The excited state dynamics and critically regulated factors of reverse intersystem crossing(RISC)in through-space charge transfer(TSCT)molecules have received insufficient attention.Here,five molecules of through spac...The excited state dynamics and critically regulated factors of reverse intersystem crossing(RISC)in through-space charge transfer(TSCT)molecules have received insufficient attention.Here,five molecules of through space/bond charge transfer inducing thermally activated delayed fluorescence(TADF)are prepared,and their excited state charge transfer processes are studied by ultrafast transient absorption and theoretical calculations.DM-Z has a largerΔEST,leading to a longer lifetime of intersystem crossing(ISC),resulting in the lowest photoluminescence quantum yield(PLQY).Oppositely,ISC and RISC are demonstrated to take place with shorter lifetimes for TSCT molecules.The face-to-faceπ-πstacking interactions and electron communication enable DM-B and DM-BX to have an efficient RISC,increasing the weight coefficient of RISC from 1.7%(DM-X)to close to 50%(DM-B and DM-BX)in the solvents,which make DM-BX and DM-B to have a high PLQY.However,partial local excitation in the donor center is observed and the charge transfer is decreased for DM-G and DM-X.The triplet excited state(DM-G)or singlet excited state(DM-X)mainly undergoes inactivation through a non-radiative relaxation process,resulting in less RISC and low PLQY.This work provides theoretical hints to enhance the RISC process in the TADF materials.展开更多
A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterpr...A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.展开更多
Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/m...Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.展开更多
In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfe...In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.展开更多
In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with...In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others,which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.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
The reverse atom transfer radical polymerization(RATRP) of (-)-menthyl methacrylate ((-)-MnMA) with AIBN(AIBN/CuCl2/bipyridine(bipy) or (-)sparteine((-)Sp) =1/2/4) initiating system in THF has been studied. The depen...The reverse atom transfer radical polymerization(RATRP) of (-)-menthyl methacrylate ((-)-MnMA) with AIBN(AIBN/CuCl2/bipyridine(bipy) or (-)sparteine((-)Sp) =1/2/4) initiating system in THF has been studied. The dependence of the specific rotation on molecular weight was investigated.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
In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attac...In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.展开更多
Thin-film composite(TFC)reverse osmosis(RO)membranes have attracted considerable attention in water treatment and desalination processes due to their specific separation advantages.Nevertheless,the trade-off effect be...Thin-film composite(TFC)reverse osmosis(RO)membranes have attracted considerable attention in water treatment and desalination processes due to their specific separation advantages.Nevertheless,the trade-off effect between water flux and salt rejection poses huge challenges to further improvement in TFC RO membrane performance.Numerous research works have been dedicated to optimizing membrane fabrication and modification for addressing this issue.In the meantime,several reviews summarized these approaches.However,the existing reviews seldom analyzed these methods from a theoretical perspective and thus failed to offer effective optimization directions for the RO process from the root cause.In this review,we first propose a mass transfer model to facilitate a better understanding of the entire process of how water and solute permeate through RO membranes in detail,namely the migration process outside the membrane,the dissolution process on the membrane surface,and the diffusion process within the membrane.Thereafter,the water and salt mass transfer behaviors obtained from model deduction are comprehensively analyzed to provide potential guidelines for alleviating the trade-off effect between water flux and salt rejection in the RO process.Finally,inspired by the theoretical analysis and the accurate identification of existing bottlenecks,several promising strategies for both regulating RO membranes and optimizing operational conditions are proposed to further exploit the potential of RO membrane performance.This review is expected to guide the development of high-performance RO membranes from a mass transfer theory standpoint.展开更多
