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AbstractWe propose a new type of max-stable process that we call the Tukey max-stable process for spatial extremes. It brings additional flexibility to modeling dependence structures among spatial extremes. The statistical properties of the Tukey max-stable process are demonstrated theoretically and numerically. Simulation studies and an application to Swiss rainfall data indicate the effectiveness of the proposed process. © 2016 Elsevier B.V.
CitationXu G, Genton MG (2016) Tukey max-stable processes for spatial extremes. Spatial Statistics 18: 431–443. Available: http://dx.doi.org/10.1016/j.spasta.2016.09.002.
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Organic Micropollutants Removal from Water by Oxidation and Other Processes:QSAR Models, Decision Support System and Hybrids of ProcessesSudhakaran, Sairam (2013-08)The presence of organic micropollutants (OMPs) in water is of great environmental concern. OMPs such as endocrine disruptors and certain pharmaceuticals have shown alarming effects on aquatic life. OMPs are included in the priority list of contaminants in several government directorate frameworks. The low levels of OMPs concentration (ng/L to μg/L) force the use of sophisticated analytical instruments. Although, the techniques to detect OMPs are progressing, the focus of current research is only on limited, important OMPs due to the high amount of time, cost and effort involved in analyzing them. Alternatively, quantitative structure activity relationship (QSAR) models help to screen processes and propose appropriate options without considerable experimental effort. QSAR models are well-established in regulatory bodies as a method to screen toxic chemicals. The goal of the present thesis was to develop QSAR models for OMPs removal by oxidation. Apart from the QSAR models, a decision support system (DSS) based on multi-criteria analysis (MCA) involving socio-economic-technical and sustainability aspects was developed. Also, hybrids of different water treatment processes were studied to propose a sustainable water treatment train for OMPs removal. In order to build the QSAR models, the ozone/hydroxyl radical rate constants or percent removals of the OMPs were compiled. Several software packages were used to 5 compute the chemical properties of OMPs and perform statistical analyses. For DSS, MCA was used since it allows the comparison of qualitative (non-monetary, non-metric) and quantitative criteria (e.g., costs). Quadrant plots were developed to study the hybrid of natural and advanced water treatment processes. The QSAR models satisfied both chemical and statistical criteria. The DSS resulted in natural treatment and ozonation as the preferred processes for OMPs removal. The QSAR models can be used as a screening tool for OMPs removal by oxidation. Moreover, the QSAR - defining molecular descriptors help in detailed understanding of oxidation. The DSS can be considered as an aid in assessing a multi-barrier approach to remove OMPs. Hybrids of natural and advanced treatment processes help to develop a more sustainable multi-barrier approach for OMPs removal.
Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urineEmwas, Abdul-Hamid M.; Saccenti, Edoardo; Gao, Xin; McKay, Ryan T.; dos Santos, Vitor A. P. Martins; Roy, Raja; Wishart, David S. (Springer Nature, 2018-02-12)1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.
Automated process flowsheet synthesis for membrane processes using genetic algorithm: role of crossover operatorsShafiee, Alireza; Arab, Mobin; Lai, Zhiping; Liu, Zongwen; Abbas, Ali (Elsevier BV, 2016-06-25)In optimization-based process flowsheet synthesis, optimization methods, including genetic algorithms (GA), are used as advantageous tools to select a high performance flowsheet by ‘screening’ large numbers of possible flowsheets. In this study, we expand the role of GA to include flowsheet generation through proposing a modified Greedysub tour crossover operator. Performance of the proposed crossover operator is compared with four other commonly used operators. The proposed GA optimizationbased process synthesis method is applied to generate the optimum process flowsheet for a multicomponent membrane-based CO2 capture process. Within defined constraints and using the random-point crossover, CO2 purity of 0.827 (equivalent to 0.986 on dry basis) is achieved which results in improvement (3.4%) over the simplest crossover operator applied. In addition, the least variability in the converged flowsheet and CO2 purity is observed for random-point crossover operator, which approximately implies closeness of the solution to the global optimum, and hence the consistency of the algorithm. The proposed crossover operator is found to improve the convergence speed of the algorithm by 77.6%.