Search results for: communal practice network

Authors: J. Gochermann

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Due to complex markets and products, and increasing need to innovate, cooperation between small and medium size enterprises arose during the last decades, which are not prior driven by process optimization or sales enhancement. Especially small and medium sized enterprises (SME) collaborate increasingly in innovation and knowledge networks to enhance their knowledge and innovation potential, and to find strategic partners for product and market development. These networks are characterized by dual objectives, the superordinate goal of the total network, and the specific objectives of the network members, which can cause target conflicts. Moreover, most SMEs do not have structured innovation processes and they are not accustomed to collaborate in complex innovation projects in an open network structure. On the other hand, SMEs have suitable characteristics for promising networking. They are flexible and spontaneous, they have flat hierarchies, and the acting people are not anonymous. These characteristics indeed distinguish them from bigger concerns. Investigation of German SME networks have been done to identify success factors for SME innovation networks. The fundamental network principles, donation-return and confidence, could be confirmed and identified as basic success factors. Further factors are voluntariness, adequate number of network members, quality of communication, neutrality and competence of the network management, as well as reliability and obligingness of the network services. Innovation and knowledge networks with an appreciable number of members from science and technology institutions need also active sense-making to bring different disciplines into successful collaboration. It has also been investigated, whether and how the involvement in an innovation network impacts the innovation structure and culture inside the member companies. The degree of reaction grows with time and intensity of commitment.

Keywords: innovation and knowledge networks, SME, success factors, innovation structure and culture

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Authors: Dimitri Golenko-Ginzburg

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A CPM network project with deterministic activity durations, in which activities require homogenous resources with fixed capacities, is considered. The problem is to determine the optimal schedule of starting times for all network activities within their maximal allowable limits (in order not to exceed the network's critical time) to minimize the maximum required resources for the project at any point in time. In case when a non-critical activity may start only at discrete moments with the pregiven time span, the problem becomes NP-complete and an optimal solution may be obtained via a look-over algorithm. For the case when a look-over requires much computational time an approximate algorithm is suggested. The algorithm's performance ratio, i.e., the relative accuracy error, is determined. Experimentation has been undertaken to verify the suggested algorithm.

Keywords: resource smoothing problem, CPM network, lookover algorithm, lexicographical order, approximate algorithm, accuracy estimate

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Authors: Dae Il Kim, Jungho Moon, Tae Yun Chung

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This paper presents a car parking monitoring system using a wireless sensor network. The presented sensor network has a line-shaped topology and adopts a TDMA-based protocol for allowing multi-hop communications. Sensor nodes are deployed in the ground of an outdoor parking lot in such a way that a sensor node monitors a parking space. Each sensor node detects the availability of the associated parking space and transmits the detection result to a sink node via intermediate sensor nodes existing between the source sensor node and the sink node. We evaluate the feasibility of the presented sensor network and the TDMA-based communication protocol through experiments using 11 sensor nodes deployed in a real parking lot. The result shows that the presented car parking monitoring system is robust to changes in the communication environments and efficient for monitoring parking spaces of outdoor parking lots.

Keywords: multi-hop communication, parking monitoring system, TDMA, wireless sensor network

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Authors: Sul-Ah Ahn, Youngim Jung

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The research activities of the computational scientists using high-performance computing are analyzed using bibliometric approaches. This study aims at providing computational scientists using high-performance computing and relevant policy planners with useful bibliometric results for an assessment of research activities. In order to achieve this purpose, we carried out a co-authorship network analysis of journal articles to assess the research activities of computational scientists using high-performance computing as a case study. For this study, we used journal articles of the Scopus database from Elsevier covering the time period of 2006-2015. We extracted the author rank in the computational science field using high-performance computing by the number of papers published during ten years from 2006. Finally, we drew the co-authorship network for 50 top-authors and their coauthors and described some features of the co-authorship network in relation to the author rank. Suggestions for further studies are discussed.

