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Multistage fracturing is a commonly-used method to improve gas production in tight hydrocarbon reservoirs. Stress shadowing effect among multi-fractures is crucial in effectively connecting the pre-existing natural fracture of reservoir and forming a complex fracture network that facilitates gas flow. Challenges remain in accurately characterizing the fracture structure and propogation patterns of naturally fractured reservoirs. In this study, we adopt an adaptive finite-discrete element method to simulate the multistage fracturing of a naturally fractured reservoir by improving the mesh auto-refinement and identification of multiple fracture propagation. The numerical model covers interactions among hydraulic fractures, pre-existing fractures, and microscale pores, while integrates the nonlinear Carter leak-off criterion to describe fluid leak-off and hydromechanical coupling effects during multistage fracturing. We introduce the proppant transport equation for idealised parallel plate flow in fractures, and Darcy's law is adopted to analyse the seepage flow in the fracture network and determine gas recovery. We then compare the fracture network and consequent fluid flow induced by the hydrofracturing of unfractured and naturally fractured models to assess the influence of pre-existing fractures on multistage fracturing behaviour and gas production. This study provides a new approach to determine and optimize fracturing cluster spacing in tight gas reservoirs.

ObjectiveTo investigate the impact of acute respiratory infections in children under 12 years old in Pudong New Area， Shanghai from 2019 to 2023.MethodsAcute respiratory infection samples of children under 12 years old from three sentinel hospitals in Pudong New Area， Shanghai from 2019 to 2023 were collected， and 42 respiratory infection pathogens， including influenza virus， adenovirus， parainfluenza virus， respiratory syncytial virus， human enterovirus/rhinovirus， human pulmonary virus， human bokavirus， coronavirus （229E， HKU1， NL63 and OC43）， and novel coronavirus， were detected with microfluidic chips. The situation of acute respiratory infections among outpatient and inpatient children in this area was analyzed for the before the implementation of non pharmacological intervention measures （2019.12‒2020.1）， during the period of non pharmacological intervention measures （2020.2‒2022.12）， and after non pharmacological intervention measures （2023.1‒2023.6）.ResultsFrom 2019 to 2023， a total of 1 770 samples were collected， and 445 pathogens were detected， with a detection rate of 25.14% （445/1 770）. The main pathogens detected during the study period were influenza virus： 8.70% （154/1 770）， respiratory syncytial virus： 4.41% （78/1 770）， human enterovirus/rhinovirus： 2.66% （47/1 770）， human adenovirus： 2.49% （44/1 770）， and parainfluenza virus： 2.20% （39/1 770）. Before the implementation of non pharmacological intervention measures， outpatients were primarily infected with influenza， parainfluenza virus， and respiratory syncytial virus， with detection rates of 8.09%， 4.49%， and 4.04%， respectively； inpatients were mainly infected with influenza， respiratory syncytial virus， and parainfluenza virus， with detection rates of 4.49%， 3.82%， and 3.15%， respectively. During the period of non pharmacological intervention measures， influenza， rhinovirus and respiratory syncytial virus were the main viruses detected in the samples of outpatient children， with detection rates of 4.04%， 3.60%， and 2.47%， respectively； inpatient samples mainly detected respiratory syncytial virus， rhinovirus， and influenza virus， with detection rates of 3.60%， 2.02%， and 1.80%， respectively. After non pharmacological intervention measures， influenza， rhinovirus and respiratory syncytial virus were the main pathogens detected in the outpatients， with detection rates of 9.89%， 2.92% and 2.02%， respectively； influenza， respiratory syncytial virus， and rhinovirus were the main pathogens detected in inpatient children， with detection rates of 6.29%， 1.57%， and 1.35%， respectively.ConclusionThe prevalence of pathogens related to acute respiratory infections in children is influenced by non pharmacological preventive measures.

Based on peer feedback, this paper further explores the application of peer feedback training to English writing teaching in China. From the theoretical perspective, compared with teacher feedback, peer feedback is beneficial to motivate students to take the initiative in learning, practice the student-centered concept, and promote cooperative learning among students. Peer feedback training, namely peer feedback under teacher intervention in this paper, combines the advantages of teacher feedback and peer feedback, which can not only be accepted by learners but also achieve significant pragmatic effects. From a practical point of view, peer feedback training can be applied to teach large groups of students, thus reducing the pressure and burden of teachers and improving the quality of peer feedback. The results also show that peer feedback training mainly works during the training stage rather than the modification stage. It is crucial to pay attention to the mechanism of peer feedback training and apply it to practice to promote the quality of English writing teaching.

