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http://dx.doi.org/10.4158/EP.14.4.516 | DOI Listing |
Acta Psychol (Amst)
January 2025
Management Development Institute, Gurgaon, India. Electronic address:
Despite the growing cognizance of Generation Z (Gen Z) fashion consumers about the externalities of fast fashion, an attitude-behaviour gap persists in their willingness to pay for sustainable fashion. This study uses dual-processing theory to examine how nudging communications in online fashion retail influence Gen Z's sustainable fashion choices and willingness to pay. It also explores how Gen Z's fashion-related knowledge and involvement and ecological consciousness moderate the effects of nudging.
View Article and Find Full Text PDFColorectal Dis
January 2025
UCD Centre for Precision Surgery, School of Medicine, University College Dublin, Dublin, Ireland.
J Hosp Med
January 2025
Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.
Infant Ment Health J
January 2025
Education Department, Tufts University, Medford, Massachusetts, USA.
This blended pilot-empirical and theoretical manuscript documents a reflective journey undertaken by a group of early childhood teacher educators located across different regions of the United States as they examined their course design, materials, and syllabi construction. Grounded in reflective practice, intersectionality, and critical pedagogy, their collaborative endeavor necessitated profound self-examination and recognition of oppressive structures inherent within the field and reproduced throughout course syllabi, thereby perpetuating societal inequities inside and outside the classroom context. Their iterative, evolving effort resembled a reflective consultation group, marked by continuous self-reflection, challenging assumptions, and transforming actions, vividly portrayed in their vignettes.
View Article and Find Full Text PDFBioinformatics
January 2025
Department of Pathology and Department of Immunobiology, Yale School of Medicine.
Summary: With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS). We applied nipalsMCIA to both bulk and single cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension.
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