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A drug dataset containing international proprietary names is essential for researchers investigating different drugs from different countries worldwide. However, many websites on the internet offer free access for a single drug searching service to identify international drug trade names, but not for a list of drugs to be searched and identified. Therefore, it will be problematic if the researcher has a list of hundreds or thousands of drug trade names to be identified. In this project, we have created an International Drug Dictionary (IDD) by curating collected drug lists from open access websites belonging to official drug regulatory agencies, official healthcare systems, or recognized scientific bodies from 44 countries around the world in addition to the European public assessment reports (EPAR) and the DRUGBANK vocabulary published in the public domain. Researchers interested in pharmacovigilance, pharmacoepidemiology, or pharmacoeconomics can benefit from this dataset, especially when identifying lists of proprietary drug names, particularly of multi-national origin.To enhance its adaptability, we also mapped the IDD to the standardized drug vocabulary RxNorm. The IDD can also be used as a tool for mapping international drug trade names to RxNorm. Each drug entity in the IDD mapped to a unique identification number for each entity called Atom Unique Identifier (RXAUI) from RxNorm.

The emerging computing paradigms like mobile cloud, mobile edge computing, fog computing, AI, and 5G provide the scope to advance mobile learning (m-learning) performances across educational disciplines. Edge computing brings the computing power at the edge of the network and closer to mobile learning actors. This study investigates the potentials of emerging computing paradigms and proposes a novel mobile edge-based hierarchical architecture to optimize m-learning performance efficiency. The architecture's design follows the ETSI MEC ISG framework and ISG MEC technical requirements. An m-learning use case was executed to evaluate the architecture's performance efficiency in a university facility. The implementation results show reduced latencies and quick response time. The architecture handles data locally on-site, lowers demand on radio access bandwidth, increases confidential data privacy, and continues the application execution even the network is disrupted. The architecture is open to redesign, reconfigure, and integrate with other computing paradigms. IoT-based m-learning use cases should be considered by reflecting learners' scaling of resource needs. Execution of complex use cases will expand the architecture base, MEC-based learning models usage, and change learning-teaching dynamics across educational disciplines.

In this work, we introduce a new definition of weighted derivatives along with corresponding integral operators, which aim to facilitate the solution of both linear and non-linear differential equations. A significant finding is that the fractional derivative of Caputo–Fabrizio type is a special case within this framework, allowing us to build upon existing research in this area. Additionally, we provide closed-form analytical solutions for autonomous and logistic equations using our newly defined derivatives and integrals. We thoroughly explore the properties associated with these weighted derivatives and integrals. To demonstrate the reliability and practical applicability of our results, we include several examples and applications that highlight the effectiveness of our approach.

This paper presents a Galerkin finite element-based study investigating the magnetohydrodynamic (MHD) mixed convective behavior of Ostwald-de Waele nanofluids in a lid-driven wavy chamber. The study addresses a critical aspect of heat transfer with applications in diverse fields, including nuclear power plants and solar thermal collectors. Utilizing Galerkin finite element methods, we analyze the complex interplay of factors in the 2D stable motion of nanofluids. Our investigation involves a comparative analysis of thermal Grashof numbers, emphasizing their impact on the thermo-hydrodynamic behavior within the chamber. Additionally, the magnetic field effects, characterized by parameters such as Hartmann number, Reynolds number, Darcy number, power-law index, and undulation number, are systematically explored. The study introduces geometric representations for understanding the isothermal behaviors of Ostwald-de Waele nanofluids. The findings underscore the significance of physical factors, including the transition to turbulent flow, increased porosity, enhanced nanoparticle strength, and smaller particle sizes, in influencing fluid flow within the chamber. Notably, at the highest Reynolds number, elevating Grashof and Darcy numbers enhances the Nusselt number by 3% and 19%, respectively, while a reduction in the Hartmann number leads to a remarkable 23% increase. This Galerkin finite element-based study provides a comprehensive examination of MHD mixed convective behavior in a lid-driven wavy chamber with Ostwald-de Waele nanofluids. The results offer quantitative insights into the intricate dynamics of nanofluid heat transfer and contribute novel perspectives to the existing literature.

The focus of this study is to establish the existence and uniqueness of solutions for differential and integral equations within specific metric spaces. Our investigation begins by introducing the concept of the so-called cone Eb-metric space and presenting crucial findings in this particular space. We have presented fixed point results for specific contractions, particularly in the context of non-solid cones that possess semi-interior points. Not only do the results enhance specific previous fixed points outcomes, but they also encompass and extend previous findings documented in the literature. Furthermore, we apply our findings in the cone Eb-metric space to various examples and applications. The ultimate outcome is the rigorous validation of the existence and uniqueness of solutions for certain differential and integral equations.