Data-driven algorithms for enhanced transportation infrastructure asset management

State highway agencies collect a considerable amount of digital data to document as well as support a variety of decision-making processes. This data is used to develop insights and extract information to enhance serval decision-making systems. However, digital data collected by highway agencies has been consistently underutilized especially in supporting data-driven or evidence-based decision-making systems. This underutilization is a result of a poor established connection between the data collected and its final possible usage. This study analyzes the digital data collected by highway agencies to enhance the reliability of decision-making systems by utilizing Geographic Information Systems (GIS) and data analytics. This study will a) develop an enhanced Life-Cycle Cost Analysis (LCCA) for pavement rehabilitation investment decisions by establishing a novel cost classification system , b) identifying the barriers and challenges faced by agencies to adopt a data-driven pavement performance evaluation process, and c) develop a dynamic pavement delineation algorithm that aggregates the pavement condition data at the distress level. In order to achieve these objectives, the study uses different digital dataset including a) pavement rehabilitation historical bid-data, b) pavement rehabilitation as-built drawings, c) pavement condition data, and d) pavement maintenance and rehabilitation geospatial data. The study developed an enhanced life-cycle cost analysis practice that would significantly improve the economic evaluation accuracy of investment decisions. Additionally, the study identified seven major barriers and challenges that hinder the adoption of a data-driven pavement performance evaluation. Finally, the study developed and automated a pavement delineation algorithm using Python programming language. This study is expected help highway agencies utilize their historical digital datasets to support a variety of decision-making systems. Furthermore, the study paves the way to adopting and implementing data-driven and evidence based decision-making processes

A novel admission control scheme for network slicing based on squatting and kicking strategies

New services and applications impose differentquality of service (QoS) requirements on network slicing. Tomeet differentiated service requirements, current Internet servicemodel has to support emerging real-time applications from 5Gnetworks. The admission control mechanisms are expected tobe one of the key components of the future integrated serviceInternet model, for providing multi-level service guarantees withthe different classes (slices) of services. Therefore, this paperintroduces a new flexible admission control mechanism, basedon squatting and kicking techniques (SKM), which can beemployed under network slicing scenario. From the results, SKMprovides 100% total resource utilization in bandwidth contextand 100% acceptance ratio for highest priority class underdifferent input traffic volumes, which cannot be achieved byother existing schemes such as AllocTC-Sharing model due topriority constraints.Peer ReviewedPostprint (published version

Iowa Pavement Asset Management Decision-Making Framework

Most local agencies in Iowa currently make their pavement treatment decisions based on their limited experience due primarily to lack of a systematic decision-making framework and a decision-aid tool. The lack of objective condition assessment data of agency pavements also contributes to this problem. This study developed a systematic pavement treatment selection framework for local agencies to assist them in selecting the most appropriate treatment and to help justify their maintenance and rehabilitation decisions. The framework is based on an extensive literature review of the various pavement treatment techniques in terms of their technical applicability and limitations, meaningful practices of neighboring states, and the results of a survey of local agencies. The treatment selection framework involves three different steps: pavement condition assessment, selection of technically feasible treatments using decision trees, and selection of the most appropriate treatment considering the return-on-investment (ROI) and other non-economic factors. An Excel-based spreadsheet tool that automates the treatment selection framework was also developed, along with a standalone user guide for the tool. The Pavement Treatment Selection Tool (PTST) for Local Agencies allows users to enter the severity and extent levels of existing distresses and then, recommends a set of technically feasible treatments. The tool also evaluates the ROI of each feasible treatment and, if necessary, it can also evaluate the non-economic value of each treatment option to help determine the most appropriate treatment for the pavement. It is expected that the framework and tool will help local agencies improve their pavement asset management practices significantly and make better economic and defensible decisions on pavement treatment selection

A Comparison Between Commercial and Open-Source Software for Finite Element Analysis of Elasto-Plastic Bending

