Effectiveness of structured teaching program on knowledge and attitude toward foot care

Objective:The study was aimed to assess the knowledge and attitude toward foot care and evaluate the effectiveness of structured teaching program(STP)on the knowledge and attitude of patients with diabetes mellitus regarding foot care.Materials and Methods:A preexperimental research design was used in this study,which was conducted in medical-surgical wards of a secondary hospital in Northeast India,Nagaland.A total of 30 subjects were recruited by consecutive sampling technique.A closed-ended structured questionnaire and 5-point Likert scale was used to assess knowledge and attitude toward foot care.A STP was administered after which posttest was done after 24 h.Results:The majority(86.6%)of the participants in the pretest had inadequate knowledge and 80%had negative attitudes toward foot care.After STP,70%had moderately adequate knowledge and 53.3%had a positive attitude regarding foot care.The structured teaching program was effective in improving the knowledge and attitude at P<0.01 which is highly significant and there was a significant association between the pretest level knowledge and duration of diabetes at P<0.01.Conclusion:STP was effective in enhancing the level of knowledge and attitude toward foot care among diabetic inpatients.This type of interaction can be organized at regular intervals to reinforce knowledge and attitude toward foot care among inpatients.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

A Practical Study of Knowledge Mapping Empowering the Construction of Engineering Costing Software Application Courses in Undergraduate Colleges and Universities

Relying on the talent cultivation program of Chongqing Institute of Engineering,this paper combines the characteristics of the Engineering Costing Software Application course with its current status quo,analyzes the existing problems and teaching pain points,and introduces knowledge mapping.The course construction is carried out in terms of the combing of knowledge points,the optimization of the course structure,the innovation of the teaching method,and the evaluation and feedback of the teaching in order to improve the systematicity and structural nature of the teaching content,enhance students’learning experience,and enhance their learning effect.It is conducive to the formation of students’knowledge systems and provides a basis for discussion and reference for improving the quality of teaching and cultivating application-oriented talents who meet the market demand.

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Structure of Chinese City Network as Driven by Technological Knowledge Flows

Based on patent cooperation data,this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows.The results revealed the spatial structure,composition structure,hierarchical structure,group structure,and control structure of Chinese city network,as well as its dynamic factors.The major findings are:1) the spatial pattern presents a diamond structure,in which Wuhan is the central city;2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network,it is weaker than the utility model patent;3) as the senior level cities,Beijing,Shanghai and the cities in the Zhujiang(Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge;4) whilst a national technology alliance has preliminarily formed,regional alliances have not been adequately established;5) even though the cooperation level amongst weak connection cities is not high,such cities still play an important role in the network as a result of their location within ′structural holes′ in the network;and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity,hierarchical proximity and technological proximity.

Safety evaluation system for hydraulic metal structures based on knowledge engineering

A comprehensive safety evaluation system taking the most influential factors into account has been developed to evaluate the reliability of hydraulic metal structures. Applying the techniques of AI and DB, the idea of a one-machine and three-base system is proposed. The framework of the three-base system has been designed and the structural framework constructed in turn. A practical example is given to illustrate the process of using this system and it can be used for comparison and analysis purposes. The key technology of the system is its ability to reorganize and improve the expert system＇s knowledge base by establishing the expert system. This system utilizes the computer technology inference process, making safety evaluation conclusions more reasonable and applicable to the actual situation. The system is not only advanced, but also feasible, reliable, artificially intelligent, and has the capacity to constantly grow.

Evolution of the Socio-cognitive Structure of Knowledge Management(1986–2015): An Author Co-citation Analysis

Purpose: The evolution of the socio-cognitive structure of the field of knowledge management(KM) during the period 1986–2015 is described. Design/methodology/approach: Records retrieved from Web of Science were submitted to author co-citation analysis(ACA) following a longitudinal perspective as of the following time slices: 1986–1996, 1997–2006, and 2007–2015. The top 10% of most cited first authors by sub-periods were mapped in bibliometric networks in order to interpret the communities formed and their relationships.Findings: KM is a homogeneous field as indicated by networks results. Nine classical authors are identified since they are highly co-cited in each sub-period, highlighting Ikujiro Nonaka as the most influential authors in the field. The most significant communities in KM are devoted to strategic management, KM foundations, organisational learning and behaviour, and organisational theories. Major trends in the evolution of the intellectual structure of KM evidence a technological influence in 1986–1996, a strategic influence in 1997–2006, and finally a sociological influence in 2007–2015.Research limitations: Describing a field from a single database can offer biases in terms of output coverage. Likewise, the conference proceedings and books were not used and the analysis was only based on first authors. However, the results obtained can be very useful to understand the evolution of KM research.Practical implications: These results might be useful for managers and academicians to understand the evolution of KM field and to(re)define research activities and organisational projects.Originality/value: The novelty of this paper lies in considering ACA as a bibliometric technique to study KM research. In addition, our investigation has a wider time coverage than earlier articles.

Structure, Evolution and Hot Spots of Cooperation Innovation Knowledge Network

Based on the statistics analysis of cooperation innovation articles, the paper analyses the distributing characteristics of cooperation innovation research papers. The distribution, structure and evolution of cooperation innovation research are studied through the social network and co-word method, combined with the information visualization technology. This study is based on the papers in the SSCI database during 2000-2014. The analysis and statistics of papers reveal distribution characteristics of the subject, time, institutions, countries and areas. The statistics shows that the research perspective of cooperation innovation from business and economics. The time distribution presents that research of cooperation innovation attracts enormous attention and the research achievement is increasing. From the distribution of organizations, countries and areas, North America and Europe countries maintain the leading position in this research field. But institutions and universities of China and other Asian countries or areas are witnessed their outstanding achievements in cooperation innovation research. Combining the co-word network analysis method, the paper studies the structure and content of cooperation innovation knowledge network in five stages and makes the net spectrum, visually showing the hot spots at various periods. The top highly cited papers in all five stages are reviewed simultaneously;their research hot spots and evolution process are concluded. The study shows 11 subjects including strategic alliances, social networks, R&D cooperation, R&D, technology transfer, alliances, knowledge management, social capital, entrepreneurship, trust, biotechnology are always the central issues in last 15 years. The focus of research is from the relationship between alliance and technology innovation to networking and knowledge innovation and then to open innovation.

A Systemic Functional Linguistics Approach to Knowledge Structure in Educational Discourse

This paper makes a literature review of Bernstein＇s knowledge structure theory and its influence on systemic functional linguistics. Then it explores systemic functional linguists＇ research on knowledge building and discusses Martin＇s theory of field as a powerful tool to analyze the knowledge structure in educational discourse.

Effectiveness of structured teaching programme on knowledge regarding therapeutic nurse- patient communication among the staff nurses

Introduction:The therapeutic nurse patient relationship is viewed as central or foundational to the practice of nursing.Such relationship not only improve patients’physical and emotional state but also facilitate their adjustments to their illness,ease pain and can ultimately lead to a good health experience.Methodology:The pre experimental one group pre test post test study was undertaken to assess the effectiveness of structured teaching programme on knowledge regarding therapeutic nurse-patient communication among the staff nurses working in SGT Hospital,Haryana.Total 30 study participants were selected by convenient sampling technique and Data were collected by using a structured knowledge questionnaire.Result:The findings of the study revealed that half of the participants 50%had average and poor knowledge regarding therapeutic nurse patient communication in the pre test.Similarly,the post test scores depicted that majority(63%)had average knowledge and 37%had good knowledge regarding therapeutic nurse patient communication.Most of the demographic variable was not significantly associated with level of pre knowledge of staff nurses regarding therapeutic nurse patient communication.Only staff nurses years of experience P=0.021 were found to statistically significant at 0.05 level of significance.There was significant difference between the knowledge of staff nurses before and after the implementation of structured teaching programme(t=11.844,P=0.0001).Conclusion:It was concluded that structured teaching programme was effective strategy which can help to increase their knowledge regarding therapeutic nurse patient communication.

Study on the Interior Market Network Structure and Its Efficiency in a Knowledge Based Company

A knowledge based company is the microcosmic foundation of the knowledge economy, the design of its organization structure should amplify the company competence to be agile to the knowledge elements. This paper expounds an interior market network structure which is fit for the company intellectual capital operation, and analyses this organization pattern about the reasons of existence, the effectiveness of growing up in scale, the economies of knowledge distribution and the efficiency of operation, and it will provide some beneficial theoretical guidance about how can a company improve its competition competence in the knowledge environment through organization innovation.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Structural choice based on knowledge discovery system

Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self organizing, self learning and open.

Generation of scale-free knowledge network with local world mechanism

In order to simulate the real growing process, a new type of knowledge network growth mechanism based on local world connectivity is constructed. By the mean-field method, theoretical prediction of the degree distribution of the knowledge network is given, which is verified by Matlab simulations. When the new added node＇s local world size is very small, the degree distribution of the knowledge network approximately has the property of scale-free. When the new added node＇s local world size is not very small, the degree distribution transforms from pure power-law to the power-law with an exponential tailing. And the scale-free index increases as the number of new added edges decreases and the tunable parameters increase. Finally, comparisons of some knowledge indices in knowledge networks generated by the local world mechanism and the global mechanism are given. In the long run, compared with the global mechanism, the local world mechanism leads the average knowledge levels to slower growth and brings homogenous phenomena.

Acquisition and Active Navigation of Knowledge Particles throughout Product Variation Design Process

The variation design of complex products has such features as multivariate association, weak theory coupling and implicit knowledge iteration. However, present CAD soft wares are still restricted to making decisions only according to current design status in dynamic navigation which leads to the huge drain of the knowledge hidden in design process. In this paper, a method of acquisition and active navigation of knowledge particles throughout product variation design process is put forward. The multi-objective decision information model of the variation design is established via the definition of condition attribute set and decision attribute set in finite universe. The addition and retrieval of the variation semantics is achieved through bidirectional association between the transplantable structures and variation design semantics. The mapping relationships between the topology lapping geometry elements set and constraint relations set family is built by means of geometry feature analysis. The acquisition of knowledge particles is implemented by attribute reduction based on rough set theory to make multi-objective decision of variation design. The topology lapping status of transplantable substructures is known from DOF reduction. The active navigation of knowledge particles is realized through embedded event-condition-action（ECA） rules. The independent prototype system taking Alan, Charles, Ian＇s system（ACIS） as kernel has been developed to verify the proposed method by applying variation design of complex mechanical products. The test results demonstrate that the navigation decision basis can be successfully extended from static isolated design status to dynamic continuous design process so that it more flexibly adapts to the different designers and various variation design steps. It is of profound significance for enhancing system intelligence as well as improving design quality and efficiency.

Knowledge-Based Multifaceted Modeling Methodology for Open Complex Giant Systems

In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.

Extension of Rough Set on Granular Structures and Axiomatic Characterizations

In granular computing granular structures represent knowledge on universe,in this paper several important granular structures are considered.In a general granular structure the notions of interior point, accumulation point and boundary point etc are proposed,by use of these notions and referring to topological method,the lower and upper approximations of a subset of universe are defined such that they are one kind of generalization of the existing approximations based on some special granular structure.Basic properties of new rough set approximations are investigated.Furthermore,granular structures on universe are characterized by the lower and upper approximation operators.

The Relationship between Assessment Skills and Nursing Knowledge

This research aimed to clarify the relationship between nursing students’ levels of structural knowledge and assessment skills. Participants were 17 third-year university students majoring in nursing who participated individually in the experiments. The experiments included a nursing-scene task, free-recall task, and sorting task. Experiments were conducted before and after their clinical practice. Each student’s level of structural knowledge was calculated from the free-recall and sorting task responses, and each student’s assessment skill was calculated from the nursing-scene task responses. Levels of structural knowledge were significantly higher in post-examination compared with pre-examination (p p n = 5) had significantly higher scores for their conclusions and reasons than the low-structured group (n = 5) (p p < 0.10). Well-structured knowledge of students in the high-structured group seemed to enable them to acquire and activate highly related information during assessment. Students in the high-structured group apparently made assessments not only by using information given but also by over viewing information comprehensively.

Knowledge-based potentials in bioinformatics: From a physicist's viewpoint

Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.

A Rule Management System for Knowledge Based Data Cleaning

In this paper, we propose a rule management system for data cleaning that is based on knowledge. This system combines features of both rule based systems and rule based data cleaning frameworks. The important advantages of our system are threefold. First, it aims at proposing a strong and unified rule form based on first order structure that permits the representation and management of all the types of rules and their quality via some characteristics. Second, it leads to increase the quality of rules which conditions the quality of data cleaning. Third, it uses an appropriate knowledge acquisition process, which is the weakest task in the current rule and knowledge based systems. As several research works have shown that data cleaning is rather driven by domain knowledge than by data, we have identified and analyzed the properties that distinguish knowledge and rules from data for better determining the most components of the proposed system. In order to illustrate our system, we also present a first experiment with a case study at health sector where we demonstrate how the system is useful for the improvement of data quality. The autonomy, extensibility and platform-independency of the proposed rule management system facilitate its incorporation in any system that is interested in data quality management.