As an important technique involved in the novel writing,the focalization is studied in detail in Narratology.The paper is an attempt to analyze the arrangement of focalizations and their functions in Heart of Dartness...As an important technique involved in the novel writing,the focalization is studied in detail in Narratology.The paper is an attempt to analyze the arrangement of focalizations and their functions in Heart of Dartness according to Narratology.展开更多
Studies about Catch-22 have been numerous,but the study of the novel’s narrative device is scarcely found.Thus,the narrative device of Catch-22 is analyzed in terms of focalization,one of Gernard Genette's narrat...Studies about Catch-22 have been numerous,but the study of the novel’s narrative device is scarcely found.Thus,the narrative device of Catch-22 is analyzed in terms of focalization,one of Gernard Genette's narrative theories.The analysis exposes the hypocrisy and meaninglessness of the bureaucratic behaviors and policies and simultaneously emphasizes bureaucracy’s devastative and corrosive effect on people.展开更多
INTRODUCTION.On January 7,2025,at 9:05 AM BJT,a MS6.8 earthquake(CENC epicenter:28.50°N,87.45°E)struck Dingri County,Xizang Province(hereinafter referred to as the Dingri mainshock).The inferred moment magni...INTRODUCTION.On January 7,2025,at 9:05 AM BJT,a MS6.8 earthquake(CENC epicenter:28.50°N,87.45°E)struck Dingri County,Xizang Province(hereinafter referred to as the Dingri mainshock).The inferred moment magnitude,based on regional/teleseismic waveform inversion and back-projection,is approximately MW7.1.Focal mechanism solutions,aftershock distribution,and field surveys indicate that the Dingri mainshock was a normal-faulting event,with a nearly north-south strike and a westward-dipping fault plane.展开更多
为了有效应对玉米地杂草对玉米产量和品质的影响,实现玉米与杂草的快速、准确检测,提出了一种基于改进YOLOv8n(You Only Look Once Version 8 nano)的玉米与杂草检测模型。首先,提出了ACMConv(Accurate and Computationally Minimal Con...为了有效应对玉米地杂草对玉米产量和品质的影响,实现玉米与杂草的快速、准确检测,提出了一种基于改进YOLOv8n(You Only Look Once Version 8 nano)的玉米与杂草检测模型。首先,提出了ACMConv(Accurate and Computationally Minimal Convolution)新型卷积方式,显著减少了模型计算量,使模型更加轻量化;其次,使用SELU激活函数,引入非线性因素,有效缓解了梯度消失问题;最后,引入Focal Loss作为边界框损失函数,使模型更加容易收敛。实验结果表明,相较于原始YOLOv8n模型,改进后的YOLOv8n模型的平均精度均值提升了1.3百分点,计算量降低了7.3%,实现了对玉米与杂草的高效、准确检测。展开更多
The Hualien M 7.3 earthquake on April 3,2024,was a significant and strong earthquake in Taiwan,China in the past two decades.The rupture process of the main shock and strong aftershocks is of great significance to the...The Hualien M 7.3 earthquake on April 3,2024,was a significant and strong earthquake in Taiwan,China in the past two decades.The rupture process of the main shock and strong aftershocks is of great significance to the subsequent seismic activity and seismogenic tectonic research.Based on local strong-motion data,we used the IDS(Iterative Deconvolution and Stacking)method to obtain the rupture process of the mainshock and two strong aftershocks on the 23rd.The rupture of the mainshock was mainly unilateral,lasting 31 s,with a maximum slip of 2m,and the depth of the large slip zone is about 41–49 km.There is a clear difference between the rupture depth of the main shock and the two strong aftershocks.The depths of the large slip zones of the latter two are 3–9 km and 8–10 km,respectively.There is also a significant difference in the seismogenic fault between the mainshock and the aftershocks,and we believe that there are two seismogenic fault zones in the study area,the deep and the shallow fault zone.The slip of the deep faults activates the shallow faults.展开更多
Source properties and stress fields are critical to understand fundamental mechanisms for fluid-induced earthquakes.In this study,we identify the focal mechanism solutions(FMSs)of 360 earthquakes with local magnitude ...Source properties and stress fields are critical to understand fundamental mechanisms for fluid-induced earthquakes.In this study,we identify the focal mechanism solutions(FMSs)of 360 earthquakes with local magnitude M_(L)≥1.5 in the Changning shale gas field from January 2016 to May 2017 by fitting three-component waveforms.We then constrain the directions of the maximum horizontal stress(σ_(H_(max)))for four dense earthquake clusters using the stress tensor inversion method.The stress drops of 121 earthquakes with M_(L)≥1.5 are calculated using the spectral ratio method.We examine the spatiotemporal heterogeneity of stress field,and discuss the cause of non-double-couple(non-DC)components in seismicity clusters.Following the Mohr-Coulomb criterion,we estimate the fluid overpressure thresholds from FMS for different seismic clusters,providing insights into potential physical mechanisms for induced seismicity.The FMS results indicate that shallow reverse earthquakes,with steep dip angles,characterize most events.The source mechanisms of earthquakes with M_(L)≥1.5 are dominated by DC components(>70%),but several earthquakes with M_(L)>3.0 and the microseismic events nearby during injection period display significant non-DC components(>30%).Stress inversion results reveal that the σ_(H_(max)) direction ranges from 120°to 128°.Stress drops of earthquakes range between 0.10 and 64.49 MPa,with high values occurring on reverse faults situated at a greater distance from the shale layer,accompanied by a moderate rotation(≤25°)in the trend of σ_(H_(max)).The seismic clusters close to the shale layer exhibit low fluid overpressure thresholds,prone to being triggered by high pore-pressure fluid.The integrated results suggest that the diffusion of high pore pressures is likely to be the primary factor for observed earthquakes.The present results are expected to offer valuable insights into the origin of anomalous seismicity near the shale gas sites.展开更多
Determining the orientation of in-situ stresses is crucial for various geoscience and engineering appli-cations.Conventional methods for estimating these stress orientations often depend on focal mechanism solutions(F...Determining the orientation of in-situ stresses is crucial for various geoscience and engineering appli-cations.Conventional methods for estimating these stress orientations often depend on focal mechanism solutions(FMSs)derived from earthquake data and formation micro-imager(FMI)data from well logs.However,these techniques can be costly,depth-inaccurate,and may lack spatial coverage.To address this issue,we introduce the use of three-dimensional(3D)seismic data(active sources)as a lateral constraint to approximate the 3D stress orientation field.Recognizing that both stress and fracture patterns are closely related to seismic velocity anisotropy,we derive the orientation of azimuthal anisotropy from multi-azimuth 3D seismic data to compensate for the lack of spatial stress orientation information.We apply our proposed workflow to a case study in the Weiyuan area of the Sichuan Basin,China,a region targeted for shale gas production.By integrating diverse datasets,including 3D seismic,earthquakes,and well logs,we develop a comprehensive 3D model of in-situ stress(orientations and magnitudes).Our results demonstrate that the estimated anisotropy orientations from 3D seismic data are consistent with the direction of maximum horizontal principal stress(SHmax)obtained from FMIs.We analyzed 12 earthquakes(magnitude>3)recorded between 2016 and 2020 for their FMSs and compressional axis(P-axis)orientations.The derived SHmax direction from our 3D stress model is 110°ES(East-South),which shows excellent agreement with the FMSs(within 3.96°).This close alignment validates the reliability and precision of our integrated method for predicting 3D SHmax orientations.展开更多
鉴于传统轮毂分类检测流程中存在的劳动重复度高、人力成本高企及生产效率低下等挑战,文章提出了一种应用YOLOv8网络的汽车轮毂自动分类系统。该系统的工作流程包括:首先,收集丰富的汽车轮毂X光图像,构建一个囊括多种轮毂类型的综合性...鉴于传统轮毂分类检测流程中存在的劳动重复度高、人力成本高企及生产效率低下等挑战,文章提出了一种应用YOLOv8网络的汽车轮毂自动分类系统。该系统的工作流程包括:首先,收集丰富的汽车轮毂X光图像,构建一个囊括多种轮毂类型的综合性数据集;接着,利用YOLOv8算法对该数据集实施训练,以生成一个能够精确分辨轮毂种类的模型。在模型训练阶段,针对轮毂分类的具体特性,对YOLOv8算法进行了改进,引入了Focal Loss作为损失函数,从而有效缓解了正负样本不均衡的问题,进一步提升了轮毂分类的精确度。在实验验证环节,文章在不同噪声干扰和光照条件下对轮毂图像进行了测试。实验结果显示,该系统能迅速且准确地识别出各类轮毂,平均识别准确率高达98.43%,展现出了卓越的分类精度和强大的鲁棒性。In response to the challenges of high labor repetition, escalating labor costs, and low production efficiency in traditional hub classification and inspection processes, this paper proposes an automatic automobile hub classification system using the YOLOv8 network. The workflow of this system includes: first, collecting extensive X-ray images of automobile hubs to construct a comprehensive dataset encompassing various hub types;next, utilizing the YOLOv8 algorithm to train this dataset to generate a model capable of accurately distinguishing hub types. During the model training phase, improvements were made to the YOLOv8 algorithm based on the specific characteristics of hub classification, with Focal Loss introduced as the loss function, effectively mitigating the issue of imbalance between positive and negative samples and further enhancing the accuracy of hub classification. In the experimental validation stage, hub images were tested under different noise interference and lighting conditions. The experimental results demonstrate that the system can swiftly and accurately identify various types of hubs, with an average recognition accuracy of 98.43%, showcasing excellent classification accuracy and robustness.展开更多
文摘As an important technique involved in the novel writing,the focalization is studied in detail in Narratology.The paper is an attempt to analyze the arrangement of focalizations and their functions in Heart of Dartness according to Narratology.
