Performance evaluation of the Cognitive Packet Network in the presence of network worms

Reliable networks that provide good service quality are expected to become more crucial in every aspect of communication, especially as the information transferred between network users gets more complex and demanding and as malicious users try to deliberately degrade or altogether deny legitimate network service. The Cognitive Packet Network (CPN) routing protocol provides Quality of Service (QoS) driven routing and performs self-improvement in a distributed manner, by learning from the experience of special packets, which gather on-line QoS measurements and discover new routes. Although CPN is generally very resilient to network changes, it may suffer worse performance during node failures caused by network threats, such as network worms. Here we evaluate the performance of CPN in such crises and compare it with the Open Shortest Path First (OSPF) routing protocol, an industry standard and widely used in Internet Protocol networks. We also improve it by introducing a failure detection element that reduces packet loss and delay during failures. Our experiments were performed in a real networking testbed

CAM04-1: Admission control in self aware networks

The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis

Perception and Understanding of Greek Dentists on Periodontal Regenerative Procedures: A Questionnaire Based Study

Objectives: The aim of this cross-sectional questionnaire study was to evaluate the perception and preferences of Greek dentists who either specialised in or had an interest in periodontal regenerative procedures and to compare the results with corresponding findings from two previous studies from different countries. Materials and methods: The questionnaire was divided in two main sections and included multiple choice and/or open/closed questions. The first section consisted of six questions and was designed to collect demographic data of the sample and the second section, consisting of 15 questions, included general questions regarding periodontal regeneration procedures and questions based on specific clinical cases. 200 questionnaires were distributed at selected venues in Greece by the investigators. The participants were given one month to complete and return to the questionnaires to the School of Dentistry in Thessaloniki. Statistical analysis: Data management and analysis was performed using both Microsoft Excel 2007® (Microsoft Corporation, Reading, UK) and SPSS® version 22.0 software (IBM United Kingdom Ltd, Portsmouth, UK). Frequencies and associations between the demographic profiles of the participants were evaluated and presented in the form of frequency tables, charts, and figures. Results: 104 questionnaires (67 males, 37 females: mean age 43.2 years [±9.8]) (52% response rate) were received. Of those who responded 56.7% (n=59) specialized in Periodontics and 43.3% (n=45) specialized in a variety of other dental disciplines (General Dentistry, Oral Surgery and Implantology). Guided tissue regeneration procedures and the use of enamel matrix derivative were recommended for the reconstruction of bony defects and both subepithelial connective tissue graft and coronally advanced flap with or without enamel matrix derivative were the most popular choices for root coverage. Smoking was considered a contraindication by most of the participants and conflicting responses were given regarding the use of antibiotics as part of the post-operative care following regenerative procedures. Conclusions: The participants incorporated both traditional and “novel” techniques and products in reconstructive procedures and appeared to be up to date with the evidence from the dental literature. However, it was evident that there was confusion regarding the role of antibiotics in regenerative procedures

Using energy criteria to admit flows in a wired network

Admission control in wired networks has been traditionally used as a way to control traffic congestion and guarantee quality of service. Here, we propose an admission control mechanism which aims to keep the power consumption at the lowest possible level by restricting the more energy-demanding users. This work relies on the fact that power consumption of networking devices, and of the network as a whole, is not proportional to the carried traffic, as would be the ideal case [1]. As a result some operating regions may be more efficient than others and ”jumps” may arise in power consumption when new traffic is added in the network. The proposed mechanism aims to keep power consumption in the lowest possible power consumption level, hopping to the next level only when necessary

Energy-efficiency evaluation of computation offloading in personal computing

Cloud computing has become common practice for a wide variety of user communities. Yet, the energy efficiency and end-to-end performance benefits of cloud computing are not fully understood. Here, we focus specifically on the trade-off between local power saving and increased execution time when work is offloaded from a user’s PC to a cloud environment. We have set up a 14-node private cloud and have executed a variety of applications with different processing demands. We have measured the energy cost at the level of the individual user’s PC, at the level of the cloud, as well as at the two combined, contrasted to the execution time for each application when running on the PC and when running on the cloud. Our results indicate that the tradeoff between energy cost and performance differs considerably between applications of different types. In most cases investigated, the total increase in energy consumption, incurred by running that additional application, was reduced significantly. This shows that research on using cloud computing as a means to reduce the overall carbon footprint of IT is warranted. Of course, the energy gains were more pronounced for energy-selfish users, who are only interested in reducing their own carbon footprint, but these savings came at the expense of performance, with execution time increase ranging from 1% to 84% for different applications

Dynamic decision support for resource offloading in heterogeneous internet of things environments

Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices' resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage based on the type of the IoT device. Yet, the conditions that determine the optimality of this decision, such as the arrival rate, nature and sizes of the tasks, and crucially the real-time condition of the networks involved, keep changing. At the same time, the energy consumption of IoT devices is usually a key requirement, which is affected primarily by the time it takes to complete tasks, whether for the actual computation or for offloading them through the network. Here, we model the expected time and energy costs for the different options of offloading a task to the edge or the cloud, as well as of carrying out on the device itself. We use this model to allow the device to take the offloading decision dynamically as a new task arrives and based on the available information on the network connections and the states of the edge and the cloud. Having extended EdgeCloudSim to provide support for such dynamic decision making, we are able to compare this approach against IoT-first, edge-first, cloud-only, random and application-oriented probabilistic strategies. Our simulations on four different types of IoT applications show that allowing customisation and dynamic offloading decision support can improve drastically the response time of time-critical and small-size applications, and the energy consumption not only of the individual IoT devices but also of the system as a whole. This paves the way for future IoT devices that optimise their application response times, as well as their own energy autonomy and overall energy efficiency, in a decentralised and autonomous manner

Enhanced biomedical heat-triggered carriers via nanomagnetism tuning in ferrite-based nanoparticles

Biomedical nanomagnetic carriers are getting a higher impact in therapy and diagnosis schemes while their constraints and prerequisites are more and more successfully confronted. Such particles should possess a well-defined size with minimum agglomeration and they should be synthesized in a facile and reproducible high-yield way together with a controllable response to an applied static or dynamic field tailored for the specific application. Here, we attempt to enhance the heating efficiency in magnetic particle hyperthermia treatment through the proper adjustment of the core–shell morphology in ferrite particles, by controlling exchange and dipolar magnetic interactions at the nanoscale. Thus, core–shell nanoparticles with mutual coupling of magnetically hard (CoFe2O4) and soft (MnFe2O4) components are synthesized with facile synthetic controls resulting in uniform size and shell thickness as evidenced by high resolution transmission electron microscopy imaging, excellent crystallinity and size monodispersity. Such a magnetic coupling enables the fine tuning of magnetic anisotropy and magnetic interactions without sparing the good structural, chemical and colloidal stability. Consequently, the magnetic heating efficiency of CoFe2O4 and MnFe2O4 core–shell nanoparticles is distinctively different from that of their counterparts, even though all these nanocrystals were synthesized under similar conditions. For better understanding of the AC magnetic hyperthermia response and its correlation with magnetic-origin features we study the effect of the volume ratio of magnetic hard and soft phases in the bimagnetic core−shell nanocrystals. Eventually, such particles may be considered as novel heating carriers that under further biomedical functionalization may become adaptable multifunctional heat-triggered nanoplatforms

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype