College of Engineering (COE)

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Note: The Department of Biological Systems Engineering is listed within the College of Agriculture and Life Sciences (CALS).

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5SL: A Language for Declarative Specification and Generation of Digital Libraries
Goncalves, Marcos A.; Fox, Edward A. (2002-07-01)
Digital Libraries (DLs) are among the most complex kinds of information systems, due in part to their intrinsic multi-disciplinary nature. Nowadays DLs are built within monolithic, tightly integrated, and generally inflexible systems- or by assembling disparate components together in an ad-hoc way, with resulting problems in interoperability and adaptability. More importantly, conceptual modeling, requirements analysis, and software engineering approaches are rarely supported, making it extremely difficult to tailor DL content and behavior to the interests, needs, and preferences of particular communities. In this paper, we address these problems. In particular, we present 5SL, a declarative language for specifying and generating domain-specific digital libraries. 5L is based on the 5S formal theory for digital libraries and enables high-level specification of DLs in five complementary dimensions, including: the kinds of multimedia information the DL supports (Stream Model); how that information is structured and organized (Structural Model); different logical and presentational properties and operations of DL components (Spatial Model); the behavior of the DL (Scenario Model); and the different societies of actors and managers of services that act together to carry out the DL behavior (Societal Model). The practical feasibility of the approach is demonstrated by the presentation of a 5SL digital library generator for the MARIAN digital library system.
Accurate and Efficient Gene Function Prediction using a Multi-Bacterial Network
Law, Jeffrey N.; Kale, Shiv D.; Murali, T. M. (2019-05-24)
The rapid rise in newly sequenced genomes requires the development of computational methods to supplement experimental functional annotations. The challenge that arises is to develop methods for gene function prediction that integrate information for multiple species while also operating on a genomewide scale. We develop a label propagation algorithm called FastSinkSource and apply it to a sequence similarity network integrated with species-specific heterogeneous data for 19 pathogenic bacterial species. By using mathematically-provable bounds on the rate of progress of FastSinkSource during power iteration, we decrease the running time by a factor of 100 or more without sacrificing prediction accuracy. To demonstrate scalability, we expand to a 73-million edge network across 200 bacterial species while maintaining accuracy and efficiency improvements. Our results point to the feasibility and promise of multi-species, genomewide gene function prediction, especially as more experimental data and annotations become available for a diverse variety of organisms.
Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation
Chennault, Austin; Popov, Andrey A.; Subrahmanya, Amit N.; Cooper, Rachel; Karpatne, Anuj; Sandu, Adrian (2021-11-16)
The data assimilation procedures used in many operational numerical weather forecasting systems are based around variants of the 4D-Var algorithm. The cost of solving the 4D-Var problem is dominated by the cost of forward and adjoint evaluations of the physical model. This motivates their substitution by fast, approximate surrogate models. Neural networks offer a promising approach for the data-driven creation of surrogate models. The accuracy of the surrogate 4D-Var problem’s solution has been shown to depend explicitly on accurate modeling of the forward and adjoint for other surrogate modeling approaches and in the general nonlinear setting. We formulate and analyze several approaches to incorporating derivative information into the construction of neural network surrogates. The resulting networks are tested on out of training set data and in a sequential data assimilation setting on the Lorenz-63 system. Two methods demonstrate superior performance when compared with a surrogate network trained without adjoint information, showing the benefit of incorporating adjoint information into the training process.
Adoption of High-Performance Housing Technologies Among U.S. Homebuilding Firms, 2000 Through 2010
McCoy, Andrew P.; Koebel, C. Theodore; Sanderford, Andrew R.; Franck, Christopher T.; Keefe, Matthew J. (HUD, 2015)
This article describes foundational processes of a larger project examining U.S. home builders’ choices to adopt innovative housing technologies that improve the environmental performance of new single-family homes. Home builders sit at a critical juncture in the housing creation decision chain and can influence how new housing units change related to energy consumption, and the units they produce can also reflect shifting technology, demography, and policy landscapes. With some exceptions, U.S. home builders have been characterized as being slow to adopt or resistant to the adoption of product and process innovations, largely because of path-dependent and risk-averse behavior. This article focuses on home builder choices by analyzing a summary of innovation adoption literature and that literature’s relationship to homebuilding. Researchers then describe analytical approaches for studying home builders’ choices and markets at a Core Based Statistical Area level, the data and statistical methodologies used in the study, and the policy implications for promoting energy efficiency in housing. Future work will draw on the foundation presented in this article to specify versions of this generic model and report results using improved quantitative analyses.
Algorithm-derived feature representations for explainable AI in catalysis
Omidvar, Noushin; Xin, Hongliang (Elsevier, 2021-12-01)
Machine learning (ML) has emerged as a critical tool in catalysis, attributed to its capability of finding complex patterns in high dimensional and heterogeneous data. A recently published article in Chem Catalysis (Esterhuizen et al.) used unsupervised ML for uncovering electronic and geometric descriptors of the surface reactivity of metal alloys and oxides.
Alternating directions implicit integration in a general linear method framework
Sarshar, Arash; Roberts, Steven; Sandu, Adrian (Elsevier, 2021-05-15)
Alternating Directions Implicit (ADI) integration is an operator splitting approach to solve parabolic and elliptic partial differential equations in multiple dimensions based on solving sequentially a set of related one-dimensional equations. Classical ADI methods have order at most two, due to the splitting errors. Moreover, when the time discretization of stiff one-dimensional problems is based on Runge–Kutta schemes, additional order reduction may occur. This work proposes a new ADI approach based on the partitioned General Linear Methods framework. This approach allows the construction of high order ADI methods. Due to their high stage order, the proposed methods can alleviate the order reduction phenomenon seen with other schemes. Numerical experiments are shown to provide further insight into the accuracy, stability, and applicability of these new methods.
Antibiotics in Agroecosystems: Introduction to the Special Section
Franklin, Alison M.; Aga, Diana S.; Cytryn, Eddie; Durso, Lisa M.; McLain, Jean E.; Pruden, Amy; Roberts, Marilyn C.; Rothrock, Michael J. Jr.; Snow, Daniel D.; Watson, John E.; Dungan, Robert S. (2016-03)
The presence of antibiotic drug residues, antibiotic resistant bacteria, and antibiotic resistance genes in agroecosystems has become a significant area of research in recent years and is a growing public health concern. While antibiotics are used in both human medicine and agricultural practices, the majority of their use occurs in animal production where historically they have been used for growth promotion, in addition to the prevention and treatment of disease. The widespread use of antibiotics and the application of animal wastes to agricultural lands play major roles in the introduction of antibiotic-related contamination into the environment. Overt toxicity in organisms directly exposed to antibiotics in agroecosystems is typically not a major concern because environmental concentrations are generally lower than therapeutic doses. However, the impacts of introducing antibiotic contaminants into the environment are unknown, and concerns have been raised about the health of humans, animals, and ecosystems. Despite increased research focused on the occurrence and fate of antibiotics and antibiotic resistance over the past decade, standard methods and practices for analyzing environmental samples are limited and future research needs are becoming evident. To highlight and address these issues in detail, this special collection of papers was developed with a framework of five core review papers that address the (i) overall state of science of antibiotics and antibiotic resistance in agroecosystems using a causal model, (ii) chemical analysis of antibiotics found in the environment, (iii) need for background and baseline data for studies of antibiotic resistance in agroecosystems with a decision-making tool to assist in designing research studies, as well as (iv) culture- and (v) molecular-based methods for analyzing antibiotic resistance in the environment. With a focus on the core review papers, this introduction summarizes the current state of science for analyzing antibiotics and antibiotic resistance in agroecosystems, discusses current knowledge gaps, and develops future research priorities. This introduction also contains a glossary of terms used in the core reivew papers of this special section. The purpose of the glossary is to provide a common terminology that clearly characterizes the concepts shared throughout the narratives of each review paper.
