Green development level assessment and obstacle analysis of China's coal-resource-based regions.

Coal is the main source of energy in China, however, in the context of carbon neutrality, how coal-resource-based regions can not only undertake the national supply of terminal energy and industrial raw materials, but also achieve regional green development is an important issue. In this paper, first, we constructed a green development indicator system for coal-resource-based regions named the green development indicator system of coal-resource-based regions (GDISCR), which could coordinate the relationship among the economy, energy, and environment when evaluating the green development level. Second, we proposed a new evaluation model named dynamic spatial TOPSIS, which comprehensively considered the spatial differences of research subjects and the differences over time in the evaluation process. Third, we introduced the obstacle analysis model to find the obstacle factors preventing green development of coal-resource-based regions. Finally, we evaluated ten coal-resource-based provinces to evaluate their green growth levels and demonstrate the effectiveness of our methodology. The following were the major conclusions: (1) The average comprehensive evaluation value of the 10 coal-resource-based provinces was 0.3956, based on which the coal-resource-based provinces could be divided into two types, namely, provinces with better or worse green development levels. (2) The obstacles restricting the green development of provinces with coal resources were dynamic, but the importance of an obstacle factor for provinces was relatively fixed. (3) The greatest obstacle to the green development of provinces with coal resources was technological capacity in the economy, with an average obstacle degree of 27.48% in 2022, and they had similar difficulties in energy transition but different difficulties in environmental protection. On the basis of these findings, some feasible recommendations for the environmentally friendly growth of coal-resource-based provinces are discussed.

Evaluation of carbon neutrality capacity based on a novel comprehensive model.

The Chinese government actively participates in global climate governance and has proposed to achieve the goal of carbon neutrality by 2060. Due to large differences in regional development, local governments need to comprehend their own carbon neutrality status and then scientifically plan a path to achieve carbon neutrality. In this study, we constructed a new carbon neutrality capacity evaluation indicator system named CNCIS, which can dynamically reflect the balance of energy, economy and environment in the process of reducing carbon emissions. In addition, to scientifically evaluate the carbon neutrality capacity, we proposed a novel comprehensive evaluation model, namely, the BWM-Entropy TOPSIS method, which can solve the unbalanced weighting and low efficiency problem in weighting indicators and improve the applicability of TOPSIS. Finally, based on real data from 30 provinces in China, we proved the effectiveness of our method and analyse the reasons for the different carbon neutrality capacities of the provinces. The main findings are as follows: (1) Clean and efficient utilization of energy had the greatest impact on achieving carbon neutrality, which is mainly represented by carbon emissions intensity, CO2 emissions per capita and coal consumption per capita. (2) In the energy, economy and environmental aspects, the factors that most affect carbon neutrality were carbon emissions intensity, the volume of technology marketing and water consumption per capita respectively. (3) Sorted by carbon neutrality capacities, the provinces could be divided into three categories, in which economically developed provinces more easily achieve carbon neutrality while resource-based provinces are the hardest. Based on these results, corresponding suggestions were proposed to help local governments scientifically plan a path to achieve carbon neutrality.

VFS-based OFSP model for groundwater pollution study of domestic waste landfill.

The groundwater quality is essential for high quality of life and social development. Thus, the importance and necessity of the accurate and rigorous requirements for contaminated groundwater assessment has increasingly attracted engineers' and researchers' attentions. In order to improve the precision and robustness of the groundwater quality evaluation of domestic waste landfills, based on the variable fuzzy set (VFS) pair and the optimized N.L. Nemerow index, we develop an optimized fuzzy set pair (OFSP) model for groundwater quality assessment. Then, we devise the OFSP model by five key elements of optimized synthesis operator "C", relative difference[Formula: see text], connection degree"ui", optimized N.L. Nemerow index "Pi," and pollution load ratio "Ji", which can achieve the reasonable groundwater quality assessment model, the stable groundwater quality evaluation process, and the convincing evaluation results. Finally, a case study on groundwater quality assessment of various domestic landfills in China is conducted to explore the comprehensive impacts of domestic landfills in different regions and types on groundwater pollution from multiple perspectives, and demonstrate the effectiveness of the proposed OFSP model. The groundwater quality assessment results of various domestic landfills indicate that the pollution level of groundwater under unregulated domestic landfills in eastern and southern China is the worst. Based on the assessment results of groundwater quality, we compare the groundwater quality levels obtained by various mainstream methods. In line with precision (0.985), correlation (0.934), robustness (0.953), and rationality (0.946), our designed OFSP model has the best performance. In addition, according to the indexes of discrimination (0.217) and versatility (0.837), the designed OFSP model also has a good ability. Results of experiments well prove that the proposed OFSP model could play a good performance on groundwater quality evaluation in domestic landfills, compared with other mainstream models.

