies are key inputs to obtain a pooled estimate of the mean and its confidence interval. We often encounter the situation that these quantities are not directly reported in the literatures. Instead, other summary statistics are reported such as median, minimum, maximum, quartiles, and study sample size. Based on available summary statistics, we need to estimate estimates of mean and standard deviation for meta-analysis.We developed an R Shiny code based on approximate Bayesian computation (ABC), ABCMETA, to deal with this situation. In this article, we present an interactive and user-friendly R Shiny application for implementing the proposed method (named ABCMETAapp). In ABCMETAapp, users can choose an underlying outcome distribution other than the normal distribution when the distribution of the outcome variable is skewed or heavy tailed. We show how to run ABCMETAapp with examples. ABCMETAapp provides an R Shiny implementation. This method is more flexible than the existing analytical methods since estimation can be based on five different distributions (Normal, Lognormal, Exponential, Weibull, and Beta) for the outcome variable.

Graduate teaching assistants (GTAs) play an important instructional role in teaching undergrad- and graduate-level courses, yet they receive very little training. The most common form of teaching professional development to GTAs is a pre-semester workshop held at the course, department, or college level. In this study, we describe the development, implementation, and evaluation of GTA training programs using the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). We used observation and interviews for data collection. ADDIE is generally used in instructional design. The results show the value of utilizing ADDIE in developing and evaluating a training program. It is intended to analyze the multi-dimensional connection of designing a training program: meeting expectations of trainees seeking to acquire skills sets as well as understanding the nuances and navigating the complex system that is needed to be successful on the job.