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Traditional development models are being slowly replaced by green economic development models. This paper views regional green economic development as a large complex system and develops a conceptual DPSIR (drivers, pressures, state, impact, response model of intervention) to construct a regional green economy development measurement index system, after which an entropy weight-TOPSIS-coupling coordination degree evaluation model is developed to quantitatively horizontally and vertically analyze regional green economy sustainable development trends and the coupled coordination status of each subsystem. The evaluation model is then employed to analyze the sustainable development of the green economy in Shandong Province from 2010 to 2016. The analysis results were found to be in line with the actual green economy development situation in Shandong Province, indicating that the measurement model had strong practicability for regional green economy development. Meanwhile, this model can demonstrate clearly how those indicators impact on the regional green economy sustainable development and fill the absence of existing studies on regional green economy sustainable development.

Summary In the processes controlling ecosystem fertility, fungi are increasingly acknowledged as key drivers. However, our understanding of the rules behind fungal community assembly regarding the effect of soil fertility level remains limited. Using soil samples from typical tea plantations spanning c. 2167 km north‐east to south‐west across China, we investigated the assemblage complexity and assembly processes of 140 fungal communities along a soil fertility gradient. The community dissimilarities of total fungi and fungal functional guilds increased with increasing soil fertility index dissimilarity. The symbiotrophs were more sensitive to variations in soil fertility compared with pathotrophs and saprotrophs. Fungal networks were larger and showed higher connectivity as well as greater potential for inter‐module connection in more fertile soils. Environmental factors had a slightly greater influence on fungal community composition than spatial factors. Species abundance fitted the Zipf–Mandelbrot distribution (niche‐based mechanisms), which provided evidence for deterministic‐based processes. Overall, the soil fungal communities in tea plantations responded in a deterministic manner to soil fertility, with high fertility correlated with complex fungal community assemblages. This study provides new insights that might contribute to predictions of fungal community complexity.

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The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.

In recent years, sustainable supply chains that balance economic development and the environment have become an inevitable focus for many businesses and industries. Supply chain finance as the core driving force for supply chain development, plays a vital role in resolving any financing difficulties that exist in many small and medium-sized enterprises (SMEs) in the upstream and downstream of the supply chain. However, most SME supply chain financing assessments currently use economic indicators as the sole measure of the evaluation system and rarely consider sustainability. While existing supply chain financing decision-making systems can resolve SME financing problems to some extent, the one-sided pursuit of maximum economic benefits is contrary to sustainable development and does not assist financial institutions in avoiding finance risks. Therefore, this paper, based on the theory of the triple bottom line (economy, environment, and society) from a sustainable development perspective, innovatively proposes an SME financing evaluation model for supply chain finance that applies a fuzzy multi-criteria evaluation method combined with Topsis. Additionally, at the end, an example is given to demonstrate model validity and evaluate the best possible SME financing model for financial institutions.

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