Six sigma approach to improve quality in E-services : an empirical study in Jordan

In the last two decades, Six Sigma approach has found its success in manufacturing sectors. The relevance of Six Sigma methodologies in service sector has been realised more recently. This paper investigates the application of Six Sigma approach to improve quality in electronic services (e-services) as more and more countries are adopting e-services as a means of providing services to their community and people through the Web. In particular, this paper presents a case study about the use of Six Sigma model to measure the customer satisfaction and quality levels achieved in e-services that were recently launched by public sector organisations in a developing country, such as Jordan. An empirical study consisting of 280 participating customers of Jordan‘s e-services is conducted and the problems are identified through the DMAIC phases of Six Sigma. The service quality levels are measured and analysed using six main criteria, namely, Website Design, Reliability, Responsiveness, Personalization, Information Quality, and System Quality. The overall result of the study indicating a 74% customer satisfaction with a Six Sigma level of 2.12 has enabled the Greater Amman Municipality to identify the usability issues associated with their e-services offered by public sector organisations and to take the leads from the results of the study to improve customer satisfaction. The aim of the paper is not only to implement Six Sigma as a measurement-based strategy for improving e-customer service quality in a newly launched e-service programme, but also to help widen its scope in investigating other service dimensions and perform comparative studies in other developing countries as future research.

Investigation of process dynamics and control of polystyrene batch reactor using hybrid model

The effects of operating conditions such as initiator and monomer concentration as well as reactor temperature of polymerization reactors are studied in this work. A recently developed hybrid model for polystyrene batch reactor is utilized in simulation study. The simulation results reveal the sensitivity of polymer properties and monomer conversion to variation of process operating conditions. In the second phase of this study, the optimization problem involving minimum time optimal temperature policy is considered for control study. An advanced neural network-based model predictive controller (NN-MPC) is designed and tested online. The experimental studies reveal that the developed controller is able to track the optimal setpoint with a minor oscillation and overshoot.

Single cell stiffness measurement at various humidity conditions by nanomanipulation of a nano-needle

This paper presents a method for single cell stiffness measurement based on a nano-needle and nanomanipulation. The nano-needle with a buffering beam was fabricated from an atomic force microscope cantilever by the focused ion beam etching technique. Wild type yeast cells (W303) were prepared and placed on the sample stage inside an environmental scanning electron microscope (ESEM) chamber. The nanomanipulator actuated the nano-needle to press against a single yeast cell. As a result, the deformation of the cell and nano-needle was observed by the ESEM system in real-time. Finally, the stiffness of the single cell was determined based on this deformation information. To reveal the relationship between the cell stiffness and the environmental humidity conditions, the cell stiffness was measured at three different humidity conditions, i.e. 40, 70 and 100%, respectively. The results show that the stiffness of a single cell is reduced with increasing humidity.

Formulation and simulation of a 3D mechanical model of embryos for microinjection

The understanding of cell manipulation, for example in microinjection, requires an accurate model of the cells. Motivated by this important requirement, a 3D particlebased mechanical model is derived for simulating the deformation of the fish egg membrane and the corresponding cellular forces during microrobotic cell injection. The model is formulated based on the kinematic and dynamic of spring- damper configuration with multi-particle joints considering the visco-elastic fluidic properties. It simulates the indentation force feedback as well as cell visual deformation during microinjection. A preliminary simulation study is conducted with different parameter configurations. The results indicate that the proposed particle-based model is able to provide similar deformation profiles as observed from a real microinjection experiment of the zebrafish embryo published in the literature. As a generic modelling approach is adopted, the proposed model also has the potential in applications with different types of manipulation such as micropipette cell aspiration.

Generative experimentation and social simulation : exploring gaming for model vVerification and validation

The agent-based modelling paradigm has been actively applied to address social normative issues such as values, cognition, morality and behaviours. The abstraction of human and human-like social processes and mechanisms result in misalignment of computational model with existing verification and validation techniques and expose significant challenges. We argue that human sources represent a sound approach for verification and validation of agent-based social simulation models. We propose a novel conceptual gaming framework that extracts required information from relevant sources as part of game play

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

Prediction interval-based neural network controller for nonlinear processes

Prediction interval (PI) has been extensively used to predict the forecasts for nonlinear systems as PI-based forecast is superior over point-forecast to quantify the uncertainties and disturbances associated with the real processes. In addition, PIs bear more information than point-forecasts, such as forecast accuracy. The aim of this paper is to integrate the concept of informative PIs in the control applications to improve the tracking performance of the nonlinear controllers. In the present work, a PI-based controller (PIC) is proposed to control the nonlinear processes. Neural network (NN) inverse model is used as a controller in the proposed method. Firstly, a PI-based model is developed to construct PIs for every sample or time instance. The PIs are then fed to the NN inverse model along with other effective process inputs and outputs. The PI-based NN inverse model predicts the plant input to get the desired plant output. The performance of the proposed PIC controller is examined for a nonlinear process. Simulation results indicate that the tracking performance of the PIC is highly acceptable and better than the traditional NN inverse model-based controller.

A new evidence-based approach to weight management in primary care: the Counterweight Programme

Background/Aims Obesity has become a global epidemic, and a major preventable cause of morbidity and mortality. Management strategies and treatment protocols are however poorly developed and evaluated. The aim of the Counterweight Programme is to develop an evidence-based model for the management of obesity in primary care.

Methods The Counterweight Programme is based on the theoretical model of Evidence-Based Quality Assessment aimed at improving the management of obese adults (18–75 years) in primary care. The model consists of four phases: (1) practice audit and needs assessment, (2) practice support and training, (3) practice nurse-led patient intervention, and (4) evaluation. Patient intervention consisted of screening and treatment pathways incorporating evidence-based approaches, including patient-centred goal setting, prescribed eating plans, a group programme, physical activity and behavioural approaches, anti-obesity medication and weight maintenance strategies. Weight Management Advisers who are specialist obesity dietitians facilitated programme implementation. Eighty practices were recruited of which 18 practices were randomized to act as controls and receive deferred intervention 2 years after the initial audit.

Results By February 2004, 58 of the 62 (93.5%) intervention practices had been trained to run the intervention programme, 47 (75.8%) practices were active in implementing the model and 1256 patients had been recruited (74% female, 26% male, mean age 50.6 years, SD 14). At baseline, 75% of patients had at one or more co-morbidity, and the mean body mass index (BMI) was 36.9 kg/m2 (SD 5.4). Of the 1256 patients recruited, 91% received one of the core lifestyle interventions in the first 12 months. For all patients followed up at 12 months, 34% achieved a clinical meaningful weight loss of 5% or more. A total of 51% of patients were classed as compliant in that they attended the required level of appointments in 3, 6, and 12 months. For fully compliant patients, weight loss improved with 43% achieving a weight loss of 5% or more at 12 months.

Conclusion The Counterweight Programme is an evidence-based weight management model which is feasible to implement in primary care.

Amalgamation of health services in South-West Victoria: reinvention or survival?

In Australia 'the hospital' has long been considered the cornerstone of small, rural health services. However, this premise has been altered significantly by the introduction of casemix loading and diagnostic-related groups that promote a rationalised output-based model of management. In the light of these changes, many rural health services have struggled to reinvent themselves by establishing a range of service models such as Multi-purpose Service (MPS) and Health Streams, while maintaining traditional models (i.e. bush nursing centres, nursing homes and aged-care facilities). These changes are about survival. This paper analyses one such case in south-west Victoria, the Macarthur and District Community Outreach Service, and compares the outcomes with other similar Victorian rural health research projects. Particular attention is paid to the nature of the health services, the management of change and the proposed health outcomes for the local rural communities. In conclusion, it is argued that this study adds to the body of knowledge surrounding the construction of models of community health and development programming, These models impact upon future rural and remote area initiatives throughout Australia.

