Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral...Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.展开更多
Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,w...Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.展开更多
The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,w...The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,which is important for object retrieval and treatment of the patient.This case report describes a male child,2 years and 2 mo of age,who accidentally swallowed a lithium battery while playing at home.A plain X-ray showed that the battery was in the abdomen,but it was unclear whether the object was still inside the stomach.A second X-ray examination performed after oral administration of a bloating agent to produce expansion of the stomach and provide negative contrast confirmed that the ingested battery was still in the stomach.The battery was then carefully removed using magnetic and balloon catheters under fluoroscopic guidance.This case report describes the successful use of an orally administered bloating agent without pain to the child in orderto determine the precise location of a foreign object in the abdomen.展开更多
In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Paul...In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Pauline Melville, we argue that Cartesianism has influenced a view on education which tends to consider good and valuable what is ＂scientific＂, ＂objective＂ and ＂universal＂. The subjective and the local seem to be considered undesirable and unreliable. Brazilian scholars on the education field, such as Coracini and Souza are important support for our argument.展开更多
针对输电线路上附着异物容易对周围环境造成污染甚至导致短路停电及发生火灾等问题,提出了一种基于改进YOLOv8n的输电线路及铁塔异物实时检测算法,采用的基线算法为YOLOv8n,为了提高对小目标的检测准确率并且提升对复杂背景下异物的检...针对输电线路上附着异物容易对周围环境造成污染甚至导致短路停电及发生火灾等问题,提出了一种基于改进YOLOv8n的输电线路及铁塔异物实时检测算法,采用的基线算法为YOLOv8n,为了提高对小目标的检测准确率并且提升对复杂背景下异物的检测能力,将原始YOLOv8n算法的检测头改进为动态检测头DynamicHead,提高了模型对多个维度特征的提取能力与应对不同输入的动态调整能力,将非极大值抑制(Non-Maximum Suppression,NMS)算法改进为Soft-NMS,提高了模型的泛化能力和整体检测性能。试验结果表明,改进后的算法检测平均精度均值(mean Average Precision,mAP)为95.7%,相比于原YOLOv8n算法提升了4.4个百分点,在保证可满足实时检测速度的同时实现了较高的检测精度,具有较好的有效性和实用性。展开更多
针对铁路轨道异物侵限检测精度偏低、速度偏慢、易出现漏检与误检的问题,提出一种基于浅层特征融合的轻量级铁轨异物侵限检测算法(YOLO Lightweight and Shallow-feature Fusion,YOLO-LSF).首先,结合YOLOv8n特征提取网络,基于GhostConv...针对铁路轨道异物侵限检测精度偏低、速度偏慢、易出现漏检与误检的问题,提出一种基于浅层特征融合的轻量级铁轨异物侵限检测算法(YOLO Lightweight and Shallow-feature Fusion,YOLO-LSF).首先,结合YOLOv8n特征提取网络,基于GhostConv改进C2f模块以构建C2f_Ghost模块,从而降低模型的参数量和计算量;其次,在骨干网络尾端引入MLCA注意力机制,增强目标区域的特征信息,优化模型的特征提取效率;再次,利用可变形卷积DCNv2替换YO-LOv8n中C2f模块的部分普通卷积,构建了C2f_DCNv2模块,增强模型的特征提取能力;最后,在颈部网络中融入主干网络中的浅层特征信息,较好地解决了经多次卷积操作所导致的细节信息丢失问题,以提升模型对远距离异物(小目标)的检测能力.实验结果表明:在自建的铁轨异物入侵检测数据集上,相比于原YOLOv8n算法,采用YOLO-LSF算法处理的平均精度提升了5.2%,每秒帧数(Frames Per Second,FPS)提升了3.37%,参数量减少了20.1%,计算量减少了22.2%,有效提升了复杂环境下铁轨异物目标的检测精度与检测速度,降低了漏检与误检的概率.展开更多
为解决YOLOv8n算法在机场异物检测中存在计算复杂度高、计算资源消耗大的问题,通过在YOLOv8n算法中引入轻量化模块的方法研究了机场异物检测的问题,提出了Fast-BiYOLOv8n算法。首先,设计了C2f_FasterEMA模块并引入YOLOv8n算法的骨干网络...为解决YOLOv8n算法在机场异物检测中存在计算复杂度高、计算资源消耗大的问题,通过在YOLOv8n算法中引入轻量化模块的方法研究了机场异物检测的问题,提出了Fast-BiYOLOv8n算法。首先,设计了C2f_FasterEMA模块并引入YOLOv8n算法的骨干网络中,该模块融合了FasterBlock模块和高效多尺度注意力(efficient multi-scale attention,EMA)注意力机制,增强了图像的特征提取能力,同时降低了算法计算量;其次,在路径聚合网络(path aggregation network,PANet,)网络架构中融合了骨干网络中的P2特征层并设计了双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)网络架构,增加了跨尺度连接促进了不同特征图之间的信息融合,同时加入C2f_Faster模块提高了特征融合的效率并进一步降低了算法的计算量;最后,通过改进损失函数为Inner-CIoU(intersection over union,complete intersection over union loss)加快了算法的收敛速度,提高了检测准确率。结果表明,Fast-BiYOLOv8n算法的检测准确率达到99.0%,召回率为98.8%,平均精度均值(mean average precision,mAP)提升了3.5个百分点,达到99.3%,参数量比原模型降低了27%,模型的权重大小降低了21%,实现了在降低算法参数量的同时,提升检测准确率的目的。展开更多
