Concomitant with the advancement of contemporary medical technology,the significance of perioperative nursing has been increasingly accentuated,necessitating elevated standards for the pedagogy of perioperative nursin...Concomitant with the advancement of contemporary medical technology,the significance of perioperative nursing has been increasingly accentuated,necessitating elevated standards for the pedagogy of perioperative nursing.Presently,the PBL(problem-based learning)pedagogical approach,when integrated with CBL(case-based learning),has garnered considerable interest.An extensive literature review has been conducted to analyze the application of the PBL-CBL fusion in the education of perioperative nursing.Findings indicate that this integrative teaching methodology not only enhances students’theoretical knowledge,practical competencies,and collaborative skills but also contributes to the elevation of teaching quality.In conclusion,the PBL-CBL teaching approach holds immense potential for broader application in perioperative nursing education.Nevertheless,it is imperative to continually refine this combined pedagogical strategy to further enhance the caliber of perioperative nursing instruction and to cultivate a greater number of exceptional nursing professionals in the operating room setting.展开更多
In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring mi...In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.展开更多
In the face of intelligent manufacturing(or smart manufacturing)human resource shortage,the training of industrial engineers in the field of intelligent manufacturing is of great significance.In academia,the positive ...In the face of intelligent manufacturing(or smart manufacturing)human resource shortage,the training of industrial engineers in the field of intelligent manufacturing is of great significance.In academia,the positive link between learning transfer and knowledge innovation is recognized by most scholars,while the learner’s attitude toward big data decision-making,as a cognitive perception,affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm.Thus,learning transfer can be regarded as a result of the learner’s attitude,and it becomes the intermediary state between their attitude and knowledge innovation.This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude,knowledge transfer,and knowledge innovation.展开更多
Objective: To explore the utilization of implicit nursing knowledge in the teaching of cardiovascular internal medicine nursing and to provide a reference for improving the quality and efficiency of cardiovascular int...Objective: To explore the utilization of implicit nursing knowledge in the teaching of cardiovascular internal medicine nursing and to provide a reference for improving the quality and efficiency of cardiovascular internal medicine nursing work. Methods: Thirty-six trainee nurses working in the cardiovascular internal medicine department of our hospital from September 2022 to September 2023 were selected and randomly divided into a control group and an observation group of 18 trainees each. The control adopted the traditional teaching methods while the observation group adopted the implicit nursing knowledge in their clinical practice work. The assessment scores and teamwork ability of the two groups were analyzed and compared. Results: The performance of the observation group was better than that of the control group, and the difference between the two groups was statistically significant (P < 0.05). The teamwork ability of the observation group was significantly better than that of the control group in teamwork ability (P < 0.05). Conclusion: Implicit nursing knowledge teaching is conducive to the cultivation of high-quality nursing talents and meets the development needs of hospitals. Therefore, the importance of implicit nursing knowledge should be strengthened in the teaching of cardiovascular internal medicine nursing and it should be comprehensively organized to improve the quality of nursing services.展开更多
Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global...Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.展开更多
With market competition becoming fiercer,enterprises must update their products by constantly assimilating new big data knowledge and private knowledge to maintain their market shares at different time points in the b...With market competition becoming fiercer,enterprises must update their products by constantly assimilating new big data knowledge and private knowledge to maintain their market shares at different time points in the big data environment.Typically,there is mutual influence between each knowledge transfer if the time interval is not too long.It is necessary to study the problem of continuous knowledge transfer in the big data environment.Based on research on one-time knowledge transfer,a model of continuous knowledge transfer is presented,which can consider the interaction between knowledge transfer and determine the optimal knowledge transfer time at different time points in the big data environment.Simulation experiments were performed by adjusting several parameters.The experimental results verified the model’s validity and facilitated conclusions regarding their practical application values.The experimental results can provide more effective decisions for enterprises that must carry out continuous knowledge transfer in the big data environment.展开更多
Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficul...Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.展开更多
