Individuals' subjective expectations are important in explaining heterogeneity in individual choices, but their elicitation poses some challenges, in particular when one is interested in the subjective probability distribution of an individual. We have developed an innovative visual representation for Internet surveys that has some advantages over previously used formats. In this paper we present our findings from testing this visual representation in the context of individuals' Social Security expectations. Respondents are asked to allocate a total of 20 balls across seven bins to express what they believe the chances to be that their future Social Security benefits would fall into any one of those bins. Our data come from the Internet Survey of respondents to the Health and Retirement Study, a representative survey of the U.S. population age 51 and older. To contrast the results from the visual format with a previously used format we divided the sample into two random groups and administered both, the visual format and the more standard percent chance format. Our findings suggest that the main advantage of the visual format is that it generates usable answers for virtually all respondents in the sample while in the percent chance format a significant fraction (about 20 percent) of responses is lost due to inconsistencies. Across various other dimensions the visual format performs similarly to the percent chance format, leading us to conclude that the bins-and-balls format is a viable alternative that leads to more complete data.
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http://dx.doi.org/10.1093/poq/nfn062 | DOI Listing |
Anal Methods
January 2025
Jiangsu Beier Machinery Co. Ltd, Jiangsu, 215600, China.
Plastic waste management is one of the key issues in global environmental protection. Integrating spectroscopy acquisition devices with deep learning algorithms has emerged as an effective method for rapid plastic classification. However, the challenges in collecting plastic samples and spectroscopy data have resulted in a limited number of data samples and an incomplete comparison of relevant classification algorithms.
View Article and Find Full Text PDFFront Child Adolesc Psychiatry
December 2024
Brain Balance Achievement Centers, Naperville, IL, United States.
Accessibility to developmental interventions for children and adolescents could be increased through virtual, at-home delivery of training programs. Virtual childhood training programs and their effects on cognitive outcomes have not been well studied. To that end, this study examined the effects of the at-home Brain Balance® (BB) program on the cognitive task performance of children and adolescents with baseline developmental and attentional difficulties.
View Article and Find Full Text PDFAnn Ital Chir
January 2025
Medical Department, Ningbo No.9 Hospital, 315020 Ningbo, Zhejiang, China.
Aim: This study aimed to develop a reliable and efficient system for predicting and locating rib fractures in medical images using an ensemble of convolutional neural networks (CNNs).
Methods: We employed five CNN architectures-Visual Geometry Group Network 16 (VGG16), Densely Connected Convolutional Network 169 (DenseNet169), Inception Version 4 (Inception V4), Efficient Network B7 (EfficientNet-B7), and Residual Network Next 50 layers (ResNeXt-50)-trained on a dataset of 840 grayscale computed tomography (CT) scan images in .jpg format collected from 42 patients at a local hospital.
Bioinformatics
January 2025
Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA.
Summary: Time-lapse 3D imaging is fundamental for studying biological processes but requires software able to handle terabytes of voxel data. Although many multidimensional viewing applications exist, they mostly lack support for heterogeneous voxel counts, datatypes, and modalities in a single timeline. Open Chrono-Morph Viewer provides a straightforward graphical user interface to quickly investigate multi-timescale datasets represented as separate volume files in the common NRRD format for compatibility between toolchains.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain; valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain.
Digital pathology is now a standard component of the pathology workflow, offering numerous benefits such as high-detail whole slide images and the capability for immediate case sharing between hospitals. Recent advances in deep learning-based methods for image analysis make them a potential aid in digital pathology. However, A significant challenge in developing computer-aided diagnostic systems for pathology is the lack of intuitive, open-source web applications for data annotation.
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