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Objective To explore the impact of macrophage-to-myofibroblast transition (MMT) on pulmonary fibrosis induced by acute lung injury by LPS. Methods Totally 21 male mice were randomly classified into 7 groups: control group, model group (LPS-PF) at different time points and intervention group of clodronate-liposomes (CL-LIP)treatement at different time points(n=3). Pulmonary fibrosis was identified by HE and Masson staining microscopy. The immuno-fluorescence technology was used for the evaluation of numbers of macrophage- to- myofibroblast transition cells (MMT cell which co-expressed CD68 and α-SMA). Bone marrow-derived macrophages (BMDMs) were randomly classified into two group: control (Ctrl) group and TGF-β1-treated group induced by transforming growthfactor-β1. α-SMA, FN and Col1 were detected by RT-qPCR. The expression of α-SMA, Smad3 and p-Smad3 protein was evaluated by Western blot. Results At day 7, the Ashcroft score of lung tissue in LPS-PF mouse model was significantly increased when compared with the Ctrl group (P＜0.01); While the score significantly declined when the model was pretreated with CL-LIP (P＜0.05). As detected by immuno-fluorescence staining, in CL-LIP group the number of CD68-positive cells co-labeled with α-SMA was obviously less then that of LPS-PF group of the corresponding time point(P＜0.01). When the BMDMs were stimulated by TGF-β1 at 24 h, 48 h and 96 h respectively, a higher expression of α-SMA,FN,Col1, were found in TGF-β1-treated group than that in Ctrl group at the corresponding time point (P＜0.01). The expression of Smad3, p-Smad3 significantly higher in LPS-PF group (at both day 7 and day 10) and TGF-β1-treated group (at both 48 h and 96 h) as compared to corresponding control group (P＜0.01). Conclusions MMT promotes pulmonary fibrosis induced by ALI via LPS. Smad3 is proved to be involved in the MMT process.

Abstract Background NAT10 is the firstly recognized RNA acetyltransferase that participates in multiple cellular biological processes and human disease. However, the role of N-acetyltransferase 10 (NAT10) in ankylosing spondylitis (AS) is still poorly elaborated. Methods Fifty-six patients with New-Onset AS, 52 healthy controls (HC), 20 patients with rheumatoid arthritis (RA) and 16 patients with systemic lupus erythematosus (SLE) were recruited from The First Afliated Hospital of Nanchang University, and their clinical characteristics were recorded. The expression level of NAT10 in peripheral blood mononuclear cell (PBMC) was examined using reverse transcription-quantitative PCR analysis. The correlations between the expression level of NAT10 in the New-Onset AS patients and disease activity of AS were examined, and receiver operating characteristic (ROC) curves were built to evaluate predictive value in AS. Univariate analysis and multivariate regression analysis were used to analyze the risk factors and construct predictive model. Results The mRNA expressions of NAT10 in PBMC from new-onset AS patients were significantly low and there were negative correlation between mRNA NAT10 and ASDAS-CRP, BASDIA in new-onset AS patients. ROC analysis suggested that mRNA NAT10 has value in distinguishing new-onset AS patients from HC, RA and SLE. Furthermore, a novel predictive model based on mRNA NAT10 and neutrophil percentages (N%) was constructed for distinguishing new-onset AS patients from HC (AUC = 0.880, sensitivity = 84.62%, specificity = 76.92%) and the predictive model correlated with the activity of new-onset AS. Furthermore, the predictive model could distinguish new-onset AS patients from RA and SLE (AUC = 0.661, sensitivity = 90.38%, specificity = 47.22%). Moreover, the potential predictive value of the combination of predictive model-HLA-B27 for AS vs. HC with a sensitivity of 92.86% (39/42), a specificity of 100.00% (52/52) and an accuracy of 96.81% (91/94) was superior to that of HLA-B27, which in turn had a sensitivity of 84.44% (38/45), a specificity of 100.00% (52/52) and an accuracy of 92.78% (90/97). Conclusion The present study suggested that the decreased mRNA NAT10 may play a role in AS pathogenesis and predictive model based on mRNA NAT10 and N% act as bioindicator for forecast and progression of diseases.