Nowadays, simulation is becoming more and more important in industries. Here we consider a typical industrial application in the field of sheet metal bending. A high number of simulations is necessary during the development process to perform parameter studies and optimizations. On the other hand, simulation tools should be also available for the customers of these machines, e.g., to plan the production of very specific profiles. In such cases, the optimal process parameters only can be found by simulation. Very important in this context are the license costs for commercial simulation software. Frequently, the simulations are not limited by computational power but by the number of available licenses, such that the duration for parameter studies is elongated. Also, with license costs it very expensive to provide a simulation platform to the customers. The presented case study has been carried out with the goal of comparing possible open source alternatives to expensive commercial Finite Element software. Exemplarily, we consider the elasto-plastic bending of a cantilever, using the Johnson Cook constitutive law. For this test case, a three dimensional Finite Element analysis is performed, comparing the results of open-source software (Salome-Meca) and a com mercial counterpart (Abaqus). Different element types and mesh sizes are compared, the usability of both tools, and the computational time. Considering the obvious price difference, both platforms show comparable results. Comparing the functionality of both programs, both are capable for modelling highly detailed and complex models for elasto-plastic material processing. However, for under standing the structure of the user interface of Salome-Meca is far more time consuming. Additionally, the performance of Salome-Meca on different operating systems is com pared: Salome-Meca on Linux, Salome-Meca on Linux, installed in a virtual machine on Windows, and finally Salome-Meca on Windows. All in all, it turned out that depending on the specific application Salome-Meca can be a powerful alternative to Abaqus for the considered industrial application

Squatting and kicking model evaluation for prioritized sliced resource management

© Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Effective management and allocation of resources remains a challenging paradigm for future large-scale networks such as 5G, especially under a network slicing scenario where the different services will be characterized by differing Quality of Service (QoS) requirements. This makes the task of guaranteeing the QoS levels and maximizing the resource utilization across such networks a complicated task. Moreover, the existing allocation strategies with link sharing tend to suffer from inefficient network resource usage. Therefore, we focused on prioritized sliced resource management in this work and the contributions of this paper can be summarized as formally defining and evaluating a self-provisioned resource management scheme through a smart Squatting and Kicking model (SKM) for multi-class networks. SKM provides the ability to dynamically allocate network resources such as bandwidth, Label Switched Paths (LSP), fiber, slots among others to different user priority classes. Also, SKM can guarantee the correct level of QoS (especially for the higher priority classes) while optimizing the resource utilization across networks. Moreover, given the network slicing scenarios, the proposed scheme can be employed for admission control. Simulation results show that our model achieves 100% resource utilization in bandwidth-constrained environments while guaranteeing higher admission ratio for higher priority classes. From the results, SKM provided 100% acceptance ratio for highest priority class under different input traffic volumes, which, as we articulate, cannot be sufficiently achieved by other existing schemes such as AllocTC-Sharing model due to priority constraints.Peer ReviewedPostprint (author's final draft

Evaluating the impact of delay constraints in network services for intelligent network slicing based on SKM model

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Solving the problem of network resource allocation with delay constraint is a significant challenge for realizing future Internet and 5G networks services such as advanced mobile broadband services and Internet of things (IoT), especially under the network slicing scenario. The impact of delay constraints may lead to rejection of demands, resulting in low resource utilization of network resources. This is especially severe when dynamic traffic is considered. Therefore, intelligent resource allocation algorithms are required to use the network resources in delay constrained scenario efficiently. Moreover, these algorithms should guarantee quality of service (QoS) between different priority slices during congestion case. Therefore, in this paper, we analyze the impact of delay constraint on the performance of an online resource allocation algorithm based on an intelligent efficient squatting and kicking model (SKM), proved in other works to be the most effective up to the present time yet. SKM incorporates kicking and squatting of resources as innovative techniques enabling it to achieve 100% resource utilization and acceptance ratio for higher priority slices in scenarios where the other state of art algorithms not able to reach by far in some scenarios. Simulation results showed that incorporating delay constraints has a significant impact on the performance, resulting in up to 10% and 4% reduction in terms of average resource utilization and acceptance ratios respectively.Peer ReviewedPostprint (published version

