Making navigation easier in object-oriented programming systems

It has been reported that non-expert users have difficulties in finding reusable software components in large object-oriented programming systems and there is a need for help tools. The research reported in this thesis addresses this issue. Described in this thesis is the design of a tool called BRRR, which aims to help non-expert users overcome such difficulties. It is developed for Smalltalk-80, the target system of this research.BRRR is a query tool with a browsing capacity. It allows users to find necessary components by query. Its design is based on the 'retrieval by reformulation' paradigm (Williams, 1984) which was originally used in the domain of information retrieval. This paradigm allows users to incrementally specify a query by reformulation. When users specify an initial query, BRRR presents the users with an example component which satisfies the query. The users can then construct further queries by using the information presented by the system. In this way, users who are not familiar with the system or who do not know exactly what they want can be guided towards the appropriate information.During this research, two versions of BRRR were developed: BRRRl and BRRR2. BRRRl was developed initially, based on the 'retrieval by reformulation' principle. After its implementation, a formative, empirical evaluation was conducted on it with a group of users. Based on the findings of the evaluation, BRRR2, an improved version of BRRR1 was developed. BRRR2 incorporates enhanced classification methods and explanation facilities. This new version of the tool was then evaluated empirically with a group of ten users.The empirical evaluation of BRRR2 showed encouraging results. It demonstrates that the 'retrieval by reformulation' approach used in this research could be used successfully in helping users find reusable software components in object-oriented programming systems

Application of Artificial Neural Networks in Predicting Abrasion Resistance of Solution Polymerized Styrene-Butadiene Rubber Based Composites

Abrasion resistance of solution polymerized styrene-butadiene rubber (SSBR) based composites is a typical and crucial property in practical applications. Previous studies show that the abrasion resistance can be calculated by the multiple linear regression model. In our study, considering this relationship can also be described into the non-linear conditions, a Multilayer Feed-forward Neural Networks model with 3 nodes (MLFN-3) was successfully established to describe the relationship between the abrasion resistance and other properties, using 23 groups of data, with the RMS error 0.07. Our studies have proved that Artificial Neural Networks (ANN) model can be used to predict the SSBR-based composites, which is an accurate and robust process

Intrusion of polyethylene glycol into solid-state nanopores

The intrusion of PEG aqueous solution into solid-state-nanopores upon mechanical pressure is experimentally investigated. By using hydrophobic nanoporous silica with a broad range of pore sizes, the characteristic size of PEG chains in water while penetrating nanopores is measured and analyzed, which increases with molecular weight and decreases with concentration of PEG. Its sensitivity to molecular weight is relatively limited due to nano-confinement. The inclusion of PEG as an intruding liquid imposes a rate effect on the intrusion pressure, and inhibits the extrusion from the nanopores

Insight into improving antidepressant adherence and symptoms by pharmacist intervention: A review

Purpose: To assess the effectiveness of antidepressant medication adherence-improving intervention by a pharmacist and its impact on clinical symptoms of depression among outdoor depressive patients.Methods: Various databases such as PubMed, Embase, and Scopus were used sources for the literature published during the last 20 years. Pharmacist intervention studies involving adult depressed patients (≥ 17 years old) and treated with antidepressants were included. Twelve studies met the inclusion criteria.Results: These studies depicted various levels of interventions in which pharmacist counseled and educated the patients to support medication adherence. In only one of the studies, pharmacist intervention exercised significant effect on the depression features of patients.Conclusion: The findings suggest that the implication of antidepressant medication adherenceimproving intervention by pharmacist leads to the improved adherence of adult depressive patients to antidepressants. However, pharmacist intervention did not show any significant influence on depression symptomology, necessitating further studies on the topic.Keywords: Pharmacist care, Depression, Antidepressants, Intervention, Medication adherenc

Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management

Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm