Quantitative characterization of stimulated reservoir volume (SRV) fracturing effects in naturally fractured unconventional hydrocarbon reservoirs

Stimulated reservoir volume (SRV) fracturing has become the most efficient technology in the treatment of unconventional hydrocarbon reservoir formations. This process aims to optimize well productivity by establishing an intricate network of fractures that integrate hydraulic and natural fractures, distal to the wellbore, thereby amplifying the contact area with the subterranean formations and fracture systems. This study introduces a quantitative framework designed to characterize the fracturing effects within naturally fractured unconventional hydrocarbon reservoirs. Leveraging existing fracturing treatment designs and production performance data, the study formulates a mathematical model of the complex fracture network, predicated on the principle of material balance. The model comprehensively accounts for the development degree of natural fractures, the morphological impact of stress differentials on the fracture network, and the imbibition displacement effects of the fracturing fluids. The model’s accuracy is verified through an integration with microseismic monitoring data and an enhanced understanding of reservoir development. Building upon this foundation, the study quantitatively dissects the impact of various engineering parameters on the efficacy of SRV fracturing. The proposed quantitative characterization method is adept for widespread application across multiple wells in oil and gas fields, offering a distinct advantage for the swift and precise assessment of SRV fracturing outcomes in naturally fractured unconventional hydrocarbon reservoirs. The research method, which is based on readily accessible fracturing construction data and is more convenient, can to a certain extent improve the efficiency of hydraulic fracturing evaluation work

Xuebijing Ameliorates Sepsis-Induced Lung Injury by Downregulating HMGB1 and RAGE Expressions in Mice

Xuebijing (XBJ) injection, a traditional Chinese medicine, has been reported as a promising approach in the treatment of sepsis in China. However, its actual molecular mechanisms in sepsis-induced lung injury are yet unknown. Therefore, this study aimed to investigate the beneficial effects of XBJ on inflammation and the underlying mechanisms in a model of caecal ligation and puncture-(CLP-) induced lung injury. The mice were divided into CLP group, CLP+XBJ group (XBJ, 4 mL/kg per 12 hours), and sham group. The molecular and histological examinations were performed on the lung, serum, and bronchoalveolar lavage (BAL) fluid samples of mice at the points of 6, 24, and 48 hours after CLP. The results show that XBJ reduces morphological destruction and neutrophil infiltration in the alveolar space and lung wet/dry weight ratio, which improves mortality of CLP-induced lung injury. Meanwhile, XBJ treatment downregulates high mobility group box protein 1 (HMGB1) and the receptor for advanced glycation end products (RAGE) expression, as well as neutrophil counts, production of IL-1β, IL-6, and TNF-α in the BAL fluids. In conclusion, these results indicate that XBJ may reduce the mortality through inhibiting proinflammatory cytokines secretion mediated by HMGB1/RAGE axis

TipScreener: A Framework for Mining Tips for Online Review Readers

User-generated content explodes in popularity daily on e-commerce platforms. It is crucial for platform manipulators to sort out online reviews with repeatedly expressed opinions and a large number of irrelevant topics in order to reduce the information processing burden on review readers. This study proposes a framework named TipScreener that generates a set of useful sentences that cover all of the information of features of a business. Called tips in this work, the sentences are selected from the reviews in their original, unaltered form. Firstly, we identify information tokens of the business. Second, we filter review sentences that contain no tokens and remove duplicates. We then use a convolutional neural network to filter uninformative sentences. Next, we find the tip set with the smallest cardinality that contains all off the tokens, taking opinion words into account. The sentences of the tip set contain a full range of information and have a very low repetition rate. Our work contributes to the work of online review organizing. Review operators of e-commerce platforms can adopt tips generated by TipScreener to facilitate decision makings of review readers. The convolutional neural network that classifies sentences into two classes also enriches deep learning studies on text classification

TipScreener: A Framework for Mining Tips for Online Review Readers

User-generated content explodes in popularity daily on e-commerce platforms. It is crucial for platform manipulators to sort out online reviews with repeatedly expressed opinions and a large number of irrelevant topics in order to reduce the information processing burden on review readers. This study proposes a framework named TipScreener that generates a set of useful sentences that cover all of the information of features of a business. Called tips in this work, the sentences are selected from the reviews in their original, unaltered form. Firstly, we identify information tokens of the business. Second, we filter review sentences that contain no tokens and remove duplicates. We then use a convolutional neural network to filter uninformative sentences. Next, we find the tip set with the smallest cardinality that contains all off the tokens, taking opinion words into account. The sentences of the tip set contain a full range of information and have a very low repetition rate. Our work contributes to the work of online review organizing. Review operators of e-commerce platforms can adopt tips generated by TipScreener to facilitate decision makings of review readers. The convolutional neural network that classifies sentences into two classes also enriches deep learning studies on text classification

Research of Arc Segment Metal Block Contact Character of Metal Belt Continuously Variable Transmission

Taking the contact characteristic of the arc segment metal block with metal belt type continuously variable transmission as study object,the mathematical model and finite element model are established.The results show that the numerical solution of the stress and deformation is basically the same as that of the analytical solution,but the numerical value is higher,the reason is that the contact area of the metal block is reduced. With the increase of the thickness of the metal block,the compressive stress decreases gradually in the z direction,but the rate is very fast,it meets to Saint Venant principle. This study lays a theoretical foundation for the optimization of metal block structure and so on

