Migration of existing software systems to mobile computing platforms:a systematic mapping study

Mobile computing has fast emerged as a pervasive technology to replace the old computing paradigms with portable computation and context-aware communication.Existing software systems can be migrated(while preserving their data and logic)to mobile computing platforms that support portability,context-sensitivity,and enhanced usability.In recent years,some research and development efforts have focused on a systematic migration of existing software systems to mobile computing platforms.To investigate the research state-of-the-art on the migration of existing software systems to mobile computing platforms.We aim to analyze the progression and impacts of existing research,highlight challenges and solutions that reflect dimensions of emerging and futuristic research.We followed evidence-based software engineering(EBSE)method to conduct a systematic mapping study(SMS)of the existing research that has progressed over more than a decade(25 studies published from 1996–2017).We have derived a taxonomical classification and a holistic mapping of the existing research to investigate its progress,impacts,and potential areas of futuristic research and development.The SMS has identified three types of migration namely Static,Dynamic,and State-based Migration of existing software systems to mobile computing platforms.Migration to mobile computing platforms enables existing software systems to achieve portability,context-sensitivity,and high connectivity.However,mobile systems may face some challenges such as resource poverty,data security,and privacy.The emerging and futuristic research aims to support patterns and tool support to automate the migration process.The results of this SMS can benefit researchers and practitioners-by highlighting challenges,solutions,and tools,etc.,-to conceptualize the state-of-the-art and futuristic trends that support migration of existing software to mobile computing.

Curvature Motion on Dual Hyperbolic Unit Sphere H^(2)_(0)

In this paper, we define dual curvature motion on the dual hyperbolic unit sphere H2<sub style="margin-left:-8px;">0 in the dual Lorentzian space D31 with dual signature (+,+-) . We carry the obtained results to the Lorentzian line space R3<sub style="margin-left:-8px;">1 by means of Study mapping. Then we make an analysis of the orbits during the dual hyperbolic spherical curvature motion. Finally, we find some line congruences, the family of ruled surfaces and ruled surfaces in R3<sub style="margin-left:-8px;">1.

Transcriptome-wide association studies: a view from Mendelian randomization

Background:Genome-wide association studies(GWASs)have identified thousands of genetic variants that are associated with many complex traits.However,their biological mechanisms remain largely unknown.Transcriptome-wide association studies(TWAS)have been recently proposed as an invaluable tool for investigating the potential gene regulatory mechanisms underlying variant-trait associations.Specifically,TWAS integrate GWAS with expression mapping studies based on a common set of variants and aim to identify genes whose GReX is associated with the phenotype.Various methods have been developed for performing TWAS and/or similar integrative analysis.Each such method has a different modeling assumption and many were initially developed to answer different biological questions.Consequently,it is not straightforward to understand their modeling property from a theoretical perspective.Results:We present a technical review on thirteen TWAS methods.Importantly,we show that these methods can all be viewed as two-sample Mendelian randomization(MR)analysis,which has been widely applied in GWASs for examining the causal effects of exposure on outcome.Viewing different TWAS methods from an MR perspective provides us a unique angle for understanding their benefits and pitfalls.We systematically introduce the MR analysis framework,explain how features of the GWAS and expression data influence the adaptation of MR for TWAS,and re-interpret the modeling assumptions made in different TWAS methods from an MR angle.We finally describe future directions for TWAS methodology development.Conclusions:We hope that this review would serve as a useful reference for both methodologists who develop TWAS methods and practitioners who perform TWAS analysis.