The interaction between metal and support is critical in oxygen catalysis as it governs the charge transfer between these two entities,influences the electronic structures of the supported metal,affects the adsorption...The interaction between metal and support is critical in oxygen catalysis as it governs the charge transfer between these two entities,influences the electronic structures of the supported metal,affects the adsorption energies of reaction intermediates,and ultimately impacts the catalytic performance.In this study,we discovered a unique charge transfer reversal phenomenon in a metal/carbon nanohybrid system.Specifically,electrons were transferred from the metal-based species to N-doped carbon,while the carbon support reciprocally donated electrons to the metal domain upon the introduction of nickel.This led to the exceptional electrocatalytic performances of the resulting Ni-Fe/Mo_(2)C@nitrogen-doped carbon catalyst,with a half-wave potential of 0.91 V towards oxygen reduction reaction(ORR)and a low overpotential of 290 m V at 10 mA cm^(-2)towards oxygen evolution reaction(OER)under alkaline conditions.Additionally,the Fe-Ni/Mo_(2)C@carbon heterojunction catalyst demonstrated high specific capacity(794 mA h g_(Zn)~(-1))and excellent cycling stability(200 h)in a Zn-air battery.Theoretical calculations revealed that Mo_(2)C effectively inhibited charge transfer from Fe to the support,while secondary doping of Ni induced a charge transfer reversal,resulting in electron accumulation in the Fe-Ni alloy region.This local electronic structure modulation significantly reduced energy barriers in the oxygen catalysis process,enhancing the catalytic efficiency of both ORR and OER.Consequently,our findings underscore the potential of manipulating charge transfer reversal between the metal and support as a promising strategy for developing highly-active and durable bi-functional oxygen electrodes.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
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
This paper constructs a model on the factors that influence knowledge transfer in mergers and acquisitions(M&A) and validates it via questionnaire surveys. Using 125valid collected questionnaires, multiple linear ...This paper constructs a model on the factors that influence knowledge transfer in mergers and acquisitions(M&A) and validates it via questionnaire surveys. Using 125valid collected questionnaires, multiple linear regression analysis and hierarchical regression analysis showed that five out of the ten factors had a positive effect on knowledge transfer effect. The ranking of factor importance, from high to low, was knowledge explicitness, relationship quality, learning intent, advanced transfer activities, and learning capability, which is fairly consistent with positive factors observed in other interorganizational knowledge transfer researches. Our results also showed that one of the control variables(size of acquired firm) had neither a direct or indirect effect on knowledge transfer in M&A. Additionally, our research found that knowledge distance and degree of M&A integration had a positive influence on knowledge transfer effect at the early stage after M&A, but had a negative influence at the late stage. Based on this research, several suggestions for knowledge transfer in M&A are proposed.展开更多
Purpose: The process of scientific literature use can be regarded as that of knowledge transfer. With the help of the knowledge transfer theory and data from scientific literature databases, we explored the behavior o...Purpose: The process of scientific literature use can be regarded as that of knowledge transfer. With the help of the knowledge transfer theory and data from scientific literature databases, we explored the behavior of scientific researchers during their scholarly communication, and studied the factors that influenced the behavior of researchers under network environment. Design/methodology/approach: Based on the literature databases of CNKI, Elsevier Science Direct and Springer Link, we used the knowledge transfer theory to construct a model for describing the scholarly communication process, which attempts to find out factors that may influence the communication behavior of researchers. With a focus laid on the absorption behavior of researchers during the knowledge acceptance process, we defined the independent variables of the model and proposed hypotheses on the basis of a comprehensive literature study. Afterwards, college students were invited to participate in a questionnaire survey, which was designed to prove our research model and hypotheses.Findings: Our results showed that during the scholarly communication, it is not the professional knowledge, but the ability and willingness for knowledge acceptance, organizations’ importance and internal atmosphere as well as knowledge authority and relevance that have played a positive significant role in the knowledge transfer performance. In addition, our distance indicators showed that knowledge distance and knowledge transfer performance have significant negative correlations. Research limitations: This study is mainly based on a questionnaire survey of college students, which may limit the generalization of our research results. In addition, more resource types need be considered for further studies.Practical implications: Under network environment, scholarly communication performance based on knowledge transfer theory could greatly contribute to the enrichment of the contentof the knowledge transfer theory, and stretch out the range of the field. In addition, our result could help commercial scientific database providers to learn more about the users’ needs, which would not only benefit both scientific communities and content providers, but also promote scholarly communication effectively. Originality/value: Compared with existing researches which mainly emphasized the model construction of scholarly communication, our study focused the knowledge relevance during the scholarly communication and influence factors that impacted on the performance of knowledge acceptance under the network environment, which could provide helpful guides for further studies.展开更多