文摘Studies about Catch-22 have been numerous,but the study of the novel’s narrative device is scarcely found.Thus,the narrative device of Catch-22 is analyzed in terms of focalization,one of Gernard Genette's narrative theories.The analysis exposes the hypocrisy and meaninglessness of the bureaucratic behaviors and policies and simultaneously emphasizes bureaucracy’s devastative and corrosive effect on people.
基金supported by the“CUG Scholar”Scientific Research Funds at China University of Geosciences(Wuhan)(No.2021230)supported by the National Natural Science Foundation of China(Nos.41922025,42204062)。
文摘INTRODUCTION.On January 7,2025,at 9:05 AM BJT,a MS6.8 earthquake(CENC epicenter:28.50°N,87.45°E)struck Dingri County,Xizang Province(hereinafter referred to as the Dingri mainshock).The inferred moment magnitude,based on regional/teleseismic waveform inversion and back-projection,is approximately MW7.1.Focal mechanism solutions,aftershock distribution,and field surveys indicate that the Dingri mainshock was a normal-faulting event,with a nearly north-south strike and a westward-dipping fault plane.
文摘为了有效应对玉米地杂草对玉米产量和品质的影响,实现玉米与杂草的快速、准确检测,提出了一种基于改进YOLOv8n(You Only Look Once Version 8 nano)的玉米与杂草检测模型。首先,提出了ACMConv(Accurate and Computationally Minimal Convolution)新型卷积方式,显著减少了模型计算量,使模型更加轻量化;其次,使用SELU激活函数,引入非线性因素,有效缓解了梯度消失问题;最后,引入Focal Loss作为边界框损失函数,使模型更加容易收敛。实验结果表明,相较于原始YOLOv8n模型,改进后的YOLOv8n模型的平均精度均值提升了1.3百分点,计算量降低了7.3%,实现了对玉米与杂草的高效、准确检测。
基金sponsored by the Earthquake Spark Technology Project(XH23051B)。
文摘The Hualien M 7.3 earthquake on April 3,2024,was a significant and strong earthquake in Taiwan,China in the past two decades.The rupture process of the main shock and strong aftershocks is of great significance to the subsequent seismic activity and seismogenic tectonic research.Based on local strong-motion data,we used the IDS(Iterative Deconvolution and Stacking)method to obtain the rupture process of the mainshock and two strong aftershocks on the 23rd.The rupture of the mainshock was mainly unilateral,lasting 31 s,with a maximum slip of 2m,and the depth of the large slip zone is about 41–49 km.There is a clear difference between the rupture depth of the main shock and the two strong aftershocks.The depths of the large slip zones of the latter two are 3–9 km and 8–10 km,respectively.There is also a significant difference in the seismogenic fault between the mainshock and the aftershocks,and we believe that there are two seismogenic fault zones in the study area,the deep and the shallow fault zone.The slip of the deep faults activates the shallow faults.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.U20A20266 and 12302503)Scientific and technological research projects in Sichuan province(Grant No.2024NSFSC0973).