An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content

This work attempts to develop a novel framework to reveal the preferences of Chinese car users from online user-generated content (UGC) and guides automotive companies to allocate resources reasonably for sustainable design and improve existing product or service attributes. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words, and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users’ satisfaction with different attributes based on the rules and weigh the importance of attributes using the TF-IDF method, thus constructing an importance-satisfaction gap analysis (ISGA) model. Finally, a case study is used to realize the framework evaluation and analysis of the twenty top-mentioned attributes of a small-sized sedan, and the dynamic ISGA-time model is constructed to analyze the changing trend of the importance of user demand and satisfaction. The results show the priority of resource allocation/adjustment. Fuel consumption and driving experience urgently need resource input and management

Research on the Impact of Online Promotions on Consumers’ Impulsive Online Shopping Intentions

Online shopping has developed rapidly, but recently, the sales of some online stores have suffered due to the decrease in people’s income caused by the epidemic. How to grasp the psychology and behavior of consumers and formulate effective marketing strategies is important for increasing sales. This paper puts forward a research model and eight hypotheses based on the research on the promotion situation and the types of products promoted on consumers’ impulse shopping, and uses regression analysis, t-test, stepwise regression and analysis of variance to conduct data analysis. The results show that online promotion has a significant impact on consumers’ willingness, and the anticipated regrets in different directions have totally different effect on willingness; the type of product promoted, and the impulsive characteristics of consumers play a moderating role; online promotion affects consumers’ impulsive online shopping intentions through the intermediary effect of expected regret. The influence of anticipated regrets on impulsive online shopping intention is proposed creatively, and the results also provide e-commerce merchants and customers with new insights in managing and treating online promotions. Managerial implications like controlling the duration of promotions and the number of preferential goods are put forward based on our analysis

Fatigue Crack Length Sizing Using a Novel Flexible Eddy Current Sensor Array

The eddy current probe, which is flexible, array typed, highly sensitive and capable of quantitative inspection is one practical requirement in nondestructive testing and also a research hotspot. A novel flexible planar eddy current sensor array for the inspection of microcrack presentation in critical parts of airplanes is developed in this paper. Both exciting and sensing coils are etched on polyimide films using a flexible printed circuit board technique, thus conforming the sensor to complex geometric structures. In order to serve the needs of condition-based maintenance (CBM), the proposed sensor array is comprised of 64 elements. Its spatial resolution is only 0.8 mm, and it is not only sensitive to shallow microcracks, but also capable of sizing the length of fatigue cracks. The details and advantages of our sensor design are introduced. The working principal and the crack responses are analyzed by finite element simulation, with which a crack length sizing algorithm is proposed. Experiments based on standard specimens are implemented to verify the validity of our simulation and the efficiency of the crack length sizing algorithm. Experimental results show that the sensor array is sensitive to microcracks, and is capable of crack length sizing with an accuracy within ±0.2 mm

Chloroplast genomes of seven species of Coryloideae (Betulaceae): structures and comparative analysis

Coryloideae is a subfamily in the family Betulaceae consisting of four extant genera: Carpinus, Corylus, Ostrya, and Ostryopsis. We sequenced the plastomes of six species of Corylus and one species of Ostryopsis for comparative and phylogenetic analyses. The plastomes are 159–160 kb long and possess typical quadripartite cp architecture. The plastomes show moderate divergence and conserved arrangement. Five mutational hotspots were identified by comparing the plastomes of seven species of Coryloideae: trnG-atpA, trnF-ndhJ, accD-psaI, ndhF-ccsA, and ycf1. We assembled the most complete phylogenomic tree for the family Betulaceae using 68 plastomes. Our cp genomic sequence phylogenetic analyses placed Carpinus, Ostrya, and Ostryopsis in a clade together and left Corylus in a separate clade. Within the genus Corylus, these analyses indicate the existence of five subclades reflecting the phylogeographical relationships among the species. The data offer significant genetic information for the identification of species of the Coryloideae, taxonomic and phylogenetic studies, and molecular breeding.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Mechanisms of improved aortic stiffness by arotinolol in spontaneously hypertensive rats.

OBJECTIVES: This study investigates the effects on aortic stiffness and vasodilation by arotinolol and the underlying mechanisms in spontaneously hypertensive rats (SHR). METHODS: The vasodilations of rat aortas, renal and mesenteric arteries were evaluated by isometric force recording. Nitric oxide (NO) was measured in human aortic endothelial cells (HAECs) by fluorescent probes. Sixteen-week old SHRs were treated with metoprolol (200 mg·kg-1·d⁻¹), arotinolol (30 mg·kg-1·d⁻¹) for 8 weeks. Central arterial pressure (CAP) and pulse wave velocity (PWV) were evaluated via catheter pressure transducers. Collagen was assessed by immunohistochemistry and biochemistry assay, while endothelial nitric oxide synthase (eNOS) and eNOS phosphorylation (p-eNOS) of HAECs or aortas were analyzed by western blotting. RESULTS: Arotinolol relaxed vascular rings and the relaxations were attenuated by Nω-nitro-L-arginine methyl ester (L-NAME, NO synthase inhibitor) and the absence of endothelium. Furthermore, arotinolol-induced relaxations were attenuated by 4-aminopyridine (4-AP, Kv channels blocker). Arotinolol produced more nitric oxide compared to metoprolol and increased the expression of p-eNOS in HAECs. These results indicated that arotinolol-induced vasodilation involves endothelium-derived NO and Kv channels. The treatement with arotinolol in 8 weeks, but not metoprolol, markedly decreased CAP and PWV. Biochemistry assay and immunohistochemistry showed that aortic collagen depositions in the arotinolol groups were reduced compared with SHRs with metoprolol. Moreover, eNOS phosphorylation was significantly increased in aortinolol-treated SHR compared with SHRs with metoprolol. CONCLUSIONS: Arotinolol improves arterial stiffness in SHR, which involved in increasing NO and decreasing collagen contents in large arteries