The growing energy consumption and associated carbon emission of Bitcoin mining could potentially undermine global sustainable efforts. By investigating carbon emission flows of Bitcoin blockchain ...operation in China with a simulation-based Bitcoin blockchain carbon emission model, we find that without any policy interventions, the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emission correspondingly. Internationally, this emission output would exceed the total annualized greenhouse gas emission output of the Czech Republic and Qatar. Domestically, it ranks in the top 10 among 182 cities and 42 industrial sectors in China. In this work, we show that moving away from the current punitive carbon tax policy to a site regulation policy which induces changes in the energy consumption structure of the mining activities is more effective in limiting carbon emission of Bitcoin blockchain operation.
Regional trade agreements (RTAs) have been widely adopted to facilitate international trade and cross-border investment and promote economic development. However, ex ante measurements of the ...environmental effects of RTAs to date have not been well conducted. Here, we estimate the CO
emissions burdens of the Regional Comprehensive Economic Partnership (RCEP) after evaluating its economic effects. We find that trade among RCEP member countries will increase significantly and economic output will expand with the reduction of regional tariffs. However, the results show that complete tariff elimination among RCEP members would increase the yearly global CO
emissions from fuel combustion by about 3.1%, doubling the annual average growth rate of global CO
emissions in the last decade. The emissions in some developing members will surge. In the longer run, the burdens can be lessened to some extent by the technological spillover effects of deeper trade liberalization. We stress that technological advancement and more effective climate policies are urgently required to avoid undermining international efforts to reduce global emissions.
In this paper, the new structural characteristics and core influencing factors of the crude oil prices are summarized based on previous representative research results. Firstly, a newly dynamic ...Bayesian structural time series model (DBSTS) is developed to investigate the oil prices. In particular, Google trend is introduced as an indicator to reflect the impact of search data on the oil price. Secondly, the spike and slab method is employed to select core influence factors. Finally, the Bayesian model average (BMA) is utilized to predict the oil price. Experimental results confirm that the supply and demand of global crude oil and the financial market are still the main factors affecting the oil price. Furthermore, Google trend can reflect the changes in the crude oil price to a certain extent. Moreover, the impact of shale oil production on the oil price is gradually increasing, yet remains relatively small. In addition, the DBSTS model can identify turning points in historical data (such as the 2008 financial crisis). Finally, the findings suggest the DBSTS model has good predictive capabilities in short-term prediction, making it suitable for analyzing the crude oil prices.
•A novel dynamic Bayesian structural time series model is developed.•415 explanatory variables are included, especially, Google trend search data.•Spike-slab regression is used to extract core factors and analyze new structural characteristics.•The impact of shale oil production on oil price is small relatively.•Turning points of historical oil price are identified and analyzed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Presenting a conditional adaptation networks for cross-domain image classification.•Solving the categories mismatch and class prior bias problems by conditional adaptation.•Proposing a label ...correlation transfer algorithm to preserve the domain information.•Experiments are performed to show the usefulness of the proposed method.
Unsupervised domain adaptation aims to improve the performance of an unknown target domain by utilizing the knowledge learned from a related source domain. Given that the target label information is unavailable in the unsupervised situation, it is challenging to match the domain distributions and to transfer the source model to target applications. In this paper, a Deep Conditional Adaptation Networks (DCAN) is proposed to address the unsupervised domain adaptation problem. DCAN is implemented based on a deep neural network and attempts to learn domain invariant features based on the Wasserstein distance. A conditional adaptation strategy is presented to reduce the domain distribution discrepancy and to address category mismatch and class prior bias, which are usually ignored in marginal adaptation approaches. Furthermore, we propose a label correlation transfer algorithm to address the unsupervised issues, by generating more effective pseudo target labels based on the underlying cross-domain relationship. A set of comparative experiments were performed on standard domain adaptation benchmarks and the results demonstrate that the proposed DCAN outperforms previous adaptation methods.
