Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Faculty Publications

Discipline
Institution
Keyword
Publication Year
File Type

Articles 6961 - 6990 of 45738

Full-Text Articles in Entire DC Network

Hybrid Quantum-Classical Unit Commitment, Reza Mahroo, Amin Kargarian Jan 2022

Hybrid Quantum-Classical Unit Commitment, Reza Mahroo, Amin Kargarian

Faculty Publications

This paper proposes a hybrid quantum-classical algorithm to solve a fundamental power system problem called unit commitment (UC). The UC problem is decomposed into a quadratic subproblem, a quadratic unconstrained binary optimization (QUBO) subproblem, and an unconstrained quadratic subproblem. A classical optimization solver solves the first and third subproblems, while the QUBO subproblem is solved by a quantum algorithm called quantum approximate optimization algorithm (QAOA). The three subproblems are then coordinated iteratively using a three-block alternating direction method of multipliers algorithm. Using Qiskit on the IBM Q system as the simulation environment, simulation results demonstrate the validity of the proposed …

Go to article

Flood-Aware Optimal Power Flow For Proactive Day-Ahead Transmission Substation Hardening, Mohadese Movahednia, Amin Kargarian Jan 2022

Flood-Aware Optimal Power Flow For Proactive Day-Ahead Transmission Substation Hardening, Mohadese Movahednia, Amin Kargarian

Faculty Publications

Power system components, particularly electrical substations, may be severely damaged due to flooding, resulting in prolonged power outages and resilience degradation. This problem is more severe in low-elevated regions such as Louisiana. Protective operational actions such as placing tiger dams around substations before flooding can reduce substation vulnerability, damage costs, and energy not supplied cost, thus enhancing power grid resilience. This paper proposes a stochastic mixed-integer programming model for protecting transmission substations one day before flood events using tiger dams. A flood-aware optimal power flow problem is formulated for transmission system operators with respect to the protected/unprotected status of substations. …

Go to article

A Deep Learning Approach To Optimal Sampling Problems, Xinxin Wang, Xiangyu Meng, Fangfei Li Jan 2022

A Deep Learning Approach To Optimal Sampling Problems, Xinxin Wang, Xiangyu Meng, Fangfei Li

Faculty Publications

Time-triggered and event-triggered sampling methods have been widely adopted in control systems. Optimal sampling problems of the two mechanisms have also received great attentions. However, for high-dimensional systems, analytical methods have some limitations. In this study, we propose a model-free method, called soft greedy policy for neural network fitting, to calculate the optimal sampling period of the time-triggered impulse control and the optimal threshold of the event-triggered impulse control. A neural network is used to approximate the objective function and then is trained. This approach is more widely applicable than the analytical method. At the same time, compared with different …

Go to article

Uncertainty-Autoencoder-Based Privacy And Utility Preserving Data Type Conscious Transformation, Bishwas Mandal, George Amariucai, Shuangqing Wei Jan 2022

Uncertainty-Autoencoder-Based Privacy And Utility Preserving Data Type Conscious Transformation, Bishwas Mandal, George Amariucai, Shuangqing Wei

Faculty Publications

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the categorical variables are represented by a collection of scores, one for each class. We use a neural network architecture consisting of a generator and a discriminator, where the generator consists of an encoder-decoder pair, and the discriminator consists of an adversary and a utility provider. Unlike previous research considering this kind of architecture, …

Go to article

Optimization Of Multi-Mode Classification For Process Monitoring, Z. T. Webb, M. Nnadili, E. E. Seghers, L. A. Briceno-Mena, J. A. Romagnoli Jan 2022

Optimization Of Multi-Mode Classification For Process Monitoring, Z. T. Webb, M. Nnadili, E. E. Seghers, L. A. Briceno-Mena, J. A. Romagnoli

