Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties

The biofuel management of a biofuel-penetrated district heating system is complicated due to its association with multiple and polymorphic uncertainties. To handle uncertainties and system dynamic complexities, an inexact two-stage compound-stochastic mixed-integer programming technique is proposed, innovatively based on the integration of different uncertain optimization approaches. The proposed technique can not only address the inexact recourse problems sourced from multiple and compound uncertainties existing in the pre-regulated biofuel supply–demand match mode, but can also quantitatively analyze the conflicts between the economic target that minimizes the system cost and the risk preference that maximizes the heating service satisfaction. The developed model is applied to a real-world biofuel management case study of a district heating system to obtain the optimal biofuel management schemes subject to supply–demand, policy requirement constraints, and the financial minimization objective. The results indicate that biofuel allocation and expansion schemes are sensitive to the multiple and compound uncertainty inputs, and the corresponding biofuel-deficit change trends of three heat sources are obviously distinct with the system’s condition, varying due to the complicated interactions of the system’s components. Beyond that, a potential trade-off relationship between the heating cost and the constraint-violation risk can be obtained by observing system responses with thermalization coefficient varying

Integrated Optimization for Biofuel Management Associated with a Biomass-Penetrated Heating System under Multiple and Compound Uncertainties

The biofuel management of a biofuel-penetrated district heating system is complicated due to its association with multiple and polymorphic uncertainties. To handle uncertainties and system dynamic complexities, an inexact two-stage compound-stochastic mixed-integer programming technique is proposed, innovatively based on the integration of different uncertain optimization approaches. The proposed technique can not only address the inexact recourse problems sourced from multiple and compound uncertainties existing in the pre-regulated biofuel supply–demand match mode, but can also quantitatively analyze the conflicts between the economic target that minimizes the system cost and the risk preference that maximizes the heating service satisfaction. The developed model is applied to a real-world biofuel management case study of a district heating system to obtain the optimal biofuel management schemes subject to supply–demand, policy requirement constraints, and the financial minimization objective. The results indicate that biofuel allocation and expansion schemes are sensitive to the multiple and compound uncertainty inputs, and the corresponding biofuel-deficit change trends of three heat sources are obviously distinct with the system’s condition, varying due to the complicated interactions of the system’s components. Beyond that, a potential trade-off relationship between the heating cost and the constraint-violation risk can be obtained by observing system responses with thermalization coefficient varying

Interfacial Electronic Effects in Co@N-Doped Carbon Shells Heterojunction Catalyst for Semi-Hydrogenation of Phenylacetylene

Metal-supported catalyst with high activity and relatively simple preparation method is given priority to industrial production. In this work, this study reported an easily accessible synthesis strategy to prepare Mott-Schottky-type N-doped carbon encapsulated metallic Co (Co@Np+gC) catalyst by high-temperature pyrolysis method in which carbon nitride (g-C3N4) and dopamine were used as support and nitrogen source. The prepared Co@Np+gC presented a Mott-Schottky effect; that is, a strong electronic interaction of metallic Co and N-doped carbon shell was constructed to lead to the generation of Mott-Schottky contact. The metallic Co, due to high work function as compared to that of N-doped carbon, transferred electrons to the N-doped outer shell, forming a new contact interface. In this interface area, the positive and negative charges were redistributed, and the catalytic hydrogenation mainly occurred in the area of active charges. The Co@Np+gC catalyst showed excellent catalytic activity in the hydrogenation of phenylacetylene to styrene, and the selectivity of styrene reached 82.4%, much higher than those of reference catalysts. The reason for the promoted semi-hydrogenation of phenylacetylene was attributed to the electron transfer of metallic Co, as it was caused by N doping on carbon

A novel ferroptosis-related gene signature for overall survival prediction in patients with gastric cancer

Abstract The global diagnosis rate and mortality of gastric cancer (GC) are among the highest. Ferroptosis and iron-metabolism have a profound impact on tumor development and are closely linked to cancer treatment and patient’s prognosis. In this study, we identified six PRDEGs (prognostic ferroptosis- and iron metabolism-related differentially expressed genes) using LASSO-penalized Cox regression analysis. The TCGA cohort was used to establish a prognostic risk model, which allowed us to categorize GC patients into the high- and the low-risk groups based on the median value of the risk scores. Our study demonstrated that patients in the low-risk group had a higher probability of survival compared to those in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden (TMB) and a longer 5-year survival period when compared to the high-risk group. In summary, the prognostic risk model, based on the six genes associated with ferroptosis and iron-metabolism, performs well in predicting the prognosis of GC patients