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Mounting studies have substantiated that abrogating autophagy contributes to cardiac hypertrophy (CH). Sirtuin 1 (SIRT1) has been reported to support autophagy and inhibit CH. However, the upstream ...regulation mechanism behind the regulation of SIRT1 level in CH remains unclear. Circular RNAs (circRNAs) are vital modulators in diverse human diseases including CH. This study intended to investigate the regulatory mechanism of circRNA on SIRT1 expression in CH. CH model was established by angiotensin II (Ang II) fusion or transverse aortic constriction (TAC) surgery and Ang II treatment on hiPSC-CMs and H9c2 cells in vitro. Our results showed that circ-SIRT1 (hsa_circ_0093884) expression was downregulated in Ang II-treated hiPSC-CMs, and confirmed that its conserved mouse homolog circ-Sirt1 (mmu_circ_0002354) was expressed at low levels in Ang II-treated H9c2 cells and TAC-induced mice model. Functionally, circ-SIRT1/circ-Sirt1 attenuated Ang II-induced CH and induced autophagy in hiPSC-CMs and H9c2 cardiomyocytes. Mechanistically, circ-SIRT1 could upregulate its host gene SIRT1 at the post-transcriptional level by sponging miR-3681-3p/miR-5195-3p and stabilized SIRT1 protein at the post-translational level by recruiting USP22 to induce deubiquitination on SIRT1 protein. Further, SIRT1 knockdown could rescue the effect of circ-SIRT1 upregulation on Ang II-induced CH and autophagy in vitro and in vivo. In conclusion, we first uncovered that circ-SIRT1 restrains CH via activating SIRT1 to promote autophagy, indicating circ-SIRT1 as a promising target to alleviate CH.
•Tillage selected specific rhizosphere bacterial communities during crop growth.•Different tillage practices shape the dynamics of rhizosphere bacterial communities with crop growth.•Effects of ...tillage as a historical event on rhizosphere bacterial community assembly.
Long-term tillage practices can shape unique soil environments. The heavy disturbance caused by plow tillage can cause a series of ecological problems; thus, zero tillage is widely used as a low-disturbance conservation practice. However, it is unclear how tillage practices with different soil disturbances affect the rhizosphere microbial communities of crops during the succession of their growth stages on a background of continuous management. The rhizosphere environment may shape different microbial communities depending on the growth stage, and soil disturbance before crop planting may also have a lasting effect on the microbial community. In this study, we used 16S rRNA sequencing to analyze the assembly and composition of the rhizosphere bacterial communities of wheat at different growth stages under different tillage practices. The results showed that under different tillage practices and growth stages, the assembly and composition of the rhizosphere bacterial community changed significantly. Rhizosphere bacterial communities under the zero tillage condition appear to be more stable than those under the plow tillage condition, which may be related to the relatively low soil disturbance and unique rhizosphere environment under the zero tillage condition. In addition, our results further suggest that several soil variables may affect bacterial community assembly and composition.
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Tillage has a considerable effect on the soil ecosystem and its services, including microbial communities. Harnessing beneficial microbes is a sustainable way to optimizing crop management and ...agricultural production. Although diazotrophs play a major role in global biological nitrogen fixation, the effects of tillage on diazotrophic communities in the rhizosphere are not fully understood. In the present study, we investigated the diazotrophic community in wheat rhizosphere soil under different tillage treatments in a long-term experiment, i.e., plow tillage (considered as conventional tillage), chisel plow tillage (considered as conservation tillage), and zero tillage (considered as conservation tillage). Tillage led to a divergent distribution in the rhizosphere diazotrophic community and significant changes in community structure. Tillage caused specific responses from members/modules of the rhizosphere diazotrophic community co-occurrence network, and the relative abundance of keystone taxa was higher under conservation tillage than under conventional tillage. The increased abundance of tillage-sensitive modules under conservation tillage had a broad and significant positive correlation with rhizosphere nutrient availability, whereas the opposite was true for conventional tillage. Differences in nutrients under different tillage practices may lead to different assembly processes of diazotrophs. Overall, our findings indicate that tillage significantly affects the assembly and composition of the rhizosphere diazotrophic community, emphasizing the importance of improved substrate availability for rhizosphere diazotrophic modules under conservation tillage. This knowledge could deepen our understanding of the rhizosphere functional microbial community (e.g., biological nitrogen fixation).
