Can environmental supervision improve air quality? Quasi-experimental evidence from China

Environmental supervision is significantly disrupted by local economic development and typically characterized by a lack of independence in China. This paper investigates the impacts and mechanisms of the vertical management reform of environmental protection department in China on urban air quality. We construct a principal–agent model suitable for explaining the interactions between the central and local governments and elaborate the intrinsic mechanism of EVM on strengthening environmental supervision. Using manually collected data, we conduct EVM as a quasi-experiment and construct a time-varying difference-in-difference (DID) model. Our empirical results show that the EVM significantly strengthens regional environmental supervision and reduces urban air pollution, bringing abatement in the PM2.5 concentration. The mechanism shows that EVM increases enterprises’ green innovation and attracts new entrants, further promoting industrial upgrading. Our study provides a new perspective on environmental governance and urban air quality in emerging countries such as China

Implications for Cation Selectivity and Evolution by a Novel Cation Diffusion Facilitator Family Member From the Moderate Halophile Planococcus dechangensis

In the cation diffusion facilitator (CDF) family, the transported substrates are confined to divalent metal ions, such as Zn2+, Fe2+, and Mn2+. However, this study identifies a novel CDF member designated MceT from the moderate halophile Planococcus dechangensis. MceT functions as a Na+(Li+, K+)/H+ antiporter, together with its capability of facilitated Zn2+ diffusion into cells, which have not been reported in any identified CDF transporters as yet. MceT is proposed to represent a novel CDF group, Na-CDF, which shares significantly distant phylogenetic relationship with three known CDF groups including Mn-CDF, Fe/Zn-CDF, and Zn-CDF. Variation of key function-related residues to “Y44-S48-Q150” in two structural motifs explains a significant discrimination in cation selectivity between Na-CDF group and three major known CDF groups. Functional analysis via site-directed mutagenesis confirms that MceT employs Q150, S158, and D184 for the function of MceT as a Na+(Li+, K+)/H+ antiporter, and retains D41, D154, and D184 for its facilitated Zn2+ diffusion into cells. These presented findings imply that MceT has evolved from its native CDF family function to a Na+/H+ antiporter in an evolutionary strategy of the substitution of key conserved residues to “Q150-S158-D184” motif. More importantly, the discovery of MceT contributes to a typical transporter model of CDF family with the unique structural motifs, which will be utilized to explore the cation-selective mechanisms of secondary transporters

Ultra-broadband and tunable infrared absorber based on VO2 hybrid multi-layer nanostructure

We propose an ultra-broadband near- to mid-infrared (NMIR) tunable absorber based on VO2 hybrid multi-layer nanostructure by hybrid integration of the upper and the lower parts. The upper part is composed of VO2 nanocylinder arrays prepared on the front illuminated surface of quartz substrate, and VO2 square films and VO2/SiO2/VO2 square nanopillar arrays prepared on the back surface. The lower part is an array of SiO2/Ti/VO2 nanopillars on Ti substrate. The effects of different structural parameters and temperature on the absorption spectra were analyzed by the finite-difference time-domain method. An average absorption rate of up to 94.7% and an ultra-wide bandwidth of 6.5 μm were achieved in NMIR 1.5–8 μm. Neither vertical incident light with different polarization angles nor large inclination incident light has a significant effect on the absorption performance of the absorber. The ultra-broadband high absorption performance of this absorber will be widely used in NMIR photodetectors and other new optoelectronic devices

2023 roadmap for potassium-ion batteries

The heavy reliance of lithium-ion batteries (LIBs) has caused rising concerns on the sustainability of lithium and transition metal and the ethic issue around mining practice. Developing alternative energy storage technologies beyond lithium has become a prominent slice of global energy research portfolio. The alternative technologies play a vital role in shaping the future landscape of energy storage, from electrified mobility to the efficient utilization of renewable energies and further to large-scale stationary energy storage. Potassium-ion batteries (PIBs) are a promising alternative given its chemical and economic benefits, making a strong competitor to LIBs and sodium-ion batteries for different applications. However, many are unknown regarding potassium storage processes in materials and how it differs from lithium and sodium and understanding of solid–liquid interfacial chemistry is massively insufficient in PIBs. Therefore, there remain outstanding issues to advance the commercial prospects of the PIB technology. This Roadmap highlights the up-to-date scientific and technological advances and the insights into solving challenging issues to accelerate the development of PIBs. We hope this Roadmap aids the wider PIB research community and provides a cross-referencing to other beyond lithium energy storage technologies in the fast-pacing research landscape

A coupled thermal-electrical-structural model for balloon-based thermoplasty treatment of atherosclerosis

AbstractObjectives The outcome of balloon-based atherosclerosis thermoplasty is closely related to the temperature/stress distribution during the treatment. For precise prediction of a required thermal lesion in the heterogeneous and thin atherosclerotic vessel, a numerical model incorporating heat-induced tissue expansion or shrinkage and the strain caused by balloon dilation is necessary.Methods A fully coupled thermal-electrical-structural new model was established. The model features a heterogeneous structure including eccentric plaque, healthy artery and surrounding tissue. Tissue expansion/shrinkage and hyperelasticity material model were taken into consideration. Different heating strategies and plaque mechanical properties were investigated. The temperature distribution was compared with the traditional thermal-electrical coupled model. The possibility of thermoplasty treatment using balloons with different sizes was also explored.Results The temperature, the electrical intensity and the stress during the thermoplasty were obtained. Lower stress was found in the heating region where tissue shrinkage occurred. The ablation depth was predicted to be ∼0.42 mm larger without coupling the biomechanical influence. The mechanical properties and input condition significantly affect the temperature and stress distribution considering the small dimensions of the tissue. Besides, with a 12.5% reduction of balloon diameter, the largest Von Mises stress decreases by 25.4%.Conclusions It is confirmed that a coupled thermal-electrical-structural model is needed for precise temperature prediction in the balloon-based thermoplasty of the heterogeneous and thin tissue. The model presented may help with future development of optimized treatment planning considering both ablation depth and minimum stress