Knowledge transfer is widely emphasized as a strategic issue for firm competition. A model for intra-firm horizontal knowledge transfer is proposed to model horizontal knowledge transfer to solve some demerits in curr...Knowledge transfer is widely emphasized as a strategic issue for firm competition. A model for intra-firm horizontal knowledge transfer is proposed to model horizontal knowledge transfer to solve some demerits in current knowledge transfer researches. The concept model of intra-firm horizontal knowledge transfer was described and a framework was provided to define the main components of the transfer process. Horizontal knowledge transfer is that knowledge is transferred from the source to the same hierarchical level recipients as the target. Horizontal knowledge transfer constitutes a strategic area of knowledge management research. However, little is known about the circumstances under which one particular mechanism is the most appropriate. To address these issues, some significant conclusions are drawn concerning knowledge transfer mechanisms in a real-world setting.展开更多
A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process an...A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process and knowledge transfer attributes,a special type of knowledge field(KF)is introduced and the knowledge diffusion equation(KDE)is developed.The evolution of knowledge potential is modeled by lattice kinetic equation and verified by numerical experiments.The new equation-based modeling developed in this paper is meaningful to simulate and predict the knowledge transfer process in firms.The development of the lattice kinetic model(LKM)for knowledge transfer can contribute to the knowledge management theory,and the managers can also simulate the knowledge accumulation process by using the LKM.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
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
基金supported by the National Natural Science Foundation of China(No.22273057)the Universities Joint Laboratory of Guangdong,Hong Kong and Macao(No.2021LSYS009)+2 种基金the Natural Science Foundation of Guangdong Province(Nos.2022A1515011661,2023A1515012631)the Chemistry and Chemical Engineering Guangdong Laboratory(No.1922003)Guangdong Major Project of Basic and Applied Basic Research(No.2019B030302009)。
文摘The excited state dynamics and critically regulated factors of reverse intersystem crossing(RISC)in through-space charge transfer(TSCT)molecules have received insufficient attention.Here,five molecules of through space/bond charge transfer inducing thermally activated delayed fluorescence(TADF)are prepared,and their excited state charge transfer processes are studied by ultrafast transient absorption and theoretical calculations.DM-Z has a largerΔEST,leading to a longer lifetime of intersystem crossing(ISC),resulting in the lowest photoluminescence quantum yield(PLQY).Oppositely,ISC and RISC are demonstrated to take place with shorter lifetimes for TSCT molecules.The face-to-faceπ-πstacking interactions and electron communication enable DM-B and DM-BX to have an efficient RISC,increasing the weight coefficient of RISC from 1.7%(DM-X)to close to 50%(DM-B and DM-BX)in the solvents,which make DM-BX and DM-B to have a high PLQY.However,partial local excitation in the donor center is observed and the charge transfer is decreased for DM-G and DM-X.The triplet excited state(DM-G)or singlet excited state(DM-X)mainly undergoes inactivation through a non-radiative relaxation process,resulting in less RISC and low PLQY.This work provides theoretical hints to enhance the RISC process in the TADF materials.
基金supported by NSFC(Grant No.71373032)the Natural Science Foundation of Hunan Province(Grant No.12JJ4073)+3 种基金the Scientific Research Fund of Hunan Provincial Education Department(Grant No.11C0029)the Educational Economy and Financial Research Base of Hunan Province(Grant No.13JCJA2)the Project of China Scholarship Council for Overseas Studies(201208430233201508430121)
文摘A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.
基金supported by the National Natural Science Foundation of China,Grant numbers:71974167 and 71573225。
文摘Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.
基金supported by the National Natural Science Foundation ofChina (Grant No. 71704016,71331008, 71402010)the Natural Science Foundation of HunanProvince (Grant No. 2017JJ2267)+1 种基金the Educational Economy and Financial Research Base ofHunan Province (Grant No. 13JCJA2)the Project of China Scholarship Council forOverseas Studies (201508430121, 201208430233).
文摘In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.
基金supported by the National Key R&D Program of China (2018AAA0101400)the National Natural Science Foundation of China (62173251+3 种基金61921004U1713209)the Natural Science Foundation of Jiangsu Province of China (BK20202006)the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control。
文摘In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others,which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.
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.