Faculty Publications

Process monitoring seeks to identify anomalous plant operating states so that operators can take the appropriate actions for recovery. Instrumental to process monitoring is the labeling of known operating states in historical data, so that departures from these states can be identified. This task can be challenging and time consuming as plant data is typically high dimensional and extensive. Moreover, automation of this procedure is not trivial since ground truth labels are often unavailable. In this contribution, this problem is approached as a multi-mode classification one, and an automatic framework for labeling using unsupervised Machine Learning (ML) methods is presented. …

Go to article

Machine Learning-Based Surrogate Models And Transfer Learning For Derivative Free Optimization Of Htpem Fuel Cells, Luis A. Briceno-Mena, Christopher G. Arges, Jose A. Romagnoli Jan 2022

Machine Learning-Based Surrogate Models And Transfer Learning For Derivative Free Optimization Of Htpem Fuel Cells, Luis A. Briceno-Mena, Christopher G. Arges, Jose A. Romagnoli

Faculty Publications

Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. In this contribution, knowledge-based modelling and data-driven modelling are combined using Few-Shot Learning and implementing an Automated Machine Learning framework for the generation of Machine Learning-based surrogate models.

Go to article

Effect Of The Demethanizer Improved Control Strategy On The Separation Train For The Ngl Separation Process, Marta Mandis, Jorge A. Chebeir, José A. Romagnoli, Roberto Baratti, Stefania Tronci Jan 2022

Effect Of The Demethanizer Improved Control Strategy On The Separation Train For The Ngl Separation Process, Marta Mandis, Jorge A. Chebeir, José A. Romagnoli, Roberto Baratti, Stefania Tronci

Faculty Publications

In recent years the attention on natural gas production and utilization is growing due to different fundamental aspects. First, the availability of natural gas has increased thanks to technological improvements in the extraction techniques that have made possible the production from unconventional reservoirs. Second, the interest in clean energy is growing, aiming to reduce CO2 emission and thus global warming. Natural gas is a cleaner fossil fuel compared with other traditional energy sources such as oil and coal. Another reason that drives the attention on this fossil fuel is the increasing economic interest of recovering the heavier hydrocarbon fractions contained …

Go to article

Effect Of Ldh On The Dissolution And Adsorption Behaviors Of Sulfate In Portland Cement Early Hydration Process, Zedong Qiu, Limin Deng, Shuang Lu, Guoqiang Li, Zhen Tang Jan 2022

Effect Of Ldh On The Dissolution And Adsorption Behaviors Of Sulfate In Portland Cement Early Hydration Process, Zedong Qiu, Limin Deng, Shuang Lu, Guoqiang Li, Zhen Tang

Faculty Publications

In recent years, it has been widely recognized that the incorporation of Mg-Al-LDH into cement-based materials can improve the salt corrosion resistance of cement-based materials. The reason for the improvement comes from the anion adsorption capacity of Mg-Al-LDH. It was confirmed that the addition of Mg-Al-LDH would accelerate the setting and hardening of cement paste. With the increase in the Mg-Al-LDH content, the initial setting time of cement slurry with different gypsum contents will decrease by 10-50% and the viscosity of the cement slurry will increase by 100-200%. Depending on different gypsum contents, the degree of cement hydration varied. This …

Go to article

Event-Triggered Control For Discrete-Time Systems Using A Positive Systems Approach, Frederic Mazenc, Michael Malisoff, Corina Barbalata, Zhong Ping Jiang Jan 2022

Event-Triggered Control For Discrete-Time Systems Using A Positive Systems Approach, Frederic Mazenc, Michael Malisoff, Corina Barbalata, Zhong Ping Jiang

Faculty Publications

We provide an output feedback event-triggered controller for discrete-time linear systems. We make novel use of positive systems, interval observers, an event-triggered state estimator, and triggering times that are computed from estimator values. This provides a discrete-time analog of our recent positive systems approach for continuous-time systems. A key novel ingredient in our discrete-time event triggers is their use of vectors of absolute values, instead of the usual Euclidean norm. We illustrate the benefits of our method using a model for event-triggered BlueROV2 underwater vehicles.

