Search results for: network service.

Authors: G. Lingam, N. Lingam, K. Raghuwaiya

Abstract:

The reported study focuses on pre-service teachers’ professional development during the teaching practice. The cohort studied comprised participants in their final year in the Bachelor of Arts and Bachelor of Science with Graduate Certificate in Education programmes of a university in Fiji. Analysis of the data obtained using a survey questionnaire indicates that overall, the pre-service teachers were satisfied with the practicum experience. This is assumed to demonstrate that the practicum experience contributed well towards their professional preparation for work expected of them in Fiji secondary schools. Participants also identified some concerns as needing attention. To conclude, the paper provides suggestions for improving the preparation of teachers by strengthening the identified areas of the practicum offered by the university. The study has implications for other teacher education providers in small developing island states and even beyond for the purpose of enhancing learning in student teachers’ for future work.

Keywords: Pre-service, teacher education, practicum, teachers’ world of work, student teachers.

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Authors: Christian Endisch, Christoph Hackl, Dierk Schröder

Abstract:

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg- Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach.

Keywords: System identification, dynamic neural network, recurrentneural network, GDNN, optimization, Levenberg Marquardt, realtime recurrent learning, network pruning, quasi-online learning.

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Authors: Qiang Fu, Md. Abu Saleh

Abstract:

Research studies are highly fragmented when an Innovation Management Framework is being discussed. With the aim to identify an Innovation Management Framework/Assessment Tool suitable for Small & Medium Enterprises (SMEs) in the service industry, this researcher critically reviewed existing innovation management frameworks and assessment models/tools and discovered a number of literature gaps. It is established that the existing literature lacks generally agreed innovation management dimensions, commonly accepted knowledge creation through empirical studies on innovation management in SMEs, effective innovation management performance measurements, suitable innovation management framework in SMEs, and studies on innovation management in the service industry, in particular in retail SMEs. As such, there is a dire need to develop an appropriate firm-level innovation management framework suitable for SMEs in the service industry for future research projects and further studies. In addition, this researcher also discussed the significance of establishing such an innovation management framework.

Keywords: innovation management, innovation management framework, innovation management assessment tools, SMEs, service industry

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Authors: Sotiria Tzivinikou, Dimitra Kagkara

Abstract:

Teacher’s sense of self-efficacy can affect significantly both teacher’s and student’s performance. More specific, self-efficacy is associated with the learning outcomes as well as student’s motivation and self-efficacy. For example, teachers with high sense of self-efficacy are more open to innovations and invest more effort in teaching. In addition to this, effective inclusive education is associated with higher levels of teacher’s self-efficacy. Pre-service teachers with high levels of self-efficacy could handle student’s behavior better and more effectively assist students with special educational needs. Teacher preparation programs are also important, because teacher’s efficacy beliefs are shaped early in learning, as a result the quality of teacher’s education programs can affect the sense of self-efficacy of pre-service teachers. Usually, a number of pre-service teachers do not consider themselves well prepared to work with students with special educational needs and do not have the appropriate sense of self-efficacy. This study aims to investigate the factors that contribute to the improvement of the sense of self-efficacy of pre-service special educators by using an academic practicum training program. The sample of this study is 159 pre-service special educators, who also participated in the academic practicum training program. For the purpose of this study were used quantitative methods for data collection and analysis. Teacher’s self-efficacy was assessed by the teachers themselves with the completion of a questionnaire which was based on the scale of Teacher’s Sense of Efficacy Scale. Pre and post measurements of teacher’s self-efficacy were taken. The results of the survey are consistent with those of the international literature. The results indicate that a significant number of pre-service special educators do not hold the appropriate sense of self-efficacy regarding teaching students with special educational needs. Moreover, a quality academic training program constitutes a crucial factor for the improvement of the sense of self-efficacy of pre-service special educators, as additional for the provision of high quality inclusive education.

