A Decision Support System to Enhance Electricity Grid Resilience against Flooding Disasters

In different areas across the U.S., there are utility poles and other critical infrastructure that are vulnerable to flooding damage. The goal of this multidisciplinary research is to assess and minimize the probability of utility pole failure through conventional hydrological, hydrostatic, and geotechnical calculations embedded to a unique mixed-integer linear programming (MILP) optimization framework. Once the flow rates that cause utility pole overturn are determined, the most cost-efficient subterranean pipe network configuration can be created that will allow for flood waters to be redirected from vulnerable infrastructure elements. The optimization framework was simulated using the Julia scientific programming language, for which the JuMP interface and Gurobi solver package were employed to solve a minimum cost network flow objective function given the numerous decision variables and constraints across the network. We implemented our optimization framework in three different watersheds across the U.S. These watersheds are located near Whittier, NC; Leadville, CO; and London, AR. The implementation of a minimum cost network flow optimization model within these watersheds produced results demonstrating that the necessary amount of flood waters could be conveyed away from utility poles to prevent failure by flooding.

A crowdsourced dynamic repositioning strategy for public bike sharing systems

<p style='text-indent:20px;'>Public bike sharing systems have become the most popular shared economy application in transportation. The convenience of this system depends on the availability of bikes and empty racks. One of the major challenges in operating a bike sharing system is the repositioning of bikes between rental sites to maintain sufficient bike inventory in each station at all times. Most systems hire trucks to conduct dynamic repositioning of bikes among rental sites. We have analyzed a commonly used repositioning scheme and have demonstrated its ineffectiveness. To realize a higher quality of service, we proposed a crowdsourced dynamic repositioning strategy: first, we analyzed the historical rental data via the random forest algorithm and identified important factors for demand forecasting. Second, considering 30-minute periods, we calculated the optimal bike inventory via integer programming for each rental site in each time period with a sufficient crowd for repositioning bikes. Then, we proposed a minimum cost network flow model in a time-space network for calculating the optimal voluntary rider flows for each period based on the current bike inventory, which is adjusted according to the forecasted demands. The results of computational experiments on real-world data demonstrate that our crowdsourced repositioning strategy may reduce unmet rental demands by more than 30% during rush hours compared to conventional trucks.</p>

Emergent Diversity and Persistent Turnover in Evolving Microbial Cross-Feeding Networks

A distinguishing feature of many ecological networks in the microbial realm is the diversity of substrates that could potentially serve as energy sources for microbial consumers. The microorganisms are themselves the agents of compound diversification via metabolite excretion or overflow metabolism. It has been suggested that the emerging richness of different substrates is an important condition for the immense biological diversity in microbial ecosystems. In this work, we study how complex cross-feeding networks (CFN) of microbial species may develop from a simple initial community given some elemental evolutionary mechanisms of resource-dependent speciation and extinctions using a network flow model. We report results of several numerical experiments and report an in-depth analysis of the evolutionary dynamics. We find that even in stable environments, the system is subject to persisting turnover, indicating an ongoing co-evolution. Further, we compare the impact of different parameters, such as the ratio of mineralization, as well as the metabolic versatility and variability on the evolving community structure. The results imply that high microbial and molecular diversity is an emergent property of evolution in cross-feeding networks, which affects transformation and accumulation of substrates in natural systems, such as soils and oceans, with potential relevance to biotechnological applications.

Lexicographically Maximum Flows under an Arc Interdiction

Network interdiction problem arises when an unwanted agent attacks the network system to deteriorate its transshipment efficiency. Literature is flourished with models and solution approaches for the problem. This paper considers a single commodity lexicographic maximum flow problem on a directed network with capacitated vertices to study two network flow problems under an arc interdiction. In the first, the objective is to find an arc on input network to be destroyed so that the residual lexicographically maximum flow is lexicographically minimum. The second problem aims to find a flow pattern resulting lexicographically maximum flow on the input network so that the total residual flow, if an arc is destroyed, is maximum. The paper proposes strongly polynomial time solution procedures for these problems.

Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision

<p><a></a><a>Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21^st century, permeability quantification from nano-micro porous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To effic­­­iently, consistently predict permeability with high quality, we propose the Morphology Decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-Ray Micro Computerized Tomography (μCT) and Nuclear Magnetic Resonance (MRI). For 3D vision, we introduce controllable-measurable-volume as new supervised semantic segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies' boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes, which we describe in this paper, these novel processes are (1) Geometrical: 3D Permeability Governing Equation, (2) Machine Learning: Guided 3D Properties Recognition of Rock Morphology, (3) Analytical: 3D Image Properties Integration Model for Permeability, (4) Experimental: MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).</a></p>

Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision

<p><a></a><a>Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21^st century, permeability quantification from nano-micro porous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To effic­­­iently, consistently predict permeability with high quality, we propose the Morphology Decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-Ray Micro Computerized Tomography (μCT) and Nuclear Magnetic Resonance (MRI). For 3D vision, we introduce controllable-measurable-volume as new supervised semantic segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies' boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes, which we describe in this paper, these novel processes are (1) Geometrical: 3D Permeability Governing Equation, (2) Machine Learning: Guided 3D Properties Recognition of Rock Morphology, (3) Analytical: 3D Image Properties Integration Model for Permeability, (4) Experimental: MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).</a></p>

The Impact of COVID-19 on the Dynamic Topology and Network Flow of World Stock Markets

Studies examining the impact of COVID-19 using network dynamics are scant and tend to evaluate a specific local stock market. We present a thorough investigation of 58 world stock market networks using a complex network approach spanning across the uncertain times that have resulted from the coronavirus outbreak. First, we use the daily closing prices of the world stock market indices to construct dynamic complex networks and sixteen minimum spanning tree (MST) maps for the period from December 2019 to March 2021. Second, we present the topological evolution properties of time-varying MSTs by applying normalized tree length, diameter, average path length, and centrality measures. Moreover, the empirical results suggest that (1) the highest correlation among the world stock markets is observed during the first wave of the COVID-19 pandemic in the months of February–March 2020; (2) most of the MSTs appear lower in hierarchy, and many chain-like structures are formed due to the sheer impact of pandemic-related crises; (3) Germany remained a hub node in many of the MSTs; and (4) the tree severely contracted during the first wave of the COVID-19 outbreak (during the months of February and March 2020) and expanded slightly afterwards. Moreover, the results obtained from this study can be used for the development of financial stability policies and stock market regulations worldwide.