Not all pipelines are created equal. Pipelines have different characteristics, and would therefore show different levels of integrity and fail differently. The failure mode and cause of a given pipeline depends on several factors including the design, operating and environmental parameters. A new tool was developed to evaluate pipeline integrity and assess its potential failure mode, patterns, and rate based on the critical pipeline parameters. These parameters include the pipeline material of construction, wall thickness, operating pressure, service material, backfill medium/material, age, coating, pipeline size and other relevant parameters. The new tool was developed using pipeline data collected from the European Union, UK, and USA for pipeline failures over four decades. Failure models and patterns were analyzed, and over 60,000 failure modes/pattern combination were identified. The tool predicts the failure mode and patterns in terms of failure rate distribution by size of leak and its causes. It also shows the relative Pipeline Risk Index, defined as the pipeline’s potential failure rate relative to average pipeline population in the industry within similar pipeline categories. Ignition probabilities for pipeline failures were also analyzed and are predicted by this tool for each pipeline leak depending on the leak characteristics.

PresentationNot all pipelines are created equal. Pipelines have different characteristics, and would therefore show different levels of integrity and fail differently. The failure mode and cause of a given pipeline depends on several factors including the design, operating and environmental parameters. A new tool was developed to evaluate pipeline integrity and assess its potential failure mode, patterns, and rate based on the critical pipeline parameters. These parameters include the pipeline material of construction, wall thickness, operating pressure, service material, backfill medium/material, age, coating, pipeline size and other relevant parameters. The new tool was developed using pipeline data collected from the European Union, UK, and USA for pipeline failures over four decades. Failure models and patterns were analyzed, and over 60,000 failure modes/pattern combination were identified. The tool predicts the failure mode and patterns in terms of failure rate distribution by size of leak and its causes. It also shows the relative Pipeline Risk Index, defined as the pipeline’s potential failure rate relative to average pipeline population in the industry within similar pipeline categories. Ignition probabilities for pipeline failures were also analyzed and are predicted by this tool for each pipeline leak depending on the leak characteristics

Development of Empirical Method to Calculate Natural Gas Pipelines Rupture Exposure Radius

PresentationNatural Gas pipeline location classification are designed following an approach similar to ASME B31.8, which considers segmenting the pipeline length and count the population in each segment within a given distance from the pipeline (width of segment). ASMEB 31.8 utilizes fixed distance of 400m for the segment width, while other operators use the pipeline Rupture Exposure Radius (RER). This is a distance determined by the consequences modeling for pipeline full rupture. Since, the population density within the segment width affects the design factors of the pipeline, i.e. wall thickness requirements, over-predicting the distance can have significant cost implications. Some operators use default RER values on conservative estimates, while industrial best practices allow for detailed dispersion to calculate representative RER distances. Detailed dispersion modeling was performed for a large number of Natural Gas Pipeline scenarios, and an empirical formula was developed to estimate the RER for these pipelines as a function of the pipeline diameter and pressure. The dispersion calculations results show that the default RER values current used by some operators are very conservative, and that the cost of pipeline design/construction can be optimized by using the empirical formula developed in this work. The formula, which produces the RER value in terms of the distance from the pipeline to the point of 1⁄2 lower flammable limit is easy to use, and accurately represents the dispersion results. This eliminates the need to using sophisticated modeling software/tools to assess the RER values of Natural Gas pipelines. The formula also uses minimum number of data/information available about the pipelines (diameter and pressure only) increasing its effectiveness as a tool replacing the modeling software. In addition, for pipeline projects, lower RER distances result in more flexibility in route selection, lower pipeline location class and hence thinner wall thicknesses, less emergency isolation valves required and longer span between sectionalizing valves, which all translate to cost savings and reduces potential sources of leak (sectionalizing valves)

Thermal injury in tonsils and its relation to postoperative pain—a histopathological and clinical study

Objectives: The aim of this study was to compare thermal injury and depth of necrosis of using different monopolar power settings in partial tonsillectomy and correlate the results with the postoperative pain score. Results: The study included a total of 15 patients with mean of age of 5.7 ± 2.57 years. The mean depth of injury was significantly higher for the 25 W side (0.973 ± 0.613) versus the 15 W side (0.553 ± 0.218) (p = 0.023). The postoperative pain score showed no significant differences between both sides. Conclusion: The histopathologic depth of thermal injury is significantly higher with the 25 W monopolar microdissection in comparison to the 15 W; however, it does not seem to correlate with the postoperative pain level. Apparently, power settings of 25 W can be safely used for pediatric intracapsular tonsillectomies, without added postoperative morbidity despite the deeper tissue injury observed in the tonsil.The authors are grateful to the Histology and Electron Microscopy Service (HEMS) team at the i3S (Institute for Research and Innovation in Health, University of Porto) for providing the necessary equipment and the technical support for the electron microscopic analysis