Keywords: Inclusive education, pre-service, self-efficacy, training program.

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Authors: Han Bing, Yin Zhenjie

Abstract:

In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: Fault prediction, Neural network, GM (1.5), Genetic algorithm, GBPGA.

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Authors: M. Pollar, M. Jaroensutasinee, K. Jaroensutasinee

Abstract:

The population structure of the Tor tambroides was investigated with morphometric data (i.e. morphormetric measurement and truss measurement). A morphometric analysis was conducted to compare specimens from three waterfalls: Sunanta, Nan Chong Fa and Wang Muang waterfalls at Khao Nan National Park, Nakhon Si Thammarat, Southern Thailand. The results of stepwise discriminant analysis on seven morphometric variables and 21 truss variables per individual were the same as from a neural network. Fish from three waterfalls were separated into three groups based on their morphometric measurements. The morphometric data shows that the nerual network model performed better than the stepwise discriminant analysis.

Keywords: Morphometric, Tor tambroides, Stepwise Discriminant Analysis , Neural Network Analysis.

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Authors: Salinee Thumronglaohapun

Abstract:

The proper number and appropriate locations of service centers can save cost, raise revenue and gain more satisfaction from customers. Establishing service centers is high-cost and difficult to relocate. In long-term planning periods, several factors may affect the service. One of the most critical factors is uncertain demand of customers. The opened service centers need to be capable of serving customers and making a profit although the demand in each period is changed. In this work, the capacitated location-allocation problem with stochastic demand is considered. A mathematical model is formulated to determine suitable locations of service centers and their allocation to maximize total profit for multiple planning periods. Two heuristic methods, a local search and genetic algorithm, are used to solve this problem. For the local search, five different chances to choose each type of moves are applied. For the genetic algorithm, three different replacement strategies are considered. The results of applying each method to solve numerical examples are compared. Both methods reach to the same best found solution in most examples but the genetic algorithm provides better solutions in some cases.

Keywords: Location-allocation problem, stochastic demand, local search, genetic algorithm.

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Authors: Earleen Jane Fuentes, Regeene Melarese Lim, Franklin Benjamin Tapia, Alexis Pantola

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Mobile Ad-hoc Network (MANET) is an infrastructure-less network of mobile devices, also known as nodes. These nodes heavily rely on each other’s resources such as memory, computing power, and energy. Thus, some nodes may become selective in forwarding packets so as to conserve their resources. These nodes are called misleading nodes. Several reputation-based techniques (e.g. CORE, CONFIDANT, LARS, SORI, OCEAN) and acknowledgment-based techniques (e.g. TWOACK, S-TWOACK, EAACK) have been proposed to detect such nodes. These techniques do not appropriately punish misleading nodes. Hence, this paper addresses the limitations of these techniques using a system called MINDRA.

Keywords: Mobile ad-hoc network, selfish nodes, reputation-based techniques, acknowledgment-based techniques.

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Authors: Mangesh R. Phate, V. H. Tatwawadi

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This paper is intended to develop an artificial neural network (ANN) based model of material removal rate (MRR) in the turning of ferrous and nonferrous material in a Indian small-scale industry. MRR of the formulated model was proved with the testing data and artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between inputs and output parameters during the turning of ferrous and nonferrous materials. The input parameters of this model are operator, work-piece, cutting process, cutting tool, machine and the environment.

The ANN model consists of a three layered feedforward back propagation neural network. The network is trained with pairs of independent/dependent datasets generated when machining ferrous and nonferrous material. A very good performance of the neural network, in terms of contract with experimental data, was achieved. The model may be used for the testing and forecast of the complex relationship between dependent and the independent parameters in turning operations.

Keywords: Field data based model, Artificial neural network, Simulation, Convectional Turning, Material removal rate.