Application of Cybernetics and Control Theory for a New Paradigm in Cybersecurity
Adams, Michael D.; Hitefield, Seth D.; Hoy, Bruce; Fowler, Michael C.; Clancy, Thomas Charles III (Virginia Tech, 2013-11-01)
A significant limitation of current cyber security research and techniques is its reactive and applied nature. This leads to a continuous ‘cyber cycle’ of attackers scanning networks, developing exploits and attacking systems, with defenders detecting attacks, analyzing exploits and patching systems. This reactive nature leaves sensitive systems highly vulnerable to attack due to un-patched systems and undetected exploits. Some current research attempts to address this major limitation by introducing systems that implement moving target defense. However, these ideas are typically based on the intuition that a moving target defense will make it much harder for attackers to find and scan vulnerable systems, and not on theoretical mathematical foundations. The continuing lack of fundamental science and principles for developing more secure systems has drawn increased interest into establishing a ‘science of cyber security’. This paper introduces the concept of using cybernetics, an interdisciplinary approach of control theory, systems theory, information theory and game theory applied to regulatory systems, as a foundational approach for developing cyber security principles. It explores potential applications of cybernetics to cyber security from a defensive perspective, while suggesting the potential use for offensive applications. Additionally, this paper introduces the fundamental principles for building non-stationary systems, which is a more general solution than moving target defenses. Lastly, the paper discusses related works concerning the limitations of moving target defense and one implementation based on non-stationary principles.
Atomic Layer Deposited Tantalum Silicate on Crystallographically-Oriented Epitaxial Germanium: Interface Chemistry and Band Alignment
Clavel, Michael B.; Bhattacharya, Shuvodip; Hudait, Mantu K. (Royal Society of Chemistry, 2022-05-13)
The interface chemistry and energy band alignment properties of atomic layer deposited (ALD) tantalum silicate (TaSiOx) dielectrics on crystallographically-oriented, epitaxial (001)Ge, (110)Ge, and (111)Ge thin-films, grown on GaAs substrates by molecular beam epitaxy, were investigated. The ALD process, consisting of a 6 : 1 Ta : Si precursor super-cycle, was analyzed via sputter depth-dependent elemental analysis utilizing X-ray photoelectron spectroscopy (XPS). The XPS investigations revealed uniform Si incorporation throughout the TaSiOx dielectric, and a measurable amount of cross-diffusion between Ge and Ta atomic species in the vicinity of the oxide/semiconductor heterointerface. The formation of a thin SiO2 interfacial oxide, through the intentional pre-pulsing of the Si precursor prior to the Si : Ta super-cycle process, was observed via cross-sectional transmission electron microscopy analysis. Moreover, the bandgap of Ta-rich Ta0.8Si0.2Ox dielectrics, analyzed using the photoelectron energy loss technique centered on the O 1s binding energy spectra, was determined to be in the range of 4.62 eV-4.66 eV (±0.06 eV). Similarly, the XPS-derived valence band and conduction band offsets (ΔEV and ΔEC, respectively) were found to be ΔEV > 3.0 ± 0.1 eV and ΔEC > 0.6 ± 0.1 eV for the (001)Ge, (110)Ge, and (111)Ge orientations, promoting the increased carrier confinement necessary for reducing operational and off-state leakage current in metal-oxide-semiconductor devices. Thus, the empirical TaSiOx/Ge interfacial energy band offsets, coupled with the uniform dielectric deposition observed herein, provides key guidance for the integration of TaSiOx dielectrics with Ge-based field-effect transistors targeting ultra-low power logic applications.