Go to article

Cable Decoupling And Cable-Based Stiffening Of Continuum Robots, Parsa Molaei, Nekita A. Pitts, Genevieve Palardy, Ji Su, Matthew K. Mahlin, James H. Neilan, Hunter B. Gilbert Jan 2022

Cable Decoupling And Cable-Based Stiffening Of Continuum Robots, Parsa Molaei, Nekita A. Pitts, Genevieve Palardy, Ji Su, Matthew K. Mahlin, James H. Neilan, Hunter B. Gilbert

Faculty Publications

Cable-driven continuum robots, which are robots with a continuously flexible backbone and no identifiable joints that are actuated by cables, have shown great potential for many applications in unstructured, uncertain environments. However, the standard design for a cable-driven continuum robot segment, which bends a continuous backbone along a circular arc, has many compliant modes of deformation which are uncontrolled, and which may result in buckling or other undesirable behaviors if not ameliorated. In this paper, we detail an approach for using additional cables to selectively stiffen planar cable-driven robots without substantial coupling to the actuating cables. A mechanics-based model based …

Go to article

Decay Of Oil Residues In The Soil Is Enhanced By The Presence Of Spartina Alterniflora, With No Additional Effect From Microbiome Manipulation, Stephen K. Formel, Allyson M. Martin, John H. Pardue, Vijaikrishnah Elango, Kristina Johnson, Claudia K. Gunsch, Emilie Lefèvre, Paige M. Varner, Yeon Ji Kim, Brittany M. Bernik, Sunshine A. Van Bael Jan 2022

Decay Of Oil Residues In The Soil Is Enhanced By The Presence Of Spartina Alterniflora, With No Additional Effect From Microbiome Manipulation, Stephen K. Formel, Allyson M. Martin, John H. Pardue, Vijaikrishnah Elango, Kristina Johnson, Claudia K. Gunsch, Emilie Lefèvre, Paige M. Varner, Yeon Ji Kim, Brittany M. Bernik, Sunshine A. Van Bael

Faculty Publications

Recent work has suggested that the phytoremediation potential of S. alterniflora may be linked to a selection by the plant for oil-degrading microbial communities in the soil, in combination with enhanced delivery of oxygen and plant enzymes to the soil. In salt marshes, where the soil is saline and hypoxic, this relationship may be enhanced as plants in extreme environments have been found to be especially dependent on their microbiome for resilience to stress and to respond to toxins in the soil. Optimizing methods for restoration of oiled salt marshes would be especially meaningful in the Gulf of Mexico, where …

Go to article

Classification Of Surface Pavement Cracks As Top-Down, Bottom-Up, And Cement-Treated Reflective Cracking Based On Deep Learning Methods, Nirmal Dhakal, Mostafa A. Elseifi, Zia U.A. Zihan, Zhongjie Zhang, Christophe N. Fillastre, Jagannath Upadhyay Jan 2022

Classification Of Surface Pavement Cracks As Top-Down, Bottom-Up, And Cement-Treated Reflective Cracking Based On Deep Learning Methods, Nirmal Dhakal, Mostafa A. Elseifi, Zia U.A. Zihan, Zhongjie Zhang, Christophe N. Fillastre, Jagannath Upadhyay

Faculty Publications

The treatment and repair strategies for reflective and fatigue cracking that initiate at the pavement surface (i.e., top-down cracking) and at the bottom of the asphalt concrete layer (i.e., bottom-up cracking) are noticeably different. However, pavement management engineers are facing difficulties in identifying these cracks in the field because they usually appear in visually identical patterns. The objective of this study was to develop artificial neural network (ANN) and convolutional neural network (CNN) applications to differentiate and classify top-down, bottom-up, and cement-treated reflective cracking in in-service flexible pavements using deep-learning models. The developed CNN model achieved an accuracy of 93.8% …

Go to article

Event-Triggered Prediction-Based Delay Compensation Approach, Frederic Mazenc, Michael Malisoff, Corina Barbalata Jan 2022

Event-Triggered Prediction-Based Delay Compensation Approach, Frederic Mazenc, Michael Malisoff, Corina Barbalata

