Use of Crack Characteristics in Crack Sealing Performance Modeling and Network-Level Project Selection

Author(s):  
Zhaohua Wang ◽  
Yichang (James) Tsai ◽  
Menghua Ding

Crack sealing (CS) and crack filling (CF) are commonly used crack treatment methods. However, the study of their performance is still very limited, making it difficult for highway agencies to systematically and optimally select network-level CS-CF projects within available budgets. To address this issue, a generalized performance model for CS-CF–treated pavements is proposed. Detailed crack characteristics—including crack type, density, and width—are employed in the model. In the proposed performance model, crack density related to three types of cracks (transverse cracks, nonwheelpath longitudinal cracks, and wheelpath longitudinal cracks) is used to determine performance gain. Two discount functions are incorporated to consider the negative impact caused by alligator cracks and cracks that are very tight or very wide. The proposed model is instantiated and estimated using the practices of the Georgia Department of Transportation on CS and CF and the department’s pavement distress survey protocol. The case study—which uses three-dimensional laser data collected from a 1-mi pavement section on State Route 26 (US-80) near Savannah, Georgia, from 2011 to 2016—validates the feasibility and reasonableness of the model. An integer programming method is formulated for network-level CS-CF project selection. The testing results of 53 pavement segments show that the model and programming method can be used to select CS-CF projects within budget constraints while maximizing the length-weighted average performance gain. The proposed performance model and integer programming method show promise for use in incorporating CS and CF into a highway agency’s pavement management system. Conclusions and recommendations are offered.

Author(s):  
Emilio Moretti ◽  
Elena Tappia ◽  
Martina Mauri ◽  
Marco Melacini

AbstractIn a context where companies are striving to produce highly customised goods in small batches and within short lead times, increasing attention is being put on the design and management of part feeding systems. This research is the first to model automated part feeding to supermarkets in a factory environment, considering an innovative technology called vertical robotic storage and retrieval systems. This technology allows automating the storage, picking, and internal transportation activities in an integrated process, thanks to rack-climbing robots roaming in both the shop floor and the storage racks. We develop an analytical model based on the queuing network approach to analyse the system performance, and we use it to perform numerical experiments and to evaluate the design trade-offs with reference to a real case in the automotive industry. Results show that an increase in the number of robots leads to better performance since the positive impact on the response time is stronger than the negative impact on the waiting times of robots at the supermarkets due to congestion. Furthermore, a configuration with multiple small supermarkets improves the efficiency of the replenishment process, compared to a setting with few big supermarkets.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Danying Shao ◽  
Nabeel Ahmed ◽  
Nishant Soni ◽  
Edward P. O’Brien

Abstract Background Translation is a fundamental process in gene expression. Ribosome profiling is a method that enables the study of transcriptome-wide translation. A fundamental, technical challenge in analyzing Ribo-Seq data is identifying the A-site location on ribosome-protected mRNA fragments. Identification of the A-site is essential as it is at this location on the ribosome where a codon is translated into an amino acid. Incorrect assignment of a read to the A-site can lead to lower signal-to-noise ratio and loss of correlations necessary to understand the molecular factors influencing translation. Therefore, an easy-to-use and accurate analysis tool is needed to accurately identify the A-site locations. Results We present RiboA, a web application that identifies the most accurate A-site location on a ribosome-protected mRNA fragment and generates the A-site read density profiles. It uses an Integer Programming method that reflects the biological fact that the A-site of actively translating ribosomes is generally located between the second codon and stop codon of a transcript, and utilizes a wide range of mRNA fragment sizes in and around the coding sequence (CDS). The web application is containerized with Docker, and it can be easily ported across platforms. Conclusions The Integer Programming method that RiboA utilizes is the most accurate in identifying the A-site on Ribo-Seq mRNA fragments compared to other methods. RiboA makes it easier for the community to use this method via a user-friendly and portable web application. In addition, RiboA supports reproducible analyses by tracking all the input datasets and parameters, and it provides enhanced visualization to facilitate scientific exploration. RiboA is available as a web service at https://a-site.vmhost.psu.edu/. The code is publicly available at https://github.com/obrien-lab/aip_web_docker under the MIT license.


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3876 ◽  
Author(s):  
Zhongjian Ma ◽  
Yuanyuan Ding ◽  
Baoqing Li ◽  
Xiaobing Yuan

Pooling layer in Convolutional Neural Networks (CNNs) is designed to reduce dimensions and computational complexity. Unfortunately, CNN is easily disturbed by noise in images when extracting features from input images. The traditional pooling layer directly samples the input feature maps without considering whether they are affected by noise, which brings about accumulated noise in the subsequent feature maps as well as undesirable network outputs. To address this issue, a robust Local Binary Pattern (LBP) Guiding Pooling (G-RLBP) mechanism is proposed in this paper to down sample the input feature maps and lower the noise impact simultaneously. The proposed G-RLBP method calculates the weighted average of all pixels in the sliding window of this pooling layer as the final results based on their corresponding probabilities of being affected by noise, thus lowers the noise impact from input images at the first several layers of the CNNs. The experimental results show that the carefully designed G-RLBP layer can successfully lower the noise impact and improve the recognition rates of the CNN models over the traditional pooling layer. The performance gain of the G-RLBP is quite remarkable when the images are severely affected by noise.


Author(s):  
José Carlos Soto-Monterrubio ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Juan Frausto-Solís ◽  
Laura Cruz-Reyes ◽  
Rodolfo Pazos R. ◽  
...  

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