文摘Source properties and stress fields are critical to understand fundamental mechanisms for fluid-induced earthquakes.In this study,we identify the focal mechanism solutions(FMSs)of 360 earthquakes with local magnitude M_(L)≥1.5 in the Changning shale gas field from January 2016 to May 2017 by fitting three-component waveforms.We then constrain the directions of the maximum horizontal stress(σ_(H_(max)))for four dense earthquake clusters using the stress tensor inversion method.The stress drops of 121 earthquakes with M_(L)≥1.5 are calculated using the spectral ratio method.We examine the spatiotemporal heterogeneity of stress field,and discuss the cause of non-double-couple(non-DC)components in seismicity clusters.Following the Mohr-Coulomb criterion,we estimate the fluid overpressure thresholds from FMS for different seismic clusters,providing insights into potential physical mechanisms for induced seismicity.The FMS results indicate that shallow reverse earthquakes,with steep dip angles,characterize most events.The source mechanisms of earthquakes with M_(L)≥1.5 are dominated by DC components(>70%),but several earthquakes with M_(L)>3.0 and the microseismic events nearby during injection period display significant non-DC components(>30%).Stress inversion results reveal that the σ_(H_(max)) direction ranges from 120°to 128°.Stress drops of earthquakes range between 0.10 and 64.49 MPa,with high values occurring on reverse faults situated at a greater distance from the shale layer,accompanied by a moderate rotation(≤25°)in the trend of σ_(H_(max)).The seismic clusters close to the shale layer exhibit low fluid overpressure thresholds,prone to being triggered by high pore-pressure fluid.The integrated results suggest that the diffusion of high pore pressures is likely to be the primary factor for observed earthquakes.The present results are expected to offer valuable insights into the origin of anomalous seismicity near the shale gas sites.
基金supported by the National Key R&D Program of China(Grant No.2020YFA0710604)NSFC(Grant No.42374064).
文摘Determining the orientation of in-situ stresses is crucial for various geoscience and engineering appli-cations.Conventional methods for estimating these stress orientations often depend on focal mechanism solutions(FMSs)derived from earthquake data and formation micro-imager(FMI)data from well logs.However,these techniques can be costly,depth-inaccurate,and may lack spatial coverage.To address this issue,we introduce the use of three-dimensional(3D)seismic data(active sources)as a lateral constraint to approximate the 3D stress orientation field.Recognizing that both stress and fracture patterns are closely related to seismic velocity anisotropy,we derive the orientation of azimuthal anisotropy from multi-azimuth 3D seismic data to compensate for the lack of spatial stress orientation information.We apply our proposed workflow to a case study in the Weiyuan area of the Sichuan Basin,China,a region targeted for shale gas production.By integrating diverse datasets,including 3D seismic,earthquakes,and well logs,we develop a comprehensive 3D model of in-situ stress(orientations and magnitudes).Our results demonstrate that the estimated anisotropy orientations from 3D seismic data are consistent with the direction of maximum horizontal principal stress(SHmax)obtained from FMIs.We analyzed 12 earthquakes(magnitude>3)recorded between 2016 and 2020 for their FMSs and compressional axis(P-axis)orientations.The derived SHmax direction from our 3D stress model is 110°ES(East-South),which shows excellent agreement with the FMSs(within 3.96°).This close alignment validates the reliability and precision of our integrated method for predicting 3D SHmax orientations.
文摘鉴于传统轮毂分类检测流程中存在的劳动重复度高、人力成本高企及生产效率低下等挑战,文章提出了一种应用YOLOv8网络的汽车轮毂自动分类系统。该系统的工作流程包括:首先,收集丰富的汽车轮毂X光图像,构建一个囊括多种轮毂类型的综合性数据集;接着,利用YOLOv8算法对该数据集实施训练,以生成一个能够精确分辨轮毂种类的模型。在模型训练阶段,针对轮毂分类的具体特性,对YOLOv8算法进行了改进,引入了Focal Loss作为损失函数,从而有效缓解了正负样本不均衡的问题,进一步提升了轮毂分类的精确度。在实验验证环节,文章在不同噪声干扰和光照条件下对轮毂图像进行了测试。实验结果显示,该系统能迅速且准确地识别出各类轮毂,平均识别准确率高达98.43%,展现出了卓越的分类精度和强大的鲁棒性。In response to the challenges of high labor repetition, escalating labor costs, and low production efficiency in traditional hub classification and inspection processes, this paper proposes an automatic automobile hub classification system using the YOLOv8 network. The workflow of this system includes: first, collecting extensive X-ray images of automobile hubs to construct a comprehensive dataset encompassing various hub types;next, utilizing the YOLOv8 algorithm to train this dataset to generate a model capable of accurately distinguishing hub types. During the model training phase, improvements were made to the YOLOv8 algorithm based on the specific characteristics of hub classification, with Focal Loss introduced as the loss function, effectively mitigating the issue of imbalance between positive and negative samples and further enhancing the accuracy of hub classification. In the experimental validation stage, hub images were tested under different noise interference and lighting conditions. The experimental results demonstrate that the system can swiftly and accurately identify various types of hubs, with an average recognition accuracy of 98.43%, showcasing excellent classification accuracy and robustness.
