Latency and Reliability Aware Edge Computation Offloading in 5G Networks

Empowered by recent technological advances and driven by the ever-growing population density and needs, the conception of 5G has opened up the expectations of what mobile networks are capable of to heights never seen before, promising to unleash a myriad of new business practices and paving the way for a surging number of user equipments to carry out novel service operations. The advent of 5G and networks beyond will hence enable the vision of Internet of Things (IoT) and smart city with its ubiquitous and heterogeneous use cases belonging to various verticals operating on a common underlying infrastructure, such as smart healthcare, autonomous driving, and smart manufacturing, while imposing extreme unprecedented Quality of Service (QoS) requirements in terms of latency and reliability among others. Due to the necessity of those modern services such as traffic coordination, industrial processes, and mission critical applications to perform heavy workload computations on the collected input, IoT devices such as cameras, sensors, and Cyber-Physical Systems (CPSs), which have limited energy and processing capabilities are put under an unusual strain to seamlessly carry out the required service computations. While offloading the devices' workload to cloud data centers with Mobile Cloud Computing (MCC) remains a possible alternative which also brings about a high computation reliability, the latency incurred from this approach would prevent from satisfying the services' QoS requirements, in addition to elevating the load in the network core and backhaul, rendering MCC an inadequate solution for handling the 5G services' required computations. In light of this development, Multi-access Edge Computing (MEC) has been proposed as a cutting edge technology for realizing a low-latency computation offloading by bringing the cloud to the vicinity of end-user devices as processing units co-located within base stations leveraging the virtualization technique. Although it promises to satisfy the stringent latency service requirements, realizing the edge-cloud solution is coupled with various challenges, such as the edge servers' restricted capacity, their reduced processing reliability, the IoT devices' limited offloading energy, the wireless offloading channels' often weak quality, the difficulty to adapt to dynamic environment changes and to under-served networks, and the Network Operators (NOs)' cost-efficiency concerns. In light of those conditions, the NOs are consequently looking to devise efficient innovative computation offloading schemes through leveraging novel technologies and architectures for guaranteeing the seamless provisioning of modern services with their stringent latency and reliability QoS requirements, while ensuring the effective utilization of the various network and devices' available resources. Leveraging a hierarchical arrangement of MEC with second-tier edge servers co-located within aggregation nodes and macro-cells can expand the edge network's capability, while utilizing Unmanned Aerial Vehicles (UAVs) to provision the MEC service via UAV-mounted cloudlets can increase the availability, flexibility, and scalability of the computation offloading solution. Moreover, aiding the MEC system with UAVs and Intelligent Reflecting Surfaces (IRSs) can improve the computation offloading performance by enhancing the wireless communication channels' conditions. By effectively leveraging those novel technologies while tackling their challenges, the edge-cloud paradigm will bring about a tremendous advancement to 5G networks and beyond, opening the door to enabling all sorts of modern and futuristic services. In this dissertation, we attempt to address key challenges linked to realizing the vision of a low-latency and high-reliability edge computation offloading in modern networks while exploring the aid of multiple 5G network technologies. Towards that end, we provide novel contributions related to the allocation of network and devices' resources as well as the optimization of other offloading parameters, and thereby efficiently utilizing the underlying infrastructure such as to enable energy and cost-efficient computation offloading schemes, by leveraging several customized solutions and optimization techniques. In particular, we first tackle the computation offloading problem considering a multi-tier MEC with a deployed second-tier edge-cloud, where we optimize its use through proposed low-complexity algorithms, such as to achieve an energy and cost-efficient solution that guarantees the services' latency requirements. Due to the significant advantage of operating MEC in heterogeneous networks, we extend the scenario to a network of small-cells with the second-tier edge server being co-located within the macro-cell which can be reached through a wireless backhaul, where we optimize the macro-cell server use along with the other offloading parameters through a proposed customized algorithm based on the Successive Convex Approximation (SCA) technique. Then, given the UAVs' considerable ability in expanding the capabilities of cellular networks and MEC systems, we study the latency and reliability aware optimized positioning and use of UAV-mounted cloudlets for computation offloading through two planning and operational problems while considering tasks redundancy, and propose customized solutions for solving those problems. Finally, given the IRSs' ability to also enhance the channel conditions through the tuning of their passive reflecting elements, we extend the latency and reliability aware study to a scenario of an IRS-aided MEC system considering both a single-user and multi-user OFDMA cases, where we explore the optimized IRSs' use in order to reveal their role in reducing the UEs' offloading consumption energy and saving the network resources, through proposed customized solutions based on the SCA approach and the SDR technique

Genome-Wide Association Study in a Lebanese Cohort Confirms PHACTR1 as a Major Determinant of Coronary Artery Stenosis

The manifestation of coronary artery disease (CAD) follows a well-choreographed series of events that includes damage of arterial endothelial cells and deposition of lipids in the sub-endothelial layers. Genome-wide association studies (GWAS) of multiple populations with distinctive genetic and lifestyle backgrounds are a crucial step in understanding global CAD pathophysiology. In this study, we report a GWAS on the genetic basis of arterial stenosis as measured by cardiac catheterization in a Lebanese population. The locus of the phosphatase and actin regulator 1 gene (PHACTR1) showed association with coronary stenosis in a discovery experiment with genome wide data in 1,949 individuals (rs9349379, OR = 1.37, p = 1.57×10−5). The association was replicated in an additional 2,547 individuals (OR = 1.31, p = 8.85×10−6), leading to genome-wide significant association in a combined analysis (OR = 1.34, p = 8.02×10−10). Results from this GWAS support a central role of PHACTR1 in CAD susceptibility irrespective of lifestyle and ethnic divergences. This association provides a plausible component for understanding molecular mechanisms involved in the formation of stenosis in cardiac vessels and a potential drug target against CAD

Large Scale Association Analysis Identifies Three Susceptibility Loci for Coronary Artery Disease

Genome wide association studies (GWAS) and their replications that have associated DNA variants with myocardial infarction (MI) and/or coronary artery disease (CAD) are predominantly based on populations of European or Eastern Asian descent. Replication of the most significantly associated polymorphisms in multiple populations with distinctive genetic backgrounds and lifestyles is crucial to the understanding of the pathophysiology of a multifactorial disease like CAD. We have used our Lebanese cohort to perform a replication study of nine previously identified CAD/MI susceptibility loci (LTA, CDKN2A-CDKN2B, CELSR2-PSRC1-SORT1, CXCL12, MTHFD1L, WDR12, PCSK9, SH2B3, and SLC22A3), and 88 genes in related phenotypes. The study was conducted on 2,002 patients with detailed demographic, clinical characteristics, and cardiac catheterization results. One marker, rs6922269, in MTHFD1L was significantly protective against MI (OR = 0.68, p = 0.0035), while the variant rs4977574 in CDKN2A-CDKN2B was significantly associated with MI (OR = 1.33, p = 0.0086). Associations were detected after adjustment for family history of CAD, gender, hypertension, hyperlipidemia, diabetes, and smoking. The parallel study of 88 previously published genes in related phenotypes encompassed 20,225 markers, three quarters of which with imputed genotypes The study was based on our genome-wide genotype data set, with imputation across the whole genome to HapMap II release 22 using HapMap CEU population as a reference. Analysis was conducted on both the genotyped and imputed variants in the 88 regions covering selected genes. This approach replicated HNRNPA3P1-CXCL12 association with CAD and identified new significant associations of CDKAL1, ST6GAL1, and PTPRD with CAD. Our study provides evidence for the importance of the multifactorial aspect of CAD/MI and describes genes predisposing to their etiology

Effect of Different Surface Treatments on the Shear Bond Strength of Metal Orthodontic Brackets Bonded to CAD/CAM Provisional Crowns

Background: The aim of this study was to find the best surface treatment for CAD/CAM provisional crowns allowing the optimal bond strength of metal brackets. Methods: The sample consists of 30 lower bicuspids and 180 provisional crowns. The provisional crowns were randomly divided into six different groups. Orthophosphoric acid etching (37%) was applied to 30 lower bicuspids. The provisional crowns had undergone different surface treatments. Group 1: No treatment (Control Group). Group 2: Diamond bur. Group 3: Sandblasting. Group 4: Plastic Conditioner. Group 5: Diamond bur and Plastic Conditioner. Group 6: Sandblasting and Plastic Conditioner. The brackets in all groups were identically placed using Transbond XT® Primer and Transbond XT® Paste. Then, the entire sample underwent an artificial aging procedure, and a measurement of the bond strength was conducted. After debonding, the surface of the crowns was examined to determine the quantity of the adhesive remnant. Results: Bonding to natural crowns recorded the highest average, followed by the averages of groups 5 and 6. However, group 1 recorded the lowest average. Groups 2 and 4 had very close averages, as well as groups 5 and 6. A statistically significant difference between the averages of all groups was recorded (p < 0.001) except for groups 2 and 4 (p = 0.965) on the one hand, and groups 5 and 6 (p = 0.941) on the other hand. Discussion: The bonding of brackets on provisional crowns is considered a delicate clinical procedure. In fact, unlike natural crowns, the orthophosphoric acid usually used does not have any effect on the surface of provisional crowns. Conclusions: Using a diamond bur combined with the plastic conditioner and sandblasting combined with that same product resulted in a bond strength close to natural crown

Effect of Different Surface Treatments on the Shear Bond Strength of Metal Orthodontic Brackets Bonded to CAD/CAM Provisional Crowns

