Kinetics of CO2 with primary and secondary amines in aqueous solutions - I. Zwitterion deprotonation kinetics for DEA and DIPA in aqueous blends of alkanolamines

The deprotonation kinetics of the DEA—CO2 and the DIPA—CO2 zwitterions have been studied in aqueous blends of amines at 298 K. Amine mixtures investigated were: DEA—TEA, DEA—MDEA, DEA—DMMEA, DEA—DEMEA, DIPA—TEA. DIPA—MDEA, DIPA—DMMEA, DIPA—DEMEA. For each blend the zwitterion deprotonation constant of the additional base present in solution (i.e. the tertiary amine) was determined. The observed deprotonation rate constants for the DEA-zwitterion and for the DIPA-zwitterion could be summarized in two Brønsted-type relationships. These relationships can be used to estimate the overall reaction rate of CO2 with DEA or DIPA in aqueous blends of amines. The present work on the zwitterion deprotonation kinetics of the reaction of CO2 with DEA and DIPA in aqueous amine blends provides additional verification for the validity of the zwitterion mechanism proposed by Caplow(1968) for the description of the reaction between CO2 and primary and secondary alkanolamines

Microfinance institutions and efficiency

Microfinance Institutions (MFIs) are special financial institutions. They have both a social nature and a for-profit nature. Their performance has been traditionally measured by means of financial ratios. The paper uses a Data Envelopment Analysis (DEA) approach to efficiency to show that ratio analysis does not capture DEA efficiency.Special care is taken in the specification of the DEA model. We take a methodological approach based on multivariate analysis. We rank DEA efficiencies under different models and specifications; e.g., particular sets of inputs and outputs. This serves to explore what is behind a DEA score. The results show that we can explain MFIs efficiency by means of four principal components of efficiency, and this way we are able to understand differences between DEA scores. It is shown that there are country effects on efficiency; and effects that depend on Non-governmental Organization (NGO)/non-NGO status of the MFI

Multi-level DEA Approach in Research Evaluation

It is well known that the discrimination power of DEA models will be diminishing if too many inputs or outputs are used. It is a dilemma if the decision makers want to select comprehensive indicators to present a relatively holistic evaluation using DEA. In this work we show that by utilizing hierarchical structures of input-output data DEA can handle quite large numbers of inputs and outputs. We present two approaches in a pilot evaluation of 15 institutes for basic research in Chinese Academy of Sciences using DEA models

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

Fractional regression models for second stage DEA efficiency analyses

Data envelopment analysis (DEA) is commonly used to measure the relative efficiency of decision-making units. Often, in a second stage, a regression model is estimated to relate DEA efficiency scores to exogenous factors. In this paper, we argue that the traditional linear or tobit approaches to second-stage DEA analysis do not constitute a reasonable data-generating process for DEA scores. Under the assumption that DEA scores can be treated as descriptive measures of the relative performance of units in the sample, we show that using fractional regression models are the most natural way of modeling bounded, proportional response variables such as DEA scores. We also propose generalizations of these models and, given that DEA scores take frequently the value of unity, examine the use of two-part models in this framework. Several tests suitable for assessing the specification of each alternative model are also discussed.Second-stage DEA; Fractional data; Specification tests; One outcomes; Two-part models.

L\'{e}vy scaling: the Diffusion Entropy Analysis applied to DNA sequences

We address the problem of the statistical analysis of a time series generated by complex dynamics with a new method: the Diffusion Entropy Analysis (DEA) (Fractals, {\bf 9}, 193 (2001)). This method is based on the evaluation of the Shannon entropy of the diffusion process generated by the time series imagined as a physical source of fluctuations, rather than on the measurement of the variance of this diffusion process, as done with the traditional methods. We compare the DEA to the traditional methods of scaling detection and we prove that the DEA is the only method that always yields the correct scaling value, if the scaling condition applies. Furthermore, DEA detects the real scaling of a time series without requiring any form of de-trending. We show that the joint use of DEA and variance method allows to assess whether a time series is characterized by L\'{e}vy or Gauss statistics. We apply the DEA to the study of DNA sequences, and we prove that their large-time scales are characterized by L\'{e}vy statistics, regardless of whether they are coding or non-coding sequences. We show that the DEA is a reliable technique and, at the same time, we use it to confirm the validity of the dynamic approach to the DNA sequences, proposed in earlier work.Comment: 24 pages, 9 figure

The use of disjunct eddy sampling methods for the determination of ecosystem level fluxes of trace gases

The concept of disjunct eddy sampling (DES) for use in measuring ecosystem-level micrometeorological fluxes is re-examined. The governing equations are discussed as well as other practical considerations and guidelines concerning this sampling method as it is applied to either the disjunct eddy covariance (DEC) or disjunct eddy accumulation (DEA) techniques. A disjunct eddy sampling system was constructed that could either be combined with relatively slow sensors (response time of 2 to 40 s) to measure fluxes using DEC, or could also be used to accumulate samples in stable reservoirs for later laboratory analysis (DEA technique). Both the DEC and DEA modes of this sampler were tested against conventional eddy covariance (EC) for fluxes of either CO2 (DEC) or isoprene (DEA). Good agreement in both modes was observed relative to the EC systems. However, the uncertainty in a single DEA flux measurement was considerable (40%) due to both the reduced statistical sampling and the analytical precision of the concentration difference measurements. We have also re-investigated the effects of nonzero mean vertical wind velocity on accumulation techniques as it relates to our DEA measurements. Despite the higher uncertainty, disjunct eddy sampling can provide an alternative technique to eddy covariance for determining ecosystem-level fluxes for species where fast sensors do not currently exist