Ionic Liquid Modified Fe-Porphyrinic Metal-Organic Frameworks as Efficient and Selective Photocatalysts for CO2 Reduction.

Porphyrin-based metal-organic frameworks (MOFs) are ideal platforms for heterogeneous photocatalysts toward CO2 reduction. To further explore photocatalytic MOF systems, it is also necessary to consider their ability to fine-tune the microenvironments of the active sites, which affects their overall catalytic operation. Herein, a kind of ionic liquid (IL, here is 3-butyric acid-1-methyl imidazolium bromide, BAMeImBr) was anchored to iron-porphyrinic Zr-MOFs with different amounts to obtain ILx@MOF-526 (MOF-526 = Zr6O4(OH)4(FeTCBPP)3, FeTCBPP = iron 5,10,15,20-tetra[4-(4'-carboxyphenyl)phenyl]-porphyrin, x = 100, 200, and 400). ILx@MOF-526 series was designed to investigate the effects of the microenvironmental and electronic structural modification on the efficiency and selectivity of the photochemical reduction of CO2 after introducing IL fragments. Compared to parent MOF-526, the production and selectivity of CO were greatly improved in the absence of any photosensitizer under visible light by the ILx@MOF-526 series. Among them, the CO yield of IL200@MOF-526 was up to 14.0 mmol g-1 within 72 h with a remarkable CO selectivity of 97%, which is superior to that of MOF-526 without BAMeIm+ modification and other amounts of BAMeIm+ loaded. Furthermore, density functional theory calculations were performed to study the mechanism of the CO2 reduction.

Green finance reform and application intensity of chemical fertilizer and pesticide: policy evidence from China.

The environmental hazards resulting from the excessive application of pesticides and fertilizers have been an inevitable agricultural production issue in various countries around the world. New technologies and policies are constantly trying to improve their application efficiency. This paper utilizes panel data of the provincial level in China from 2009 to 2019 to empirically study the effect of green finance reform policies on the chemical fertilizer application intensity (FAI) and pesticide application intensity (PAI). Standard difference-in-differences (DID), synthetic DID, difference-in-difference-in differences (DDD), and spatial DID models are constructed for specific empirical analysis. The findings can be concluded as follows: (1) A unit of the green finance reform policy reduces FAI by 0.0144 and PAI by 1.7921 by promoting green technology innovation. (2) Government financial extractive capacity hinders the reduction effect of green finance on PAI. (3) Coastal geographical location is conducive to reducing PAI through green finance reform. (4) FAI and PAI show positive spatial autocorrelations, and the influence of green finance reform overflows to surrounding areas. The research results can provide policy references for countries around the world to promote the green development of agriculture and reduce environmental pollution.

FEUSNet: Fourier Embedded U-Shaped Network for Image Denoising.

Deep convolution neural networks have proven their powerful ability in comparing many tasks of computer vision due to their strong data learning capacity. In this paper, we propose a novel end-to-end denoising network, termed Fourier embedded U-shaped network (FEUSNet). By analyzing the amplitude spectrum and phase spectrum of Fourier coefficients, we find that low-frequency features of an image are in the former while noise features are in the latter. To make full use of this characteristic, Fourier features are learned and are concatenated as a prior module that is embedded into a U-shaped network to reduce noise while preserving multi-scale fine details. In the experiments, we first present ablation studies on the Fourier coefficients' learning networks and loss function. Then, we compare the proposed FEUSNet with the state-of-the-art denoising methods in quantization and qualification. The experimental results show that our FEUSNet performs well in noise suppression and preserves multi-scale enjoyable structures, even outperforming advanced denoising approaches.

Robotic Grasp Detection Network Based on Improved Deformable Convolution and Spatial Feature Center Mechanism.

In this article, we propose an effective grasp detection network based on an improved deformable convolution and spatial feature center mechanism (DCSFC-Grasp) to precisely grasp unidentified objects. DCSFC-Grasp includes three key procedures as follows. First, improved deformable convolution is introduced to adaptively adjust receptive fields for multiscale feature information extraction. Then, an efficient spatial feature center (SFC) layer is explored to capture the global remote dependencies through a lightweight multilayer perceptron (MLP) architecture. Furthermore, a learnable feature center (LFC) mechanism is reported to gather local regional features and preserve the local corner region. Finally, a lightweight CARAFE operator is developed to upsample the features. Experimental results show that DCSFC-Grasp achieves a high accuracy (99.3% and 96.1% for the Cornell and Jacquard grasp datasets, respectively) and even outperforms the existing state-of-the-art grasp detection models. The results of real-world experiments on the six-DoF Realman RM65 robotic arm further demonstrate that our DCSFC-Grasp is effective and robust for the grasping of unknown targets.

The carbon emission reduction effect of auditing outgoing officials' natural resource asset management--Evidence from China.

Audit outgoing officials' natural resource asset management is an institutional innovation in the field of ecological civilization construction to promote the modernization of national governance system and governance capacity. Focusing on the carbon emission reduction effect of this policy, this paper takes the audit pilot as a quasi-natural experiment and constructs a difference-in-difference model and a spatial difference-in-difference model to explore the carbon emission reduction effect and spatial spillover effect of this policy. The results reveal that the audit pilot has a significant negative impact on carbon emission intensity. Additionally, the impacts are heterogeneous in the east, center, west, northeast, and on both sides of the "Heihe-Tengchong" Line. What's more, this policy influences the environmental performance of surrounding areas manifesting significant spatial spillover effects. Finally, based on the summary of findings, this study proposes a series of countermeasures and suggestions to optimize audit outgoing officials' natural resource asset management.

Carbon Footprint Research Based on Input-Output Model-A Global Scientometric Visualization Analysis.

Reducing the effect of mankind's activities on the climate and improving adaptability to global warming have become urgent matters. The carbon footprint (CF), derived from the concept of ecological footprint, has been used to assess the threat of climate change in recent years. As a "top to bottom" method, input-output analysis (IOA) has become a universally applicable CF assessment tool for tracing the carbon footprint embodied in economic activities. A wide range of CF studies from the perspective of the IOA model have been presented and have made great progress. It is crucial to have a better understanding of what the relevant research focuses on in this field, yet so far a systematic synopsis of the literature is missing. The purpose of this paper is to explore the knowledge structure and frontier trends in respect of the IOA model applied to CF research using scientometric visualization analysis. The main findings of this paper are as follows. (1) Published articles show a two-stage increase in the period 2008 to 2021, and present a complex academic network of countries, authors, and institutions in this important domain. (2) The classic studies are mainly divided into three categories: literature reviews, database application introduction, and CF accounting in different scales. (3) The research hotspots and trends show that the research scales tend to be more microscopic and applications of models tend to be more detailed. In addition, supply-chain analysis and driver-factor analysis will probably become the main research directions in the future.

Knowledge mapping of planetary boundaries based on bibliometrics analysis.

The planetary boundaries concept has triggered a vast amount of pure and applied scientific research, as well as policy and governance activities globally. Indeed, it has rapidly become a centerpiece of sustainability study. It is crucial to review the scientific state of the planetary boundaries (PB) concept systematically. However, there is a lack of research on drawing a scientific investigation map of planetary boundaries. Therefore, to clarify the spatial and temporal distribution characteristics, research hotspots, and frontiers of planetary boundaries, a scientometric analysis was performed based on 530 academic publications on planetary boundaries from 2009 to 2021. This paper conducted the analysis by visualizing the social network, dual-map overlay, co-cited references, structure variation article, and co-occurrence keywords with CiteSpace. The results show that as a new achievement and paradigm in sustainable development research, the planetary boundaries framework is gradually getting global attention and promotion, which has increasingly become an interdisciplinary hot research topic. The most productive authors and institutions are concentrated in England, the USA, Germany, and Sweden. Relevant articles were mainly published in journals focusing on ecology, earth, marine, veterinary, animal, economics, and politics. In addition, we summarized four predominant research themes by clustering keywords: the calculation of single boundary threshold and present value, the integration with assessment methods such as life cycle assessment and footprint families, the downscaling of planetary boundaries, and the expansion to economic and social domains. For scholars who are interested in this topic, this paper would be a useful reference and guideline.