Faculty Publications

We provide a new event-triggered delay compensation approach for linear systems with arbitrarily long constant input delays. Our prediction map is expressible as a solution of a discrete time system. Our method ensures input-to-state stability. We also provide an analog under measurement delays, where the prediction map is expressible as a solution of a continuous-discrete system. Significant novel features are our combined use of matrices of absolute values and our prediction based event triggers, instead of Euclidean norms, and the fact that the predictor dynamics always has the same dimension as that of the original system. Our marine robotic example …

Go to article

Estimation For Model Parameters And Maximum Power Points Of Photovoltaic Modules Using Stochastic Fractal Search Algorithms, Duy C. Huynh, Matthew W. Dunnigan, Corina Barbalata Jan 2022

Estimation For Model Parameters And Maximum Power Points Of Photovoltaic Modules Using Stochastic Fractal Search Algorithms, Duy C. Huynh, Matthew W. Dunnigan, Corina Barbalata

Faculty Publications

The performance of a photovoltaic (PV) power generation system could be improved through the optimal control and operation of a PV module which is one of the fundamental components of this system. Thus, an appropriate PV module model along with precise knowledge of its parameters is necessary. This paper proposes a novel technique to estimate the source current, the saturation current of diodes, the shunt resistance, the series resistance, the ideality coefficient of diodes and the maximum power points (MPPs) of PV modules at the same time. This estimation problem can be described by the minimization of the root mean …

Go to article

Event-Triggered Control For Continuous-Time Linear Systems With A Delay In The Input, Frederic Mazenc, Michael Malisoff, Corina Barbalata Jan 2022

Event-Triggered Control For Continuous-Time Linear Systems With A Delay In The Input, Frederic Mazenc, Michael Malisoff, Corina Barbalata

Faculty Publications

We provide an event-triggered control technique for a family of linear time-varying continuous-time systems with a constant known pointwise delay in the input. We adopt a subpredictor based prediction technique, and we provide sufficient conditions that ensure that Zeno behavior does not occur. At each time, only delayed measurements are needed to implement the control. Also, the delay can be an arbitrarily large constant. We prove an input-to-state stability property for the closed-loop system, using the theory of cooperative systems. We apply our method to a gyroscopic control problem for a curve tracking dynamics arising in marine robotics.

Go to article

Thermodynamic Characterization Of Grease Oxidation–Thermal Stability Via Pressure Differential Scanning Calorimetry, Jude A. Osara, Piet M. Lugt, Michael D. Bryant, Michael M. Khonsari Jan 2022

Thermodynamic Characterization Of Grease Oxidation–Thermal Stability Via Pressure Differential Scanning Calorimetry, Jude A. Osara, Piet M. Lugt, Michael D. Bryant, Michael M. Khonsari

Faculty Publications

This study investigates and characterizes, via thermodynamic laws, a widely employed standard for measuring oxidation-thermal stability of lubricating greases—ASTM 5483—based on first-order chemical kinetics, using pressure differential scanning calorimetry (PDSC). Steps and active mechanisms in the controlled test are analyzed and modeled using energy and entropy transformations. Applying the degradation-entropy generation (DEG) theorem, oxidation induction time is related to accumulated oxidation entropy and entropy transfer by mass flow to obtain characteristic degradation coefficients. These DEG coefficients are calibrated using available measured data from the literature and subsequently used to predict induction times at various temperatures. DEG elements—trajectories, planes, and domain—presented …

Go to article

Multiple-Inputs Convolutional Neural Network For Covid-19 Classification And Critical Region Screening From Chest X-Ray Radiographs: Model Development And Performance Evaluation, Zhongqiang Li, Zheng Li, Luke Yao, Qing Chen, Jian Zhang, Xin Li, Ji Ming Feng, Yanping Li, Jian Xu Jan 2022

Multiple-Inputs Convolutional Neural Network For Covid-19 Classification And Critical Region Screening From Chest X-Ray Radiographs: Model Development And Performance Evaluation, Zhongqiang Li, Zheng Li, Luke Yao, Qing Chen, Jian Zhang, Xin Li, Ji Ming Feng, Yanping Li, Jian Xu