China's rural areas are vast, and housing construction is the primary organization of farmers' living spaces and an essential focus of the national implementation of rural revitalization. However, there is a lack of rural housing census data and methods that quickly and accurately establish a rural three-dimensional (3D)-building model. The existing 3D-building modeling techniques, including manual modeling and oblique photography modeling, are encountering the problem of high cost and do not meet the construction requirements of low cost and comprehensive coverage. With the development of satellite remote sensing technology in China, building recognition based on high-resolution remote sensing images has become a convenient and rapid technical tool. At the same time, with the widespread use of smartphones and their potent computing power, people can easily and quickly access the Internet and receive a three-dimensional display. Compared with two-dimensional products, three-dimensional products can show rural buildings, terrain, and landscape more clearly, enhance the refined management of rural areas, and improve enthusiasm to participate in rural construction. Therefore, this study proposes a simple rural 3D building modeling method based on multi-source data fusion, namely intelligent identification and 3D modeling for rural buildings. This method consists of two stages: rough model generation and deepening. In the rough model generation stage, the building is identified based on high-resolution remote sensing images and Mask R_CNN technology, the location of the building is determined, and the basic white model is obtained by stretching. In the deepening stage, field collectors replace the basic white model with a more refined and parameterized model based on the rural building model library, according to the actual situation. Subsequently, they supplement the building facade texture through smartphone photography and texture processing. Finally, a physical, storable, and exchangeable 3D-building model is obtained through coordinate matching, image terrain fusion, 3D-lightweight technology, and other technologies. This study adopts the modeling strategy of gradual deepening to reduce the modeling cost. The associated high-resolution remote sensing image recognition technology and mobile phone-based 3D modeling and display technologies are relatively advanced. Based on the characteristics of architectural styles in rural areas, a set of rural building-model libraries based on CSG technology was constructed. Model replacement, model size adjustment, texture mapping, and other operations were quickly used to build a refined 3D model. Finally, the 3D models were lightly processed and fused with the image topography and other data, which meets the demand for smooth browsing from various aspects. The 3D model of Hecun Village in Xinxing County was experimentally reconstructed, illustrating that the method can support applications such as rural surveys, rural planning, rural construction, and co-production. The modeling results show that the method is simple, easy to use, and reduces the high requirements of conventional modeling in terms of data acquisition and processing. It can provide a low-cost, highly efficient, and prevalent 3D-reconstruction method for rural regions, which is suitable for widespread promotion.

The development of the automatic fare collection (AFC) systems provides significant support for predicting passenger flow on urban rail transit. This paper extracts passenger travel patterns using AFC data on urban rail transit in Chengdu, China, over a one-month period. Passengers are divided into two categories based on their travel habits and data mining models, and multinomial logit (MNL) models are separately used to predict their destinations. Furthermore, a two-way search algorithm is developed to search the optimal paths between origin-destination (OD) pairs by considering interchange constraints. Start a path search through the origin point and destination point, respectively, until the shortest path is found. The maximum effectiveness of a path is measured by travel time, interchange time, and the number of interchanges between the OD pairs. Finally, the validity of the proposed passenger flow path prediction method is verified by using the AFC data of Chengdu metropolitan rail transit from April 2018., Author(s): Yu Wang [1]; Qixuan Qin (corresponding author) [1,2]; Jialiang Chen [3]; Jiangbo Wang [3]; Kai Liu [3] 1. Introduction By March 2022, there were 49 cities in mainland China [...]

Epoxy resin (EP) has good mechanical and insulating properties and is widely used in the aerospace and nuclear industry. However, it can become degraded and failure-prone in high-energy radiation environments. Although boron nitride (BN) is stable in high-radiation fields, the actual performance of the radiation stability of BN is limited owing to its poor compatibility with the polymer matrix. In this study, boron nitride nanosheets (BNNSs) were modified by applying radiation to prepare BNNSs modified by glycidyl-methacrylate (BNNSs@PGMA), which has good compatibility with EP. Then, BNNSs@PGMA were introduced into the EP matrix by employing solution blending to prepare EP composites: EP, BNNSs/EP, and BNNSs@PGMA/EP. Moreover, the changes in the properties of these composites before and after γ-ray irradiation were investigated. The results indicated that the tensile strength of EP composites first increased and then decreased with an increasing absorbed dose. Radiation crosslinking dominated in the case of an absorbed dose of 0-400 kGy, whereas radiation cracking played a leading role for an absorbed dose of 400-1 000 kGy. For the absorbed dose of 1 000 kGy, the tensile strength of EP decreased by 42.9%, whereas that of BNNSs@PGMA/EP decreased by only 21.3%, which indicated more excellent radiation resistance. In addition, the resistance of the composites was improved by increasing the degree of modification in BNNSs@PGMA.