University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

Assessing model fit: caveats and recommendations for confirmatory factor analysis and exploratory structural equation modeling

Abstract: Despite the limitations of overgeneralizing cutoff values for confirmatory factor analysis (CFA; e.g., Marsh, Hau, & Wen, 2004), they are still often employed as golden rules for assessing factorial validity in sport and exercise psychology. The purpose of this study was to investigate the appropriateness of using the CFA approach with these cutoff values for typical multidimensional measures. Furthermore, we ought to examine how a model could be respecified to achieve acceptable fit and explored whether exploratory structural equation modeling (ESEM) provides a more appropriate assessment of model fit. Six multidimensional measures commonly used in sport and exercise psychology research were examined using CFA and ESEM. Despite demonstrating good validity in previous research, all eight failed to meet the cutoff values proposed by Hu and Bentler. ESEM improved model fit in all measures. In conclusion, we suggest that model misfit in this study demonstrates the problem with interpreting cutoff values rigi...

Pulmonary Arterial Hypertension: A Current Perspective on Established and Emerging Molecular Genetic Defects.

Abstract: Pulmonary arterial hypertension (PAH) is an often fatal disorder resulting from several causes including heterogeneous genetic defects. While mutations in the bone morphogenetic protein receptor type II (BMPR2) gene are the single most common causal factor for hereditary cases, pathogenic mutations have been observed in approximately 25% of idiopathic PAH patients without a prior family history of disease. Additional defects of the transforming growth factor beta pathway have been implicated in disease pathogenesis. Specifically, studies have confirmed activin A receptor type II-like 1 (ACVRL1), endoglin (ENG), and members of the SMAD family as contributing to PAH both with and without associated clinical phenotypes. Most recently, next-generation sequencing has identified novel, rare genetic variation implicated in the PAH disease spectrum. Of importance, several identified genetic factors converge on related pathways and provide significant insight into the development, maintenance, and pathogenetic transformation of the pulmonary vascular bed. Together, these analyses represent the largest comprehensive compilation of BMPR2 and associated genetic risk factors for PAH, comprising known and novel variation. Additionally, with the inclusion of an allelic series of locus-specific variation in BMPR2, these data provide a key resource in data interpretation and development of contemporary therapeutic and diagnostic tools.

Do smartphone usage scales predict behavior

Abstract: Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite a growing body of resources that can quantify smartphone use, research within psychology and social science overwhelmingly relies on self-reported assessments. These have yet to convincingly demonstrate an ability to predict objective behavior. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviors derived from Apple's Screen Time application. While correlations between psychometric scales and objective behavior are generally poor, single estimates and measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favorably with subsequent behavior. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.

Agricultural Robotics: The Future of Robotic Agriculture

Abstract: Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over £108bn p.a., with 3.9m employees in a truly international industry and exports £20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector.