Exploring the relationship between compact urban form and green infrastructure

Compact Urban Form (CUF) and Green Infrastructure (GI) are widely used in sustainability approaches. GI can be understood as a system of green components (e.g. parks, gardens, allotments, etc.) and has multiple benefits for the green and blue urban agendas. Alongside, CUF is an effective strategy used to address urban sprawl. The integration of the two approaches is challenging due to the limited availability of space in CUF and the lack of an analysis of existing and potential GI offerings in compact built environments. This paper looks at the relationship between urban form patterns and green space patterns at the urban scale. It seeks to identify the variables that can describe the compactness and greenness of CUF and the structure of GI, the typologies of CUF and GI, and their potential interrelationships. The method introduces selected variables for quantitative description of CUF and GI, and cluster-based typologies of CUF and GI based on the reproduced components. The three pattern variables are identified (using statistical analysis and spatial analysis) for CUF and GI respectively based on the degree of the greenness, density (e.g. Berghauser Pont and Haupt, 2007), landscape structure (FRAGSTATS) and space syntax measurements (e.g. connectivity). Subsequently, the clusters of CUF and GI are generated using fuzzy c-means clustering analysis (FCM). The method is applied to London, UK. Overall, this paper introduces a quantitative approach to understand CUF and GI as well as their relationship. The methods – which are reproducible because of the use of open-access data – take a fundamental step towards a deeper understanding of the way compact urban fabrics can become greener by activating and embedding green networks into the urban fabric

Curating sustainable urban development: a study of green infrastructure in compact cities by designing and applying an analytical framework in three English cities

Curating sustainable urban development (SUD) is an inherent requirement of the Sustainable Development Goals (SDGs). Green Infrastructure (GI) is a long-term and sustainable method, involving a system of green components (e.g., parks or gardens) that can deliver multiple benefits (i.e., ecosystem services) to the urban environment. Compact cities have serious environmental problems (e.g., greenhouse gas emissions or lack of green spaces), and can be described as having an urban form (UF) quantified by urban compactness variables. The aim of this research is to address the current gap in GI quantitative analysis and spatial-morphological research in the compact urban form context and contribute to the improvement of current green space qualities, e.g., the arrangement of GI’s types, functions, and elements, for benefiting landscape architects, urban designers and urban planners. The research focuses on the identification of spatial-morphological characteristics of GI and UF, and the understudied spatial interrelationship between GI and UF. It develops a GI-UF analytical framework (GUAF) that combines landscape metrics, space syntax, and urban morphometric techniques from the research field of urban morphology (e.g., clustering analysis and ‘spacemate’), socio-ecological spatial morphology (SESM) and urban morphometrics. This framework is a comprehensive process using open reproducible data and is applied to three English cities, London, Manchester and Birmingham. The results can be used to pro- pose GI-guided strategies (i.e., GI spatial-morphological characteristics-guided strategies) for GI quality improvement in compact urban fabrics. The framework can also be applied to different spatial scales and cities in contemporary and future research.</p

Molecular Mechanism of Curcumin Derivative on YAP Pathway against Ovarian Cancer

The purpose of this study is to study the effect of curcumin derivative WZ10 on the proliferation, invasion and apoptosis of ovarian cancer OVCAR3 cells, and to explore its mechanism. MTT assay was used to detect the effect of WZ10 on the proliferation of ovarian cancer OVCAR3 cells; Annexin V/PI double staining flow cytometry was used to detect the effect of WZ10 on cell apoptosis; Transwell method was used to detect the effect of WZ10 on cell invasiveness; Western blot was used to investigate the effect of WZ10 Mechanisms affecting OVCAR3 activity in ovarian cancer in vitro. Our results show that WZ10 treatment could significantly inhibit the proliferation and invasion of OVCAR3 cells, induce apoptosis of OVCAR3 cells in a dose-dependent manner. After knockdown of Hippo expression with sh-RNA, further combined treatment with WZ10 had no significant image on ovarian cancer OVCAR3 cells. In conclusion: WZ10 can significantly inhibit the proliferation of OVCAR3 cells, reduce cell invasion and proliferation by downregulating the activation of Hippo-YAP pathway, and induce cell apoptosis

Battery recycling policies for boosting electric vehicle adoption: evidence from a choice experimental survey