Keywords: co-authorship network analysis, computational science, high performance computing, research activity

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Authors: Dalu Zhang, Xiang Jin, Dejiang Zhou, Jianpeng Wang, Haiying Jiang

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Traditional TCP/IP network is showing lots of shortages and research for future networks is becoming a hotspot. FIA (Future Internet Architecture) and FIA-NP (Next Phase) are supported by US NSF for future Internet designing. Moreover, virtual machine migration is a significant technique in cloud computing. As a network application, it should also be supported in XIA (expressive Internet Architecture), which is in both FIA and FIA-NP projects. This paper is an experimental study aims at verifying the feasibility of VM migration over XIA. We present three ways to maintain VM connectivity and communication states concerning DAG design and routing table modification. VM migration experiments are conducted intra-AD and inter-AD with KVM instances. The procedure is achieved by a migration control protocol which is suitable for the characters of XIA. Evaluation results show that our solutions can well supports full live VM migration over XIA network respectively, keeping services seamless.

Keywords: DAG, downtime, virtual machine migration, XIA

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Çağrı Özgün Kibiroğlu, Zeynep Turgut

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This paper considers a hub location problem where the network service area partitioned into predetermined zones (represented by node clusters is given) and potential hub nodes capacity levels are determined a priori as a selection criteria of hub to investigate congestion effect on network. The objective is to design hub network by determining all required hub locations in the node clusters and also allocate non-hub nodes to hubs such that the total cost including transportation cost, opening cost of hubs and penalty cost for exceed of capacity level at hubs is minimized. A mixed integer linear programming model is developed introducing additional constraints to the traditional model of capacitated multiple allocation hub location problem and empirically tested.

Keywords: hub location problem, p-hub median problem, clustering, congestion

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Authors: Cheng Fang, Lingwei Quan, Cunyue Lu

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Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks.

Keywords: computer vision, pose estimation, pose tracking, Siamese network

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Authors: Pichamon Chansuchai

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This qualitative research paper aimed to study the best practice regulation of the Government Pension Fund of Thailand or GPF to explore the importance of good corporate governance and to identify and compare impacts towards the organizational operation and image before and after adopting the corporate good governance practice. The study employed the six principles of good corporate governance and best practice including accountability, responsibility, equitable treatment, transparency, value creation and ethics. The study pointed out that the GPF was a good example of the organization that regained public trust and receiving a positive image and credibility after implementing corporate good governance in all aspects of its organizational management.

Keywords: corporate governance, government, pension funds, organizational operation

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Hassan Abubakar

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This paper will serve as a guide to good design and implementation of wireless networking for campus institutions in Nigeria. It can be implemented throughout the primary, secondary and tertiary institutions. This paper describe the some technical functions, standard configurations and layouts of the 802.11 wireless LAN(Local Area Network) that can be implemented across the campus network. The paper also touches upon the wireless infrastructure standards involved with enhanced services, such as voice over wireless and wireless guest hotspot. The paper also touch the benefits derived from implementing campus wireless network and share some lights on how to arrive at the success in increasing the performance of wireless and using the campus wireless to share resources like software applications, printer and documents.

Keywords: networking, standards, wireless local area network (WLAN), radio frequency (RF), campus

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Authors: Y. B. Kirankumar, J. D. Mallapur

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Wireless Sensor Network (WSN) is an emerging technology, which has great invention for various low cost applications both for mass public as well as for defence. The wireless sensor communication technology allows random participation of sensor nodes with particular applications to take part in the network, which results in most of the uncovered simulation area, where fewer nodes are located at far distances. The drawback of such network would be that the additional energy is spent by the nodes located in a pattern of dense location, using more number of nodes for a smaller distance of communication adversely in a region with less number of nodes and additional energy is again spent by the source node in order to transmit a packet to neighbours, thereby transmitting the packet to reach the destination. The proposed work is intended to develop Energy Efficient Node Placement Algorithm (EENPA) in order to place the sensor node efficiently in simulated area, where all the nodes are equally located on a radial path to cover maximum area at equidistance. The total energy consumed by each node compared to random placement of nodes is less by having equal burden on fewer nodes of far location, having distributed the nodes in whole of the simulation area. Calculating the network lifetime also proves to be efficient as compared to random placement of nodes, hence increasing the network lifetime, too. Simulation is been carried out in a qualnet simulator, results are obtained on par with random placement of nodes with EENP algorithm.