Knowledge transfer within university-led innovative research teams helps to maximally gather knowledge sources and promote knowledge dissemination,exchange and digestion among different disciplines. T he effect of tra...Knowledge transfer within university-led innovative research teams helps to maximally gather knowledge sources and promote knowledge dissemination,exchange and digestion among different disciplines. T he effect of transfer directly affects the team's capacity of knowledge innovation and its outcomes. In this paper,a WSB-based research framework for the influencing factors of knowledge transfer within university-led innovative research teams is established by means of grounded theory with help of in-depth interviews,in which five fundamental categories that affect knowledge transfer within teams,namely,knowledge source,knowledge receiver,knowledge transfer context,knowledge characteristics and knowledge transfer medium,are proposed to elaborate on the relationship between the fundamental categories and the effect of knowledge transfer within teams.Finally,a theoretical saturation test is conducted to verify the rationality and scientific tenability of this theoretical framework.展开更多
Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without an...Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application scenarios.The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context.Moreover,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information.To address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different clauses.Based on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed.The authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events.Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.展开更多
In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors o...In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.展开更多
This article reviewed the principles and outcomes of tendon transfer procedures described in the literature to restore function following injuries delivered in a workshop as a way of improving basic science and anatom...This article reviewed the principles and outcomes of tendon transfer procedures described in the literature to restore function following injuries delivered in a workshop as a way of improving basic science and anatomical knowledge in surgical trainees preparing for surgical examinations. Post intervention surveys showed an improvement in trainees’ familiarity with musculoskeletal anatomy and engagement in learning with improved readiness for surgical examinations.展开更多
Knowledge transfer model of software process improvement (SPI) and the conceptual framework of influencing factors are established. The model includes five elements which are knowledge of transfer, sources of knowledg...Knowledge transfer model of software process improvement (SPI) and the conceptual framework of influencing factors are established. The model includes five elements which are knowledge of transfer, sources of knowledge, recipients of knowledge, relationship of transfer parties, and the environment of transfer. The conceptual framework includes ten key factors which are ambiguity, systematism, transfer willingness, capacity of impartation, capacity of absorption, incen-tive mechanism, culture, technical support, trust and knowledge distance. The research hypothesis is put forward. Em-pirical study concludes that the trust relationship among SPI staffs has the greatest influence on knowledge transfer, and organizational incentive mechanism can produce positive effect to knowledge transfer of SPI. Finally, some sug-gestions are put forward to improve the knowledge transfer of SPI: establishing a rational incentive mechanism, exe-cuting some necessary training to transfer parties and using software benchmarking.展开更多
Knowledge transfer(KT)is an attempt by an entity to copy and utilize an explicit type of knowledge from another entity.The main reason is none other than to expand the ability and increasing the value through inter-or...Knowledge transfer(KT)is an attempt by an entity to copy and utilize an explicit type of knowledge from another entity.The main reason is none other than to expand the ability and increasing the value through inter-organization collaborative affiliation.Nonetheless,questions may arise as to what extent do capabilities,mechanism and performance or success is associated.Using inputs from 154 respondents which consist of various KTP(knowledge transfer program)partners namely from the community(total 94)and industry(total 60),this article highlights the associations between the three main categories of variables.Using Smart PLS(partial least squares),the study provides evidence that academia knowledge,academia readiness,academia skills,and ethics and conduct affect KTP performance through the mediation role of KT mechanism.Academia readiness was also found to be the most significant predictor to KT mechanism.In summary,all the significant capabilities have indirect positive impact towards KTP performance.Thus,higher education institutions must emphasize their internal strength in order to continue supporting the success of inter-organization collaborative affiliation.展开更多