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Authors: Ehab N. Alkhanak, Salimah Mokhtar

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The main aim of this paper is to present the research findings on the solution of centralized Web-Services for students by adopting a framework and a prototype for Service Oriented Architecture (SOA) Web-Services. The current situation of students- Web-based application services has been identified and proposed an effective SOA to increase the operational efficiency of Web-Services for them it was necessary to identify the challenges in delivering a SOA technology to increase operational efficiency of Web-Services. Moreover, the SOA is an emerging concept, used for delivering efficient student SOA Web-Services. Therefore, service reusability from SOA Web-Services is provided and logically divided services into smaller services to increase reusability and modularity. In this case each service is a modular unit by itself and interoperability services.

Keywords: Services Oriented Architecture (SOA), Web-based Application services, and Web-Services.

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Authors: Garima Gupta, Daya Gupta

Abstract:

Virtually all existing networked system management tools use a Manager/Agent paradigm. That is, distributed agents are deployed on managed devices to collect local information and report it back to some management unit. Even those that use standard protocols such as SNMP fall into this model. Using standard protocol has the advantage of interoperability among devices from different vendors. However, it may not be able to provide customized information that is of interest to satisfy specific management needs. In this dissertation work, different approaches are used to collect information regarding the devices attached to a Local Area Network. An SNMP aware application is being developed that will manage the discovery procedure and will be used as data collector.

Keywords: ICMP Scanner, Network Discovery, NetworkManagement, SNMP Scanner.

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Authors: Masaki Ito, Hideyuki Tokuda, Takao Kawamura, Kazunori Sugahara

Abstract:

With the advancement of wireless sensor network technology, its practical utilization is becoming an important challange. This paper overviews my past environmental monitoring project, and discusses the process of starting the monitoring by classifying it into four steps. The steps to start environmental monitoring can be complicated, but not well discussed by researchers of wireless sensor network technology. This paper demonstrates our activity and challenges in each of the four steps to ease the process, and argues future challenges to enable quick start of environmental monitoring.

Keywords: Environmental Monitoring, Wireless Sensor Network, Field Experiment and Research Challenges.

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Authors: N. Bhalaji, A. R. Sivaramkrishnan, Sinchan Banerjee, V. Sundar, A. Shanmugam

Abstract:

Nodes in mobile Ad Hoc Network (MANET) do not rely on a central infrastructure but relay packets originated by other nodes. Mobile ad hoc networks can work properly only if the participating nodes collaborate in routing and forwarding. For individual nodes it might be advantageous not to collaborate, though. In this conceptual paper we propose a new approach based on relationship among the nodes which makes them to cooperate in an Adhoc environment. The trust unit is used to calculate the trust values of each node in the network. The calculated trust values are being used by the relationship estimator to determine the relationship status of nodes. The proposed enhanced protocol was compared with the standard DSR protocol and the results are analyzed using the network simulator-2.

Keywords: Reliable Routing, DSR, Grudger, Adhoc network.

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Authors: Xin Bai, Han Yu, Yongchang Ji, Dan C. Marinescu

Abstract:

We discuss the application of matching in the area of resource discovery and resource allocation in grid computing. We present a formal definition of matchmaking, overview algorithms to evaluate different matchmaking expressions, and develop a matchmaking service for an intelligent grid environment.

Keywords: Grid, Matchmaking, Ontology

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Authors: Charru Malhotra, Girija Krishnaswamy

Abstract:

Setting up of rural telecentres, popularly referred to as Common Service Centres (CSCs), are considered one of the initial forerunners of rural e-Governance initiatives under the Government of India-s National e-Governance Plan (NeGP). CSCs are implemented on public-private partnership (PPP) – where State governments play a major role in facilitating the establishment of CSCs and investments are made by private companies referred to as Service Centre Agencies (SCAs). CSC implementation is expected to help in improving public service delivery in a transparent and efficient manner. However, there is very little research undertaken to study the actual impact of CSC implementation at the grassroots level. This paper addresses the gap by identifying the circumstances, concerns and expectations from the point-of-view of citizens and examining the finer aspects of social processes in the context of rural e-Governance.

