Effects of Highway Landscapes on Drivers’ Eye Movement Behavior and Emergency Reaction Time: A Driving Simulator Study

This study aims at investigating the effects of highway landscapes and alignments on drivers’ eye movement behavior and emergency reaction time, based on a driving simulator experiment. In this study, four simulation scenarios are evaluated including open space, semiopen space, semiclosed space, and enclosed space landscapes on highways in Yunnan Province, China. Twenty-four experienced drivers participated in a 6-kilometer driving experiment in each landscape scenario. Each subject was required to drive at 80 km/h in the scenarios and the driving behavior data were collected. Three different data analysis methods were employed: (1) descriptive analysis of the characteristics of drivers’ visual fixation area; (2) statistical tests of emergency reaction time with drivers’ demographic characteristics, highway landscapes, and alignments; and (3) multiple linear regression analysis of emergency reaction time, highway landscapes, and alignments. The results show that emergency reaction time is significantly influenced by highway landscapes and alignments, and the multiple linear regression model built in this experiment could accurately predict drivers’ emergency reaction time in different highway landscapes and alignments. Document type: Articl

Improved performance and stability of perovskite solar modules by interface modulating with graphene oxide crosslinked CsPbBr3quantum dots

Perovskite solar cells (PSCs) are one of the most prominent photovoltaic technologies. However, PSCs still encounter great challenges of scaling up from laboratorial cells to industrial modules without serious performance loss while maintaining excellent long-term stability, owing to the resistive losses and extra instability factors that scale quadratically with the device area. Here, we manifest a concept of multifunctional interface modulation for highly efficient and stable perovskite solar modules (PSMs). The advisably designed multifunctional interface modulator GO/QD crosslinks the CsPbBr3 perovskite quantum dots (QDs) on the conductive graphene oxide (GO) surfaces, which significantly improve charge transport and energy band alignment at the perovskite/hole transporting layer interface to reduce the charge transport resistance while passivating the surface defects of the perovskite to inhibit carrier recombination resistive losses. Moreover, the GO/QD interlayer acts as a robust permeation barrier that modulates the undesirable interfacial ion and moisture diffusion. Consequently, we adopt a scalable vacuum flash-assisted solution processing (VASP) method to achieve a certified stabilized power output efficiency of 17.85% (lab-measured champion efficiency of 18.55%) for the mini-modules. The encapsulated PSMs achieve over 90% of their initial efficiency after continuous operation under 1 sun illumination and the damp heat test at 85 °C, respectively. This journal isThe authors acknowledge financial from the National Natural Science Foundation of China (21875081, 91733301, and 51972251), the Chinese National 1000-Talent-Plan program, the Foundation of State Key Laboratory of Coal Conversion (Grant No. J18-19-913), and the Frontier Project of the Application Foundation of Wuhan Science and Technology Plan Project (2020010601012202)

A genome-wide association study of anorexia nervosa suggests a risk locus implicated in dysregulated leptin signaling

J. Kaprio, A. Palotie, A. Raevuori-Helkamaa ja S. Ripatti ovat työryhmän Eating Disorders Working Group of the Psychiatric Genomics Consortium jäseniä. Erratum in: Sci Rep. 2017 Aug 21;7(1):8379, doi: 10.1038/s41598-017-06409-3We conducted a genome-wide association study (GWAS) of anorexia nervosa (AN) using a stringently defined phenotype. Analysis of phenotypic variability led to the identification of a specific genetic risk factor that approached genome-wide significance (rs929626 in EBF1 (Early B-Cell Factor 1); P = 2.04 x 10(-7); OR = 0.7; 95% confidence interval (CI) = 0.61-0.8) with independent replication (P = 0.04), suggesting a variant-mediated dysregulation of leptin signaling may play a role in AN. Multiple SNPs in LD with the variant support the nominal association. This demonstrates that although the clinical and etiologic heterogeneity of AN is universally recognized, further careful sub-typing of cases may provide more precise genomic signals. In this study, through a refinement of the phenotype spectrum of AN, we present a replicable GWAS signal that is nominally associated with AN, highlighting a potentially important candidate locus for further investigation.Peer reviewe

Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases

Genome-wide association studies (GWASs) have identified hundreds of susceptibility genes, including shared associations across clinically distinct autoimmune diseases. We performed an inverse χ(2) meta-analysis across ten pediatric-age-of-onset autoimmune diseases (pAIDs) in a case-control study including more than 6,035 cases and 10,718 shared population-based controls. We identified 27 genome-wide significant loci associated with one or more pAIDs, mapping to in silico-replicated autoimmune-associated genes (including IL2RA) and new candidate loci with established immunoregulatory functions such as ADGRL2, TENM3, ANKRD30A, ADCY7 and CD40LG. The pAID-associated single-nucleotide polymorphisms (SNPs) were functionally enriched for deoxyribonuclease (DNase)-hypersensitivity sites, expression quantitative trait loci (eQTLs), microRNA (miRNA)-binding sites and coding variants. We also identified biologically correlated, pAID-associated candidate gene sets on the basis of immune cell expression profiling and found evidence of genetic sharing. Network and protein-interaction analyses demonstrated converging roles for the signaling pathways of type 1, 2 and 17 helper T cells (TH1, TH2 and TH17), JAK-STAT, interferon and interleukin in multiple autoimmune diseases

Behavioral Patterns of Drivers under Signalized and Unsignalized Urban Intersections

Under the general trend of mixed traffic flow, an in-depth understanding of the driving behaviors of traditional vehicles is of great significance for the design of autonomous vehicles and the improvement in the safety and acceptance of autonomous vehicles. This study first obtained microdata on the behaviors of drivers through driving simulation experiments and conducted research in stages. Then, generalized linear mixed-effects models were constructed to study the main effects and interaction effects of driver attributes and traffic conditions on driving behaviors. The data analysis shows that the overall speed of drivers passing through intersections follows a “deceleration acceleration” mode, but the fluctuations are more pronounced at signalized intersections, and the signal control significantly changes the position of the lowest speed when turning left. According to the different signal control and driving tasks, there are significant differences in a driver’s acceleration patterns between the entry and exit stages. A driver’s heart rate fluctuates greatly during the exit phase, especially during straight tasks. Compared with other indicators, the change in the gaze duration is not significant. In addition, interaction effects were observed between driver attributes and traffic conditions, with participants exhibiting different behavioral patterns based on their different attributes. The research results can provide a basis for the design of driving assistance systems and further improve the interactions between autonomous vehicles and traditional vehicles at intersections

Wavelet-Based Correlation Identification of Scales and Locations between Landscape Patterns and Topography in Urban-Rural Profiles: Case of the Jilin City, China

Landscapes display overlapping sets of correlations in different regions at different spatial scales, and these correlations can be delineated by pattern analysis. This study identified the correlations between landscape pattern and topography at various scales and locations in urban-rural profiles from Jilin City, China, using Pearson correlation analysis and wavelet method. Two profiles, 30 km (A) and 35 km (B) in length with 0.1-km sampling intervals, were selected. The results indicated that profile A was more sensitive to the characterization of the land use pattern as influenced by topography due to its more varied terrain, and three scales (small, medium, and large) could be defined based on the variation in the standard deviation of the wavelet coherency in profile A. Correlations between landscape metrics and elevation were similar at large scales (over 8 km), while complex correlations were discovered at other scale intervals. The medium scale of cohesion and Shannon’s diversity index was 1–8 km, while those of perimeter-area fractal dimension and edge density index were 1.5–8 km and 2–8 km, respectively. At small scales, the correlations were weak as a whole and scattered due to the micro-topography and landform elements, such as valleys and hillsides. At medium scales, the correlations were most affected by local topography, and the land use pattern was significantly correlated with topography at several locations. At large spatial scales, significant correlation existed throughout the study area due to alternating mountains and plains. In general, the strength of correlation between landscape metrics and topography increased gradually with increasing spatial scale, although this tendency had some fluctuations in several locations. Despite a complex calculating process and ecological interpretation, the wavelet method is still an effective tool to identify multi-scale characteristics in landscape ecology

A comprehensive metric scheme for characterizing the heterogeneity of urban thermal landscapes: A case study of 14-year evaluation in Beijing

Urbanization has affected land surface temperature (LST) significantly. The spatial average of LST is widely applied to evaluate urban heat islands (UHI), but a simple comparison of mean temperature is insufficient to evaluate the heterogeneity of LST and the effectiveness of urban heat mitigation strategies. This study took the spatial distribution of LST as the thermal landscape and firstly employed 3D landscape metrics to analyze its spatial heterogeneity. To perform collaborative analysis with UHI, the urban–rural differences of landscape metrics were defined as thermal metric islands (TMI). Significant UHI and TMI were revealed by temperature amplitude, aggregation, and complexity parameters, indicating higher temperature fluctuation and fragmentation in urban regions. Before 2007, the standard deviation of thermal surface, landscape fragmentation, and shape irregularity increased significantly, particularly in the suburbs. Later, benefiting from the Summer Olympic Games, the temperature fluctuation, fragmentation, and shape complexity decreased obviously, and UHI intensity hit the lowest. Both UHI intensities and TMI increased after 2010, and various metrics suggest that maximum temperatures, landscape fragmentation, and shape complexity declined over most of the district, even as the mean temperatures increased. This study concludes that urban temperatures have risen at a higher rate than suburban areas, but the thermal landscape composition and configuration have changed more dramatically in rural areas