With the rapid development of satellite remote sensing technology, carbon-cycle research, as a key focus of global climate change, has also been widely developed in terms of carbon source/sink-research methods. The internationally recognized “top-down” approach, which is based on satellite observations, is an important means to verify greenhouse gas-emission inventories. This article reviews the principles, categories, and development of satellite detection payloads for greenhouse gases and introduces inversion algorithms and datasets for satellite remote sensing of XCO2. It emphasizes inversion methods based on machine learning and assimilation algorithms. Additionally, it presents the technology and achievements of carbon-assimilation systems used to estimate carbon fluxes. Finally, the article summarizes and prospects the future development of carbon-assimilation inversion to improve the accuracy of estimating and monitoring Earth’s carbon-cycle processes.

Driven by the goal of ‘carbon peak, carbon neutrality’, an integrated demand response strategy for integrated electricity–hydrogen energy systems is proposed for low-carbon energy supply parks considering the multi-level and multi-energy characteristics of campus-based microgrids. Firstly, considering the spatial and temporal complementary nature of wind and photovoltaic generation and energy utilization, the energy flow framework of the park is built based on the electricity and hydrogen energy carriers. Clean energy is employed as the main energy supply, and power, heat, cooling, and gas loads are considered energy consumption. Secondly, the operation mechanism of coupled hydrogen storage, hydrogen fuel cell, and carbon capture equipment is analyzed in the two-stage power-to-gas conversion process. Thirdly, considering the operating costs and environmental costs of the park, an integrated demand response dispatch model is constructed for the coupled electricity–hydrogen–carbon system while satisfying the system equipment constraints, network constraints and energy balance constraints of the park system. Finally, Case study in an energy supply park system is implemented. The dispatch results of the integrated demand response with customer participation in the conventional, electricity–hydrogen and electricity–hydrogen–carbon modes are compared to verify the effectiveness of the proposed strategy in renewable accommodation, environmental protection, and economic benefits.

This study focuses on the carbon emissions of key industries in Henan Province, employing techniques of seasonal adjustment, frequency transformation, and statistical modeling to construct an industry-level “Electricity–Energy–Carbon” model to aid in the high-frequency monitoring of carbon emissions in the province’s industries. Based on relevant data, this research performs high-frequency calculations of carbon emissions from energy consumption in 34 typical industries and from the production processes of 53 typical sub-categories in the industrial sector of Henan. The findings reveal the following: Firstly, industrial energy consumption in Henan accounts for over half of the total provincial energy consumption, with most months seeing proportions around 60%. Industries such as energy, non-ferrous metals, building materials, steel, chemicals, petrochemicals, and paper making contribute to over 80% of the industrial energy consumption’s carbon emissions, often nearing 90% in most months. Secondly, among the major industries, such as non-ferrous metals, chemicals, building materials, and steel, there is a dual challenge of being restricted under the “high energy consumption and high emissions” project while also being required to build key industrial bases, leading to fluctuating trends in historical annual carbon emissions data. Thirdly, six sub-categories, namely plastic products, cement, flat glass, steel, ten types of non-ferrous metals, and alumina, have significant carbon emissions in their production processes, accounting for about 72.3% of the total production-related emissions.

Background: The prognostic value of follow-up cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) patients is unclear. We aimed to investigate the prognostic value of cardiac function, structure, and tissue characteristics at mid-term CMR follow-up. Methods: The study population was a prospectively enrolled cohort of DCM patients who underwent guideline-directed medical therapy with baseline and follow-up CMR, which included measurement of biventricular volume and ejection fraction, late gadolinium enhancement, native T1, native T2, and extracellular volume. During follow-up, major adverse cardiac events (MACE) were defined as a composite endpoint of cardiovascular death, heart transplantation, and heart-failure readmission. Results: Among 235 DCM patients (median CMR interval: 15.3 months; interquartile range: 12.5–19.2 months), 54 (23.0%) experienced MACE during follow-up (median: 31.2 months; interquartile range: 20.8–50.0 months). In multivariable Cox regression, follow-up CMR models showed significantly superior predictive value than baseline CMR models. Stepwise multivariate Cox regression showed that follow-up left ventricular ejection fraction (LVEF; hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.91–0.96; p 1273 ms or LVEF 15%). Conclusions: Follow-up CMR provided better risk stratification than baseline CMR. Improvements in the LVEF and T1 mapping are associated with a lower risk of MACE.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Shale, which is a fine-grained sedimentary rock, differs in reservoir quality (RQ) and completion quality (CQ) as the result of variations in its mineral and organic-matter compositions. The RQ and CQ directly affect shale gas exploitation. Therefore, classification of the lithofacies types of shale, analysis of the reservoir differences of different lithofacies, and identification of the superior shale lithofacies with the best RQ and CQ are important for decisions on well placement, targeting the “sweet spot” zone and optimizing the expected ultimate recovery of a shale gas well. In this study, several methods including microscopic observation of thin sections, X-ray diffraction analysis, core analysis and elemental geochemistry were used to investigate the lithofacies types, reservoir characteristics and silica origin of the marine shales of the Wufeng Formation–Longmaxi Formation (WF Fm.–LMX Fm.) in the Luzhou area, southern Sichuan Basin, China. The lithofacies characteristics exhibit marked differences through the WF–LMX shale succession. Specifically, the lower gas-bearing zone (LMX-1) is mainly composed of siliceous shales and siliceous–argillaceous mixed shales, whereas the upper gas-bearing zone (LMX-4) mostly contains argillaceous shales and argillaceous–siliceous mixed shales. The lower shale gas reservoirs have a higher silica content and lower clay and carbonate contents than the upper zones, and thus superior petrophysical qualities. The upper part of the WF Fm. (WF-2) and the lower part of the LMX Fm (LMX-1, LMX-2) are rich in bio-chemical authigenic silica, whereas the silica minerals of other intervals are mainly derived from allochthonous terrigenous material. The siliceous shales and siliceous–argillaceous mixed shales with a biogenic quartz content greater than 40% have the best RQ and CQ and are the most promising lithofacies for shale gas in the Luzhou area. Although the upper gas-bearing zones represented by LMX-4 are also good shale gas reservoirs, the RQ, CQ and gas content are generally worse than those of the lower gas-bearing zones because of the silica origin and lithofacies types; thus, extracting shale gas from the upper zones would be a greater challenge.

Objectives: To develop an evaluation model for, and identify key factors contributing to, burnout in orthopedic surgeons, providing a reference for the management of burnout among orthopedic surgeons in hospitals.Methods: We developed an analytic hierarchy process (AHP) model with 3 dimensions and 10 sub-criteria based on an extensive literature review and expert assessment. We used expert and purposive sampling and 17 orthopedic surgeons were selected as research subjects. The AHP process was then used to obtain the weights and to prioritize the dimensions and criteria for burnout in orthopedic surgeons.Results: The dimension of C1 (personal/family) was the key factor affecting burnout in orthopedic surgeons, and in the sub-criteria, the top four sub-criteria were C11 (little time for family), C31 (anxiety about clinical competence), C12 (work-family conflict), and C22 (heavy work load).Conclusion: This model was effective in analyzing the key factors contributing to job burnout risk, and the results can inform improved management of the levels of burnout affecting orthopedic surgeons in hospitals.

Aim: A decision analysis model was constructed to explore the key factors affecting the job satisfaction of hospital nurses and to analyze the key satisfaction gaps in the case hospital. Background: In China, medical institutions are facing greater pressure and challenges in the normalization of epidemic prevention and control. Nurses play a critical role in the delivery of medical care services. Past studies have shown that improving job satisfaction among hospital nurses is important for both reducing nurse turnover and improving the quality of care. Methods: McCloskey/Mueller satisfaction scale (MMSS-31) was used to survey 25 nursing specialists in a case hospital in Zhejiang. Then, the Consistent Fuzzy Preference Relation (CFPR) method was used to analyze the degree of importance of dimensions and corresponding sub-criteria. Finally, the importance-performance analysis method was applied to identify critical satisfaction gaps for the case hospital. Results: In terms of local weight for dimensions, “Control/Responsibility (C8)” ≻ “Praise/Recognition (C7)” ≻ “Extrinsic Rewards (C1)” are the top three key factors for nurses’ work environment satisfaction in the case of a hospital. In addition, the sub-criteria “Salary (C11)”, “Benefits (C13)”, “Child care (C33)”, “Recognition-peers (C73)”, “Encouragement/feedback (C74)”, and “Decision making (C85)” are the key factors for improving clinical nursing satisfaction in the case hospital. Conclusion: The issues that nurses care about but for which they have not attained expectations mainly involved extrinsic rewards, recognition/encouragement and control over their working process. The findings of this study could offer an academic reference for management and remind them to consider the above factors in exploring future reform, further improving nurses' job satisfaction and motivating them to provide better nursing services.

In modern power systems, analyzing the behaviors of the end users can help to improve the system’s security, stability, and economy. Load classification provides an efficient way to implement awareness of the user’s behaviors. However, due to the development of data collection, transmission, and storage technologies, the volumes of the load data keep increasing. Meanwhile, the structure and knowledge hidden in the data become ever more complicated. Therefore, the parallelized ensemble learning method has been widely employed in recent load classification research. Although the positive performance of ensemble learning has been proven, two critical issues remain: class imbalance and base classifier redundancy. These issues raise challenges of improving the classification accuracy and saving computational resources. Therefore, to solve the issues, this article presents an improved selective ensemble learning approach to enable load classification considering base classifier redundancy and class imbalance. First, a Gaussian SMOTE based on density clustering (GSDC) is introduced to handle the class imbalance, which aims to achieve higher classification accuracy. Second, the classifier pruning strategy and the optimization strategy of the ensemble learning are further introduced to handle the base classifier redundancy. The experimental results indicate that when combined with the popular classifiers, the presented approach shows effectiveness for serving the load classification tasks.

A non-destructive method to monitor lamb freshness was developed by analysing the impedance spectrum of lamb meat. As a result of lamb storing, its biological tissue structure and intracellular ionic conductivity change, which can be effectively monitored by impedance amplitude and phase angle. Initially, the coefficient of variation method was employed to identify three frequency ranges: low, medium, and high. These ranges were used to establish a predictive model for lamb freshness using Fisher linear discriminant analysis. Independent variables included impedance, phase angle, and measurement quality, while dependent variables encompassed fresh and aged lamb meat states. The subsequent evaluation of lamb meat morphology aimed to uncover how these independent variables impacted lamb freshness. Results highlighted the significance of impedance parameters in fresh lamb meat, particularly in the lowfrequency range. The discriminant analysis model demonstrated a robust predictive capacity, achieving a 94% accuracy rate for the discriminant function. The impedance variable had a significant impact on the model accuracy, and the introduction of independent variables improved the predictive accuracy of fresh and aged lamb meat by 22% and 15% respectively. Moreover, the study observed interconnected relationships among lamb freshness, water status, and impedance state changes. [ABSTRACT FROM AUTHOR]

Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental results indicate a high nonlinear degree of battery degradation. Considering the nonlinearity of Li-ion batteries aging, the extreme learning machine (ELM), which has good learning and fitting ability for highly nonlinear, highly nonstationary, and time-varying data, is adopted for prediction. A battery life prediction model based on the sparrow search algorithm (SSA) is proposed in this paper to optimize the random weights and bias of the ELM network and verified by experimental data. The results show that compared with traditional ELM and back-propagation neural networks, the prediction results of ELM optimized by SSA have lower mean absolute error percentages and root mean square errors, indicating that the SSA-ELM model has higher prediction accuracy and better stability and has obvious advantages in processing data with a high nonlinear degree.