Benefits of integrated screening and vaccination for infection control
Rabil, Marie Jeanne; Tunc, Sait; Bish, Douglas R.; Bish, Ebru K. (2021-12)
Importance: Screening and vaccination are essential in the fight against infectious diseases, but need to be integrated and customized based on community and disease characteristics. Objective: To develop effective screening and vaccination strategies, customized for a college campus, to reduce COVID-19 infections, hospitalizations, deaths, and peak hospitalizations. Design, Setting, and Participants: We construct a compartmental model of disease spread for vaccination and routine screening, and study the efficacy of four mitigation strategies (routine screening only, vaccination only, vaccination with partial routine screening, vaccination with full routine screening), and a no-intervention strategy. The study setting is a hypothetical college campus of 5,000 students and 455 faculty members, with 11 undetected, asymptotic SARS-CoV-2 infections at the start of an 80-day semester. For sensitivity analysis, we vary the screening frequency, daily vaccination rate, initial vaccination coverage, and screening and vaccination compliance; and consider three scenarios that represent low/medium/high transmission rates and test efficacy. Model parameters come from publicly available or published sources. Results: With low initial vaccination coverage, even aggressive vaccination and screening result in a high number of infections: 1,024/2,040 (1,532/1,773) with routine daily (every other day) screening of the unvaccinated; 275/895 with daily screening extended to the newly vaccinated in base- and worst-case scenarios, with reproduction numbers 4.75 and 6.75, respectively, representative of COVID-19 Delta variant. With the emergence of the Omicron variant, the reproduction number may increase and/or effective vaccine coverage may decrease if a booster shot is needed to maximize vaccine efficacy. Conclusion: Integrated vaccination and routine screening can allow for a safe opening of a college when initial vaccination coverage is sufficiently high. The interventions need to be customized considering the initial vaccination coverage, estimated compliance, screening and vaccination capacity, disease transmission and adverse outcome rates, and the number of infections/peak hospitalizations the college is willing to tolerate.
Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation
Maruf, M.; Daw, Arka; Dutta, Amartya; Bu, Jie; Karpatne, Anuj (2023)
A biologically accurate model of directional hearing in the parasitoid fly Ormia ochracea
Mikel-Stites, Max R.; Salcedo, Mary K.; Socha, John J.; Marek, Paul E.; Staples, Anne E. (Cold Spring Harbor Laboratory, 2021-09-17)
Although most binaural organisms localize sound sources using neurological structures to amplify the sounds they hear, some animals use mechanically coupled hearing organs instead. One of these animals, the parasitoid fly Ormia ochracea, has astoundingly accurate sound localization abilities and can locate objects in the azimuthal plane with a precision of 2°, equal to that of humans. This is accomplished despite an intertympanal distance of only 0.5 mm, which is less than 1/100th of the wavelength of the sound emitted by the crickets that it parasitizes. In 1995, Miles et al. developed a model of hearing mechanics in O. ochracea, which works well for incoming sound angles of less than ±30°, but suffers from reduced accuracy (up to 60% error) at higher angles. Even with this limitation, it has served as the basis for multiple bio-inspired microphone designs for decades. Here, we present critical improvements to the classic O. ochracea hearing model based on information from 3D reconstructions of O. ochracea’s tympana. The 3D images reveal that the tympanal organ has curved lateral faces in addition to the flat front-facing prosternal membranes represented in the Miles model. To mimic these faces, we incorporated spatially-varying spring and damper coefficients that respond asymmetrically to incident sound waves, making a new quasi-two-dimensional (q2D) model. The q2D model has high accuracy (average errors of less than 10%) for the entire range of incoming sound angles. This improved biomechanical hearing model can inform the development of new technologies and may help to play a key role in developing improved hearing aids. Significance Statement: The ability to identify the location of sound sources is critical to organismal survival and for technologies that minimize unwanted background noise, such as directional microphones for hearing aids. Because of its exceptional auditory system, the parasitoid fly Ormia ochracea has served as an important model for binaural hearing and a source of bioinspiration for building tiny directional microphones with outsized sound localization abilities. Here, we performed 3D imaging of the fly’s tympanal organs and used the morphological information to improve the current model for hearing in O. ochracea. This model greatly expands the range of biological accuracy from ±30° to all incoming sound angles, providing a new avenue for studies of binaural hearing and further inspiration for fly-inspired technologies.