A New Conformal Penetrating Heating Strategy for Atherosclerotic Plaque

(1) Background: A combination of radiofrequency (RF) volumetric heating and convection cooling has been proposed to realize plaque ablation while protecting the endothelial layer. However, the depth of the plaque and the thickness of the endothelial layer vary in different atherosclerotic lesions. Current techniques cannot be used to achieve penetrating heating for atherosclerosis with two targets (the specified protection depth and the ablation depth). (2) Methods: A tissue-mimicking phantom heating experiment simulating atherosclerotic plaque ablation was conducted to investigate the effects of the control parameters, the target temperature (Ttarget), the cooling water temperature (Tf), and the cooling water velocity (Vf). To further quantitatively analyze and evaluate the ablation depth and the protection depth of the control parameters, a three-dimensional model was established. In addition, a conformal penetrating heating strategy was proposed based on the numerical results. (3) Results: It was found that Ttarget and Tf were factors that regulated the ablation results, and the temperatures of the plaques varied linearly with Ttarget or Tf. The simulation results showed that the ablation depth increased with the Ttarget while the protection depth decreased correspondently. This relationship reversed with the Tf. When the two parameters Ttarget and Tfwere controlled together, the ablation depth was 0.47 mm–1.43 mm and the protection depth was 0 mm–0.26 mm within 2 min of heating. (4) Conclusions: With the proposed control algorithm, the requirements of both the ablation depth and the endothelium protection depth can be met for most plaques through the simultaneous control of Ttarget and Tf

A Study on Double-Headed Entities and Relations Prediction Framework for Joint Triple Extraction

Relational triple extraction, a fundamental procedure in natural language processing knowledge graph construction, assumes a crucial and irreplaceable role in the domain of academic research related to information extraction. In this paper, we propose a Double-Headed Entities and Relations Prediction (DERP) framework, which divides the entity recognition process into two stages: head entity recognition and tail entity recognition, using the obtained head and tail entities as inputs. By utilizing the corresponding relation and the corresponding entity, the DERP framework further incorporates a triple prediction module to improve the accuracy and completeness of the joint relation triple extraction. We conducted experiments on two English datasets, NYT and WebNLG, and two Chinese datasets, DuIE2.0 and CMeIE-V2, and compared the English dataset experimental results with those derived from ten baseline models. The experimental results demonstrate the effectiveness of our proposed DERP framework for triple extraction

GL-Net: semantic segmentation for point clouds of shield tunnel via global feature learning and local feature discriminative aggregation

Has gradually become the first choice of modern urban public transportation due to its advantages of safety and high-efficiency. Shield tunnel is an important type of subway tunnel, and its structural stability and safety play an important role in subway operation. The shield tunnels are prone to problems such as water leakage and tunnel collapse, which affect the safe operation of subways. Efficient monitoring methods are required to detect the status of subway tunnels. The data collection and accurate segmentation of key components of shield tunnels are the basis and key to the automatic monitoring of subway tunnels. This research presents a novel semantic segmentation method of three-dimensional (3-D) point clouds of typical structural elements (e.g., longitudinal joint, circumferential joints, bolt hole and grouting hole) in shield tunnel based on deep learning. In this method, we focus on how to make the network learn robust global features and complex local distribution patterns. Further, we propose a global and local feature encoding block (namely GL-block) to discriminatively aggregate local features while learning global representation. After multiple encodings by the GL-block, we design a global correlation modeling (GCM) module to establish a global awareness of each point. Finally, a weighted cross-entropy loss function is designed to solve the problem of unbalanced number of samples in each category of shield tunnel. In the experiments, we make a dataset of shield tunnel point clouds with a length of about 1,000 m collected by CNU-TS-1 (DU et al., 2018) mobile tunnel monitoring system, and use the dataset to train and test the segmentation ability of our method on the typical structural elements of shield tunnels. Experiments verify the effectiveness of our method by comparing with the other state-of-the-art 3-D point cloud semantic segmentation methods, and our method has an mIoU score of 73.02 %, which is at least 14.54 % higher than the other compared state-of-the-art networks. Also, we further verify the adaptability of our method to different tunnels and different laser scanning equipment, such as FARO, Leica and Z + F, and achieve very advanced performance

Deep-reinforcement-learning-based water diversion strategy

Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation. Its effectiveness depends on changes in the source water and lake conditions. However, the challenge of optimizing water diversion remains because it is difficult to simultaneously improve lake water quality and minimize the amount of diverted water. Here, we propose a new approach called dynamic water diversion optimization (DWDO), which combines a comprehensive water quality model with a deep reinforcement learning algorithm. We applied DWDO to a region of Lake Dianchi, the largest eutrophic freshwater lake in China and validated it. Our results demonstrate that DWDO significantly reduced total nitrogen and total phosphorus concentrations in the lake by 7% and 6%, respectively, compared to previous operations. Additionally, annual water diversion decreased by an impressive 75%. Through interpretable machine learning, we identified the impact of meteorological indicators and the water quality of both the source water and the lake on optimal water diversion. We found that a single input variable could either increase or decrease water diversion, depending on its specific value, while multiple factors collectively influenced real-time adjustment of water diversion. Moreover, using well-designed hyperparameters, DWDO proved robust under different uncertainties in model parameters. The training time of the model is theoretically shorter than traditional simulation-optimization algorithms, highlighting its potential to support more effective decision-making in water quality management