Study on rock mechanics characteristics of deep shale in Luzhou block and the influence on reservoir fracturing

Abstract This paper aims to study the influence of deep formation conditions on the mechanical properties of shale, which is of great significance for the engineering application of efficient development of deep shale gas. In this study, a real‐time high‐temperature (25°C–170°C) triaxial compression experiment was first carried out on shale samples. Then, based on the analysis results of rock mineral components and discrete element numerical simulation technology, a thermo‐mechanical coupling model (TMCM) was constructed, and the accuracy of the numerical model was verified. Finally, based on the micromechanical parameters, a numerical fracturing mechanism model considering the influence of temperature, natural fracture density, and fracture length was established, and the influence of current reservoir conditions on hydraulic fracturing was discussed. The results show that confining pressure has a greater effect on rock mechanics than temperature. When the temperature exceeds 110°C, the plasticity of rock samples increases, and the fracture morphology becomes complex. In addition, the increase in temperature promotes the fracture of microcracks to a certain extent. The research results are expected to provide a sufficient theoretical basis for the development and utilization of deep shale gas resources

Alpha-L-Fucosidase Serves as a Prognostic Indicator for Intrahepatic Cholangiocarcinoma and Inhibits Its Invasion Capacity

Alpha-L-fucosidase (AFU) has been reported to be a predictor of survival in patients with several cancers, but it is unclear whether AFU is associated with prognosis in patients with intrahepatic cholangiocarcinoma (iCCA). In this study, we used receiver operating characteristic (ROC) analysis to generate the cutoff point of AFU for overall survival (OS). The prognostic influence of the AFU level in serum on OS was studied using Kaplan-Meier curves. Moreover, invasion assays and Western blotting were performed to explore the effects of AFU on iCCA invasion in vitro. We found that higher AFU levels (≥20.85 U/L) were significantly associated with favorable median OS (44.3 months versus 20.1 months; P=0.022) in iCCA patients. Cox regression models’ analyses showed that the AFU level was an independent predictor for OS (P=0.006). Moreover, our results revealed that the AFU could impair the invasion capability of the iCCA cells, HuH28, and also downregulated the expression of matrix metalloproteinase 2 and matrix metalloproteinase 9. In conclusion, our results indicate that AFU is a significantly favorable prognostic factor in iCCA patients

A hybrid distance-based outlier detection approach

Conference Name:2012 International Conference on Systems and Informatics, ICSAI 2012. Conference Address: Yantai, China. Time:May 19, 2012 - May 20, 2012.Most real-world datasets have outliers. Outliers can imply abnormal states that often indicate significant performance degradation or danger in certain circumstances. Therefore, the outlier detection plays an important role in the field of data mining. This paper proposes a hybrid distance-based outlier detection approach. It uses the average distance as neighborhood distance, and records the number of data objects within the neighborhood. Therefore, the average number of neighbors can be calculated. Using this average value as a threshold, the data set can be divided into two parts: non-outlier data set and candidate data set. Calculating the distances between a candidate object and its k-nearest neighbors can filter out outliers from the candidate data set. Experimental results show that the approach can effectively detect outliers. 漏 2012 IEEE

Review of the productivity evaluation methods for shale gas wells

Abstract The influence of geological and engineering factors results in the complex production characteristics of shale gas wells. The productivity evaluation method is effective to analyze the production decline law and estimate the ultimate recovery in the shale gas reservoir. This paper reviews the production decline method, analytical method, numerical simulation method, and machine learning method. which analyzes the applicable conditions, basic principles, characteristics, and limitations of different methods. The research found that the production decline method can mainly account for the gas well production and pressure data by fitting type curve analysis. The analytical method is able to couple multiple transport mechanisms and quantify the impact of different mechanisms on shale gas well productivity. Numerical simulation builds multiple pore media in shale gas reservoirs and performs production dynamics as well as capacity prediction visually. Machine learning methods are a nascent approach that can efficiently use available production data from shale gas wells to predict productivity. Finally, the research discusses the future directions and challenges of shale gas well productivity evaluation methods