Biomechanics of hair cell kinocilia: experimental measurement of kinocilium shaft stiffness and base rotational stiffness with Euler-Bernoulli and Timoshenko beam analysis
Spoon, Corrie E.; Grant, John Wallace (Company of Biologists, 2011-03-01)
Vestibular hair cell bundles in the inner ear contain a single kinocilium composed of a 9+2 microtubule structure. Kinocilia play a crucial role in transmitting movement of the overlying mass, otoconial membrane or cupula to the mechanotransducing portion of the hair cell bundle. Little is known regarding the mechanical deformation properties of the kinocilium. Using a force-deflection technique, we measured two important mechanical properties of kinocilia in the utricle of a turtle, Trachemys (Pseudemys) scripta elegans. First, we measured the stiffness of kinocilia with different heights. These kinocilia were assumed to be homogenous cylindrical rods and were modeled as both isotropic Euler-Bernoulli beams and transversely isotropic Timoshenko beams. Two mechanical properties of the kinocilia were derived from the beam analysis: flexural rigidity (El) and shear rigidity (kGA). The Timoshenko model produced a better fit to the experimental data, predicting El=10,400 pN mu m(2) and kGA=247 pN. Assuming a homogenous rod, the shear modulus (G=1.9 kPa) was four orders of magnitude less than Young's modulus (E=14.1 MPa), indicating that significant shear deformation occurs within deflected kinocilia. When analyzed as an Euler-Bernoulli beam, which neglects translational shear, El increased linearly with kinocilium height, giving underestimates of El for shorter kinocilia. Second, we measured the rotational stiffness of the kinocilium insertion (kappa) into the hair cell's apical surface. Following BAPTA treatment to break the kinocilial links, the kinocilia remained upright, and kappa was measured as 177 +/- 47 pN mu m rad(-1). The mechanical parameters we quantified are important for understanding how forces arising from head movement are transduced and encoded by hair cells.
Blasted Flies and Nanoparticles for TBI
Hockey, Kevin S.; Sholar, Christopher A.; Sajja, Venkata Siva Sai Sujith; Hubbard, W. Brad; Thorpe, Chevon; VandeVord, Pamela J.; Rzigalinski, Beverly A. (Brain Injuries and Biomechanics Symposium, 2013-09-19)
This presentation briefly summaries two major areas of work in our lab, development of a Drosophila model of blast injury and treatment of traumatic brain injury (TBI) with cerium oxide nanoparticles. First, we discuss the design, methodology, and results for the Drosophila blast model, and its relevance to human head injury. Briefly, we found that the Drosophila model was able to reproduce the decreased lifespan and early death seen in military personnel exposed to repetitive mild blast and NFL players exposed to repeated mild head injury. Next we discuss our in vitro and in vivo work with cerium oxide nanoparticles as neuroprotective and regenerative agents for treatment of TBI. Using a tissue culture model for TBI, we found that cerium oxide nanoparticles, delivered up to 6 hrs. post-injury, improved neuronal survival and maintained near-normal glutamate signaling in neurons of mixed organotypic brain cell cultures. In vivo, we found that delivery of cerium oxide nanoparticles prior to lateral fluid percussion brain injury in the rat, improved motor performance, learning and memory.
Boulder dislodgement during coastal storms and tsunamis: Insights from a new ensemble model
Weiss, Robert; Irish, Jennifer L.; Goodman Tchernov, Beverly (American Geophysical Union, 2022-03-01)
Boulders are excellent candidate deposits to study coastal inundation events by storms and tsunamis due to their significant preservation potential. However, it is difficult to infer how and what forcing dislodged the boulder. We present a new model that enables ensemble and Monte-Carlo-type simulations to study the sensitivity of boulder, the fluid flow, and environmental parameters. Our examples show that boulder transport is complex and nonlinear, and to acknowledge the uncertainties of the boulder's preexisting transport conditions, a range of velocities and environmental parameters should be used to quantify the flow that caused boulder dislodgement.
