Energy is an important material basis for people’s life and production, as well as one of the important input factors for economic development. The relationship between the two plays a direct role in ...the energy consumption policy of a certain region. In addition, the long-term energy consumption structure dominated by oil and natural gas has brought certain adverse effects on the living environment on which we depend. Therefore, it has certain research value and significance to explore the role of energy consumption on economic growth. This study analyzes the concepts and definitions of energy and theoretical models of regional economic growth. It also adopts the economic methods of cluster division and correlation analysis to study the dynamic relationship between economic growth and energy consumption in different regions. The experimental data show that there is not only an extremely significant mutual influence relationship between them, but also the differences in each region are also very obvious. This is mainly manifested in the fact that the economic growth of the three regions (economically developed region, fast-developing region, and steady-slow development region) can stimulate energy consumption, that is, it is a one-way causal relationship. Among them, the economic growth in the economically developed areas does not promote energy consumption so clearly. It even lags other areas at times. In economically developed regions, the correlation between energy consumption and economic growth is lower than 0.2. In the economic development zone, the correlation coefficient reached 0.8.
Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great ...challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder-decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves F -measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.
Activated macrophages switch from oxidative phosphorylation to aerobic glycolysis, similar to the Warburg effect, presenting a potential therapeutic target in inflammatory disease. The endogenous ...metabolite itaconate has been reported to regulate macrophage function, but its precise mechanism is not clear. Here, we show that 4-octyl itaconate (4-OI, a cell-permeable itaconate derivative) directly alkylates cysteine residue 22 on the glycolytic enzyme GAPDH and decreases its enzyme activity. Glycolytic flux analysis by U
C glucose tracing provides evidence that 4-OI blocks glycolytic flux at GAPDH. 4-OI thereby downregulates aerobic glycolysis in activated macrophages, which is required for its anti-inflammatory effects. The anti-inflammatory effects of 4-OI are replicated by heptelidic acid, 2-DG and reversed by increasing wild-type (but not C22A mutant) GAPDH expression. 4-OI protects against lipopolysaccharide-induced lethality in vivo and inhibits cytokine release. These findings show that 4-OI has anti-inflammatory effects by targeting GAPDH to decrease aerobic glycolysis in macrophages.
This paper reviews recent developments in the preparation, surface functionalization, and applications of Fe3O4 magnetic nanoparticles. Especially, it includes preparation methods (such as ...electrodeposition, polyol methods, etc.), organic materials (such as polymers, small molecules, surfactants, biomolecules, etc.) or inorganic materials (such as silica, metals, and metal oxidation/sulfide, functionalized coating of carbon surface, graphene, etc.) and its applications (such as magnetic separation, protein fixation, magnetic catalyst, environmental treatment, medical research, etc.). In the end, some existing challenges and possible future trends in the field were discussed.
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•Comprehensive summary of the main aspects of Fe3O4 magnetic nanoparticles related to their preparation and application.•Classification and intrinsic properties of Fe3O4 magnetic nanoparticles were studied.•Perspectives for the future developments of Fe3O4 magnetic nanoparticles were proposed.
•Novel interior penalty formulation for large-scale bounded NCPs in engineering.•Proof of unique solvability of the penalty equation.•Establishment of convergence theory for the penalty ...method.•Jacobian matrix in Newton method shown to be positive-definite and an M-matrix.•Linearized system in Newton method decomposable into two decoupled subsystems.
We propose and analyze an interior penalty method for a finite-dimensional large-scale bounded Nonlinear Complementarity Problem (NCP) arising from the discretization of a differential double obstacle problem in engineering. Our approach is to approximate the bounded NCP by a nonlinear algebraic equation containing a penalty function with a penalty parameter μ > 0. The penalty equation is shown to be uniquely solvable. We also prove that the solution to the penalty equation converges to the exact one at the rate O(μ1/2) as μ → 0. A smooth Newton method is proposed for solving the penalty equation and it is shown that the linearized system is reducible to two decoupled subsystems. Numerical experiments, performed on some non-trivial test examples, demonstrate the computed rate of convergence matches the theoretical one.
Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes ...with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers' performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California's Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.
Soil net nitrogen mineralization rate (Nmin), which is critical for soil nitrogen availability and plant growth, is thought to be primarily controlled by climate and soil physical and/or chemical ...properties. However, the role of microbes on regulating soil Nmin has not been evaluated on the global scale. By compiling 1565 observational data points of potential net Nmin from 198 published studies across terrestrial ecosystems, we found that Nmin significantly increased with soil microbial biomass, total nitrogen, and mean annual precipitation, but decreased with soil pH. The variation of Nmin was ascribed predominantly to soil microbial biomass on global and biome scales. Mean annual precipitation, soil pH, and total soil nitrogen significantly influenced Nmin through soil microbes. The structural equation models (SEM) showed that soil substrates were the main factors controlling Nmin when microbial biomass was excluded. Microbe became the primary driver when it was included in SEM analysis. SEM with soil microbial biomass improved the Nmin prediction by 19% in comparison with that devoid of soil microbial biomass. The changes in Nmin contributed the most to global soil NH4+‐N variations in contrast to climate and soil properties. This study reveals the complex interactions of climate, soil properties, and microbes on Nmin and highlights the importance of soil microbial biomass in determining Nmin and nitrogen availability across the globe. The findings necessitate accurate representation of microbes in Earth system models to better predict nitrogen cycle under global change.