Innovation is the primary driving force behind the development of China as a modern economic power. This study examines the impact of paternalistic leadership on innovation, proposing a theoretical model using the three dimensions of paternalistic leadership (i.e., benevolence, morality, and authoritarianism) as independent variables, constructive deviance as a mediating variable, and innovation performance as the dependent variable. Empirical results showed that benevolent and moral leadership has a positive impact on innovation performance while authoritarian leadership has a negative impact. Constructive deviance by employees has a positive impact on innovation performance. Benevolent and moral leadership has a positive impact on the constructive deviance of employees, while authoritarian leadership has a negative impact on constructive deviance. In addition, benevolent and moral leadership has a positive impact on innovation performance through the constructive deviance of employees, while the impact of authoritarian leadership is negative. In practice, leaders should recognize that constructive deviance is a double-edged sword and guide employees to engage in reasonable constructive deviant behavior, thereby creating sound organizational environments to foster innovation, eliminate barriers, and benefit from the positive impact of the constructive deviance of employees to enhance innovation performance.

The design and development of machine learning programs require selecting appropriate data and algorithms, and coding and debugging based on specific task requirements and the programming experience of developers. However, the current knowledge structure in the field of machine learning is relatively complex, lacking systematic organization, and developers often face the problem of lack of experience when choosing algorithms and designing programs, resulting in a long development cycle and easy errors in machine learning programs. In response to the above issues, this article proposes and designs a machine learning program generation algorithm based on the AORBCO model. The program generation ability includes two sub abilities: algorithm decision-making ability and code generation ability. AD-EKG has been designed for algorithmic decision-making ability, allowing Ego to select appropriate machine learning algorithms based on datasets in massive data. This algorithm combines the characteristics of the AORBCO model's domain knowledge base, knowledge graph based recommendation algorithm, and collaborative filtering algorithm. By calculating the descriptive and structural information between the dataset and algorithm, the interaction probability between the dataset and algorithm is obtained, allowing Ego to make algorithmic decisions interaction probability based. Results of the experiment have shown that the AD-EKG algorithm can fully utilize structural and descriptive information to improve the accuracy of Ego algorithm decision-making. CodeT5-EKG has been designed for code generation capability, allowing Ego to automatically generate machine learning program code. This algorithm combines the CodeT5 generative model with the domain knowledge base of the AORBCO model, by adding auxiliary information extracted using DPR technology to the code generation task, and performing diversified fusion operations to improve code generation quality. The CodeT5-EKG algorithm combines the creativity and efficiency of generative models and DPR technology, and is an algorithm that can improve the quality of generated code while also having the advantages of generative models. The experiments have proved that the code generated by this algorithm has better quality compared to other generative models with the same number of parameters. [ABSTRACT FROM AUTHOR]

Urban digital twin is a key step to build a smart city, digital twin is an important application scenario for smart city platform, and the relationship between the two are both current research hotspots. In this paper, we will start from the demand of digital twin on smart city platform, study the architecture method and key technology of smart city platform, in this paper's platform construction method compared to the traditional construction method, reduces the difficulty of digital twin smart city construction, and also reduces the coupling degree between smart city platform modules, and use a smart city platform for engineering verification. Finally compared with the traditional smart platform construction techniques, the techniques in this paper are better than the traditional ones in terms of coupling, difficulty and cost. Through engineering verification and experimental results show that this paper on the digital twin-oriented smart city construction technology, the coupling degree of each module is the lowest, and in the development efficiency experiments, this paper by comparing with the traditional technology, the experimental development cycle compared to the traditional technology can be shortened by 61.7% of the development cycle, greatly reducing the development cost and improving the construction efficiency. [ABSTRACT FROM AUTHOR]

The Sichuan Basin is a major target for shale gas exploration in present China because of its rich gas stored in abundant black shales with multiple bed series. For further guidance or reference, field exploration and development practices in the shale reservoirs Upper Ordovician Wufeng–Lower Silurian Longmaxi shale reservoirs were studied in terms of development stages and progress, favorable conditions for shale gas accumulation, bottlenecking issues on theories and technologies related to shale gas development, and so on. The following findings were obtained. (1) Shale with rich organic matters originated from the deep shelf has a good quality and great thickness in the continuous beds. The relatively stable wide buffer zones in synclines (anticlines) provides favorable conditions for shale gas accumulation and preservation with well-developed micro-fractures and overpressure as necessary factors for a great potential of high shale gas productivity. (2) The bottlenecking technical issues restricting the shale gas industrial development in this study area include the following aspects: understandings of rich-organic matter shale sedimentary facies and modes, shale reservoir diagenetic process and evaluation systems, shale gas generation and accumulation mechanism, geophysical logging identification and prediction of shale gas layers, low resource utilization rate, great uncertainty of shale gas development, no technological breakthrough in the exploration of shale gas reservoirs buried deeper than 3500 m. In conclusion, this study area will be the major target for the shale gas exploration and development in China in a rather long period in the future. Keywords: Sichuan Basin, Upper Ordovician Wufeng–Lower Silurian Longmaxi, Shale gas, Exploration and development, Progress, Challenge, Prospect

