Cascading failures in networks of heterogeneous node behavior

Variability in the dynamical function of nodes comprising a complex network impacts upon cascading failures that can compromise the network's ability to operate. Node types correspond to sources, sinks or passive conduits of a current ow, applicable to renewable electrical power micro-grids containing a variable number of intermittently operating generators and consumers of power. The resilience to cascading failures of ensembles of synthetic networks with di_erent topology is examined as a function of the edge current carrying capacity and mix of node types, together with exemplar real-world networks. Whilst a network with homogeneous node type can be resilient to failure, one with identical topology but heterogeneous node function can be strongly susceptible to failure. For networks with similar numbers of sources, sinks and passive nodes the mean resilience decreases as networks become more disordered. Nevertheless all network topologies have enhanced regions of resilience, accessible by manipulation of node composition and functionality

Understanding the policy instruments mix in higher education r&d : a policy scale development

Purpose: The purpose of this paper is to understand the policy instruments mix in higher education research and development (HERD) using structural equation modeling. This modeling helps us to understand the total structure of the factors affecting the policy mix as well as its main actors in a political system. Design/Methodology/Approach: Thirty two identified actors (official institutions) through upstream documents were designed by the method of social network analysis in the form of a political network and their role in policy instruments mix was investigated through their amount of centrality in the network. Also, indicators affecting policy instrument mix were identified using the view of 13 Iranian higher education policy experts. These indicators were categorized in the form of causal, contextual, intervening factors, main phenomena, mechanisms and outcomes. Structural equation modeling was used to confirm the model. Findings: According to the results, the lack of policy logic is the main reason for the lack of justice in the policy instruments mix. Choosing a logic or theory of justice that is the basis of all the instruments in research and development decisions can lead to the integration of concepts and instruments mix. Practical Implications: There is no doubt that the dominant range of thought can have a greater impact on politics in any state, but choosing observers from other aspects of thought will always lead to more effective policies. Originality/Value: How to form policy instruments mix in policymakers' mind has not been investigated in any study so far, and this study explores the indicators governing policy instrument mix.peer-reviewe

Fleet Prognosis with Physics-informed Recurrent Neural Networks

Services and warranties of large fleets of engineering assets is a very profitable business. The success of companies in that area is often related to predictive maintenance driven by advanced analytics. Therefore, accurate modeling, as a way to understand how the complex interactions between operating conditions and component capability define useful life, is key for services profitability. Unfortunately, building prognosis models for large fleets is a daunting task as factors such as duty cycle variation, harsh environments, inadequate maintenance, and problems with mass production can lead to large discrepancies between designed and observed useful lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A simple numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consist of predicting fatigue crack length for a synthetic fleet of airplanes subject to different mission mixes. The model is trained using full observation inputs (far-field loads) and very limited observation of outputs (crack length at inspection for only a portion of the fleet). The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer perceptron, the stress intensity and Paris law layers, the cumulative damage cell, as well as python driver scripts) used in this manuscript are publicly available on GitHub at https://github.com/PML-UCF/pinn. The data and code are released under the MIT Licens

Predicting Scheduling Failures in the Cloud

Cloud Computing has emerged as a key technology to deliver and manage computing, platform, and software services over the Internet. Task scheduling algorithms play an important role in the efficiency of cloud computing services as they aim to reduce the turnaround time of tasks and improve resource utilization. Several task scheduling algorithms have been proposed in the literature for cloud computing systems, the majority relying on the computational complexity of tasks and the distribution of resources. However, several tasks scheduled following these algorithms still fail because of unforeseen changes in the cloud environments. In this paper, using tasks execution and resource utilization data extracted from the execution traces of real world applications at Google, we explore the possibility of predicting the scheduling outcome of a task using statistical models. If we can successfully predict tasks failures, we may be able to reduce the execution time of jobs by rescheduling failed tasks earlier (i.e., before their actual failing time). Our results show that statistical models can predict task failures with a precision up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of such predictions using the tool kit GloudSim and found that they can improve the number of finished tasks by up to 40%. We also perform a case study using the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene expression correlations analysis study from breast cancer research. We find that when extending the scheduler of Hadoop with our predictive models, the percentage of failed jobs can be reduced by up to 45%, with an overhead of less than 5 minutes