Evaluation of hydrocortisone, vitamin c, and thiamine for the treatment of septic shock: a randomized controlled trial (the HYVITS trial)

Purpose: The aim of the study is to evaluate the effect of combined hydrocortisone, vitamin C, and thiamine (triple therapy) on the mortality of patients with septic shock. Methods: This multicenter, open-label, two-arm parallel-group, randomized controlled trial was conducted in four intensive care units in Qatar. Adult patients diagnosed with septic shock requiring norepinephrine at a rate of ≥0.1 μg/kg/min for ≥6 h were randomized to a triple therapy group or a control group. The primary outcome was in-hospital mortality at 60 days or at discharge, whichever occurred first. Secondary outcomes included time to death, change in Sequential Organ Failure Assessment (SOFA) score at 72 h of randomization, intensive care unit length of stay, hospital length of stay, and vasopressor duration. Results: A total of 106 patients (53 in each group) were enrolled in this study. The study was terminated early because of a lack of funding. The median baseline SOFA score was 10 (interquartile range, 8–12). The primary outcomes were similar between the two groups (triple therapy, 28.3% vs.control, 35.8%; P = 0.41). Vasopressor duration among the survivors was similar between the two groups (triple therapy, 50 h vs. control, 58 h; P = 0.44). Other secondary and safety endpoints were similar between the two groups. Conclusion:Triple therapy did not improve in-hospital mortality at 60 days in critically ill patients with septic shock or reduce the vasopressor duration or SOFA score at 72 h

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians

Hybrid and Composite Crystalline Materials

A facile approach for developing an interfacial solar evaporator by heat localization of solar-thermal energy conversion at water-air liquid composed by in-situ polymerization of Fe2O3 nanoparticles (Fe2O3@PPy) deposited over a facial sponge is proposed. The demonstrated system consists of a floating solar receiver having a vertically cross-linked microchannel for wicking up saline water. The in situ polymerized Fe2O3@PPy interfacial layer promotes diffuse reflection and its rough black surface allows Omni-directional solar absorption (94%) and facilitates efficient thermal localization at the water/air interface and offers a defect-rich surface to promote heat localization (41.9 °C) and excellent thermal management due to cellulosic content. The self-floating composite foam reveals continuous vapors generation at a rate of 1.52 kg m−2 h−1 under one 1 kW m−2 and profound evaporating efficiency (95%) without heat losses that dissipates in its surroundings. Indeed, long-term evaporation experiments reveal the negligible disparity in continuous evaporation rate (33.84 kg m−2/8.3 h) receiving two sun solar intensity, and ensures the stability of the device under intense seawater conditions synchronized with excellent salt rejection potential. More importantly, Raman spectroscopy investigation validates the orange dye rejection via Fe2O3@PPy solar evaporator. The combined advantages of high efficiency, self-floating capability, multimedia rejection, low cost, and this configuration are promising for producing large-scale solar steam generating systems appropriate for commercial clean water yield due to their scalable fabrication

Solar Thermal Collector Education Using Polysun Simulations Software

There are a variety of solar thermal collectors available in the market today. These collectors are typically manufactured in diverse countries and have different performance characteristics. For homeowners and commercial solar solution providers, it is important to know how these collectors will perform to ensure maximum return on investment. Therefore, engineers and technicians need to be trained into how different collectors will perform in different locations. In this article, we demonstrate how a Swiss simulations software package called Polysun can be used to accurately determine the performance of a particular system under real operating conditions. To demonstrate the accuracy of the simulations tool, we show performance comparisons with experimental results for different types of flat plate and evacuated tube solar collectors. We also show examples of exercises that can be implemented in an undergraduate course in solar thermal systems. According to our investigations, the thermal performance predicted by Polysun was in close agreement with our experimental measurements. The outcomes of our investigations can help educators make informed decisions regarding teaching solar thermal systems to undergraduates using state-of-the art simulation and visualization tools