Over the past five years, great progress has been made in shale gas exploitation, which has become the most driving force for global gas output growth. Hydrocarbon extraction from shale helps drive the USA on the road to energy independence. Besides, shale oil & gas production has been kept in a sustained growth by continuous improvement in drilling efficiency and well productivity in the case of tumbling oil prices and rig counts. Shale gas reserves and production have been in a rapid growth in China owing to the Lower Paleozoic Wufeng and Longmaxi shale gas exploitation in the Sichuan Basin, which has become an important sector for the future increment of gas reserves and output in China. However, substantial progress has been made neither in non-marine shale gas exploitation as previously expected nor in the broad complicated tectonic areas in South China for which a considerable investment was made. Analysis of the basic situation and issues in domestic shale gas development shows that shale gas exploitation prospects are constrained by many problems in terms of resources endowment, horizontal well fracturing technology, etc. especially in non-marine shale deposits and complicated tectonic areas in South China where hot shales are widely distributed but geological structures are found severely deformed and over matured. Discussion on the prospects shows that the sustained and steady growth in shale gas reserves and production capacity in the coming years lies in the discovery and supersession of new shale plays in addition to Wufeng and Longmaxi shale plays, and that a technological breakthrough in ultra-high-pressure and ultra-deep (over 3500 m buried in the Sichuan Basin) marine shale gas exploitation is the key and hope. Keywords: Shale gas, Exploitation, Marine facies, Hot shale, Resource endowment, Sichuan Basin, South China, Complicated tectonic area, Gas play

Searching for some reliable evidences that can verify the carbonification of organic matters in marine shales is a major scientific issue in selecting shale gas fairways in old strata. To this end, based on core, logging and testing data, the electric property of two organic-rich shale layers in the Lower Cambrian Qiongzhusi Fm. and the Lower Silurian Longmaxi Fm. in the southern Sichuan Basin was compared to examine the carbonification signs of organic matters in the Qiongzhusi shale and its influence on gas occurrence in the shales. The following conclusions were reached: (1) the electric property experiment shows that the Qiongzhusi shale in the study area has had carbonification of organic matters. The low resistivity of dry samples from this highly mature organic-rich shale and ultra-low resistivity on downhole logs can be used to directly judge the degree of organic matter carbonification and the quality of source rocks; (2) in the Changning area, the Qiongzhusi shale shows low resistivity of dry samples and low to ultra-low resistivity on logs, indicating that organic matters are seriously carbonized, while in the Weiyuan area, the Qiongzhusi shale shows a basically normal resistivity on log curves, indicating its degree of graphitization between the Longmaxi Fm. and Qiongzhusi Fm. in the Changning area; (3) shale with medium-to-high resistivity is remarkably better than that with ultra-low resistivity in terms of gas generation potential, matrix porosity and gas adsorption capacity; (4) industrial gas flow has been tested in the organic shales with medium-to-high resistivity in the Jianwei–Weiyuan–Tongnan area in the north, where the Qiongzhusi shale is a favorable shale gas exploration target.

Nowadays smart grid and intelligent power consumption community develop rapidly, and the interactive coupling of power system with intelligent community has become more in-depth. Based on differential evolution algorithm, a grid dynamic economic dispatch method with the intelligent community is proposed in this realistic context. The objective function is to minimize generation cost. And multiple constraint conditions are power balance, reserve capacity of electric power system, generator output, generator climbing speeds and et al. The accuracy and validity of the method are validated through analyzing the example of a power system including two intelligent communities.