文摘The reverse atom transfer radical polymerization(RATRP) of (-)-menthyl methacrylate ((-)-MnMA) with AIBN(AIBN/CuCl2/bipyridine(bipy) or (-)sparteine((-)Sp) =1/2/4) initiating system in THF has been studied. The dependence of the specific rotation on molecular weight was investigated.
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This work was supported by the National Key Research and Development Program(No.2016YFB0901900)the National Natural Science Foundation of China(No.61733018)The authors would like to thank Prof.Peng Yi for his helpful suggestions.
文摘In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.
基金supported by the Natural Science Foundation of Sichuan Province(No.2022NSFSC1042)National Natural Science Foundation of China(No.52200051)+1 种基金Outstanding Youth Fund of Heilongjiang Natural Science Foundation(No.YQ2023E021)Open Project of State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(No.HC202236).
文摘Thin-film composite(TFC)reverse osmosis(RO)membranes have attracted considerable attention in water treatment and desalination processes due to their specific separation advantages.Nevertheless,the trade-off effect between water flux and salt rejection poses huge challenges to further improvement in TFC RO membrane performance.Numerous research works have been dedicated to optimizing membrane fabrication and modification for addressing this issue.In the meantime,several reviews summarized these approaches.However,the existing reviews seldom analyzed these methods from a theoretical perspective and thus failed to offer effective optimization directions for the RO process from the root cause.In this review,we first propose a mass transfer model to facilitate a better understanding of the entire process of how water and solute permeate through RO membranes in detail,namely the migration process outside the membrane,the dissolution process on the membrane surface,and the diffusion process within the membrane.Thereafter,the water and salt mass transfer behaviors obtained from model deduction are comprehensively analyzed to provide potential guidelines for alleviating the trade-off effect between water flux and salt rejection in the RO process.Finally,inspired by the theoretical analysis and the accurate identification of existing bottlenecks,several promising strategies for both regulating RO membranes and optimizing operational conditions are proposed to further exploit the potential of RO membrane performance.This review is expected to guide the development of high-performance RO membranes from a mass transfer theory standpoint.
基金financially supported by the Outstanding Youth Scientific Research Project for Colleges and Universities of Anhui Province of China (2022AH020054)the Anhui Provincial Natural Science Foundation (2208085Y06)+2 种基金the National Natural Science Foundation of China (Nos.21975001 and U2002213)the Support Program of Excellent Young Talents in Anhui Provincial Colleges and Universities (gxyq ZD2022034)the Double Tops Joint Fund of the Yunnan Science and Technology Bureau and Yunnan University (2019FY003025)。
文摘The interaction between metal and support is critical in oxygen catalysis as it governs the charge transfer between these two entities,influences the electronic structures of the supported metal,affects the adsorption energies of reaction intermediates,and ultimately impacts the catalytic performance.In this study,we discovered a unique charge transfer reversal phenomenon in a metal/carbon nanohybrid system.Specifically,electrons were transferred from the metal-based species to N-doped carbon,while the carbon support reciprocally donated electrons to the metal domain upon the introduction of nickel.This led to the exceptional electrocatalytic performances of the resulting Ni-Fe/Mo_(2)C@nitrogen-doped carbon catalyst,with a half-wave potential of 0.91 V towards oxygen reduction reaction(ORR)and a low overpotential of 290 m V at 10 mA cm^(-2)towards oxygen evolution reaction(OER)under alkaline conditions.Additionally,the Fe-Ni/Mo_(2)C@carbon heterojunction catalyst demonstrated high specific capacity(794 mA h g_(Zn)~(-1))and excellent cycling stability(200 h)in a Zn-air battery.Theoretical calculations revealed that Mo_(2)C effectively inhibited charge transfer from Fe to the support,while secondary doping of Ni induced a charge transfer reversal,resulting in electron accumulation in the Fe-Ni alloy region.This local electronic structure modulation significantly reduced energy barriers in the oxygen catalysis process,enhancing the catalytic efficiency of both ORR and OER.Consequently,our findings underscore the potential of manipulating charge transfer reversal between the metal and support as a promising strategy for developing highly-active and durable bi-functional oxygen electrodes.
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.
基金supported by the National Planning Office of Philosophy and Social Science(Grant No.07BTQ011)
文摘This paper constructs a model on the factors that influence knowledge transfer in mergers and acquisitions(M&A) and validates it via questionnaire surveys. Using 125valid collected questionnaires, multiple linear regression analysis and hierarchical regression analysis showed that five out of the ten factors had a positive effect on knowledge transfer effect. The ranking of factor importance, from high to low, was knowledge explicitness, relationship quality, learning intent, advanced transfer activities, and learning capability, which is fairly consistent with positive factors observed in other interorganizational knowledge transfer researches. Our results also showed that one of the control variables(size of acquired firm) had neither a direct or indirect effect on knowledge transfer in M&A. Additionally, our research found that knowledge distance and degree of M&A integration had a positive influence on knowledge transfer effect at the early stage after M&A, but had a negative influence at the late stage. Based on this research, several suggestions for knowledge transfer in M&A are proposed.
基金jointly supported by the National Natural Science Foundation of China(Grant No:71373124)Assemble Technology Infrastructure Projects(Grant No.QTQNJ20121QB04)
文摘Purpose: The process of scientific literature use can be regarded as that of knowledge transfer. With the help of the knowledge transfer theory and data from scientific literature databases, we explored the behavior of scientific researchers during their scholarly communication, and studied the factors that influenced the behavior of researchers under network environment. Design/methodology/approach: Based on the literature databases of CNKI, Elsevier Science Direct and Springer Link, we used the knowledge transfer theory to construct a model for describing the scholarly communication process, which attempts to find out factors that may influence the communication behavior of researchers. With a focus laid on the absorption behavior of researchers during the knowledge acceptance process, we defined the independent variables of the model and proposed hypotheses on the basis of a comprehensive literature study. Afterwards, college students were invited to participate in a questionnaire survey, which was designed to prove our research model and hypotheses.Findings: Our results showed that during the scholarly communication, it is not the professional knowledge, but the ability and willingness for knowledge acceptance, organizations’ importance and internal atmosphere as well as knowledge authority and relevance that have played a positive significant role in the knowledge transfer performance. In addition, our distance indicators showed that knowledge distance and knowledge transfer performance have significant negative correlations. Research limitations: This study is mainly based on a questionnaire survey of college students, which may limit the generalization of our research results. In addition, more resource types need be considered for further studies.Practical implications: Under network environment, scholarly communication performance based on knowledge transfer theory could greatly contribute to the enrichment of the contentof the knowledge transfer theory, and stretch out the range of the field. In addition, our result could help commercial scientific database providers to learn more about the users’ needs, which would not only benefit both scientific communities and content providers, but also promote scholarly communication effectively. Originality/value: Compared with existing researches which mainly emphasized the model construction of scholarly communication, our study focused the knowledge relevance during the scholarly communication and influence factors that impacted on the performance of knowledge acceptance under the network environment, which could provide helpful guides for further studies.
文摘Knowledge transfer is widely emphasized as a strategic issue for firm competition. A model for intra-firm horizontal knowledge transfer is proposed to model horizontal knowledge transfer to solve some demerits in current knowledge transfer researches. The concept model of intra-firm horizontal knowledge transfer was described and a framework was provided to define the main components of the transfer process. Horizontal knowledge transfer is that knowledge is transferred from the source to the same hierarchical level recipients as the target. Horizontal knowledge transfer constitutes a strategic area of knowledge management research. However, little is known about the circumstances under which one particular mechanism is the most appropriate. To address these issues, some significant conclusions are drawn concerning knowledge transfer mechanisms in a real-world setting.
基金supported by the National Natural Science Foundation of China(71472055 71871007)+2 种基金National Social Science Foundation of China(16AZD0006)Heilongjiang Philosophy and Social Science Research Project(19GLB087)the Fundamental Research Funds for the Central Universities(HIT.NSRIF.2019033)
文摘A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process and knowledge transfer attributes,a special type of knowledge field(KF)is introduced and the knowledge diffusion equation(KDE)is developed.The evolution of knowledge potential is modeled by lattice kinetic equation and verified by numerical experiments.The new equation-based modeling developed in this paper is meaningful to simulate and predict the knowledge transfer process in firms.The development of the lattice kinetic model(LKM)for knowledge transfer can contribute to the knowledge management theory,and the managers can also simulate the knowledge accumulation process by using the LKM.
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.
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.
