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In recent years, with the rapid growth of the internet of things (IoT), the number of devices connected to the network has increased significantly. The rising trend of mass data that objects send to the network has become a big challenge for cloud networks. The next generation IoT applications require low delay and real-time responses. Hence, IoT applications need a new processing paradigm to be able to access distributed cloud services at the network edge. In recent years, fog computing has emerged as a distributed computing model compatible with heterogeneous environments that can provide cloud services with low delay at the network edge. Meanwhile, services provided by the IoT suffer from varying workload changes over time, and approaches are needed to automatically provision resources and satisfy quality of service (QoS) requirements. With this motivation, this study presents an improved political optimizer-based resource management scheme for fog-enabled cloud environments, which is named IPORM. IPORM seeks to efficiently allocate resources to IoT requests with the purpose of maximizing resource utilization. In addition, other objectives such as energy, bandwidth, and delay are also included in IPORM. Simulation results show that our scheme significantly improves network performance in terms of various metrics compared to its counterparts. [ABSTRACT FROM AUTHOR]

This paper suggests a multi-layer neural network for deep learning based on fractional differential equations, and parallel computing is used to search an optimal structure. First, the Caputo derivative is approximated by L 1 numerical scheme and an unconstrained discretization minimization problem is presented. Then, parameters are adjusted by use of the Adam algorithm. Analytical approximate solutions of two fractional logistic equations (FLEs) are obtained which demonstrate the method's efficiency. Furthermore, with real-life data, fractional order and other parameters of FLEs are estimated by the gradient descent algorithm meanwhile. The proposed optimal NN method is used in forecasting. Through the comparative study, FLEs have more parameter freedom degrees and perform better than the classical logistic model. [ABSTRACT FROM AUTHOR]

Background & Aim: Panax notoginseng saponins are believed to promote wound healing due to its anti‐proliferative effect on fibroblasts. The present work was therefore aimed to examine the beneficial effect of PNS on wound healing in vitro and in a murine model of cutaneous wound. Methods: The in vitro effects of Panax notoginseng saponins on the proliferation of and nitric oxide (NO) synthesis in human fibroblast 3T3 cells were studied. The in vivo effects of Panax notoginseng saponins were examined in C57 mice with dorsal cutaneous wound. The healing rate and scar formation were followed after treatment with Panax notoginseng saponins. The histology and fibroblast accumulation in the wounds were studied using hematoxylin and eosin (H&E) staining. Expression of α‐smooth muscle actin (α‐SMA) was examined by immunohistochemistry. Results: Panax notoginseng saponins inhibited the proliferation of human fibroblast 3T3 with an EC50 of 1.825 mM Panax notoginseng saponins (0.1 mM) significantly promoted NO production (P < 0.01) and NO synthase activity (P < 0.01) of 3T3. In C57 mice with dorsal cutaneous wounds, 0.1 mM Panax notoginseng saponins significant expedited wound healing by reducing the size of lesions and suppressing the formation of scar. H&E staining revealed that treatment with Panax notoginseng saponins suppressed fibroblast accumulation in wound areas, while immunohistochemistry showed a significant reduction in α‐SMA expression by 0.1 mM Panax notoginseng saponins. Conclusion: Panax notoginseng saponins are a promising drug candidate that can accelerate wound healing and reduce scar formation. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)