Faculty Publications

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 …

Go to article

From Merging Frameworks To Merging Stars: Experiences Using Hpx, Kokkos And Simd Types, Gregor Dais, Srinivas Yadav Singanaboina, Patrick Diehl, Hartmut Kaiser, Dirk Pfluger Jan 2022

From Merging Frameworks To Merging Stars: Experiences Using Hpx, Kokkos And Simd Types, Gregor Dais, Srinivas Yadav Singanaboina, Patrick Diehl, Hartmut Kaiser, Dirk Pfluger

Faculty Publications

Octo-Tiger, a large-scale 3D AMR code for the merger of stars, uses a combination of HPX, Kokkos and explicit SIMD types, aiming to achieve performance-portability for a broad range of heterogeneous hardware. However, on A64FX CPUs, we encountered several missing pieces, hindering performance by causing problems with the SIMD vectorization. Therefore, we add std:experimental:simd as an option to use in Octo-Tiger's Kokkos kernels alongside Kokkos SIMD, and further add a new SVE (Scalable Vector Extensions) SIMD backend. Additionally, we amend missing SIMD implementations in the Kokkos kernels within Octo-Tiger's hydro solver. We test our changes by running Octo-Tiger on three …

Go to article

Preface, Kisung Lee, Liang Jie Zhang Jan 2022

Preface, Kisung Lee, Liang Jie Zhang

Faculty Publications

No abstract provided.

Go to article

Analyzing Tweeting Patterns And Public Engagement On Twitter During The Recognition Period Of The Covid-19 Pandemic: A Study Of Two U.S. States, Misbah Ul Hoque, Kisung Lee, Jessica L. Beyer, Sara R. Curran, Katie S. Gonser, Nina S.N. Lam, Volodymyr V. Mihunov, Kejin Wang Jan 2022

Analyzing Tweeting Patterns And Public Engagement On Twitter During The Recognition Period Of The Covid-19 Pandemic: A Study Of Two U.S. States, Misbah Ul Hoque, Kisung Lee, Jessica L. Beyer, Sara R. Curran, Katie S. Gonser, Nina S.N. Lam, Volodymyr V. Mihunov, Kejin Wang

Faculty Publications

The abundance of available information on social media can provide invaluable insights into people's responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social media, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 …

Go to article

Including Unmarried Women In The Homestead Act Of 1862, Hannah Haksgaard Jan 2022

Including Unmarried Women In The Homestead Act Of 1862, Hannah Haksgaard

Faculty Publications

When Congress passed the Homestead Act of 1862, it decided to distribute land to single, unmarried women. Most Congressional members who supported including unmarried women did so because women were a necessary part of empire building—women were expected to marry, bear children, and engage in building permanent communities. Few Congressional members cared about women’s equality or the progressive goals of the women’s rights movements, although some Congressional members thought women would be incapable of successfully homesteading. This Article presents the fascinating history of including unmarried women in the Homestead Act of 1862 by conducting an intensive study of the Act’s …

Go to article

The Trial Of Cinque - Steven Spielberg's Amistad, Jonathan Van Patten Jan 2022

The Trial Of Cinque - Steven Spielberg's Amistad, Jonathan Van Patten

Faculty Publications

No abstract provided.

Go to article

Tribute To Frank Pommersheim (In The Trial Of Cinque - Steven Spielberg's Amistad), Jonathan Van Patten Jan 2022

Tribute To Frank Pommersheim (In The Trial Of Cinque - Steven Spielberg's Amistad), Jonathan Van Patten

Faculty Publications

No abstract provided.

