Mouse C-MET/HGFR ELISA Kit

Internet of Things (IoT) is an emerging paradigm that transforms everyday devices (Things) into Internet-connected devices with sensing, processing, and actuation capabilities. These devices have limited storage and processing capacity, so they have been integrated with Cloud computing to overcome these limitations. Cloud computing offers various benefits such as offload data storage and processing burden at the Cloud side. Nevertheless, because Cloud is not an efficient solution for IoT latency-sensitive applications, Fog computing was introduced to address this limitation. Although Fog-Cloud IoT environments have begun to be adopted in the last few years, such environments have not been properly assessed in terms of their capacity to meet the growing demand of IoT devices. In this work, we present a Deterministic and Stochastic Petri Net (DSPN) approach for evaluating Fog-Cloud IoT environments composed of hundreds physical Things. Our approach allows evaluating the trade-offs of many performability metrics (e.g., utilization, response time, throughput, and availability) and, consequently, may help system designers to choose the most suitable Fog-Cloud IoT environment. We demonstrate the feasibility of our approach through a real-world case study. The results revealed that adopting a Fog device can improve availability. However, the performance is only improved in certain conditions like when the environment is not at full capacity.

Land cover mapping in complex environments can be challenging due to their landscape heterogeneity. With the increasing availability of various open-access remotely sensed datasets, more images acquired by different sensors and on different dates tend to be used to improve land cover classification accuracy. Selecting an appropriate feature domain with the best landscape separability is therefore crucial in meeting the requirement of computational efficiency and model interpretability. Variable selection is widely used in pattern recognition to enhance model parsimony. This study focused on the variable selection process and proposed a series of methods to select the optimal feature domain to improve land cover classification in a complex urbanized coastal area. Two decision tree models (CART-Classification and Regression Tree and CIT-Conditional Inference Tree) and five variable importance measures (GINI, PVIM-Permutated Variable Importance Measure, MDMinimum Depth, IPM-Intervention of Prediction Measure, and CPVIM-Conditional Permutation Variable Importance Measure) based on random forests were considered. Variable importance measures were applied to a set of spectral, spatial and temporal features derived from medium-resolution satellite images. Backward elimination methods were used to select the optimal feature subset. It is found that compared to the traditional band-only model, the variable selection process can significantly improve the model parsimony and computational efficiency. The CPVIM based on CIT decision tree model was more reliable in selecting relevant features regardless their correlations, but CART tended to generate higher classification accuracy. Therefore, the combination of the CART model and the ranking from the CPVIM variable measure is recommended to achieve higher classification accuracy and better data interpretability. The novelty of our work is with the insight into the merits of integrating variable selection in the land cover classification process over complex environments.