Theoretical studies on 2,3,5,6-Tetra(1H-tetrazol-5-yl)pyrazine and 1,1-diamino-2,2-Dinitroethylene blending system.

To study the properties of 2,3,5,6-tetra(1H-tetrazol-5-yl)pyrazine (H4TTP) and 1,1'-diamino-2,2'-dinitroethylene (FOX-7) blending system, the structures of H4TTP, FOX-7, and H4TTP/FOX-7 dimers were optimized using density functional theory (DFT), and the mechanical properties and cohesive energy densities (CED) of H4TTP/FOX-7 blends with different mass ratios were calculated by molecular dynamics (MD) simulation. The results show that the HOMO of H4TTP is distributed on the pyrazine and tetrazole rings, while the LUMO is mainly distributed on the pyrazine ring, with a small contribution from the tetrazole ring. The HOMO of FOX-7 molecules is mainly located on the CC bonds, while the LUMO is mainly located on the nitro groups. The most stable dimer, (I), was formed when the interaction between frontier MOs is possible and hydrogen bond is formed between two monomers, which was confirmed by the Reduced Density Gradient (RDG) isosurface graph. MD studies were carried out to examine the mechanical properties and cohesive energy density of the blending systems. In monomer systems, FOX-7 has the strongest rigidity and best ductility, while H4TTP has the largest elasticity and best toughness. In the blending systems, we found that various mechanical properties and CED values were different from those of monomers, which improves the sensitivity of H4TTP and the safety of explosives.

Can urban sprawl be the cause of environmental deterioration? Based on the provincial panel data in China.

Based on the provincial panel data in China for 2001-2017, this article explores the impact of incoordination between land and population urbanization on environmental quality from the perspective of urban sprawl. It can be found that: (1) Most provinces are suffering from the urban sprawl, land finance has made urban sprawl more serious. (2) The results of SDM show that the impact of urban sprawl on the CO2 emissions can be regarded as a N-shaped curve. Both excessively rapid expansion of urban space and excessively rapid growth of urban population can intensify the CO2 emissions. (3) The tests of regional heterogeneity show that in underdeveloped provinces, the effect of urban sprawl on CO2 emissions could also be a N-shaped curve, but it is an inversed U-shaped for the developed provinces. Thus, it can diminish the emissions of CO2 in developed provinces, through strengthening environmental regulations and restraining the excessively rapid growth of the urban population. However, the underdeveloped provinces should actively promote economic development and create more jobs to avoid the loss of labors. These conclusions are also applicable to the tests of regional heterogeneity based on total factor productivity.

A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG.

Constructing a reliable and stable emotion recognition system is a critical but challenging issue for realizing an intelligent human-machine interaction. In this study, we contribute a novel channel-frequency convolutional neural network (CFCNN), combined with recurrence quantification analysis (RQA), for the robust recognition of electroencephalogram (EEG) signals collected from different emotion states. We employ movie clips as the stimuli to induce happiness, sadness, and fear emotions and simultaneously measure the corresponding EEG signals. Then the entropy measures, obtained from the RQA operation on EEG signals of different frequency bands, are fed into the novel CFCNN. The results indicate that our system can provide a high emotion recognition accuracy of 92.24% and a relatively excellent stability as well as a satisfactory Kappa value of 0.884, rendering our system particularly useful for the emotion recognition task. Meanwhile, we compare the performance of the entropy measures, extracted from each frequency band, in distinguishing the three emotion states. We mainly find that emotional features extracted from the gamma band present a considerably higher classification accuracy of 90.51% and a Kappa value of 0.858, proving the high relation between emotional process and gamma frequency band.