Keywords: Capacity Building, Citizens' Participation, e- Government, NeGP, PPP, Rural Telecentres

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Authors: S. Anna Durai, E. Anna Saro

Abstract:

Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly.

Keywords: Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.

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Authors: Rumyana Peytcheva-Forsyth, Blagovesna Yovkova

Abstract:

This paper presents the outcomes of a qualitative study which aims to investigate the pedagogical potentials of serious games in the preparation of future teachers. The authors discuss the existing problems and barriers associated with the organization of teaching practices in Bulgaria as part of the pre-service teacher training, as well as the attitudes and perceptions of the interviewed academics, teachers and trainees concerning the integration of serious games in the teaching practicum. The study outcomes strongly confirm the positive attitudes of the respondents to the introduction of virtual learning environments for the development of professional skills of future teachers as a supplement to the traditional forms of education. Through the inclusion of serious games it is expected to improve the quality of practical training of pre-service teachers as they overcome many of the problems identified in the existing teaching practices. The outcomes of the study will inform the design of the educational simulation software which is part of the project SimAula Tomorrow's Teachers Training.

Keywords: pre-service teacher training, serious games, virtual practicum, simulations

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Authors: E. Praynlin, P. Latha

Abstract:

Software Effort Estimation is the process of estimating the effort required to develop software. By estimating the effort, the cost and schedule required to estimate the software can be determined. Accurate Estimate helps the developer to allocate the resource accordingly in order to avoid cost overrun and schedule overrun. Several methods are available in order to estimate the effort among which soft computing based method plays a prominent role. Software cost estimation deals with lot of uncertainty among all soft computing methods neural network is good in handling uncertainty. In this paper Radial Basis Function Network is compared with the back propagation network and the results are validated using six data sets and it is found that RBFN is best suitable to estimate the effort. The Results are validated using two tests the error test and the statistical test.

Keywords: Software cost estimation, Radial Basis Function Network (RBFN), Back propagation function network, Mean Magnitude of Relative Error (MMRE).

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Authors: Satya Pilla

Abstract:

This study explores how the mechanics of learning paves the way to engineering innovation. Theories related to learning in the new product/service innovation are reviewed from an organizational perspective, behavioral perspective, and engineering perspective. From this, an engineering team-s external interactions for knowledge brokering and internal composition for skill balance are examined from a learning and innovation viewpoints. As a result, an integrated learning model is developed by reconciling the theoretical perspectives as well as developing propositions that emphasize the centrality of learning, and its drivers, in the engineering product/service development. The paper also provides a review and partial validation of the propositions using the results of a previously published field study in the aerospace industry.

Keywords: Engineering Services, Integrated Learning, NewProduct Development, Service Innovation

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Authors: H.Mohammadi Majd, M.Jalali Azizpour , Foad Saadi

Abstract:

In this paper back-propagation artificial neural network (BPANN) is employed to predict the deformation of the upsetting process. To prepare a training set for BPANN, some finite element simulations were carried out. The input data for the artificial neural network are a set of parameters generated randomly (aspect ratio d/h, material properties, temperature and coefficient of friction). The output data are the coefficient of polynomial that fitted on barreling curves. Neural network was trained using barreling curves generated by finite element simulations of the upsetting and the corresponding material parameters. This technique was tested for three different specimens and can be successfully employed to predict the deformation of the upsetting process

Keywords: Back-propagation artificial neural network(BPANN), prediction, upsetting

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Authors: E. Ahvar, M. Fathy

Abstract:

Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols. We compare the performance of three routing protocols for mobile ad hoc networks: Dynamic Source Routing (DSR), Ad Hoc On-Demand Distance Vector Routing (AODV), location-aided routing (LAR1).Our evaluation is based on energy consumption in mobile ad hoc networks. The performance differentials are analyzed using varying network load, mobility, and network size. We simulate protocols with GLOMOSIM simulator. Based on the observations, we make recommendations about when the performance of either protocol can be best.

Keywords: Ad hoc Network, energy consumption, Glomosim, routing protocols.

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Authors: Kejun Zhuang, Zhaohui Wen

Abstract:

In this paper, a tri–neuron network model with time delay is investigated. By using the Bendixson-s criterion for high– dimensional ordinary differential equations and global Hopf bifurcation theory for functional differential equations, sufficient conditions for existence of periodic solutions when the time delay is sufficiently large are established.

Keywords: Delay, global Hopf bifurcation, neural network, periodicsolutions.

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Authors: Mukunda Bastola, Frank Nyame-Asiamah

Abstract:

Economics explanation for privatisation, drawing on neo-liberal market structures and technical efficiency principles has failed to address social imbalance and, distribute the efficiency benefits accrued from privatisation equitably among service users and different classes of people in society. Stakeholders’ interest, which cover ethical values and changing human needs are ignored due to shareholders’ profit maximising strategy with higher service charges. The consequence of these is that, the existing justifications for privatisation have fallen short of customer quality expectations because the underlying plan-based models fail to account for the nuances of customer expectations. We draw on the theory of deferred action to develop a context-based privatisation model, the deferred-based privatisation model, to explain how privatisation could be strategised for the emergent reality of the wider stakeholders’ interests and everyday quality demands of customers which are unpredictable.

Keywords: Privatisation, service quality, shareholders, deferred action, deferred-based privatisation model.

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Authors: Tal Ben-Zvi, Donald N. Lombardi

Abstract:

This study uses a simulation to establish a realistic environment for laboratory research on Accountable Care Organizations. We study network attributes in order to gain insights regarding healthcare providers- conduct and performance. Our findings indicate how network structure creates significant differences in organizational performance. We demonstrate how healthcare providers positioning themselves at the central, pivotal point of the network while maintaining their alliances with their partners produce better outcomes.

Keywords: Social Networks, Decision-Making, Accountable Care Organizations, Performance

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Authors: M. R. Martinez-Torres, S. L. Toral, M. Olmedilla

Abstract:

Typically, virtual communities exhibit the well-known phenomenon of participation inequality, which means that only a small percentage of users is responsible of the majority of contributions. However, the sustainability of the community requires that the group of active users must be continuously nurtured with new users that gain expertise through a participation process. This paper analyzes the time evolution of Open Source Software (OSS) communities, considering users that join/abandon the community over time and several topological properties of the network when modeled as a social network. More specifically, the paper analyzes the role of those users rejoining the community and their influence in the global characteristics of the network.

Keywords: Open source communities, social network analysis, time series, virtual communities.

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Authors: Fengxia Zheng, Shouming Zhong

Abstract:

ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression (AR) model based on binomial smoothing (BS) technique which is efficient in data processing, which is called BSRBFAR. This method is examined by using the data of Canadian Lynx data. Empirical results indicate that the over-fitting problem can be eased using RBF neural network based on binomial smoothing which is called BS-RBF, and the hybrid model–BS-RBFAR can be an effective way to improve forecasting accuracy achieved by BSRBF used separately.

Keywords: Binomial smoothing (BS), hybrid, Canadian Lynx data, forecasting accuracy.

