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
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
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
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
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
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
With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.He...With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.展开更多
Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning techn...Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.展开更多
Traditional medicine(TM)has been more popular among pregnant women worldwide and has played a significant part in maternal health-care services in many nations.Herbs,herbal preparations,and finished herbal products al...Traditional medicine(TM)has been more popular among pregnant women worldwide and has played a significant part in maternal health-care services in many nations.Herbs,herbal preparations,and finished herbal products all contain active substances that are derived from plant parts or other plant components that are thought to have medicinal advantages.To diagnose,prevent,and treat illnesses as well as to enhance general well-being,about 80%of people use a variety of TM,including herbal remedies.A systematic search of Google Scholar and PubMed was performed utilizing an established scoping review framework by Joanna Briggs Institute from January 2012 to December 2022.A consequent title and abstract review of articles published on TM in the African context were completed.Of over 15,000 published studies identified,15 meeting the inclusion criteria were integrated into the following seven categorical themes:prevalence of TM use,source of information on TM use,reasons for use of TM,route of administration,common herbs used in pregnancy and labor,the effect of herbs used in pregnancy and labor,and predictors of use of TM.The studies reviewed were primarily in the context of an African setting on the use of TM regarding herbal medicine.Of all the articles,the highest number of studies was conducted in Zimbabwe.This review shows increased use of TM by women during pregnancy and labor with a reported prevalence rate varying from 12%to 60%.However,a decrease in use in the third trimester of pregnancy was reported.The most frequent source of information on the use of TM was from family and friends,while age,parity,education,and income were factors affecting use.In conclusion,the participants do not often disclose the use of TM during their antenatal attendance and the reason for use was accessibility and cost.Therefore,there is a need for further study on the safety and efficacy of TM use in pregnancy and labor.展开更多
In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more ...In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more useful conditions for students to learn, which requires students to master enough self-learning abilities to adapt to this model. The study in the paper shows that students are usually interested in autonomous learning in a multimodal environment, but the degree of strategy choice is relatively low, and the learning process is blind and passive with the lack of self-confidence. Facing the future, schools should actively integrate into network thinking, and teachers should change their roles and train and guide students’ learning strategies and learning motivations, so as to achieve better teaching results.展开更多
Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues...Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal size.Spatio-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding grids.Secondly,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from grids.And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation.Thirdly,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency.Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O architecture.The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.展开更多
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.
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.
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With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)(No.2021R1C1C1013133)funded by BK21 FOUR(Fostering Outstanding Universities for Research)(No.5199990914048)supported by the Soonchunhyang University Research Fund.
文摘Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.
文摘Traditional medicine(TM)has been more popular among pregnant women worldwide and has played a significant part in maternal health-care services in many nations.Herbs,herbal preparations,and finished herbal products all contain active substances that are derived from plant parts or other plant components that are thought to have medicinal advantages.To diagnose,prevent,and treat illnesses as well as to enhance general well-being,about 80%of people use a variety of TM,including herbal remedies.A systematic search of Google Scholar and PubMed was performed utilizing an established scoping review framework by Joanna Briggs Institute from January 2012 to December 2022.A consequent title and abstract review of articles published on TM in the African context were completed.Of over 15,000 published studies identified,15 meeting the inclusion criteria were integrated into the following seven categorical themes:prevalence of TM use,source of information on TM use,reasons for use of TM,route of administration,common herbs used in pregnancy and labor,the effect of herbs used in pregnancy and labor,and predictors of use of TM.The studies reviewed were primarily in the context of an African setting on the use of TM regarding herbal medicine.Of all the articles,the highest number of studies was conducted in Zimbabwe.This review shows increased use of TM by women during pregnancy and labor with a reported prevalence rate varying from 12%to 60%.However,a decrease in use in the third trimester of pregnancy was reported.The most frequent source of information on the use of TM was from family and friends,while age,parity,education,and income were factors affecting use.In conclusion,the participants do not often disclose the use of TM during their antenatal attendance and the reason for use was accessibility and cost.Therefore,there is a need for further study on the safety and efficacy of TM use in pregnancy and labor.
文摘In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more useful conditions for students to learn, which requires students to master enough self-learning abilities to adapt to this model. The study in the paper shows that students are usually interested in autonomous learning in a multimodal environment, but the degree of strategy choice is relatively low, and the learning process is blind and passive with the lack of self-confidence. Facing the future, schools should actively integrate into network thinking, and teachers should change their roles and train and guide students’ learning strategies and learning motivations, so as to achieve better teaching results.
基金supported by the National Natural Science Foundation of China(41871320,61873316)the Key Project of Hunan Provincial Education Department(19A172)+1 种基金the Scientific Research Fund of Hunan Provincial Education Department(18K060)the Postgraduate Scientific Research Innovation Project of Hunan Province(CX20211000).
文摘Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal size.Spatio-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding grids.Secondly,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from grids.And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation.Thirdly,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency.Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O architecture.The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.
