In the quest to mitigate the environmental impact of the aviation sector, we seek to optimise fuel consumption during taxi-out—aiming for reduced emissions and cost efficiency—while ensuring safety remains paramount. Partnering with an airline, we access a comprehensive aircraft taxiing database. Through data analysis and statistical inference, we establish suitable probability distributions for the key variables involved in the taxi-out phase. We then advocate for the systematic adoption of two operational strategies: streamlining the delay time for the second engine’s start-up and suggesting revised taxi-out loads. Both procedures should be regarded as multi-objective optimisation problems, wherein we must assess not only their benefits but also their potential adverse consequences—whether in terms of safety or non-compliance with existing regulations—and strive to minimise them to the greatest possible extent. We conduct simulations of future operations at high-traffic airports to predict the advantages and viability of our fuel-saving measures and quantify associated uncertainties. Results show that significant savings could be achieved, outweighing the relatively low risk of non-compliance. Additional recommendations to airports and air traffic controllers could further mitigate these risks. We believe these protocols could be valuable decision-making tools for the airline, contributing to environmental preservation and operational efficiency. Rather than asserting absolute truths, our goal is to provide a technical basis for informed decision-making under standard conditions, while acknowledging that rigid rule-following may overlook key human factors and real-world deviations.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to impact the world even three years after its outbreak. International border closures and health alerts severely affected the air transport industry, resulting in substantial financial losses. This study analyzes the global data on infected individuals alongside aircraft types, flight durations, and passenger flows. Using a Bayesian framework, we forecast the risk of infection during commercial flights and its potential spread across an air transport network. Our model allows us to explore the effect of mitigation measures, such as closing individual routes or airports, reducing aircraft occupancy, or restricting access for infected passengers, on disease propagation, while allowing the air industry to operate at near-normal levels. Our novel approach combines dynamic network modeling with discrete event simulation. A real-case study at major European hubs illustrates our methodology.

Adversarial Risk Analysis (ARA) allows for much more realistic modeling of game theoretical decision problems than Bayesian game theory. While ARA solutions for various applications have been discussed in the literature, we have not encountered a manuscript that assesses ARA in a real-life case study involving actual decision-makers. In this study, we present an ARA solution for the Parole Board decision problem. To elicit the Parole Board's probabilities and utilities regarding the convict's choices and resulting consequences, as well as their own subjective beliefs about such probabilities and utilities, we conducted a detailed interview with two current members of the New Zealand Parole Board, using a realistic case report. Subsequently, we derived the optimal ARA decision for different scenarios. This study highlights the advantages and challenges of the ARA methodology for real-life decision-making in the presence of an adversary.

It is shown that every polyhedron in R3 can be guarded by at most 56 of its edges. This result holds even if the boundary of the polyhedron is disconnected (i.e., if the polyhedron has “holes”), and regardless of the genus of each connected component of its boundary.When a polyhedron is homeomorphic to a ball and all its faces are triangles, the bound can be improved slightly to 2936 of its edges.

Multimodal optimization deals with problems where multiple feasible global solutions coexist. Despite sharing a common objective function value, some global optima may be preferred to others for various reasons. In such cases, it is paramount to devise methods that are able to find as many global optima as possible within an affordable computational budget. Niching strategies have received an overwhelming attention in recent years as the most suitable technique to tackle these kinds of problems. In this paper we explore a different approach, based on a systematic yet versatile use of traditional direct search methods. When tested over reference benchmark functions, our proposal, despite its apparent simplicity, noticeably resists the comparison with state-of-the-art niching methods in most cases, both in the number of global optima found and in the number of function evaluations required. However, rather than trying to outperform niching methods-far more elaborated-our aim is to enrich them with the knowledge gained from exploiting the distinctive features of direct search methods. To that end, we propose two new performance measures that can be used to evaluate, compare and monitor the progress of optimization algorithms of (possibly) very different nature in their effort to find as many global optima of a given multimodal objective function as possible. We believe that adopting these metrics as reference criteria could lead to more sophisticated and computationally-efficient algorithms, which could benefit from the brute force of derivative-free local search methods.

Purpose Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed. Design/methodology/approach This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results. Findings The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration. Originality/value This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

Jet engine malfunctions and pitot probe blocking are two safety events for which the formation of High Altitude Ice Crystals (HAIC) is a relevant contributing factor. Power loss and damage in jet engine under such conditions has drawn considerable attention of air transport authorities and industry. In turn, little interest has been paid to pitot probe clogging by HAIC, despite it being the main cause of two fatal accidents occurred in recent years. Aiming to increase flight safety, indications of erroneous Total Air Temperature (TAT) measurements due to built-up HAIC could be used to improve crew situation awareness. To that end, we propose data-based, practical and cost-effective mitigation measures to reduce the associated risk.