Keywords: energy, WSN, wireless sensor network, energy approach

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Fariba Ebrahimi, Mehdi Ghorbani

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Achieving to integrated Water management fundamentally needs to effective relation, coordination, collaboration and synergy among various actors who have common but different responsibilities. In this sense, the foundation of comprehensive and integrated management is not compatible with centralization and top-down strategies. The aim of this paper is analysis institutional network of water relevant stakeholders and water policy monitoring in Shemiranat. In this study collaboration networks between informal and formal institutions co-management process have been investigated. Stakeholder network analysis as a quantitative method has been implicated in this research. The results of this study indicate that institutional cohesion is medium; sustainability of institutional network is about 40 percent (medium). Additionally the core-periphery index has measured in this study according to reciprocity index. Institutional capacities for integrated natural resource management in regional level are measured in this study. Furthermore, the necessity of centrality reduction and promote stakeholders relations and cohesion are emphasized to establish a collaborative natural resource governance.

Keywords: policy monitoring, water management, social network, stakeholder, shemiranat

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Authors: Lee Soo-Kyoung, Kim Young-Su

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This study established a network related to metabolic syndrome intervention by conducting a social network analysis of titles, keywords, and abstracts, and it identified emerging topics of research. It visualized an interconnection between critical keywords and investigated their frequency of appearance to construe the trends in metabolic syndrome intervention measures used in studies conducted over 38 years (1979–2017). It examined a collection of keywords from 8,285 studies using text rank analyzer, NetMiner 4.0. The analysis revealed 5 groups of newly emerging keywords in the research. By examining the relationship between keywords with reference to their betweenness centrality, the following clusters were identified. Thus if new researchers refer to existing trends to establish the subject of their study and the direction of the development of future research on metabolic syndrome intervention can be predicted.

Keywords: intervention, metabolic syndrome, network analysis, research, the trend

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Authors: Sanat Ramesh, Tarun Dutt, Abhilasha Aswal, Anushka Chandrababu, G. N. Srinivasa Prasanna

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Optimal Schedule Generation for a large national railway network operating thousands of passenger trains with tens of thousands of kilometers of track is a grand computational challenge in itself. We present heuristics based on a Mixed Integer Program (MIP) formulation for local optimization. These methods provide flexibility in scheduling new trains with varying speed and delays and improve utilization of infrastructure. We propose methods that provide a robust solution with hundreds of trains being scheduled over a portion of the railway network without significant increases in delay. We also provide techniques to validate the nominal schedules thus generated over global correlated variations in travel times thereby enabling us to detect conflicts arising due to delays. Our validation results which assume only the support of the arrival and departure time distributions takes an order of few minutes for a portion of the network and is computationally efficient to handle the entire network.

Keywords: mixed integer programming, optimization, railway network, train scheduling

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Authors: S. Bangphan, P. Bangphan, T.Boonkang

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Quality control helps industries in improvements of its product quality and productivity. Statistical Process Control (SPC) is one of the tools to control the quality of products that turning practice in bringing a department of industrial engineering process under control. In this research, the process control of a turning manufactured at workshops machines. The varying measurements have been recorded for a number of samples of a rice polished cylinder obtained from a number of trials with the turning practice. SPC technique has been adopted by the process is finally brought under control and process capability is improved.

Keywords: rice polished cylinder, statistical process control, control charts, process capability

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Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Nopsarun Vannasirikul

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The purposes of this research paper were to study the current situations of community development, women’s potentials, women’s participation, network, and government policy as well as to study the factors influencing women’s potentials, women’s participation, network, and government policy that have on the community development. The population included the women age of 18 years old who were living in the communities of Bangkok areas. This study was a mix research method of quantitative and qualitative method. A simple random sampling method was utilized to obtain 400 sample groups from 50 districts of Bangkok and to perform data collection by using questionnaire. Also, a purposive sampling method was utilized to obtain 12 informants for an in-depth interview to gain an in-sight information for quantitative method.

Keywords: community development, participation, network, women’s right, management

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Authors: Hanbhin Ryu, Ilkwon Yun

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Automotive industry targets to provide an improvement in safety and convenience through realizing fully autonomous vehicle. For partially realizing fully automated driving, Current vehicles already feature varieties of advanced driver assistance system (ADAS) for safety and infotainment systems for the driver’s convenience. This paper presents Cloud Connected Vehicle (CCV) which connected vehicles with cloud data center via the access network to control the vehicle for achieving next autonomous driving form and describes its features. This paper also describes the shortcoming of the existing In-Vehicle Network (IVN) to be a next generation IVN of CCV and organize the 802.3 Ethernet, the next generation of IVN, related research issue to verify the feasibility of using Ethernet. At last, this paper refers to additional considerations to adopting Ethernet-based IVN for CCV.

