Lessons from the submission and approval process of energy-efficiency CDM baseline and monitoring methodologies

Energy efficiency is a CDM project type that suffers from high methodology rejection rates. 43 baseline and monitoring methodologies for CDM energy efficiency projects are analyzed with respect to reasons for approval / rejection by the CDM Executive Board. Most methodologies have been rejected because they did not comply with implicit quality standards regarding presentation and conservativeness. Also, tools to select the baseline scenario and to prove additionality were frequently lacking. If the level or the quality of production in the baseline or the project scenario changes, a simple before-after-comparison is not valid. Black box models are not accepted and methodologies should be sufficiently differentiated to account for specific (technical) circumstances. The remaining lifetime of equipment has to be taken into account. Often, elements of small-scale methodologies have been retained in approvals of large-scale methodologies. --

influence.ME: tools for detecting influential data in mixed effects models

influence.ME provides tools for detecting influential data in mixed effects models. The application of these models has become common practice, but the development of diagnostic tools has lagged behind. influence.ME calculates standardized measures of influential data for the point estimates of generalized mixed effects models, such as DFBETAS, Cook’s distance, as well as percentile change and a test for changing levels of significance. influence.ME calculates these measures of influence while accounting for the nesting structure of the data. The package and measures of influential data\ud are introduced, a practical example is given, and strategies for dealing with influential data are suggested

Fabrication of Bone Substitute Material by Rapid Prototyping

Bone tissue engineering has gained much attention in recent years. A key requirement in this field is the development of scaffold structures, on which cells adhere. This can be done by fabricating scaffolds by direct procedures like 3D-printing or by indirect procedures like casting. With the 3D-printing process different structures were build up by using hydroxyapatite powder (HA) and a special binder material. Afterwards the printed 3D structures were sintered. For the casting process molds have been made of different resins by stereolithography and other processes using polymers and waxes. These structures were filled by a suspension of HA. By heating the resulting polymer/ceramic composite to a specific temperature it is possible to combust the polymer or wax. By further heating the remaining body, the HA is sintered. Compared to the 3D printing a better resolution can be obtained here. But there are restrictions regarding the ratio of polymer and the HA ceramic during the heating process which means a limitation for the level of porosity.Mechanical Engineerin

Ecological panel inference in repeated cross sections

This paper presents a Markov chain model for the estimation of individual-level binary transitions from a time series of independent repeated cross-sectional (RCS) samples. Although RCS samples lack direct information on individual turnover, it is demonstrated here that it is possible with these data to draw meaningful conclusions on individual state-to-state transitions. We discuss estimation and inference using maximum likelihood, parametric bootstrap and Markov chain Monte Carlo approaches. The model is illustrated by an application to the rise in ownership of computers in Dutch households since 1986, using a 13-wave annual panel data set. These data encompass more information than we need to estimate the model, but this additional information allows us to assess the validity of the parameter estimates. We examine the determinants of the transitions from 'have-not' to 'have' (and back again) using well-known socio-economic and demographic covariates of the digital divide. Parametric bootstrap and Bayesian simulation are used to evaluate the accuracy and the precision of the ML estimates and the results are also compared with those of a first-order dynamic panel model. To mimic genuine repeated cross-sectional data, we additionally analyse samples of independent observations randomly drawn from the panel. Software implementing the model is available.

Inferring transition probabilities from repeated cross sections: a cross-level inference approach to US presidential voting

This paper outlines a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro-level from a time series of independent cross-sectional samples with a binary outcomevariable. The model has its origins in the work of Moffitt (1993) and shares features with standard statistical methods for ecological inference. We show how ML estimates of the parameters can be obtained by the method-of-scoring, how to estimate time-varying covariate effects, and how to include non-backcastable variables in the model. The latter extension of the basic model is an important one as it strongly increases its potential application in a wide array of research contexts. The example illustration uses survey data on American presidential vote intentions from a five-wavepanel study conducted by Patterson (1980) in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with the observations in the panel. Directions for future work are discussed.Markov model;transition probabilities

Development of 121Sb Nuclear Forward Scattering and high pressure applications

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Planters against Peasants

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Student characteristics used to place children in the learning disabled category

The purposes of this study were to examine the characteristics that contribute to the placement of children in the learning disabilities category and to contribute to the development of a manual that can be used as a criterion for learning disabilities placement. To accomplish the purposes, graduate students examined five hypothetical case histories which contained fourteen student characteristics. However, five of the fourteen student characteristics were selected for data analysis. The five student characteristics were: Classroom Achievement, Intellectual Functioning, Perceptual-Motor Skills, Achievement Test Scores, and Classroom Grades. Subjects were divided into two groups: the experimental group which examined hypothetical case histories and discussed each as a group before making individual diagnostic decisions and the control group which also examined hypothetical case histories but did not participate in a group discussion before responding to the hypothetical case histories;Subjects of this study were 31 graduate students enrolled at Iowa State University during the spring semester of 1986. They were either enrolled in the graduate degree program in School Psychology, Counseling Psychology, or Education (Learning Disabilities or Emotional Disabilities). The students were randomly assigned to one of two groups, to which a treatment was then randomly assigned;Overall, judges were similar in how they responded to the recommended placement, ratings of severity of learning disability, and ratings of importance of student characteristics for the five hypothetical case histories. The findings from this study also suggested that judges were similar in how they made diagnostic decisions about the learning disabled regardless if they participated in group discussion or made individual decisions