Stress sensitivity of multiscale pore structure of shale gas reservoir under fracturing fluid imbibition

Generally, huge amounts of fracturing fluid are used in a shale gas well but the flowback efficiency is low. Since the distribution characteristics of imbibed fracturing fluid in shale are complex, they need further evaluation. This paper takes the Longmaxi Shale as the research object, including matrix cores, natural fracture cores and cores of artificial fracture with proppant. Stress sensitivity experiments are carried out on the above three kinds of cores under different degrees of imbibition and retention state of fracturing fluid. The results show that when the degree of aqueous phase retention is 0-0.78 pore volume, water mainly appears in the pores with a diameter of 2-50 nm. As the water saturation increases to more than 0.9 pore volume, the amounts of aqueous phase in the pores or fractures with a hydraulic diameter of 100-1,000 nm and larger than 1,000 nm increase significantly. Both the stress sensitivity of nanopores and natural fractures are enhanced by aqueous phase retention. With the increase in effective stress, the permeability damage rate of artificial fracture cores with proppant is inversely proportional to the degree of fracturing fluid retention. Aqueous phase retention in the pores with a diameter of 2-50 nm significantly contributes to the stress sensitivity of matrix cores. With the increase in effective stress, aqueous phase retention in pores with diameter larger than 100 nm increases the stress sensitivity of natural fracture cores. It is recommended that the retention degree of fracturing fluid in a shale gas reservoir should be controlled below 0.5 pore volume. In this case, the stress sensitivity of natural fractures will be less aggravated by fracturing fluid retention, and the stress sensitivity of artificial fracture with proppant will be reduced to a certain extent.Document Type: Original articleCited as: Chen, M., Yan, M., Kang, Y., Cao, W., Bai, J., Li, P. Stress sensitivity of multiscale pore structure of shale gas reservoir under fracturing fluid imbibition. Capillarity, 2023, 8(1): 11-22. https://doi.org/10.46690/capi.2023.07.0

A dynamic and scalable user-centric route planning algorithm based on Polychromatic Sets theory

Existing navigation services provide route options based on a single metric without considering user's preference. This results in the planned route not meeting the actual needs of users. In this paper, a personalized route planning algorithm is proposed, which can provide users with a route that meets their requirements. Based on the multiple properties of the road, the Polychromatic Sets (PS) theory is introduced into route planning. Firstly, a road properties description scheme based on the PS theory was proposed. By using this scheme, users' travel preferences can be quantified, and then personalized property combination schemes can be constructed according to these properties. Secondly, the idea of setting priority for road segments was utilized. Based on a user's travel preference, all the property combination schemes can be prioritized at relevant levels. Finally, based on the priority level, an efficient path planning scheme was proposed, in which priority is given to the highest road segments in the target direction. In addition, the system can constantly obtain real-time road information through mobile terminals, update road properties, and provide other users with more accurate road information and navigation services, so as to avoid crowded road segments without excessively increasing time consumption. Experiment results show that our algorithm can realize personalized route planning services without significantly increasing the travel time and distance. In addition, source code of the algorithm has been uploaded on GitHub for this algorithm to be used by other researchers

Intrusion detection for IoT based on improved genetic algorithm and deep belief network

With the advent of the Internet of Things, the network security of the transport layer in the Internet of Things is getting more and more attention. Traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of the Internet of Things. Therefore, it is extremely urgent to study the intrusion detection system corresponding to today's Internet of Things security. This paper presents an intrusion detection model based on Genetic Algorithm (GA) and Deep Belief Network (DBN). Through multiple iterations of GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate. Finally, the KDDCUP99 data set was used to simulate and evaluate the model and algorithm. Experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the network

