Magnetism in metastable and annealed compositionally complex alloys

Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local environments exist with a large variations in the magnetic energy terms, which can manifest large changes in the magnetic behavior. In the current work, the magnetic properties of (Cr, Mn, Fe, Ni) alloys are presented. These materials were prepared by room-temperature combinatorial sputtering, resulting in a range of compositions with a single bcc structural phase and no chemical ordering. The combinatorial growth technique allows CCMs to be prepared outside of their thermodynamically stable phase, enabling the exploration of otherwise inaccessible order. The mixed ferromagnetic and antiferromagnetic interactions in these alloys causes frustrated magnetic behavior, which results in an extremely low coercivity (<1mT), which increases rapidly at 50 K. At low temperatures, the coercivity achieves values of nearly 500 mT, which is comparable to some high-anisotropy magnetic materials. Commensurate with the divergent coercivity is an atypical drop in the temperature dependent magnetization. These effects are explained by a mixed magnetic phase model, consisting of ferro-, antiferro-, and frustrated magnetic regions, and are rationalized by simulations. A machine-learning algorithm is employed to visualize the parameter space and inform the development of subsequent compositions. Annealing the samples at 600 °C orders the sample, more-than doubling the Curie temperature and increasing the saturation magnetization by as much as 5×. Simultaneously, the large coercivities are suppressed, resulting in magnetic behavior that is largely temperature independent over a range of 350 K. The ability to transform from a hard magnet to a soft magnet over a narrow temperature range makes these materials promising for heat-assisted recording technologies.

A stochastic programming model with endogenous uncertainty for proactive supplier risk mitigation of low-volume-high-value manufacturers considering decision-dependent supplier performance

Poor supplier performance can result in delays that disrupt manufacturing operations. By proactively managing supplier performance, the likelihood and severity of supplier risk can be minimized. In this paper, we study the problem of selecting optimal supplier development programs (SDPs) to improve suppliers’ performance with a limited budget to proactively reduce supplier risks for a manufacturer. A key feature of our research is that it incorporates the uncertainty in supplier performance in response to SDPs selection decisions. This uncertainty is endogenous (decision-dependent), as the probability of supplier performance depends on the selection of SDPs, which introduces modeling and algorithmic challenges. We formulate this problem as a two-stage stochastic program with decision-dependent uncertainty. We implement a sample-based greedy algorithm and an accelerated Benders’ decomposition method to solve the developed model. We evaluate our methodology using the numerical cases of four low-volume, high-value manufacturing firms. The results provide insights into the effects of the budget amount and of the number of SDPs on the firm’s expected profit. Numerical experiments demonstrate that an increase in budget results in profit growth, e.g., 5.09% profit growth for one firm. At a lower budget level, increasing the number of available SDPs results in more profit growth. The results also demonstrate the significance of considering uncertainty in supplier performance and considering multiple supplier risks for the firm. In addition, computational experiments demonstrate that our algorithms, especially our greedy approximation algorithm, can solve large-sized problems in a reasonable time.

Risk-averse Bi-level Stochastic Network Interdiction Model for Cyber-security

Bhuiyan, Tanveer Hossain, Hugh R. Medal, Apurba K. Nandi, and Mahantesh Halappanavar. “Risk-averse bi-level stochastic network interdiction model for cyber-security risk management.” International Journal of Critical Infrastructure Protection 32 (2021): 100408.

Atomistic modeling of meso-timescale processes with SEAKMC: A perspective and recent developments

On-the-fly kinetic Monte Carlo (kMC) methods have recently garnered significant attentions after successful applications to various atomic-scale problems using a timescale outside the reach of classical molecular dynamics. These methods play a critical role in modeling atomistic meso-timescale processes, and it is therefore essential to further improve their capabilities. Herein, we review one of the on-the-fly kMC methods, Self-Evolving Atomistic kinetic Monte Carlo (SEAKMC) and propose two schemes that considerably enhance the efficiency of saddle point searches (SPSs) during the simulations. The performance of these schemes is tested using the diffusion of point defects in bcc Fe. In addition, we discuss approaches to significantly mitigate limitations of these schemes, which further improves their efficiencies. Importantly, these schemes improve the SPS efficiency not only for SEAKMC but also for other on-the-fly kMC methods, broadening the applications of on-the-fly kMC simulations to complex meso-timescale problems.