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Authors: Kgabo H. Badimo, Sheryl Buckley

Abstract:

Knowledge is increasingly recognised in this, the knowledge era, as a strategic resource, by public sector organisations, in view of the public sector reform initiatives. People and knowledge play a vital role in attaining improved organisational performance and high service quality. Many government departments in the public sector have started to realise the importance of knowledge management in streamlining their operations and processes. This study focused on knowledge management in the public healthcare service organisations, where the concept of service provider competitiveness pales to insignificance, considering the huge challenges emanating from the healthcare and public sector reforms. Many government departments are faced with challenges of improving organisational performance and service delivery, improving accountability, making informed decisions, capturing the knowledge of the aging workforce, and enhancing partnerships with stakeholders. The purpose of this paper is to examine the knowledge management practices of the Gauteng Department of Health in South Africa, in order to understand how knowledge management practices influence improvement in organisational performance and healthcare service delivery. This issue is explored through a review of literature on dominant views on knowledge management and healthcare service delivery, as well as results of interviews with, and questionnaire responses from, the general staff of the Gauteng Department of Health. Web-based questionnaires, face-to-face interviews and organisational documents were used to collect data. The data were analysed using both the quantitative and qualitative methods. The central question investigated was: To what extent can the conditions required for successful knowledge management be observed, in order to improve organisational performance and healthcare service delivery in the Gauteng Department of Health. The findings showed that the elements of knowledge management capabilities investigated in this study, namely knowledge creation, knowledge sharing and knowledge application, have a positive, significant relationship with all measures of organisational performance and healthcare service delivery. These findings thus indicate that by employing knowledge management principles, the Gauteng Department of Health could improve its ability to achieve its operational goals and objectives, and solve organisational and healthcare challenges, thereby improving organisational performance and enhancing healthcare service delivery in Gauteng.

Keywords: Knowledge Management, Healthcare Service Delivery, Public Healthcare, Public Sector.

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Authors: C. W. Hsu, C. N. Liang, L. Y. Ke, F. Y. Huang

Abstract:

Many accidents were happened because of fast driving, habitual working overtime or tired spirit. This paper presents a solution of remote warning for vehicles collision avoidance using vehicular communication. The development system integrates dedicated short range communication (DSRC) and global position system (GPS) with embedded system into a powerful remote warning system. To transmit the vehicular information and broadcast vehicle position; DSRC communication technology is adopt as the bridge. The proposed system is divided into two parts of the positioning andvehicular units in a vehicle. The positioning unit is used to provide the position and heading information from GPS module, and furthermore the vehicular unit is used to receive the break, throttle, and othersignals via controller area network (CAN) interface connected to each mechanism. The mobile hardware are built with an embedded system using X86 processor in Linux system. A vehicle is communicated with other vehicles via DSRC in non-addressed protocol with wireless access in vehicular environments (WAVE) short message protocol. From the position data and vehicular information, this paper provided a conflict detection algorithm to do time separation and remote warning with error bubble consideration. And the warning information is on-line displayed in the screen. This system is able to enhance driver assistance service and realize critical safety by using vehicular information from the neighbor vehicles.KeywordsDedicated short range communication, GPS, Control area network, Collision avoidance warning system.

Keywords: Dedicated short range communication, GPS, Control area network, Collision avoidance warning system.

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Authors: Mohamed Watfa, William Daher, Hisham Al Azar

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The main idea behind in network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Existing sensor network data aggregation techniques assume that the nodes are preprogrammed and send data to a central sink for offline querying and analysis. This approach faces two major drawbacks. First, the system behavior is preprogrammed and cannot be modified on the fly. Second, the increased energy wastage due to the communication overhead will result in decreasing the overall system lifetime. Thus, energy conservation is of prime consideration in sensor network protocols in order to maximize the network-s operational lifetime. In this paper, we give an energy efficient approach to query processing by implementing new optimization techniques applied to in-network aggregation. We first discuss earlier approaches in sensors data management and highlight their disadvantages. We then present our approach “Energy Efficient Indexed Aggregation" (EEIA) and evaluate it through several simulations to prove its efficiency, competence and effectiveness.

Keywords: Sensor Networks, Data Base, Data Fusion, Aggregation, Indexing, Energy Efficiency

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Authors: Defu Zhang, Qingshan Jiang, Xin Li

Abstract:

This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.

Keywords: Data mining, neural network, stock forecasting.

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