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With the unprecedented urbanization processes around the world, cities have become the main areas of political, cultural, and economic creation, but these regions have also caused environmental degradation and even affected public health. Ecological landscape is considered as an important way to mitigate the impact of environmental exposure on urban residents. Therefore, quantifying the quality of urban road landscape and exploring its spatial heterogeneity to obtain basic data on the urban environment and provide ideas for urban residents to improve the environment will be a meaningful preparation for further urban planning. In this study, we proposed a framework to achieve automatic quantifying urban street quality by integrating a mass of street view images based on deep learning and landscape ecology. We conducted a case study in Xiamen Island and mapped a series of spatial distribution for ecological indicators including PLAND, LPI, AI, DIVISION, FRAC_MN, LSI and SHDI. Additionally, we quantified street quality by the entropy weight method. Our results showed the streetscape quality of the roundabout in Xiamen was relatively lower, while the central urban area presented a belt-shaped area with excellent landscape quality. We suggested that managers could build vertical greening on some streets around the Xiamen Island to improve the street quality in order to provide greater well-being for urban residents. In this study, it was found that there were still large uncertainties in the mechanism of environmental impact on human beings. We proposed to strengthen the in-depth understanding of the mechanism of environmental impact on human beings in the process of interaction between environment and human beings, and continue to form general models to enhance the ability of insight into the urban ecosystem. [ABSTRACT FROM AUTHOR]

Abstract: The transactional memory in multicore processors has been a major research area over past several years. Many transactional memory systems have been proposed to be used to solve the synchronization problem of multicore processors. Hardware transactional memory is one of the critical methods to speedup communications in multicore environment. In this paper, we give a review of the current hardware transactional memory systems for multicore processors. We take a top-down approach to characterizing and classifying various hardware transactional design issues and present a taxonomy of hardware transactional memory systems which is consist of the five fundamental design issues: version management, conflict detection, contention management, virtualization and nesting. Finally, we discussed the active research challenge: the relationship between transactional memory and Input/Output operations and system calls. [Copyright &y& Elsevier]

Failure detectors are one of the fundamental components for maintaining the high availability of vehicular cloud computing. In vehicular cloud computing, lots of RSUs are deployed along the road to improve the connectivity. Many of them are equipped with solar battery due to the unavailability or excess expense of wired electrical power. So it is important to reduce the battery consumption of RSU. However, the existing failure detection algorithms are not designed to save battery consumption RSU. To solve this problem, a new energy-efficient failure detector 2E-FD has been proposed specifically for vehicular cloud computing. 2E-FD does not only provide acceptable failure detection service, but also saves the battery consumption of RSU. Through the comparative experiments, the results show that our failure detector has better performance in terms of speed, accuracy and battery consumption. [ABSTRACT FROM AUTHOR]

Failure detectors are used to build high availability distributed systems as the fundamental component. To meet the requirement of a complicated large-scale distributed system, accrual failure detectors that can adapt to multiple applications have been studied extensively. However, several implementations of accrual failure detectors do not adapt well to the cloud service environment. To solve this problem, a new accrual failure detector based on Weibull Distribution, called the Weibull Distribution Failure Detector, has been proposed specifically for cloud computing. It can adapt to the dynamic and unexpected network conditions in cloud computing. The performance of the Weibull Distribution Failure Detector is evaluated and compared based on public classical experiment data and cloud computing experiment data. The results show that the Weibull Distribution Failure Detector has better performance in terms of speed and accuracy in unstable scenarios, especially in cloud computing. [ABSTRACT FROM AUTHOR]