A quantitative framework to evaluate urban ecological resilience: broadening understanding through multi-attribute perspectives

Intensive human and economic activities in urban areas have had adverse effects on local resources and ecology, leading to a decline in ecological resilience. Enhancing ecological resilience is crucial for improving the urban ecosystem's ability to withstand and recover from external risks. However, quantitative research on urban ecological resilience remains somewhat ambiguous, with many studies lacking comprehensive assessment methods from multiple perspectives. In this study, we established a comprehensive framework to assess urban ecological resilience based on four regime attributes. The study's results indicated the following key findings: The average urban ecological resilience value exhibited a trend of initially declining and then recovering. Cities proposed different approaches when considering and managing social and ecological relationships during the development process. A significant correlation between urbanization levels and ecological resilience was observed, with urban ecological resilience increasing in areas with low urbanization levels and sharply decreasing in areas with high urbanization levels. The findings from this study provide a specific theoretical foundation for decision-makers involved in urban planning and development strategies

Observations on the Quantum Circuit of the SBox of AES

In this paper, we propose some improved quantum circuits to implement the Sbox of AES. Our improved quantum circuits are based on the following strategies. First, we try to find the minimum set of the intermediate variables that can be used to compute the 8-bit output of the Sbox. Second, we check whether some wires store intermediate variables and remain idle until the end. And we can reduce the number of qubit by reusing some certain wires. Third, we try to compute the output of the Sbox without ancillas qubits, because we do not need to be clean up the wires storing the output of the Sbox. This operation will reduce the number of Toffoli gates. Our first quantum circuit only needs 26 qubits and 46 Toffoli gates, while quantum circuit proposed by Langenberg \emph{et al.} required 32 qubits and 55 Toffoli gates. Furthermore, we can also construct our second quantum circuit with 22 qubits and 60 Toffoli gates

QTLs and candidate genes analyses for fruit size under domestication and differentiation in melon (Cucumis melo L.) based on high resolution maps

Background: Melon is a very important horticultural crop produced worldwide with high phenotypic diversity. Fruit size is among the most important domestication and differentiation traits in melon. The molecular mechanisms of fruit size in melon are largely unknown. Results: Two high-density genetic maps were constructed by whole-genome resequencing with two F2 segregating populations (WAP and MAP) derived from two crosses (cultivated agrestis × wild agrestis and cultivated melo × cultivated agrestis). We obtained 1,871,671 and 1,976,589 high quality SNPs that show differences between parents in WAP and MAP. A total of 5138 and 5839 recombination events generated 954 bins in WAP and 1027 bins in MAP with the average size of 321.3 Kb and 301.4 Kb respectively. All bins were mapped onto 12 linkage groups in WAP and MAP. The total lengths of two linkage maps were 904.4 cM (WAP) and 874.5 cM (MAP), covering 86.6% and 87.4% of the melon genome. Two loci for fruit size were identified on chromosome 11 in WAP and chromosome 5 in MAP, respectively. An auxin response factor and a YABBY transcription factor were inferred to be the candidate genes for both loci. Conclusion: The high-resolution genetic maps and QTLs analyses for fruit size described here will provide a better understanding the genetic basis of domestication and differentiation, and provide a valuable tool for map-based cloning and molecular marker assisted breeding.info:eu-repo/semantics/publishedVersio

Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Very high resolution (VHR) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district

Modeling and Deploying IoT-Aware Business Process Applications in Sensor Networks

The concept of the Internet of Things (IoT) is an important part of the next generation of information. Wireless sensor networks are composed of independent distributed smart sensor nodes and gateways. These discrete sensors constantly gather external physical information, such as temperature, sound, and vibration. Owing to the diversity of sensor devices and the complexity of the sensor sensing environment, the direct modeling of an IoT-aware business process application is particularly difficult. In addition, how to effectively deploy those designed applications to discrete servers in the heterogeneous sensor networks is also a pressing problem. In this paper, we propose a resource-oriented modeling approach and a dynamic consistent hashing (DCH)-based deploying algorithm to solve the above problems. Initially, we extended the graphic and machine-readable model of Business Process Model Notation (BPMN) 2.0 specification, making it able to support the direct modeling of an IoT-aware business process application. Furthermore, we proposed the DCH-based deploying algorithm to solve the problem of dynamic load balancing and access efficiency in the distributed execution environment. Finally, we designed an actual extended BPMN plugin in Eclipse. The approach presented in this paper has been validated to be effective

An integrated approach for evaluating dynamics of urban eco-resilience in urban agglomerations of China

The resilience of the urban ecological network is an indispensable aspect of reflecting the recovery capacity after the external risk shock. Improving the resilience of the ecological network is conducive to enhancing the ecological benefits. However, the current studies are lack of resilience evaluation from a comprehensive perspective. Thus, in this study, taking three typical urban agglomerations as cases, a comprehensive evaluation framework was proposed to assess ecological network resilience. First, we selected the comprehensive reserve as ecological sources and combined them with ecological corridors to construct the ecological network. Then we evaluated the resilience of ecological networks from the perspectives of robustness and redundancy. Finally, we analyzed the resilience of the network under different importance nodes failed. The results showed that (1) the average values of the comprehensive reserve value (CRV) in three urban agglomerations were over than 0.8. (2) the resilience of ecological networks in three urban agglomerations was blow the optimal value throughout during the period from 1985 to 2020. (3) the resilience change trends of multiple urban agglomerations were significant differences after removing certain important nodes, indicating the current ecological network is redundant. The research will help to improve the evaluation of urban ecological resilience and enhance the improvement of sustainable urban management and ecological restoration