Identifying Users' Gender via Social Representations

Gender prediction has evoked great research interests due to its potential applications like targeted advertisement and personalized search. Most of existing studies rely on the content texts. However, the text information is hard to access. This makes it difficult to extract text features. In this paper, we propose a novel framework which only involves the users' ids for gender prediction. The key idea is to represent users in the embedding connection space. We present two strategies to modify the word embedding technique for user embedding. The first is to sequentialize users' ids to get the order of social context. The second is to embed users into a large-sized sliding window of contexts. We conduct extensive experiments on two real data sets from Sina Weibo. Results show that our method is significantly better than the state-of-the-art graph embedding baselines. Its accuracy also outperforms that of the content based approaches

Multi-model running latency optimization in an edge computing paradigm

Recent advances in both lightweight deep learning algorithms and edge computing increasingly enable multiple model inference tasks to be conducted concurrently on resource-constrained edge devices, allowing us to achieve one goal collaboratively rather than getting high quality in each standalone task. However, the high overall running latency for performing multi-model inferences always negatively affects the real-time applications. To combat latency, the algorithms should be optimized to minimize the latency for multi-model deployment without compromising the safety-critical situation. This work focuses on the real-time task scheduling strategy for multi-model deployment and investigating the model inference using an open neural network exchange (ONNX) runtime engine. Then, an application deployment strategy is proposed based on the container technology and inference tasks are scheduled to different containers based on the scheduling strategies. Experimental results show that the proposed solution is able to significantly reduce the overall running latency in real-time applications

Extracellular Matrix Protein Tenascin C Increases Phagocytosis Mediated by CD47 Loss of Function in Glioblastoma.

Glioblastomas (GBM) are highly infiltrated by myeloid-derived innate immune cells that contribute to the immunosuppressive nature of the brain tumor microenvironment (TME). CD47 has been shown to mediate immune evasion, as the CD47-SIRPα axis prevents phagocytosis of tumor cells by macrophages and other myeloid cells. In this study, we established CD47 homozygous deletion (CD47-/-) in human and mouse GBM cells and investigated the impact of eliminating the "don't eat me" signal on tumor growth and tumor-TME interactions. CD47 knockout (KO) did not significantly alter tumor cell proliferation in vitro but significantly increased phagocytosis of tumor cells by macrophages in cocultures. Compared with CD47 wild-type xenografts, orthotopic xenografts derived from CD47-/- tumor cells grew significantly slower with enhanced tumor cell phagocytosis and increased recruitment of M2-like tumor-associated microglia/macrophages (TAM). CD47 KO increased tumor-associated extracellular matrix protein tenascin C (TNC) in xenografts, which was further examined in vitro. CD47 loss of function upregulated TNC expression in tumor cells via a Notch pathway-mediated mechanism. Depletion of TNC in tumor cells enhanced the growth of CD47-/- xenografts in vivo and decreased the number of TAM. TNC knockdown also inhibited phagocytosis of CD47-/- tumor cells in cocultures. Furthermore, TNC stimulated release of proinflammatory factors including TNFα via a Toll-like receptor 4 and STAT3-dependent mechanism in human macrophage cells. These results reveal a vital role for TNC in immunomodulation in brain tumor biology and demonstrate the prominence of the TME extracellular matrix in affecting the antitumor function of brain innate immune cells. SIGNIFICANCE: These findings link TNC to CD47-driven phagocytosis and demonstrate that TNC affects the antitumor function of brain TAM, facilitating the development of novel innate immune system-based therapies for brain tumors

Distribution of transparent exopolymer particles and their response to phytoplankton community structure changes in the Amundsen Sea, Antarctica

To understand the response of transparent exopolymer particles (TEP) to the changes in phytoplankton communities caused by melting sea ice, we collected samples from the polynya and open ocean affected by the Antarctic circumpolar current in the Amundsen Sea. TEP, pigments, and other environmental factors were analyzed. The results showed that high TEP content was mainly found in the polynya, and was higher in the surface layer than in the deep layer. The main factor that affected TEP distribution was the phytoplankton community. In the polynya area, the phytoplankton were dominated by low-iron Haptophyta. In the Antarctic circumpolar current region affected by ice-melting water, the dominant species was diatom type II. Our results revealed that low-iron Haptophyta may be the main contributors to TEP content