According to the literature, landscape (panoramas, heritage objects e.g. landmarks) affects people in various ways. Data are primarily developed by asking people (interviews, photo sessions, focus groups) about their preferences, but to a lesser degree by measuring how the body reacts to such objects. Personal experience while driving a car through a landscape is even more rare. In this paper we study how different types of objects in the landscape affect drivers during their drive. A high-fidelity moving-base driving simulator was used to measure choice of speed and lateral position in combination with stress (heart rate measure) and eye tracking. The data were supplemented with questionnaires. Eighteen test drivers (8 men and 10 women) with a mean age of 37 were recruited. The test drivers were exposed to different new and old types of landscape objects such as 19th century church, wind turbine, 17th century milestone and bus stop, placed at different distances from the road driven. The findings are in some respect contradictory, but it was concluded that that 33% of the test drivers felt stressed during the drive. All test drivers said that they had felt calm at times during the drive but the reason for this was only to a minor degree connected with old and modern objects. The open landscape was experienced as conducive to acceleration. Most objects were, to a small degree, experienced (subjective data) as having a speed-reducing effect, much in line with the simulator data (objective data). Objects close to the road affected the drivers' choice of' lateral position. No significant differences could be observed concerning the test drivers' gaze between old or modern objects, but a significant difference was observed between the test drivers' gaze between road stretches with faraway objects and stretches without objects. No meaningful, significant differences were found for the drivers' stress levels as measured by heart rate.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.aap.2013.09.021 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Readmissions and Mortality After "Before Medically Advised" Hospital Discharges Among Medicare Beneficiaries with Opioid Use Disorder.
J Gen Intern Med
January 2025
Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, RI, USA.
Background: "Before medically advised" (BMA) discharges are rising among hospitalized people with opioid use disorder (OUD) and associated with worse outcomes. However, little is known about BMA discharge among the growing share of U.S.
View Article and Find Full Text PDFVery Early Health Technology Assessment for Potential Predictive Biomarkers in the Treatment of Advanced Non-Small Cell Lung Cancer.
Pharmacoecon Open
January 2025
Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Objectives: Immune checkpoint inhibitor (ICI)-containing treatment is currently prescribed as first-line treatment for all patients with advanced non-small cell lung cancer (NSCLC) without targetable driver mutations. However, only 30-45% of patients show no progression within 12 months after treatment start. Various biomarkers are being studied to save costly and potentially harmful treatment in non-responders.
View Article and Find Full Text PDFOil palm smallholders and the road to certification: Insights from Indonesia.
J Environ Manage
January 2025
University of Göttingen, Department of Agricultural Economics and Rural Development, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany. Electronic address:
Smallholder-managed oil palm plantations are a major driver of economic welfare and rural development. However, compared to industrial producers, smallholders are associated with lower farm productivity and disproportionately higher rates of illegal land clearing. Therefore, a balance must be struck between mitigating adverse externalities and strengthening favorable outcomes to ensure smallholders' sustainable integration into the palm oil industry.
View Article and Find Full Text PDFMutations in hnRNP A1 drive neurodegeneration and alternative RNA splicing of neuronal gene targets.
Neurobiol Dis
January 2025
Office of the Saskatchewan Multiple Sclerosis Clinical Research Chair, University of Saskatchewan, Saskatoon, SK S7K 0M7, Canada; Neurology Division, Department of Medicine, University of Saskatchewan, Saskatoon, SK S7N 0X8, Canada. Electronic address:
RNA binding protein dysfunction is a pathogenic feature of multiple neurological diseases, including multiple sclerosis (MS). Neurodegeneration (the loss of, or damage to neurons and axons) is the primary driver of disease progression in MS. Herein, we utilized a novel, neuron-specific model of neurodegeneration by transducing primary mouse neurons with mutant forms of the RNA binding protein heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) identified from MS patients, including one within the M9-nuclear localization sequence of hnRNP A1 (A1(P275S)) and a second in the prion-like domain of hnRNP A1 (A1(F263S)) to test the hypothesis that neuronal hnRNP A1 dysfunction drives neurodegeneration in MS.
View Article and Find Full Text PDFCompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials.
Comput Struct Biotechnol J
December 2024
NovaMechanics Ltd, Nicosia 1070, Cyprus.
The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced models, approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!