Mechanical performance of glass-based geopolymer matrix composites reinforced with cellulose fibers

Glass-based geopolymers, incorporating fly ash and borosilicate glass, were processed in conditions of high alkalinity (NaOH 10-13 M). Different formulations (fly ash and borosilicate in mixtures of 70-30 wt% and 30-70 wt%, respectively) and physical conditions (soaking time and relative humidity) were adopted. Flexural strength and fracture toughness were assessed for samples processed in optimized conditions by three-point bending and chevron notch testing, respectively. SEM was used to evaluate the fracture micromechanisms. Results showed that the geopolymerization efficiency is strongly influenced by the SiO2/Al2O3 ratio and the curing conditions, especially the air humidity. The mechanical performances of the geopolymer samples were compared with those of cellulose fiber-geopolymer matrix composites with different fiber contents (1 wt%, 2 wt%, and 3 wt%). The composites exhibited higher strength and fracture resilience, with the maximum effect observed for the fiber content of 2 wt%. A chemical modification of the cellulose fiber surface was also observe

RMSA algorithms resilient to multiple node failures in dynamic EONs

In Elastic Optical Networks (EONs), the way different service demands are supported in the network is ruled by the Routing, Modulation and Spectrum Assignment (RMSA) algorithm, which decides how the spectrum resources of the optical network are assigned to each service demand. In a dynamic EON, demand requests arrive randomly one at a time and the accepted demands last in the network for a random time duration. So, one important goal of the RMSA algorithm is the efficient use of the spectrum resources to maximize the acceptance probability of future demand requests. On the other hand, multiple failure events are becoming a concern to network operators as such events are becoming more frequent in time. In this work, we consider the case of multiple node failure events caused by malicious attacks against network nodes. In order to obtain RMSA algorithms resilient to such events, a path disaster availability metric was recently proposed which takes into account the probability of each path not being disrupted by an attack. This metric was proposed in the offline variant of the RMSA problem where all demands are assumed to be known at the beginning. Here, we exploit the use of the path disaster availability metric in the RMSA of dynamic EONs. In particular, we propose RMSA algorithms combining the path disaster availability metric with spectrum usage metrics in a dynamic way based on the network load level. The aim is that the efficient use of the resources is relaxed for improved resilience to multiple node failures when the EON is lightly loaded, while it becomes the most important goal when the EON becomes heavily loaded. We present simulation results considering a mix of unicast and anycast services in 3 well-known topologies. The results show that the RMSA algorithms combining the path disaster availability metric with spectrum usage metrics are the best trade-off between spectrum usage efficiency and resilience to multiple node failures.publishe

A stochastic intra-ring synchronous optimal network design problem

We develop a stochastic programming approach to solving an intra-ring Synchronous Optical Network (SONET) design problem. This research differs from pioneering SONET design studies in two fundamental ways. First, while traditional approaches to solving this problem assume that all data are deterministic, we observe that for practical planning situations, network demand levels are stochastic. Second, while most models disallow demand shortages and focus only on the minimization of capital Add-Drop Multiplexer (ADM) equipment expenditure, our model minimizes a mix of ADM installations and expected penalties arising from the failure to satisfy some or all of the actual telecommunication demand. We propose an L-shaped algorithm to solve this design problem, and demonstrate how a nonlinear reformulation of the problem may improve the strength of the generated optimality cuts. We next enhance the ba-sic algorithm by implementing powerful lower and upper bounding techniques via an assortment of modeling, valid inequality, and heuristic strategies. Our computational results conclusively demonstrate the efficacy of our proposed algorithm as opposed to standard L-shaped and extensive form approaches to solving the problem