Background: The aim of this study was to find the best surface treatment for CAD/CAM provisional crowns allowing the optimal bond strength of metal brackets. Methods: The sample consists of 30 lower bicuspids and 180 provisional crowns. The provisional crowns were randomly divided into six different groups. Orthophosphoric acid etching (37%) was applied to 30 lower bicuspids. The provisional crowns had undergone different surface treatments. Group 1: No treatment (Control Group). Group 2: Diamond bur. Group 3: Sandblasting. Group 4: Plastic Conditioner. Group 5: Diamond bur and Plastic Conditioner. Group 6: Sandblasting and Plastic Conditioner. The brackets in all groups were identically placed using Transbond XT® Primer and Transbond XT® Paste. Then, the entire sample underwent an artificial aging procedure, and a measurement of the bond strength was conducted. After debonding, the surface of the crowns was examined to determine the quantity of the adhesive remnant. Results: Bonding to natural crowns recorded the highest average, followed by the averages of groups 5 and 6. However, group 1 recorded the lowest average. Groups 2 and 4 had very close averages, as well as groups 5 and 6. A statistically significant difference between the averages of all groups was recorded (p p = 0.965) on the one hand, and groups 5 and 6 (p = 0.941) on the other hand. Discussion: The bonding of brackets on provisional crowns is considered a delicate clinical procedure. In fact, unlike natural crowns, the orthophosphoric acid usually used does not have any effect on the surface of provisional crowns. Conclusions: Using a diamond bur combined with the plastic conditioner and sandblasting combined with that same product resulted in a bond strength close to natural crown

External Anchorage Failure And Tendon Pull-Out Tests On Bridge Piers

Post-tensioning tendons in segmental bridge construction are often only anchored within the deviator and pier segments. The effectiveness of the post-tensioning (PT) system is therefore dependent on proper functioning of the anchorages. On August 28, 2000 a routine inspection of the Mid-Bay Bridge (Okaloosa County, Florida) revealed corrosion in numerous PT tendons. Moreover, one of the 19-strand tendons was completely slacked, with later inspection revealing a corrosion-induced failure at the pier anchor location. Anchorage failure caused all PT force to transfer to the steel duct located within the pier segment that in turn slipped and caused the tendon to go completely slack. After the application of PT force, the anchorage assembly and steel pipes that house the tendon are filled with grout. These short grouted regions could, in the event of anchorage failure, provide a secondary anchorage mechanism preventing the scenario mentioned above from occurring. This paper presents the results of a full-scale experimental investigation on anchorage tendon pull-out. The study focuses on the length required to develop the in-service PT force within the pier segment grouted steel tube assembly. Seven, twelve, and nineteen 0.6 diameter strand tendons with various development lengths were considered. Recommendations for pier section pipe detailing and design will be discussed. © 2010 American Society of Civil Engineers

Secondary Anchorage In Post-Tensioned Bridge Systems

Post-tensioning (PT) tendons in segmental bridges are often anchored within the deviator and pier segments. The effectiveness of the PT system is therefore dependent on proper anchorage function. However, anchorage failure may occur due to corrosion of the strand at the anchor head and subsequently cause the PT force to transfer within the pier segment or slacking of the tendon to occur. Following tendon stressing, the anchorage assembly and ducts that house the tendon are filled with grout. These short bonded regions could, in the event of anchorage failure, provide secondary anchorage. This paper presents the results of a full-scale experimental investigation on bonded anchorage tendon pullout. The study focuses on the embedment length required to develop the in-service PT force within the pier segment. Seven, twelve, and nineteen 15 mm (0.6 in.) diameter low-relaxation strand tendons with various bonded lengths were considered. Copyright © 2013, American Concrete Institute. All rights reserved

Robust Deep Learning For Emulating Turbulent Viscosities

From the simplest models to complex deep neural networks, modeling turbulence with machine learning techniques still offers multiple challenges. In this context, the present contribution proposes a robust strategy using patch-based training to learn turbulent viscosity from flow velocities, and demonstrates its efficient use on the Spalart-Allmaras turbulence model. Training datasets are generated for flow past twodimensional obstacles at high Reynolds numbers and used to train an auto-encoder type convolutional neural network with local patch inputs. Compared to a standard training technique, patch-based learning not only yields increased accuracy but also reduces the computational cost required for training

Robust Deep Learning For Emulating Turbulent Viscosities

From the simplest models to complex deep neural networks, modeling turbulence with machine learning techniques still offers multiple challenges. In this context, the present contribution proposes a robust strategy using patch-based training to learn turbulent viscosity from flow velocities, and demonstrates its efficient use on the Spalart-Allmaras turbulence model. Training datasets are generated for flow past twodimensional obstacles at high Reynolds numbers and used to train an auto-encoder type convolutional neural network with local patch inputs. Compared to a standard training technique, patch-based learning not only yields increased accuracy but also reduces the computational cost required for training

Deep learning model to assist multiphysics conjugate problems

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