A Framework for Automated Pavement Condition Monitoring

Pavement condition monitoring is mainly performed manually. Inspectors are driving or walking the road network bare eyed to look for irregularities. Moreover, processing the collected data for understanding the road condition is also a manual task. In this paper a framework that automates the process is presented. Video data collected from the car’s parking camera is utilized to detect defects in frames. Simultaneously, elevation signals collected from accelerometers attached to the car are processed to reconstruct the profile of the road and detect defects associated with its z-axis, such as bumps. A GPS device is synchronized with the other sensors to acquire the data’s geolocation. Detected defects are then classified according to their type and their severity is assessed. All information is then transferred via 4G network to a central server, where the Road Condition Index of road segments necessary to classify roads is calculated. Finally, everything is saved in a Pavement Management System. Preliminary results on the processing of video data demonstrate the frameworks’ promising application. The initial identification of frames including defects produces an accuracy of 96% and approximately 97% precision. Further experiments on such frames, aiming at the detection of potholes, patches and three different types of cracks result in over 84% overall accuracy and over 85% precision.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via http://dx.doi.org/10.1061/9780784479827.07

Mycophenolic acid in the treatment of birdshot chorioretinopathy: long-term follow-up

Aim: To assess the long-term efficacy and tolerability of both derivatives of mycophenolic acid, mycophenolate mofetil (MMF) and mycophenolate sodium (MPS), in the therapy of patients with birdshot chorioretinopathy (BSCR). Methods: Retrospective analysis of 24 patients (48 eyes) with BSCR, treated with MMF or MPS with a follow-up of at least 1 year. The main outcome measures included control of inflammation, steroid-sparing potential and side effects. Secondary outcome measure was the development of retinal function during the therapy measured by best-corrected visual acuity (BCVA), visual field and/or electroretinography (ERG). Results: Twelve patients (50%) were treated with MMF and 12 patients (50%) with MPS. Control of intraocular inflammation, defined as complete lack of clinical and angiographic signs of inflammatory activity, was achieved in 16 of 24 patients (67%). The angiographic signs of activity were significantly reduced during the follow-up (p0.05). In 20 out of 21 patients (95%) who received systemic corticosteroids, the corticosteroids could be tapered to a daily dose of ≤10 mg (rate 0.26/patient-year). Drug-related side effects occurred in 12 patients (50%, rate 0.16/patient-year). In four patients (17%), a therapy switch from MMF to MPS was undertaken due to gastrointestinal discomfort. Conclusions: Derivatives of mycophenolic acid are effective and safe drugs for the treatment of BSCR. In cases with gastrointestinal side effects, a therapy switch from MMF to MPS should be considered

Implementing textural features on GPUs for improved real-time pavement distress detection

The condition of municipal roads has deteriorated considerably in recent years, leading to large scale pavement distress such as cracks or potholes. In order to enable road maintenance, pavement distress should be timely detected. However, manual investigation, which is still the most widely applied approach toward pavement assessment, puts maintenance personnel at risk and is time-consuming. During the last decade, several efforts have been made to automatically assess the condition of the municipal roads without any human intervention. Vehicles are equipped with sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface. Yet, this data are usually not processed while driving, but instead it is recorded and later analyzed off-line. As a result, a vast amount of memory is required to store the data and the available memory may not be sufficient. To reduce the amount of saved data, the authors have previously proposed a graphics processing units (GPU)-enabled pavement distress detection approach based on the wavelet transform of pavement images. The GPU implementation enables pavement distress detection in real time. Although the method used in the approach provides very good results, the method can still be improved by incorporating pavement surface texture characteristics. This paper presents an implementation of textural features on GPUs for pavement distress detection. Textural features are based on gray-tone spatial dependencies in an image and characterize the image texture. To evaluate the computational efficiency of the GPU implementation, performance tests are carried out. The results show that the speedup achieved by implementing the textural features on the GPU is sufficient to enable real-time detection of pavement distress. In addition, classification results obtained by applying the approach on 16,601 pavement images are compared to the results without integrating textural features. There results demonstrate that an improvement of 27% is achieved by incorporating pavement surface texture characteristics

A Framework for Automated Pavement Condition Monitoring

Pavement condition monitoring is mainly performed manually. Inspectors are driving or walking the road network bare eyed to look for irregularities. Moreover, processing the collected data for understanding the road condition is also a manual task. In this paper a framework that automates the process is presented. Video data collected from the car’s parking camera is utilized to detect defects in frames. Simultaneously, elevation signals collected from accelerometers attached to the car are processed to reconstruct the profile of the road and detect defects associated with its z-axis, such as bumps. A GPS device is synchronized with the other sensors to acquire the data’s geolocation. Detected defects are then classified according to their type and their severity is assessed. All information is then transferred via 4G network to a central server, where the Road Condition Index of road segments necessary to classify roads is calculated. Finally, everything is saved in a Pavement Management System. Preliminary results on the processing of video data demonstrate the frameworks’ promising application. The initial identification of frames including defects produces an accuracy of 96% and approximately 97% precision. Further experiments on such frames, aiming at the detection of potholes, patches and three different types of cracks result in over 84% overall accuracy and over 85% precision

Ethnicity predicts metabolic syndrome after liver transplant

Post-transplant metabolic syndrome (PTMS) is associated with important causes of morbidity and mortality in solid organ transplant recipients. Our aim was to investigate predictors of PTMS in liver transplant (LT) recipients. We randomly selected 343 adults (>18 years of age) from a large cohort of 1,262 ethnically diverse patients who received LTs during 2000-2010. Of 343 patients included, 68.2 % were male, with a mean age of 54 ± 10 years, 87 % White, and 31 % Hispanic. Prior to LTs, 6.2 % were on lipid-lowering agents and 24.5 % had BMI ≥ 30 (mean 26.9 ± 5 kg/m(2)). More Hispanics had diabetes before LTs compared to non-Hispanics (p = 0.037). Among those with follow-up of >6 months (n = 304) after LTs, the proportion of patients with diabetes and hypertension increased from 21.9 to 27 % (p < 0.0001), and from 11.5 to 51.6 % (p < 0.0001), respectively. Cholesterol levels increased from 150 ± 115 to 167 ± 70 (p < 0.0001). BMI remained unchanged. PTMS developed in 41 (13.5 %) and cardiovascular events in 31 (10.2 %) patients. Hispanics had higher risk of developing PTMS compared to non-Hispanics (OR 2.30, 95 % CI 1.18-4.49). Survival was not affected by PTMS (p = 0.3), ethnicity (p = 0.52), or nonalcoholic steatohepatitis as the etiology of liver disease (p = 0.50). More Hispanics had diabetes before LTs (29 to 18 %, p < 0.05) and were more prone to developing PTMS after LTs compared to non-Hispanics

Stabilized mixed micelles with a temperature-responsive core and a functional shell

Formation and stabilization of multiresponsive micelles with a mixed poly(ethylene oxide)/polyelectrolyte shell and a temperature-responsive poly(propylene oxide) core were studied. Various poly(ethylene oxide)block-poly(propylene oxide)-block-poly(ethylene oxide) (PEO-PPO-PEO) triblock copolymers were mixed with poly(acrylic acid)-block-poly(propylene oxide)-block-poly(acrylic acid) (PAA-PPO-PAA) or poly(dimethylaminoethyl methacrylate)-block-poly(propylene oxide)-block-poly(dimethylaminoethyl methacrylate) (PD-MAEMA-PPO-PDMAEMA) triblock copolymers. The micelles formed by binary mixtures of well-defined compositions at a specific pH were additionally stabilized by loading with pentaerythritol tetraacrylate (PETA), that was polymerized and cross-linked "in situ" with UV assistance. Depending on both the composition of the copolymers and the experimental conditions, either spherical or wormlike "stabilized polymeric micelles with a mixed shell" (SPMMS) were observed by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The SPMMS that contained PAA blocks in the shell were pH-sensitive, such that a reversible transition from well-dispersed SPMMS to precipitate could be promoted. In contrast, the SPMMS with a PEO/PDMAEMA mixed shell remained well-dispersed in the 2-11 pH range. Finally, SPMMS were successfully exploited as templates for the preparation of Ag nanoparticles (Ag NPs)