We describe how to support a decision maker who faces an adversary. To that end, we consider general interactions entailing sequences of both agents' decisions, some of them possibly being simultaneous or repeated across time. We model their joint problem as a bi-agent influence diagram. Unlike previous solutions framed under a standard game-theoretic perspective, we provide a decision-analytic methodology to support the decision maker based on an adversarial risk analysis paradigm. This allows the avoidance of non-realistic strong common knowledge assumptions typical of non-cooperative game theory as well as a better apportion of uncertainty sources. We illustrate the methodology with a schematic critical infrastructure protection problem. (C) 2018 The Authors. Published by Elsevier B.V.

This paper presents a novel distributed intelligent video surveillance architecture based on Wireless Multimedia Sensor Networks (WMSNs). This architecture is part of a video surveillance project and has been built using the Robot Operating System (ROS). ROS allows to develop and connect (through wireless TCP-IP) several modules to process and manage multimedia data in an easy way. The real-time intelligent surveillance system has been trained for detecting, tracking and monitoring people and vehicles in an indoor-outdoor real environment. The test process shows the reliability of the developed system as a tool for the identification of security incidents. Besides, using wireless connections and a distributed architecture together, we have achieved a really flexible, easy to install and lower-maintenance system that supports many different devices. Thus, the proposed architecture can be applied in distributed locations such as smart cities.

Runway excursions at landing constitute a major threat to aviation safety. Among them, runway overruns, defined as those occurrences when an aircraft departs the end of a runway, are the most frequent events. Although their occurrence rate is low, the entailed consequences may be very severe in terms of lives and aircraft damage. The main contributing factors to this event and their relationships are studied with the aid of a Bayesian network, while also modeling the nodes' conditional distributions. Then, inferences and predictions are made for the quantities of interest. The issues uncovered suggest several operational recommendations to reduce the probability of facing a runway overrun when landing.

We use the adversarial risk analysis (ARA) framework to deal with the protection of a critical networked infrastructure from the attacks of intelligent adversaries. We deploy an ARA model for each relevant element (node, link, hotspot in link) in the network, using a Sequential Defend–Attack–Defend template as a reference. Such ARA models are related by resource constraints and result aggregation over various sites, for both the Defender and the Attacker. As a case study, we consider the protection of a section of the Spanish railway network from a potential terrorist attack.

We describe in this paper the implementation of E-Water, an open software Decision Support System (DSS), designed to help local managers assess the Water Energy Food Environment (WEFE) nexus. E-Water aims at providing optimal management solutions to enhance food crop production at river basin level. The DSS was applied in the transboundary Mékrou river basin, shared among Benin, Burkina Faso and Niger. The primary sector for local economy in the region is agriculture, contributing significantly to income generation and job creation. Fostering the productivity of regional agricultural requires the intensification of farming practices, promoting additional inputs (mainly nutrient fertilizers and water irrigation) but, also, a more efficient allocation of cropland. In order to cope with the heterogeneity of data, and the analyses and issues required by the WEFE nexus approach, our DSS integrates the following modules: (1) the EPIC biophysical agricultural model; (2) a simplified regression metamodel, linking crop production with external inputs; (3) a linear programming and a multiobjective genetic algorithm optimization routines for finding efficient agricultural strategies; and (4) a user-friendly interface for input/output analysis and visualization. To test the main features of the DSS, we apply it to various real and hypothetical scenarios in the Mékrou river basin. The results obtained show how food unavailability due to insufficient local production could be reduced by, approximately, one third by enhancing the application and optimal distribution of fertilizers and irrigation. That would also affect the total income of the farming sector, eventually doubling it in the best case scenario. Furthermore, the combination of optimal agricultural strategies and modified optimal cropland allocation across the basin would bring additional moderate increases in food self-sufficiency, and more substantial gains in the total agricultural income. The proposed software framework proves to be effective, enabling decision makers to identify efficient and site-specific agronomic management strategies for nutrients and water. Such practices would augment crop productivity, which, in turn, would allow to cope with increasing future food demands, and find a balanced use of natural resources, also taking other economic sectors-like livestock, urban or energy-into account.

Functional complex networks have meant a pivotal change in the way we understand complex systems, being the most outstanding one the human brain. These networks have classically been reconstructed using a frequentist approach that, while simple, completely disregards the uncertainty that derives from data finiteness. We provide here an alternative solution based on Bayesian inference, with link weights treated as random variables described by probability distributions, from which ensembles of networks are sampled. By using both statistical and topological considerations, we prove that the role played by links’ uncertainty is equivalent to the introduction of a random rewiring, whose omission leads to a consistent overestimation of topological structures. We further show that this bias is enhanced in short time series, suggesting the existence of a theoretical time resolution limit for obtaining reliable structures. We also propose a simple sampling process for correcting topological values obtained in frequentist networks. We finally validate these concepts through synthetic and real network examples, the latter representing the brain electrical activity of a group of people during a cognitive task.