In recent years,knowledge management(KM)theory has become an omnipresent and important element of organisational development.It includes processes intended to improve organisational effectiveness and it describes the ...In recent years,knowledge management(KM)theory has become an omnipresent and important element of organisational development.It includes processes intended to improve organisational effectiveness and it describes the convergence of people,processes,and systems.However,its application is limited to the development of technology for document repository and sharing.To promote new ways of approaching KM,this paper focuses on four knowledge topics:the use of human capital,social capital,structural capital,and artificial intelligence.Accepting that the four components of KM:people,processes,tools,and organisation,are interdependent,nested,and porous,then getting relevant knowledge to those who need it,when they need it,is critical for knowledge transfer.This paper considers whether the recovery of forgotten knowledge will create value for organisations.It proposes a new holistic framework to enhance the transferability of tacit and implicit knowledge in emergency relief organisations.It considers the application of artificial intelligence in the aid sector as a means of achieving this,and it proposes its use for providing ready-to-use knowledge for decision making in emergencies.Using a quantitative and qualitative research approach,this research resolves several ambiguities in the application of the KM discipline within emergency relief organisations.It found that there is no relationship between the employees’age and their attitude to communicating across organisational boundaries to exchange knowledge,yet age is a factor in the use of organisational social networks as a communication tool.Further,it found little difference in the way employees of various designations comprehend the human,structural,and social capital elements of an organisation,yet the importance,selection,and use of each of these elements is dependent on the employees’designation and/or position in the organisational hierarchy.Finally,it found that age is a key factor in the frequency of changing jobs,which contributes to the loss of tacit and implicit knowledge in aid organisations.This paper concludes by providing recommendations for action within each of the five knowledge sharing dimensions:individual,social,managerial,cultural,and structural.展开更多
Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the ...Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.展开更多
In each act or process of knowledge, all components can be classified into two kinds: tacit (implicit) components and focal (explicit) components. This article, first of all introduces the terms of implicit knowledge,...In each act or process of knowledge, all components can be classified into two kinds: tacit (implicit) components and focal (explicit) components. This article, first of all introduces the terms of implicit knowledge, explicit knowledge and their distinctions in the process of English language learning and then provides interactive instruction design to improve learners' communicative competence.展开更多
A mathematical model has been developed to describe the dynamic heat transfer in the clothing microclimate under transient wear conditions. This model is solved numerically by the implicit finite difference method. If...A mathematical model has been developed to describe the dynamic heat transfer in the clothing microclimate under transient wear conditions. This model is solved numerically by the implicit finite difference method. If the physical activity and ambient conditions are specified, the model can predict the thermoregulatory response of the body. Experimental measurements with garments made of fibers with different levels of hygroscopicity are compared with predictions by the model. There is good agreement between prediction and experiment for the temperature of the clothing microclimate.展开更多
文摘Concomitant with the advancement of contemporary medical technology,the significance of perioperative nursing has been increasingly accentuated,necessitating elevated standards for the pedagogy of perioperative nursing.Presently,the PBL(problem-based learning)pedagogical approach,when integrated with CBL(case-based learning),has garnered considerable interest.An extensive literature review has been conducted to analyze the application of the PBL-CBL fusion in the education of perioperative nursing.Findings indicate that this integrative teaching methodology not only enhances students’theoretical knowledge,practical competencies,and collaborative skills but also contributes to the elevation of teaching quality.In conclusion,the PBL-CBL teaching approach holds immense potential for broader application in perioperative nursing education.Nevertheless,it is imperative to continually refine this combined pedagogical strategy to further enhance the caliber of perioperative nursing instruction and to cultivate a greater number of exceptional nursing professionals in the operating room setting.
基金supported by Key Laboratory of Information System Requirement,No.LHZZ202202Natural Science Foundation of Xinjiang Uyghur Autonomous Region(2023D01C55)Scientific Research Program of the Higher Education Institution of Xinjiang(XJEDU2023P127).
文摘In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.
基金Natural Science Foundation of Inner Mongolia(Project No.2023LHMS07016)the Fundamental Research Fund for the directly affiliated university of Inner Mongolia(Project No.2022 JBQN056)。
文摘In the face of intelligent manufacturing(or smart manufacturing)human resource shortage,the training of industrial engineers in the field of intelligent manufacturing is of great significance.In academia,the positive link between learning transfer and knowledge innovation is recognized by most scholars,while the learner’s attitude toward big data decision-making,as a cognitive perception,affects learning transfer from the learner’s experienced engineering paradigm to the intelligent manufacturing paradigm.Thus,learning transfer can be regarded as a result of the learner’s attitude,and it becomes the intermediary state between their attitude and knowledge innovation.This paper reviews prior research on knowledge transfer and develops hypotheses on the relationships between learner acceptance attitude,knowledge transfer,and knowledge innovation.