井下运煤带式输送机是煤炭生产运输的关键环节。由于运煤皮带上出现的锚杆、槽钢、铁棍等异物,皮带在运行过程中容易出现纵向撕裂甚至断带等事故。针对煤矿井下的皮带异物检测问题,提出了一种基于迁移学习和在线难例挖掘的井下皮带异物...井下运煤带式输送机是煤炭生产运输的关键环节。由于运煤皮带上出现的锚杆、槽钢、铁棍等异物,皮带在运行过程中容易出现纵向撕裂甚至断带等事故。针对煤矿井下的皮带异物检测问题,提出了一种基于迁移学习和在线难例挖掘的井下皮带异物检测模型。首先,利用迁移学习策略,提高模型泛化能力,解决数据集较小的问题;其次,在特征融合层的改进型空间金字塔池化模块(Spatial Pyramid Pooling Fast,SPPF)添加坐标注意力(Coordinate Attention,CA)机制,提升模型特征的表达能力;之后,使用损失函数WIoU(Wise Intersection over Union)代替损失函数CIoU(Complete Intersection over Union),加快模型训练速度;最后,利用在线难例挖掘(Online Hard Example Mining,OHEM)策略,帮助模型更好地学习难分类的样本。试验结果表明,井下皮带异物检测模型在自建异物检测数据集上mAP@0.5和mAP@0.5-0.95分别取得了92.5%和79.4%的检测效果,与原YOLOv8相比分别增加了2.6百分点和1.8百分点,并且本模型在实际矿山的检测中取得了90.4%的检测效果,表明模型在实际矿井环境中具有较强的适用性,可为井下皮带异物的检测提供技术支持。展开更多
文摘Transorbital craniocerebral injury is a relatively rare type of penetrating head injury that poses a significant threat to the ocular and cerebral structures.^([1])The clinical prognosis of transorbital craniocerebral injury is closely related to the size,shape,speed,nature,and trajectory of the foreign object,as well as the incidence of central nervous system damage and secondary complications.The foreign objects reported to have caused these injuries are categorized into wooden items,metallic items,^([2-8])and other materials,which penetrate the intracranial region via fi ve major pathways,including the orbital roof (OR),superior orbital fissure (SOF),inferior orbital fissure(IOF),optic canal (OC),and sphenoid wing.Herein,we present eight cases of transorbital craniocerebral injury caused by an unusual metallic foreign body.
基金supported by a grant from the National Key Research and Development Project(2023YFB4302100)Key Research and Development Project of Jiangxi Province(No.20232ACE01011)Independent Deployment Project of Ganjiang Innovation Research Institute,Chinese Academy of Sciences(E255J001).
文摘Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.
文摘The location of an ingested foreign object is often difficult to determine by X-ray if gastric air bubbles are not clear in the image.Methods that provide negative contrast can facilitate precise object localization,which is important for object retrieval and treatment of the patient.This case report describes a male child,2 years and 2 mo of age,who accidentally swallowed a lithium battery while playing at home.A plain X-ray showed that the battery was in the abdomen,but it was unclear whether the object was still inside the stomach.A second X-ray examination performed after oral administration of a bloating agent to produce expansion of the stomach and provide negative contrast confirmed that the ingested battery was still in the stomach.The battery was then carefully removed using magnetic and balloon catheters under fluoroscopic guidance.This case report describes the successful use of an orally administered bloating agent without pain to the child in orderto determine the precise location of a foreign object in the abdomen.
文摘In this paper we propose to discuss the issue of subjectivity versus objectivity teaching practice of foreign language, especially English, in Brazil. Starting from the short story ＂The Parrot and Descartes＂ by Pauline Melville, we argue that Cartesianism has influenced a view on education which tends to consider good and valuable what is ＂scientific＂, ＂objective＂ and ＂universal＂. The subjective and the local seem to be considered undesirable and unreliable. Brazilian scholars on the education field, such as Coracini and Souza are important support for our argument.
文摘针对输电线路上附着异物容易对周围环境造成污染甚至导致短路停电及发生火灾等问题,提出了一种基于改进YOLOv8n的输电线路及铁塔异物实时检测算法,采用的基线算法为YOLOv8n,为了提高对小目标的检测准确率并且提升对复杂背景下异物的检测能力,将原始YOLOv8n算法的检测头改进为动态检测头DynamicHead,提高了模型对多个维度特征的提取能力与应对不同输入的动态调整能力,将非极大值抑制(Non-Maximum Suppression,NMS)算法改进为Soft-NMS,提高了模型的泛化能力和整体检测性能。试验结果表明,改进后的算法检测平均精度均值(mean Average Precision,mAP)为95.7%,相比于原YOLOv8n算法提升了4.4个百分点,在保证可满足实时检测速度的同时实现了较高的检测精度,具有较好的有效性和实用性。
文摘针对铁路轨道异物侵限检测精度偏低、速度偏慢、易出现漏检与误检的问题,提出一种基于浅层特征融合的轻量级铁轨异物侵限检测算法(YOLO Lightweight and Shallow-feature Fusion,YOLO-LSF).首先,结合YOLOv8n特征提取网络,基于GhostConv改进C2f模块以构建C2f_Ghost模块,从而降低模型的参数量和计算量;其次,在骨干网络尾端引入MLCA注意力机制,增强目标区域的特征信息,优化模型的特征提取效率;再次,利用可变形卷积DCNv2替换YO-LOv8n中C2f模块的部分普通卷积,构建了C2f_DCNv2模块,增强模型的特征提取能力;最后,在颈部网络中融入主干网络中的浅层特征信息,较好地解决了经多次卷积操作所导致的细节信息丢失问题,以提升模型对远距离异物(小目标)的检测能力.实验结果表明:在自建的铁轨异物入侵检测数据集上,相比于原YOLOv8n算法,采用YOLO-LSF算法处理的平均精度提升了5.2%,每秒帧数(Frames Per Second,FPS)提升了3.37%,参数量减少了20.1%,计算量减少了22.2%,有效提升了复杂环境下铁轨异物目标的检测精度与检测速度,降低了漏检与误检的概率.
文摘为解决YOLOv8n算法在机场异物检测中存在计算复杂度高、计算资源消耗大的问题,通过在YOLOv8n算法中引入轻量化模块的方法研究了机场异物检测的问题,提出了Fast-BiYOLOv8n算法。首先,设计了C2f_FasterEMA模块并引入YOLOv8n算法的骨干网络中,该模块融合了FasterBlock模块和高效多尺度注意力(efficient multi-scale attention,EMA)注意力机制,增强了图像的特征提取能力,同时降低了算法计算量;其次,在路径聚合网络(path aggregation network,PANet,)网络架构中融合了骨干网络中的P2特征层并设计了双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)网络架构,增加了跨尺度连接促进了不同特征图之间的信息融合,同时加入C2f_Faster模块提高了特征融合的效率并进一步降低了算法的计算量;最后,通过改进损失函数为Inner-CIoU(intersection over union,complete intersection over union loss)加快了算法的收敛速度,提高了检测准确率。结果表明,Fast-BiYOLOv8n算法的检测准确率达到99.0%,召回率为98.8%,平均精度均值(mean average precision,mAP)提升了3.5个百分点,达到99.3%,参数量比原模型降低了27%,模型的权重大小降低了21%,实现了在降低算法参数量的同时,提升检测准确率的目的。
文摘井下运煤带式输送机是煤炭生产运输的关键环节。由于运煤皮带上出现的锚杆、槽钢、铁棍等异物,皮带在运行过程中容易出现纵向撕裂甚至断带等事故。针对煤矿井下的皮带异物检测问题,提出了一种基于迁移学习和在线难例挖掘的井下皮带异物检测模型。首先,利用迁移学习策略,提高模型泛化能力,解决数据集较小的问题;其次,在特征融合层的改进型空间金字塔池化模块(Spatial Pyramid Pooling Fast,SPPF)添加坐标注意力(Coordinate Attention,CA)机制,提升模型特征的表达能力;之后,使用损失函数WIoU(Wise Intersection over Union)代替损失函数CIoU(Complete Intersection over Union),加快模型训练速度;最后,利用在线难例挖掘(Online Hard Example Mining,OHEM)策略,帮助模型更好地学习难分类的样本。试验结果表明,井下皮带异物检测模型在自建异物检测数据集上mAP@0.5和mAP@0.5-0.95分别取得了92.5%和79.4%的检测效果,与原YOLOv8相比分别增加了2.6百分点和1.8百分点,并且本模型在实际矿山的检测中取得了90.4%的检测效果,表明模型在实际矿井环境中具有较强的适用性,可为井下皮带异物的检测提供技术支持。
