China Electronic Information Industry Development

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.

Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal graph convolutional network (AST-GCN). We model the external factors as dynamic attributes and static attributes and design an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model. Experiments on real datasets show the effectiveness of considering external information on traffic speed forecasting tasks when compared with traditional traffic prediction methods. Moreover, under different attribute-augmented schemes and prediction horizon settings, the forecasting accuracy of the AST-GCN is higher than that of the baselines. The source code of the AST-GCN is available at https://github.com/lehaifeng/T-GCN/AST-GCN.

With the large-scale use of lithium-ion batteries, the global demand for lithium resources has increased dramatically. It is essential to extract lithium resources from liquid lithium sources such as brine and seawater, as well as recycled waste lithium-ion batteries. Among various liquid lithium extraction technologies, lithium ion-sieve (LIS) adsorption is considered to be the most promising method for its low energy consumption and environment-friendly. This method has advantages of excellent lithium uptake capacity, high selective, and good regeneration performance. In this review, we summarized the development of lithium manganese oxides (LMO)-type LIS, including the chemical structure, lithium intercalation/de-intercalation mechanism, preparation methods and forming technology of this material. The problems in the industrial application of ion-sieves are put forward, and the future research directions are prospected.

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.

As one of the hot issues in cloud computing, task scheduling is an important way to meet user needs and achieve multiple goals. With the increasing number of cloud users and growing demand for cloud computing, how to reduce the task completion time and improve the system load balancing ability have attracted increasing interest from academia and industry in recent years. To meet the two aforementioned goals, this paper develops an EDA-GA hybrid scheduling algorithm based on EDA (estimation of distribution algorithm) and GA (genetic algorithm). First, the probability model and sampling method of EDA are used to generate a certain scale of feasible solutions. Second, the crossover and mutation operations of GA are used to expand the search range of solutions. Finally, the optimal scheduling strategy for assigning tasks to virtual machines is realized. This algorithm has advantages of fast convergence speed and strong search ability. The algorithm proposed in this paper is compared with EDA and GA via the CloudSim simulation experiment platform. The experimental results show that the EDA-GA hybrid algorithm can effectively reduce the task completion time and improve the load balancing ability.

Abstract As one of the most common disasters in deep mine roadway, floor heave has caused serious obstacles to mine transportation and normal production activities. The third section winch roadway in the third mining area of Qitaihe Longhu coal mine has a serious floor heave due to the large buried depths of the roadway and the semicoal rock roadway, and the maximum floor heave is 750 mm. For the problem of floor stability, this paper establishes a mechanical model to analyze the stability of roadway floor heave by analogy with the basement heave of deep foundation pit. It provides a model reference for analyzing the problem of roadway floor heave. Aiming at the problem of roadway floor heave in Longhu coal mine, the roadway model is established by using FLAC3D, and the roadway model after support is established according to the on-site support measures. Through the analysis of the distribution of roadway plastic area, stress nephogram, and displacement field simulation results, the results show that the maximum displacement of roadway roof and floor after support is reduced by 15% and 23%, but the maximum floor heave is still 770 mm, which is close to the measured floor heave of roadway. In order to solve the problem of roadway floor heave and integrate economic factors, this paper puts forward three support optimization schemes, simulates the support effect of each scheme, and finally determines that scheme 3 is the best support optimization scheme. Compared with that under the original support, the amount of floor heave is reduced by 81%, and the final amount of floor heave is 150 mm, which can meet the requirements of roadway floor deformation. The results provide a scheme and guidance for roadway support optimization.

Beginning with the explanation of smart city, this paper firstly gave the definition of smart city based on the existing researches and discussed the four characteristics of smart city, which were about interconnection, integration, cooperation and application for the urban information systems. Secondly the development processes of smart city were discussed, and it showed that smart city was the consequence of information technology evolution. Thirdly, the construction modes of smart city in China were summarized, which included constructing integrating smart city, developing smart industry, smart management and service, smart technology and infrastructure, and smart humanity and living. Fourthly construction modes of smart city in China were analyzed from the point of construction routes and countermeasures. Finally, the six trends of smart city were given for government decision-makers and relevant researchers.

The correlation between dimensionality and active sites on deciding the catalytic performance of an MOF catalyst in CO₂–epoxide cycloaddition reactions has been studied.