文摘Objective: To explore the utilization of implicit nursing knowledge in the teaching of cardiovascular internal medicine nursing and to provide a reference for improving the quality and efficiency of cardiovascular internal medicine nursing work. Methods: Thirty-six trainee nurses working in the cardiovascular internal medicine department of our hospital from September 2022 to September 2023 were selected and randomly divided into a control group and an observation group of 18 trainees each. The control adopted the traditional teaching methods while the observation group adopted the implicit nursing knowledge in their clinical practice work. The assessment scores and teamwork ability of the two groups were analyzed and compared. Results: The performance of the observation group was better than that of the control group, and the difference between the two groups was statistically significant (P < 0.05). The teamwork ability of the observation group was significantly better than that of the control group in teamwork ability (P < 0.05). Conclusion: Implicit nursing knowledge teaching is conducive to the cultivation of high-quality nursing talents and meets the development needs of hospitals. Therefore, the importance of implicit nursing knowledge should be strengthened in the teaching of cardiovascular internal medicine nursing and it should be comprehensively organized to improve the quality of nursing services.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006)the National Natural Science Foundation of China (61972102)+1 种基金the Guangzhou Science and Technology Plan Project (023A04J1729)the Science and Technology development fund (FDCT),Macao SAR (015/2020/AMJ)。
文摘Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.
基金supported by the National Natural Science Foundation of China(Grant No.71704016,71331008)the Natural Science Foundation of Hunan Province(Grant No.2017JJ2267)+1 种基金Key Projects of Chinese Ministry of Education(17JZD022)the Project of China Scholarship Council for Overseas Studies(201208430233,201508430121),which are acknowledged.
文摘With market competition becoming fiercer,enterprises must update their products by constantly assimilating new big data knowledge and private knowledge to maintain their market shares at different time points in the big data environment.Typically,there is mutual influence between each knowledge transfer if the time interval is not too long.It is necessary to study the problem of continuous knowledge transfer in the big data environment.Based on research on one-time knowledge transfer,a model of continuous knowledge transfer is presented,which can consider the interaction between knowledge transfer and determine the optimal knowledge transfer time at different time points in the big data environment.Simulation experiments were performed by adjusting several parameters.The experimental results verified the model’s validity and facilitated conclusions regarding their practical application values.The experimental results can provide more effective decisions for enterprises that must carry out continuous knowledge transfer in the big data environment.
基金supported by the National Natural Science Foundation of China (42274144,42304122,and 41974155)the Key Research and Development Program of Shaanxi (2023-YBGY-076)+1 种基金the National Key R&D Program of China (2020YFA0713404)the China Uranium Industry and East China University of Technology Joint Innovation Fund (NRE202107)。
文摘Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.
基金Project supported by the MOE Planned Fund for Humanities and Social Sciences(Project Name:Empirical Research into the Influencing Factors of Knowledge Transfer within University-led Innovative Research TeamsGrant No.:12YJA630169)
文摘Knowledge transfer within university-led innovative research teams helps to maximally gather knowledge sources and promote knowledge dissemination,exchange and digestion among different disciplines. T he effect of transfer directly affects the team's capacity of knowledge innovation and its outcomes. In this paper,a WSB-based research framework for the influencing factors of knowledge transfer within university-led innovative research teams is established by means of grounded theory with help of in-depth interviews,in which five fundamental categories that affect knowledge transfer within teams,namely,knowledge source,knowledge receiver,knowledge transfer context,knowledge characteristics and knowledge transfer medium,are proposed to elaborate on the relationship between the fundamental categories and the effect of knowledge transfer within teams.Finally,a theoretical saturation test is conducted to verify the rationality and scientific tenability of this theoretical framework.
基金National Natural Science Foundation of China,Grant/Award Numbers:61671064,61732005National Key Research&Development Program,Grant/Award Number:2018YFC0831700。
文摘Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application scenarios.The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context.Moreover,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information.To address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different clauses.Based on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed.The authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events.Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.
基金supported by the National Natural Science Foundation of China(62103104)the Natural Science Foundation of Jiangsu Province(BK20210215)the China Postdoctoral Science Foundation(2021M690615).
文摘In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.
文摘This article reviewed the principles and outcomes of tendon transfer procedures described in the literature to restore function following injuries delivered in a workshop as a way of improving basic science and anatomical knowledge in surgical trainees preparing for surgical examinations. Post intervention surveys showed an improvement in trainees’ familiarity with musculoskeletal anatomy and engagement in learning with improved readiness for surgical examinations.
