A platform for initial testing of multiple camouflage patterns

The human visual system is still an important factor in military warfare; military personnel receive training on effective search strategies, and camouflage that can effectively conceal objects and personnel is a key component of a successful integrated survivability strategy. Previous methods of camouflage assessment have, amongst others, used psychophysics to generate distinctiveness metrics. However, the population from which the human observers are drawn is often not well defined, or necessarily appropriate. In this experiment we designed a new platform for testing multiple patterns based on a camouflaged object detection task, and investigate whether trained military observers perform better than civilians. We use a two-alternative forced choice paradigm, with participants searching images of woodland for a replica military helmet displaying Olive Green, Multi Terrain Pattern, US Marine Pattern or, as a conspicuous control, UN Peacekeeper Blue. Our data show that there is no difference in detection performance between the two observer groups but that there are clear differences in the effectiveness of the different helmet colour patterns in a temperate woodland environment. We conclude that when tasks involve very short stimulus presentation times, task-specific training has little effect on the success of target detection and thus this paradigm is particularly suitable for robust estimates of camouflage efficacy.     

Field test and probabilistic analysis of irregular steel debris casualty risks from a person-borne improvised explosive device

Person-borne improvised explosive devices (PBIEDs) are often used in terrorist attacks in Western countries. This study aims to predict the trajectories of PBIED fragments and the subsequent safety risks for people exposed to this hazard. An explosive field test with a typical PBIED composed of a plastic explosive charge and steel nut enhancements was performed to record initial fragment behaviour, including positions, velocity, and trajectory angles. These data were used to predict the full trajectory of PBIED fragments using a probabilistic analysis. In the probabilistic analyses a probability of fatality or serious injury was computed. Based on the results presented, many practical conclusions can be drawn, for instance, regarding safe evacuation distances if a person were exposed to a suspected PBIED.     

Improvement of VIS and IR camouflage properties by impregnating cotton fabric with PVB/IF-WS2

In order to examine the possibility to improve its camouflage properties standard cotton fabric with camouflage print was impregnated with poly(vinyl butyral), PVB and fullerene-like nanoparticles of tungsten disulfide, PVB/IF-WS₂. FTIR analysis excluded any possible chemical interaction of IF-WS₂ with PVB and the fabric. The camouflage behavior of the impregnated fabric has been examined firstly in the VIS part of the spectrum. Diffuse reflection, specular gloss and color coordinates were measured for three different shades (black, brown and dark green). Thermal imaging was applied to examine the camouflage abilities of this impregnation in IR part of the spectrum. The obtained results show that PVB/IF-WS₂ impregnation system induced enhacement of the materials camouflage properties, i.e. that IF-WS₂ have a positive effect on spectrophotometric characteristics of the fabric.     

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical-military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical-military experts.Both the design and the results of the evaluation are revealed in this article.     

Emergency transshipment in decentralized dealer networks: When to send and accept transshipment requests

While there has been significant previous literature on inventory transshipment, most research has focused on the dealers' demand filling decision (when to fill transshipment requests from other dealers), ignoring the requesting decision (when to send transshipment requests to other dealers). In this paper we develop optimal inventory transshipment policies that incorporate both types of decisions. We consider a decentralized system in which the dealers are independent of the manufacturer and of each other. We first study a network consisting of a very large number of dealers. We prove that the optimal inventory and transshipment decisions for an individual dealer are controlled by threshold rationing and requesting levels. Then, in order to study the impact of transshipment among independent dealers in a smaller dealer network, we consider a decentralized two‐dealer network and use a game theoretic approach to characterize the equilibrium inventory strategies of the individual dealers. An extensive numerical study highlights the impact of the requesting decision on the dealers' equilibrium behavior in a decentralized setting. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Optimal maintenance policies for serial,multi‐machine systems with non‐instantaneous repairs

We study optimal age‐replacement policies for machines in series with non‐instantaneous repair times by formulating two nonlinear programs: one that minimizes total cost‐rate subject to a steady‐state throughput requirement and another that maximizes steady‐state throughput subject to a cost‐rate budget constraint. Under reasonable assumptions, the single‐machine cost‐optimal and throughput‐optimal solutions are unique and orderable, and the multi‐machine optimal solutions have appealing structure. Furthermore, we establish equivalence between the two formulations and provide an illustrative numerical example. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

Transient analysis of Markov models of fault‐tolerant systems with deferred repair using split regenerative randomization

The (standard) randomization method is an attractive alternative for the transient analysis of continuous time Markov models. The main advantages of the method are numerical stability, well‐controlled computation error, and ability to specify the computation error in advance. However, the fact that the method can be computationally very expensive limits its applicability. In this paper, we develop a new method called split regenerative randomization, which, having the same good properties as standard randomization, can be significantly more efficient. The method covers reliability‐like models with a particular but quite general structure and requires the selection of a subset of states and a regenerative state satisfying some conditions. For a class of continuous time Markov models, model class C₂, including typical failure/repair reliability‐like models with exponential failure and repair time distributions and deferred repair, natural selections are available for both the subset of states and the regenerative state and, for those natural selections, theoretical results are available assessing the efficiency of the method in terms of “visible” model characteristics. Those results can be used to anticipate when the method can be expected to be competitive. We illustrate the application of the method using a large class C₂ model and show that for models in that class the method can indeed be significantly more efficient than previously available randomization‐based methods. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

WHO IS THE ENEMY?

We examine who benefits when there is a strong leader in place, and who benefits when a situation lacks a proper leader. There are fractious terrorist groups who seek to serve the same people in common cause against a common enemy. The groups compete for rents obtained from the public by engaging in actions against the common enemy. We determine the leadership structure under which each group is better off, as well as the circumstance that the common enemy prefers. We are able to state simple and general conditions for each group and the common enemy to benefit.     

DESIGNING A VIABLE PREDICTION MARKET TO FORECAST DEFENSE ACQUISITION COST AND SCHEDULE OUTCOMES

This paper is the first to demonstrate a viable prediction market that successfully forecasts defense acquisition cost and schedule outcomes, and to provide insights for defense executive decision‐making. Already used in private industry, prediction markets can also accurately forecast outcomes and their associated risks for government programs. Using virtual money, prediction markets allow traders to ‘bet’ on some future outcome. This market mechanism turns out to be a relatively simple and accurate way to discover, aggregate, and communicate to a defense executive the collective market’s beliefs about the likelihood of an eventual outcome of an acquisition program of interest.