Context Aware Routing Management Architecture for Airborne Networks

Military environments require highly dynamic mobile ad hoc networks (MANETs) to meet operational mission requirements. Decision makers rely on the timely delivery of critical battlefield information to make informed determinations quickly and as accurately as possible. However, traditional MANET routing protocols do not provide quality of service (QoS). Furthermore, they do not implement active controls to minimise the impact of network congestion. This study proposes the use of the information embedded in an air tasking order (ATO) during the planning phase of military missions to optimise the network performance. The trajectories of relevant nodes (airborne platforms) participating in the MANET can be forecasted by parsing key information contained in the ATO. Using this idea it is possible to optimise network routes to minimise edge overutilisation and increase network throughput. In onesimulated test case, there was a 25% improvement of network throughput, and 23% reduction on dropped packets. Using this technique, the authors can selectively preserve the QoS by establishing network controls that drop low-priority packets when necessary. The algorithm improves the overall MANET throughput while minimising the packets dropped due to network congestion

Benchmarking Approach for Empirical Comparison of Pricing Models in DRMS

emand response management systems often involve the use of pricing schemes to motivate the efficient use of electrical power. Achieving this efficiency requires the detection of electrical power patterns. The detection of these patterns normally involves use of non-linear, quasi-non-linear, and at times linear data pattern detection models. The behavioural disparities of these models and specifically when used for a specific set of data make it hard to select the most efficient model. The contribution of this study is devising an empirical benchmark (reference) ( perfect ) control pricing (PCP) model through which various models are compared in order to select the most efficient model. In this study, the authors elect neural networks, sliding window–multiple linear regression, and a proportional controller models to be representative of non-linear, quasi-non-linear, and linear models, respectively, in order to demonstrate the effectiveness of PCP. The dataset used for demonstrating both the operation of PCP and the elected models for comparisons is collected from Green Button project and Pacific Gas and Electric

Con-Resistant Trust for Improved Reliability in a Smart Grid Special Protection System

This paper applies a con-resistant trust mechanism to improve the performance of a communications-based special protection system to enhance its effectiveness and resiliency. Smart grids incorporate modern information technologies to increase reliability and efficiency through better situational awareness. However, with the benefits of this new technology come the added risks associated with threats and vulnerabilities to the technology and to the critical infrastructure it supports. The research in this paper uses con-resistant trust to quickly identify malicious or malfunctioning (untrustworthy) protection system nodes to mitigate instabilities. The con-resistant trust mechanism allows protection system nodes to make trust assessments based on the node\u27s cooperative and defective behaviors. These behaviors are observed via frequency readings which are prediodically reported. The trust architecture is tested in experiments by comparing a simulated special protection system with a con-resistant trust mechanism to one without the mechanism via an analysis of the variance statistical model. Simulation results show promise for the proposed con-resistant trust mechanism. © IEE

Improving Optimization of Convolutional Neural Networks through Parameter Fine-tuning

In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter fine-tuning over random initialization. We find that training should not be reduced after transferring weights, larger, more similar networks tend to be the best source task, and parameter fine-tuning can often outperform randomly initialized ensembles. The experimental framework and findings will help to train models with improved accuracy

Using Modeling and Simulation to Examine the Benefits of a Network Tasking Order

The Global Information Grid (GIG) is the military’s computer and communications network which supports the myriad of military missions. Military missions are highly planned, passing through many hands in the strategy-to-task methodology to ensure completeness, accuracy, coordination, cohesion, and appropriateness. A benefit of this planning is the possibility to collect knowledge of future conditions that could be of use to network designers whose goals include optimizing and protecting the GIG. This advanced knowledge includes which networked military equipment will be involved, what their capabilities are, where they will be, when they will be there, and particulars on the required data flows. A Network Tasking Order process is proposed as a means of collecting this information, analyzing the information to generate network taskings, and disseminating those taskings. Tactical integration of assets in mobile networks is introduced as another planning variable in the battlefield; not unlike logistical considerations such as fuel, ammunition, water, and so on used currently in operation planning. Modeling and simulation is used to support the proposed benefits

Beyond outputs: pathways to symmetrical evaluations of university sustainable development partnerships

As the United Nations Decade of Education for Sustainable Development (2005–2014) draws to a close, it is timely to review ways in which the sustainable development initiatives of higher education institutions have been, and can be, evaluated. In their efforts to document and assess collaborative sustainable development program outcomes and impacts, universities in the North and South are challenged by similar conundrums that confront development agencies. This article explores pathways to symmetrical evaluations of transnationally partnered research, curricula, and public-outreach initiatives specifically devoted to sustainable development. Drawing on extensive literature and informed by international development experience, the authors present a novel framework for evaluating transnational higher education partnerships devoted to sustainable development that addresses design, management, capacity building, and institutional outreach. The framework is applied by assessing several full-term African higher education evaluation case studies with a view toward identifying key limitations and suggesting useful future symmetrical evaluation pathways. University participants in transnational sustainable development initiatives, and their supporting donors, would be well-served by utilizing an inclusive evaluation framework that is infused with principles of symmetry

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Implications of serial measurements of natriuretic peptides in heart failure: insights from BIOSTAT‐CHF

No abstract available

Agent Technology Applied to Adaptive Relay Setting for Multi-TerminalLines

Abstract: This paper discusses the adaptation of the settings of distance relays for multi-terminal lines employing agents. Agents are software processes capable of searching for information in networks, interacting with pieces of equipment and performing tasks on behalf of their owners (relays). Results illustrating the performance of the adaptive method proposed compared to conventional fixed settings are presented. It is shown that the digital relays and agents acting within a communication structure (also called middleware) can alter adaptive settings to ensure correct performance over a wide variety of operation conditions, without the need of an additional communication link. The proposed relaying scheme can also be utilized for first zone clearing over the entire line