Introducing the Principles of Tendon Transfer for Surgical Trainees to Improve Anatomical Knowledge

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.

Research into the Influencing Factors of Knowledge Transfer within Innovative Research Teams

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.

Progress and Knowledge Transfer from Science to Technology in the Research Frontier of CRISPR Based on the LDA Model

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.

Time Optimization of Multiple Knowledge Transfers in the Big Data Environment

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.

Optimal Model of Continuous Knowledge Transfer in the Big Data Environment

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.

Knowledge Transfer and Management Consulting:The Effect of Consultant and Client Characteristics

Knowledge transfer(KT)from the consultant to the client is an important area that needs to be repeatedly addressed and thoroughly understood.The aim of this research was to examine the assumption that client characteristics and consultant competencies play a defining role in the effective transfer of knowledge to the client party.The authors examined the critical aspects and competencies required of the consultant,and the characteristics and attitudes required of the client,which would contribute to a successful transfer of knowledge,through unstructured in-depth interviews and concise questionnaires.Eighty consulting assignments were studied from both the client side and the consultant side.A conceptual model is presented,factor analysis was used to validate the constructs,and partial least squares were used to test the model.The findings showed that the consultants’professionalism,skills,and behavior were significant contributors to KT to the client.Surprisingly,neither the consultant knowledge nor client characteristics had any significance to the KT to the client.

Fourth-Order Compact Formulation for the Resolution of Heat Transfer in Natural Convection of Water-Cu Nanofluid in a Square Cavity with a Sinusoidal Boundary Thermal Condition

In the present work, we numerically study the laminar natural convection of a nanofluid confined in a square cavity. The vertical walls are assumed to be insulated, non-conducting, and impermeable to mass transfer. The horizontal walls are differentially heated, and the low is maintained at hot condition (sinusoidal) when the high one is cold. The objective of this work is to develop a new height accurate method for solving heat transfer equations. The new method is a Fourth Order Compact (F.O.C). This work aims to show the interest of the method and understand the effect of the presence of nanofluids in closed square systems on the natural convection mechanism. The numerical simulations are performed for Prandtl number ( ), the Rayleigh numbers varying between and for different volume fractions varies between 0% and 10% for the nanofluid (water + Cu).

A Polarized Radiative Transfer Model Based on Successive Order of Scattering

Based on Successive Order of Scattering approach, a full Vector Radiative Transfer model （SOSVRT） for vertically inhomogeneous plane-parallel media has been developed. To overcome the computational burdens of convergence, a simple approximation technique by truncating scattering orders with a geometric series is used to reduce computational time. Analytical Fourier decomposition of phase matrix with three symmetry relationships and two mutual inverse operators have been implemented to further improve the computational efficiency. To improve the accuracy, a post-processing procedure is implemented to accurately interpolate the Stokes vector at arbitrary angles. Comparisons with the benchmarks for an atmosphere of randomly orientated oblate spheroids show excellent agreement for each Stokes parameter （within 0.1%）. SOSVRT has been tested for different atmospheric condition against RT3, which is based on doubling-adding method, and the results prove that SOSVRT is accurate and much more efficient in vector radiative transfer modeling, especially for optical thin atmosphere, which is the most common case in polarized radiative transfer simulations. SOSVRT is written in Fortran 90 and the code is freely accessible by contacting the author.

Exploring the barriers of utilizing theoretical knowledge in clinical settings: A qualitative study

Objectives: Although effective performance in clinical settings requires the integration between theory and practice,there is a gap between theoretical knowledge as taught in the classroom and what the students experience in clinical settings.This study aimed to elicit and explore the barriers of utilizing theoretical knowledge in clinical settings.Methods: A qualitative study was adopted with a conventional content analysis approach.Fifteen nursing and paramedic's students,faculty members and experienced nursing staff participated in the study.Data were collected by semi-structured individual interviews until data saturation and concurrently analyzed via MAXQDA 10.Results: Five main categories emerged as barriers of utilizing theoretical knowledge in the clinical settings i.e.non-standard practices in clinical settings;lack of trust in clinical competence;lack of perceived professional support;insufficiencies in teaching and learning process;and differences between doing things in simulated and real clinical situations.Conclusion: Transferring theory into practice in a structured manner requires professional support in the workplace,trust and the opportunity for direct experience,using valid and up-to-date knowledge by clinical staff and bridging the simulated situations with real life scenarios.

A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text.However,only some labeled data for agricultural knowledge graph domain training are available.Furthermore,labeling is costly due to the need for more data openness and standardization.This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction.Knowledge distillation between the target and source data domain is performed,where Bi-LSTM and CRF models are constructed for entity recognition.The experimental result is shown that we only need to label less than one-tenth of the data for model training.Furthermore,the agricultural domain ontology is constructed by BILSTM-CRF named entity recognition model and relationship extraction model.Moreover,there are a total of 13,983 entities and 26,498 relationships built in the neo4j graph database.

Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

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.

Scene image recognition with knowledge transfer for drone navigation

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.

Knowledge transfer in multi-agent reinforcement learning with incremental number of agents

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.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

Decision Model of Knowledge Transfer in Big Data Environment

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.

Attribute Disturbance of Knowledge and Attribute Disturbance Theorems

由使用一个方向的动态特征S不平的集合(一个方向单个不平的集合)并且一个方向双S不平的集合(一个方向双单个不平的集合)，知识的属性骚乱的概念，知识的属性骚乱度，并且知识的骚乱系数是采用这些概念的 given.By ，属性骚乱知识的最重要的顺序定理，属性骚乱知识的联合起来的圆定理。

Modeling the factors that influence knowledge transfer in mergers and acquisitions

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.

Knowledge exchange:A strategy for open access success at The University of Hong Kong

The University of Hong Kong’s statement on vision now has three themes:1) Research, 2) teaching & learning, and 3) knowledge exchange(KE). KE emphasizes HKU’s desire to interact with its community for a mutual benefit. A new five-year strategic plan(2009-2014) sets out operational priorities and key indicators to enable knowledge exchange at HKU. Chief among these is the establishment of an exchange hub to make HKU researchers and their research products highly visible. The institutional repository of HKU, the HKU Scholars Hub, developed by its University Libraries, has become this KE exchange hub. Now the Hub includes HKU ResearcherPages, featuring the accomplishments of each HKU professoriate staff. HKU’s policy on knowledge exchange and the HKU ResearcherPages have increased the incentive for faculties, departments, and authors to place more items in open access(OA). This paper will discuss what KE is, the benefits it can bring to the university and its reputation, and how it can increase OA deposit.

Transformation and influencing factors of scholarly communication based on knowledge transfer: A case study of science and technology literature

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.

Synthesis of End Functional Polymers via Atom Transfer Radical Polymerization in Immobilized Catalytic System

Cross-linked polystyrene with azo-crown ether functional side chain (PSt-1, 10-dicarbonyl-3,6,9-trizaocylcodecane) was prepared under microwave irradiation and the structure was characterized through FT-IR and element analysis. The functionalized cross-linked polystyrene (cross-link degree, 3.5%) combining with immobilized catalyst system (CuBr and ethyl α-bromo-isobutyrate) can catalyze atom transfer radical polymerization of Styrene. Neat polymer products can be obtained then. Complex of La and the polymer end group (EBiB) was synthesized. The third order nonlinear optical property of the polymer-La complex was investigated and the structure was also characterized by FT-IR and XPS.