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Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature-physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature's potential contribution to creating more equitable, green, livable cities with active inhabitants.

Fault localization is a complex, costly and time‐consuming task in software debugging. Numerous automated techniques have been developed to expedite this process. Mutation‐based fault localization (MBFL) is one of the most widely studied techniques which uses mutation analysis to generate mutants for revealing potential faults in the program. However, our theoretical analysis exposes an inherent conflict between the fundamental assumption and the essential meaning of existing MBFL suspiciousness. This conflict is caused by mutant bias. Intuitively, the suspiciousness can be corrected by eliminating the mutant bias for more accurately measuring the faulty probability of the corresponding mutant statement. In this paper, we introduce Delta4Ms, a fault localization approach designed to eliminate mutant bias. Delta4Ms integrates the principles of signal theory, modelling the actual suspiciousness and mutant bias as the desired and false signal components, respectively. Based on theoretical derivation, the average suspiciousness of mutants serves as an estimate of mutant bias. Delta4Ms effectively mitigates mutant bias, extracting the desired signal and yielding corrected suspiciousness for fault localization. To precisely estimate mutant bias, higher order mutants (HOMs) are incorporated. We conduct an extensive experimental evaluation of Delta4Ms on 320 real‐fault programs from Codeflaws. The results indicate that our model significantly outperforms existing SBFL and MBFL techniques, showing a considerable improvement in fault localization effectiveness. We further assessed the robustness of Delta4Ms by examining different HOM ratios and HOM generation strategies. Moreover, Delta4Ms achieves a substantial reduction in mutation execution cost and minimal accuracy loss through the implementation of test case reduction. Finally, we perform preliminary experiments on 15 real‐fault programs from the Defects4J benchmark to assess the generalization of the model's fault localization effectiveness. [ABSTRACT FROM AUTHOR]

With increasing urbanization in China, the traffic-induced urban environmental noise pollution problem is becoming more and more serious, and it has become a common urban malady that cannot be ignored. Traffic green belts are an important part of the urban ecosystem and play a role in traffic noise reduction, and simultaneously provide ecosystem services, such as creating a natural landscape and retaining dust. Therefore, they are a category of Nature-based Solutions (NbSs) that have multiple ecosystem service provisions. The relationship between NbSs and urban ecosystem services is one of the current research hot spots. However, regarding the assessment of ecosystem services on the urban scale, the role of vegetation in reducing noise pollution as a service has rarely been studied. Taking Shenzhen City as an example, through monitoring 217 sample plots in the city, this paper analyzes the relationship between vegetation coverage and the ability of green belts to reduce noise by using the IUEMS platform combined with the high-resolution spatial distribution data of green spaces. Then, we evaluated the product amount and the value of the roadside green belts in Shenzhen when acting as a noise reduction service. The work of this study, to a certain extent, improves the problems related to the inadequate consideration of vegetation characteristics in current urban-scale noise assessment models. The results show the following: (i) In the respective analysis buffer zones of the Grade I to Grade IV roads in Shenzhen, on average, for every 1% increase in the vegetation coverage of green belts, noise can be reduced by 0.4 dB, 1.0 dB, 0.2 dB, and 0.6 dB, respectively. (ii) The product value of the noise reduction service provided by roadside green belts is CNY 1.16 billion in Shenzhen. (iii) The road traffic noise greatly exceeds the standard in Shenzhen, but traffic noise can be decreased by increasing the vegetation coverage of green belts. This is not only beneficial to the scientific understanding of the ecological service value of green spaces by evaluating the noise reduction service of traffic green belts, as well as its influencing factors, but is also beneficial to making improvements in construction and management ideas for urban green spaces. [ABSTRACT FROM AUTHOR]