Go to article

Injection Data Analysis Using Material Balance Time For Co2 Storage Capacity Estimation In Deep Closed Saline Aquifers, Mohamed Abdelaal, Mehdi Zeidouni Jan 2022

Injection Data Analysis Using Material Balance Time For Co2 Storage Capacity Estimation In Deep Closed Saline Aquifers, Mohamed Abdelaal, Mehdi Zeidouni

Faculty Publications

Estimating the ultimate storage capacity of deep saline aquifers is important to address the formation potential to store the envisioned large volumes of CO2. Injection data (i.e. injection rate, bottomhole pressure, and cumulative injected volume of CO2) are routinely recorded during storage operations. These data contain valuable information on the subsurface (e.g. the reservoir pore volume and the formation storage capacity) that can be extracted. In this paper, we present a two-step graphical technique to infer the pore volume and the ultimate storage capacity of closed saline aquifers by analyzing the available injection data. First, the pore volume is inferred …

Go to article

Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi, Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …

Go to article

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

Go to article

Effect Of Trigonometric Transformations On The Machine Learning Prediction And Quality Control Of Air Temperature, Andrea Fenoglio, Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Effect Of Trigonometric Transformations On The Machine Learning Prediction And Quality Control Of Air Temperature, Andrea Fenoglio, Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Conducting effective quality control of weather observations in real time is vital to the 14th Weather Squadron’s mission of providing authoritative climate data. This study explored automated quality control of weather observations by applying multiple machine learning techniques to 43,487 surface weather observations from 5 years of data at a single location. Temperature predictors were evaluated using recursive feature elimination on linear regression and XGBoost algorithms, as well as using a neural network hyperparameter sweep. Modeling was repeated after calculating trigonometric transforms of temporal variables to give the models insight into the diurnal heating cycle of the Earth. All models …

Go to article

Competing For Deal Flow In Local Mortgage Markets, Darren Aiello, Mark Garmaise, Gabriel Natividad Jan 2022

Competing For Deal Flow In Local Mortgage Markets, Darren Aiello, Mark Garmaise, Gabriel Natividad

Faculty Publications

The U.S. mortgage market exhibits competitive instability in which some lenders emerge rapidly from the fringe to substantial market shares. Using inferred discontinuities in application acceptance models to generate local lending shocks, we analyze the impact on a lender of a surge in originations by its competitors. We show that the quickest-growing (not the largest) competitors divert applications and originations from other lenders. Facing a quickly-growing competitor, lenders charge higher interest rates, partially due to the increased risk of their loans. Loan performance suffers for other lenders as the quickestgrowing competitor’s originations increase.

Go to article

Young Firms, Old Capital, Song Ma, Justin Murfin, Ryan Pratt Jan 2022

Young Firms, Old Capital, Song Ma, Justin Murfin, Ryan Pratt

Faculty Publications

Across a broad range of equipment types and industries, we document a pattern of local capital reallocation from older firms to younger firms. Start-ups purchase a disproportion- ate share of old physical capital previously owned by more mature firms. The evidence is consistent with financial constraints driving differential demand for vintage capital. The local supply of used capital influences start-up entry, job creation, investment choices, and growth, particularly when capital is immobile. Meanwhile, as suppliers of used capital, in- cumbents accelerate capital replacement in the presence of younger firms. The evidence suggests previously undocumented benefits to co-location between old and …

Go to article

Embracing Brain And Behaviour: Designing Programs Of Complementary Neurophysiological And Behavioural Studies, C. Brock Kirwan, Anthony Vance, Jeffrey L. Jenkins, Bonnie Brinton Anderson Jan 2022

Embracing Brain And Behaviour: Designing Programs Of Complementary Neurophysiological And Behavioural Studies, C. Brock Kirwan, Anthony Vance, Jeffrey L. Jenkins, Bonnie Brinton Anderson

Faculty Publications

NeuroIS—the methods and knowledge of neuroscience applied to the information systems (IS) domain—has become an established research field within the IS discipline. A key advantage of NeuroIS is its ability to provide insights into human cognition beyond those obtained using behavioural techniques alone. Nevertheless, in neuroscience, there is renewed interest in examining behaviour together with neurophysiological methods to better inform our understanding of neural processes. In this research opinion article, we argue that in the field of NeuroIS, there is an opportunity for hybrid programs of study that combine neurophysiological and behavioural methods in a complementary manner. We outline four …

Go to article

First Prev Next Last