Behavioral adaptation of drivers when driving among automated vehicles

PurposeThis paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles (AVs) compared to driving among manually driven vehicles (MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations. Here, mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.Design/methodology/approachA driving simulator study is designed to explore whether such behavioral adaptations exist. Two different driving scenarios were explored on a three-lane highway: driving on the main highway and merging from an on-ramp. For this study, 18 research participants were recruited.FindingsBehavioral adaptation can be observed in terms of car-following speed, car-following time gap, number of lane change and overall driving speed. The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs. Although significant differences in behavior were found in more than 90% of the research participants, they adapted their behavior differently, and thus, magnitude of the behavioral adaptation remains unclear.Originality/valueThe observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles. This finding differs from previous studies, which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles. Furthermore, the surrounding vehicles in this study are more “free flow\u27” compared to previous studies with a fixed formation such as platoons. Nevertheless, long-term observations are required to further support this claim

Use of Antihypertensive Drugs and Risk of Malignant Melanoma: A Meta-analysis of Observational Studies

Introduction Several antihypertensive drugs are photosensitizing and may promote the development of malignant melanoma (MM), but evidence remains inconsistent. We sought to quantify the association between use of antihypertensive drugs and MM risk. Methods We systematically searched PubMed, Embase, and CENTRAL from inception to August 17, 2017 to identify observational studies that reported the MM risk associated with the use of antihypertensive drugs. A random-effects meta-analysis was used to estimate the odds ratio (OR) with 95% confidence interval (CI). Results Overall, we included eight observational studies (two cohort studies and six case–control studies). Compared with non-use, use of diuretics (OR 1.10; 95% CI 1.03–1.17) or β-adrenergic blocking agents (OR 1.19; 95% CI 1.04–1.37) was significantly associated with increased risk of MM. The use of angiotensin-converting enzyme inhibitors (OR 1.08; 95% CI 0.95–1.23), angiotensin II receptor blockers (OR 1.12; 95% CI 0.95–1.31), and calcium channel blockers (OR 1.12; 95% CI 0.72–1.74) was not significantly associated with increased risk of MM. Conclusions Current evidence from observational studies suggests that use of diuretics or β-adrenergic blocking agents may be associated with increased risk of MM. Further large well-conducted prospective studies are required to confirm our findings

Phosphodiesterase type 5 inhibitors and risk of melanoma: A meta-analysis

Background The association between phosphodiesterase type 5 (PDE5) inhibitors and melanoma risk is controversial. Objective We quantify the association between use of PDE5 inhibitors and melanoma. Methods We systematically searched PubMed, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, and ClinicalTrials.gov for studies that were conducted up to July 13, 2016, and evaluated the association between PDE5 inhibitors and skin cancer. Random effects meta-analyses were used to calculate the adjusted odds ratio (OR) with the 95% confidence interval (CI). Results Five observational studies were included. Compared with PDE5 inhibitor nonuse, PDE5 inhibitor use was slightly but significantly associated with an increased risk for development of melanoma (OR, 1.12; 95% CI, 1.03-1.21) and basal cell carcinoma (OR, 1.14; 95% CI, 1.09-1.19) but not squamous cell carcinoma. For melanoma risk, none of the prespecified factors (dose of PDE5 inhibitor, study design, and study region) significantly affected the results (P > .05). Our sensitivity analysis confirmed the stability of the results. Limitations We included only observational studies, which had some heterogeneities and inconsistent controlling for potential confounders. Conclusions Use of PDE5 inhibitors may be associated with a slightly increased risk for development of melanoma and basal cell carcinoma but not squamous cell carcinoma. However, further large well-conducted prospective studies with adequate adjustment for potential confounders are required for confirmation

Use of antihypertensive drugs and risk of keratinocyte carcinoma: A meta‐analysis of observational studies