A Novel Detection Framework About Conditions of Wearing Face Mask for Helping Control the Spread of COVID-19.

Properly wearing a face mask has become an effective way to limit the COVID-19 transmission. In this work, we target at detecting the fine-grained wearing state of face mask: face without mask, face with wrong mask, face with correct mask. This task has two main challenging points: 1) absence of practical datasets, and 2) small intra-class distance and large inter-class distance. For the first challenging point, we introduce a new practical dataset covering various conditions, which contains 8635 faces with different wearing status. For the second challenging point, we propose a novel detection framework about conditions of wearing face mask, named Context-Attention R-CNN, which enlarge the intra-class distance and shorten inter-class distance by extracting distinguishing features. Specifically, we first extract the multiple context feature for region proposals, and use attention module to weight these context feature from channel and spatial levels. And then, we decoupling the classification and localization branches to extract more appropriate feature for these two tasks respectively. Experiments show that the Context-Attention R-CNN achieves 84.1% mAP on our proposed dataset, outperforming Faster R-CNN by 6.8 points. Moreover, Context-Attention R-CNN still exceed some state-of-the-art single-stage detectors.

A Novel Method in Surface Water Quality Assessment Based on Improved Variable Fuzzy Set Pair Analysis.

In the case of surface water pollution, it is important and necessary to accurately assess the level of contaminated water and ensure the safety of drinking water for people in disaster areas during floods. However, for the assessment of the strict requirements of drinking water, traditional assessment methods still have some limitations, such as low precision and rationality. In order to overcome these limitations, in the light of the theory of set pair analysis and variable fuzzy set, we propose an improved variable fuzzy set pair analysis method (IVFSPA), which combines the analysis framework of variable fuzzy set and set pair analysis, and has made some improvements to the fusion architecture. Firstly, we present a novel game theory comprehensive weighting method, in which the objective entropy method and the subjective analytic hierarchy process(AHP) method employed to obtain the reasonable weight. Then, based on the Nemerow index method, we improve the arithmetic form of " P i " (Equation P) to replace the fuzzy comprehensive evaluation method. Furthermore, we design a double judgment mode of combining the principle of maximum membership degree with the positive and negative relationship between the standard value and the measured value, which can accurately judge the evaluation level of surface water quality. Finally, to validate and verify the effectiveness of the proposed method, experiments was conducted at the representative river collection sections of Nanking, China, employing water quality data of 14 sampling sections in their rivers in Nanking during the 2017 flood. In terms of performance metcrics of precision and rationality, based on the values of "TP", "NH3-N", "Pb", "AS" and "KMnO4" of "Ch-lh section/Chuhe gate" are 0.415, 3.77, 0.07, 0.23 and 7.12, respectively, the level of Ch-lh section/Chuhe gate is that the IVFSPA is Class V and the rest are class IV. Results of experiments show that our IVFSPA method can achieve a good performance, compared with other traditional methods.

Optimizing the performance of photocatalytic H2 generation for ZnNb2O6 synthesized by a two-step hydrothermal method.

Semiconductor-based photocatalytic H2 generation is a promising technique and the development of efficient photocatalysts has attracted great attention. Columbite-ZnNb2O6 is a wide-bandgap semiconductor capable of photocatalytic water splitting. Here we employed a two-step hydrothermal method to first dissolve Nb2O5 with a highly basic aqueous solution and further react it with Zn2+ to form nanosized ZnNb2O6. The reaction time plays an important role on its morphology and photocatalytic performance in water reduction. The sample synthesized through 7 days of reaction was the optimal one with an appropriate crystallinity and a large specific surface area, however the severe surficial defects prohibited its photocatalytic activity in pure water. The H2 generation at a rate of 23.6(5) µmol h-1 g-1 emerged when 20 vol% methanol was used as the hole-sacrificial agent. Most remarkably, once metal or metal oxide cocatalysts, including Pt, Au, NiO, RuO2, Ag2O, and Pd/PdO, were loaded appropriately, the photocatalytic H2 generation rate ultimately achieved 3200(100) or 680(20) µmol h-1 g-1 with or without using methanol, respectively. Apparent quantum yields (AQYs) at 295 nm were investigated by changing the experimental parameters, and the optimal AQYs are 4.54% and 9.25% in water and methanol solution, respectively. Further post-modifications like bandgap engineering may be performed on this highly efficient nano-ZnNb2O6.