An optimized resource allocation approach to identify and mitigate supply chain risks using fault-tree analysis

Sherwin, M., Brown, K. J., Medal, H., and Mackenzie, C. (2017) An optimized resource allocation approach to identify and mitigate supply chain risks using fault-tree analysis. Technical report, Mississippi State University. 


Connected Infrastructure Network Design Under Additive Service Utilities

An infrastructure system usually contains a number of inter-connected infrastructure links that connect users to services or products. Where to locate these infrastructure links is a challenging problem that largely determines the efficiency and quality of the network. This paper studies a new location design problem that aims to maximize the total weighted benefits between users and multiple services that are measured by the amount of connectivity between users and links in the network. This problem is investigated from both analytical and computational points of view. First, analytical properties of special cases of the problem are described. Next, two integer programming model formulations are presented for the general problem. We also test intuitive heuristics including greedy and interchange algorithms, and find that the interchange algorithm efficiently yields near-optimum solutions. Finally, a set of numerical examples demonstrate the proposed models and reveal interesting managerial insights. In particular, we found that a more distance-dependent utility measure and a higher concentration of users help achieve a better total utility. As the population becomes increasingly concentrated, the optimal link design evolves from a linear path to a cluster of links around the population center. As the budget level increases, the installed links gradually sprawl from the population center towards the periphery, and in the case of multiple population centers, they grow and eventually merge into one connected component.

A model‐based systems engineering approach to critical infrastructure vulnerability assessment and decision analysis

A Model-Based Systems Engineering Approach to Critical Infrastructure Vulnerability Assessment and Decision Analysis

Securing critical infrastructure against attack presents significant challenges. As new infrastructure is built and existing infrastructure is maintained, a method to assess the vulnerabilities and support decision makers in determining the best use of security resources is needed. In response to this need, this research develops a methodology for performing vulnerability assessment and decision analysis of critical infrastructure using model‐based systems engineering, an approach that has not been applied to this problem. The approach presented allows architects to link regulatory requirements, system architecture, subject matter expert opinion and attack vectors to a Department of Defense Architecture Framework (DoDAF)‐based model that allows decision makers to evaluate system vulnerability and determine alternatives to securing their systems based on their budget constraints. The decision analysis is done using an integer linear program that is integrated with DoDAF to provide solutions for how to allocate scarce security resources. Securing an electrical substation is used as an illustrative case study to demonstrate the methodology. The case study shows that the method presented here can be used to answer key questions, for example, what security resources should a decision maker invest in based on their budget constraints? Results show that the modeling and analysis approach provides a means to effectively evaluate the infrastructure vulnerability and presents a set of security alternatives for decision makers to choose from, based on their vulnerabilities and budget profile.

NATO Human View Executable Architectures for Critical Infrastructure Analysis

NATO Human View Executable Architectures for Critical Infrastructure Analysis

Engineering managers are responsible for the secure operation of critical infrastructure systems and need tools and methods to identify and mitigate potential insider threats such as physical damage to equipment, information leakage, malware, and identify theft. This research examines the benefit of development and analysis of the NATO Human View to aid engineering managers with this responsibility. In an illustrative case study, the NATO Human View is used to analyze electrical grid personnel; the results demonstrate that the NATO Human View can be used to enable engineering managers to make investment decisions that can mitigate security threats.

Wireless LAN transmitter location under the threat of jamming attacks

Wireless LAN transmitter location under the threat of jamming attacks

This paper studies the optimal placement of wireless access points in a network under the threat of jamming. We addressed this problem with a tri-level mixed-integer program. In the top level, the defender seeks to optimally place a set of capacity-limited access points to maximize total connectivity. In the middle level, an attacker seeks to optimally place a set of jammers that may be relocated between time periods to minimize total connectivity. In the bottom level, demand points seek to connect to capacitated access points such that their connections maximize their network utility. This model was examined from two viewpoints: a non-additive model in which connections were jammed if they fell within a jammer’s radius, and an additive model in which connections were jammed if enough jamming power was interfering with the connection. We proposed a solution methodology which solved a modified bi-level program efficiently via implicit enumeration and dynamic constraint generation. We showed that the addition of just one access point provided a significant increase to network connectivity, different topologies had different robustness when different utility functions were considered, and optimal jammer placement varied significantly across different topologies. Through our experiments on five topologies, we found the Spacious and Median topologies were closest to the optimal access point placement.