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•Tillage changes the co-occurrence network of rhizosphere diazotrophic community.•Most of keystone taxa are enriched under conservation tillage.•Tillage may change the assembly process of diazotrophic modules.•Enriched nutrients in conservation tillage benefit copiotrophs.
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•A new 3D multi-directional textile auxetic structure has been developed in current work.•The deformation behavior data simulated from FE model shows excellent agreement with ...additively manufactured 2D material.•The developed model was subsequently expanded for 3D geometry to predict the auxetic deformation behavior in each direction.•This novel textile structure can find its application in designing multi-directional auxetic composites.
Previous studies of auxetic composites reinforced with woven fiber structures focused on unidirectional tension or compression. Multiple fibers are often required to play different roles in the structure, making it difficult to fully exploit the advantages of auxetic materials in real-world applications. Here, a single fiber-composed textile structure that can be stretched in various directions to produce auxetic behavior was developed. The single-layer structure was successfully fabricated using a three-dimensional (3D) printing method and evaluated by finite element analysis (FEA). The tensile deformation behavior of the 3D structure was simulated, and Poisson’s ratio (PR) values of the single-layer and 3D structures were obtained. There was good agreement between the FEA and experimental results, and the proposed structure can exhibit auxetic behavior when stretched in three orthogonal directions. With tensile displacements of 4 mm in the X and Z directions, the 3D structure was able to achieve maximum negative Poisson's ratio (NPR) values of −0.26 and −0.43, respectively. In particular, the diameter ratio of the fibers in each direction is an influential factor in terms of the degree of auxetic deformation of the textile. Thus, the results of this study could inform the development of novel multi-directional auxetic textile composites.
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The marginal wells in low-permeability oilfields are characterized by small storage size, scattered distribution, large regional span, low production, intermittent production, etc. The production ...mode of these wells is nonpipeline mode. In our previous work (Zhang et al., 2019), a novel mixed-integer linear programming (MILP) model using a discrete-time representation was presented for the operation scheduling of nonpipelined wells. However, too many discretization time points are required to ensure the accuracy of the model. Even for moderately sized problems, computationally intractable models can arise. The present paper describes a new continuous-time representation method to reformulate this schedule optimization problem. By introducing the continuous-time representation, the binary variables are largely reduced. The solution effect for different model sizes is also investigated. When the model size increases to a certain degree, a feasible solution cannot be obtained within a limited time. The results of a case study originated from a real oilfield in China show that the continuous-time model requires less time to obtain the optimal solution compared to the discrete-time model. In details, considering a same scale problem, the solution based on the continuous-time model saves 52.25% of the time comparing with the discrete-time model. The comparison validates the new model's superiority.
There is a growing interest in scene text detection for arbitrary shapes. The effectiveness of text detection has also evolved from horizontal text detection to the ability to perform text detection ...in multiple directions and arbitrary shapes. However, scene text detection is still a challenging task due to significant differences in size and aspect ratio and diversity in shape, as well as orientation, coarse annotations, and other factors. Regression-based methods are inspired by object detection and have limitations in fitting the edges of arbitrarily shaped text due to the characteristics of their methods. Segmentation-based methods, on the other hand, perform prediction at the pixel level and thus can fit arbitrarily shaped text better. However, the inaccuracy of image text annotations and the distribution characteristics of text pixels, which contain a large number of background pixels and misclassified pixels, degrades the performance of segmentation-based text detection methods to some extent. Usually, considering whether a pixel belongs to a text region is highly dependent on the strength of the semantic information it has and the position of the pixel in the text area. Based on the above two points, we propose an innovative and robust method for scene text detection combining position and semantic information. First, we add position information to the images using a position encoding module (PosEM) to help the model learn the implicit feature relationships associated with the position. Second, we use the semantic enhancement module (SEM) to enhance the model's focus on the semantic information in the image during feature extraction. Then, to minimize the effect of noise due to inaccurate image text annotations and the distribution characteristics of text pixels, we convert the detection results into a probability map that can more reasonably represent the text distribution. Finally, we reconstruct and filter the text instances using a post-processing algorithm to reduce false positives. The experimental results show that our model improves significantly on the Total-Text, MSRA-TD500, and CTW1500 datasets, outperforming most previous advanced algorithms.
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