In this work, we describe the efficient use of improved directions of negative curvature for the solution of bound-constrained nonconvex problems. We follow an interior-point framework, in which the key point is the inclusion of computational low-cost procedures to improve directions of negative curvature obtained from a factorisation of the KKT matrix. From a theoretical point of view, it is well known that these directions ensure convergence to second-order KKT points. As a novelty, we consider the convergence rate of the algorithm with exploitation of negative curvature information. Finally, we test the performance of our proposal on both CUTEr/st and simulated problems, showing empirically that the enhanced directions affect positively the practical performance of the procedure.

Airports are critical infrastructures entailing intense human, commercial and economic activity. As such, they are preferred targets for criminal and terrorist groups, who are attracted by the promisingly high revenues they might get from an attack. Every year, airport authorities worldwide have to face, with limited resources, attacks arising from different adversaries. There are several sensible areas within an airport organization that are especially vulnerable to the terrorist threat, including, among others: (1) those related to human lives (of passengers or staff); (2) airport infrastructure (airport perimeter, main terminal, Air Traffic Control Tower, runways, hangars, etc.); (3) aircrafts and other ground vehicles; and (4) IT systems and services. Besides the more traditional ones, we are particularly concerned with attacks launched against the last type of targets, an emerging and increasingly worrisome threat. Specifically, we analyze the impact of cyber-attacks launched by organized groups whose main goal is to take hold of airport operations. In some cases, in order to have more chances to achieve their purpose (and take advantage of its eventual success), cyber attackers may be backed up by a terrorist group who will try to interfere with the Air Traffic Management network. In this paper, we aim at supporting airport authorities in their fight against both threats, by devising a security allocation plan. We provide an adversarial risk analysis model to address the problem, and apply it to obtain the optimal portfolio of preventive measures in an illustrative case study. The model is open to extensions, as e.g. larger and more complex technical infrastructures, new threats, or additional recovery measures deployed by different defensive agents.

In this paper, we illustrate how to combine supervised machine learning algorithms and unsupervised learning techniques for sentiment analysis and opinion mining purposes. To this end, we describe a multi-stage method for the automatic detection of different opinion trends. The proposal has been tested on real textual data available from comments introduced in a weblog, connected to organizational and administrative affairs in a public educational institution. The use of the described tool, given its potential impact to obtain valuable knowledge from opinion streams created by commenters, may be straightforwardly extended, for example, to the detection of opinion trends concerning policy decision making or electoral campaigns.

We analyze the case of protecting an airport, in which there is concern with terrorist threats against the Air Traffic Control Tower. To deter terrorist actions, airport authorities rely on various protective measures, which entail multiple consequences. By deploying them, airport authorities expect to reduce the probabilities and potential impacts of terrorist actions. We aim at giving advice to the airport authorities by devising a security resource allocation plan. We use the framework of adversarial risk analysis to deal with the problem.

We provide a novel adversarial risk analysis approach to security resource allocation decision processes for an organization which faces multiple threats over multiple sites. We deploy a Sequential Defend Attack model for each type of threat and site, under the assumption that different attackers are uncoordinated, although cascading effects are contemplated. The models are related by resource constraints and results are aggregated over the sites for each participant and, for the Defender, by value aggregation across threats. We illustrate the model with a case study in which we support a railway operator in allocating resources to protect from two threats: fare evasion and pickpocketing. Results suggest considerable expected savings due to the proposed investments. (C) 2016 Elsevier B.V. All rights reserved.

The quality of a process or product can be characterized by a functional relationship between a response variable and one or more explanatory variables. In this work, we develop a novel hybrid nonparametric–parametric procedure for the monitoring of nonlinear profiles, that is, realizations of a noisy nonlinear functional relationship between variables. In particular, we focus on the ‘shape’ property of profiles as a way of measuring their quality. Starting from a nonparametric reference curve, we select our model from a universe of parametric deformations of such a curve with the property of preserving certain important shape characteristics. To this aim, we design a metric based on the solution of a related optimization problem. In addition, we show that the problem is well posed from a theoretical point of view. Finally, we illustrate the performance of the proposal with numerical examples from simulated and real environments. Copyright © 2014 John Wiley & Sons, Ltd.

Let S be a set of n points in R d in general position. A setH of k-flats is called an mk-stabber of S if the relative interior of any m-simplex with vertices in S is intersected by at least one element of H. In this paper we give lower and upper bounds on the size of minimum mk-stabbers of point sets in R d . We study mainly mk-stabbers in the plane and in R 3 .