文摘Knowledge transfer model of software process improvement (SPI) and the conceptual framework of influencing factors are established. The model includes five elements which are knowledge of transfer, sources of knowledge, recipients of knowledge, relationship of transfer parties, and the environment of transfer. The conceptual framework includes ten key factors which are ambiguity, systematism, transfer willingness, capacity of impartation, capacity of absorption, incen-tive mechanism, culture, technical support, trust and knowledge distance. The research hypothesis is put forward. Em-pirical study concludes that the trust relationship among SPI staffs has the greatest influence on knowledge transfer, and organizational incentive mechanism can produce positive effect to knowledge transfer of SPI. Finally, some sug-gestions are put forward to improve the knowledge transfer of SPI: establishing a rational incentive mechanism, exe-cuting some necessary training to transfer parties and using software benchmarking.
文摘Knowledge transfer(KT)is an attempt by an entity to copy and utilize an explicit type of knowledge from another entity.The main reason is none other than to expand the ability and increasing the value through inter-organization collaborative affiliation.Nonetheless,questions may arise as to what extent do capabilities,mechanism and performance or success is associated.Using inputs from 154 respondents which consist of various KTP(knowledge transfer program)partners namely from the community(total 94)and industry(total 60),this article highlights the associations between the three main categories of variables.Using Smart PLS(partial least squares),the study provides evidence that academia knowledge,academia readiness,academia skills,and ethics and conduct affect KTP performance through the mediation role of KT mechanism.Academia readiness was also found to be the most significant predictor to KT mechanism.In summary,all the significant capabilities have indirect positive impact towards KTP performance.Thus,higher education institutions must emphasize their internal strength in order to continue supporting the success of inter-organization collaborative affiliation.
文摘In recent years,knowledge management(KM)theory has become an omnipresent and important element of organisational development.It includes processes intended to improve organisational effectiveness and it describes the convergence of people,processes,and systems.However,its application is limited to the development of technology for document repository and sharing.To promote new ways of approaching KM,this paper focuses on four knowledge topics:the use of human capital,social capital,structural capital,and artificial intelligence.Accepting that the four components of KM:people,processes,tools,and organisation,are interdependent,nested,and porous,then getting relevant knowledge to those who need it,when they need it,is critical for knowledge transfer.This paper considers whether the recovery of forgotten knowledge will create value for organisations.It proposes a new holistic framework to enhance the transferability of tacit and implicit knowledge in emergency relief organisations.It considers the application of artificial intelligence in the aid sector as a means of achieving this,and it proposes its use for providing ready-to-use knowledge for decision making in emergencies.Using a quantitative and qualitative research approach,this research resolves several ambiguities in the application of the KM discipline within emergency relief organisations.It found that there is no relationship between the employees’age and their attitude to communicating across organisational boundaries to exchange knowledge,yet age is a factor in the use of organisational social networks as a communication tool.Further,it found little difference in the way employees of various designations comprehend the human,structural,and social capital elements of an organisation,yet the importance,selection,and use of each of these elements is dependent on the employees’designation and/or position in the organisational hierarchy.Finally,it found that age is a key factor in the frequency of changing jobs,which contributes to the loss of tacit and implicit knowledge in aid organisations.This paper concludes by providing recommendations for action within each of the five knowledge sharing dimensions:individual,social,managerial,cultural,and structural.
基金supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004).
文摘Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.
文摘In each act or process of knowledge, all components can be classified into two kinds: tacit (implicit) components and focal (explicit) components. This article, first of all introduces the terms of implicit knowledge, explicit knowledge and their distinctions in the process of English language learning and then provides interactive instruction design to improve learners' communicative competence.
文摘A mathematical model has been developed to describe the dynamic heat transfer in the clothing microclimate under transient wear conditions. This model is solved numerically by the implicit finite difference method. If the physical activity and ambient conditions are specified, the model can predict the thermoregulatory response of the body. Experimental measurements with garments made of fibers with different levels of hygroscopicity are compared with predictions by the model. There is good agreement between prediction and experiment for the temperature of the clothing microclimate.