Keywords: autonomous vehicle, cloud connected vehicle, ethernet, in-vehicle network

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Amrah Maryam

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The emergence of online social networks (OSNs) has transformed the way we pursue and share information. On the one hand, OSNs provide great ease for the spreading of positive information while, on the other hand, they may also become a channel for the spreading of malicious rumors and misinformation throughout the social network. Thus, to assure the trustworthiness of OSNs to its users, it is of vital importance to detect the misinformation propagation in the network by placing network monitors. In this paper, we aim to place monitors near the suspected nodes with the intent to limit the diffusion of misinformation in the social network, and then we also detect the most significant nodes in the network for propagating true information in order to minimize the effect of already diffused misinformation. Thus, we initiate two heuristic monitor placement using articulation points and truth propagation using eigenvector centrality. Furthermore, to provide real-time workings of the system, we integrate both the monitor placement and truth propagation entities as well. To signify the effectiveness of the approaches, we have carried out the experiment and evaluation of Stanford datasets of online social networks.

Keywords: online social networks, monitor placement, independent cascade model, spread of misinformation

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Sisira Edirippulige, Anthony C. Smith, Sumudu Wickramasinghe, Nigel R. Armfield

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Background: E-Health is steadily integrating into modern health services, making significant changes in the way health services are traditionally delivered. To work in this new environment, healthcare workers are required to have new knowledge, skills, and competencies specific to e-Health. The aim of this study was to understand the self-reported perceptions of graduates regarding the influence of an e-Health postgraduate program on their professional careers. Methods: All graduates from 2005 to 2015 were surveyed using an online questionnaire that consisted of a mixture of closed and open-ended questions. Results: The number of participants in the study was 32. Response rate was 62%. Graduates thought that the postgraduate e-Health program had an influence on their professional practice. The majority of the participants mentioned that they had worked in the e-Health field since their graduation. Their professional roles mainly involved implementation of e-Health in health service settings and the use of e-Health in clinical practice. Conclusions: While e-Health may be steadily integrating into modern health services, e-Health specific job opportunities are still relatively limited. E-Health workforce development must be given priority.

Keywords: e-health, postgraduate education, clinical practice, curriculum

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Authors: H. Gorine, M. Ramadan Elmezughi

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In this paper, we investigate security issues and challenges facing researchers in wireless sensor networks and countermeasures to resolve them. The broadcast nature of wireless communication makes Wireless Sensor Networks prone to various attacks. Due to resources limitation constraint in terms of limited energy, computation power and memory, security in wireless sensor networks creates different challenges than wired network security. We will discuss several attempts at addressing the issues of security in wireless sensor networks in an attempt to encourage more research into this area.

Keywords: wireless sensor networks, network security, light weight encryption, threats

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Mostafa Sefidgar, Kaamran Raahemifar, Hossein Bazmara, Madjid Soltani

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The computational methods provide condition for investigation related to the process of drug delivery, such as convection and diffusion of drug in extracellular matrices, and drug extravasation from microvascular. The information of this process clarifies the mechanisms of drug delivery from the injection site to absorption by a solid tumor. In this study, an advanced numerical method is used to solve fluid flow and solute transport equations simultaneously to show how capillary network structure induced by tumor affects drug delivery. The effect of heterogeneous capillary network induced by tumor on interstitial fluid flow and drug delivery is investigated by this multi scale method. The sprouting angiogenesis model is used for generating capillary network induced by tumor. Fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network and fluid flow in normal and tumor tissues. The Starling’s law is used for closing this system of equations and coupling the intravascular and extravascular flows. Finally, convection-diffusion-reaction equation is used to simulate drug delivery. The dynamic approach which changes the capillary network structure based on signals sent by hemodynamic and metabolic stimuli is used in this study for more realistic assumption. The study indicates that drug delivery to solid tumors depends on the tumor induced capillary network structure. The dynamic approach generates the irregular capillary network around the tumor and predicts a higher interstitial pressure in the tumor region. This elevated interstitial pressure with irregular capillary network leads to a heterogeneous distribution of drug in the tumor region similar to in vivo observations. The investigation indicates that the drug transport properties have a significant role against the physiological barrier of drug delivery to a solid tumor.

