HTHP syngas cleaning concept of two stage biomass gasification for FT synthesis

The EU intends to increase the fraction of fuels from biogenic energy sources from 2% in 2005 to 8% in 2020. This means a minimum of 30 million TOE/a of fuels from biomass. This makes technical-scale generation of syngas from high-grade biomass, e.g. straw, hay, bark, or paper/cardboard waste, and the production of synthetic fuels by Fischer-Tropsch (FT) synthesis highly attractive. The BTL concept (Biomass to Liquids) of the Karlsruhe Research Center, labeled bioliq, focuses on this challenge by locally concentrating the biomass energy content by fast pyrolysis in a coke/oil slurry followed by slurry conversion to syngas in a central entrained flow gasifier at 1200C and pressures above 4MPa. FT synthesis generates intermediate products for synthetic fuels. To prevent the sensitive catalysts from being poisoned the syngas must be free of tar and particulates. Trace concentrations of H2S, COS, CS2, HCl, NH3, and HCN must be on the order of a few ppb. Moreover, maximum conversion efficiency will be achieved by cleaning the gas above the synthesis conditions. (T>350C, P>4MPa). The concept of an innovative dry HTHP syngas cleaning process is presented. Based on HT particle filtration and suitable sorption and catalysis processes for the relevant contaminants, an overall concept will be derived, which leads to a syngas quality required for FT synthesis in only two combined stages. Results of filtration experiments on a pilot scale are presented. The influence of temperature on the separation and conversion, respectively, of particulates and gaseous contaminants is discussed on the basis of experimental results obtained on a laboratory and pilot scale. Extensive studies of this concept are performed in a scientific network comprising the Karlsruhe Research Center and five universities; funding is provided by the Helmholtz Association of National Research Centers in Germany.

A two-stage win–win multiattribute negotiation model:optimization and then concession

Many automated negotiation models have been developed to solve the conflict in many distributed computational systems. However, the problem of finding win-win outcome in multiattribute negotiation has not been tackled well. To address this issue, based on an evolutionary method of multiobjective optimization, this paper presents a negotiation model that can find win-win solutions of multiple attributes, but needs not to reveal negotiating agents' private utility functions to their opponents or a third-party mediator. Moreover, we also equip our agents with a general type of utility functions of interdependent multiattributes, which captures human intuitions well. In addition, we also develop a novel time-dependent concession strategy model, which can help both sides find a final agreement among a set of win-win ones. Finally, lots of experiments confirm that our negotiation model outperforms the existing models developed recently. And the experiments also show our model is stable and efficient in finding fair win-win outcomes, which is seldom solved in the existing models. © 2012 Wiley Periodicals, Inc.

Gaussian process quantile regression using expectation propagation

Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagation algorithm. We describe how this work relates to other quantile regression methods and apply the method on both synthetic and real data sets. The method is shown to be competitive with state of the art methods whilst allowing for the leverage of the full Gaussian process probabilistic framework.

A two-stage model of orientation integration for Battenberg-modulated micropatterns

The visual system pools information from local samples to calculate textural properties. We used a novel stimulus to investigate how signals are combined to improve estimates of global orientation. Stimuli were 29 × 29 element arrays of 4 c/deg log Gabors, spaced 1° apart. A proportion of these elements had a coherent orientation (horizontal/vertical) with the remainder assigned random orientations. The observer's task was to identify the global orientation. The spatial configuration of the signal was modulated by a checkerboard pattern of square checks containing potential signal elements. The other locations contained either randomly oriented elements (''noise check'') or were blank (''blank check''). The distribution of signal elements was manipulated by varying the size and location of the checks within a fixed-diameter stimulus. An ideal detector would only pool responses from potential signal elements. Humans did this for medium check sizes and for large check sizes when a signal was presented in the fovea. For small check sizes, however, the pooling occurred indiscriminately over relevant and irrelevant locations. For these check sizes, thresholds for the noise check and blank check conditions were similar, suggesting that the limiting noise is not induced by the response to the noise elements. The results are described by a model that filters the stimulus at the potential target orientations and then combines the signals over space in two stages. The first is a mandatory integration of local signals over a fixed area, limited by internal noise at each location. The second is a taskdependent combination of the outputs from the first stage. © 2014 ARVO.

Optimisation of two-stage raman converter based on phosphosilicate core fibre:Modelling and experiment

Parameter optimization of a two-stage Raman fibre converters (RFC) based on phosphosilicate core fiber was presented. The optimal operational regime was determined and tolerance of the converter against variations of laser parameters was analyzed. Converter was pumped by ytterbium-doped double-clad fibre laser with a maximum output power of 3.8W at 1061 nm. A phosphosilicate-core RFC with enhanced performance was fabricated using the results of numerical modelling.

