Blockchain technology has been developed for more than ten years and has become a trend in various industries. As the oil and gas industry is gradually shifting toward intelligence and ...digitalization, many large oil and gas companies were working on blockchain technology in the past two years because of it can significantly improve the management level, efficiency, and data security of the oil and gas industry. This paper aims to let more people in the oil and gas industry understand the blockchain and lead more thinking about how to apply the blockchain technology. To the best of our knowledge, this is one of the earliest papers on the review of the blockchain system in the oil and gas industry. This paper first presents the relevant theories and core technologies of the blockchain, and then describes how the blockchain is applied to the oil and gas industry from four aspects: trading, management and decision making, supervision, and cyber security. Finally, the application status, the understanding level of the blockchain in the oil and gas industry, opportunities, challenges, and risks and development trends are analyzed. The main conclusions are as follows: 1) at present, Europe and Asia have the fastest pace of developing the application of blockchain in the oil and gas industry, but there are still few oil and gas blockchain projects in operation or testing worldwide; 2) nowadays, the understanding of blockchain in the oil and gas industry is not sufficiently enough, the application is still in the experimental stage, and the investment is not enough; and (3) blockchain can bring many opportunities to the oil and gas industry, such as reducing transaction costs and improving transparency and efficiency. However, since it is still in the early stage of the application, there are still many challenges, primarily technological, and regulatory and system transformation. The development of blockchains in the oil and gas industry will move toward hybrid blockchain architecture, multi-technology combination, cross-chain, hybrid consensus mechanisms, and more interdisciplinary professionals.
In recent years, with the emphasis on environmental protection, the global energy landscape is changing: the proportion of traditional energy is gradually decreasing, and renewable energy are ...developing rapidly. In this context, the oil and gas company is also in the early stages of the low-carbon emission energy transition. However, this concept is relatively new for many oil and gas companies. Thus, this paper aims to introduce the low-carbon emission transition practices of several large oil and gas companies so that more companies can learn from the experience. This paper summarizes the transition targets, investment, and actions of some large oil and gas companies employing enterprise surveys, and analyses the low-carbon transition paths, opportunities, and challenges. The analysis shows that (1) vigorous development of natural gas business is the first step for oil and gas companies to transition to low-carbon emission stage; (2) increasing investment in renewable energy is a long-term action of oil and gas companies and the key to transforming oil and gas companies into integrated energy companies; (3) oil and gas companies should have rich experience in developing geothermal energy. In addition, the paper also proposes policy recommendations for the low-carbon transition of oil and gas companies.
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•The low-carbon transition actions of nine major oil and gas companies are summarized.•Three low-carbon transition paths are analyzed and summarized.•The opportunities, challenges and emerging technologies of low-carbon transition for oil and gas companies are summarized.
The texts published in the Russian mass media on the digitalization processes in China are analysed in the article. The relevance of the study is due to the attention of the Chinese government to the ...implementation of international cooperation with various countries, including Russia. It is noted that the “Chinese theme” is one of the most relevant in Russian publications. Attention is paid to one of the most popular and dynamically developing areas — digitalization. The author proceeds from the assumption that the media not only reflect news information, but also in a certain way form ideas about a particular object being described. The author notes that significant attention is paid to the development of digitalization in the PRC in Russian newspapers and magazines. It is shown that in the publications of the Russian media, China is represented by one of the world leaders in digitalization, which is supported by the state and its citizens. The contexts, indicating that digitalization is penetrating into different spheres of the country’s life: education, industry, trade, etc., are given. The author comes to the conclusion that an exclusively positive image of digital China is being constructed in the Russian media.
Feature selection (FS), which aims to select informative feature subsets and improve classification performance, is a crucial data-mining technique. Recently, swarm intelligence has attracted ...considerable attention and has been successfully applied to FS. Ant colony optimization (ACO), a swarm intelligence algorithm, has shown great potential in FS owing to its graphical representation and search ability. However, designing an effective ACO-based approach for FS is challenging because of issues originating from feature interactions and premature convergence problems. In this study, a novel ACO is proposed that incorporates symmetric uncertainty (SU). By constructing a probabilistic sequence-based graphical representation, the proposed algorithm significantly outperformed six other algorithms on 16 problems in terms of the classification error rate. This study also considers an extensive investigation of the contribution of the two components, namely, probabilistic sequence and SU. The experimental results indicated that these components significantly improved the performance of the ACO-based approach.
Quantitative flow ratio (QFR) is a novel angiography-based method for deriving fractional flow reserve (FFR) without pressure wire or induction of hyperemia. The accuracy of QFR when assessed online ...in the catheterization laboratory has not been adequately examined to date.
The goal of this study was to assess the diagnostic performance of QFR for the diagnosis of hemodynamically significant coronary stenosis defined by FFR ≤0.80.
This prospective, multicenter trial enrolled patients who had at least 1 lesion with a diameter stenosis of 30% to 90% and a reference diameter ≥2 mm according to visual estimation. QFR, quantitative coronary angiography (QCA), and wire-based FFR were assessed online in blinded fashion during coronary angiography and re-analyzed offline at an independent core laboratory. The primary endpoint was that QFR would improve the diagnostic accuracy of coronary angiography such that the lower boundary of the 2-sided 95% confidence interval (CI) of this estimate exceeded 75%.
Between June and July 2017, a total of 308 patients were consecutively enrolled at 5 centers. Online QFR and FFR results were both obtained in 328 of 332 interrogated vessels. Patient- and vessel-level diagnostic accuracy of QFR was 92.4% (95% CI: 88.9% to 95.1%) and 92.7% (95% CI: 89.3% to 95.3%), respectively, both of which were significantly higher than the pre-specified target value (p < 0.001). Sensitivity and specificity in identifying hemodynamically significant stenosis were significantly higher for QFR than for QCA (sensitivity: 94.6% vs. 62.5%; difference: 32.0% p < 0.001; specificity: 91.7% vs. 58.1%; difference: 36.1% p < 0.001). Positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for QFR were 85.5%, 97.1%, 11.4, and 0.06. Offline analysis also revealed that vessel-level QFR had a high diagnostic accuracy of 93.3% (95% CI: 90.0% to 95.7%).
The study met its prespecified primary performance goal for the level of diagnostic accuracy of QFR in identifying hemodynamically significant coronary stenosis. (The FAVOR Functional Diagnostic Accuracy of Quantitative Flow Ratio in Online Assessment of Coronary Stenosis II China study; NCT03191708)
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Video anomaly detection is important in various practical applications. This paper proposes an unsupervised method for video anomaly detection. In the core of the method lies a new prediction model ...for anomaly detection with novel anomaly score mechanism and self-training mechanism combined with prediction model. In the first stage, we use two conventional unsupervised anomaly detection methods to obtain pseudo normal and anomalous frames from the original unlabeled data. In the second stage, we train the prediction model with the pseudo normal frames to learn normal patterns. In the last stage, a three-branch decision module is constructed using prediction model and decision function to calculate the anomaly score of frames and update the pseudo frames for subsequent iterative training. The model then enters the second stage, until the last iterative training is completed. After several iterative training and evaluations, the optimal anomaly scores of the original unlabeled data are finally obtained, and a stable model is generated at the same time. Experimental results on four real-world video datasets demonstrate that the proposed method outperforms state-of-the-art methods without labeled data by a significant margin.
•The average prediction losses of the pseudo normal and anomalous frames are used to calculate anomaly score.•The application of self-training mechanism eliminates the need for manually labeled training data.•The proposed unsupervised method adopts the idea of anchors and achieves effective anomalous region localization.
Recycled carbon fiber (RCF) can be used in fused deposition modeling (FDM), which can not only improve the reuse value of carbon fiber but also make up for the insufficient performance of general FDM ...products. According to the recycling principle, the device special for carbon fiber-reinforced resin matrix composite (CFRP) recycling is developed; soft and fluffy carbon fibers were obtained under the optimal process parameters in this work. RCF was remanufactured into composites by grinding, extrusion, and FDM. The microtopography and monofilament tensile strength of RCF were analyzed, the particle size distribution of chopped RCF and the interface combination in composites were observed, and the mechanical properties of RCF-reinforced polylactic acid (PLA) composites (RCF/PLA) were studied. Results show that the monofilament tensile strength of RCF obtained under the optimal process parameters was 8% higher than that of original carbon fiber (OCF). Compared with PLA, the tensile strength of OCF-reinforced PLA composites (OCF/PLA) and RCF/PLA composites was reduced by 25% and 12.5%, respectively. The chemical bonding between RCF and resin matrix and the nozzle temperature, layer height, and printing speed in FDM process have an important influence on the tensile strength of the composites. Compared with that of PLA, the bending strength of OCF/PLA composites increased by about 7.8%, the flexural modulus increased by about 81%, the bending strength of RCF/PLA composites increased by 10.4%, and the flexural modulus increased by 87%. Through FDM, RCF can be used to enhance the resin matrix with higher requirements for stiffness, bending strength, and wear resistance.
Below-cloud aerosol scavenging process by precipitation is important for cleaning the polluted aerosols in the atmosphere, and is also a main process for acid rain formation. However, the related ...physical mechanism has not been well documented and clarified yet. In this paper, we investigated the below-cloud PM
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(particulate matter with aerodynamic diameter being 2.5 μm or less) scavenging by different-intensity rains under polluted conditions characterized by high PM
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concentrations, based on in-situ measurements from March 2014 to July 2016 in Beijing city. It was found that relatively more intense rainfall events were more efficient in removing the polluted aerosols in the atmosphere. The mean PM
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scavenging ratio and its standard deviation (SD) were 5.1% ± 25.7%, 38.5% ± 29.0%, and 50.6% ± 21.2% for light, moderate, and heavy rain events, respectively. We further found that the key impact factors on below-cloud PM
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scavenging ratio for light rain events were rain duration and wind speed rather than raindrop size distribution. However, the impacts of rain duration and wind speed on scavenging ratio were not important for moderate and heavy rain events. To our knowledge, this is the first statistical result about the effects of rain intensity, rain duration, and raindrop size distribution on below-cloud scavenging in China.
Metagenomics technology can directly extract microbial genetic material from the environmental samples to obtain their sequencing reads, which can be further assembled into contigs through assembly ...tools. Clustering methods of contigs are subsequently applied to recover complete genomes from environmental samples. The main problems with current clustering methods are that they cannot recover more high-quality genes from complex environments. Firstly, there are multiple strains under the same species, resulting in assembly of chimeras. Secondly, different strains under the same species are difficult to be classified. Thirdly, it is difficult to determine the number of strains during the clustering process.
In view of the shortcomings of current clustering methods, we propose an unsupervised clustering method which can improve the ability to recover genes from complex environments and a new method for selecting the number of sample's strains in clustering process. The sequence composition characteristics (tetranucleotide frequency) and co-abundance are combined to train the probability model for clustering. A new recursive method that can continuously reduce the complexity of the samples is proposed to improve the ability to recover genes from complex environments. The new clustering method was tested on both simulated and real metagenomic datasets, and compared with five state-of-the-art methods including CONCOCT, Maxbin2.0, MetaBAT, MyCC and COCACOLA. In terms of the number and quality of recovered genes from metagenomic datasets, the results show that our proposed method is more effective.
A new contigs clustering method is proposed, which can recover more high-quality genes from complex environmental samples.
This paper studies robust gait features against pedestrian carrying and clothing condition changes. Inspired by the fact that humans pay more attention to pose details based on part movements when ...completing a gait recognition task, we introduce pose information into the convolutional network without complex computation of human modeling. We construct a multimodal set-based deep convolutional network (mmGaitSet). The mmGaitSet consists of two independent feature extractors which extract the body features from silhouettes and the part features from pose heatmaps, respectively. Joint training of two feature extractors make them complement each other. We combine intra-modal fusion and inter-modal fusion into the network. The intra-modal fusion integrates the low-level structural features and high-level semantic features, to improve the discrimination of single modality features. The inter-modal fusion fully aggregates the complementary information between different modalities to enhance the pedestrian gait presentation. The state-of-the-art results are achieved on the challenging CASIA-B dataset outperforming recent competing methods, with up to 92.5% and 80.3% average rank-1 accuracies under bag-carrying and coat-wearing walking conditions, respectively.
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