A New Self-Adjusting CVT Configuration Using Compliant Mechanisms

ABSTRACT This paper introduces a new configuration of a Continuously Variable Transmission (CVT) that is self-adjusting and designed as a compliant mechanism. This new configuration is called the Pivot-Arm CVT. The criteria for classification as a Pivot-Arm CVT is discussed. An analytical model describing the performance of the Pivot-Arm CVT is developed. Special design considerations which may be useful in implementing Pivot-Arm CVTs are introduced and explained. The Pivot-Arm CVT model is validated through controlled testing of two Pivot-Arm CVT prototypes

Thermodynamic model fitting of the calorimetric output obtained for aqueous solutions of oxyethylene-oxypropylene-oxyethylene triblock copolymers

A high-sensitivity differential scanning calorimetry (HSDSC) study of aggregation transitions in dilute aqueous solutions of oxyethylene-oxypropylene-oxyethylene (EO-PO-EO) triblock copolymers (poloxamers) is reported. The data have been analyzed using a previously described thermodynamic model (Armstrong, J. K.; et al. J. Chem. Res. 1994, 364) based upon a mass action description of aggregation which has been further elaborated to include the effect of changes in the heat capacity of the initial and final states. As a consequence the model incorporates the underlying changes in the heat capacity of the system, thus obviating the need for baseline fitting and as such provides a useful mechanism for the analysis of the data. Model-fitting results are presented for aqueous solutions of various concentrations of the poloxamers P237 (EO62PO39EO62) and P333 (EO19PO56EO19) In addition model-derived results are presented for a number of other poloxamer solutions. The thermodynamic data obtained are further used to produce phase diagrams of the aggregation process as a function of concentration and temperature. Furthermore the calorimetric output is also used to compute critical micelle concentration and critical micelle temperature data. Data obtained for P333 complement spectroscopic data reported in the literature. The thermodynamic data obtained show a number of important trends. The heat capacity change values obtained are invariably negative, pointing toward the loss of solvating water structure on aggregation. Two measures of enthalpy are computed: the calorimetric enthalpy-obtained from integration of the calorimetric output-and the van't Hoff enthalpy-obtained from the change of the equilibrium constant characterizing aggregation with temperature. Both these measure of enthalpy are positive. The computed entropy changes are Likewise positive, indicating that aggregation in these systems is an entropy-driven process. The van't Hoff enthalpy/calorimetric enthalpy ratio further indicates the aggregation process to be cooperative. The temperature at which aggregation is half completed (T-1/2) varies with copolymer concentration. The corresponding change in the van't Hoff enthalpy results from the temperature dependence of the enthalpy. Data are also obtained for aqueous solutions of a further 12 EO-PO-EO block copolymers. Multiple linear regression analysis of the van't Hoff enthalpy normalized to 298.15 K as a function of PO and EO block length points to the importance of the PO block in determining the size of the van't Hoff enthalpy. Finally an enthalpy-entropy compensation plot indicates that the same solvent-solute interactions are responsible for the transitions in all the samples regardless of the copolymer composition and concentration

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future