Understanding Knowledge Sharing Within Communities of Practice. A Study of Engagement Patterns and Intervention within Community of Practice.

Online Communities of Practices (CoPs) is emerging as a major form for knowledge sharing in this era of information revolution. Due to the advancement of technology and ease of internet access in every part of the world, people began to get more and more involved in online CoPs to share knowledge. The defining characteristic of a Community of Practice is the interaction between members in order to jointly determine and embrace goals, eventually resulting in shared practices. Crucial to the success of a Community of Practice is the engagement between community members. Without engagement, a Community of Practice can not share knowledge and achieve its negotiated goals. To that end, there is a need to examine, why do people engage in an online discussion, what role domain experts play to keep on-line discussion alive and how to develop a ''right intervention'' to maintain and stimulate participants for engagement in on-line community. This thesis studied eight Communities of Practices that are being deliberately formed to facilitate knowledge sharing in the online community and describes an exploratory study of knowledge sharing within Communities of Practices (CoPs) by investigating eight CoPs ¿Start up Nation, All nurses, Young Enterpener, Teneric, SCM Focus, Systems Dynamics, Mahjoob and Alnj3 CoPs. The CoPs under investigation shared the following characteristics: permanent life span, created by interested members (i.e. bottom-up rather than top-down management creation), have a high level of boundary crossing, have more than 700 members who come from disparate locations and organizations, have voluntary membership enrollment, high membership diversity, high topic¿s relevance to members, high degree of reliance on technology, and are moderated. Data were gathered on the eight CoPs through online observations and online questionnaire survey. Results show that in each of the case study the most common type of activity performed by members of each CoP was sharing knowledge, followed by socialsing. Regarding the types of knowledge shared, the most common one across all CoPs was practical and general knowledge. The types of practical knowledge, however, varied in each CoP. The study also discovered that storytelling extensively enhances knowledge transfer and participants¿ interpersonal communications in eight communities under investigation. What were also notable in this study were the stories discussed in a CoP remains in the archive, what are more likely to generate interest and curiosity on the topic among inactive members who ultimately facilitates knowledge transfer. In this study it is also evident that successful topics with successful conclusion (in terms that the original query was answered) will not necessary get high responses and vice versa. An analysis of selected topics in the eight case studies has shown that some successful topics have few replies and vice versa, where many topics ended with open conclusion or they were unsuccessful in terms that the original query was not answered satisfactory. Therefore, it is not necessary that successful topic will get high number of responses as there are some successful topics which have limited number of replies. Overall, it is found that, topic may play a major role in the success of online discussion. It is observed in the study that members normally use short messages rather long messages and usually discusses more than one topic within one thread. Practical implications for knowledge sharing in online communities of practice were discussed, along with some recommendations for future research

Knowledge sharing by entrepreneurs in a virtual community of practice (VCoP)

PurposeThis paper examines how entrepreneurs engage in a Virtual Community of Practice (VCoP) to share knowledge. Intensity of engagement is taken as a proxy to measure the strength of knowledge sharing.Design/methodology/approachThe archival data spanning over a three-year period from ‘Start-up-Nation©’ (a VCoP purposefully setup for entrepreneurs) is used for analysis. A set of indices are introduced to measure participants’ intensity of engagement in terms of message length, message frequency and reciprocity in the knowledge sharing process. Content analysis is employed to test a sample of ‘highly engaged’, ‘moderately engaged’, ‘low engaged’ and ‘not engaged’ discussion topics as part of the on-line discourse.FindingsWe find that entrepreneurs normally use short (fewer than 100 words) or medium (fewer than 250 words) message size to contribute to the discussions. In addition, we find that senior members and discussion moderators play important roles in igniting the ‘reciprocity’ behaviour in stimulating the interest of the community with the topic discussion. We also findthat highly engaged topics usually lead to further discussion threads.Originality/valueThis is the first study of its kind to explore how entrepreneurs engage in a VCoP to share their knowledge and experiences. The set of measurement indices tested here provide a tool for the owner, designer and moderator of the VCoP to measure the utility of their website in terms of its members’ participation. In addition, the set of textual and subjective interventions identified here enable the moderator (administrator) of a VCoP to design effective interventions to facilitate on-line discourse and augment the knowledge sharing process amongst its community members

Partially Lazy Classification of Cardiovascular Risk via Multi-way Graph Cut Optimization

