Cost-(in)effective public good provision: an experimental exploration

This paper investigates the determinants of cost-(in)effective giving to public goods. We conduct a pre-registered experiment to elucidate how factors at the institutional and individual levels shape individual contributions and the cost-effectiveness of those contributions in a novel public good game. In particular, we examine the role of consequential uncertainty over the value of public good contributions (institutional level) as well as individual characteristics like risk and ambiguity attitudes, giving type, and demographics (individual level). We find cost-ineffective contributions in all institutions, but total contribution levels and the degree of cost-ineffectiveness are similar across institutions. Meanwhile, cost-effectiveness varies by giving type—which is a novel result that is consistent with hypotheses we generate from theory—but other individual characteristics have little influence on the cost-effectiveness of contributions. Our work has important positive and normative implications for charitable giving and public good provision in the real world, and it is particularly germane to emerging online crowdfunding and patronage platforms that confront users with a multitude of competing opportunities for giving

In Search of Competitive Givers

Much of the theoretical and experimental research on charitable giving allows for three main types of donor: pure altruists, impure altruists, and pure warm-glow givers. For none of these types should donations be increasing in the amount donated by others, and the fact that some experimental subjects do behave in this way suggests a fourth type, the "competitive giver". Our experimental results provide evidence for the existence of competitive givers. The results also suggest that most (but not all) competitive giving is a result of uncertainty about the social norm: when information about the norm is revealed, the incidence of competitive giving is much lower

Cost-(in)effective public good provision: An experimental exploration

This paper investigates the determinants of cost-(in)effective giving to public goods. We conduct a pre-registered experiment to elucidate how factors at the institutional and individual levels shape individual contributions and the cost-effectiveness of those contributions in a novel public good game. In particular, we examine the role of consequential uncertainty over the value of public good contributions (institutional level) as well as individual characteristics like risk and ambiguity attitudes, giving type, and demographics (individual level). We nd that consequential uncertainty tends to reduce overall contributions, but not the cost-effectiveness of those contributions. Meanwhile, cost-effectiveness varies by giving type-which is a novel result that is consistent with hypotheses we generate from theory-but other individual characteristics have little influence on contributions or cost-effectiveness. Our work has important positive and normative implications for charitable giving and public good provision in the real world, and it is particularly germane to emerging online crowdfunding and patronage platforms that confront users with a multitude of competing opportunities for giving

Radiographs in Clinical Periodontal Trials

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141530/1/jper0381.pd

New insights on neutral binary representations for evolutionary optimization

This paper studies a family of redundant binary representations NNg(l, k), which are based on the mathematical formulation of error control codes, in particular, on linear block codes, which are used to add redundancy and neutrality to the representations. The analysis of the properties of uniformity, connectivity, synonymity, locality and topology of the NNg(l, k) representations is presented, as well as the way an (1+1)-ES can be modeled using Markov chains and applied to NK fitness landscapes with adjacent neighborhood.The results show that it is possible to design synonymously redundant representations that allow an increase of the connectivity between phenotypes. For easy problems, synonymously NNg(l, k) representations, with high locality, and where it is not necessary to present high values of connectivity are the most suitable for an efficient evolutionary search. On the contrary, for difficult problems, NNg(l, k) representations with low locality, which present connectivity between intermediate to high and with intermediate values of synonymity are the best ones. These results allow to conclude that NNg(l, k) representations with better performance in NK fitness landscapes with adjacent neighborhood do not exhibit extreme values of any of the properties commonly considered in the literature of evolutionary computation. This conclusion is contrary to what one would expect when taking into account the literature recommendations. This may help understand the current difficulty to formulate redundant representations, which are proven to be successful in evolutionary computation. (C) 2016 Elsevier B.V. All rights reserved

Passive control of critical speeds of a rotating shaft using eccentric sleeves: model development (GT2016-58155)

This paper considers the passive control of lateral critical speeds in high-speed rotating shafts through application of eccentric balancing sleeves. Equations of motion for a rotating flexible shaft with eccentric sleeves at the free ends are derived using the extended Hamilton Principle, considering inertial, non-constant rotating speed, Coriolis and centrifugal effects. A detailed analysis of the passive control characteristics of the eccentric sleeve mechanism and its impact on the shaft dynamics, is presented. Results of the analysis are compared with those from three-dimensional finite element simulations for 3 practical case studies. Through a comparison and evaluation of the relative differences in critical speeds from both approaches it is shown that consideration of eccentric sleeve flexibility becomes progressively more important with increasing sleeve length. The study shows that the critical speed of high-speed rotating shafts can be effectively controlled through implementation of variable mass/stiffness eccentric sleeve systems

Searching for Imaging Biomarkers of Psychotic Dysconnectivity

Background: Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging. Methods: We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics. Results: Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results. Conclusions: Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers

Growth Kinetics in Layer‐by‐Layer Assemblies of Organic Nanoparticles and Polyelectrolytes

The growth rates of layer‐by‐layer (LbL) assemblies of polyelectrolytes (PEs) with oppositely charged polystyrene (PS) nanoparticles (NPs) as a function of molecular weight (MW) of the PEs, ionic strength of the media, and NP size and charge are systematically investigated. To optimize LbL growth, the effects of suspension concentration, pH of the media, and deposition time on the growth rate of multilayers are assessed. Both linear and exponential growth behaviors are observed and, under optimal conditions, films of up to around 1 μm thick can readily be assembled after 10 or so bilayers have been deposited. For many of the cases studied, an intermediate MW of PE leads to the fastest film buildup, for both cationic poly(ethyleneimine) deposited alternately with anionic PS NPs and for anionic poly(acrylic acid) deposited alternately with cationic PS NPs. The existence of an optimal MW suggests that growth rate is determined by a balance of thermodynamic factors, including density of polymer bridges between particles, and kinetic factors, specifically the diffusivity of polymer in the film. The optimal MW, however, is very sensitive to the materials used. Moreover, depending on the MW of the PE, increasing salinity could increase or decrease the growth kinetics. Finally, the surface morphology of the films is characterized with AFM and SEM to reveal that the roughness increases less than linearly with film thickness.Growth factors: The growth rates of layer‐by‐layer (LbL) assemblies of polyelectrolytes (PEs) with oppositely charged polystyrene nanoparticles are systematically investigated. The molecular weight of a PE has a considerable effect on LbL film growth and its surface morphology (see figure).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135666/1/cphc201600789_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135666/2/cphc201600789-sup-0001-misc_information.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135666/3/cphc201600789.pd

DYX1C1 is required for axonemal dynein assembly and ciliary motility

Dyx1c1 has been associated with dyslexia and neuronal migration in the developing neocortex. Unexpectedly, we found that deletion of Dyx1c1 exons 2–4 in mice caused a phenotype resembling primary ciliary dyskinesia (PCD), a genetically heterogeneous disorder characterized by chronic airway disease, laterality defects, and male infertility. This phenotype was confirmed independently in mice with a Dyx1c1c.T2A start codon mutation recovered from an ENU mutagenesis screen. Morpholinos targeting dyx1c1 in zebrafish also created laterality and ciliary motility defects. In humans, recessive loss-of-function DYX1C1 mutations were identified in twelve PCD individuals. Ultrastructural and immunofluorescence analyses of DYX1C1-mutant motile cilia in mice and humans revealed disruptions of outer and inner dynein arms (ODA/IDA). DYX1C1 localizes to the cytoplasm of respiratory epithelial cells, its interactome is enriched for molecular chaperones, and it interacts with the cytoplasmic ODA/IDA assembly factor DNAAF2/KTU. Thus, we propose that DYX1C1 is a newly identified dynein axonemal assembly factor (DNAAF4)