Project description:Anger is an emotion that drivers often feel and express while driving, and it is believed by researchers to be an important cause of dangerous driving behavior. In this study, the relationships between driving trait anger, driving anger expression, and dangerous driving behaviors were analyzed. The Driving Anger Scale (DAS) was used to measure driving trait anger, whereas the Driving Anger Expression (DAX) Inventory was used to measure expressions of driving anger. A sample of 38 drivers completed the DAS, DAX, and a driving simulation session on a simulator where their driving behaviors were recorded. Correlation analysis showed that the higher scores on the DAS were associated with longer durations of speeding in the simulator. The more participants expressed their anger in verbal and physical ways, the more likely they were to crash the virtual vehicle during the simulation. Regression analyses illustrated the same pattern. The findings suggest that, although trait anger is related to speeding, the passive expression of anger is the real factor underling traffic accidents. This study extends findings about the predictive effects of self-report scales of driving behaviors to behaviors recorded on a simulator. Thus, if in traffic safety propaganda, guiding drivers to use positive ways to cope with driving anger is recommended by our findings.
Project description:The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.
Project description:Studies of long-term trends in phenology often rely on climatic averages or accumulated heat, overlooking climate variability. Here we test the hypothesis that unusual weather conditions are critical in driving adult insect phenology. First, we generate phenological estimates for Lepidoptera (moths and butterflies) across the Eastern USA, and over a 70 year period, using natural history collections data. Next, we assemble a set of predictors, including the number of unusually warm and cold days prior to, and during, the adult flight period. We then use phylogenetically informed linear mixed effects models to evaluate effects of unusual weather events, climate context, species traits, and their interactions on flight onset, offset and duration. We find increasing numbers of both warm and cold days were strong effects, dramatically increasing flight duration. This strong effect on duration is likely driven by differential onset and termination dynamics. For flight onset, impact of unusual climate conditions is dependent on climatic context, but for flight cessation, more unusually cold days always lead to later termination particularly for multivoltine species. These results show that understanding phenological responses under global change must account for unusual weather events, especially given they are predicted to increase in frequency and severity.
Project description:Individuals often form more reasonable judgments from complex information after a period of distraction vs. deliberation. This phenomenon has been attributed to sophisticated unconscious thought during the distraction period that integrates and organizes the information (Unconscious Thought Theory; Dijksterhuis and Nordgren, 2006). Yet, other research suggests that experiential processes are strengthened during the distraction (relative to deliberation) period, accounting for the judgment and decision benefit. We tested between these possibilities, hypothesizing that unconscious thought is distinct from experiential processes, and independently contributes to judgments and decisions during a distraction period. Using an established paradigm, Experiment 1 (N = 319) randomly induced participants into an experiential or rational mindset, after which participants received complex information describing three roommates to then consider consciously (i.e., deliberation) or unconsciously (i.e., distraction). Results revealed superior roommate judgments (but not choices) following distraction vs. deliberation, consistent with Unconscious Thought Theory. Mindset did not have an influence on roommate judgments. However, planned tests revealed a significant advantage of distraction only within the rational-mindset condition, which is contrary to the idea that experiential processing alone facilitates complex decision-making during periods of distraction. In a second experiment (N = 136), we tested whether effects of unconscious thought manifest for a complex analytical reasoning task for which experiential processing would offer no advantage. As predicted, participants in an unconscious thought condition outperformed participants in a control condition, suggesting that unconscious thought can be analytical. In sum, the current results support the existence of unconscious thinking processes that are distinct from experiential processes, and can be rational. Thus, the experiential vs. rational nature of a process might not cleanly delineate conscious and unconscious thought.
Project description:Arrhythmogenic cardiomyopathy (AC) is a clinical entity that has evolved conceptually over the past 30 years. Advances in cardiac imaging and the introduction of genetics into everyday practice have revealed that AC comprises multiple phenotypes that are dependent on genetic or acquired factors. In this study, the authors summarise the approach to the identification of the AC phenotype and its underlying causes. They believe that AC represents a paradigm for personalised medicine in cardiology and that better stratification of the disease will enhance the development of mechanism-based treatments.
Project description:RDE is becoming a necessary element of the emissions certification of automotive vehicles. Real Driving Emissions (RDE) helps to ensure that the regular operation of a car, or heavy vehicle, is still within the acceptable emissions standards while driving under normal conditions. RDE is monitored by connecting a Portable Emissions Measurement System (PEMS) to the exhaust of the tested vehicle, which measures the pollutant concentrations as the car or truck drives along a standardised route. The data described in this paper is the raw, detailed PEMS records of a heavy goods vehicle, recorded at a rate of 1Hz, over multiple trips on an urban route in South Africa. The data includes the pollutant concentrations of CO, CO 2 , NO and NO 2 , ambient conditions, and vehicle diagnostics collected from different sensors mounted to the vehicle during the field tests. We performed no additional analysis on the data. The value of the data is in allowing researchers to (a) develop and test machine learning algorithms that predict the instantaneous pollutant concentrations or (b) studying the variance of pollutant concentrations that occurs under typical driving conditions.
Project description:Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.
Project description:Because the properties of horizontally-transferred genes will reflect the mutational proclivities of their donor genomes, they often show atypical compositional properties relative to native genes. Parametric methods use these discrepancies to identify bacterial genes recently acquired by horizontal transfer. However, compositional patterns of native genes vary stochastically, leaving no clear boundary between typical and atypical genes. As a result, while strongly atypical genes are readily identified as alien, genes of ambiguous character are poorly classified when a single threshold separates typical and atypical genes. This limitation affects all parametric methods that examine genes independently, and escaping it requires the use of additional genomic information. We propose that the performance of all parametric methods can be improved by using a multiple-threshold approach. First, strongly atypical alien genes and strongly typical native genes would be identified using conservative thresholds. Genes with ambiguous compositional features would then be classified by examining gene context, including the class (native or alien) of flanking genes. By including additional genomic information in a multiple-threshold framework, we observed a remarkable improvement in the performance of several popular, but algorithmically distinct, methods for alien gene detection.
Project description:We report an NMR chemical shift study of conformationally challenging seven-membered lactones (1-11); computed and experimental data sets are compared. The computations involved full conformational analysis of each lactone, Boltzmann-weighted averaging of the chemical shifts across all conformers, and linear correction of the computed chemical shifts. DFT geometry optimizations [M06-2X/6-31+G(d,p)] and GIAO NMR chemical shift calculations [B3LYP/6-311+G(2d,p)] provided the computed chemical shifts. The corrected mean absolute error (CMAE), the average of the differences between the computed and experimental chemical shifts for each of the 11 lactones, is encouragingly small (0.02-0.08 ppm for (1)H or 0.8-2.2 ppm for (13)C). Three pairs of cis versus trans diastereomeric lactones were used to assess the ability of the method to distinguish between stereoisomers. The experimental shifts were compared with the computed shifts for each of the two possible isomers. We introduce the use of a "match ratio"--the ratio of the larger CMAE (worse fit) to the smaller CMAE (better fit). A greater match ratio value indicates better distinguishing ability. The match ratios are larger for proton data [2.4-4.0 (av = 3.2)] than for carbon [1.1-2.3 (av = 1.6)], indicating that the former provide a better basis for discriminating these diastereomers.