Purpose Current epidemiologic evidence on the association between antihypertensive drugs and keratinocyte carcinoma (KC) risk is inconsistent. We sought to quantify this association by meta‐analysis of observational studies. Methods We systematically reviewed observational studies published through August 2016 and reported the KC risk (basal cell carcinoma [BCC] and squamous cell carcinoma [SCC]) associated with antihypertensive drugs, including diuretics, angiotensin‐converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), beta‐adrenergic blocking agents (β‐blockers), and calcium channel blockers (CCBs). Random‐effects meta‐analysis was used to estimate the odds ratio (OR) with 95% confidence interval (CI). Results Ten eligible studies were included. Compared with nonuse, diuretic use was significantly associated with increased risk of both BCC (OR, 1.10; 95% CI, 1.01‐1.20) and SCC (OR, 1.40; 95% CI, 1.19‐1.66). Use of β‐blockers or CCBs was associated with increased risk of BCC (but not SCC); the OR with β‐blockers was 1.09 (95% CI, 1.04‐1.15) and with CCBs was 1.15 (95% CI, 1.09‐1.21). Use of ACE inhibitors or ARBs was associated with decreased risk of both BCC (OR, 0.53; 95% CI, 0.39‐0.71) and SCC (OR, 0.58; 95% CI, 0.42‐0.80) in high‐risk individuals. Conclusions Current evidence indicates that use of diuretics might be associated with increased risk of KC, while ACE inhibitors or ARBs might be associated with decreased risk in high‐risk individuals. β‐blockers or CCBs might be positively associated with BCC risk. Further postmarketing surveillance studies and investigations to clarify the possible underlying mechanisms are warranted

Pioglitazone and bladder cancer risk: a systematic review and meta-analysis

Current evidence about the association between pioglitazone and bladder cancer risk remains conflict. We aimed to assess the risk of bladder cancer associated with the use of pioglitazone and identify modifiers that affect the results. We systematically searched PubMed, Embase, and Cochrane Central Register of Controlled Trials from inception to 25 August 2016 for randomized controlled trials (RCTs) and observational studies that evaluated the association between pioglitazone and bladder cancer risk. Conventional and cumulative meta-analyses were used to calculate the odds ratio (OR) with 95% confidence interval (CI). A restricted spline regression analysis was used to examine the dose-response relationship with a generalized least-squares trend test. We included two RCTs involving 9114 patients and 20 observational studies (n = 4,846,088 individuals). An increased risk of bladder cancer in patients treated with pioglitazone versus placebo was noted from RCTs (OR, 1.84; 95%CI, 0.99 to 3.42). In observational studies, the increased risk of bladder cancer was slight but significant among ever-users of pioglitazone versus never-users (OR, 1.13; 95%CI, 1.03 to 1.25), which appeared to be both time- (P = 0.003) and dose-dependent (P = 0.05). In addition, we observed the association differed by region of studies (Europe, United States, or Asia) or source of funding (sponsored by industry or not). Current evidence suggests that pioglitazone may increase the risk of bladder cancer, possibly in a dose- and time-dependent manner. Patients with long-term and high-dose exposure to pioglitazone should be monitored regularly for signs of bladder cancer

Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channel ρHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.This research was funded in part by the National Natural Science Foundation of China (Grant No. 41,804,004, 41,820,104,005, 41,531,068, 41,904,004), the Canadian Space Agency SOAR-E Program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P

Law enforcement resource optimization with response time guarantees

National Research Foundation Singapore under its Corp Lab@University schem

Integration of Metabolomics and Transcriptomics Reveals the Therapeutic Mechanism Underlying Paeoniflorin for the Treatment of Allergic Asthma

Objectives: Asthma is a chronic airway inflammatory disease, which is characterized by airway remodeling, hyperreactivity and shortness of breath. Paeoniflorin is one of the major active ingredients in Chinese peony, which exerts anti-inflammatory and immune-regulatory effects in multiple diseases. However, it remains unclear whether paeoniflorin treatment can suppress allergic asthma.Methods: In this study, we evaluated the effect of paeoniflorin on lung function and airway inflammation in asthmatic mice. These asthmatic Balb/c mice were first sensitized and constructed through ovalbumin (OVA) motivation. Subsequently, we determined the mechanism of action of paeoniflorin in treating allergic asthma through integrated transcriptomic and metabolomic data sets.Results: Our results demonstrated that many genes and metabolites were regulated in the paeoniflorin-treated mice. Moreover, the potential target proteins of paeoniflorin played important roles in fatty acid metabolism, inflammatory response, oxidative stress and local adhesion.Conclusion: Paeoniflorin has a beneficial effect on asthma, which may be achieved through regulating fatty acid metabolism, inflammatory response and the adhesion pathway at system level

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.This research was funded in part by the Canadian Space Agency SOAR-E program (Grant No. SOAR-E-5489), the National Natural Science Foundation of China (Grant No. 41804004, 41820104005, 41531068), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P

COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference

The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.Comment: BuildSys 201