Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One f...Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.展开更多
In recent years,the rapid integration of artificial intelligence(AI)with various industries has led to an intelligent transformation in people’s learning and working patterns.In the field of higher vocational public ...In recent years,the rapid integration of artificial intelligence(AI)with various industries has led to an intelligent transformation in people’s learning and working patterns.In the field of higher vocational public music course teaching,AI provides intelligent teaching tools and learning platforms,while offering students timely and scientific support and companionship,enabling them to complete learning tasks more efficiently.How to explore the transformation path of teaching modes for higher vocational public music courses in the AI context has become a key consideration for frontline teachers.Based on this,this paper first analyzes the significance of teaching higher vocational public music courses in the AI context,and then proposes feasible transformation paths for teaching modes in combination with course characteristics for reference.展开更多
Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has bec...Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has become a mainstream solution but losing focus on the inter-sample class information.This paper proposes a new Credible Local Context Representation approach for SFDA.Our main idea is to exploit the credible local context for more discriminative representation.Specifically,we enhance the source model's discrimination by information regulating.To capture the context,a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context.In the epoch-wise adaptation,deep clustering-like training is conducted with two major updates.First,the context for all target data is constructed and then the context-fused pseudo-labels providing semantic guidance are generated.Second,for each target data,a weighting fusion on its context forms the anchored neighbourhood structure;thus,the deep clustering is switched from individual-based to coarse-grained.Also,a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse-grained learning.Experiments on three benchmarks indicate that the proposed method can achieve stateof-the-art results.展开更多
Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or ...Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.展开更多
With the development of modern educational concepts and technologies,corporate financial audit is facing unprecedented challenges and opportunities.This paper first analyzes the new characteristics of corporate financ...With the development of modern educational concepts and technologies,corporate financial audit is facing unprecedented challenges and opportunities.This paper first analyzes the new characteristics of corporate financial audit in the context of modern education,including the widespread application of digital audit tools,the diversification of audit content,and the increased requirements for audit efficiency.Then,it explores the innovative practices in corporate financial audit,such as the introduction of big data analysis technology,the construction of intelligent audit platforms,and the implementation of continuous audit.The paper also conducts an in-depth study on the impact of these innovative practices on the processes,quality,and risk management of corporate financial audit.Finally,it summarizes the effectiveness of the innovation and practice of corporate financial audit in the context of modern education,and looks forward to future development trends,providing references for theoretical research and practical operations in related fields.展开更多
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal ...In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.展开更多
Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the pattern...Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the patterns of interchanges,which are indispensable parts of urban road networks.In the SC-GCNN model,an interchange is modeled as a graph,wherein nodes and edges represent the interchange segments and their connections,respectively.Then,a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes.Finally,a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns.The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap.The classification accuracy was 87.06%,which was higher than that of the image-based AlexNet,GoogLeNet,and Random Forest models.展开更多
Context information is significant for semantic extraction and recovery of messages in semantic communication.However,context information is not fully utilized in the existing semantic communication systems since re-l...Context information is significant for semantic extraction and recovery of messages in semantic communication.However,context information is not fully utilized in the existing semantic communication systems since re-lationships between sentences are often ignored.In this paper,we propose an Extended Context-based Semantic Communication(ECSC)system for text transmission,in which context information within and between sentences is explored for semantic representation and recovery.At the encoder,self-attention and segment-level relative attention are used to extract context information within and between sentences,respectively.In addition,a gate mechanism is adopted at the encoder to incorporate the context information from different ranges.At the decoder,Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery.Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.展开更多
Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variati...Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.展开更多
This work presents a study of the Paleogene sandstones of the Manika plateau in Kolwezi, DR Congo. These sandstones belong to the “Grès polymorphes” group, which together with the overlying “Sables ocre” make...This work presents a study of the Paleogene sandstones of the Manika plateau in Kolwezi, DR Congo. These sandstones belong to the “Grès polymorphes” group, which together with the overlying “Sables ocre” makes up the Kalahari Supergroup. Sedimentological and geochemical analyses have enabled us to characterize these sandstones and determine their origin, the conditions of their formation and the tectonic context in which they were developed. The results show that the sandstones are quartz arenites with a high level of mineralogical, textural and chemical maturity. They are recycled sandstones, formed in an intracratonic sedimentary basin, in the context of a passive continental margin, after a long fluvial transport of sediments. These sandstones initially come from intense alteration of magmatic rocks with felsic composition, mainly tonalite-trondhjemite-granodiorite (TTG) complexes, in hot, humid palaeoclimatic conditions and oxidizing environments.展开更多
This study investigates the differences in pragmatic competence between Hong Kong and Chinese mainland university students.Participants included 19 native speakers of English,115 Chinese mainland students,divided into...This study investigates the differences in pragmatic competence between Hong Kong and Chinese mainland university students.Participants included 19 native speakers of English,115 Chinese mainland students,divided into those who had spent time abroad in an English-speaking country(CM A)and those who had not(CM NA),and 97 Hong Kong students,divided into those from an English-medium secondary school(Hong Kong EMI)and those from a Chinese-medium school(Hong Kong CMI).Linguistic proficiency was measured by a C-test,and pragmatic competence by a Metapragmatic Knowledge Test,an Irony Test and a Monologic Role Play.Group scores were compared using ANCOVAs to control for differences in proficiency.The results point to a continuum of pragmatic competence—EMI>CMI>CM A>CM NA—reflecting the groups’access to English in real-life contexts.The differences between the Hong Kong groups and the Chinese mainland groups were clearest in those tests measuring processing capacity(i.e.,Irony Response Time and the Monologic Role Play).CM A,but not CM NA,performed as well as the Hong Kong groups on measures of metapragmatic awareness.The results are discussed in terms of Bialystok’s(1993)distinction between analyzed representation and control of processing.展开更多
Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view ...Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.展开更多
Taking Zhaoyu Historical City in Qixian County as an example,this paper explores the production process of tourism space in Zhaoyu Historical City in the context of consumption,based on Lefebvre's triadic dialecti...Taking Zhaoyu Historical City in Qixian County as an example,this paper explores the production process of tourism space in Zhaoyu Historical City in the context of consumption,based on Lefebvre's triadic dialectic theory.The study reveals that,driven by the development of tourism,subjects such as the government and planners possess absolute dominance over spatial representations,while residents demonstrate receptive and adaptive action strategies and social relations are reproduced,presenting a harmonious state.Further exploring the tourism community in the environmental performance of the subject of action,social relations,consumption demand,daily life practice,cultural capital,etc.,the daily life practice of the tourism community has transcended the original logic of tourism spatial production and has a certain extension.The mechanism analysis in this paper can help guide the healthy development of tourism space in the neighboring historical cities or communities and achieve the dual purpose of promoting the economic development of the community and heritage protection.展开更多
In the context of the dual-carbon goals(carbon peaking and carbon neutrality),China’s new energy vehicle industry,as a key component of the green economy,has increasingly attracted international attention for its exp...In the context of the dual-carbon goals(carbon peaking and carbon neutrality),China’s new energy vehicle industry,as a key component of the green economy,has increasingly attracted international attention for its export competitiveness.Drawing on the Porter Diamond Model,this paper selects four major variables—R&D investment,production factors,demand factors,and spare parts services—to establish a linear regression equation.Through this model,the trade competitiveness of China’s new energy vehicles is analyzed in detail,and corresponding suggestions are proposed to enhance the competitiveness index.It is anticipated that Chinese new energy vehicle enterprises can strengthen overseas competitiveness by advancing core technology innovation,improving comprehensive industrial chain management,and diversifying sales channels.展开更多
文摘Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.
文摘In recent years,the rapid integration of artificial intelligence(AI)with various industries has led to an intelligent transformation in people’s learning and working patterns.In the field of higher vocational public music course teaching,AI provides intelligent teaching tools and learning platforms,while offering students timely and scientific support and companionship,enabling them to complete learning tasks more efficiently.How to explore the transformation path of teaching modes for higher vocational public music courses in the AI context has become a key consideration for frontline teachers.Based on this,this paper first analyzes the significance of teaching higher vocational public music courses in the AI context,and then proposes feasible transformation paths for teaching modes in combination with course characteristics for reference.
基金National Key R&D Program of China,Grant/Award Numbers:2018YFE0203900,2020YFB1313600German Research Foundation,Hamburg Landesforschungsförderungsprojekt Cross,Grant/Award Number:Sonderforschungsbereich Transregio 169+2 种基金Shanghai Artificial Intelligence Innovation Development Special Support Project,Grant/Award Number:3920365001Horizon2020 RISE project STEP2DYNA,Grant/Award Number:691154National Natural Science Foundation of China,Grant/Award Numbers:61773083,62206168,62276048,U1813202。
文摘Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has become a mainstream solution but losing focus on the inter-sample class information.This paper proposes a new Credible Local Context Representation approach for SFDA.Our main idea is to exploit the credible local context for more discriminative representation.Specifically,we enhance the source model's discrimination by information regulating.To capture the context,a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context.In the epoch-wise adaptation,deep clustering-like training is conducted with two major updates.First,the context for all target data is constructed and then the context-fused pseudo-labels providing semantic guidance are generated.Second,for each target data,a weighting fusion on its context forms the anchored neighbourhood structure;thus,the deep clustering is switched from individual-based to coarse-grained.Also,a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse-grained learning.Experiments on three benchmarks indicate that the proposed method can achieve stateof-the-art results.
基金Supported by the National Key R&D Program of China(2022YFC3803600).
文摘Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.
文摘With the development of modern educational concepts and technologies,corporate financial audit is facing unprecedented challenges and opportunities.This paper first analyzes the new characteristics of corporate financial audit in the context of modern education,including the widespread application of digital audit tools,the diversification of audit content,and the increased requirements for audit efficiency.Then,it explores the innovative practices in corporate financial audit,such as the introduction of big data analysis technology,the construction of intelligent audit platforms,and the implementation of continuous audit.The paper also conducts an in-depth study on the impact of these innovative practices on the processes,quality,and risk management of corporate financial audit.Finally,it summarizes the effectiveness of the innovation and practice of corporate financial audit in the context of modern education,and looks forward to future development trends,providing references for theoretical research and practical operations in related fields.
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
基金supported in part by the 2023 Key Supported Project of the 14th Five Year Plan for Education and Science in Hunan Province with No.ND230795.
文摘In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.
基金supported by the National Natural Science Foundation of China[grant numbers 42071450 and 42001415].
文摘Pattern recognition is critical to map data handling and their applications.This study presents a model that combines the Shape Context(SC)descriptor and Graph Convolutional Neural Network(GCNN)to classify the patterns of interchanges,which are indispensable parts of urban road networks.In the SC-GCNN model,an interchange is modeled as a graph,wherein nodes and edges represent the interchange segments and their connections,respectively.Then,a novel SC descriptor is implemented to describe the contextual information of each interchange segment and serve as descriptive features of graph nodes.Finally,a GCNN is designed by combining graph convolution and pooling operations to process the constructed graphs and classify the interchange patterns.The SC-GCNN model was validated using interchange samples obtained from the road networks of 15 cities downloaded from OpenStreetMap.The classification accuracy was 87.06%,which was higher than that of the image-based AlexNet,GoogLeNet,and Random Forest models.
基金supported in part by the National Natural Science Foundation of China under Grant No.61931020,U19B2024,62171449,,62001483in part by the science and technology innovation Program of Hunan Province under Grant No.2021JJ40690.
