Synergies between the circular economy and carbon emission reduction.

Climate change and sustainable development drive transformation in economic development models. Carbon emission reduction and the circular economy propel climate change and sustainable development, yet it's unclear if they synergize or counteract each other. This study examines the question from theoretical and practical perspectives. Using a theory-practice framework, bibliometric and big data analyses were conducted on the Web of Science and Chinese case data, totaling 2.29GB, to explore synergies between carbon emission reduction and the circular economy. The study finds predominantly synergistic interactions between the circular economy and carbon emission reduction, with minimal offsetting effects. That is, the circular economy markedly enhances carbon emissions reduction. At the theoretical level, the two fields are gradually evolving towards in-depth research, while at the practical level, collaboration is coalescing around four areas: hot fields, potential fields, auxiliary fields and common goals. A noteworthy contribution of this study is the development of a framework that synergizes theory and practice, providing a structured approach for future research in this domain. By quantifying the synergistic and offsetting relationship between the circular economy and carbon emissions reduction through systematic big data analysis, this research offers insights essential for achieving the UN's Sustainable Development Goals. We also stress the need for diverse case studies and multi-dimensional analyses in ongoing research.

Cross-modality cerebrovascular segmentation based on pseudo-label generation via paired data.

Accurate segmentation of cerebrovascular structures from Computed Tomography Angiography (CTA), Magnetic Resonance Angiography (MRA), and Digital Subtraction Angiography (DSA) is crucial for clinical diagnosis of cranial vascular diseases. Recent advancements in deep Convolution Neural Network (CNN) have significantly improved the segmentation process. However, training segmentation networks for all modalities requires extensive data labeling for each modality, which is often expensive and time-consuming. To circumvent this limitation, we introduce an approach to train cross-modality cerebrovascular segmentation network based on paired data from source and target domains. Our approach involves training a universal vessel segmentation network with manually labeled source domain data, which automatically produces initial labels for target domain training images. We improve the initial labels of target domain training images by fusing paired images, which are then used to refine the target domain segmentation network. A series of experimental arrangements is presented to assess the efficacy of our method in various practical application scenarios. The experiments conducted on an MRA-CTA dataset and a DSA-CTA dataset demonstrate that the proposed method is effective for cross-modality cerebrovascular segmentation and achieves state-of-the-art performance.

3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection.

Vascular structure segmentation plays a crucial role in medical analysis and clinical applications. The practical adoption of fully supervised segmentation models is impeded by the intricacy and time-consuming nature of annotating vessels in the 3D space. This has spurred the exploration of weakly-supervised approaches that reduce reliance on expensive segmentation annotations. Despite this, existing weakly supervised methods employed in organ segmentation, which encompass points, bounding boxes, or graffiti, have exhibited suboptimal performance when handling sparse vascular structure. To alleviate this issue, we employ maximum intensity projection (MIP) to decrease the dimensionality of 3D volume to 2D image for efficient annotation, and the 2D labels are utilized to provide guidance and oversight for training 3D vessel segmentation model. Initially, we generate pseudo-labels for 3D blood vessels using the annotations of 2D projections. Subsequently, taking into account the acquisition method of the 2D labels, we introduce a weakly-supervised network that fuses 2D-3D deep features via MIP to further improve segmentation performance. Furthermore, we integrate confidence learning and uncertainty estimation to refine the generated pseudo-labels, followed by fine-tuning the segmentation network. Our method is validated on five datasets (including cerebral vessel, aorta and coronary artery), demonstrating highly competitive performance in segmenting vessels and the potential to significantly reduce the time and effort required for vessel annotation. Our code is available at: https://github.com/gzq17/Weakly-Supervised-by-MIP.

Hierarchy performance assessment of industrial solid waste utilization - tracking resource recycling and utilization centers in China.

