Weigh Station Bypassing

A Study was initiated in May of 1990 to investigate the problem of trucks bypassing or avoiding weigh/enforcement stations in Kentucky. A literature review identified two major studies (Wisconsin and Florida) on the subject, both of which are summarized in this report. In addition, a 1989, limited-scope study of truck bypassing of weigh stations in Kentucky was reviewed and summarized. The primary data collection effort for this study took place in the fall of 1990, centered around the Simpson County enforcement station on Interstate-55. Three potential bypass routes were identified. Automatic vehicle classification (AVC) and weight-in-motion (WIM) equipment was installed on 1-65 and on all three bypass routes. Data collection took place over a three-week period, with enforcement on the bypass routes during the second week. Significant conclusions of the study include: 1) While weigh station bypassing does occur in Kentucky, there was no indication of significant numbers of trucks modifying their route choices due to enforcement activity on the bypass routes; 2) Average truck weights and the percentage of trucks overweight are higher on bypass routes than on Interstate routes, but this is not primarily a result of bypassing activity; 3) The majority of trucks on bypass routes have legitimate reasons (in terms of origin or destination) to be on those routes; 4) A high percentage of trucks on bypass routes have violations, regardless of whether the trucks have a local origin/destination along the route; 5) The most common inspection violations on bypass routes are safety-related equipment violations, followed by driver violations; 6) Temporary enforcement efforts on bypass routes can be effective and can be self-supporting through citation revenues; 7) Due to accuracy limitations, high speed WIM data may not be appropriate for certain uses. The following recommendations were developed: 1) A statewide enforcement plan should be developed with increased emphasis on enforcement for non- Interstate routes; 2) Innovative options should be investigated to simplify or expedite weigh station operations; 3) Enforcement efforts on non-Interstate routes should be randomized and unpredictable and should include weighing of trucks; 4) Effectiveness measures should be developed and used to monitor non-Interstate enforcement efforts; 5) The accuracy of statewide WIM data should be assessed; 6) The potential for using statewide WIM data to identify problem areas and direct enforcement efforts should be explored, and a formal process should be established to foster this cooperative effort

Evaluating Deep Convolutional Neural Networks for Material Classification

Determining the material category of a surface from an image is a demanding task in perception that is drawing increasing attention. Following the recent remarkable results achieved for image classification and object detection utilising Convolutional Neural Networks (CNNs), we empirically study material classification of everyday objects employing these techniques. More specifically, we conduct a rigorous evaluation of how state-of-the art CNN architectures compare on a common ground over widely used material databases. Experimental results on three challenging material databases show that the best performing CNN architectures can achieve up to 94.99% mean average precision when classifying materials

Two prospective, multicenter studies for the identification of biomarker signatures for early detection of pulmonary hypertension (PH): the CIPHER and CIPHER‐MRI studies

A blood test identifying patients at increased risk of pulmonary hypertension (PH) could streamline the investigative pathway. The prospective, multicenter CIPHER study aimed to develop a microRNA-based signature for detecting PH in breathless patients and enrolled adults with a high suspicion of PH who had undergone right heart catheterization (RHC). The CIPHER-MRI study was added to assess the performance of this CIPHER signature in a population with low probability of having PH who underwent cardiac magnetic resonance imaging (cMRI) instead of RHC. The microRNA signature was developed using a penalized linear regression (LASSO) model. Data were modeled both with and without N-terminal pro-brain natriuretic peptide (NT-proBNP). Signature performance was assessed against predefined thresholds (lower 98.7% CI bound of ≥0.73 for sensitivity and ≥0.53 for specificity, based on a meta-analysis of echocardiographic data), using RHC as the true diagnosis. Overall, 926 CIPHER participants were screened and 888 were included in the analysis. Of 688 RHC-confirmed PH cases, approximately 40% were already receiving PH treatment. Fifty microRNA (from 311 investigated) were algorithmically selected to be included in the signature. Sensitivity [97.5% CI] of the signature was 0.85 [0.80–0.89] for microRNA-alone and 0.90 [0.86–0.93] for microRNA+NT-proBNP, and the corresponding specificities were 0.33 [0.24–0.44] and 0.28 [0.20–0.39]. Of 80 CIPHER-MRI participants with evaluable data, 7 were considered PH-positive by cMRI whereas 52 were considered PH-positive by the microRNA signature. Due to low specificity, the CIPHER miRNA-based signature for PH (either with or without NT-proBNP in model) did not meet the prespecified diagnostic threshold for the primary analysis