A Spatial Comparison of Roadway Lighting and Nonmotorist Crashes in Cambridge, MA

2021 ◽  
pp. 036119812110266
Author(s):  
Emily Rose Hennessy ◽  
Chengbo Ai
Keyword(s):  
Hot Spot ◽  
Spatial Relationship ◽  
Network Connectivity ◽  
Estimation Methods ◽  
Future Research ◽  
Control Analysis ◽  
Crash Frequency ◽  
Lighting Conditions ◽  
Pedestrian Network ◽  
Roadway Lighting

Dark lighting conditions, including those occurring at dawn and dusk, are correlated with increased nonmotorist crash frequency owing to reduced visibility, but little research has been done that investigates the spatial relationship between roadway lights and nonmotorist crashes on a community scale. This research used kernel density estimation methods to calculate the commonalities between geolocated streetlight data and non-motorist-vehicle crashes from 2010 to 2018 in Cambridge, Massachusetts. It was observed that dawn, dusk, and darkness showed a significant correlation between nonmotorist crashes and the absence of roadway lighting, all exceeding the control analysis undertaken with crashes occurring in daylight. The Getis-Ord [Formula: see text] hot spot cluster analysis indicated that areas with the greatest density of streetlights were associated with fewer nonmotorist crash hot spots. Future research seeks to corroborate these findings with data from other cities and to assess roadway lighting as a facet of pedestrian network connectivity.

scholarly journals Sensory over-responsivity is related to GABAergic inhibition in thalamocortical circuits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Emily T. Wood ◽  
Kaitlin K. Cummings ◽  
Jiwon Jung ◽  
Genevieve Patterson ◽  
Nana Okada ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Sensory Information ◽  
Network Connectivity ◽  
Sensory Stimulation ◽  
Autism Spectrum ◽  
Future Research ◽  
Resonance Spectroscopy ◽  
Gamma Aminobutyric Acid ◽  
Sensory Stimuli ◽  
Brain Responses

AbstractSensory over-responsivity (SOR), extreme sensitivity to or avoidance of sensory stimuli (e.g., scratchy fabrics, loud sounds), is a highly prevalent and impairing feature of neurodevelopmental disorders such as autism spectrum disorders (ASD), anxiety, and ADHD. Previous studies have found overactive brain responses and reduced modulation of thalamocortical connectivity in response to mildly aversive sensory stimulation in ASD. These findings suggest altered thalamic sensory gating which could be associated with an excitatory/inhibitory neurochemical imbalance, but such thalamic neurochemistry has never been examined in relation to SOR. Here we utilized magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging to examine the relationship between thalamic and somatosensory cortex inhibitory (gamma-aminobutyric acid, GABA) and excitatory (glutamate) neurochemicals with the intrinsic functional connectivity of those regions in 35 ASD and 35 typically developing pediatric subjects. Although there were no diagnostic group differences in neurochemical concentrations in either region, within the ASD group, SOR severity correlated negatively with thalamic GABA (r = −0.48, p < 0.05) and positively with somatosensory glutamate (r = 0.68, p < 0.01). Further, in the ASD group, thalamic GABA concentration predicted altered connectivity with regions previously implicated in SOR. These variations in GABA and associated network connectivity in the ASD group highlight the potential role of GABA as a mechanism underlying individual differences in SOR, a major source of phenotypic heterogeneity in ASD. In ASD, abnormalities of the thalamic neurochemical balance could interfere with the thalamic role in integrating, relaying, and inhibiting attention to sensory information. These results have implications for future research and GABA-modulating pharmacologic interventions.

scholarly journals Discrepancies between Global Forest Net Primary Productivity Estimates Derived from MODIS and Forest Inventory Data and Underlying Factors