The massive production and accumulation of industrial solid waste (ISW) have led to environmental pollution and natural resource underutilization. China's efforts to build trial industrial waste resource utilization centers provide strong support for sustainable development. However, these centers and the factors driving ISW utilization have yet to be evaluated. This paper utilizes context-dependent data envelopment analysis models without explicit inputs (DEA-WEI) to evaluate the overall utilization performance of 48 industrial waste resource utilization centers in China from 2018 to 2020. It also builds a Tobit model to assess which indicators and waste types affect overall ISW utilization. The results show overall ISW utilization performance of centers in the sample has improved, with the average value falling from 1.7193 in 2018 to 1.5624 in 2020. However, there are clear regional performance gaps, with East China having the highest utilization performance (1.3113) while the Southwest had the lowest (2.2958). Finally, this paper proposes measures to improve the overall utilization of industrial waste resources based on an analysis of the factors driving solid waste utilization.

Rapid Increase in China's Industrial Ammonia Emissions: Evidence from Unit-Based Mapping.

Ammonia (NH3) is an important precursor of secondary inorganic aerosols and greatly impacts nitrogen deposition and acid rain. Previous studies have mainly focused on the agricultural NH3 emissions, while recent research has noted that industrial sources could be significant in China. However, detailed estimates of NH3 emitted from industrial sectors in China are lacking. Here, we established an unprecedented high-spatial-resolution data set of China's industrial NH3 emissions using up-to-date measurements of NH3 and point source-level information covering eight major industries and 27 subdivided process categories. We found that China emitted 798 (90% confidence interval: 668-933) gigagrams of industrial NH3 into the atmosphere in 2019, equivalent to 44 ± 20% of the industrial emissions worldwide; this flux is 3-fold larger than that in 1998 and has fluctuated since 2014. Furthermore, although fertilizer production is responsible for approximately half of the emissions in China, the emissions from cement production and coal-fired power plants increased dramatically from near zero to 164 and 41 gigagrams, respectively, in the past two decades, primarily due to the NH3 escape caused by the large-scale application of the denitration process. Our results reveal that, unlike other major air pollutants, China's industrial NH3 emission control is still in a critical period, and stricter NH3 emission standards and innovation in pollution control technologies are highly desirable.

Urban ecological transition: The practice of ecological civilization construction in China.

Ecological civilization construction is an essential means of achieve sustainable development in China. It promotes not only the decoupling of environmental degradation from economic development, but additionally the coupling of positive ecological development with economic development. Presently, most of the research on ecological civilization focuses on its indices and evaluation methods. However, there exist some gaps such as the use of incomplete scientific indicators, and insufficient practice caused by inadequate sample size. In this study, we first take the evaluation framework for ecological civilization pilot areas combined with academic research to construct a comprehensive framework and indicator system. Second, we calculate the Coupling Coordination Degree (CCD) for each of the pilot areas based on the entropy weight and identify typical industries that promote the coupling of ecology and economy. Third, we use the Relative Development Coefficient (RDC) to measure the development of ecology and economy between 2014 and 2019, and study the different kinds of development models for cities. Results of the study found that the regional economy is highly positive correlated with CCD, indicating a mutually reinforcing relationship between economic development and ecological development. Further, the RDC reveals that the level of urban ecological development is relatively higher at the stage of decoupling and coordination with economic system. Finally, strategic emerging industries are a common element in pilot areas with a high level of ecological development, as they offer higher economic output without the ecological degradation associated with traditional industries.

11,20-Dihy-droxy-3-oxopregna-1,4-dien-21-oic acid monohydrate.

The title compound, C(21)H(28)O(5)·H(2)O, is the hydrate of a steroid derivative and was obtained by degradation of solid prednisolone sodium phosphate. The six C atoms in ring A are nearly co-planar with a mean deviation of 0.015 Å. Rings B and C are both in chair conformations, while ring D has an envelope form. In the crystal, inter-molecular O-H⋯O hydrogen-bonding inter-actions occur between the hy-droxy groups, carbonyl O atoms and solvent water mol-ecules, resulting in an overall three-dimensional structure.