Keywords: solid tumor, physiological barriers to drug delivery, angiogenesis, microvascular network, solute transport

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Authors: Junaid Ahmed, Nandita Shenoy

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Maintaining lifelong knowledge and skills is essential for safe clinical practice. Continuing Professional Development (CPD) is an established method that can facilitate lifelong learning. It focuses on maintaining or developing knowledge, skills and relationships to ensure competent practice.To date, relatively little has been done to comprehensively and systematically synthesize evidence to identify subjects of interest among practising dentist. Hence the aim of our study was to identify areas in clinical practice that would be favourable for continuing professional dental education amongst practicing dentists. Participants of this study consisted of the practicing dental surgeons of Mangalore, a city in Dakshina Kannada, Karnataka. 95% of our practitioners felt that regular updating as a one day program once in 3-6 months is required, to keep them abreast in clinical practice. 60% of subjects feel that CPD programs enrich their theoretical knowledge and helps in patient care. 27% of them felt that CPD programs should be related to general dentistry. Most of them felt that CPD programs should not be charged nominally between one to two thousand rupees. The acronym ‘CPD’ should be seen in a broader view in which professionals continuously enhance not only their knowledge and skills, but also their thinking,understanding and maturity; they grow not only as professionals, but also as persons; their development is not restricted to their work roles, but may also extend to new roles and responsibilities.

Keywords: continuing professional development, competent practice, dental education, practising dentist

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Authors: Seipati Baloyi-Mothibedi, Wendy Setlalentoa

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Recently scholars have shown much interest in the engagement and involvement of educational stakeholders in early childhood development (ECD) research, which has yielded positive results for ECD globally, especially in South Africa. Realising this gap, this study reports on the establishment of the research group comprising teachers, parents, and education stakeholders, which aimed to enhance curriculum practice in a grade R mathematics class. We adopted bricolage as a theoretical lens, mainly for its multi-layered, multi-methodological, multi-perspectival, and metatheoretical benefits to make sense in reviewing the literature as well as the empirical part of the study. A participatory action research (PAR) study using collaborative information sessions, meetings, workshops, and as well transcend movements were employed in order to engage the team to have first-hand information in enhancing curriculum practice in a grade R mathematics class was conducted. We adopted audiovisuals, photo voices, and lesson demonstrations to generate the data. The generated data were transcribed into texts that were further analysed using three levels based on the spoken or written texts and social and discursive practices. At the end of the discourses, the findings showed that engagement, involvement, and inclusion of different education stakeholders were instrumental in enhancing curriculum practice in a grade R mathematics class for the highest attainment. From the findings, we developed a strategy for engagement and involvement of teachers, parents, and the education stakeholders in enhancing curriculum practice in grade R mathematics class.

Keywords: engagement, involvement, curriculum practice, grade R, mathematics class

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Authors: Elif D. Oguz Erkal, David Krackhardt, Erica Cochran-Hameen

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Even though social network analysis (SNA) is an abundantly studied concept for many organizations and industries, a clear SNA approach to the project teams has not yet been adopted by the construction industry. The main challenges for performing SNA in construction and the apparent reason for this gap is the unique and complex structure of each construction project, the comparatively high circulation of project team members/contributing parties and the variety of authentic problems for each project. Additionally, there are stakeholders from a variety of professional backgrounds collaborating in a high-stress environment fueled by time and cost constraints. Within this case study on Project RE, a design & build project performed at the Urban Design Build Studio of Carnegie Mellon University, social network analysis of the project design team will be performed with the main goal of applying social network theory to construction project environments. The research objective is to determine a correlation between the network of how individuals relate to each other on one’s perception of their own professional strengths and weaknesses and the communication patterns within the team and the group dynamics. Data is collected through a survey performed over four rounds conducted monthly, detailed follow-up interviews and constant observations to assess the natural alteration in the network with the effect of time. The data collected is processed by the means of network analytics and in the light of the qualitative data collected with observations and individual interviews. This paper presents the full ethnography of this construction design team of fourteen architecture students based on an elaborate social network data analysis over time. This study is expected to be used as an initial step to perform a refined, targeted and large-scale social network data collection in construction projects in order to deduce the impacts of social networks on project performance and suggest better collaboration structures for construction project teams henceforth.

Keywords: construction design teams, construction project management, social network analysis, team collaboration, network analytics

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