2021 ◽  
Vol 13 (8) ◽  
pp. 1441
Author(s):  
Jin Han Park ◽  
Jianbang Gan ◽  
Chan Park
Keyword(s):  
Low Income ◽  
Primary Productivity ◽  
Carbon Stock ◽  
Forest Cover ◽  
Net Primary Productivity ◽  
Estimation Methods ◽  
Future Research ◽  
Important Indicator ◽  
Sequestration Potential ◽  
Carbon Losses

The net primary productivity (NPP) of a forest is an important indicator of its potential for the provision of ecosystem services such as timber, carbon, and biodiversity. However, accurately and consistently quantifying global forest NPP remains a challenge in practice. We converted carbon stock changes using the Global Forest Resources Assessment (FRA) data and carbon losses associated with disturbances and timber removals into an NPP equivalent measurement (FRA NPP*) and compared it with the NPP derived from the MODIS satellite data (MOD17 NPP) for the world’s forests. We found statistically significant differences between the two NPP estimates, with the FRA NPP* being lower than the MOD17 NPP; the differences were correlated with forest cover, normalized difference vegetation index (NDVI), and GDP per capita in countries, and may also stem from the NPP estimation methods and scopes. While the former explicitly accounts for carbon losses associated with timber removals and disturbances, the latter better reflects the principles of photosynthesis. The discrepancies between the two NPP estimates increase in countries with a low income or low forest cover, calling for enhancing their forest resource assessment capacity. By identifying the discrepancies and underlying factors, we also provide new insights into the relationships between the MOD17 NPP and global forest carbon stock estimates, motivating and guiding future research to improve the robustness of quantifying global forest NPP and carbon sequestration potential.

scholarly journals The Importance of Pedestrian Network Connectivity for Adolescent Health: A Cross-sectional Examination of Associations between Neighbourhood Built Environments and Metabolic Health in the Pacific Islands Families Birth Cohort Study

2019 ◽  
Vol 16 (18) ◽  
pp. 3375 ◽  
Author(s):  
Melody Smith ◽  
Vlad Obolonkin ◽  
Lindsay Plank ◽  
Leon Iusitini ◽  
Euan Forsyth ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Pacific Islands ◽  
International Diabetes Federation ◽  
Network Connectivity ◽  
Metabolic Health ◽  
Mediating Effect ◽  
Fat Free Mass ◽  
Metabolic Risk ◽  
Pedestrian Network

The research aim was to investigate associations between objectively-assessed built environment attributes and metabolic risk in adolescents of Pacific Islands ethnicity, and to consider the possible mediating effect of physical activity and sedentary time. Youth (n = 204) undertook a suite of physical assessments including body composition, blood sampling, and blood pressure measurements, and seven day accelerometry. Objective measures of the neighbourhood built environment were generated around individual addresses. Logistic regression and linear modelling were used to assess associations between environment measures and metabolic health, accounting for physical activity behaviours. Higher pedestrian connectivity was associated with an increase in the chance of having any International Diabetes Federation metabolic risk factors for males only. Pedestrian connectivity was related to fat free mass in males in unadjusted analyses only. This study provides evidence for the importance of pedestrian network connectivity for health in adolescent males. Future research is required to expand the limited evidence in neighbourhood environments and adolescent metabolic health.

Safety Evaluation of Destination Lighting Treatment at Stop Controlled Cross-Intersections

2018 ◽  
Vol 2672 (16) ◽  
pp. 113-121 ◽  
Author(s):  
Amrita Goswamy ◽  
Shauna Hallmark ◽  
Theresa Litteral ◽  
Michael Pawlovich
Keyword(s):  
Rural Areas ◽  
Negative Binomial ◽  
Safety Evaluation ◽  
Negative Binomial Regression ◽  
State Agencies ◽  
Crash Frequency ◽  
Binomial Regression ◽  
Manual Selection ◽  
Roadway Lighting ◽  
Selection Of