Fault detection in a cold forging process through feature extraction with a neural network

This paper investigates the application of neural networks to the recognition of lubrication defects typical to an industrial cold forging process employed by fastener manufacturers. The accurate recognition of lubrication errors, such as coating not being applied properly or damaged during material handling, is very important to the quality of the final product in fastener manufacture. Lubrication errors lead to increased forging loads and premature tool failure, as well as to increased defect sorting and the re-processing of the coated rod. The lubrication coating provides a barrier between the work material and the die during the drawing operation; moreover it needs be sufficiently robust to remain on the wire during the transfer to the cold forging operation. In the cold forging operation the wire undergoes multi-stage deformation without the application of any additional lubrication. Four types of lubrication errors, typical to production of fasteners, were introduced to a set of sample rods, which were subsequently drawn under laboratory conditions. The drawing force was measured, from which a limited set of features was extracted. The neural network based model learned from these features is able to recognize all types of lubrication errors to a high accuracy. The overall accuracy of the neural network model is around 98% with almost uniform distribution of errors between all four errors and the normal condition.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Considering the receiver in knowledge sharing: when the receiver seems ready the sharer appears

The knowledge needs and knowledge-related behaviours of receivers are among the most crucial, yet of ten overlooked, aspects of successful knowledge-sharing. This research examines how sharers consider receivers' knowledge needs and knowledge-related behaviours when choosing whether to share their knowledge and which channels to use for the transmission of that knowledge. A new theory of knowledge sharing - Receiver Theory - is introduced, and a receiver-based model of knowledge sharing is developed from existing literature. Two exploratory case studies are conducted using the model as a guiding framework. A key finding shows that perceived receiver knowledge needs and behaviours are Important motivators and inhibitors in sharer choices in intra-organisational knowledge sharing. This finding was suggested for both personalised and codified knowledge sharing strategies. The study suggests that for companies to realise more effective knowledge sharing, they should develop better ways to connect potential sharers with receivers' real knowledge needs. The study also suggests that sharing on a need-to know basis impedes change In organisational power structures and prevents the integration of isolated pockets of knowledge that may yield new value.

Radar target tracking via robust linear filtering

In this letter, we provide a robust version of a linear Kalman filter for target tracking based on a measurement conversion technique on the nonlinear radar measurements. We prove that the state estimation error is bounded in a probabilistic sense. We compare our approach with the current state of the art in converted radar measurement-based linear filtering.

Propagation characteristics of lamb waves in stringer-stiffened panels

Stringer-stiffened plate-like structure is a typical engineering structure and its structural integrity is critical. A guided Lamb wave-based damage identification scheme and an online structural health monitoring (SHM) system with an integrated PZT-sensor network were developed. In the previous studies, the specimens were relatively simple. In this paper, the above mentioned method was extended to the stiffened plate-like structure—a flat plate reinforced by stringer. FE dynamic simulation was applied to investigate the Lamb wave propagation characteristics due to the existence of stringer with the consideration of its material and geometric configurations.

Modeling the flow behavior, recrystallization, and crystallographic texture in hot-deformed Fe-30 Wt Pct Ni Austenite

The present work describes a hybrid modeling approach developed for predicting the flow behavior, recrystallization characteristics, and crystallographic texture evolution in a Fe-30 wt pct Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 °C and 1050 °C and strain rates between 0.1 and 10 s⁻¹. The evolution of grain structure, crystallographic texture, and dislocation substructure was characterized in detail for a deformation temperature of 950 °C and strain rates of 0.1 and 10 s⁻¹, using electron backscatter diffraction and transmission electron microscopy. The hybrid modeling method utilizes a combination of empirical, physically-based, and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallization behavior is predicted from the measured microstructural state variables of internal dislocation density, subgrain size, and misorientation between subgrains using a physically-based model. The texture evolution is modeled using artificial neural networks.