EPS-TRA: Energy efficient Peer Selection and Time switching Ratio Allocation for SWIPT-enabled D2D Communication

This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure selfsustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained back-up, when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efficiency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID), however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for selfsustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied . In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efficient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e. data rate and harvested energy

A new best proximity point results in partial metric spaces endowed with a graph

For a given mapping f in the framework of different spaces, the fixed-point equations of the form f x = x can model several problems in different areas, such as differential equations, optimization, and computer science. In this work, the aim is to find the best proximity point and to prove its uniqueness on partial metric spaces where the symmetry condition is preserved for several types of contractive non-self mapping endowed with a graph. Our theorems generalize different results in the literature. In addition, we will illustrate the usability of our outcomes with some examples. The proposed model can be considered as a theoretical foundation for applications to real cases

Improving Fog Computing Performance via Fog-2-Fog Collaboration

In the Internet of Things (IoT) era, a large volume of data is continuously emitted from a plethora of connected devices. The current network paradigm, which relies on centralized data centers (aka Cloudcomputing), has become inefficient to respond to IoT latency concern. To address this concern, fog computing allows data processing and storage \close" to IoT devices. However, fog is still not efficient due to spatial and temporal distribution of these devices, which leads to fog nodes' unbalanced loads. This paper proposes a new Fog-2-Fog (F2F) collaboration model that promotes offloading incoming requests among fog nodes, according to their load and processing capabilities, via a novel load balancing known as Fog Resource manAgeMEnt Scheme (FRAMES). A formal mathematical model of F2F and FRAMES has been fomulated, and a set of experiments has been carried out demonstrating the technical doability of F2F collaboration. The performance of the proposed fog load balancing model is compared to other load balancing models

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this paper, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN and DT) using a standard security dataset (NSL-KDD)

An Edge Computing Based Smart Healthcare Framework for Resource Management

The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time

Providing Secure and Reliable Communication for Next Generation Networks in Smart Cities

Finding a framework that provides continuous, reliable, secure and sustainable diversified smart city services proves to be challenging in today’s traditional cloud centralized solutions. This article envisions a Mobile Edge Computing (MEC) solution that enables node collaboration among IoT devices to provide reliable and secure communication between devices and the fog layer on one hand, and the fog layer and the cloud layer on the other hand. The solution assumes that collaboration is determined based on nodes’ resource capabilities and cooperation willingness. Resource capabilities are defined using ontologies, while willingness to cooperate is described using a three-factor node criteria, namely: nature, attitude and awareness. A learning method is adopted to identify candidates for the service composition and delivery process. We show that the system does not require extensive training for services to be delivered correct and accurate. The proposed solution reduces the amount of unnecessary traffic flow to and from the edge, by relying on nodeto-node communication protocols. Communication to the fog andcloud layers is used for more data and computing-extensive applications, hence, ensuring secure communication protocols to the cloud. Preliminary simulations are conducted to showcase the effectiveness of adapting the proposed framework to achieve smart city sustainability through service reliability and security. Results show that the proposed solution outperforms other semicooperative and non-cooperative service composition techniques in terms of efficient service delivery and composition delay, service hit ratio, and suspicious node identification

PriNergy: A Priority-based Energy Efficient Routing Method for IoT Systems

The Internet of Things (IoT) devices gather a plethora of data by sensing and monitoring the surrounding environment. Transmission of collected data from the IoT devices to the cloud through relay nodes is one of the many challenges that arise from IoT systems. Fault tolerance, security, energy consumption and load balancing are all examples of issues revolving around data transmissions. This paper focuses on energy consumption, where a priority-based and energy-efficient routing (PriNergy) method is proposed. The method is based on the routing protocol for low-power and lossy network (RPL) model, which determines routing through contents. Each network slot uses timing patterns when sending data to the destination, while considering network traffic, audio and image data. This technique increases the robustness of the routing protocol and ultimately prevents congestion. Experimental results demonstrate that the proposed PriNergy method reduces overhead on the mesh, end-to-end delay and energy consumption. Moreover, it outperforms one of the most successful routing methods in an IoT environment, namely the quality of service RPL (QRPL)