A linear relational DEA model to evaluate two-stage processes with shared inputs

Two-stage data envelopment analysis (DEA) efficiency models identify the efficient frontier of a two-stage production process. In some two-stage processes, the inputs to the first stage are shared by the second stage, known as shared inputs. This paper proposes a new relational linear DEA model for dealing with measuring the efficiency score of two-stage processes with shared inputs under constant returns-to-scale assumption. Two case studies of banking industry and university operations are taken as two examples to illustrate the potential applications of the proposed approach.

On the comparative performance of socially responsible and Islamic mutual funds

This is the first study to provide comprehensive analyses of the relative performance of both socially responsible investment (SRI) and Islamic mutual funds. The analysis proceeds in two stages. In the first, the performance of the two categories of funds is measured using partial frontier methods. In the second stage, we use quantile regression techniques.By combining two variants of the Free Disposal Hull (FDH) methods (order-m and order-?) in the first stage of analysis and quantile regression in the second stage, we provide detailed analyses of the impact of different covariates across methods and across different quantiles. In spite of the differences in the screening criteria and portfolio management of both types of funds, variation in the performance is only found for some of the quantiles of the conditional distribution of mutual fund performance. We established that for the most inefficient funds the superior performance of SRI funds is significant. In contrast, for the best mutual funds this evidence vanished and even Islamic funds perform better than SRI.These results show the benefits of performing the analysis using quantile regression.

On the comparative performance of socially responsible and Islamic mutual funds

This is the first study to provide comprehensive analyses of the relative performance of both socially responsible investment (SRI) and Islamic mutual funds. The analysis proceeds in two stages. In the first, the performance of the two categories of funds is measured using partial frontier methods. In the second stage, we use quantile regression techniques. By combining two variants of the Free Disposal Hull (FDH) methods (order- m and order- α) in the first stage of analysis and quantile regression in the second stage, we provide detailed analyses of the impact of different covariates across methods and across different quantiles. In spite of the differences in the screening criteria and portfolio management of both types of funds, variation in the performance is only found for some of the quantiles of the conditional distribution of mutual fund performance. We established that for the most inefficient funds the superior performance of SRI funds is significant. In contrast, for the best mutual funds this evidence vanished and even Islamic funds perform better than SRI. These results show the benefits of performing the analysis using quantile regression. © 2013 Elsevier B.V.

The use of correlation and regression methods in optometry

Correlation and regression are two of the statistical procedures most widely used by optometrists. However, these tests are often misused or interpreted incorrectly, leading to erroneous conclusions from clinical experiments. This review examines the major statistical tests concerned with correlation and regression that are most likely to arise in clinical investigations in optometry. First, the use, interpretation and limitations of Pearson's product moment correlation coefficient are described. Second, the least squares method of fitting a linear regression to data and for testing how well a regression line fits the data are described. Third, the problems of using linear regression methods in observational studies, if there are errors associated in measuring the independent variable and for predicting a new value of Y for a given X, are discussed. Finally, methods for testing whether a non-linear relationship provides a better fit to the data and for comparing two or more regression lines are considered.

Breast cancer teams:the impact of constitution, new cancer workload and methods of operation on their effectiveness

National guidance and clinical guidelines recommended multidisciplinary teams (MDTs) for cancer services in order to bring specialists in relevant disciplines together, ensure clinical decisions are fully informed, and to coordinate care effectively. However, the effectiveness of cancer teams was not previously evaluated systematically. A random sample of 72 breast cancer teams in England was studied (548 members in six core disciplines), stratified by region and caseload. Information about team constitution, processes, effectiveness, clinical performance, and members' mental well-being was gathered using appropriate instruments. Two input variables, team workload (P=0.009) and the proportion of breast care nurses (P=0.003), positively predicted overall clinical performance in multivariate analysis using a two-stage regression model. There were significant correlations between individual team inputs, team composition variables, and clinical performance. Some disciplines consistently perceived their team's effectiveness differently from the mean. Teams with shared leadership of their clinical decision-making were most effective. The mental well-being of team members appeared significantly better than in previous studies of cancer clinicians, the NHS, and the general population. This study established that team composition, working methods, and workloads are related to measures of effectiveness, including the quality of clinical care. © 2003 Cancer Research UK.

Interocular masking and summation indicate two stages of divisive contrast gain control

Our understanding of early spatial vision owes much to contrast masking and summation paradigms. In particular, the deep region of facilitation at low mask contrasts is thought to indicate a rapidly accelerating contrast transducer (eg a square-law or greater). In experiment 1, we tapped an early stage of this process by measuring monocular and binocular thresholds for patches of 1 cycle deg-1 sine-wave grating. Threshold ratios were around 1.7, implying a nearly linear transducer with an exponent around 1.3. With this form of transducer, two previous models (Legge, 1984 Vision Research 24 385 - 394; Meese et al, 2004 Perception 33 Supplement, 41) failed to fit the monocular, binocular, and dichoptic masking functions measured in experiment 2. However, a new model with two-stages of divisive gain control fits the data very well. Stage 1 incorporates nearly linear monocular transducers (to account for the high level of binocular summation and slight dichoptic facilitation), and monocular and interocular suppression (to fit the profound 42 Oral presentations: Spatial vision Thursday dichoptic masking). Stage 2 incorporates steeply accelerating transduction (to fit the deep regions of monocular and binocular facilitation), and binocular summation and suppression (to fit the monocular and binocular masking). With all model parameters fixed from the discrimination thresholds, we examined the slopes of the psychometric functions. The monocular and binocular slopes were steep (Weibull ߘ3-4) at very low mask contrasts and shallow (ߘ1.2) at all higher contrasts, as predicted by all three models. The dichoptic slopes were steep (ߘ3-4) at very low contrasts, and very steep (ß>5.5) at high contrasts (confirming Meese et al, loco cit.). A crucial new result was that intermediate dichoptic mask contrasts produced shallow slopes (ߘ2). Only the two-stage model predicted the observed pattern of slope variation, so providing good empirical support for a two-stage process of binocular contrast transduction. [Supported by EPSRC GR/S74515/01]

The impact of ICT on productivity of airline industry

Airline industry is at the forefront of many technological developments and is often a pioneer in adopting such innovations in a large scale. It needs to improve its efficiency as the current trends for input prices and competitive pressures show that any airline will face increasingly challenging market conditions. This paper has focused on the relationship between ICT investments and efficiency in the airline industry and employed a two-stage analytical investigation, DEA, SFA and the Tobit regression model. In this study, we first estimate the productivity of the airline industry using a balanced panel of 17 airlines over the period 1999–2004 by the Data Envelop Analysis (DEA) and the Stochastic Frontier Analysis (SFA) methods. We then evaluate the impacts of the determinants of productivity in the industry concentrating on ICT. The results suggest that regardless of all the negative shocks to the airline industry during the sample period, ICT had a positive effect on productivity during 1999-2004.

Statnote 18: comparison of regression lines

In many circumstances, it may be of interest to discover whether two or more regression lines are the same. Regression lines may differ in three properties, viz., in residual variance, in slope, and in elevation; all of which can be tested using analysis of covariance. If there are no significant differences between regression lines, an investigator may which to combine the data from different studies and fit a single regression line to the whole of the data.

A non-parametric data envelopment analysis approach for improving energy efficiency of grape production

Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production.In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming.The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. © 2013.

The Long-Term impact of Business Support? - Exploring the Role of Evaluation Timing using Micro Data

The original contribution of this work is threefold. Firstly, this thesis develops a critical perspective on current evaluation practice of business support, with focus on the timing of evaluation. The general time frame applied for business support policy evaluation is limited to one to two, seldom three years post intervention. This is despite calls for long-term impact studies by various authors, concerned about time lags before effects are fully realised. This desire for long-term evaluation opposes the requirements by policy-makers and funders, seeking quick results. Also, current ‘best practice’ frameworks do not refer to timing or its implications, and data availability affects the ability to undertake long-term evaluation. Secondly, this thesis provides methodological value for follow-up and similar studies by using data linking of scheme-beneficiary data with official performance datasets. Thus data availability problems are avoided through the use of secondary data. Thirdly, this thesis builds the evidence, through the application of a longitudinal impact study of small business support in England, covering seven years of post intervention data. This illustrates the variability of results for different evaluation periods, and the value in using multiple years of data for a robust understanding of support impact. For survival, impact of assistance is found to be immediate, but limited. Concerning growth, significant impact centres on a two to three year period post intervention for the linear selection and quantile regression models – positive for employment and turnover, negative for productivity. Attribution of impact may present a problem for subsequent periods. The results clearly support the argument for the use of longitudinal data and analysis, and a greater appreciation by evaluators of the factor time. This analysis recommends a time frame of four to five years post intervention for soft business support evaluation.