In this paper, we present a distributed complex event processing (CEP) engine for Internet of Things and its applications. A CEP Engine combines information from a variety of sources. It looks for patterns in these event streams and then responds in real-time. Complex event processing is an effective mechanism to analyze object data, to reason sensing events, and to trigger responding actions for the intelligence of IoT application. A building controlled by a building automation system (BAS) is often referred to as an intelligent building, smart building, or a smart home. BAS core functionality keeps building climate within a specified range, lights rooms based on an occupancy schedule, monitors performance and device failures in all systems, and provides malfunction alarms to building engineering contractors and maintenance staff. The advantages of employing a distributed CEP engine for smart building are demonstrated.

In December 2019, an outbreak of pneumonia, which was named COVID-2019, emerged as a global health crisis. Scientists worldwide are engaged in attempts to elucidate the transmission and pathogenic mechanisms of the causative coronavirus. COVID-19 was declared a pandemic by the World Health Organization in March 2020, making it critical to track and review the state of research on COVID-19 to provide guidance for further investigations. Here, bibliometric and knowledge mapping analyses of studies on COVID-19 were performed, including more than 1,500 papers on COVID-19 available in the PubMed and China National Knowledge Infrastructure databases from January 1, 2020 to March 8, 2020. In this review, we found that because of the rapid response of researchers worldwide, the number of COVID-19-related publications showed a high growth trend in the first 10 days of February; among these, the largest number of studies originated in China, the country most affected by pandemic in its early stages. Our findings revealed that the epidemic situation and data accessibility of different research teams have caused obvious difference in emphases of the publications. Besides, there was an unprecedented level of close cooperation and information sharing within the global scientific community relative to previous coronavirus research. We combed and drew the knowledge map of the SARS-CoV-2 literature, explored early status of research on etiology, pathology, epidemiology, treatment, prevention, and control, and discussed knowledge gaps that remain to be urgently addressed. Future perspectives on treatment, prevention, and control are also presented to provide fundamental references for current and future coronavirus research.

Menopause leads to an increased risk for osteoporosis in women. Although drug therapies exist, increasing numbers of people prefer alternative therapies such as dietary supplements, for example, calcium, vitamin D, and collagen hydrolysates for the prevention and treatment of osteoporosis. We have previously shown that a 3-month intervention using a calcium-collagen chelate (CC) dietary supplement was efficacious in improving bone mineral density (BMD) and blood biomarkers of bone turnover in osteopenic postmenopausal women. This study reports the long-term efficacy of CC in reducing bone loss in postmenopausal women with osteopenia. Thirty-nine women were randomly assigned to one of two groups: 5 g of CC containing 500 mg of elemental calcium and 200 IU vitamin D (1,25-dihydroxyvitamin D3) or control (500 mg of calcium and 200 IU vitamin D) daily for 12 months. Total body, lumbar, and hip BMD were evaluated at baseline, 6 and 12 months using dual-energy X-ray absorptiometry. Blood was collected at baseline, 6 and 12 months to assess levels of blood biomarkers of bone turnover. Intent-to-treat (ITT) analysis was performed using repeated measures analysis of variance pairwise comparisons and multivariate analysis to assess time and group interactions. The loss of whole body BMD in women taking CC was substantially lower than that of the control group at 12 months in those who completed the study and the ITT analysis, respectively (CC: -1.33% and -0.33% vs. control: -3.75% and -2.17%; P=.026, P=.035). The CC group had significantly reduced levels of sclerostin and tartrate-resistant acid phosphatase isoform 5b (TRAP5b) (P<.05), and higher bone-specific alkaline phosphatase/TRAP5b ratio (P<.05) than control at 6 months. These results support the use of CC in reducing bone loss in osteopenic postmenopausal women.

This study aims to analyze massive data in cities through data vitalization, and quantitatively evaluate smart city services, so as to promote the construction and development of smart cities. Due to the great difference between cities, a single evaluation method cannot accurately describe the development of a city. In this study, we classify cities by multiple labels according to various bases to give cities comprehensive description. Then, a Multi-level Service Evaluation System (MSES) is introduced. It considers individual weights to cities with different characteristics and evaluates the smart city services from different aspects. In addition to putting forward the iterative development of smart city services, a Maturity Model-based Service Evaluation (MMSE) framework is proposed based on the evaluation results of the MSES. It constructs a standardized and high confident evaluation framework to analyze the current state of cities and make feedback to the government policies. Finally, we take 10 cities in China to demonstrate the effectiveness of MMSE during the development of smart city services.

Timing correctness of hard real-time systems is guaranteed by schedulability analysis and worst-case execution time (WCET) analysis of programs. Traditional WCET analysis mainly deals with application programs and has achieved success in industry. Timing analysis of application programs along cannot guarantee correctness of complete systems consisting RTOS. WCET tools designed for application program analysis have been applied to analyze RTOS routines by several research groups, but poor WCET estimations have been reported. Timing analysis of real-time systems considering both applications and RTOS has not been fully studied. So we intend to give a survey of related work on WCET analysis of RTOS. By summarizing previous work, challenges of WCET analysis of complete real-time systems are presented, and some possible further research potentials are unleashed.

Parcel-level cropland maps are an essential data source for crop yield estimation, precision agriculture, and many other agronomy applications. Here, we proposed a rice field mapping approach that combines agricultural field boundary extraction with fine-resolution satellite images and pixel-wise cropland classification with Sentinel-1 time series SAR (Synthetic Aperture Radar) imagery. The agricultural field boundaries were delineated by image segmentation using U-net-based fully convolutional network (FCN) models. Meanwhile, a simple decision-tree classifier was developed based on rice phenology traits to extract rice pixels with time series SAR imagery. Agricultural fields were then classified as rice or non-rice by majority voting from pixel-wise classification results. The evaluation indicated that SeresNet34, as the backbone of the U-net model, had the best performance in agricultural field extraction with an IoU (Intersection over Union) of 0.801 compared to the simple U-net and ResNet-based U-net. The combination of agricultural field maps with the rice pixel detection model showed promising improvement in the accuracy and resolution of rice mapping. The produced rice field map had an IoU score of 0.953, while the User‘s Accuracy and Producer‘s Accuracy of pixel-wise rice field mapping were 0.824 and 0.816, respectively. The proposed model combination scheme merely requires a simple pixel-wise cropland classification model that incorporates the agricultural field mapping results to produce high-accuracy and high-resolution cropland maps.

The growing knowledge-based and service economies in megacities like Beijing and Shanghai have attracted large numbers of highly educated migrants, whereas their living conditions have drawn plenty of attention. In examining these issues, we conducted an empirical study regarding the precarity among highly educated migrants in Beijing. There are some structural and institutional factors underneath the highly educated migrants’ precarity, as the household registration (hukou) system still plays a significant role in accessing social welfare (urban public housing) and job opportunities in Beijing. Although the new urban poverty has occurred in the city as a result of the questionable policies regarding social distribution and welfare, it can also be argued that some of these migrants view temporary precarity as a strategy toward future upward social mobility – the hope of doing better over the long term.

With the development of cloud computing, many critical applications have been supported to provide many key services in the cloud computing. So the availability of cloud computing services turns to be higher and higher. Because resources of cloud computing are distributed, dynamic and heterogeneous, traditional research on availability cannot be good to adapt to the cloud computing new features. This paper does research on QoS-oriented cloud computing resources availability. First, a monitoring model of cloud computing resources availability is created. Then, according to the dynamic process of the cloud computing service, the availability of cloud computing resources is analyzed from QoS of a single cloud resource node which is described by common attribution and special attribution to QoS of some cloud resources which are connected by series model, parallel model and mix model to provide service. According to the three models and the analysis of the single cloud service resource, the availability of cloud computing service is monitored.

Rice is one of the most important staple food sources worldwide. Effective and cheap monitoring of rice planting areas is demanded by many developing countries. This study proposed a weakly supervised paddy rice mapping approach based on long short-term memory (LSTM) network and dynamic time warping (DTW) distance. First, standard temporal synthetic aperture radar (SAR) backscatter profiles for each land cover type were constructed on the basis of a small number of field samples. Weak samples were then labeled on the basis of their DTW distances to the standard temporal profiles. A time series feature set was then created that combined multi-spectral Sentinel-2 bands and Sentinel-1 SAR vertical received (VV) band. With different combinations of training and testing datasets, we trained a specifically designed LSTM classifier and validated the performance of weakly supervised learning. Experiments showed that weakly supervised learning outperformed supervised learning in paddy rice identification when field samples were insufficient. With only 10% of field samples, weakly supervised learning achieved better results in producer’s accuracy (0.981 to 0.904) and user’s accuracy (0.961 to 0.917) for paddy rice. Training with 50% of field samples also presented improvement with weakly supervised learning, although not as prominent. Finally, a paddy rice map was generated with the weakly supervised approach trained on field samples and DTW-labeled samples. The proposed data labeling approach based on DTW distance can reduce field sampling cost since it requires fewer field samples. Meanwhile, validation results indicated that the proposed LSTM classifier is suitable for paddy rice mapping where variance exists in planting and harvesting schedules.