Cardiovascular disease (CVD) is considered a leading cause of human mortality with rising trends worldwide. Therefore, early identification of seemingly healthy subjects at risk is a priority. For this purpose, we propose a novel classification algorithm that provides a sound individual risk prediction, based on a non-invasive assessment of retinal vascular function. so-called lazy classification methods offer reduced time complexity by saving model construction time and better adapting to newly available instances, when compared to well-known eager methodS. Lazy methods are widely used due to their simplicity and competitive performance. However, traditional lazy approaches are more vulnerable to noise and outliers, due to their full reliance on the instances' local neighbourhood for classification. In this work, a learning method based on Graph Cut Optimization called GCO mine is proposed, which considers both the local arrangements and the global structure of the data, resulting in improved performance relative to traditional lazy methodS. We compare GCO mine coupled with genetic algorithms (hGCO mine) with established lazy and eager algorithms to predict cardiovascular risk based on Retinal Vessel Analysis (RVA) data. The highest accuracy of 99.52% is achieved by hGCO mine. The performance of GCO mine is additionally demonstrated on 12 benchmark medical datasets from the UCI repository. In 8 out of 12 datasets, GCO mine outperforms its counterpartS. GCO mine is recommended for studies where new instances are expected to be acquired over time, as it saves model creation time and allows for better generalization compared to state of the art methodS

Hopf Bifurcation and Stability of Periodic Solutions for Delay Differential Model of HIV Infection of CD4 +

This paper deals with stability and Hopf bifurcation analyses of a mathematical model of HIV infection of CD4+ T-cells. The model is based on a system of delay differential equations with logistic growth term and antiretroviral treatment with a discrete time delay, which plays a main role in changing the stability of each steady state. By fixing the time delay as a bifurcation parameter, we get a limit cycle bifurcation about the infected steady state. We study the effect of the time delay on the stability of the endemically infected equilibrium. We derive explicit formulae to determine the stability and direction of the limit cycles by using center manifold theory and normal form method. Numerical simulations are presented to illustrate the results

A Robust UWSN Handover Prediction System Using Ensemble Learning.

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors' knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model

Different methods of termination of second trimester pregnancy at Women′s Health Hospital, Assiut University: efficacy and complications

Background: Termination of pregnancy in second trimester is one of the greatest challenges in modern obstetrics practice and is more risky than during first trimester. Now the main concern of the obstetrician is to provide the most effective, safest, and cost-effective regimen with least or no complications. Describe the different indications, technique and complications of different methods of TOP used at Women’s Health Hospital, Assiut University.  Identify gap between current practice and guidelines and setting recommendations for filling gap to improve outcomeMethods: Studying the different methods used for all cases with gestational age 13-24 weeks attending at Women′s Health Hospital, Assiut University from the 1st July 2015 to the 1st June 2016, for second trimester termination of pregnancy who are eligible for termination of pregnancy, with exclusion criteria including any case with scared uterus, multiple pregnancy and rupture of membranes.Results: Of the 146 patients, 55 patients received misoprostol alone, 13 cases used foley’s catheter alone, 67 cases received misoprostol in combination with foley’s catheter and hysterotomy done in 9 patients (4 after failed induction and the rest as primary procedure). In present work the most common complication recorded was retained placental parts, 39 patients (26,5%) followed by surgical evacuation. Uterine perforation occurred accidentally in 3 cases during evacuation followed by laparotomy and repair of perforation without hysterectomy. Infection recorded in 3 cases (1.7%). Sever haemorhage occurred in 4 cases where they needed hysterotomy.Conclusions: All methods used in the department showed efficacy. Misoprostol induction was associated with a shorter induction-abortion interval but was associated with higher risk of retained placenta. Foley's catheter induction was more prolonged but it was associated with almost no complication. The most common complication was retained placenta except those who used Foley's catheter as they had no retained placental parts

A new approach for achievement of inulin accumulation in suspension cultures of Jerusalem artichoke (Helianthus tuberosus) using biotic elicitors

AbstractA promising protocol for achievement the accumulation rate of inulin compound in a suspension culture of Jerusalem artichoke (Helianthus tuberosus) was established. The effect of incorporated of cell cultures in combining with two type of biotic elicitors Aspergillus niger extract and Methyl-Jasmonate incorporation feeding medium on leaf cell growth patterns and production of inulin was investigated. The maximum value of cell growth parameters and highest content of inulinase activity (0.395u/ml) were resulted from elicitation of augmented MS-medium with A. niger extract at the level of 0.2% in combination with Methyl-Jasmonate (150μM) as compared with other concentrations after 2weeks of cultivation. The chemical analyses of the different cell lines were spectro-photometerically performed. This study clearly indicates that combining of A. niger and Methyl-Jasmonate elicitors plays a critical role on inulin process and its accumulation in Jerusalem artichoke cell cultures