Model description and kinetic parameter analysis of MTBE biodegradation in a packed bed reactor

A dynamic modeling approach was used to estimate in-situ model parameters, which describe the degradation of methyl tert-butyl ether (MTBE) in a laboratory packed bed reactor. The measured dynamic response of MTBE pulses injected at the reactor's inlet was analyzed by least squares and parameter response surface methodologies. Response surfaces were found to be statistically significant and thus suitable for estimating the global minimum as well as the 95% parameter uncertainty regions. The linear parameter uncertainty estimates for the half-saturation constant (K(S)) and the maximum growth rate (micro(max)) were: 0相似文献   

Metacognition and system usability: Incorporating metacognitive research paradigm into usability testing

There is an agreement that perceived usability is important beyond actual effectiveness of software systems. Perceived usability is often obtained by self-reports provided after system use. Aiming to improve summative usability testing, we propose a methodology to enhance in-depth testing of users' performance and perceived usability at the task level. The metacognitive research approach allows detailed analysis of cognitive processes. Adapting its methodologies, we propose the Metacognitive Usability Profile (MUP) which includes a comprehensive set of measures based on collecting confidence in the success of each particular task and triangulating it with objective measures. We demonstrate using the MUP by comparing two versions of a project management system. Based on a task analysis we allocated tasks that differ between the versions and let participants (N = 100) use both versions. Although no difference was found between the versions in system-level perceived usability, the detailed task-level analysis exposed many differences. In particular, overconfidence was associated with low performance, which suggests that user interfaces better avoid illusions of knowing. Overall, the study demonstrates how the MUP exposes challenges users face. This, in turn, allows choosing the better task implementation among the examined options and to focus attempts for usability improvement.     

A novel approach for coal seam terrain prediction through information fusion of improved D–S evidence theory and neural network

In order to effectively and accurately forecast the distribution of coal seam terrain, a novel prediction approach through information fusion of improved D–S evidence theory and neural network was proposed. An improved strategy based on confidence level was presented for evidence theory to reduce the conflicts between evidences and enhance the fusion effect. Moreover, BPAs function was constructed reasonably through extracting weights from preliminary prediction values of four neural networks, and the flowchart of proposed approach was designed. Furthermore, a simulation example was provided and some comparisons with other fusion prediction methods were carried out. The simulation example and comparison results indicated that the proposed approach was feasible, high-precision and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to specify the effect of proposed system.     

A Procedure for the Sequential Interval Estimation of the Shape Parameter of the Gamma Distribution

A procedure for interval estimation of the shape parameter of the gamma distribution is proposed which enables the experimenter to obtain upper and lower confidence limits after each observation is sampled. An approximate lower bound for the probability that the resulting sequence of confidence intervals will contain the shape parameter is obtained. The proposed procedure may be useful in estimating the coefficient of variationassociated with non-negative variables whose distribution is skewed to the right. Comparisons with a single sample procedure are discussed.     

Confidence limits for Weibull parameters estimated using linear least squares analysis

Confidence limits (at selected levels of 68.27%, 90%, 95% and 99%) for unbiased estimation of Weibull parameters obtained using linear least squares (LLS) analysis were investigated in this paper. A Monte Carlo procedure was used to obtain probability distributions for unbiased estimates of Weibull modulus, m, and Weibull scale parameter, S_o, as a function of total specimen number, N (10 ≤ N ≤ 200), and m (1 ≤ m ≤ 25). Inspection of the probability distributions indicated that confidence limits for m depended only on N whereas those for S_o depended on both N and m. Whilst the determination of confidence limits for m proved to be relatively straightforward, the respective values for S_o were obtained by fitting an empirical equation to the S_o probability distributions approximated by a Gaussian curve. Example values of m and S_o confidence limits for selected N have been presented in this work.     

Confidence interval optimization for testing hypotheses under data with low statistics

The notion of a confidence interval (CI) is used in the both major sections of the mathematical statistics: parameter estimation and hypothesis testing. While estimating parameters a CI defines the bounds of the parameter estimates with a certain confidence probability, and while testing hypotheses a CI defines with a certain confidence probability an interval of values of the random variable which do not contradict the tested hypothesis about them.     

On the Determination of Single Sampling Attribute Plans Based upon a Linear Cost Model and a Prior Distribution

The linear cost model previously formalized by Hald [4], [5], [9] is reviewed. Techniques are described which permit easy determination of sampling plans based on that model. The degenerate, the beta, and the two point distributions are considered as prior distributions of p, the process fraction defective. For calculations only standard tables and a desk calculator are required.     

Consumers' confidence,reflections and response strategies following the horsemeat incident

Following the discovery of horsemeat in beef products in Europe in 2013, restoring consumers' confidence in processed meat products as well as in all the agencies involved – producers, food safety authorities, retailers – soon became a key priority. However, the European public's confidence in processed meat products and their views about government and industry actions to manage fraudulent practices in the wake of this incident are poorly understood. The objective of this study was to identify the core issues affecting consumers' confidence in the food industry, particularly in the meat processing sector, and to explore the impact of the horsemeat incident on consumers' purchasing and eating behaviour. It involved the use of an online deliberation tool VIZZATA™ to collect detailed views of 61 consumers in the UK and the Republic of Ireland. Many participants reported buying fewer products containing processed meat as a result of the horsemeat incident. These respondents also claimed that their confidence in processed foods containing meat was lower than before the incident. Participants suggested restoring consumer confidence through improved traceability, sourcing local ingredients, providing clearer and correct labelling and stating the origin of meat on pack. Overall, findings indicate that rebuilding consumer confidence in processed meat products following a food adulteration episode is a multifaceted and difficult process. Food authorities and the food industry can benefit from the insights provided by this study to address issues affecting consumer confidence and to improve their communication strategies during future food adulteration incidents.