文摘Context information is significant for semantic extraction and recovery of messages in semantic communication.However,context information is not fully utilized in the existing semantic communication systems since re-lationships between sentences are often ignored.In this paper,we propose an Extended Context-based Semantic Communication(ECSC)system for text transmission,in which context information within and between sentences is explored for semantic representation and recovery.At the encoder,self-attention and segment-level relative attention are used to extract context information within and between sentences,respectively.In addition,a gate mechanism is adopted at the encoder to incorporate the context information from different ranges.At the decoder,Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery.Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.
基金the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2023GXJS163,ZDYF2024GXJS014)National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)+2 种基金the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012)Hainan Provincial Natural Science Foundation of China(Grant No.620MS021)Youth Foundation Project of Hainan Natural Science Foundation(621QN211).
文摘Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.
文摘This work presents a study of the Paleogene sandstones of the Manika plateau in Kolwezi, DR Congo. These sandstones belong to the “Grès polymorphes” group, which together with the overlying “Sables ocre” makes up the Kalahari Supergroup. Sedimentological and geochemical analyses have enabled us to characterize these sandstones and determine their origin, the conditions of their formation and the tectonic context in which they were developed. The results show that the sandstones are quartz arenites with a high level of mineralogical, textural and chemical maturity. They are recycled sandstones, formed in an intracratonic sedimentary basin, in the context of a passive continental margin, after a long fluvial transport of sediments. These sandstones initially come from intense alteration of magmatic rocks with felsic composition, mainly tonalite-trondhjemite-granodiorite (TTG) complexes, in hot, humid palaeoclimatic conditions and oxidizing environments.
文摘This study investigates the differences in pragmatic competence between Hong Kong and Chinese mainland university students.Participants included 19 native speakers of English,115 Chinese mainland students,divided into those who had spent time abroad in an English-speaking country(CM A)and those who had not(CM NA),and 97 Hong Kong students,divided into those from an English-medium secondary school(Hong Kong EMI)and those from a Chinese-medium school(Hong Kong CMI).Linguistic proficiency was measured by a C-test,and pragmatic competence by a Metapragmatic Knowledge Test,an Irony Test and a Monologic Role Play.Group scores were compared using ANCOVAs to control for differences in proficiency.The results point to a continuum of pragmatic competence—EMI>CMI>CM A>CM NA—reflecting the groups’access to English in real-life contexts.The differences between the Hong Kong groups and the Chinese mainland groups were clearest in those tests measuring processing capacity(i.e.,Irony Response Time and the Monologic Role Play).CM A,but not CM NA,performed as well as the Hong Kong groups on measures of metapragmatic awareness.The results are discussed in terms of Bialystok’s(1993)distinction between analyzed representation and control of processing.
文摘Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.
文摘Taking Zhaoyu Historical City in Qixian County as an example,this paper explores the production process of tourism space in Zhaoyu Historical City in the context of consumption,based on Lefebvre's triadic dialectic theory.The study reveals that,driven by the development of tourism,subjects such as the government and planners possess absolute dominance over spatial representations,while residents demonstrate receptive and adaptive action strategies and social relations are reproduced,presenting a harmonious state.Further exploring the tourism community in the environmental performance of the subject of action,social relations,consumption demand,daily life practice,cultural capital,etc.,the daily life practice of the tourism community has transcended the original logic of tourism spatial production and has a certain extension.The mechanism analysis in this paper can help guide the healthy development of tourism space in the neighboring historical cities or communities and achieve the dual purpose of promoting the economic development of the community and heritage protection.
文摘In the context of the dual-carbon goals(carbon peaking and carbon neutrality),China’s new energy vehicle industry,as a key component of the green economy,has increasingly attracted international attention for its export competitiveness.Drawing on the Porter Diamond Model,this paper selects four major variables—R&D investment,production factors,demand factors,and spare parts services—to establish a linear regression equation.Through this model,the trade competitiveness of China’s new energy vehicles is analyzed in detail,and corresponding suggestions are proposed to enhance the competitiveness index.It is anticipated that Chinese new energy vehicle enterprises can strengthen overseas competitiveness by advancing core technology innovation,improving comprehensive industrial chain management,and diversifying sales channels.
