Identification of Factors Affecting Preservative Efficacy and Chemical Stability of Lamivudine Oral Solution Through Statistical Experimental Design

To identify factors affecting the chemical stability and preservative efficacy of lamivudine oral liquid formulations, an optimization study using a central composite design was performed. In this design, five factors, each at three levels, were investigated: pH (4.5, 5.5, and 73, sucrose (5%, 20%. and 50% w/v), propylene glycol (0% 2%, and 5% w/v), glycerin (4% 8%, and 12% w/v). and EDTA (0.100. 0.175, and 0.250 mg/mL). All formulations contained a constant concentration of lamivudine, parabens, and artificial strawberry and banana flavors. All formulations were evaluated for preservative effectiveness against USP and BP standards and for chemical stability at 30°C and 40°C for three months. All formulations were effective against bacteria and yeasts, but indicated reduced preservative effectiveness against the mold Aspergillus niger. Preservative effectiveness improved with increasing pH (4.5 to 7.5) and to a lesser extent with increasing EDTA concentration (0.100 to 0.250 mg/mL). Increasing glycerin concentration (4% to 12% w/v) slightly decreased preservative effectiveness. Over the concentration ranges tested, no change in preservative effectiveness was noted with concentration changes in sucrose or propylene glycol. The pH was the main factor influencing the chemical stability of the drug and preservatives in this study. Lamivudine chemical stability increased with increasing pH from 4.5 to 7.5. Methyl and propylparaben showed extensive degradation at pH 7.5.     

Michael addition polymers from bisacetoacetates

Summary The Michael reaction was used to synthesize polymers from bisacetoacetyl esters and amides and a diacrylate. The reaction occurred readily at room temperature yielding semi-solid polymers with broad molecular weight distributions which narrowed as the reaction progressed. The polymers based on the amide had lower molecular weights than those based on the ester.     

Using rainbow net simulation to measure fault tolerance in a multiprocessor

The Rainbow net simulation technique is applied to modelling the impact of system load and fault handling on the availability of a fault-tolerant multiprocessor architecture. Rainbow nets are described along with the motivation for creating this modelling technique. A Rainbow net fault-handling model is created for the fault-tolerant multiprocessor architecture and the topology is shown to remain constant in size, independent of the number of processor, memory and I/O elements configured in the system. Simulation is performed with a varying load in terms of the number of active jobs the system must support. Results are given showing how the fault-tolerant capability varies with load. Two new metrics for evaluating fault tolerance are introduced; namely full fault-tolerability and partial fault-tolerability. They are based on simple observations in the model.     

Thermoplastic vulcanizates obtained by reaction-induced phase separation: Interplay between phase separation dynamics,final morphology and mechanical properties

Reaction-induced phase separation (RIPS) of miscible blends of poly(?-caprolactone) (PCL) and an epoxy resin based on poly(propylene oxide) (PPO) was used to prepare thermoplastic vulcanizates (TPVs) with fine rubber dispersions. Scanning electron microscopy (SEM) confirmed the formation of cross-linked rubber particles dispersed in the thermoplastic matrix at PCL contents ≥20 wt%. The morphology development during phase separation was studied by optical microscopy (OM) and time-resolved small-angle light scattering (SALS). It was shown that higher curing temperatures lead to a decrease in rubber particle size, but at the same time lead to an increase in the extent of particle connectivity. In some cases, gelation of the PPO-rich phase limits full structure development, which leads to extensive connectivity between the dispersed rubber particles and a strong deterioration in tensile properties.     

On the effectiveness of early life cycle defect prediction with Bayesian Nets

Standard practice in building models in software engineering normally involves three steps: collecting domain knowledge (previous results, expert knowledge); building a skeleton of the model based on step 1 including as yet unknown parameters; estimating the model parameters using historical data. Our experience shows that it is extremely difficult to obtain reliable data of the required granularity, or of the required volume with which we could later generalize our conclusions. Therefore, in searching for a method for building a model we cannot consider methods requiring large volumes of data. This paper discusses an experiment to develop a causal model (Bayesian net) for predicting the number of residual defects that are likely to be found during independent testing or operational usage. The approach supports (1) and (2), does not require (3), yet still makes accurate defect predictions (an R ² of 0.93 between predicted and actual defects). Since our method does not require detailed domain knowledge it can be applied very early in the process life cycle. The model incorporates a set of quantitative and qualitative factors describing a project and its development process, which are inputs to the model. The model variables, as well as the relationships between them, were identified as part of a major collaborative project. A dataset, elicited from 31 completed software projects in the consumer electronics industry, was gathered using a questionnaire distributed to managers of recent projects. We used this dataset to validate the model by analyzing several popular evaluation measures (R ², measures based on the relative error and Pred). The validation results also confirm the need for using the qualitative factors in the model. The dataset may be of interest to other researchers evaluating models with similar aims. Based on some typical scenarios we demonstrate how the model can be used for better decision support in operational environments. We also performed sensitivity analysis in which we identified the most influential variables on the number of residual defects. This showed that the project size, scale of distributed communication and the project complexity cause the most of variation in number of defects in our model. We make both the dataset and causal model available for research use.