ALBERT

Description: Context—Internet of Thing (IoT) based systems support any-time/place computations by interconnecting humans, systems, services, devices, and things that enabling autonomous systems to shape-up digitized societies. Software architecture, as the blue-print of software-intensive systems, abstracts the complexities of modeling, design, development, and evolution phases of a software to engineer complex IoT driven systems effectively and efficiently. Objectives and Method—Research and development efforts are required to exploit architectural principle and practices to design and develop IoT systems to go beyond the state-of-the-art for IoTs. The objectives of this research are to empirically investigate and systematically classify the state-of-the-art on architecting IoT based software. We have used the Evidence Based Software Engineering (EBSE) method to conduct a mapping study of the existing IoT solutions by investigating 88 qualitatively selected studies. Results and Implications—The results of the mapping study highlight various research themes that exploit software architecture models to develop IoT systems. The identified research themes include, but are not limited to, cloud-based software ecosystems, software defined networking, autonomous, and adaptive software and agent-based systems that IoTs drive. The mapping study suggests that futuristic research on architecting IoT software is focused on architectural languages and patterns that support reusability, automation, and human decision support to develop and dynamically adapt IoT software. The mapping study represents a concentrated knowledge regarding architectural principle and practices to facilitate knowledge transfer—benefiting researchers and practitioners—on the role of software architecture for IoT systems.

Description: The air-blood partition coefficient (Kab) is extensively employed in human health risk assessment for chemical exposure. However, current Kab estimation approaches either require an extensive number of parameters or lack precision. In this study, we present two novel and parsimonious models to accurately estimate Kab values for individual neutral organic compounds, as well as their complex mixtures. The first model, termed the GC×GC model, was developed based on the retention times of nonpolar chemical analytes on comprehensive two-dimensional gas chromatography (GC×GC). This model is unique in its ability to estimate the Kab values for complex mixtures of nonpolar organic chemicals. The GC×GC model successfully accounted for the Kab variance (R2 = 0.97) and demonstrated strong prediction power (RMSE = 0.31 log unit) for an independent set of nonpolar chemical analytes. Overall, the GC×GC model can be used to estimate Kab values for complex mixtures of neutral organic compounds. The second model, termed the partition model (PM), is based on two types of partition coefficients: octanol to water (Kow) and air to water (Kaw). The PM was able to effectively account for the variability in Kab data (n = 344), yielding an R2 value of 0.93 and root-mean-square error (RMSE) of 0.34 log unit. The predictive power and explanatory performance of the PM were found to be comparable to those of the parameter-intensive Abraham solvation models (ASMs). Additionally, the PM can be integrated into the software EPI Suite, which is widely used in chemical risk assessment for initial screening. The PM provides quick and reliable estimation of Kab compared to ASMs, while the GC×GC model is uniquely suited for estimating Kab values for complex mixtures of neutral organic compounds. In summary, our study introduces two novel and parsimonious models for the accurate estimation of Kab values for both individual compounds and complex mixtures.