Electric vehicles must be widely accepted because of environmental concerns and carbon restrictions. Previous research has looked at consumer policy preferences and their influence on electric vehicle adoption. However, none have investigated the impact of policies linked to battery recycling on electric vehicle adoption. This study used a discrete choice model (the panel-data mixed logit model) to evaluate 552 actual consumer choice data from Southwest China collected via an online questionnaire. Our results indicate that (1) 75% of respondents feel that electric vehicles enhance the environment and are eager to embrace them. However, the lack of strong recycling policies may hinder their adoption of electric vehicles. Specifically, the four battery recycling policies significantly impact electric vehicle adoption. (2) Consumers appreciate producer-oriented incentives more than consumer-oriented incentives to a lesser extent, such as mandated battery recycling policies and electric vehicle battery flow tracing policies. (3) Consumers place a larger willingness to pay on charging station density than vehicle attributes. (4) Regarding consumer heterogeneity, the usual young group in higher-rated cities prefers electric vehicles, while customers who own a car are more inclined to buy electric vehicles. Finally, more management insights and policy recommendations are provided based on these findings to help government and producer policymakers

MYOCARDIUM SEGMENTATION COMBINING T2 AND DE MRI USING MULTI- COMPONENT BIVARIATE GAUSSIAN MIXTURE MODEL

Accurately delineating the myocardium from cardiac T2 and delayed enhanced (DE) MRI is a prerequisite to identifying and quantifying the edema and infarcts. The automatic delineation is however challenging due to the heterogeneous intensity distribution of the myocardium. In this paper, we propose a fully automatic method, which combines the complementary information from the two sequences using the newly proposed Multi-Component Bivariate Gaussian (MCBG) mixture model. The expectation maximization (EM) framework is adopted to estimate the segmentation and model parameters, where a probabilistic atlas is also used. This method performs the segmentation on the two MRI sequences simultaneously, and hence improves the robustness and accuracy. The results on six clinical cases showed that the proposed method significantly improved the performance compared to the atlas-based methods: myocardium Dice scores 0.643±0.084 versus 0.576±0.103 (P=0.002) on DE MRI, and 0.623±0.129 versus 0.484±0.106 (P=0.002) on T2 MRI. Index Terms—multi-component bivariate Gaussian mixture model, expectation maximization, probabilistic atlas, myocardial infarction, Magnetic Resonance Imaging 1

Digital supply chain management in the COVID-19 crisis:an asset orchestration perspective

Although many firms are actively deploying various digital technology (DT) assets across their supply chains to mitigate the negative impact of the COVID-19 pandemic on operations, whether these DT assets are truly helpful remains unclear. To disentangle this puzzle, we investigate whether firms that have higher levels of DT asset deployment achieve better supply chain performance in the COVID-19 crisis than firms with lower levels. From an asset orchestration perspective, we focus on two dimensions of DT asset deployment: breadth and depth, which reflect the scope and scale of DT assets, respectively. The empirical results from 175 Chinese firms that have deployed DT assets to varying degrees reveal that both the breadth and the depth of DT asset deployment show positive relationships with supply chain visibility. In contrast, the depth but not the breadth of DT asset deployment poses a positive relationship with supply chain agility. Most importantly, high levels of supply chain visibility and supply chain agility were prerequisites for excellent supply chain performance in the COVID-19 crisis. We contribute to the digital supply chain management literature by uncovering the mechanism through which DT asset deployment generates impacts on supply chain performance from an asset orchestration perspective. Our study also assists firms in improving their digital transformation strategies to combat the COVID-19 pandemic

Myocardium Segmentation from de MRI Using Multicomponent Gaussian Mixture Model and Coupled Level Set

© 1964-2012 IEEE. Objective: In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information. Methods: We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework. Results: The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods. Conclusion: The results can provide a benchmark for the myocardial segmentation in the literature. Significance: DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients

Research Data for Venous malformation presenting as <i>Mauritia arabica</i>-like bronchial wall thickness: a case report

Research Data for Venous malformation presenting as Mauritia arabica-like bronchial wall thickness: a case report by Miaochan Lao, Ping Gao, Yanhui Liu, Lixu Yan and Xinglin Gao in Journal of International Medical Research</p