Spontaneous coal combustion is one of the most common disasters in coal mine production. In order to explore the mechanism of coal spontaneous combustion more deeply, coal samples from the Yangdong wellfield of Jizhong Energy were selected for oxidative heat energy analysis experiments. A temperature-programmed experiment was selected to study the changes in characteristic parameters during the low-temperature oxidation of coal under different air supply conditions. TG-DSC experiments were conducted to study the characteristic temperature changes and thermodynamic characteristics of coal combustion processes at different heating rates. The study results show that the coal is most easily oxidised in the low-temperature oxidation stage when the air supply is 120 ml/min. The oxygen consumption rate, CO generation rate, and maximum and minimum heat release intensity are all greater at this airflow than under other conditions. The process of spontaneous combustion of coal has six characteristic temperature points and is divided into five stages. The characteristic temperature of the coal sample increased with the increase of the heating rate, and the TG/DTG curve showed a hysteresis phenomenon. DSC temperature curve shifts toward the high temperature with the increase of the heating rate, and the exothermic region is expanded. Isokinetic analysis (F-W-O and V-W) and Coats-Redfern model for calculating thermodynamic parameters. The activation energy of the samples decreased with the increase of the heating rate in the range of 2∼20°C·min−1 and showed a decreasing trend with the increase of the conversion rate.

BACKGROUND: Although the grape berries are deliberated as a non-climacteric fruit, ethylene seems to be involved in grape berry ripening. However, the precise role of ethylene in regulating the ripening of non-climacteric fruits is poorly understood. RESULTS: Exogenous ethephon (ETH) can stimulate the concentration of internal ethylene and accelerate the accumulation of anthocyanins in berries of 'Fujiminori', including malvidin-, delphinidin-, and petunidin-derivatives (3',4',5'-trihydroxylated anthocyanins) and cyanidin-derivatives (3',4'-dihydroxylated anthocyanins). The content of 3',4',5'-trihydroxylated anthocyanins was extremely higher than 3',4'-dihydroxylated anthocyanins, and ethylene did not affect the composition of anthocyanins in grape. Furthermore, we observed the expression of anthocyanin structural and regulatory genes as well as ethylene biosynthesis and response genes in response to ETH treatment. The anthocyanins accumulation is significantly associated with increased expression of anthocyanin structural (VvPAL, Vv4CH, VvCHS, VvCHI, VvF3H, and VvUFGT) and regulatory genes (VvMYBA1, VvMYBA2, and VvMYBA3), which persisted over the 12 days. In addition, exogenous ETH affected the endogenous ethylene biosynthesis (VvACO2 and VvACO4) and the downstream ethylene regulatory network (VvERS1, VvETR2, VvCTR1, and VvERF005). CONCLUSIONS: These findings bring new insights into the physiological and molecular function of ethylene during berry development and ripening in grapes. © 2021 Society of Chemical Industry.

Abstract The Pb(Ni 1/3 Nb 2/3 )O 3 -Pb(Zr x Ti 1− x )O 3 (PNN-PZT) piezoelectric ceramics with CuO and LiBiO 2 doping were successfully fabricated by the low-temperature solid-state reaction to effectively restrain the PbO volatilization. The microstructure and electrical properties of the PNN-PZT ceramics were characterized. The experimental results reveal that the PNN-PZT ceramics are composed of a pure perovskite structure in which the rhombohedral and tetragonal phases coexist. Meanwhile, the good electric properties, including low dielectric loss, outstanding diffusion phase transition and palpable dielectric relaxation, are exhibited in PNN-PZT ceramics with 0.2 wt.% CuO and 1 wt.% LiBiO 2 addition. This piezoceramic composition possibly provides a reference for the application of multi-layer piezoelectric actuators.