Intersection crashes during nighttime hours may occur because of poor driver visual cognition of conflicting traffic or intersection presence. In rural areas, the only source of lighting is typically provided by vehicle headlights. Roadway lighting enhances driver recognition of intersection presence and visibility of signs and markings. Destination lighting provides some illumination for the intersection but is not intended to fully illuminate all approaches. Destination lighting has been widely used in Iowa but the effectiveness has not been well documented. This study, therefore, sought to evaluate the effect on safety of destination lighting at rural intersections. As part of an extensive data collection effort, locations with destination/street lighting were gathered with the assistance of several state agencies. After manual selection of a similar number of control intersections, propensity score matching using the caliper width technique was used to match 245 treatments with 245 control sites. Negative binomial regression was used to evaluate crash frequency data. The presence of destination lighting at stop-controlled cross-intersections generally reduced the night-to-day crash ratio by 19%. The presence of treatment or destination lighting was associated with a 33%–39% increase in daytime crashes across all models but was associated with an 18%–33% reduction in nighttime crashes. Injuries in nighttime crashes decreased by 24% and total nighttime crashes reduced by 33%. Property damage crashes were reduced by 18%.

Case Study of Crash Severity Spatial Pattern Identification in Hot Spot Analysis

2019 ◽  
Vol 2673 (9) ◽  
pp. 684-695 ◽  
Author(s):  
Myungwoo Lee ◽  
Aemal J. Khattak
Keyword(s):  
Density Estimation ◽  
Motor Vehicle ◽  
Safety Concern ◽  
Hot Spot ◽  
Estimation Method ◽  
Spatial Relationship ◽  
Crash Severity ◽  
Hot Spot Analysis ◽  
Spot Analysis

Traffic crash hot spot analyses allow identification of roadway segments that may be of safety concern. Understanding geographic patterns of existing motor vehicle crashes is one of the primary steps for geostatistical-based hot spot analysis. Much of the current literature, however, has not paid particular attention to differentiating among cluster types based on crash severity levels. This study aims at building a framework for identifying significant spatial clustering patterns characterized by crash severity and analyzing identified clusters quantitatively. A case study using an integrated method of network-based local spatial autocorrelation and the Kernel density estimation method revealed a strong spatial relationship between crash severity clusters and geographic regions. In addition, the total aggregated distance and the density of identified clusters obtained from density estimation allowed a quantitative analysis for each cluster. The contribution of this research is incorporating crash severity into hot spot analysis thereby allowing more informed decision making with respect to highway safety.

scholarly journals Disrupted Intrinsic Connectivity among Default, Dorsal Attention, and Frontoparietal Control Networks in Individuals with Chronic Traumatic Brain Injury

2016 ◽  
Vol 22 (2) ◽  
pp. 263-279 ◽  
Author(s):  
Kihwan Han ◽  
Sandra B. Chapman ◽  
Daniel C. Krawczyk
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Network Connectivity ◽  
Higher Order ◽  
Future Research ◽  
Resting State Functional Connectivity ◽  
Post Injury ◽  
Graph Theoretic ◽  
Multiple Networks ◽  
Network Disruptions

AbstractObjectives:Individuals with chronic traumatic brain injury (TBI) often show detrimental deficits in higher order cognitive functions requiring coordination of multiple brain networks. Although assessing TBI-related deficits in higher order cognition in the context of network dysfunction is promising, few studies have systematically investigated altered interactions among multiple networks in chronic TBI.Method:We characterized disrupted resting-state functional connectivity of the default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FPCN) whose interactions are required for internally and externally focused goal-directed cognition in chronic TBI. Specifically, we compared the network interactions of 40 chronic TBI individuals (8 years post-injury on average) with those of 17 healthy individuals matched for gender, age, and years of education.Results:The network-based statistic (NBS) on DMN-DAN-FPCN connectivity of these groups revealed statistically significant (pNBS<.05; |Z|>2.58) reductions in within-DMN, within-FPCN, DMN-DAN, and DMN-FPCN connectivity of the TBI group over healthy controls. Importantly, such disruptions occurred prominently in between-network connectivity. Subsequent analyses further exhibited the disrupted connectivity patterns of the chronic TBI group occurring preferentially in long-range and inter-hemispheric connectivity of DMN-DAN-FPCN. Most importantly, graph-theoretic analysis demonstrated relative reductions in global, local and cost efficiency (p<.05) as a consequence of the network disruption patterns in the TBI group.Conclusion:Our findings suggest that assessing multiple networks-of-interest simultaneously will allow us to better understand deficits in goal-directed cognition and other higher order cognitive phenomena in chronic TBI. Future research will be needed to better understand the behavioral consequences related to these network disruptions. (JINS, 2016,22, 263–279)

scholarly journals Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2272 ◽  
Author(s):  
Faisal Khan ◽  
Saqib Salahuddin ◽  
Hossein Javidnia
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Research Work ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Future Research ◽  
Comprehensive Overview ◽  
Ill Posed ◽  
Monocular Depth

Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.

scholarly journals Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research

2019 ◽  
pp. 089443931988845 ◽  
Author(s):  
Alexander Christ ◽  
Marcus Penthin ◽  
Stephan Kröner
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Predictive Modeling ◽  
Hot Spots ◽  
Quantitative Research ◽  
Hot Spot ◽  
Future Research ◽  
Original Research ◽  
Cultural Education ◽  
Data Volume

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE.

scholarly journals Flag and boost theories for hot spot forecasting: An application of NIJ’s Real-Time Crime forecasting algorithm using Colorado Springs crime data

2019 ◽  
Vol 22 (1) ◽  
pp. 4-15
Author(s):  
YongJei Lee ◽  
SooHyun O
Keyword(s):  
Hot Spots ◽  
High Efficiency ◽  
Hot Spot ◽  
State Dependence ◽  
Population Heterogeneity ◽  
Future Research ◽  
Theoretical Frameworks ◽  
Data Set ◽  
Original Algorithm ◽  
Colorado Springs

By operationalizing two theoretical frameworks, we forecast crime hot spots in Colorado Springs. First, we use a population heterogeneity (flag) framework to find places where the hot spot forecasting is consistently successful over months. Second, we use a state dependence (boost) framework of the number of crimes in the periods prior to the forecasted month. This algorithm is implemented in Microsoft Excel®, making it simple to apply and completely transparent. Results shows high accuracy and high efficiency in hot spot forecasting, even if the data set and the type of crime we used in this study were different from what the original algorithm was based on. Results imply that the underlying mechanisms of serious and non-serious crime for forecasting are different from each other. We also find that the spatial patterns of forecasted hot spots are different between calls for service and crime event. Future research should consider both flag and boost theories in hot spot forecasting.

The Effect of Gamma Ray on the Mechanical and Molecular Structure Changes of Silk Fibroin

2012 ◽  
Vol 502 ◽  
pp. 269-272
Author(s):  
Huan Yu Jin ◽  
Hua Yin ◽  
Yu Liu ◽  
Yan An
Keyword(s):  
Molecular Structure ◽  
Silk Fibroin ◽  
Gamma Ray ◽  
Hot Spot ◽  
Basic Material ◽  
Future Research ◽  
Structure Changes ◽  
The Future ◽  
Long Time ◽  
Accelerate Degradation

Silk fibroin is regarded as a biomaterial for a long time. The fantastic biocompatibility of silk fibroin makes it a hot spot in the biomedical area and many researches has chosen it as a basic material to create new biomaterials. However, it has a long biodegradation period in body. Gamma ray is a method to accelerate degradation of other material in some experiments. This research combines silk fibroin with gamma ray and tries to figure out whether gamma ray can make the biodegradation of silk fibroin shorten. In this article, we give various doses of gamma ray to silk fibroin films and take some measurements about the mechanical and molecular structure of post-radiated silk fibroin, thus to choose an appropriate dose for the future research.

Sign in / Sign up

Export Citation Format

Share Document