This study provides a comprehensive evaluation of the determinants of global Nmin with a focus on soil microbial biomass. The results demonstrated that soil microbial biomass predominantly controlled the variability of Nmin at a global scale. Climate, soil properties, and substrates influenced Nmin via their impacts on soil microbial biomass. The study highlights the importance of microbial biomass in determining soil nitrogen cycling, which challenges the conventional view that climate and soil properties are the dominant drivers of soil Nmin and advances our current understanding on the global patterns of nitrogen cycle. The findings suggest that changes in soil microbial biomass under global change would result in profound consequences on ecosystem processes by changing soil Nmin.
Photovoltaic (PV) power is volatile in nature and raises the level of uncertainty in power systems. PV power forecasting is an important measure to solve this problem. It helps to improve the ...reliability and reduces the generation cost. Advances in computer technology and sensors make the numeric modeling methods a hotspot in the field of PV power forecasting. However, data modeling methods strongly rely on the accuracy of measurement data. Unavoidable outliers in the measured meteorological data have an adverse effect on the model due to their heteroscedasticity. Although many studies can be found focusing on outlier detection, only a few have incorporated outlier detection with regression models. In this study, an innovative method employing the weighted Gaussian process regression approach is proposed, such that data samples with higher outlier potential have a low weight. A density-based local outlier detection approach is introduced to compensate the deterioration of Euclidean distance for high-dimensional data. A novel concept of the degree of nonlinear correlation is incorporated to compute the contribution of every individual data attribute. Effectiveness of the proposed method is demonstrated by performing an experimental analysis and making comparisons with other typical data-based approaches, and the results exhibit higher estimation accuracy.
Precise binding towards structurally similar substrates is a common feature of biomolecular recognition. However, achieving such selectivity—especially in distinguishing subtle differences in ...substrates—with synthetic hosts can be quite challenging. Herein, we report a novel design strategy involving the combination of different rigid skeletons to adjust the distance between recognition sites within the cavity, which allows for the highly selective recognition of hydrogen‐bonding complementary substrates, such as 4‐chromanone. X‐ray single‐crystal structures and density functional theory calculations confirmed that the distance of endo‐functionalized groups within the rigid cavity is crucial for achieving high binding selectivity through hydrogen bonding. The thermodynamic data and molecular dynamics simulations revealed a significant influence of the hydrophobic cavity on the binding affinity. The new receptor possesses both high selectivity and high affinity, which provide valuable insights for the design of customized receptors.
Endo‐functionalized hosts with different rigid skeletons were successfully synthesized. The well‐controlled distances of the recognition sites within the hydrophobic cavities enable the highly selective recognition of structurally similar guests. This approach presents a viable solution for achieving the precise recognition of specific guests in aqueous environments through adjustments in the distance of recognition sites.
The riverine system is usually considered as a natural reservoir of antibiotic resistance genes (ARGs) and more susceptible to anthropogenic activities. In this study, the occurrence and distribution ...of 14 antibiotics belonging to 7 categories together with 23 corresponding ARGs in Ba River of Xi'an China were investigated in March and July 2017. Sulfonamides, quinolones, macrolides and tetracyclines were detected in high frequencies ranged from 85.7% to 100%. Tetracyclines were detected with high concentrations in water samples (up to 8.54 × 102 ng L−1) and sediment samples (up to 2.08 × 103 μg kg−1), respectively. The total concentrations of antibiotics were much higher in July in comparison with March. The sul1, tetA, tetC, tetZ, gyrA, ermF, cmlA and blaTEM were the predominant ARGs in terms of absolute abundance. For both water and sediment samples in March compared with July, the relative abundance of ARGs had no significant difference except for sul3. The tetracyclines had positive correlation with tet genes, whereas the remaining antibiotics had no significant correlations with their corresponding ARGs, suggesting that environmental factors and cross-selection may significantly influenced the distribution of ARGs. Redundancy analysis was performed to further predict the influences of environmental factors on antibiotics and ARG abundance. The findings suggest that anthropogenic activities contribute significantly to the persistence of antibiotics pollution.
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•Antibiotics together with ARGs were surveyed in the urban river of Xi'an, China.•The effluent of residential area and wastewater treatment plant contributed to the antibiotic pollution.•Cross-selection was an important form of ARGs and integrons accelerated the spread speed of ARGs in riverine system.•The environmental factors had definite influence on the distribution of antibiotics and ARGs.
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