Building Digital Libraries Made Easy: Toward Open Digital Libraries
Fox, Edward A.; Suleman, Hussein; Luo, Ming (2002)
Digital libraries (DLs) promote a sharing culture among those who contribute and those who use resources. This same approach works when building Open Digital Libraries (ODLs). Leveraging the intellectual and practical investment made in the Open Archives Initiative through an eXtended Protocol for Metadata Harvesting (XPMH), one can build lightweight protocols to tie together key components that together make up the core of a DL. DL developers in various settings have learned how to apply this framework in a few hours. The ODL approach has been effective with the Computer Science Teaching Center (www.cstc.org), the Networked Digital Library of Theses and Dissertations (www.ndltd.org), and AmericanSouth.org. Hence, to support our Computing and Information Technology Interactive Digital Educational Library (www.citidel.org) and to provide a generic capability for other parts of the US National Science, technology, engineering, and mathematics education Digital Library (www.nsdl.org), we are developing a "DL-in-a-box" toolkit. When lightweight protocols, pools of components, and open standard reference mod-els are combined carefully, as suggested in the OCKHAM discussions, both the DL user and developer communities can benefit from the principle of sharing.
Celebrate the 10th Anniversary of IEEE Electrification Magazine and Embrace a New Era
Rahman, Saifur (IEEE, 2023-09)
Chain conformations and phase separation in polymer solutions with varying solvent quality
Huang, Yisheng; Cheng, Shengfeng (Wiley, 2021-10-02)
Molecular dynamics simulations are used to investigate the conformations of a single polymer chain, represented by the Kremer-Grest bead-spring model, in a solution with a Lennard-Jones liquid as the solvent when the interaction strength between the polymer and solvent is varied. Results show that when the polymer-solvent interaction is unfavorable, the chain collapses as one would expect in a poor solvent. For more attractive polymer-solvent interactions, the solvent quality improves and the chain is increasingly solvated and exhibits ideal and then swollen conformations. However, as the polymer-solvent interaction strength is increased further to be more than about twice the strength of the polymer-polymer and solvent-solvent interactions, the chain exhibits an unexpected collapsing behavior. Correspondingly, for strong polymer-solvent attractions, phase separation is observed in the solutions of multiple chains. These results indicate that the solvent becomes effectively poor again at very attractive polymer-solvent interactions. Nonetheless, the mechanism of chain collapsing and phase separation in this limit differs from the case with a poor solvent rendered by unfavorable polymer-solvent interactions. In the latter, the solvent is excluded from the domain of the collapsed chains while in the former, the solvent is still present in the pervaded volume of a collapsed chain or in the polymer-rich domain that phase separates from the pure solvent. In the limit of strong polymer-solvent attractions, the solvent behaves as a glue to stick monomers together, causing a single chain to collapse and multiple chains to aggregate and phase separate.
Connectivity Measures for Signaling Pathway Topologies
Franzese, Nicholas; Groce, Adam; Murali, T. M.; Ritz, Anna (Virginia Tech, 2019-03-30)
Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules “connected” in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway’s downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals two case studies of pathway influence, and we describe the specific reactions that contribute to the large influence score. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.
Continuous Equality Knapsack with Probit-Style Objectives
Fravel, Jamie; Hildebrand, Robert; Travis, Laurel (2022-11-04)
We study continuous, equality knapsack problems with uniform separable, non-convex objective functions that are continuous, strictly increasing, antisymmetric about a point, and have concave and convex regions. For example, this model captures a simple allocation problem with the goal of optimizing an expected value where the objective is a sum of cumulative distribution functions of identically distributed normal distributions (i.e., a sum of inverse probit functions). We prove structural results of this model under general assumptions and provide two algorithms for efficient optimization: (1) running in linear time and (2) running in a constant number of operations given preprocessing of the objective function.

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