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In the context of carbon peaking and carbon neutrality as well as the construction of a new energy system, the integrated development and intelligent regulation of fossil energy with new energy resources such as wind, solar, and geothermal energy has become a new pattern for the future energy system. The oil and gas industry is undergoing digital and intelligent transformation, and the development of smart oil and gas fields will reduce exploration and development costs and increase social and economic benefits. This study elucidates the concept and implications of smart oil and gas fields with multi-energy synergy and the critical role of new smart oil and gas fields in enhancing oil and gas reserves and production and in promoting the green, low-carbon, and intelligent transformation of the oil and gas industry. It reviews the current development status of the wind-solar-geothermal-energy storage multi-energy synergy system, the integration of oil and gas fields with the multi-energy synergy system, and the smart oil and gas fields. The study also identifies the challenges and key issues faced by the development of smart oil and gas fields in China. It summarizes future scenarios for smart oil and gas fields with multi-energy synergy: (1) utilization of green electricity, (2) new geothermal systems for thermal recovery of abandoned heavy oil reservoirs, (3) production optimization of oil well clusters based on wind/solar power microgrids, (4) in-situ conversion of natural gas and utilization of associated gas for power generation, (5) comprehensive energy management systems for efficient and low-carbon oil and gas production, and (6) intelligent collaboration and optimization of electricity, geothermal energy, and hydrogen energy storage. The core output of the construction of smart oil and gas fields with multi-energy synergy is low-carbon oil fields and super energy basins. Under the premise of ensuring oil and gas production, future development should focus on supplementing fundamental shortcomings, increasing technological advantages, strengthening application capabilities, and achieving independence, thereby achieving breakthroughs in key core technologies to form practical development solutions.

To address the difficulty faced by network security operation and maintenance personnel in timely and accurate identification of required data during network security event analysis, a recommendation algorithm based on a knowledge graph for network security events was proposed.The algorithm utilized the network threat framework ATT＆amp;CK to construct an ontology model and establish a network threat knowledge graph based on this model.It extracted relevant security data such as attack techniques, vulnerabilities, and defense measures into interconnected security knowledge within the knowledge graph.Entity data was extracted based on the knowledge graph, and entity vectors were obtained using the TransH algorithm.These entity vectors were then used to calculate data similarity between entities in network threat data.Disposal behaviors were extracted from literature on network security event handling and treated as network security data entities.A disposal behavior matrix was constructed, and the behavior matrix enabled the vector representation of network threat data.The similarity of network threat data entities was calculated based on disposal behaviors.Finally, the similarity between network threat data and threat data under network security event handling behavior was fused to generate a data recommendation list for network security events, which established correlations between network threat domains based on user behavior.Experimental results demonstrate that the algorithm performs optimally when the fusion weight α=7 and the recommended data volume K=5, achieving a recall rate of 62.37% and an accuracy rate of 68.23%.By incorporating disposition behavior similarity in addition to data similarity, the algorithm better represents factual disposition behavior.Compared to other algorithms, this algorithm exhibits significant advantages in recall rate and accuracy, particularly when the recommended data volume is less than 10.

Objective: To explore the differences in runner performance of screw groups with different differential ratios, and to clarify the effects of differential speed on the extrusion performance of polylactic acid materials. Methods: Four groups of differential twin screw groups were established by Solidworks software, meshing and finite element numerical simulation were carried out by Fluent software, the calculation results were analyzed by CFD-post software, and the contours and discounted plots of various characterization parameters were combined, and they were verified by experiments. Results: Under the condition that the inlet and outlet pressures were 0.2 MPa and 0.5 MPa, and the reference speed of a single head was 30 r/min, the screws with a differential ratio of 1∶2 and 1∶3 had their own advantages and disadvantages under different properties, and the screws with a difference ratio of 1∶2 for output and pressure in the barrel had an advantage. In terms of mixing capacity, a screw with a differential ratio of 1∶3 had an advantage. Conclusion: Compared with the traditional twin-screw extruder, the differential twin-screw extruder has a positive impact on the extrusion performance of polylactic acid materials, and can be used as a new polylactic acid production equipment.

As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods, an effective detection method for multi-type App-DDoS was proposed. Firstly, in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method, the indexes of source IP address in multiple time windows were constructed by the approach of Hash function. Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically, and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow, burst normal flow and multi-type App-DDoS flows. The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods, the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.

In order to understand the relationship between the pyrolysis characteristics of herb residues and their composition & structure, the composition structure and chemical composition of 10 typical Chinese planted herb residues were determined, and the pyrolysis weight loss characteristics were carried by the thermogravimetric analyzer. Through the correlation analysis between the characteristics of the feedstock and the pyrolysis process behavior, the physical factors affecting the pyrolysis process of herb residues and the associated coupling mechanism were discussed. Results showed that herb residues were detected with high content of nitrogen and sulfur. The root samples had high cellulose content and the leaf samples had high lignin content. The ability of thermal decomposition to remove volatile matter of leaves, stems, and seeds of herb residue samples were poor, and the activation energy of pyrolysis obtained by Coats-Redfern integration method was basically within 100 kJ/ mol. Roots of herb residue samples were easier to devolatile, and the activation energy was higher than 110 kJ/ mol. Among them, yam sample had the strongest pyrolysis ability to devolatilize, with the activation energy of 169. 83 kJ/ mol. The lowest activation energy was obtained in the artemisia annua sample, and the correspongding value was 64. 32 kJ/ mol. Cellulose and lignin were the two main physical properties affecting pyrolysis devolatilization and activation energy. The content of cellulose and lignin could be used to predict the pyrolysis devolatilization ability and activation energy of herb residues. [ABSTRACT FROM AUTHOR]

Seed quality plays an important role in the agricultural and animal husbandry production, the effective utilization of genetic resources, the conservation of biodiversity and the restoration and reconstruction of plant communities. Seed aging is a common physiological phenomenon during storage. It is a natural irreversible process that occurs and develops along with the extension of seed storage time. It is not only related to the growth, yield and quality of seed and seedling establishment, but also has an important effect on the conservation, utilization and development of plant germplasm resources. The physiological mechanisms of seed aging are complex and diverse. Most studies focus on conventional physiological characterization, while systematic and comprehensive in-depth studies are lacking. Here we review the recent advances in understanding the physiology of seed aging process, including the methods of seed aging, the effect of aging on seed germination, and the physiological and molecular mechanisms of seed aging. The change of multiple physiological parameters, including seed vigor, electrical conductivity, malondialdehyde content and storage material in the seed, antioxidant enzyme activity and mitochondrial structure, were summarized. Moreover, insights into the mechanism of seed aging from the aspects of transcriptome, proteome and aging related gene function were summarized. This study may facilitate the research of seed biology and the conservation and utilization of germplasm resources.

To establish a simple, quick and effective method to get a large amount of spider toxin JZTX-26 (35 aa) and JZTX-51 (27 aa) with 3 disulfide bonds each, the mature peptides coding gene fragments were constructed and fused with maltose-binding protein (MBP) tag in an Escherichia coli expression vector pMAL-p2x. The recombinant constructs pMAL-jz26 and pMAL-jz51 were transformed and cultured in E. coli TB1 and BL21 (DE3). After being induced by isopropyl-β-d-thiogalactoside (IPTG), the periplasmic proteins were purified by amylose affinity chromatography and analyzed by SDS-PAGE. The fusion proteins were digested with factor X, and purified by Sizes-Exclusion chromatography and Reversed Phase HPLC. Molecular weights of the purified peptides were obtained by using a MALDI-TOF-TOF mass spectrometer, which were consistent with the theoretical molecular weights. Five milligram of target protein could be purified from 1 L of culture medium. The results indicate that the peptides with three disulfide bonds can be expressed by using the prokaryotic expression system with MBP tag. Our findings suggest the possibility of genetic engineering to obtain large amount of spider peptide toxins.

Objective: To explore the predictive value of the prognosis and outcome for optic neuritis (ON) and neuromyelitis optica (NMO) by investigating the levels and variation of CXCL12, PDGF and CXCL14 in CSF of patients with ON and NMO., Methods: Retrospective study. Thirty-five patients with ON, 10 patients with NMO and 10 patients with cerebral venous sinus thrombosis (CVST) were scheduled in the research unit from September 2012 to September 2013 in Neuro-Ophthalmology Department of PLA General Hospital. Clinical data and cerebrospinal fluid (CSF) parameters were collected. CXCL12, PDGF and CXCL14 concentrations were measured in CSF using enzyme linked immunosorbent assay (ELISA). The CXCL12, PDGF and CXCL14 levels in CSF were compared by using ANOVA in different diseases with different phases and recurrent cases found by MRI. Multiple comparisons were used by LSD method. The comparison of positive rate for MRI in ON different phases was used by exact probability. Meanwhile, correlation analysis was conducted between CXCL12, PDGF, CXCL14 and white blood cells (WBC), IgG and protein in CSF., Results: Compared with NMO group (3.69±0.35, 2.04±0.24, 7.05±0.94), the CXCL12, PDGF and CXCL14 levels in CSF were higher in ON (4.39±0.51, 2.51±0.39, 8.65±1.55) and CVST(4.84± 0.49, 2.79±0.47, 10.53±1.11) group (F=14.593, 10.060, 10.003,P<0.001, <0.001, <0.001), especially the CXCL12, PDGF and CXCL14 levels in CSF of CVST group patients were higher than that in ON group. Among them, the CXCL12 and PDGF levels in CSF were higher in acute phase of ON (4.63±0.50, 2.65±0.40) and CVST(4.84±0.49, 2.79±0.47) group than stationary phase of ON (4.13±0.39, 2.34±0.32) group (F=8.823, 4.906, P=0.001, 0.012). In addition, 28 of 35 ON patients were conducted the cerebral or orbital magnetic resonance imaging (MRI). The result showed that the CXCL12 and PDGF levels in CSF of patients with positive finding in MRI (3.96±0.30, 2.23±0.16) were higher than those patients with negative finding in MRI (4.64±0.42, 2.62±0.42) (t=-4.754, -2.977, all P<0.01). Besides that, there was higher correlation between the CXCL12 level and PDGF in CSF (P<0.01)., Conclusions: Reduced concentration of cytokine that promoted remyelination such as CXCL12 and PDGF in cerebrospinal fluid of the ON and NMO patients may predict a bad myelin regeneration.